management accounting and supply chain performance of

UNIVERSITY OF GHANA

(College of Humanities)

Management Accounting and Supply Chain Performance of Healthcare

Institutions in Ghana

Nartey Edward

(10072443)

This thesis is submitted to the University of Ghana, Legon in partial fulfilment of the

requirement for the award of PhD Accounting degree

JUNE, 2018

University of Ghana http://ugspace.ug.edu.gh

i

DECLARATION

I, the undersigned, do hereby declare that this work is the result of my own research and has

not been presented by anyone for any academic award in this university or any other university.

All references used in the work have been fully acknowledged. I bear responsibility for any

shortcomings.

………………………………………… ……….………………….

NARTEY EDWARD DATE


ii

CERTIFICATION

I, the undersigned, do hereby certify that this thesis was supervised in accordance with the

procedures laid down by the university.

……………………………………………… …….………………

DR. FRANCIS ABOAGYE-OTCHERE DATE

……………………………………………… …….………………

DR. SAMUEL NANA YAW SIMPSON DATE


iii

DEDICATION

I dedicate this work to my two lovely boys: Nana and June


iv

ACKNOWLEDGEMENTS

Completing a major research effort would be virtually impossible without getting the energy

and strength given from above. First of all, I thank the almighty God for giving me the strength

to successfully complete this project. This project also couldn’t have seen the light without the

support and assistance from a number of special people. I especially appreciate the assistance

and insight from my supervisors Dr. Francis Aboagye-Otchere and Dr. Samuel Nana Yaw

Simpson who in diverse ways were invaluable in helping me progress through the various

iterations. I was always impressed about how responsive and focused they seem to be when I

need their input. Finally, I would like to express my profound gratitude to Professor Joshua

Abor, Dean of the Business School for his encouragement and support towards the successful

completion of this project.


v

TABLE OF CONTENTS

DECLARATION.................................................................................................................................... i

CERTIFICATION ................................................................................................................................ ii

DEDICATION...................................................................................................................................... iii

ACKNOWLEDGEMENTS ................................................................................................................ iv

TABLE OF CONTENTS ..................................................................................................................... v

LIST OF TABLES ............................................................................................................................... xi

LIST OF FIGURES ............................................................................................................................ xii

LIST OF ABBREVIATIONS ........................................................................................................... xiii

ABSTRACT ........................................................................................................................................ xiv

CHAPTER ONE ................................................................................................................................... 1

INTRODUCTION ................................................................................................................................. 1

1.1 Brief Overview of the Study ............................................................................................ 1

1.2 Research Background ....................................................................................................... 2

1.3. The Research Problem .................................................................................................... 5

1.4 Study Objectives ............................................................................................................ 10

1.5 General Research Questions........................................................................................... 10

1.6 Significance of the Study ............................................................................................... 11

1.7 Outline of Remainder of Thesis ..................................................................................... 13

1.8 Chapter Summary ........................................................................................................... 13

CHAPTER TWO ................................................................................................................................ 15

CONTEXTUAL BACKGROUND, MANAGEMENT ACCOUNTING SYSTEM DESIGN AND

THE HEALTH SUPPLY CHAIN ..................................................................................................... 15

2.1. Introduction ................................................................................................................... 15

2.2. Recent Studies on MAS Design and Hospital SC Performance ................................... 16

2.3 Ghana’s Health SCM System ......................................................................................... 18

2.4 Managerial and Costing Systems in Ghana’s Healthcare Management ........................ 20

2.5. Reforms in the Health Logistics and Commodity Management System ...................... 21

2.6 The Health Supply Chain ............................................................................................... 27

2.6.1 Elements of the Health Supply Chain ................................................................................... 29

2.6.2 SCM in Healthcare Context .................................................................................................. 30

2.6.3 Minimizing Health Supply Chain Cost ........................................................................ 32

2.7 Nature of MAS Design ................................................................................................... 32

2.8 Functions of the MAS Information in Healthcare Management .................................... 34

2.8.1 Costing Systems Design in Hospitals .................................................................................... 35


vi

2.8.2 Formal MAS Design in Health SCM Decisions ...................................................................... 36

2.8.3 Inter-Health Organizational Cost Management in Supply Chains ....................................... 37

2.9 Chapter Summary ........................................................................................................... 39

CHAPTER THREE ............................................................................................................................ 41

CONTINGENCY-BASED MANAGEMENT ACCOUNTING STUDIES .................................... 41

3.1 Introduction .................................................................................................................... 41

3.2 Theoretical Background of Contingency Fit .................................................................. 41

3.3 Contingency Fit Models ................................................................................................. 43

3.3.1 Cartesian vs. Configuration .................................................................................................. 43

3.3.2 Congruence vs. Contingency ................................................................................................ 45

3.4 Typology of Contingency Fit Models ............................................................................ 47

3.4.1 Selection Fit Models ............................................................................................................. 48

3.4.2 Matching Fit Models ............................................................................................................ 49

3.4.3 Interaction (Moderation) Fit Models .................................................................................... 52

3.4.4 Mediation Form of Fit .......................................................................................................... 55

3.5 Approaches to Testing Contingency Fit Models ............................................................ 56

3.5.1 Deviation Score and Residual Analysis Techniques .............................................................. 56

3.5.2 Approach to Testing Moderation Fit Models ....................................................................... 60

3.5.3 Approach to Testing Mediation Form of Fit Models ............................................................ 61

3.6 Conceptualization of Contingency Fit and its Attainment ............................................. 62

3.7 Levels of Contingency Fit Analysis ............................................................................... 64

3.8 Early Contingency-Based Management Accounting Studies ........................................ 65

3.9 Selection, Definition and Measurement of MAS Variables ........................................... 66

3.9.1 Arbitrary Selection of Variables ........................................................................................... 66

3.9.2 Empirical Estimation of Contingency Fit Models .................................................................. 70

3.10 Empirical Estimation of Moderation Forms of Fit ....................................................... 71

3.10.1 Hypotheses Formulation and Statistical Analysis .............................................................. 71

3.10.2 Strength of the Relationship .............................................................................................. 78

3.10.3 Lower-Order Effects ........................................................................................................... 79

3.10.4 Interaction and Effect Size ................................................................................................. 82

3.10.5 Multiple and Higher-Order Interaction .............................................................................. 84

3.11 The Use of Higher-Order of Abstraction Models in SEM ........................................... 86

3.12 Chapter Summary ......................................................................................................... 87

CHAPTER FOUR ............................................................................................................................... 89

THEORETICAL MODEL AND HYPOTHESES DEVELOPMENT ........................................... 89

4.1 Introduction .................................................................................................................... 89


vii

4.2 Theoretical Framework .................................................................................................. 89

4.2.1 Contingency Fit ..................................................................................................................... 90

4.3 Theoretical Foundation of Constructs ............................................................................ 96

4.3.1 Management Accounting Systems (MAS) ............................................................................ 96

4.3.2 Supply Chain Integration (SCI) ............................................................................................ 102

4.3.3 Hospital SC Performance .................................................................................................... 107

4.4 Formulation of Hypotheses .......................................................................................... 109

4.4.1 MAS Information and Hospital Supplier Partnerships ....................................................... 109

4.4.2 MAS Information and Hospital Supply Chain Integration (Internal Integration) ............... 112

4.4.3 MAS Information and Level of Information Exchange ....................................................... 114

4.4.4 MAS Information and Hospital Supply Chain Risk and Uncertainty ................................... 116

4.5. Mediating Role of MAS Information in Supply Chain Performance ......................... 118

4.5.1: MAS and Supplier Relations (External Integration) on Performance ................................ 118

4.5.2. MAS and Supply Chain Integration (Internal Integration) on Performance ...................... 121

4.5.3: MAS and Level of Knowledge Exchange on Performance ................................................. 122

4.5.4. MAS and Supply Chain Risk and Uncertainty on Performance ......................................... 123

4.6 Contingency Effect of the SCM Contextual Dimensions ............................................ 124

4.6.1. Supplier Relations (External Integration) to Performance ................................................ 124

4.6.2. Relationship of Internal Integration to Performance ........................................................ 125

4.6.3. Relationship of Level of Knowledge Exchange to Performance ........................................ 126

4.6.4. Relationship of Supply Chain Risk and Uncertainty to Performance................................. 127

4.7 Chapter Summary ...................................................................................................................... 128

CHAPTER FIVE .............................................................................................................................. 130

METHODOLOGY ........................................................................................................................... 130

5.1 Introduction .................................................................................................................. 130

5.2 The Study’s Philosophical Underpinning .................................................................... 131

5.3. Dimensions of Philosophical Assumptions ................................................................. 132

5.3.1. Ontology ............................................................................................................................ 132

5.3.2. Epistemology ..................................................................................................................... 133

5.3.3. Methodology ..................................................................................................................... 134

5.3.4. Axiology ............................................................................................................................. 134

5.4. Paradigms in Management Research .......................................................................... 135

5.4.1. Positivism .......................................................................................................................... 135

5.4.2. Social Constructivism ........................................................................................................ 136

5.4.3. Pragmatism ....................................................................................................................... 137

5.5. Paradigms in Accounting Research............................................................................. 138


viii

5.6. The Study’s Philosophical Stance ............................................................................... 139

5.7. Purpose and Design Strategies of the Survey.............................................................. 140

5.8 Survey Strategy ............................................................................................................ 143

5.9. Population, Sample and Sampling Procedure ............................................................. 143

5.9.1. Study Sample ..................................................................................................................... 145

5.9.2. Sampling Procedure .......................................................................................................... 145

5.9.3. Sample Size........................................................................................................................ 146

5.10. Approach to Data Collection ..................................................................................... 147

5.11. Scale Development and Measurements..................................................................... 148

5.12. Constructs and their Sources ..................................................................................... 149

5.13. The Modelling Process .............................................................................................. 151

5.14. Summary of Analysis Procedure ............................................................................... 152

6.15. Chapter Summary ...................................................................................................... 154

CHAPTER SIX ................................................................................................................................. 155

SURVEY ANALYSIS AND RESULTS .......................................................................................... 155

6.1 Introduction .................................................................................................................. 155

6.2 Preliminary Analysis of the Sample Data .................................................................... 156

6.3 Sample Characteristics ................................................................................................. 157

6.4 Assessment of Data for Normality ............................................................................... 159

6.4.1 Assessing Data for Multivariate Outliers ........................................................................... 162

6.4.2 Correlation Analysis ........................................................................................................... 163

6.5 Measures of Reliability and Validity of Scale Items.................................................... 165

6.6 Post Hoc Analysis of Factor Analytic Models ............................................................. 170

6.6.1 Model 2 of the Factor Analytic Models .............................................................................. 172

6.6.2 Model 3 of the Factor Analytic Models .............................................................................. 172

6.7 Structural Model Specification..................................................................................... 175

6.7.1 Specification of the Hypothesized Model ........................................................................... 176

6.7.2 Identification of the Hypothesized Model .......................................................................... 178

6.8 Fitting the Hypothesized Model ................................................................................... 180

6.8.1 Statistical Significance ........................................................................................................ 180

6.8.2 Model Estimation Process .................................................................................................. 180

6.8.3 Structural Model Fitting Process ........................................................................................ 181

6.9 Hypotheses Tests of the Structural Model ................................................................... 181

6.10 Selection Fit Model – Test of H1 (a – d), H2 (a – d), H3 (a – d), and H4 (a – d) ...... 182

6.11 Mediating Effect of MAS Construct – Test of H5 (a) to H5 (d) ................................ 187


ix

6.11.1. Confounding Variables Effect .......................................................................................... 187

6.11.2 Mediation Effect of the MAS Construct............................................................................ 187

6.13 Moderating Effects of the SCM Constructs – Hypotheses H 6(a) – H 6(d) .............. 193

6.13.2 Inclusion of Lower-Order Effects ...................................................................................... 193

6.13.1 Exclusion of Lower-Order Effects...................................................................................... 198

6.14 Chapter Summary ....................................................................................................... 201

CHAPTER SEVEN ........................................................................................................................... 202

DISCUSSION AND INTERPRETATION OF FINDINGS .......................................................... 202

7.1 Introduction .................................................................................................................. 202

7.2 Recap of Research Objectives, Questions and Hypotheses ......................................... 202

7.2.1. Research Questions ........................................................................................................... 203

7.3 Fit between SCM and MAS Information Characteristics ............................................ 203

7.3.1 Strategic Supplier Partnerships (External Integration) and MAS Information ................... 204

7.3.2 Supply Chain Integration (Internal Integration) and MAS Information ............................. 206

7.3.3 Level of Information Sharing and MAS Information .......................................................... 207

7.3.4 Supply Chain Risk and Uncertainty and MAS Information ................................................. 208

7.4 Interpretation of the MAS Mediation Effect ................................................................ 209

7.4.1 Confounding Variables and Hospital Supply Chain Performance ...................................... 209

7.4.2 Mediating Effect of MAS on Performance ......................................................................... 210

7.4.3 Joint Effect of MAS and SCM on Hospital SC Performance ................................................ 211

7.4.4 Mediating Role of the MAS Information Dimensions......................................................... 212

7.5. Contingency Effect of SCM on Performance ............................................................. 214

7.6 Implications of Findings............................................................................................... 215

7.6.1 Theoretical Implication ...................................................................................................... 216

7.6.2 Practical Implications ......................................................................................................... 216

7.6.3 Policy Implications .............................................................................................................. 218

7.7 Chapter Summary ......................................................................................................... 218

CHAPTER EIGHT ........................................................................................................................... 220

SUMMARY, CONCLUSIONS AND CONTRIBUTIONS ........................................................... 220

8.1 Introduction .................................................................................................................. 220

8.2 Summary of the Study .................................................................................................. 220

8.2.1 The Study’s Main Thrust ..................................................................................................... 220

8.2.2 The Study’s Main Findings.................................................................................................. 227

8.3 Conclusions .................................................................................................................. 230

8.4 Contributions of the Thesis .......................................................................................... 233

8.5. Recommendations ....................................................................................................... 235


x

8.6 Limitations of the Study ............................................................................................... 236

8.7 Future Research Directions .......................................................................................... 238

8.8 Chapter Summary ......................................................................................................... 239

REFERENCES ................................................................................................................... 240

Appendix A ........................................................................................................................ 262

Questionnaire for Hospital Accountants ............................................................................ 262


xi

LIST OF TABLES

Table 5.1 Ontology, Epistemology, Methodology, Axiology and Methods Distinguished 135

Table 5.2 Implications of Positivist Philosophical Assumptions 136

Table 5.3 Positivist and Social Constructionist Contrasted 137

Table 5.4 Relationship between Philosophical Assumptions and Paradigms 138

Table 5.5 Distribution of Samples by the Six Regions 147

Table 5.6 Constructs and their Sources of Measured 150

Table 5.7 Summary of Analysis Procedure 153

Table 6.1 Distribution of Hospitals in Terms of Ownerships, Profit Status and Location 158

Table 6.2 Descriptive Statistics for Scale Items 161

Table 6.3 Correlation Analysis, Reliability and Average Variance Extracted of Constructs 164

Table 6.4 Factor Loadings of Scale Items 169

Table 6.5 Post Hoc Analysis for Factor Analytic Models 171

Table 6.6 Structural Path Coefficients and Hypotheses Tests for Fit Relationships 184

Table 6.7 Summary of Goodness-of-Fit Tests for Structural Model 185

Table 6.8 Structural Path Coefficients for Mediation Analysis 189

Table 6.9 Path Coefficients for Moderation Effects – Lower-Order Effects Excluded 196

Table 6.10 Path Coefficients for Moderation Effects – Lower-Order Effects Included 199


xii

LIST OF FIGURES

Figure 1.1 Research Scope 1

Figure 1.2 Gaps Linking MAS, SCI and SCP 8

Figure 2.1 Ghana’s Health Supply Chain Management System 23

Figure 3.1 (a)-(b) Cartesian and Configuration Models 45

Figure 3.2 (a)-(c) Matching Fit Iso-performance 51

Figure 3.3 Moderation Fit Relationship 53

Figure 3.4 (a-(d) Non-Monotonic (Symmetrical) and Cross Over Interaction 54

Figure 3.5 Euclidian Approach in Determining Matching Form of Fit 59

Figure 3.6 Potential Type II Error Associated with Residual Analysis 60

Figure 3.7 (a) Mediation Form of Fit Models 62

Figure 3.7 (b) Four Step Procedure for Testing Mediation Form of Fit 63

Figure 3.8 (a) Mediation Analysis of Contingency Variables, Budget Participation

and Managerial Performance

76

Figure 3.8 (b) Moderation Analysis of Contingency Variables, Budget Participation

and Managerial Performance

77

Figure 3.9 (a)-(d) Strength Versus Form Interaction 79

Figure 4.1 Theoretical Framework 91

Figure 4.2 Classification of the MAS Dimensions 99

Figure 6.1 (a)-(b) Graphical Output of Factor Analytic Model for SCM Context Factors 166

Figure 6.1 (c) –(d) Graphical Output of Factor Analytical Model for MAS Information

Dimensions

167

Figure 6.1 (c) Graphical Output of Factor Analytic Model for SCP Dimensions 167

Figure 6.2 The Hypothesized Model 177

Figure 6.3 Structural Output for Fit Relationships 186

Figure 6.4 Structural Output of Mediation Analysis 190

Figure 6.5 Structural Output for Mediation Analysis (Second-order) 192

Figure 6.6 Structural Output for Moderation Effects (Lower-order effects

included)

197

Figure 6.7 Structural Output for Moderation Effects (Lower order effects

excluded)

200


xiii

LIST OF ABBREVIATIONS

CEO Chief Executive Officer

CMS Central Medical Stores

GHS Ghana Health Service

HCSCMP Health Commodity Supply Chain Management Plan

HSC Hospital Supply Chain

MA Management Accounting

MAS-CS Management Accounting System Supply Chain

MAS-SCM Management Accounting System Supply Chain Management

MOH Ministry of Health

NPM New Public Management

RMS Regional Medical Stores

SC Supply Chain

SCI Supply Chain Integration

SCM Supply Chain Management

SCN Supply Chain Network

SDP Service Distribution Points

TCE Transaction Cost Economics

WHO World Health Organization


xiv

ABSTRACT

This work analyzes the contingency effects of supply chain integration (SCI), information

sharing (or knowledge exchange), supply chain (SC) risk and uncertainty and management

accounting system (MAS) design on hospital SC performance in Ghana. Increasingly seen as

crucial in the management accounting (MA) literature over the past three decades has been the

design and implementation of effective MASs that extend beyond organizational boundaries

for the management and performance of relationships in the inter-firm exchanges domain. Such

studies have become one of the most important and critical areas for the CEOs and executive

leaderships of hospitals in their efforts to improve operational efficiency at minimal costs.

However, while the relationship between several SCM contextual factors and MAS

information, and the impact of SCI on SC performance have been extensively examined,

virtually no study had empirically examined in a single comprehensive study, the SCM-

performance impacts of MAS design. The underdevelopment of such studies is even more

pronounced in service oriented organizations. In addition, the literature on MAS-SCM

relationships had emphasized the design of MAS in the transaction context which in most cases

results in misaligned control. Transactions costs economics (TCE) although provides insights

on the MAS organizations should install to achieve fit, the actual observed patterns of MAS

use and the contextual factors that underpin MAS design in the inter-firm exchanges domain

are not fully explained by the theory. Hence, TCE tends to ignore the dimensions of internal fit

(i.e. internal integration) of the SC. Given this void, a survey of management accountants

drawn from 237 public and private hospitals in Ghana was used to test the contingency effects

of these relationships on hospital supply chain aggregate performance using hierarchical

factorial structures. Whereas the results partially support the presence of the selection and

mediation fit, the moderating form of fit was fully supported by the sample data. Also, internal


xv

integration was only supported by the moderation fit model. However, level of information (or

knowledge) exchange was not supported by the selection fit model although it was fully

supported by the moderation fit model and partially supported by the mediation model. This

finding suggests that choices of the MAS information characteristics to facilitate SCM

decisions among hospitals in Ghana is either excessive or insufficient. However, the findings

suggest that optimal choices of MAS design in the inter-firm exchanges domain to enhance SC

operational performance can be explained by contingency theory. Consistent with several prior

studies, it can be theorized that SCI as well as level of knowledge exchange and SC risk and

uncertainty have contingency effects in the design of MAS in the inter-organizational domains

in healthcare context. Hence, they could be considered as added external variables in the MAS-

contingency paradigm. The study’s key contribution is the development and testing of a novel

theoretical model of the relationship between the selection, mediation and moderation fit

models that link SCI, level of information sharing, and SC risk and uncertainty and MAS design

on hospital SC performance. It also offers Ghanaian hospital managers the usefulness of the

MAS-contingency framework in enhancing hospital SCM decisions.


1

CHAPTER ONE

INTRODUCTION

1.1 Brief Overview of the Study

This thesis is concerned with empirical examination of the contingency fit relationships among

four dimensions (broad scope, timeliness, integration and aggregation) of management

accounting system (MAS) information and supply chain integration (SCI), level of knowledge

exchange, and supply chain risk and uncertainty that can leverage hospital supply chain

aggregate performance in Ghana. This represents the intersection of three key areas:

management accounting systems (MAS), supply chain integration (SCI), and hospital supply

chain (SC) performance as shown in Fig 1.1. In this chapter, the background linking the

research problem and layout of the whole thesis’s logical sequence is presented. The chapter

ends with a summary.

Figure 1.1: Study Scope

(Source: Author’s own drawing based on review of literature)

Management Accounting

System (MAS) Design

Hospital Supply Chain Performance

Supply Chain Integration

(SCI)

Fit Relationships in

Healthcare

Institutions


2

1.2 Research Background

The supply chain (SC) concept which emerged in the 1990s as a strategic management tool,

has become a key driver that enhances overall organizational performance (Maestrini, Luzzini,

Maccarrone & Caniato, 2017; Ataseven & Nail, 2017). This strategic aspect of the SC has been

investigated by several research studies through empirical examination of the associations

between different SCM contextual factors and MAS design on one hand, and both operational

and financial performance on the other (van der Meer-Kooistra & Vosselman, 2000; Dekker,

2004; Flynn, Huo & Zhao, 2010; Wong, Boon-itt & Wong, 2011; Dekker, Groot & Schoute,

2013; Qi, Huo, Wang & Yeung, 2017). However, research that examines the impact of the

MAS information characteristics on either SC operational or financial performance is relatively

underdeveloped and largely remains a void (Burritt & Scaltegger, 2014). While this gap

remains, SCI, and sharing of information (or knowledge exchange) about the risks associated

with inter-firm exchanges have been identified as the most influencing factors that facilitate

the effective design and implementation of MAS information in enhancing hospital SC

performance (Kwon, Kim and Martin, 2016).

In addition, existing works on SCI and performance have predominantly been dominated by

the industrial dynamics and logistics literature as the literature has extensively focused on

product type organizations mostly discrete parts manufacturing and consumer goods (e.g.

Flynn et al, 2010; Wang et al, 2011; Qi et al, 2017). There have been relatively little research

that look at the impact of the MAS information characteristics on SCM decisions in service

oriented organizations, and more specifically, healthcare institutions (Anderson & Dekker,

2015). Consequently, increasingly seen as crucial in the literature over the past two decades

has been the design and implementation of effective MASs that align with contextual factors

within healthcare organizations (Wardhani, Utarini, van Dijk, Post & Groothoff, 2009;

Aidemark & Funck, 2009). This is because the effective formulation, management and efficient


3

functioning of the SC is highly associated with the fit between MAS information characteristics

and SCM contextual factors (Seal Cullen, Dunlop, Berry & Ahmed, 1999; Reusen &

Stouthuysen, 2017). For example, in healthcare services, information relating to SC costs such

as inventory management, procurement, transportation, warehousing, cost of operating

facilities, link between cost and cost drivers, commodity throughput as well as revealing

information on ineffective and inefficient hospital operations through the consumption of

resources etc., are provided by effective MAS design.

Furthermore, the SC constitutes the most critical element in overall organizational cost control

and has become one of the most important and critical areas for the chief executive officers

(CEOs) and executive leaderships of hospitals (Barlow, 2010c). This is because improvement

upon overall efficiency and cost reduction strategies have been a challenging issue in hospital

management (Pizzini, 2006; Abernethy, Chua, Grafton & Mahama, 2007). Also, the power

structure of healthcare has changed with the evolution of the new public management (NPM),

making MASs more dominant than medical skills in the monitoring and evaluation of clinical

performance (WHO, 2000; Malmmose, 2015). However, for effective monitoring and hence

optimal performance, the MAS design should traditionally be based on the ‘matching’ principle

where the installation of MASs by organizations align with contextual variables (Reusen &

Stouthuysen, 2017). Although these theoretical interrelationships fundamentally underpin the

installation and implementation of MAS for performance, the large volume of MAS-SC studies

had emphasized fit in the transaction context other than fit in the organizational context (van

der Meer-Kooistra & Vosselman, 2000; Dekker, 2004; Dekker et al, 2013). This is because the

theoretical lens of transaction costs economics (TCE) explains how MASs are installed as a

function of the specific transaction context that arises from contracting risks between suppliers

and buyers other than internal dimensions such as internal integration of the SC.


4

In this regard, TCE tends to ignore the dimensions of internal fit (e.g. internal integration) of

the SC. The internal fit of the supply chain represents the cross-functioning of systems and

collective responsibility across functions from a strategic perspective. Collaborations across

procurement, warehousing and distribution functions take place within internal integration to

meet customer (patient) requirements at a low total cost (Qi et al, 2017). Also, internal

integration efforts facilitate the sharing of real-time information and knowledge across key

functions through the break-down of functional barriers (Wong et al, 2011). These functional

barriers impact significantly on inventory management and warehousing, quality of hospital

processes and clinical costs, operating cost of hospital facilities, commodity throughput, costs

and cost drivers, etc. that have significant effect on SC operational performance (Chen, Preston

& Xia, 2013; Ataseven & Nair, 2017). In addition, the TCE does not seem to fully explain the

actual observed patterns of MAS use and the contextual factors that underpin its design in the

inter-firm exchanges domain, although it provides insights on the MAS organizations should

adopt to achieve fit (Anderson & Dekker, 2015). Furthermore, adoption of the TCE is mainly

based on mitigating the risk associated with the transactions in the inter-firm exchanges domain

(Langfield-Smith, 2008; Dekker et al, 2013). Risks such as heightened vulnerability and the

possibility that partners engaged in the transactions will opportunistically exploit the dependent

relationship are typical examples.

In this regard, the general contention has been that the excessive use of the MAS information

is required for transactions that suggest higher levels of risk attributes in order to foster mutual

collaboration and coordination (Reusen & Stouthuysen, 2017). Whilst there is wide acceptance

of this notion of alignment, the relationship between an organization’s MAS structure and

transactions context will often be in a state of misalignment to adversely affect performance.

Choices that entail either excessive or insufficient use of the MAS relative to the contextual

variable will result in misalignment, and negatively affect performance (Otley, 1980, 2016;


5

Burkert, Davila, Mehta & Oyon, 2014). Based on these voids, this study tests the contingency

effects of the MAS-SC performance using empirical data from healthcare institutions in Ghana.

In business strategy, the contingency perspective examines a particular set of organizational

and environmental factors at the optimal level (Burkert et al, 2014). However, the benefits of

contingency theory’s application in the inter-firm exchanges domain with respect to MAS

design has received little (or possibly no attention) in the literature (Jamal & Tayles, 2010).

Furthermore, existing works on the contingency paradigm were conducted based on samples

drawn from the developed world such as the US, UK, Canada and Australia (Jamal & Tayles,

2010). Hence arguments regarding the convergence of the contingency theory’s usefulness

remains contested. Similar works in developing economies such as Ghana will allow for the

assessment of the validity of the contingency arguments.

1.3. The Research Problem

The design of MAS information for SCM decisions in the inter-firm exchanges domain has

been considered as mere extensions of the conventional intra-firm cost accounting tools of the

traditional MAS. To this end, no attempt has been made by management accounting researchers

to test the contextual factors that underpin MAS design in the inter-firm exchanges domain as

compared to the intra-organizational domains which have been extensively studied.

Consequently, organizations have relied on the practice of imitating individuals’ MAS

structure when designing and installing MASs in inter-firm exchanges (Reusen & Stouthuysen,

2017). Reusen & Stouthuysen (2017) found this practice to be one of the root causes of

misalignment in the MAS-context relationship in the inter-firm exchanges domain.

It has been argued that ‘‘the planning and controlling processing abilities that are fundamental

to managing costs internally is the same for managing inter-firm relationships hence, can be


6

applied to SCM activities’’ (Fayard et al, 2012, p.170). In this regard, organizations that have

installed internal cost management systems considered to be at the highest level would be able

to use their expertise and knowledge to model similar costing systems for SCM decisions.

Although it is possible to use the expertise and knowledge of the traditional intra-firm cost

accounting tools to proxy SCM decisions in the inter-firm exchanges domain, the key concept

of fit is missing which ultimately results in misalignment to negatively affect performance.

Studying the impact of the MAS dimensions on SCM characteristics determines the functions

of the MAS in SCM decisions. In other words, the MAS has already been installed and is being

used to make decisions in the SCM context. This aspect of the MA-SC relationship has been

extensively studied from the theoretical lens of transaction cost economics (TCE) which

according to Anderson & Dekker (2015), does not fully explain the actual observed patterns of

the MAS use and the contextual factors that underpin its design in the inter-firm exchanges

domain. On the other hand, studying the effect of the SCM characteristics on MAS design

determines the MAS-context relationship that impact on performance in the inter-firm

exchanges domain. That is, it determines the design and type of MAS that appropriately fits

the SCM characteristics to maximize performance.

This study focuses on hospitals in Ghana because the SCM challenges that confront the health

sector could be as a result of improper design and use of the MAS for SCM decisions resulting

in misalignment between SCM practices and MAS design. (Asamoah, Abor and Opare, 2011;

Manso Annan & Anane, 2013; Denkyira, 2015). This will by extension, translate the body of

literature on MAS-contingency knowledge from the intra-organizational domains (which has

been the predominant MAS design studies in extant literature) to the inter-firm exchanges

domain in supply chains. Management of Ghana’s health commodity and SCM system has in

recent years, been characterized by ineffectiveness and weaknesses as ‘gaps’ (WHO, 2009;

Manso, Annan & Anane, 2013; Denkyira, 2015). To this end, various models (e.g. the five-


7

year development plan of the Ministry of Health (MOH) spanning the period 2008-2012, and

the MOH’s Health Commodity Supply Chain Master Plan (HCSCMP) of 2012 etc.) have been

tried and tested yet the problem remain unaddressed and have widened rather than curtailed.

For example, a study by Asamoah, Abor and Opare (2011) to examine the healthcare SC for

Artemisinin (ACT) Based Combination Therapies in Ghana revealed weaknesses such as

disruptions and delays in the SCM system due to poor SCI (both internal and external

integration) and weak inventory management systems. They further identified the integration

of the SC and weak information linkages as the main threat to the health SC and this ultimately

results in price increases at the pharmacy level.

The identified weaknesses have significant impact on the design and implementation of MAS

information because MAS serves not only to provide information to oil the wheels of a SC

network but also, integrates functional units and entities which are internal and external to

organizations (Burns, 2002; Thrane & Hald, 2006; Dacosta-Claro, 2002; Scheller & Smeltzer,

2006). It also serves to reduce costs and create value in SCs (Fayard, Lee, Leitch & Kettinger,

2012). However, these relationships have not been examined as part of the numerous models

already tried and tested. Perhaps, the MASs installed and used in hospitals might not be in

alignment (or fit) with hospital SCM activities thereby providing little value for managerial

decisions in the inter-firm exchanges domain. Studies into the contingency models linking

MAS information characteristics and SCM could offer some insights to solving the problem.

Besides, substantial literature document the link between SCI and both SC financial and

operational performance (Flynn et al, 2010; Wong et al, 2011). Another vast stream of literature

examine the relationship between MAS information characteristics and attributes of SCM in

the inter-firm exchanges domain (Copper & Slagmulder, 1999; van der Meer-Kooistra &

Vosselman, 2000; Dekker, 2004, Agndal & Nilsson, 2009; Coad & Cullen, 2006; Fayard et al,


8

2012; Dekker et al, 2013; Anderson & Dekker, 2015; Reusen & Stouthuysen, 2017). There is

however, virtually no research that simultaneously examines the MAS design, SCI and SC

performance associations in a single comprehensive study. Given the considerable effort

devoted by management accounting researchers in explaining MAS choices in inter-firm

relationships and the fact that SC performance is largely a function of relationship

characteristics, such theoretical interrelationships are important and fill a gap that develops and

tests a novel theoretical model linking both MAS design, SCM, and SC performance in

healthcare context. This is important for the advancement of theory in the inter-firm exchanges

domain. Fig 1.2 illustrates the relationships linking these gaps.

Fig 1.2. Gaps Linking MAS, SCI and SCP

As shown in Fig 1.2, the link between MAS design and SC performance still remains a gap

although they have a strong theoretical background. Besides, the link between SCM and MAS

design is recursive (Ramos, 2004). However, only one-side of this bi-directional relationship

has so far been examined. The impact of SCM on MAS design has virtually received no


9

empirical investigation. Finally, a study that examines the complete relationship between MAS,

SCM, and SCP hardly exists in the literature. Thus, this study fills a gap by developing a novel

theoretical model that links these three key constructs. Additionally, the link between SCI and

SC performance is associated with levels of effects on various performance dimensions that

differ and vary (Cousins & Menguc, 2006; Devaraj, Krajewski & Wei, 2007; Schoenherr &

Swink, 2012, 2015), rendering the findings of such studies to be inconsistent. More precisely,

the impact of both internal integration and external integration on performance has been

associated with mixed findings. This inconsistencies could be attributed to the measurement of

integration as one construct and also elimination of internal integration in most studies (Flynn

et al, 2010; Ataseven & Nail, 2017).

For example, while Cousins and Menguc (2006) show a positive association between SCI and

suppliers’ communication performance, Devaraj et al (2007) also registered a positive

association between SCI and performance, but found that customer integration and

performance were negatively related. Similarly, Flynn et al (2010) found that supplier

integration and operational performance are not directly related. Schoenherr & Swink (2012)

also found distinct associations between SCI and both financial and operational performance.

Flynn et al (2010) found in both their configuration and contingency approach that SCI was

associated with both operational and business performance and that customer and internal

integration were more strongly associated in enhancing performance than supplier integration.

In general, the literature on the relationship between SCI and performance has very mixed

findings. In order to advance theory development, it is important to ascertain whether

moderating factors underpin certain relationships which is the key concept of contingency

theory and is highly important in situations where the findings about relationships are mixed

(Ataseven & Nail, 2017). Furthermore, much of the literature in this area have been conceptual

in nature coupled with the dominance of inductive methods such as case studies involving two


10

dyad relationships and occasionally SC triad in the majority of empirical works (e.g. Cooper

& Slagmulder, 2004; Seuring & Muller, 2008; Burritt & Schaltegger, 2014). Although case

studies research can be very helpful for gaining a deeper insights and understanding into factors

that influence the installation of MASs in inter-firm relationships (van der Meer-Kooistra &

Vosselman, 2000), the findings associated with these studies show lack of generalizability and

overall conclusions. This work contributes to filling these gaps.

1.4 Study Objectives

The broad objective of this study is to examine the impact of the relationship between hospital

SCI and four dimensions of MAS design on hospital SC performance. Specifically, the study

seeks to:

1) Examine the association between hospital SCM contextual factors and dimensions of

the MAS information in Ghana.

2) Investigate the mediating effect of the MAS dimensions on SCM contextual factors and

Ghana’s healthcare SC operational performance.

3) Examine the moderating effect of SCM contextual factors on the relationship between

MAS design and Ghana’s healthcare SC operational performance.

1.5 General Research Questions

The following research questions underpin the study:

1) Do the MASs designed and used by healthcare institutions in Ghana align with

internal and external integration, level of information sharing and the risk and

uncertainties of the health supply chain?


11

2) To what extent do the MASs used in Ghana’s healthcare organizations offer a

mediating role between SCI (internal and external), level of information sharing,

and supply chain risk and uncertainty and hospital SC operational performance?

3) To what extent do SCI (external and internal), level of information sharing, and

supply chain risk and uncertainty have an interaction effect on the association

between MAS information and hospital SC operational performance of healthcare

institutions in Ghana?

1.6 Significance of the Study

The design and use of MAS information in both private and public-sector organizations has

often been encouraged by government policies across the globe (Macinati &Anessi-Pessina,

2014). However, exploitation into its actual usage and benefits in healthcare settings, and

supply chains in particular largely remains a void. Consequently, a study that provides insights

into the performance implications of the causal paths linking SCI and MAS information

characteristics in healthcare context would both extend existing knowledge and provide useful

indications to healthcare managers and policymakers. In addition, modelling and measuring

performance outcomes relating to the MAS-supply chain intersection is needed to permit an

examination of the question of whether an appropriate or inappropriate ‘matching’ between

SCI and MAS information in the hospital SC leads to higher or lower performance. The

findings in this regard, will undoubtedly serve as a reference point (or a guide) for the design

and implementation of MAS information in the SCM context of healthcare institutions. In

particular, it provides empirical evidence that could serve as guiding principles to the health

systems’ pricing decisions, performance assessment, process improvement, and cost reduction

strategies associated with their inter-organizational relations (e.g. suppliers) and the extent of

fit (or alignment) of the managerial and cost information with these decision needs.


12

This work, apart from the foregoing, also provides information that relates to the current state

of the managerial and cost information systems designed and used by healthcare institutions;

i.e. whether they positively respond to hospital SCM issues and provide management with the

relevant SCM information for effective decisions. In other words, whether the characteristics

of the MASs information and the SCM metrics have any linkage to positively impact on

performance. Given that the accounting systems in these institutions have perhaps reached their

state of obsolescence and hence become dysfunctional due to evolutionary changes in the

processes and procedures of hospital operations that have not been incorporated into their

accounting systems, this work stands in the position to provide information that could lead to

addressing some of these deficiencies and weaknesses. To achieve this, it shows the extent to

which the MAS information designed and used by healthcare institutions fits (or aligns) with

context factors in the inter-organizational domain, and whether per the outcome, these systems

need further improvement or total overhaul.

Indeed, the identified weaknesses and deficiencies in the health SCM system suggests that the

managerial and cost information systems associated with the health logistic and supply chain

have perhaps become obsolete and outdated, and hence, no longer support the strategic

direction of healthcare management although the systems are perfectly adequate for financial

reporting purposes. In this regard, issues on value relevance of the current MAS information

needed for management decision making in terms of the sophistication level of the managerial

and cost information are highly imbedded in this work. For example, issues relating to

advanced accounting techniques such as Activity-Based Costing (ABC) (which is found in

most hospitals’ accounting system) and knowledge of the application of modern costing

concepts become highly relevant.


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1.7 Outline of Remainder of Thesis

The remainder of the thesis is organized into seven chapters as follows: Chapters 2 and 3 are

devoted to systematic literature reviews. Chapter two discusses the study’s context, MAS

design and the health supply chain; its distinguishing features from that of the mainstream

discrete parts manufacturing and consumer goods, and the decision-functional role of MAS

information in minimizing hospital supply chain costs. In chapter three, a general discussion

of the study’s theoretical underpinnings is presented together with empirical reviews of the

empirical contingency-based management accounting studies. Chapter four presents the

theoretical framework, and following a refinement of the research questions develops and

formulates the verbal hypotheses. The research methodology is presented in chapter five. It

comprises a detailed discussion of the research design, data collection procedures and

processes, and the approach to statistical tests and analysis. In chapter six, the survey analysis

and results are presented. This is followed by a discussion and interpretation of the findings in

chapter seven. Chapter eight concludes the study with a summary of main findings, key

contributions and future research directions.

1.8 Chapter Summary

In this chapter, the motivation for investigating the contingency fit relationships between MAS

design and SCI, level of information sharing, and SC risk and uncertainty, and their interaction

effect on hospital SC performance in Ghana has been specified. Given that the design and use

of MAS information is known to be commonly associated with organizational contextual

factors that can leverage organizational performance, such studies lack empirical evidence in

the inter-firm exchanges domain in service-oriented organizations. Existing MAS-SC studies

have predominantly not only dominated product type organizations such as discrete parts

manufacturing and consumer goods but also, emphasized the fit in the transaction context to


14

mainly mitigate the risk between transaction partners. However, an incomplete and potentially

based picture of inter-firm control may be presented when the MAS decisions are explained

using transaction attributes alone as a focus. The actual observed patterns on MAS use, and the

contextual factors that underpin MAS design in the inter-firm exchanges domain are not fully

explained by the TCE. In line with these voids, this study tests the contingency effects of the

MAS-SC performance using empirical data from healthcare institutions in Ghana. Evidence of

the weaknesses and deficiencies that characterize the procurement, warehousing, storage and

distribution arrangements of health commodities which ultimately results in high prices to end

users, have major implications for MAS design in the SCM of Ghana’s healthcare institutions.

Finally, the study outlines the structure and logical sequence of the remainder of the thesis.


15

CHAPTER TWO

CONTEXTUAL BACKGROUND, MANAGEMENT ACCOUNTING

SYSTEM DESIGN AND THE HEALTH SUPPLY CHAIN

2.1. Introduction

The previous chapter introduced the research problem and the motivation for the study. This

chapter presents a review of the study’s context, the MAS information characteristics and SCM

interactions, and how they combine to impact on the health SC performance. The chapter

begins with a discussion of the importance of hospital SC integration and its effect on

performance. In this case, recent issues on management accounting and SC performance are

discussed. It also gives a description of the study’s context followed by the nature of MAS

design and its possible functions in SCM decisions. The chapter also presents the SCM

challenges that characterize the research sight. It discusses the structure of Ghana’s health SCM

system which basically links three key distributors of health products: the Central Medical

Stores (CMS), the Regional Medical Stores (RMS) and Service Distributed Points (SDP) (or

hospitals). A background of the weaknesses and deficiencies that characterized the current

operations of Ghana’s health logistics and supply management system is also presented.

Furthermore, the current state of the accounting system in the health sector is briefly explained

followed by the numerous reform projects launched by governments since 1998 which were

aimed at transforming the health commodity and supply management system. Finally, a review

of the health SC in terms of levels of complexity and sophistication in comparison to that of

mainstream business organizations is also presented. The chapter ends with a summary.


16

2.2. Recent Studies on MAS Design and Hospital SC Performance

In this section, a reportage of recent trending issues on the MAS design, SCI and hospital SC

performance both at the local and international levels is presented. According to Ab Talib,

Abdul Hamid and Thoo (2015) the existing body of knowledge on this topic has a number of

trending issues arising from recent studies. These studies investigate the antecedents,

prerequisites, requirements, or critical success factors that affect SCI and hospital SC

performance. Integration of the SC is currently, considered as one of the widely discussed

topics in the SCM research (Polater and Demirdogen, 2018; Abdallah, Abdallah & Saleh,

2017). Polater and Demirdogen (2018) argue that intensive integration between external

partners and internal processes enhances hospital SC operational performance hence, underlies

the basic motivation for healthcare SCM. While hospitals integrate their internal capabilities to

provide a better service for patients, they benefit from their partners’ resources as well. Several

research studies (e.g. Zhao and Huo, 2013; Skandrani et al. ,2011; Kazemzadeh et al. ,2012;

Lee et al., 2011; Rahimnia and Moghadasian, 2010) have argued that an effective integrated

SC is one of the key competitive advantages of leading organizations and is currently one of

the widely discussed topics in the SCM research.

The concept of SCI has therefore become prevalent in both the public and private health sectors

because of the impact it has on enhancing operational efficiencies, customer satisfaction and

quality of care as well as minimizing wastes and medical errors (Schneller .and Smeltzer, 2011;

Polater and Demirdogen, 2018). The supply of medical products has also become critical to all

hospitals so that performance in terms of costs, quality, responsiveness and patient satisfaction

can be enhanced (Asamoah et al, 2011). Consequently, health providers in both the public and

private sectors have paid more attention to healthcare services and their suppliers in order to

achieve higher service quality, lower costs, and enhance operational performance (Hammad,

Jusoh and Ghozali, 2013). In addition, due to the increasing trend in healthcare spending on


17

supplies, both governments and private health providers are compelled to focus on the

assessment and improvement of hospital operations efficiency especially in relation to

suppliers of medical products and the overall logistics and SC (Watcharasriroj and Tang, 2004).

According to Chakraborty, Bhattacharya and Dobrzykowski (2014) the general perception of

organizations about SCI and SCM in general has budged toward managing relationships rather

than just the purchasing function. These relationships are however, closely related to MAS

information (Dekker, 2013, 2016) which like the objective of SCM, aims at reducing SC cost

and adding value (Burritt & Scaltegger, 2014). Abdallah et al (2017) provide useful insights in

boosting hospital SC performance when they examined the effect of trust (a key construct of

MAS design) with suppliers on hospital-supplier integration and hospital SC performance.

They established that high levels of SCI not only improve hospital SC performance but also

enhance the transformation of trust benefits into SC performance. The paper further documents

that hospital SCI partially mediates the relationship between trust and hospital SC performance.

In another study, Ataseven and Nail (2017) undertook an extensive examination of the

association between SCI and various performance dimensions using a meta-analytical

methodology. They find that supplier integration, internal integration, and customer integration

have a significant impact on both operational (cost, quality, delivery, and flexibility)

performance and financial performance of a firm. They point to the specific relationships

between SCI and performance that need to examined further within contingency framework to

discern the role of moderating factors. Finally, they offered insights on the dimensions of

integration that have the largest breadth and depth of impact on various performance measures

which is relevant for managerial decision-making.

On the local front, Adu-Poku, Asamoah and Abor (2011) examined the logistics SC system of

the Adansi South District Health Directorate from users’ perspective. They document that


18

adequate information (i.e. management accounting information) needed to support effective

and efficient SCM activities is lacking hence, a poor collation of patients’ needs during

procurement. They also identified problems such as delay in the procurement system in the

district as a result of poor delivery time by suppliers, delay in evaluating bids. Manso, Annan

and Anane (2013) also assessed the logistics management of the Ghana Health Service and

reported several shortcomings associated with the health SC. They found poor procurement

planning and budgeting, poor quantification and forecasting, delay in the procurement process

and order processing, and delay in receiving insurance claims to characterized the health SC.

Asamoah et al (2011) examined the performance of the SCM activities relating the

pharmaceutical SC for artemisinim-based combination therapies (ACT) in Ghana. They

reported that both the public and private SC networks for the ACT depict evidence of long-tern

relationships with external suppliers and factor interdependence. They also reported of the high

prices that characterized the general price level of the although highly subsidised ACT as a

result of disruptions in the health SC. Frequent disruptions in the link between external

suppliers which was found to be the main threat to the health SC, result in price increases of

health commodities at the pharmacy level. As can be seen from these deficiencies, a study to

examine the interaction effect of the MAS design and SCI is crucial because effective MAS

design provides information for the smooth running of the logistics system (Burritt &

Scaltegger, 2014). The next section explains the health SCM system in Ghana.

2.3 Ghana’s Health SCM System

The mission of the Logistics, Clearing and Warehousing Department of GHS reads: Logistics:

‘‘Our mission is to offer, our clients, a competitive advantage through superior transportation

of logistics services. Through timely communications and quality information, we will meet

and exceed our client’s expectations of service. Also, through our commitment to provide


19

excellent service, Value added service, continued innovation in management, we will

accomplish our mission: 2) Warehousing: To ensure that regular availability and uninterrupted

supply of health commodities are delivered to health institutions at affordable prices. Using

best practices in storage and distribution of quality, we are capable of responding to the total

commodity requirement and as a centre of excellence, and safe efficacious health commodities”

(Ministry of Health (MOH), 2012, p.2).

The SC system of Ghana’s healthcare which manages health commodities through a three-tier

system, is made up of drug manufacturers, suppliers (wholesalers, distributors, and retailers),

the Central Medical Stores (CMS), Regional Medical Stores (RMS), Service Delivery Points

(SDP), and the transportation networks. Through this SC, supplies and drugs as well as

contraceptives are managed and sent to health facilities across the country. The receipt, storage

and distribution of all medical supplies after procurement by the Ministry of Health (MOH) is

the responsibility of the CMS. The CMS in turn, supplies the lower levels of the tier. Depending

on their geographical location, health facilities (SDPs) receive their supplies from the

appropriate RMS. However, the management of vaccines is slightly different from other

medical supplies. These are managed through refrigerated facilities and a network of

warehouses of cold storage across the regions.

The MOH exercises overall oversight control including policy formulation, evaluation and

monitoring of progress in achieving set targets for the whole system. The service delivery and

Teaching Hospitals are largely undertaken by the Ghana Health Service (GHS). Both of these

institutions constitute the bulk of the MOH institutions under the public health system. A four-

tier system made up of regional, district, sub-district and community constitutes the health

system delivery while the management of health services and health supplies as stated upfront

is operated on a three-tier system comprising the CMS, the RMS, and the SDP. The CMS is

responsible for procuring drugs and vaccines that are primarily financed by external financiers.


20

Occasionally, and this is an exceptional case, the Teaching Hospitals and the Regional

Hospitals procure directly from suppliers but approval must be sought from the MOH. Thus,

whilst the logistics and supply management system is centralized, the healthcare delivery

system is decentralized. Together with suppliers of drugs and other medical supplies at both

local and international levels, pharmaceutical manufacturers, wholesalers, distributors, and

retailers, transportation networks and other distribution networks constitute the SCN in the

health sector.

2.4 Managerial and Costing Systems in Ghana’s Healthcare Management

As a recap of chapter one, the managerial and cost information systems of most healthcare

institutions in Ghana have reached the state of obsolescence and dysfunctional due to

evolutionary changes in the processes and procedures of hospital operations that have not been

incorporated in their accounting systems. The current MASs have little value for management

decisions in the inter-firm exchanges domain as there is limitation in the use and sophistication

of cost and managerial information. Advanced accounting techniques such as Activity-Based

Costing (ABC) are perhaps not applied and modern costing concepts are largely unknown in

most healthcare institutions. Managerial and cost information are mostly associated with

decisions on pricing rather than process improvement, performance assessment, or cost

reduction strategies. The managerial and cost information systems seemed to have become

outdated and no longer support the strategic direction of healthcare institutions though the

systems are perfectly adequate for financial reporting purposes.

There are also illogical relations among operating cost of facilities, value of commodity

throughput and populations served at various levels. Neither commodity throughput nor service

population appear to drive costs as they should be in an efficiently and rationally operating

system (Manso et al, 2013). Non connection between costs and cost drivers – clear indication


21

of non-linkage between budgeting process and both commodity throughput and service

populations (WHO, 2009; Denkyira, 2015). Currently, there is the unconventional occurrence

of public sector prices for highly subsidised commodities being higher than private sector

prices (Asamoah et al, 2011; Manso et al, 2013). However, Kelle, Woosley and Schneider

(2012) noted that by paying attention to the three dimensions of SCI (supplier, customer and

internal integration) in the inter-organizational linkages and their alignment with accounting

information characteristics, a hospital can improve its performance through the performance of

other organizations in the SC.

In addition, a hospital can improve upon its procurement and logistics activities by aligning its

inventory management system with a just-in-time MAS technique. Another example is where

hospitals provide a perfect fitting information about their inventory needs by helping their

suppliers more efficiently plan their production schedules to meet their needs. In this case

hospitals’ operating costs are minimized because they perform their procurement and logistics

activities in an efficient manner. In addition, they have less of their capital tied up in stock (or

inventory). Furthermore, sharing of knowledge and expertise in the optimum is easier, and

perhaps improve operations and even provide a competitive advantage in a situation where the

MAS information characteristics are well-fitted with SCM context factors. Hence, the perfect

alignment of SCI with the MAS information characteristics is highly critical to SC performance

in the healthcare setting (Aidemark & Funck, 2009). In this regard, improvements in hospital

SC performance have increasingly gained importance as health care institutions strive to

improve upon their operational efficiency as well as minimize costs (Chen et al, 2013).

2.5. Reforms in the Health Logistics and Commodity Management System

A five-year health sector reform under the Health Sector Support Project (HSSP), and

supported by the World Bank, was undertaken by the Ghana government from 1998 to 2002.


22

From 2002 to 2006, another health sector reforms project under the five-year medium-term

was undertaken. During this period, several reform packages were implemented under several

health reform initiatives. These initiatives were designed to specifically ensure that the reform

packages are successfully implemented.

In 2008, a review to assess the strengths and weaknesses of Ghana’s health commodity supply

and management system was undertaken by the USAID DELIVER PROJECT and

Management Sciences for Health (MSH)/Strengthening Pharmaceutical System (SPS)

(Adegoke, Bruce, Chimnani, Eghan, Tetteh & Veskov, 2008). In collaboration with the

Procurement and Supply Directorate (PSD) and the Foods and Drugs Authority (FDA), the

existence of weaknesses and inefficiencies as ‘gaps’ in the health SCM system was confirmed

(WHO, 2009; MOH, 2012). Prior to this survey, the Ghana Health Service (GHS) had in 2007,

and based on an extensive review, discovered weaknesses in the pharmaceutical SCM system

as well (MOH, 2012).

Based on these voids, the health supply management system was again subjected to multiple

transformations with varying suggested, tested and tried models. For example, the Master Plan

placed much emphasis on the promotion of rational use of drugs, improvement in access to

medicines, increase in quality assurance, and improvement in supply management system. In

spite of these interventions, the identified gaps have remained unaddressed and widened rather

than bridged1. The system as shown in Fig 2.1, is currently characterized by unsound

procedures and inaccessibility of medical products. There is also high and much higher

transportation costs at service delivery points than required for an efficiently and rationally

operating system (Denkyira, 2015).

1 8th Annual General Meeting and Continuous Professional Development Programme on the Health Services Supply Chain Practitioners Association, Ghana (HESSCPAG) Held in Cape Coast on 31st October, 2015


23

The system also reveals ineffective, sub-optimal and significant non-value-added steps in the

procurement, warehousing and distribution of medical commodities which results in higher

delivery cost for the end user. For example, a significant proportion of a health budget is

represented by storage and distribution costs, and transportation/freight costs can represent

multiples of the value of the drugs distributed to remote areas (WHO, 2009). The general price

level of highly subsidized drugs such as ACT remains relatively high. In addition, procurement

and distribution activities are hampered by unsound procedures, poor information linkages and

weak operation systems to reliable inventory control and demand information (Denkyira,

2015).

Figure 2.1: Ghana’s Health SCM System

(Source: WHO/MOH 2012) Note: CMS = central medical stores, RMS = regional medical

stores; PU = procurement unit; DHMT = district health management team; RHA = regional

health administration; SSDM = CBD = community based

In the wake of these deficiencies, it is believed that healthcare expenditure and associated

problems in the SCM arena which in many countries, account for more than 10% of GDP

(Ferry & Gebreiter, 2016), will increase further due to increasingly expensive medical


24

technologies, aging population and their mutual interactions. Ferry and Gebreiter (2016) noted

that health reforms are undertaken based on expensive medical technologies, aging populations

and their purported implications for healthcare costs. They argue that issues relating to

healthcare costs are historically contingent rather than inescapable consequences of

demographic and technological change, and that one constitutive and reflective of such

concerns is health service accounting. Clemens, Michelsen, Commers, Garel, Dowdeswell and

Brand (2014) showed that hospital reforms place emphasis on cost containment measures

rather than embarking on structural redesign of the health sector. They emphasized that,

problems associated with effective healthcare management can be resolved through effective

control of health costs and increase efficiency in the health service. Gyimah, Yellu, Andrews-

Annan, Gyansa-Lutterodt and Koduah (2009) undertook an in-depth assessment of the state of

medicine supply management system in Ghana. The research revealed the procurement Law,

ACT 663, as being present and active in guiding the procurement and supplies of medical

products throughout the country; good infrastructure for central level warehousing as the

strengths in the system, and well-defined distribution network. The following were however,

found to be a weakness in the medicines supply management system: limited circulation of

procurement guidelines and the National Essential Medicines list was found to be outdated.

Other findings were; quantification and forecasting of drug supply and related cost information

were found to be weak, and this arises from non-existence of effective coordination that links

the central and the periphery. Rules and guidelines to facilitate the recruitment of personnel,

other engagements and monitoring of drug supply were not in place. Suppliers’ information for

forecasting and quantification decisions which flow to the central procurement unit from the

facility level to was not forthcoming. The engagement of suppliers was not based on any set of

guidelines and rules. Although internal stock control systems have been developed by most

facilities, standardization for effective management is highly crucial. Optional storage and


25

handling equipment are not allowed by several of the lower level facilities and no standardized

structural plan that ensures adequacy of storage conditions has been developed.

Implementation of the proposed scheduled delivery system has not taken place. Also, the

supervision and monitoring of drug supply management activities which is supposed to take

place on regular basis have not been implemented across levels.

These weaknesses and shortcomings have negatively impacted on the system within the

logistics management system. Some of these include poor cost control in the health system as

well as reduction in healthcare services’ quality offered to the public. Schneller and Smeltzer

(2006) note that, traditionally considered an area of low value in the health care industry is

inventory management and distribution. However, extant literature suggests that potential

revenue can be generated as well as tremendous cost savings with improvement in the

management of inventory and distribution. A hospital can minimize its total expenses through

inventory management and distribution of finished medical materials, by at least two percent.

This does not represent just the amount providers spend on supplies and pharmaceuticals but a

percentage of total expenses.

In addition to a comprehensive transport policy, there was also a decentralized policy

formulated for the regional, district, sub-district and community level of health care.

Transportation plays a significant role in the delivery of health services and is considered an

indispensable resource in the health sector. Procurement, operations, maintenance, disposal and

replacements are also undertaken within a prescribed guidelines and are properly

communicated to the regional and district levels. Although the transport policy at the regional,

district, sub-district and community levels of health care has been formulated and established,

little has been achieved in the transport system. This unfortunate situation is even more

pronounced and more deplorable for health care delivery at the community level.


26

The major logistical functions in the health SC include forecasting, planning, procurement,

warehousing and the distribution of essential drugs, vaccines and contraceptives. The central

ministry offices across the regions are responsible for the performance and delivery of these

functions. Notoriously, these systems have been inefficient as well as incapable of offering

adequate supplies on a timely basis in many cases. It is therefore important that all the logistical

functions are integrated together by the organizations and other industry players in order to

ensure that healthcare logistics are effectively and efficiently managed. This will ensure regular

availability of health commodities and medicines at all levels all the time. This is largely

achieved by improving management efficiency through effective coordination of the

administration within the sector and the supply systems. However, the system still exhibits lack

of effective coordination.

A study by Bossert, Bowser, Amenyah and Copeland (2004) on the Ghanaian health logistics

systems finds that a bigger space in decision making was associated with better performance

for budgeting, financing and planning; whilst inventory control, procurement, logistics

management information systems, storage, client contact and training, were found to have

performed worse. The author sees some logistics functions within the logistics systems as one

that can be decentralized effectively while for some others, they should remain centralized. In

a similar situation, the report by the USAID/DELIVER project (2011) in Addis Ababa

indicated that information is required for the status of stock levels in order to make vital

decisions about procurement and resupply of drugs. It is also to ensure that hospital logistics

are essentially managed effectively to maintain transparency and accountability within the

health care sector. The report however, showed that the information was improperly organized

or not always easily available and this situation often leads to stock-outs, overstocks, and expiry

of drugs.


27

2.6 The Health Supply Chain

The hospital SC is unique and have some features not found in those of typical industrial

settings in many respects (Shah, 2004; Chen et al, 2013). First, as noted by Beier (1995),

hospital suppliers are mission critical to sustaining the public health. The needs of patients are

diverse. Hence, adequate, accurate and constant supplies of medical products are required by

clinical operations. Barlow (2010c) has noted that SCI translates its influence and reach to just

about every operational, clinical and performance areas in contemporary hospitals. Second,

hospital supplier selections are underpinned by physician preferences which are medically-

trained oriented, context-specific demands, and experience with specific brands as against

those of discrete parts manufacturing and consumer goods which normally involves the

forecast of production/sales as well as managing costs. As Roark (2005) points out, most often

there is a non-linkage between those who make the purchasing decisions and those who do the

actual buying. However, integration of the supply chain with perfect alignment of MAS

information bridges this gap. Third, as a consequence of the rapid technological and

innovations in medical practices that characterized the diverse types of healthcare supplies,

there is an intensive requirements for healthcare SC management information and effective

sharing of knowledge (Shah, 2004).

A concise definition of the health supply chain is given by the Rockefeller Foundation (2008,

p.1) as:

‘‘the network of entities that distribute products through planning and sourcing as well as

managing the information and finances associated with the distribution from manufacturers

through intermediate warehouses and resellers to dispensing and health service delivery

points.’’


28

This definition implies that the health supply chain embodies several key players with the

pharmaceutical supply chain being a major component as it captures the chemical plants,

pharmaceutical manufacturers, wholesalers, distributors, and retailers of drugs. Thus, the

health supply chain as defined above is complex, sophisticated and much more expensive to

operate (Shah, 2004). This is perhaps due to the multiplicity of institutional formats

(government, private, non-profit/profit oriented) as well as stakeholders (health administrators,

physicians, professionals, investors, governments, and community), complexities of

technologies being used, distinctive characteristics of health service operations, and dynamic

internal and external environment with diversity in industry and reimbursement policies across

the globe.

While many healthcare organizations recognize the adoption of SCM practices as vital to

overall healthcare management it becomes problematic when the methods, best practices, and

techniques originally designed for the mainstream industrial settings are directly applied (Jan

de Vries & Huijsman, 2011). In particular, the difficulties that confront healthcare institutions

when adopting a SCM philosophy is clearly manifested in the numerous projects they embark

upon. Often, these are targeted at the implementation of planning systems from an integrated

perspective regarding patient flows, and the establishment of partnership relationships among

different healthcare organizations.

Coupled with the foregoing challenges is the fact that for over two decades that SCM has been

around in healthcare institutions, its fundamental key concepts are misunderstood by health

professionals, which makes it more problematic in applying the SCM principles of mainstream

business organizations to healthcare operations. Often, the term ‘supply chain management’ is

used by healthcare professional with no firm idea about its underlying concepts (Schneller &

Smeltzer, 2006). Unfortunately, the health supply chain has, in most parts of the world, been

confined to contract and purchasing management as group purchasing organizations (Kwon,


29

Hamilton & Hong, 2011). Even with ordinary SCM of mainstream business organizations, it

goes beyond purchasing and contract management. Perhaps, the limited knowledge in the basic

SC concepts and principles have resulted in the narrow definition (e.g. purchasing) by decision

makers in healthcare, and not paying attention to rather the vital parts of the SC unexplored or

totally neglected and hence suboptimal outcomes. The next two sub-sections present the

elements of the health SC and its effective management to minimize costs.

2.6.1 Elements of the Health Supply Chain

Like the mainstream business organizations, elements of the health SC comprises capacity and

production planning, inventory and distribution planning, facility location and design, and

detailed scheduling (Shah, 2004). However, previous studies in the management accounting

field have, to a greater extent, focused on the inventory management component with little

attention given to the other elements (Samuel, Gonapa, Chuadhary & Mishra, 2010). It is

extremely rare to find studies on MAS design for facility location, production planning and

control, or scheduling and distribution even in mainstream business organizations let alone the

health SC.

It also appears that the health SC has been limited solely to the pharmaceutical SC since past

studies focused on the latter in relation to MAS design. Whilst the pharmaceutical SC

constitutes the bulk of the health supply system, it does not provide a complete description of

the players in the healthcare system and hence not fully represented by the health SC. Among

the key players in the healthcare industry include pharmaceutical manufacturers (primary and

secondary), wholesalers/distributors, hospitals/clinics, non-hospital pharmacies, governments,

regulatory bodies, investors, and consumers.


30

2.6.2 SCM in Healthcare Context

Healthcare SCM encompasses the universally identifiable elements such as facility location

and design, inventory management, capacity and distribution planning, and detailed scheduling

associated with business organizations (Shah, 2004). This is due to the fact that the handling

and treatment of customers (patients) of healthcare organizations is entirely different from

customers of mainstream business organizations. As Kwon, Kim and Martin (2016) contend,

the application of SCM practices in the healthcare sector not only involves the

production/procurement, storage and distribution of physical commodities such as drugs,

pharmaceuticals, medical devices and health aids, but also the flow of patients. The rate at

which patients demand various healthcare services underpins decisions which match demand

and supply throughout the supply chain.

Decisions on patients’ logistics and coordination issues are often related to the complexity and

variability of demand within a hospital. For example, a typical problem that confronts designers

of pharmaceutical SC has been that of striking a balance between future capacity and

anticipated demands (Shah, 2004). What makes it even more complex is the fact that such

decisions are often taken alongside the very significant uncertainty that emanates from

competitor activities and clinical trials.

In addition to the foregoing are other factors accounting for the complexity in healthcare supply

chains. First is the conflict between physicians and health administrators regarding the

procurement of prescription medicines which constitute the bulk of all medical supplies. Whilst

key decisions in this area are taken by physicians, they possess little knowledge and

understanding of SCM systems and practices (Scheller & Smeltzer, 2006). Second, the strong

regulatory and institutional pressures that accompany the operations of the healthcare industry.

As noted by Shah (2004), accurate sales forecasts are difficult to maintain in healthcare industry

not only because of the regulatory regime but also the difficulty in measuring the magnitude of


31

competitions that emanate from generic drugs. Third, lead times of the developmental cycles

of healthcare products are relatively longer compared to other medical devices.

Situations where the overall supply chain cycle time takes 300 days is not unusual. This is due

to the low manufacturing velocities that characterized the two stages of production and the

possible obstruction to activities by the need for quality assurance at various points. The two

stages as is evident in most pharmaceutical products include primary active ingredient

production referred to as multi-stage chemical processes or bioprocess, and secondary, referred

to as formulation production. According to Shah (2004), SC strategies and capacity planning

in relation to inventory management in the health sector as a whole are significantly affected

by the long lead times that characterize the development of pharmaceutical products. Finally,

unlike other business organizations, the distinctive nature of hospital operations coupled with

the sophisticated mix of patients makes it extremely difficult to predict supply consumption.

As Jan, de Vries and Huijsman (2011) contend, the straight forward application of industrial

oriented SCM practices in a healthcare setting is often impeded by these complexities.

In spite of these complexities, the health SC is recognised as one of the tools that have attracted

the efforts of scholars, researchers, governments, and healthcare providers in managing

healthcare costs while at the same time improving quality (Kwon et al, 2016). For example,

Pricewaterhouse Health Research Institute reported in 2008 that out of the yearly expenditure

of $2.2 trillion on healthcare, $1.2 trillion are incurred as a result of wastes in the supply

management system (Kavilanz, 2009). In Ghana, not only do the cost of distribution and

storage constitute a significant portion of the health budget (MOH, 2012), but also

transportation/freight costs represent multiples of the value of drugs distributed to remote areas

(WHO, 2009). In addition, SC costs represent the single largest health expenditure after

personnel costs (MOH, 2009). These wastes, according to Dooner (2014), are generated from


32

non-conformance to standardized cost processes which underpin the design and use of MAS

information.

2.6.3 Minimizing Health Supply Chain Cost

Although the phenomenon of SCM has been studied from several different perspectives with

emergence of several aspects such as SC optimization, network coordination, IT-enabled

supply chains, supply chain integration etc., the minimization of costs through a drastic

reduction or complete elimination of wastes as well as improvement in performance through

supply chain coordination constitute the core of SCM relationships (Jan de Vries & Huijsman,

2011). However, the contextual antecedents of SCM affect MAS design which ultimately

impact on supply chain costs. Studies such as Burns (2002) and Dacosta-Claro (2002) suggest

that about 48% of health supply chain costs are reduced through the implementation of

effective SCM practices.

However, a significant scope for improving the overall performance of the supply chain

remains unexplored (Lega, Marsilio & Villa, 2013). It has been argued that effectiveness and

efficiency coexist in supply chains (Kwon et al, 2016). This translates to mean that efficient

management of the health supply chain creates surplus in resources which can be diverted or

reinvested for the benefit of customers (patients) and other stakeholders.

2.7 Nature of MAS Design

Fundamentally, a MAS design can be viewed from two main viewpoints: integrated accounting

systems design where the MAS is integrated into the financial accounting system and the

records of the latter serve as a database for the former, or a separate system for the MAS exists

where besides the financial and tax accounting records, a third set of books is created

(WeiBenberger & Angelkort, 2011). The integrated approach suggests that the management

accounting techniques (e.g. budgeting, product costing and standard costing), reporting and


33

performance measurement etc., are derived from the database of the financial accounting

records. Such MAS design, which is typical of Anglo-American firms provides information

not only at minimal incremental costs, but also facilitates the reconciliation of the internal and

financial performance measures (weiBenberger & Angelkort, 2011).

A limitation of this approach is that depending on the underlying accounting regulation, the

data on financial accounting may be unsuitable for management control and decision-

influencing purposes (Kaplan, 1984). In the ‘separate’ or ‘dual’ design which has been

traditionally employed in continental Europe particularly in German-speaking countries (Jones

& Luther, 2005), a completely different third set of books is created alongside the financial and

tax accounting records. A third approach to MAS design referred to as hybrid form or partial

integration (Angelkort, Sandt & Weißenberger, 2009) places restriction on the integration of

management accounting and financial information. For example, information relating to top

management or confidential aspects of the databases of the financial records could be restricted.

Past studies on the MAS-contingency framework paid little or possibly no attention to the

design type of the MAS adopted by organizations and the extent to which such designs might

be affected by contextual factors. To a greater extent, the focus has been on the existence of a

particular technique with varying descriptions of the MAS other than the extent and manner of

their use (Otley, 2016). Additions to the MAS variable rather than identifying which design

type is suitable within a particular context has been the norm. For example, following the focus

away from the use of budgets as the MAS variable, subsequent studies have conceptualized

and defined the MAS from diverse perspectives.

In the study of Chenhall and Morris (1986) and that of Bouwens and Abernethy (2000) for

example, the MAS was defined in terms of broad scope, timeliness, aggregation and

integration. In Gerdin (2005a), the MAS was described based on its rudimentary, broad scope


34

and traditional focus. The study of Gerdin (2005a) was based on the assumption that certain

levels of information provided by the MAS is irrelevant to some context factors hence rarely

leading to such a classification. Others have conceptualized the MAS based on Porter’s (1985)

strategic priorities of low cost strategy and differentiation strategy. However, drawing a

distinction between the MAS and the design type is an important dimension of the MAS-

contingency combination. The reason for the distinction is that the type of design adopted for

the implementation of a particular MAS information requires that the system ‘matches’ with

contextual or situational factors. This requirement of ‘matching’ the MAS with the context

factors is termed contingency-fit (Otley, 1980). As Otley (1980, p.27) states: ‘‘specific aspects

of an accounting system must be identified by a contingency theory and this must be related to

certain defined circumstances as well as demonstrate appropriate matching’’.

2.8 Functions of the MAS Information in Healthcare Management

Although the management accounting function has been conceptualized from several different

angles and assigned different variables to its design and measurement, two main functions

characterize its design: 1) reduces agency costs and 2) facilitates managerial decision-making

process (Davila & Foster, 2005). The former focuses on effective monitoring of performance

in the contracting process between principals and agents while the latter focuses on the update

of managers with both financial and nonfinancial information in their decision-making process.

In the principal-agent setting, managers are contracted to act on behalf and in shareholders’

interest who have contributed economic resources to the affairs of the organization.

Although the healthcare industry ranks among the largest economic sectors in many countries,

and its complexity and richness allows a broad range of economic theories on compensation

contracts, competition, and costs and management behaviors to be studied, the model of


35

principal-agent relationships of typical mainstream business organizations is not a descriptive

of the typical healthcare setting (Cardinnaels & Soderstrom, 2013). Therefore, the health

system requires a more sophisticated and detailed MAS information as the cost information

generated by hospitals are subsequently used by regulatory bodies for updating or setting prices

associated with public health services (Raulinajtys-Grzybek, 2014). This is because unlike

mainstream business organizations, healthcare providers in most parts of the world operate on

a refund basis (e.g. Ghana’s Health Insurance Scheme) and are legally required to allocate costs

in a predefined manner which MAS information provides (Sutherland, 2015). This legal system

in most cases, follows the step-down cost allocation mode from service departments (such as

administration, laundry, out-patient department, etc.) to internal revenue-generating

departments (such as laboratory, surgery, maternity, etc.) and to patients in some cases.

2.8.1 Costing Systems Design in Hospitals

Inputs such as capital, labour, materials and supplies are normally employed by hospitals to

offer healthcare services consumed by customers (patients) during their episode of care. Since

the services offered (including hours of nursing and X-Ray etc.), are normally associated with

intermediate products, business rules and regulations that govern the generation and

apportionment of costs associated with hospitals’ intermediate products are in most hospital

operations, codified in activity based guidelines (Chapman, Kern, Laguecir, Angele-Halgand,

Angert & Campenale, 2013). The assignment of intermediate product costs to patients’

hospitalization is a result of the output generated by activity-based costing (ABC) systems. As

a consequence, ABC systems have widely been recognized as costing system designs that

provide cost reduction and better cost management information in hospital operations

(Cardinaels, Roodhooft & van Herck, 2004; Sutherland, 2015). ABC systems provide more

detailed costing information relating to hospital activities that yield better cost aggregation for

effective decisions.


36

Despite this wide recognition and advantages associated with such costing systems as

providing cost-effective solutions to hospital operations, it has generally been documented that

the effective design and efficient implementation of such costing systems is largely influenced

by hospital specific factors (Devine, O’clock & Lyons, 2000; Lawson, 2003; Cardinaels et al,

2004). Issues bothering on fuller support of medical personnel for the use of particular costing

systems, internal financial agreement involving physicians’ type of contract, awareness of

problems with existing costing systems etc., need to be taken into account. In particular, it has

been established that unlike mainstream business organizations, different approaches are

required in cost system design in hospitals, and that the interest of physicians should not be

underestimated in the process of cost systems’ redesign.

2.8.2 Formal MAS Design in Health SCM Decisions

Whilst the accounting literature provides anecdotal evidence of the inappropriateness of

traditional MAS practices to capture and provide SC data for managerial decisions (Otley,

1980; Schulze, Seuring & Ewering, 2012), and the subsequent significant eroding of

confidence in the MAS, little empirical studies that systematically examine the link between

SCM and management accounting performance have been accumulated. The traditional

systems which focused primarily on cost control using tools like variance analysis, marginal

costing, responsibility accounting, standard costing, etc., are task segregation and efficiency

oriented, and well suited for mass producers of standard products with unchanging

characteristics (Chapman et al, 2013). In addition, the implicit task segregation in variance

analysis and responsibility accounting is antithetical to the cross-functional coordination

required in SC networks.

It has been argued that formal MAS design in healthcare organizations has, over the years, been

problematic despite its relevance in addressing concerns from the growing regulatory and

competitive pressures (Aidemark & Funck, 2009; Cardinaels & Soderstrom, 2013). Issues


37

bothering on goal congruence’s reliance on monetary incentives, the high degree of influence

over operational processes by physicians and nurses, the imposition of a wide range of priorities

by stakeholders who are classified as highly influential, and complex and diverse work methods

and objective functions, and austere budgets that generate unparalleled complexities for

effective design and use of MAS, are just a few of the numerous challenges faced by hospitals

(Abernethy et al, 2007). In addition, it has been reported by many studies in management

accounting (e.g. Abernethy & Stoelwinder, 1995; Jones, 2002; Abernethy et al, 2007) that the

regular conflicts that arise from health administrators’ professional objectives and that of

clinicians2 has been noted to be a hindrance to, and curtailment of effective MAS design.

Notwithstanding, it is strongly believed that effective design and use of MAS information

enhances the identification of value-adding processes and costs across organizational

boundaries hence investments in innovative and sophisticated cost-accounting tools and

systems by healthcare organizations in recent years (Cardinnaels & Soderstrom, 2013). To this

end, past studies in management accounting (e.g. Bai, Coronado & Krishnan, 2010; King &

Clarkson, 2015; Harlez & Malagueno, 2016) have called for research that provides more

insights and understanding of the design and use of MAS in hospitals. By this directive, the

antecedent conditions of effective performance measurement systems in hospitals have also

been emphasized by prior literature (Ballantine, Brignall & Modell, 1998; Cardinaels &

Soderstrom, 2013).

2.8.3 Inter-Health Organizational Cost Management in Supply Chains

The existence of an organization is normally linked to another organization which may be a

supplier of raw materials and/or other services for the running of the former, or a buyer of the

goods and/or services produced. Based on these arrangements, organizations of all kinds are

2 Are in dominance when it comes to core operations of hospitals but the implicit assumption is that they can be controlled through hospital strategies


38

said to be embedded in supply chains (Dekker, 2016). Like mainstream business organizations

that comprise discrete parts manufacturing and fast-moving consumer goods, healthcare

institutions are embedded in a network of supply chains made up of the focal organization,

suppliers upstream and buyers downstream. For example, none of the constituents of healthcare

delivery, i.e. hospitals/clinics, primary and secondary pharmaceutical manufacturers,

wholesalers, distributors, and retailers of medical suppliers and other health commodities exist

in isolation.

The non-existence of standards for the definition and composition of costs and the

inappropriateness of intra-firm cost accounting tools in the SCM context (Schulze et al, 2012)

have resulted in the development of inter-firm cost accounting tools referred to as inter-

organizational cost management (IOCM). Normally labelled as advanced MAS, IOCM is a

strategic cost management tool used to facilitate the management of costs across organizational

boundaries in supply chains. More specifically, it extends the application of cost management

and control strategies beyond the conventional internal cost management practices to include

cost management among SC partners (Fayard et al, 2012).

The management of costs among these entities from focal organization perspective (e.g. a

hospital) is necessary as far as cost minimization in its operational activities is concerned. As

a consequence, Dekker (2000) pointed out that the design of MAS information in organizations

must be such that they increasingly operate across organizational boundaries.

Although a number of cost management strategies or techniques such as ABC, target costing,

kaizen costing, total cost ownership etc., constitutes IOCM, a common theme that underpins

IOCM is the minimization of costs and other cost control strategies that create value for

organizations in supply chains (Fayard et al, 2012). The ability of firms to identify significant

inter-firm cost saving opportunities is facilitated by IOCM (Schulze et al, 2012). Since this


39

goal is facilitated by the cooperative and collaborative actions of members in the SC, it

demands organizational resource which takes the form of set of tools.

Advanced MAS (e.g. ABC, value chain analysis, IOCM) not only coordinate supply chain

relationships (Fayard et al, 2012; Anderson & Dekker, 2014) by planning, measuring,

supporting and assessing supply chain activities and performance (Van de Meer-Kooistra &

Vosselman, 2000; Hammad, Jusoh & Oon, 2010), but also provide information to oil the

wheels of supply chain relationships (Burritt & Schalttegger, 2014).

2.9 Chapter Summary

In this chapter, the context of the study has been presented. It outlined the SCM challenges that

characterized Ghana’s healthcare system as well as the weak accounting systems. The chapter

notes that Ghana’s health SC is highly centralised and operates on a three-tier system consisting

of the Central Medical Stores (CMS), Regional Medical Stores (RMS) and Service Delivery

Points (SDP). The supply of medical products begins from the CMS to the RMS, and then to

the SDP in turn. This arrangement is however, characterized by deficiencies and weaknesses

as identified ‘gaps’. Various solution strategies have been pursued by governments yet the

problem continues to persist and have widened than before. Individual studies have also

identified a number of shortcomings and weaknesses in the system. The accounting system has

been also identified as weak and ineffective in controlling the SCM activities of healthcare

institutions. The chapter notes that the accounting system does not support the strategic

direction of healthcare management and needs total revision. It is believed that the institution

of proper MAS which is the focus of this thesis will contribute to solving some of these

problems and challenges.

The chapter also analyzed the characteristics of the health SC and the extent to which the MAS

information relates to its effective and efficient operation. Although the health SC and that of


40

mainstream business organizations and discrete parts manufacturing concerns have elements

that are similar, the constituents of the health SC encompasses the elements commonly found

in mainstream business organizations. This suggests that the approaches and procedures

employed in MAS design and its implementation in mainstream business organizations are

unlikely to work perfectly in the SCM systems of healthcare institutions. The chapter also

explained the implications of costing system design in hospitals where ABC systems are known

to provide effective cost system design as a consequence of the nature of hospital operations

normally termed as intermediary products. Finally, the chapter highlighted the importance and

relevance of the MAS’s application in the effective management of the health SC.


41

CHAPTER THREE

CONTINGENCY-BASED MANAGEMENT ACCOUNTING STUDIES

3.1 Introduction

Having discussed the study’s context, MAS design and the health supply chain, a detailed

analysis of the empirical literature on contingency-based management accounting research is

considered in this chapter. As pointed out in the following section, the body of literature on

these studies are mixed, inconsistent and fragmentary in nature. In line with these initial

reviews, the chapter begins with the theoretical underpinnings of contingency fit and the

processes involved in conceptualizing contingency fit and its attainment in management

accounting literature. This is followed by an analysis of the modelling approaches that have

been adopted to examining contingency fit. A discussion of the diversity in the selection,

definition and measurement of contextual and MAS variables is then presented to establish a

proper relationship between extant contingency literature and the current study. Finally, a

review of the linkages among three key areas of contingency hypotheses testing: 1) formulation

of verbal hypotheses relating to specific fit models, 2) the relevant statistical methodology

employed in testing the fit hypotheses, and 3) the interpretation of results is presented. A

summary of the key findings closes the chapter.

3.2 Theoretical Background of Contingency Fit

Central to contingency theory is the concept of fit between structure and context (contingency)

characteristics of organizations although its attainment still remains contested in the

contingency literature (Donaldson, 2001). Fit is the underlying principle of contingency theory

which suggests that fit between variables is important for achieving high performance. This is

so fundamental to the context-structure relationship that failure to attain such a fit results in


42

outcomes that are inferior. These outcomes are normally a typical of some aspects of

performance (Meilich, 2006; Burkert et al, 2014). In this regard, ‘‘it is generally considered as

the ‘‘heart’’ of contingency theory’’ (Donaldson, 2001, p.181). Hence, finding an appropriate

match between context and structure has been the fundamental notion of contingency-based

MAS design. This matching concept has been appropriately labelled and termed ‘‘contingency-

fit’’ in both the organizational and management accounting literatures alike (Jermias & Gani,

2004).

Investigating the MAS-contingency fit relationships has resulted in several different types of

fit (discussed in the sub-section following), and has led to some confusion and misconceptions

about the fit concept and how it is attained. Drawing on the contingency theory of general

organizational literature, several scholars in the management accounting arena have sought to

investigate the fit relationship between context factors and MAS design. Whilst subsequent

studies expanded the MAS variables in terms of definition and measurement (e.g. Chenhall &

Morris, 1986 conceptualization of broad scope, timeliness, aggregation, integration, etc.), that

of contextual variables (e.g. strategy, size, structure, environmental uncertainty, etc.) remained

fixed and imported from the general organizational literature. However, after decades of

research, there still remains unsettled issues in the management accounting field as the results

accumulated so far have not consistently supported contingency theory’s arguments (Burkert

et al, 2014).

The challenge for management accounting researchers has been how to theoretically derive the

form of fit to be predicted and tested and fail to reject a fit which is true or actually reject one

which is false. This is a key dimension of contingency studies as it provides a basis for the

progression of management accounting knowledge that is theory-consistent. Re-stating Otley

(1980, p. 451) which states that ‘‘specific aspects of an accounting system must be identified

by contingency theory, and this should be associated with certain defined circumstances as well


43

as demonstrate an appropriate matching,’’ specific aspects point to the specificity of the MAS

variable in relation to theory formulation and usage. Defined circumstances and appropriate

matching differ in the conceptual and empirical differences respectively between a universal

theory and contingency theory of accounting. The identification and testing the true form of

contingency fit warrants an analysis of the types of contingency fit models as discussed in the

subsections that follow.

3.3 Contingency Fit Models

Two main conflicting paradigms: the Cartesian school of thought and the Configuration School

underpin the fit concept. Drawing on these paradigms, researchers from both the general

organizational and management accounting fields conceptualize the relationship between

structure (e.g. MAS variable such as low cost strategy or measurement diversity) and

contingency (e.g. Strategy or perceived environmental uncertainty) from three different levels

of analysis; 1) Cartesian versus Configurational view (Mintzberg, 1983; Miller & Friesen,

1984; Meyer, Tsui & Hinings, 1993; Donaldson, 1996), 2) Congruence versus Contingency

(Drazin & Van de Vein, 1985; Chenhall & Morris, 1986; Gerdin, 2005a, Dunk, 2011), and 3)

Matching versus Multiplicative (Gerdin & Greve, 2008; Burkert et al, 2014). Each of the above

conflicting paradigms is briefly discussed in the next three sub-sections.

3.3.1 Cartesian vs. Configuration

The principal difference between the Cartesian school and that of the Configurational school

lies in their dominant mode of enquiry (Meyers et al, 1993). Advocates of the Cartesian

approach contend that contingency fit between structure and context is a continuum with

varying degrees of fit between the dimensions of structure and pairs of contingency variables

that allow organizations to make small frequent movements from one state of fit to another


44

(Donaldson, 1996). The dominant mode of enquiry in this paradigm is characterized by what

is described as reductionism. In other words, a limited number of contingency variables are

assumed to offer explanations that are general to the organizational structure (e.g. MAS

design). The extent to which single contextual (contingency) factors influence single structural

attributes and the resulting context-structure combination on performance is the main focus.

Proponents of the configuration paradigm argue that only a few states of fit between structure

and context exist and that a quantum jump of fits from one state of fit to another will have to

be made by organizations. The configuration view opposes partial analysis of context-structure

variables rather, views the context-structure combinations in a holistic manner. This holistic

analysis assumes that the understanding of relationships can only take place based on the

simultaneous analysis of many contextual and structure variables. Whilst the configuration

approach requires researchers to explore only the non-performance effects of the context-

structure relationships, the Cartesian researcher must show that a higher degree of fit is

associated with a higher performance since it assumes varying degrees of fit. Figure. 3.1

illustrates examples of typical associations between structure and context according to the

Cartesian and Configurational approaches as indicated in (Figure 3.1a) and (Figure 3.1b)

respectively.

As Figure 3.1a depicts, both contextual and structural factors are defined as continuous

variables as well as the fit relationships between them giving rise to many points of fit

(Donaldson, 2001). The numerous points of fit result in a continuous solid fit line with high

performance associated with high levels of fit. Figure 3.1b illustrates fit as system states

(configurations) where organizations make quantum jumps to substitute one state for another

when the cost associated with being out of step becomes quite prohibitive. As theory prescribes,

most firms can be assigned to a limited set of configurations which are in existence. A

researcher only identifies the processes and set of organizational structures that are feasible and


45

effective for different context configurations, and also provides insights into both internal

consistencies and inconsistencies of organizational structure patterns and processes (Drazin &

Van de Ven, 1985, p. 521-522).

Figure 3.1: Cartesian and Configurational Models

Figure 3.1 (a) Figure 3.1 (b)

(Source: Adapted from Gerdin & Greve, 2004)

3.3.2 Congruence vs. Contingency

The congruence and contingency fit models are respectively, antecedents of the

Configurational and Cartesian paradigms. The congruence (also known as selection) model is

a bivariate form of fit model which assumes that organizations that inferiorly combine structure

and contingency variables do not survive and for that matter vanish quickly either by extinction

or by adaptation. In other words, fit is assumed to be the result of a natural selection process

which ensures that only the best performing companies survive and are visible for observation

purposes (Drazin & Van de Vein, 1985). Quick to disappear are unfit combinations of structure

and contingencies. In this model, the fact that unfit combinations of contingency variables and

structure vanish quickly presupposes an unforgiving environment for low performing firms. In

other words, surviving organizations are those whose structural characteristics are congruent

with the organization’s contingencies.


46

The research task under this model is to examine the nature of context (contingency variable)

and structure (MAS variable) relationships with no examination of their impact on

performance. The MAS variable is assumed to be dependent on contextual variables for its

design and that fit results implicitly from the natural selection forces that link the variables. Fit

is determined by the correlations between contingency and structural variables or regression of

the structural variable on the contingency variables. Testing this link with performance is

therefore regarded as unnecessary.

The underlying assumption of either survival or a complete disappearance is however

unrealistic because in real-world cases, such a harsh environment is not only a non-reflection

of the success of most organizations, but also rarely exists. Organizations fail yet they continue

to survive until things get improved. They do not disappear suddenly or entirely as suggested

by the model. In addition, this model suggests that the outcome variable assumes a categorical

dimension (survive or vanish) yet, this form of fit does not incorporate any variations of

outcome. The implication is that all surviving firms are assumed to be equally fit which is not

the case in real-world situations. Moreover, it has been argued that the non-existence of

performance in this model is a major shortcoming because as stated by Pennings (1992, p. 274)

signalling survival of the fittest as a proxy for performance is too crude (Pennings, 1992,

p.274). Furthermore, the tendency for fit to be undetected is high when the so-called selection

forces are weak. A linear correspondence between the contingency and structural variables is

imposed by this model, which may not exist in a sub-sample leading to erroneous acceptance

of fit.

Following these arguments, contingency models (e.g. matching and moderation forms of fit)

have been developed where fit is conceptualized as a positive impact of context-structure

combinations on performance (Drazin & Van de Vein, 1985). This development stems from

the argument raised by contingency researchers that fit is a continuum and that companies


47

move between states that are fit and those that are not fit on this continuum (Donaldson, 2001)

so it takes a long time for companies to reach the so-called equilibrium stage specified by the

selection model. Furthermore, since on this continuum, companies of different kinds are

situated at different places, it provides room for the analysis of the performance effects of the

context-structure combinations.

As stated previously, the fit concept is very much central to the contingency model in that

inferior outcomes result in the event of failure to attain such a fit. These inferior outcomes are

typical of some aspects of performance. Unlike the congruence model in which unfit

organizations disappear, both high-performing and low-performing organizations are assumed

to exist in the contingency model. This is due to successful combinations of context and

structure that take place to affect performance. Examining performance variations with respect

to context-structure interactions is the research task. To date, the congruence and contingency

models still remain two irreconcilable ideas about the concept of fit (Burket et al, 2014). The

typologies of contingency fit models used in management accounting studies are now

discussed.

3.4 Typology of Contingency Fit Models

The types of fit models outlined in this study can be found in both the general organizational

literature (e.g. Meilich, 2006; Donaldson, 1996, 2001; Venkatraman, 1989; Drazin & Van de

Vein, 1985; Schoonhoven, 1981; Southwood, 1978) and the management accounting literature

(e.g. Hartmann & Moers, 1999, 2003; Gerdin & Greve, 2004, 2008; Burkert et al, 2014). The

contingency theory of organizational structure is heavily drawn upon by the management

accounting discipline hence, reference to scholars in the general organization field is highly

relevant for a study of this nature. Two main forms of classifications can be identified: those

based on the statistical technique required by the fit type and those based on the fit’s theoretical


48

underpinning. In the statistical approach, the fit is classified as bivariate, deviation score or

residual analysis, and interaction (Meilich, 2006) whereas those based on theoretical

classification include selection or congruence, matching, moderation, mediation, system, and

multi-fit (Hartmann & Moers, 1999, 2003, Gerdin & Greve, 2004, 2008; Burkert et al, 2014).

The two different forms of classification stem from the fact that a specific relationship between

the structural, contingency and outcome variables is postulated by each form of fit, which is

associated with different theoretical interpretation and requires the application of different

statistical tests. The different typologies of fit and underlying theoretical interpretations and the

required statistical methodology has led to some confusion in the management accounting

literature (Chenhall & Chapman, 2006). In the next four sub-sections that follow, each

contingency fit model together with the theoretical background is discussed while a detailed

discussion of the statistical approaches to testing each model of fit is presented under section

3.5.

3.4.1 Selection Fit Models

As a recap, in this form of analysis, survival constitutes the outcome measure and requires that

surviving organizations exhibit appropriate congruence between contingency and structural

variables. The basic assumption is that structure depends on context with no examination of

whether these context-structure combinations affect performance, hence, a congruence form of

fit (Drazin & Van de Vein, 1985). Proponents of this approach argue that testing the link with

performance is unwarranted because fit is implicitly assumed to emanate from a selection

process that is natural and ensures that only organizations with best performance survive and

at any point in time, can be recognized. Some measure of association (e.g. correlation and

regression) is required to empirically examine the interdependence between context and

structure.


49

In the management accounting context, the attainment of this type of fit simply means that

organizations have adapted their MAS fully to what their contexts demand or managerial

choices (Hartmann, 2005), and that differences in performance are not expected since

companies have reached equilibrium in their choices of the context-structure combinations.

The research task merely explores the context-structure relationships with no examination of

their effect on performance. Following this approach, many management accounting scholars

have examined the determinants of MAS design from a contextual perspective without

examining how such context-MAS combinations affect performance (e.g. Chenhall & Morris,

1986; Abernethy & Lillis, 2001; Gerdin, 2005a; Ylinen, & Gullkvist, 2012). The statistical

technique used to examine the extent to which MAS variables are related to elements of context

involves tests of associations such as correlation analysis or multivariate analysis such as

multiple regression. As noted earlier, the risk that this approach reaches incorrect conclusions

is very high.

3.4.2 Matching Fit Models

The matching form of fit model examines the impact of the MAS variable and contingency

variable combinations on performance. It is the classical form of fit model with iso-

performance on the fit line (Schoonhoven, 1981; Donaldson, 2001), and assumes an optimality

between the contingency variable and specific dimensions of the MAS. A unique level of an

MAS is assumed to match each level of a contextual (contingency) variable which then

maximizes performance. More specifically, for each level of a contextual variable there is

assumed to be a unique level of a structure (MAS variable) that maximizes performance. Any

deviations (or mismatch) in either directions between the specific level of the contextual

variable and the appropriate MAS variable results in decline in performance.

The key concept underlying the matching fit model is the iso-performance assumption. This

assumption implies that fit is the result of MAS – contingency combinations that are severally


50

combined (matched), each of which is expected to predict performance at the same level. This

results in the matching fit-line which is conceptualised (under the Cartesian school of thought)

as the line that joins all the points of fit derived from matching the appropriate MAS with the

level of contextual variable. The iso-performance assumption is normally reflected in the fit

line with zero values as illustrated in Fig 3.2. In this Figure, panel ‘A’ depicts a distinct feature

exhibited by the matching form of fit. Considering the fact that the context variable is the locus

of control of an organizational structure and the structural dimension is MAS variable

represented by budgetary participation, there might be a unique level of locus of control and

budgetary participation combinations that maximizes performance.

A curvilinear (i.e. inverted U-shaped) relationship exists between the MAS variable and

performance in the matching fit model. This association implies that only one optimal MAS

variable is attained for a given level of the contingency variable. Any deviation in either

direction (either ‘‘over fit i.e. too many MAS, e.g. too many measurement diversities or under

fit, i.e. too few MAS, e.g. performance indicators that are too few’’ (Meilich, 2006, p.167)) are

equally harmful as they cause the same negative effects on performance. Panel B depicts the

iso-performance on the fit-line with the same level of performance of MAS-contingency

combinations3.

3 This unique level is determined as having an equal score by the scales of measurement used.


51

Figure 3.2: Matching Fit Iso-performance (Donaldson, 2001)

Panel C

Internal 5 −4 −3 −2 −1 𝟎

4 −3 −2 −1 𝟎 −1

Locus of 3 −2 −1 𝟎 −1 −2

Control 2 −1 𝟎 −1 −2 −3

1 𝟎 −1 −2 −3 −4

External 1 2 3 4 5

Low High

Participation

(Source: Adapted from Donaldson, 2001; Chenhall & Chapman, 2006; Burkert et al, 2014)

Panel C shows a typical example of the matching form of fit depicting an association between

budgetary participation and performance. Here, the locus of control of an employee indicates

that there is a unique level of budgetary participation at which performance is maximized.

Given the point of optimality, a locus of control of 4 which points to a more internal focus, will

match the same level of 4 for participation which is an indication of a relatively high level of

participation. Any deviation in either direction from this point will reduce performance.


52

The use of the term ‘‘iso-performance’’ was coined by Donaldson (2001). It establishes the

fact that the matching fit that indicates performance is determined by a distance away from the

fit line and not where it is positioned since performance is the same at any point on the fit line.

The matching fit model is however, not tested in this research because this model cannot be

tested via structural equation modelling (Burkert et al, 2014). This is due to its curvilinear (U-

shape) nature of the MAS – contingency combinations. Furthermore, only one MAS variable

and one contingency variable can be tested at any one time on performance.

3.4.3 Interaction (Moderation) Fit Models

Interaction form of fit models investigate the extent to which the MAS-contingency

combinations affect performance. Specifically, these models consider how the MAS variable,

contingencies (or their context) and performance can be brought together. To many accounting

researchers, the absence of performance in the selection fit model is a major shortcoming

because performance is an important variable that cannot be ignored (Chenhall & Chapman,

2006). The argument is that whilst organizations move toward optimal combinations thereby

attaining the equilibrium prescribed by the selection forces, some firms would still be lagging

behind, and in the process of reaching the optimum combination. Others might have changed

their context (e.g. strategy, size, or structure) and may be in the process of adjusting their MAS

to suit or match the new situation. The attainment of an optimal combination of the MAS –

contingency variables is characterized as a dynamic process where organizations move in and

out of equilibrium. In this regard, poor performing organizations with a mismatch of MAS –

contingency combinations are likely to be identified by researchers at any given point in time.

This situation has given rise to the matching fit (discussed in the previous sub-section) and the

moderation fit models.

The moderation fit model assumes that the association between an independent variable and a

criterion variable is a function of a third variable called the moderator. The underlying theory


53

specifies that the effect an independent variable has on a criterion variable is moderated by a

third variable which is exogenous or uncontrollable. More specifically, the impact of an

independent variable on a criterion variable is contingent upon a third variable (Chenhall &

Chapman, 2006; Luft & Shields, 2003; Venkatraman, 1989, Baron & Kenny, 1986). Unlike

the matching fit model, there is a linearity in the relationship between the MAS variable and

performance, and this gives rise to different levels of variations of the contingency variable.

Conceptually, the relationship between the MAS variable and performance is determined by

the contingency variable as shown in Fig 3.3.

Figure 3.3: Moderation form of Fit Relationship

(Source: Author’s own construction based on Barron & Kenny, 1986)

The contingency variable variations at different levels given by the MAS variable and

performance result in a monotonic or non-monotonic moderation forms of fit. These are further

classified under four sub-form of interaction fit comprising monotonic, non-monotonic

(symmetrical), monotonic and cross over, and ordinal forms of moderation fit. For non-

monotonic fit type, the impact of the MAS variable on performance is positive for the

contingency variable at one level and negative for the contingency variable at another

(Schoonhoven, 1981). In this case the lines cross each other within the range of data as shown

in Fig 3.4. Fig 3.4 (a) depicts a monotonic cross over interaction where a positive relationship

always exists between the MAS variable and performance but the lines do not cross over

MAS Variable

Performance

Contingency


54

outside the range of data. Fig 3.4 (b) on the other hand, shows a non-monotonic (symmetrical)

interaction as discussed above.

Figure 3.4: Non-Monotonic (Symmetrical) and Cross-Over Interaction

Figure 3.4 (a): Monotonic and Cross Over Interaction Figure 3.4 (b): Symmetrical and

Cross Over Interaction

The other two sub-forms of moderation fit which depict a linear monotonic interaction and

ordinal forms of fit are illustrated in Fig 3.4 (c) and (d) respectively. In Fig 3.4 (c), which shows

a monotonic interaction, a positive association always exists between the MAS variable and

performance but as Donaldson (2001, p. 191) argues, the level of the contingency variable

affects the MAS variable.

Figure 3.4 (c) Figure 3.4 (d)


55

(Source: Adapted from Burkert et al, 2014)

3.4.4 Mediation Form of Fit

In the mediation fit model, the exogenous variable impacts a criterion variable which then

impacts performance. That is to say that the association between the contingency variable and

performance is affected by the MAS variable which acts as an intervening mechanism between

the exogenous and endogenous variables. Although this form of contingency fit has been tested

in many management accounting studies (Bouwens & Abernethy, 2000; Gerdin, 2005a;

Soobaroyen & Poorundersing, 2008; Hammad et al, 2013), such studies failed to clearly

distinguish a partially-mediated variable from full-mediated variable. The interpretation often

given to mediation forms of fit hypothesis has been that an indirect relationship existing

between the contingent variable and performance or a relationship exists between the

contingency variable and performance acting through the MAS variable. As Burkert et al

(2014) point out, such analysis does not establish true mediation because there were no

theoretical arguments nor empirical tests for true mediation by those studies.

In addition, a confusion arising from both the general organizational literature and management

accounting literature regarding partial mediation and full mediation still remains (Burkert et al,

2014). Prior literature has just by mere inspection, labelled the models in Fig 3.7 (a) and (b) as

full mediation and partial mediation respectively with no consideration given to the theoretical

as well as statistical conditions that need to be satisfied (Hartmann & Moers, 2003, Hartmann,

2005; Burkert et al, 2014). In this regard, prior literature (e.g. Hartmann, 2005; Burkert et al,

2014) have argued that mediation fit models with path analysis do not belong to contingency

theory as the key concept of fit does not exist. This is because explicit tests for true mediation

in such models were not identified. Testing mediation fit models must follow certain theoretical

as well as statistical procedures. These are discussed under section 3.5 following.


56

3.5 Approaches to Testing Contingency Fit Models

In this section, the statistical methodologies adopted to test contingency fit hypotheses are

discussed. These range from deviation score analysis, residual analysis, moderated regression

analysis, and structural analysis. These are discussed in the next four subsections.

3.5.1 Deviation Score and Residual Analysis Techniques

The Deviation Score Technique (DST) and residual analysis (RA) are the two commonly

approaches used in testing for example, matching fit models (Drazin & Van de Vein, 1985;

Donaldson, 2001; Said, Hassabelnaby & Wier, 2003; Gerdin, 2005a). The former requires the

calculation of a score that deviates from the optimality matching the MAS variable and

contingency variable while the latter tests for the negative effect of misfit on performance given

the level of a contingent value and an optimal value of the MAS variable using the deviations

from a predicted value. A comparison of a predicted optimal level of the MAS variable with

the actual level is normally made by both methods. The DST technique is expressed by the

following equation:

𝑦 = 𝛽0 + 𝛽1𝑋 + 𝛽2𝑍 + 𝛽3|𝑋 − 𝑍| + 𝜀………. (3.1)

where 𝑋 and 𝑍 represent MAS variable and contingency variable respectively. A statistically

significant coefficient of 𝛽3 supports the matching form of fit. Both the DST approach and the

RA technique are discussed next.

3.5.1.1 Deviation Score Approach

Two steps necessitate the deviation score methodology: 1) determining the fit-line theoretically

and 2) estimating the fit-line empirically (Chenhall & Chapman, 2006). As shown in equation

(3.1), the theoretical approach requires the use of a fit term which consists of the variations in

the MAS variable (𝑋) and the contingency variable (𝑍) and regressing the outcome variable


57

performance (𝑦) on this term (Venkatraman, 1989; Hartmann & Moers, 1999; Gerdin & Greve.

2008). In this form of analysis, fit is attained whenever (𝑋 = 𝑍).

In step two where the fit-line is unknown (Driazin & Van de ven, 1985), but has to be

determined empirically, the space defined by the MAS and contingency variables identifies

each observation in that space. Since the fit line is theoretically unknown, the best performers

in a sub-sample is estimated empirically. The key assumption here is that companies that are

closer to the fit line are regarded as best performers. The structural (MAS) variable is initially

predicted by the contingency variable. The relationship between the predicted value of the

structural variable and the contingency variable becomes a reference point for calculating the

deviation score. From this baseline, the Euclidean distance to the fit line which is expected to

have a negative association between performance is calculated, where given the contingency

variable, performance is optimal.

Specifically, based on the corresponding value of the contingency (i.e. the residual or error

term of the regression), the deviation score is measured as the absolute (or squared) value of

the difference between the actual and predicted structural variable. In this case the MAS

variable is then regressed on the contingency variable. Finally, the fit line is predicted from the

regression coefficients from the subsample of high-performing firms. The implication is that a

linear combination of the deviation score, the structural variable and contingency variable

predicts the outcome variable (performance). The statistical test which is normally applied is a

bivariate correlation between distance and performance.

As noted by Meilich (2006), the deviation score methodology has hardly been incorporated

into empirical studies due to the unreliable and misleading results it produces. It suffers from

low reliability and hence is associated with Type II errors (Donaldson, 2001; Edwards, 2007).

More specifically, the reliability of the MAS variable and contingency variable which


58

constitute the deviation score exceeds or is higher than the reliability of the deviation score

itself. Low reliability leads to a true matching fit model lacking a statistically significant

support. It also results in Type I error leading to erroneous acceptance of a matching fit model

when a different fit model is rather present. As illustrated in panel A of Fig 3.5 and assuming

that a moderation fit model is present instead of a matching fit model. The first step of the

deviation score is to identify the best performers who are primarily associated with the high

values say 15, 20, 25, etc., in the upper right corner as depicted in panel A. In the second step,

a regression between the contingency variable and the MAS variable for this subsample is

estimated out of best performers where in this case, the fit line is derived. This supports the

matching form of fit as shown in panel B.

In moderation types of fit, an ordinal and a monotonic fit model predicts that an increase in the

MAS (e.g. more performance indicators) has a positive impact on performance. In sharp

contrast to this, the matching form of fit predicts that for a given level of the contingency

variable, performance decreases not only when the MAS is too low, but also when it is too high

which contradicts the key assumption underlying the matching fit model. This suggests that the

matching form of fit has been erroneously supported. In addition, where the fit line is positioned

above most of the observations as depicted in panel C, then it is a very likely for a monotonic

moderation fit model to result. In this regard, a statistically significant matching fit model is

again erroneously supported. These spurious supports which are systematically provided by

the deviation score approach to find a statistically significant matching fit model are associated

with Type I errors. Finally, the difference score methodology impose untested and

oversimplification assumptions: the attainment of fit whenever 𝑋 = 𝑍, and the assumption of

iso-performance.


59

Figure 3.5: Euclidian Approach in Determining Matching Form of Fit


3.5.1.2.Residual Analysis

Like the deviation score methodology, iso-performance is implicitly assumed on the fit line in

the case of residual analysis. In this approach, the MAS variable is initially regressed on one

or multiple contextual factors using the entire sample. The next step which is similar to the

Euclidian distance associated with the difference score, is to interpret the error term associated

with each observation as the distance from the fit line, of each observation (i.e. level of misfit)

which is assumed to be negatively associated with performance. A major disadvantage of this

method is that it can easily result in a potential Type II error. For example, it is difficult to

predict the optimal level of the MAS variable since its position on, below, or above the fit line

cannot easily be identified.

As illustrated in Fig 3.6, this can lead to erroneous conclusions since in the matching principle,

over fit rather than under fit has a negative relationship with performance. In Fig 3.6 below, a

position of the MAS variable in area A and B is an indication of high value of the MAS whereas

a position in area C indicates a low value of the MAS variable. In this regard, if a point on the

bold line is erroneously predicted for the optimal value of the MAS variable, organizations


60

located in area B would over-invest rather than under-invest in the MAS as the incorrect bold

fit line predicts. In this regard, a cancellation of each of the residuals in areas B and C might

result, so that no statistically significant results can be obtained for the ‘‘under-investment’’

hypotheses. In addition, it does not clarify the existence of a true fit line in cases where a

statistically significant finding is not supported. As noted by Burkert et al (2014), unless a

careful analysis is followed, this approach is associated with the risk of erroneously interpreting

a matching fit model when there is existence of another form of fit instead.

Figure 3.6: Potential Type II Error Associated with Residual Analysis


3.5.2 Approach to Testing Moderation Fit Models

In general, contingency researchers have predominantly relied on Moderated Regression

Analysis (MRA) as the main approach to testing moderation forms of fit (Hartmann & Moers,

1999, 2003). MRA is a mere extension of the ordinary least squares (OLS) multiple regression

by the introduction of an interaction term in the multiple regression equation as shown in

equation (3.2) for two independent variables. Since the interaction term in equation (3.2) is

made up of two independent variables, it is a two-way interaction. The moderation fit model is

supported for the statistically significant coefficient, 𝛽3.

𝑦 = 𝛽0 + 𝛽1𝑋 + 𝛽2𝑍 + 𝛽3𝑋𝑍 + 𝜀 …………. (3.2)


61

This approach which can take various levels of interaction (e.g. two-way interaction, three-way

interaction), has been argued to constitute a weak methodology in detecting the existence of

fit. As can be seen in the next chapter, several underlying assumptions in testing moderation

fit models pose a serious challenge to researchers. These include the inclusion in the model,

lower-order effects, matching verbal hypotheses with statistical model, form and strength of

interaction, and interpretation of results. In this regard, other powerful alternatives such as

structural equation models have been proposed. However, these methodologies have been

marginally employed in contingency fit studies.

3.5.3 Approach to Testing Mediation Form of Fit Models

According to Baron and Kenny (1986), four theoretical and statistical conditions must be met

to establish the existence of true mediation. First, as shown in Fig 3.7 (a) demonstrating that

the mere effect ‘a’ and ‘b’ are both statistically significant and that a statistically significant of

effect size ‘a’ multiplied by effect size ‘b’ establishes full mediation between the exogenous

variable and the endogenous variable is insufficient. Theoretically, the exogenous variable

causes the endogenous variable (Jacobucci, 2008); so the first step which Baron and Kenny

(1986) describe as the ‘classic four-step procedure’, is to empirically show a statistically

significant direct link between the exogenous variable and the final outcome variable as shown

in Fig 3.7 (b). Second, a test between the exogenous variable and the mediating variable

followed by another test between the mediating variable and final endogenous variable is

performed in the third step. In the final test (step 4) a statistically significant effect running

from the exogenous variable to the final endogenous variable needs to reduce for partial

mediations or vanish entirely for full mediation to be established.


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Figure 3.7 (a): Mediation Form of Fit Models

effect ‘a’ effect ‘b’

Figure 3.7 (b): Four Step Procedure for Testing Mediation Form of Fit

(Source: Adapted from Baron & Kenny, 1986)

3.6 Conceptualization of Contingency Fit and its Attainment

In this section, a systematic review of the extant empirical literature on contingency-based

MAS design is presented. Arguments in both the contingency-based management accounting

literature and general organizational literature regarding the fit concept and how it is attained

has up to date not been resolved. As Otley (2016, p.45) points out, ‘‘the design of optimal

MASs which results from a mechanistic approach that will develop into a predictive

mechanism misguided’’. Earlier on, Meilich (2006, p.161) has stated ‘‘contingency theory’s

fit concept has been modelled in ways which made its detection to be less conducive’’. These

two statements suggest that the approach to modelling the fit concept in both the management

accounting literature and general organization literature has not followed a precise and

common guidance in advancing theory-consistent knowledge. As Venkatraman (1989) noted,

Contingency

Variable MAS Variable Performance


63

although different phrases such as ‘contingency upon’, ‘matched with’, ‘fit’, ‘consistent with’,

‘congruence’, and ‘co-alignment’ etc., have been used to explain the fit concept, the provision

of precise guidelines for the translation of the above verbal statements to the required analytical

level is mostly missing. As a consequence, several attacks have been launched on the seemingly

straightforward contingency arguments by scholars such as Schoonhoven (1981) and Drazin &

Van de Ven (1985) as not clear in its predictions and subsequent operationalisations.

In the management accounting field, criticisms are based primarily on the fragmentary and

contradictory nature of contingency-based management accounting studies, which arise from

methodological limitations (see Hartmann & Moers, 1999, 2003; Gerdin & Greve, 2004, 2008;

Burkert et al, 2014; Otley, 2016). Some of these limitations are attributed to insufficient data

and underspecified models and disparate definitions of variables, (Burkert et al, 2014). Aside

the methodological issues and as discussed in the preceding chapter, are confusions

surrounding the fit concept and its conceptualization in management accounting literature.

It has been argued in the management accounting literature that certain contingency hypotheses

that have been discussed and tested (mediation forms of fit) do not belong to contingency

theory as the key concept of fit is missing in these models (Burkert et al, 2014). Hartmann

(2005) argues that mediation forms of fit constitute a violation of contingency theory’s

prediction as they are composed of a mix of matching fit and selection fit which have different

theoretical underpinnings. Gerdin (2005b) had a counter claim about the mediation form of fit

arguing that it is a multi-contingency form of fit. To Burkert et al (2014) any path model with

performance as the criterion variable does not belong to contingency theory.

In line with these arguments, the current study attempts to present a systematic analysis of

empirical contingency work in management accounting. A number of features that

characterized contingency-based empirical research in management accounting was revealed


64

in a systematic literature review. In the sections that follow, the modelling approaches to

contingency fit hypotheses, selection and measurement of variables, formulation of hypotheses

and subsequent statistical tests applied, are discussed. Also discussed in this sub-section, is an

analysis of the compatibility of verbal hypotheses with their subsequent interpretations in

management accounting literature.

3.7 Levels of Contingency Fit Analysis

Extant empirical literature on contingency-based management accounting research can be

grouped into three main levels of analysis (Otley, 2016). The first level of analysis involves

studies that investigate the relationship between one contingent exogenous variable and one

criterion variable (e.g. Hirst, 1983; Govindarajan, 1984; Brownell & Hirst, 1986; Frucot &

Shearon, 1991; Abernethy & Stoelwinder, 1995; O’Connor, 1995; Bouwens & Abernethy,

2000; Dunk, 2011). The analysis of the joint effect of multiple contingent exogenous variables

on one criterion variable is examined in the second level, where some variables moderate or

mediate (e.g. Merchant, 1981; Kim, 1988). In the third level of analysis, an examination of the

effect of multiple contingent exogenous variables on several criterion variables (e.g. Chenhall

& Morris, 1986; Jermias & Gini, 2004; Gerdin, 2005a; Cadez & Guilding, 2008; Ylinen &

Gullkvist, 2012).

Each level of analysis is based on the selection, matching, interaction (moderation) and

mediation, systems, and multi-fit models. The challenge however, has been how to identify and

test the correct fit model (Burkert et al, 2014). This is particularly associated with the matching

fit models. Although the matching fit model is classic, identifying such studies in management

accounting literature is relatively rare because the iso-performance term has been

misrepresented due perhaps to the two main approaches – residual analysis and deviation score


65

(discussed in the preceding chapter) that have been used. A classic example is the study by

Brownell (1982) and that of Frucot and Shearon (1991) which used the deviation score

approach. Both studies examined the absolute difference between locus of control (an external

variable) and budgetary participation, and test the relationship between this absolute difference

with performance. In the interest of logical explanation and understanding, a brief background

of the historical antecedents of the contingency-based management accounting literature is

initially presented in the next section. This is followed by a discussion of the selection,

definition and measurement of the MAS variables.

3.8 Early Contingency-Based Management Accounting Studies

Early empirical work in management accounting focused mainly on budgetary participation

for which a match between the MAS variable and context (contingency) variable was

examined. Such studies, to a greater extent, relied on the budgetary design component of the

MAS as the approach to examining the MAS-contingency relationship (see for example,

Argyris, 1952). This period which witnessed the development of contingency theory studies in

management accounting as well as explaining the different aspects of management accounting

practices that were apparent at that time, relied exclusively on the use and deployment of

budgets as the MAS variable.

To that end, emphasis was placed on the extent to which the design of budgets influenced the

behavioral and attitudinal aspects of subordinate managers (see for example, Hopwood, 1972;

Otley, 1978, Merchant, 1981). The concentration on the deployment and use of budgets as the

dominant methodology resulted in the capturing of the MAS variable as ‘Reliance on

Accounting Performance Measures’ (RAPM) but yielded inconsistent results. The mixed

results that characterized these early studies especially those of Hopwood (1972) and Otley


66

(1978) created the platform for other researchers to advance contingency-based management

accounting studies, which Burkert et al (2014) argue contingency theory’s usage in

management accounting research has attained a dominant position.

3.9 Selection, Definition and Measurement of MAS Variables

Review of past literature indicates that the bulk of empirical work on contingency-based

management accounting research has so far generally been mixed with inconsistent and

fragmentary results. Several factors account for the mixed and conflicting results. First, studies

in this field have generally not been systematic in the selection and measurement of both the

MAS and context variables. Generally, variables (or measures) chosen for analysis in any

particular research do not exactly correspond to those employed in previous works. As Otley

(2016, p.47) points out in his systematic review of contingency theory, ‘‘cumulative results are

rare in this field of research’’. Second, both variable selection and the techniques employed for

analysis in any particular study are arbitrarily determined. Third, past studies have tended to

increase the number of exogenous variables relative to the criterion variable (MAS) rather than

mapping out the boundaries of the field. Fourth, comparability of studies from one period to

another is difficult as most often only a small proportion of the contingency variables used by

organization theorists is employed in any particular study, although these variables have been

used extensively in the management accounting field. Each of these is discussed in the

subsections that follow.

3.9.1 Arbitrary Selection of Variables

Otley (2016) in his review argued that the comparability difficulties arising from empirical

contingency-based management accounting literature is as a result of the limited use, or the use

of a subset of the organizational contingency variables which have been extensively used by


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organizational theorists. Indeed, whilst this assertion holds for purposes of cumulative results

and theory consistent management accounting knowledge, it will at the same time be extremely

difficult if not impossible for the analysis of all the contingency variables that have been used

by organizational theorists to be executed in a single comprehensive study. This is due to the

fact that new additions to the independent variables continue to take place in the literature.

Besides, some contingency variables are organizational-specific and may not register their

presence in other organizations. For example, the variable of national culture underpinning an

organization domiciled in an industrialized economy may or may not be relevant in some

organizational types situated in emerging economies. The arguments of Otley (1980) are

genuinely advanced but some appears to be infeasible, at least.

Following the capturing of the ‘‘RAPM’’ which emanated from the budgetary measures era,

both the independent and dependent variables have increased considerably. In broad terms, the

contingent independent variables can be grouped into internal and external (Otley, 2016, p. 48).

Among the major internal independent variables include organizational strategy, structure,

size, compensation systems, information systems, employees’ participation in the control

systems, product life-cycle stage, systems change, market position, and psychological variables

(e.g. tolerance for ambiguity). Major external independent variables include market hostility or

competition, technology, national culture, and environmental uncertainty. The most commonly

used dependent variables (MAS design) include management control systems, performance

measures, effectiveness, budgeting behaviour, job satisfaction, managerial style, and product

innovation.

It can be established from the foregoing that the independent contingency variables have

expanded drastically in relation to the dependent MAS variable, and this development started

right after the contingency theories of management accounting development phase during the

1960s-1970s. Govindarajan (1984) for example, investigated the effects of organizational


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effectiveness on the strength of the relationship between organizational uncertainty and

evaluation style. In this study, the MAS variable was defined as the evaluation style of

management in manufacturing concerns. In Chenhall and Morris (1986), the MAS variable was

defined based on the perceived usefulness of four information characteristics which were

considered to be provided by MAS. These information characteristics (broad scope, timeliness,

integration, and aggregation) were regressed on context (contingent) variables composed of

structural decentralization, perceived environmental uncertainty, and organizational

interdependence.

Following from these two studies, Kim (1988) investigated the fit between organizational

context and effective design of MAS in US hospitals. In his study, the MAS variable was

defined as users’ satisfaction with information quality and the extent of system usage and

information value. These variables were regressed on context variables defined by technology

measured by task predictability, and coordination defined as impersonal coordination which

refers to integration of various organizational activities using policies, formalized rules, pre-

established plans, schedules, and procedures, which do not require human input. Using a

multiple contingency approach, Gerdin (2005a) investigated the impact of organizational

contextual factors (composed of organizational structure and departmental interdependencies)

on MAS design in manufacturing departments. He defined the MAS variable as a composite

of rudimentary, broad scope and traditional. Rudimentary MAS information depicts the

aggregation and seldom use of all types of accounting information. Broad scope MAS shows

the extent of detailing and frequent reporting of budgetary and operational information. The

traditional MAS refers to the frequent issuance of detailed budget and product cost reports.

Jermias and Gini (2004) tested contingency hypotheses about the associations between

organizational configurations, MASs, business strategy, and business unit effectiveness. The

MAS variable was defined in this study as low-cost strategy and product differentiation


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strategy. These variables were regressed on the context variables represented by competitive

strategy and degree of decentralization. Cadez and Guilding (2008) represented the MAS as

participation in strategic decision process and strategic management accounting use, which

were regressed on the contextual variables represented by prospector/defender, market

orientation and organizational size. In the study of Dunk (2011), the moderating role of budgets

on the relationship between product innovation and firm financial performance was examined.

Ylinen and Gullkvist (2012) in their study, defined the MAS as balanced dimensions and

combined dimensions of accounting information. The context variables were defined as

perceived task uncertainty and tolerance for ambiguity.

Given these few examples above, one can quickly establish that the organizational contextual

variables that underpin the supply chain remained unexplored. As noted by Anderson and

Dekker (2014), all organizations are embedded in supply chains as the existence and

functioning of one particular organization depends on the existence and functioning of another.

In other words, no organization exist and functions in isolation but is embedded in chains of

the activities of other organizations. This makes the efficient and effective management of

supply chains a crucial aspect of contemporary organizational control. The increases in the

organizational contextual variables as outlined above has so far not considered those contextual

variables that fall within the domain of inter-organizational relations in supply chains although

efforts to link MAS design to SCM has been an issue of much concern to management

accounting researchers in recent years (see for example Cooper & Slagmulder, 1998).

To a greater extent, these studies propose the design of MAS techniques (inter-organizational

cost management) for SCM practices by mere extension (or imitation) of the traditional intra

cost management accounting techniques without an examination of the extent to which they

match the contextual antecedents of SCM. The argument put forward by these studies has been

that organizations with much experience and knowledge in intra cost management techniques


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would be able to use their knowledge and expertise to develop similar techniques for the

management of supply chains. It can be established from the above literature that the choice of

both the MAS variables and the context (contingency) variables have been arbitrary and

inconsistent. This has perhaps contributed to the different conceptualizations in the

contingency literature in management accounting. The approaches to conceptualizing and

determining what constitutes contingency fit in management accounting literature are

discussed next.

3.9.2 Empirical Estimation of Contingency Fit Models

Extant literature suggests that many different statistical techniques have been used in the

management accounting field to estimate contingency fit. Empirically, estimation techniques

such as the difference in means (or deviation score technique) (Abernethy & Brownell, 1999;

Abernethy & Lillis, 2001; Chenhall & Langfield-Smith, 1998), bivariate correlation

(Khandwalla, 1972; Merchant, 1981, 1984; Haka, 1987; Simons, 1987; Govindarajan, 1988;

Duncan & Moore, 1989), difference in correlation coefficients (strength or form) (Merchant,

1981, 1984; Abernethy & Lillis, 2001; Abernethy & Brownell, 1999), moderated regression

analysis (form-monotonic, form-non-monotonic, strength) (Hirst, 1983; Brownell & Hirst,

1986; Dunk, 2011), and indirect path analysis (Chenhall & Morris, 1986; Chong & Chong,

1997; Bouwens & Abernethy, 2000; Baines & Langfield-Smith, 2003; Gerdin, 2005a) are

statistical models commonly used in estimating contingency fit in management accounting

literature.

The basis for choosing a particular statistical estimation technique for any such epistemic

analysis lies in the fact that each concept of fit is associated with different theoretical meanings,

and requires different statistical tests (Hartmann & Mores, 1999; Gerdin & Greve, 2004).

Although each of the above-mentioned techniques have had one or multiple appearances in the

management accounting literature, the difference in means (deviation score technique),


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difference in correlation and MRA have been the dominant statistical approach used in this

field (Hartmann & Moers, 1999, 2003; Dunk, 2003; Gerdin & Greve, 2004, 2008). An analysis

of the strengths and weaknesses of the statistical test conducted in estimating contingency fit

under the interaction models is presented in the sub-sections that follows.

3.10 Empirical Estimation of Moderation Forms of Fit

Management accounting scholars have relied extensively on the use of MRA in testing the

interaction (or moderation) forms of contingency fit (Burkert et al, 2014). However, a review

of empirical studies in this area (e.g. Hartmann & Moers, 1999; 2003, Burkert et al, 2014)

reveals that a weak relationship exists between interaction and (1) the formulation of

hypotheses (Brownell, 1982a, 1982b, 1983), (2) the strength of relationships (e.g. Brownell &

Hirst, 1986), (3) lower order effects (e.g. Merchant, 1990), (4) effect size, (5) multiple and

higher-order interaction (e.g. Mia & Chenhall, 1994), and (6) the issue of monotonicity (e.g.

Frucot & Shearon, 1991). Each of these deficiencies is addressed in the sub-sections that

follow.

3.10.1 Hypotheses Formulation and Statistical Analysis

A fundamental issue in ‘contingency fit’ studies is to ensure that the link between the verbal

statement of hypotheses, the statistical tests applied, and the interpretation of results are

coherent and compatible. From theory, verbal statements of hypotheses are explicitly, logically

and clearly derived, and tested subsequently using an appropriate statistical technique

(Hartmann & Moers, 2003). Failure to ensure this compatibility seriously impedes the

advancement of management accounting studies that is theory-consistent (Burkert et al, 2014).

Apart from the incompatibility of the relationship between the typical format of the underlying

contingency fit hypotheses and the appropriate statistical tests applied in most past

management accounting studies, there are cases of either non-statement of explicit hypotheses


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or the statement in a null form of the specified hypotheses. (see for example, Brownell, 1982a,

1982b, 1983; Govindarajan, 1984; Brownell, 1985; Brownell & Hirst, 1986; Chenhall, 1986;

Dunk, 1989; Merchant, 1990; Brownell & Dunk, 1991; Frucot & Shearon, 1991; Harrison,

1993; Mia & Chenhall, 1994; Tsamenyi & Mills, 2002).

Stating a hypothesis in a null form predicts a negative (rather than a positive) relationship

between the variables. In particular, and as stated in the previous chapter, each form of fit is

associated with different theoretical interpretations and different statistical tests and hence, a

null form of a hypothesis is far from adequacy in describing the specific contingency

formulation and the type of statistical test to be performed. In fact, even if the hypotheses

relating to those studies were stated in the positive form, they would still have been inadequate

in describing the specific contingency formulation. Contingency studies require that a proper

match between the underlying contingency fit (e.g. selection, matching, etc.) and the statistical

test applied, be explicitly established. The existence of differences between verbal statements

of hypotheses and statistical tests applied results in erroneous conclusions.

In the study of Frucot and Shearon (1991, p.85) for example, the hypothesis was stated in a

null form as:

‘‘In Mexico, through an interaction effect with budgetary participation, locus of control does

not have a significant effect on performance’’.

They then proceeded to estimate the following statistical model:

𝑌 = 𝛽0 + 𝛽1𝑋1 + 𝛽2𝑋2 + 𝛽3𝑋1 − 𝑋2 + 𝜀 …………… (3.3)

where 𝑋1, 𝑋2 and 𝑌 are locus of control, budgetary participation and performance respectively.

The statement of the verbal hypothesis which refers to interaction form of fit and the statistical

test used are incompatible since equation (3.3) does not measure fit as interaction but rather

measures fit as matching. According to Venkatraman (1989, p. 431), equation (3.3) estimates

a matching rather than an interaction form of fit. Since fit is hypothesized as interaction (form),


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there is incompatibility in both the verbal statement of hypothesis and the statistical measure

used; hence, the conclusions drawn and the interpretations thereof remained flawed. The

authors also argued and mentioned the deviation score as a better reflection of the relationship

among the three variables (Frucot & Shearon, 1991, p.90).

Based on this assertion, the hypothesis should have been modified as follows:

There is a unique value of budgetary participation that produces the highest value of

performance for a given value of locus of control in Mexico; deviations from this relationship

in either direction reduce the value of performance (Schoonhoven, 1981; Hartmann & Moers,

1999).

Another incompatibility between the theoretical arguments, verbal hypotheses, and statistical

test used is the work of Tsamenyi and Mills (2002). The authors’ theoretical arguments relate

to ‘‘how perceived environmental uncertainty (environmental level factors) and organizational

culture influence budget participation, and how budget participation in turn influences

managerial performance’’ (Tsamenyi & Mills, 2002, p.17). However, the verbal hypotheses

formulation of this theoretical argument and the statistical analysis (univariate ANOVA) used

were not only incompatible, but also do not fall within contingency theory’s framework and its

key concept of fit.

First, the hypotheses were stated in a null form as follows:

H1: ‘‘There is no difference in budgetary participation when perceived environmental

uncertainty is high or low

H2: There is no difference in budgetary participation when organizational culture has a:

a) high or low power distance b) high or low uncertainty avoidance c) external or

internal focus d) task or social focus e) conformity or individuality focus f) safety

or risk focus, g) adhockery or planning focus’’ (Tsamenyi & Mills, 2002, p. 34)


74

Formulating verbal statement of hypotheses in a null form in contingency studies seriously

distorts the advancement of management accounting knowledge that is theory-consistent (Luft

& Shields, 2003). Also, the null form possesses limited power in describing the specific

contingency formulation and the associated statistical tests to be employed. In addition, the

hypotheses as specified above do not only mismatch the purpose of investigating the influence

of perceived environmental uncertainty and organizational culture on budgetary participation,

and the corresponding effect of budget participation on managerial performance, but also, do

not capture the key variables that constitute the test. Second, in line with the study’s objectives,

the variable ‘‘budget participation’’ serves both as a mediating variable in which case, a

simultaneous equations analysis involving mediation design would have been the best

statistical alternative to analyze their relationships. The relationship between the four variables

as prescribed by the study objectives, can be illustrated diagrammatically as shown in Fig 3.8

(a) and Fig 3.8 (b).

In Fig 3.8 (a), organizational culture and perceived environmental uncertainty interact to affect

budget participation which then affects managerial performance. This suggests that budget

participation mediates the relationship between the contingency variables and managerial

performance which although according to Hartmann (2005) and Burkert et al (2014), is not

consistent with contingency theory, issues relating to this form of contingency fit hypothesis

still remain unaddressed. If the authors were to test the mediation form of fit, the appropriate

and alternative verbal hypotheses formulation would have been as follows:

‘‘The relationship between organizational culture, perceived environmental uncertainty and

managerial performance is explained by an indirect effect of budget participation whereby both

organizational culture and perceived environmental uncertainty reduce budget participation,

which in turn increases managerial performance’’ (cf. Hartmann & Moers, 1999, 2003)


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Also, the variable, ‘budget participation’ was labelled as the contingency variable instead of

the theoretically relevant moderators ‘perceived environmental uncertainty’ and

‘organizational culture’ (see Hartmann & Moers, 1999). The authors even failed to hypothesize

this relationship in the verbal statement when the mediation form of fit was tested. Since

statements of verbal hypotheses are explicitly and logically derived from theory (Hartmann &

Moers, 1999), the hypotheses ought to have captured the theoretical linkages between the

variables.

In Fig 3.8 (b) both perceived environmental uncertainty and organizational culture moderate

the relationship between managerial performance and budget participation. This model is

consistent with contingency theory since the moderators are the theoretically relevant variables

(perceived environmental uncertainty and organizational culture) which suggests that the

relationship between budget participation and managerial performance is contingent upon the

organizational contextual variables of perceived environmental uncertainty and organizational

culture. In conformity with contingency theory, the proper formulation of the verbal hypotheses

(assuming non-monotonic) would be given as:

‘‘For higher values of perceived environmental uncertainty and organizational culture, budget

participation will positively (negatively) affect managerial performance. For lower values of

perceived environmental uncertainty and organizational culture, budget participation will

negatively (positively) affect managerial performance’’ (cf. Hartmann & Moers, 1999, 2003)


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Figure 3.8 (a): Mediation Analysis of Contingency Variables, Budget Participation and

Managerial Performance

(Source: Author’s own modelling based on review of contingency literature)

Third, the study hypothesized the contingency variable (perceived environmental uncertainty)

as high or low values of two dichotomous sub-group variables, but these were not (and could

not have been) captured by the statistical technique used (univariate ANOVA). In contingency-

based management accounting research, the specification of a contingency variable as high or

low values reflects a sub-group analysis where the contingency variable (moderator) is divided

into two dichotomous variables taking the values of 1 and 0 for high and low values

respectively. Such relationships are appropriately analyzed using MRA to determine its

monotonicity (i.e. monotonic or non-monotonic effect) (see Hartmann & Moers, 1999, 2003;

Gerdin & Greve, 2004, 2008; Burket et al, 2014).

In this case, the contingency variable (moderator) moderates the slope of the relationship

between the independent variable and the dependent variable for the high and low values. The

slope of the relationship between the exogenous variable and the criterion variable could be

positive (negative) for high (low) values respectively of the contingency variable which is

dichotomized into two sub-groups. Since there were two contingency variables affecting an

independent variable and a criterion variable, a three-way interaction type of MRA could have

been more appropriate as follows:

Perceived

environmental

uncertainty

Organizational

culture

Budget

participation

Managerial

performance


77

𝑌 = 𝛽0 + 𝛽1𝑃 + 𝛽2𝐶 + 𝛽3𝐵 + 𝛽4𝑃 × 𝐶 + 𝛽5𝑃 × 𝐵 + 𝛽6𝐶 × 𝐵 + 𝛽7𝑃 × 𝐶 × 𝐵 + 𝜀 . (3.4)

Where 𝑌 = managerial performance

𝑃 = perceived environmental uncertainty

𝐶 = organizational culture

𝐵 = budget participation

In this model, the two contingency variables (perceived environmental uncertainty and

organizational culture) interact to affect the relationship between budget participation and

managerial performance. More specifically, the relationship between budget participation and

managerial performance is contingent upon perceived environmental uncertainty and

organizational culture. The relationship between the moderating effect of perceived

environmental uncertainty and organizational culture on the relationship between budget

participation and managerial performance is shown in Fig 3.8 (b).

Figure 3.8 (b): Moderation Analysis of Contingency Variables, Budget Participation and

Managerial Performance

Budget

participation Managerial

performance

Perceived environmental

uncertainty

Organizational

culture


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Overall, the formulation of verbal hypotheses, theoretical arguments and statistical analysis

have no match (Hartmann & Moers, 1999).

3.10.2 Strength of the Relationship

Whether an interaction form of fit relates to the ‘strength’ or ‘form’ of a relationship is an issue

that has remained contested among contingency-based management accounting scholars over

the years (Hartmann & Moers, 1999, 2003; Gerdin & Greve, 2004). Govindarajan (1984) tests

a contingency hypothesis involving three variables: organizational effectiveness, evaluation

style, and environmental uncertainty. His hypothesizes that the association between ‘evaluation

style’ and ‘environmental uncertainty’ is affected by ‘organizational effectiveness’, and

therefore formulates the following hypothesis:

‘‘organizational effectiveness affects the strength of the association between evaluation style

environmental uncertainty’’.

Although his verbal hypothesis was compatible with the statistical format used (correlation

analysis) since it contrasts the coefficient of correlations between the two subgroups, the results

which were subsequently interpreted were incompatible with the verbal hypothesis. In his

conclusion, Govindarajan (1984, p. 133) states: ‘‘there is a significant moderating effect of

environmental uncertainty on the association between organizational effectiveness and

evaluation style’’. However, his hypothesis clearly states that organizational effectiveness is

the moderator instead of the theoretically relevant moderator – environmental uncertainty.

As illustrated in Fig 3.9 (a) to (d) the distinguishing features between strength of relationships

and form of relationships can be made. The association between the variables X and Y are

represented by the shaded portions of observable clouds (or scatter diagrams) in each of the

diagrams shown in Fig 3.9. A low correlation is registered by a wide cloud while a high

correlation is represented by a narrow cloud. The first diagram (panel A) shows no correlation


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between the variables as represented by the plain area registered by the cloud. The regression

lines for both subgroups are equal. In panel B, there is equal prediction of Y by X in both

subgroups which shows non-existence of strength interaction rather a form of the relationship

between X and Y. The existence of form relationship is evidenced by the different regression

lines for the two subgroups. In sharp contrast to panel B is panel C where the slopes for the

two regression lines are the same but registers difference in strengths where X shows to be a

better predictor of Y in subgroup 1 than in subgroup 2. Panel D shows both strength and form

of relationships as a difference exists between the correlation and slope of the regression lines

for subgroups 1 and 2.

Figure 3.9: Strength vs. Form Interaction

(Source: Quoted from the Literature of Hartman & Moers, 1999, p. 297)

3.10.3 Lower-Order Effects

A statistical format (such as MRA) for testing an interaction form of fit must be such that it

contains the interaction term as well as the main effects (or lower-order effects). This is because

a statistically significant coefficient of the interaction term can be due solely to lower-order

effects and not the interaction term itself (Stone & Hollenbeck, 1984, p. 201). Therefore, in


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MRA, lower-order effects are included in order to prevent the existence of an interaction when

in reality such an interaction is due solely to the former. Apart from the misleading conclusions

that can arise if lower-order effects are omitted is the proper interpretation of the coefficients

that relate to the lower order effects. It is argued that the coefficients of lower order effects

show clearly, the main effect of a variable when the value of the other variable is zero, although

a zero value is meaningful only for ratio scale variables (Southwood, 1978, p. 1165).

Furthermore, the lower-order effects and their product (interaction term) are at high potential

of being correlated resulting in multicollinearity (Drazin & Van de vein, 1985).

The review reveals some deficiencies and shortcomings associated with lower-order effects in

terms of inclusion of main variables, proper interpretation of main effects, and the problem

with multicollinearity. In the literature of Hirst (1983) for example, a model to test an

interaction was stated in the form:

𝑌 = 𝛽0 + 𝛽1𝑋1𝑋2 + 𝜀 …………………… (3.5)

This equation does not include an interaction term. As it has been argued, a statistically

significant 𝛽1 could be misleading if the existence of in interaction is supported. This is because

a statistically significant value of 𝛽1 could result from a significant association between 𝑋1 and

𝑌 regardless of 𝑋2 and vice versa. As Cohen and Cohen (1983, p. 305) argue, an interaction

term has the potential of ‘stealing variance’ from its constituent parts because it is the product

of main (or lower-order) effects. In line with this, Stone and Hollenbeck (1984, p. 201) states:

‘‘While the multiplication term ‘‘carries’’ the interaction in a regression equation, the same

multiplication term is not the interaction’’.

According to Southwood (1978, p. 1164), Cohen and Cohen (1983, p. 348) and Stone and

Hollenbeck (1984, p. 201), a partialed out of the lower-order effects should be obtained by

making sure that they are present in the regression equation.


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Another deficiency relates to the interpretation of lower-order effects which, as the literature

indicates, are subject to both Type I and Type II errors. Whilst some studies are associated with

invalid lower-order interpretation especially in a two-way interaction and higher-order

interaction, where most are associated with Type I error (Brownell, 1982a, 1983, 1985;

Chenhall, 1986; Brownell & Hirst, 1986; Imoisili, 1989; Mia, 1989), others showed no

interpretation of lower-order effects even though they could have been interpreted (Dunk,

1992, 1993; Brownell & Dunk, 1991; Harrison, 1992, 1993; Mia & Chenhall, 1994; Gul &

Chia, 1994; Lau, Low & Eggleton, 1995), which is associated with a Type II error.

The reason for non-interpretation of lower-order effects are primarily based on Southwood’s

(1978, p.1168) contention that no theoretical meaning is associated with such coefficients.

Southwood’s (1978) argument is based on scale origins of variable measurement in the

behavioral sciences which are based on interval scales rather than ratio scales. Hence, linear

transformations according to Southwood (1978), do change the coefficients of lower-order

effects in MRA equations, and therefore, these coefficients cannot be easily interpreted.

Nevertheless, the zero value of the variables obtained in interval scale has a specific meaning

if the variables are centered on their respective means.

Contingency-based management accounting researchers have erroneously relied on this

assertion and failed to interpret the lower-order effects in MRA. It is argued that the main

effects coefficients obtained when using MRA, are generally different from those that are

obtained from a regression model without an interaction term. Southwood (1978, p. 1168) and

Cohen and Cohen (1983, p. 305-306) further argue and state: ‘‘linear transformations do not

vary the coefficient of the interaction term, not its t-statistic and level of significance’’.

Management accounting researchers have however, relied on the problems associated with

scale origins which are peculiar to interval scales compared to ratio scales. In the study of Mia

and Chenhall (1994) for example, the moderating effect of ‘‘Function’’ on the relationship


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between ‘‘the Use of Broad Scope MAS’’ and ‘‘Managerial Performance’’ was investigated.

In their analysis, the MAS variable was estimated as a difference score from the overall mean.

Since the mean is centered, the coefficient of the lower-order effect of Function was

interpretable and could have been interpreted. Referring to Southwood (1978), the authors

state: ‘‘No attempt was made to interpret the coefficients of lower-order effects that related to

the extent of use of broad-scope MAS information or function’’ (Mia & Chenhall, 1994, p. 9).

3.10.4 Interaction and Effect Size

Information about the optimal value of the dependent variable has been confused with that

which relates to changes in the relationship between the variables under consideration.

Champoux and Peters (1987) noted that a statistically significant coefficient of the interaction

term in MRA is only an indication of changes in the relationship between the variables, and

does not provide information about the value (size) of the dependent variable for a given value

of an independent variable. A recognition of the difference between significant interaction and

effect size appears to have been ignored by past studies, leading to wrong interpretation of the

interaction term.

For example, the interpretation of a positive statistically significant coefficient term in an MRA

consisting of two independent variables 𝑋1 and 𝑋2, is not based on the prediction that 𝑌 derives

the highest value for the highest values of 𝑋1 and 𝑋2 , but that 𝑋2 has a more positive effect on

𝑌 for higher values of 𝑋1. The body of literature relating to Brownell (1983), Brownell and

Hirst (1986), Mia (1989), Brownell and Dunk (1991), and Dunk (1989, 1992, 1993) incorrectly

assume that a statistically significant interaction term implies that the dependent variable is

maximized for a given set of certain combination of variables. Dunk (1992) wrongly assumes

that the significant interaction term in the MRA model he used to test his hypothesis contains

information about effect size, and subsequently offers wrong interpretation of the verbal

hypothesis. His hypothesized verbal statement reads:


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‘‘production subunit performance is higher for higher (lower) the reliance on budgetary control

and a higher (lower) the level of manufacturing process automation’’ (Dunk, 1992, p. 198)

The use of MRA to test this hypothesis which by implication, a combination of low/low and

high/high of the exogenous variables maximizes the criterion variable as specified in the verbal

hypothesis cannot be interpreted by the interaction term. Another statistically significant

negative coefficient in a three-way interaction MRA model used by Dunk (1993, p. 405-406)

was interpreted to mean that the dependent variable ‘slack’ attains a minimum value when the

independent variables consisting of ‘participation’, ‘information asymmetry’ and ‘budget

emphasis’ are all high. As the main effects (or lower-order effects) are due to the exogenous,

uncontrollable contingency factors, the question of either maximum or minimum 𝑌 value is

somehow not important in contingency models (Hartmann & Moers, 1999).

In addition to the above deficiencies that characterized some empirical contingency literature

on the effect size, confusion still surrounds the use of the Johnson-Neyman (JN) technique in

analyzing effect size in MRA. While Hartmann and Moers (1999) argue that the JN technique

plays no role in hypotheses tests regarding MRA interaction format, as used by Brownell

(1982a) and Lau et al (1995) in performing formal analysis of effect size, Burkert et al (2014),

in their extensive literature review on contingency fit hypotheses, concluded that such a ‘‘lack

of comprehensive explanations of how best to investigate the additional features of an

interaction term which are available now could account for misperception’’ (Burkert et al,

2014, p. 20). On common grounds, the JN is noted for its relevance in determining the ‘region

of significance’ in MRA for the difference between the effects of different values of the

moderator at a given value of the exogenous variable. Its relevance may however, depend on

the way and manner in which it is applied.


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3.10.5 Multiple and Higher-Order Interaction

A number of deficiencies such as over-specification of models (Imoisili, 1989), under-specified

theoretical support for interaction terms introduced (Imoisili, 1989; Harrison 1992) and

misapplication of multiple and higher-order interaction models (Harrison, 1992, 1993) etc.,

characterized most studies. Another shortcoming associated with this form of statistical

analysis is the difficulty in interpreting the coefficients of the interaction terms. These

shortcomings seriously affect the validity of the results published by most studies. In the study

of Imoisili (1989) for example, a multiple two-way and higher-order interaction model was

specified as follows:

𝑌 = 𝛽0 + 𝛽1𝑋1 + 𝛽2𝑋2 + 𝛽3𝑋3 + 𝛽4𝑋1𝑋2 + 𝛽5𝑋1𝑋3 + 𝛽6𝑋2𝑋3 + 𝛽7𝑋1𝑋2𝑋3 + 𝜀 …... (3.6)

The two-way interaction term (𝑋2𝑋3) and the three-way interaction term (𝑋1𝑋2𝑋3) as included

in the model were not hypothesized by Imoisili (1989), yet they were retained in the model for

analysis. In this case, the model is said to have contained a redundant two-way interaction and

a redundant three-way interaction which do not form part of the hypotheses, thereby indicating

an incorrect test of the hypotheses. Taking the partial derivative of (3.6) shows that the model

contains an interaction effect (𝑋1𝑋2) not hypothesized. That is

𝜕𝑌

𝜕𝑋1= 𝛽1 + 𝛽4𝑋2 + 𝛽5𝑋3 + 𝛽7𝑋2𝑋3…………………. (3.7)

which means that the association between 𝑋1 and 𝑌 is not only a function of 𝑋2 and 𝑋3 but also

a function of the interaction term (𝑋2𝑋3). The inclusion of the redundant terms was not

specified by theory. As noted by Jacaard et al (1990) any addition of two-way interaction in an

MRA equation should have a theoretical foundation. More specifically, the theory should

dictate that the association between the exogenous variable and an endogenous variable is a

function of two or more independent variables. From these analysis, the results obtained by

Imoisili (1989) do not provide an answer to the verbal hypotheses and hence the conclusions


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reached are incorrect. To provide reliable results, the two redundant terms in the equation

should have been excluded.

In another test performed by Harrison (1992) involving a test for the existence of a two-way

interaction between budgetary participation, reliance on job-related tension, accounting

performance measures and nation represented by 𝑋1, 𝑋2, 𝑋3, and 𝑌 respectively, the following

MRA equation was specified:

𝑌 = 𝛽0 + 𝛽1𝑋1 + 𝛽2𝑋2 + 𝛽3𝑋3 + 𝛽4𝑋1𝑋2 + 𝛽5𝑋1𝑋3 + 𝛽6𝑋2𝑋3 + 𝜀……… (3.8)

The aim of the test was to investigate the moderating effect of budgetary participation (𝑋1) on

the association between job-related tension (𝑌) and reliance on accounting performance

measures (𝑋2). As far as this hypothesis is concerned, the multiple interaction equation as

specified in (3.7) is of no relevance as it has no theoretical foundation. Per the verbal

hypothesis, only the first interaction (𝑋1𝑋2) are hypothesized. The other two (𝑋1𝑋3) and

(𝑋2𝑋3) are not. The equations specified in Imoisili (1989) and Harrison (1992) both have high

probability of being over-specified since the extra predictor variables which were included is

unnecessary. A model which is over-specified has the chance of increasing its standard error

of regression coefficients which is highly associated with a Type I error since it influences the

significance of the coefficients. A standard error influenced by over-specification is not

interpretable irrespective of its stability. In addition, a model which is affected by unnecessary

over-specification is hardly interpretable no matter how stable it is (Hartmann & Moers, 1999,

2003). Based on these arguments, a statistically significant coefficient 𝛽4 in Harrison’s (1992)

model could not have been interpreted. In a further analysis, we take the partial derivative of

(3.7) w.r.t. 𝑋2 to give:

𝜕𝑌

𝜕𝑋2= 𝛽2 + 𝛽4𝑋1 + 𝛽5𝑋3………………. (3.9)

which means that the association between 𝑋2 and 𝑌 is a linear function of not 𝑋1 alone, but

also 𝑋3. Consequently, answers to the hypothesis has not been provided in Harrison (1992)


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since there is no the statistically significant value of the coefficient 𝛽4 is not in this case, a

measure of the moderating effect of 𝑋1 on the relationship between 𝑋2 and 𝑌 as hypothesized.

3.11 The Use of Higher-Order of Abstraction Models in SEM

Another shortcoming associated with second or third level of contingency fit analysis (Otley,

2016), has been the modelling of multiple independent and dependent variables. These studies

have tended to bundle together, multiple constructs in a single composite score. The outcome

of such modelling is that the explication of the resultant construct generally is incomplete and

the contributions of content domains of the individual construct to the final score by the scale

will be unknown (Gerbing, Hamilton & Freeman, 1994). Alternatively, if all items or manifest

variables are posited as reflective items of a single first-order construct, then it would be

difficult to ascertain the contribution of each domain on the overall construct.

This is of particular importance with multi-dimensional constructs. Several of both the structure

(MAS) and context (contingency) constructs in contingency-based management accounting

studies can be meaningfully conceptualized as higher orders of abstraction. Although

paradigms on the use of structural equation modelling in modelling contingency fit have

appeared in the management accounting field, several SEM techniques still remain unexplored.

Studies that employ advanced SEM techniques such as higher-order modelling, latent growing

models, and multilevel modelling are relatively not in existence. None of the contingency-

based management accounting studies reviewed in this study has produced a manuscript that

employ higher-order (e.g. second-order) modelling.

Indeed, structural equation models are powerful statistical tools that can test all types of

contingency fit models (except matching form of fit) since their correct application drastically

reduces the amounts of Type I and Type II errors (Burkert et al, 2014). Besides, the mere

finding of a statistically significant coefficient of the 𝛽-value for the absolute difference value


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and that of an interaction term which are associated with the deviation score and MRA is too

crude a proxy for respectively determining the existence of moderation forms of fit. In SEM,

the test variables in addition to supporting a statistically significant test, must obtain statistical

values (or goodness-of-fit indices) that fall within the specified theoretical range of

acceptability, hence providing concrete evidence of the model adequately fitting the sample

data. In the traditional form of statistical analysis, such goodness-of-fit measures do not exist

which suggests that a statistically significant coefficient of the term representing the

contingency fit does not necessarily imply a fit. Such a statistical analysis has the high risks of

reaching incorrect conclusions.

3.12 Chapter Summary

In this chapter, the two main schools of thought underpinning contingency theory have been

discussed where a clear distinction is drawn between the Cartesian school of thought and the

Configurational school of thought. In addition, the various typologies of contingency fit models

have been presented delineating between the selection, matching, and moderation and

mediation forms of fit. Approaches to conceptualizing as well as testing the types of

contingency fit models have not been left out. Each concept of fit is associated with a different

theoretical interpretation which requires a different statistical analysis. For example, whereas

the selection form of fit requires a correlation analysis that of the matching and moderation

forms of fit require the deviation score technique and moderation regression analysis

respectively. This underscores enormous theoretical implications for management accounting

researchers. A final form of fit, the mediation form of fit, which has attracted several divergent

views as to whether it falls under contingency theory has been discussed. The short comings

and weaknesses associated with tests involving these models, and the theoretical and statistical


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conditions (the classic four-step procedure) that need to be satisfied to establish true partial or

full mediations have been fully elaborated.

The empirical review relating to contingency-based studies in management accounting has also

been presented. The review shows that contingency-fit hypotheses testing is underpinned by

three main issues; i.e. a match between the formulation of verbal hypotheses, the statistical

methodology employed, and the interpretation of results. Relating these three dimensions to

existing empirical studies reveals a number of inconsistencies as well as distortions and

fragmentations associated with contingency theory’s requirements. In addition, the review

reveals several complexities and weaknesses associated with the traditional approaches used in

testing contingency fit hypotheses which clearly shows that they are not appropriate statistical

methodology to testing contingency hypotheses especially multiple variables. The higher the

number of the test variables the more complex it becomes. For example, testing a four-way

interaction using MRA would be extremely difficult. This suggests the use of covariance-based

structural equation models which have the advantage of resolving these shortcomings

associated with the traditional approaches.


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CHAPTER FOUR

THEORETICAL MODEL AND HYPOTHESES DEVELOPMENT

4.1 Introduction

The preceding chapter reviewed the theoretical and empirical literature on contingency theory’s

application in MAS design, and identified the shortcomings associated with these studies

particularly in relation to testing of contingency hypotheses under the various fit models. The

review identified the common structural characteristics of organizations that have been

incorporated into contingency research during the past three decades. These include strategy,

decentralization, size, organizational culture, formalization, structural differentiation,

technology and perceived environmental uncertainty. This chapter presents a more detailed and

focused description of the variables/constructs that form the conceptual model and the

theoretical linkages or empirical evidence of the extent of their relationships. Building on these

theoretical foundations, the theoretical model that form this research is initially presented. A

detailed discussion of the theoretical background of the individual study variables follows.

Next is the formulation of the testable hypotheses; their formulation which are based on

evidence of theoretical linkages from empirical literature as well as in conformance with the

underlying contingency fit model. The study also controls for hospital specific variables made

up of four variables namely, hospital size, location, ownership and location which have been

shown empirically to influence hospital performance. The chapter concludes with a summary.

4.2 Theoretical Framework

The theoretical model within which this study is conducted is illustrated in Fig 4.1. This model

is developed based on empirical evidence that supply chain integration (SCI) and MAS

information characteristics are important variables that interact to enhance hospital SC


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performance. It is also based on empirical evidence that a fit (or alignment) between

organizational contextual variables and MAS information characteristics is important for

achieving high performance.

As shown in Fig 4.1, the SCM variable is represented as a multidimensional construct

comprising four contextual variables. These include SCI (strategic supplier partnerships or

external integration), internal integration, level of information (or knowledge) sharing (or

exchange), and supply chain risk and uncertainty (Li et al, 2006; Chen et al, 2013) and are

hypothesized to influence the design and implementation of MAS information in the health

SCM context. The MAS variable represented by one rectangle is also a multidimensional

construct comprising broad scope, timeliness, integration, and aggregation (Chenhall & Morris,

1986; Bouwens & Abernethy, 2000; Abernethy & Lillis, 2001). Finally, hospital supply chain

performance is although represented in the model by a single rectangle, it is a multidimensional

construct comprised four dimensions namely, cost effectiveness, utilization of hospital asset,

supply chain flexibility and speed, and supply chain quality (Chen et al, 2013; Ataseven &

Nair, 2017).

4.2.1 Contingency Fit

This research is positioned within contingency framework i.e., the three models of contingency

fit relationships: the selection, mediation and moderation fit models. The selection fit model

tests the impact of the four SCM contextual factors on the four dimensions of the MAS design.

In this type of fit, the theory says that effect of the contextual variables is tested not on

performance but the MAS variable (Hartmann & Morris, 1999). The implication is that

companies have fully adapted their MAS to the demands of their context (Hartmann, 2005).


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4.1: Theoretical Framework

(Source: Modification from Chen et al, 2013; Hammad et al, 2013; Macinati and Anessi-Pessina, 2014)

Supply Chain Integration

Supplier

relationship

(External

Integration)

Internal Integration

Level of Information

Sharing

Supply chain risk

and Uncertainty

MAS Design

Broad scope

Timeliness

Integration

Aggregation

Hospital Supply Chain

Performance

Supply Chain Cost

Effectiveness

Utilization of

Hospital Assets

Supply Chain

Quality

Supply Chain

Flexibility

Size Location

Profit Status Ownership

H1a,b,c,d H2a,b,c,d H3a,b,c,d H4a,b,c,d

H5 a,b,c,d

H6 a,b,c,d


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Because companies are assumed to be in equilibrium, no differences in performance are expected.

Sixteen sub-hypotheses are tested in this model. The other contingency fit is the mediation form

of fit which tests the dimensions of the MAS design on the relationship between SCM contextual

factors and supply chain performance. Here, the MAS serves as an intervening mechanism

between SCM and SC performance. Relating the contingency theory to SCM and MAS design

suggests an alignment of the individual dimensions of SCM with the MAS dimensions in order to

achieve the best performance. Past literature suggests that the dimensions of SCM highly interacts

with other variables to enhance both operational and financial performance (Flynn, Huo and Zhao,

2010). This suggests that the interaction of individual characteristics of SCM to affect performance

is supported by contingency theory (Flynn et al, 2010) hence, organizations make the effort to

integrate SCM characteristics with the dimensions of the MAS to maximize performance. In this

model, four hypotheses are tested. The final model is the moderation fit model which tests

moderation of the SCM contextual factors on the relationship between the dimensions of the MAS

and SC performance. As asserted by Ataseven and Nair (2017), the need for investigating specific

associations between the characteristics of SCM and performance within contingency frameworks

is to discern the role of moderating factors in the dimensions SCM.

4.2.2. Contingency Theory

Contingency theory is a well-recognized and long-standing theoretical framework for explaining

the design and success of organizational structure (Meilich, 2006; Burkert et al, 2014). On general

grounds, contingency-based studies in management accounting aim to find a ‘match’ (or

alignment) between the design and use of MAS information and the context (or environment) in

which it is used for optimal performance.


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Contingency theory is known not only to be a major theory and overarching area of management

accounting research, but also a theory that has attained a pivotal stand within the field of general

organizational literature. In management accounting research, it has attained a dominant position

(Hartmann & Moers, 1999; Otley, 2016). As noted by Scott (1998, p.97), ‘‘it is the most

contemporary theoretical approach that has been widely utilized to the study of organizations’’.

The importance of contingency theory is reflected not only in the frequency of its nomination by

scholars of organizational for its relevance (Miner, 1984; Lawrence & Lorsch, 1967), but also the

assertion made by Lawrence (1993, p. 16). He states that ‘‘the strongest research-based body of

knowledge that continues to be relevant to the practical problems of organizational design has been

contingency theory’’. For effective organization and structuring of a firm involved contingencies

that need to be taken into consideration (Miner, 1984). In other words, the effect of organizational

structure on organizational performance depends on contextual aspects that need to be accounted

for.

Contingency theory assumes that no optimal organizational structure exists rather, the design of

an organizational structure is contingent upon both external and internal and (contextual and

environmental) factors such as strategy, size, technology, and perceived environmental uncertainty

(Otley, 1978; 1980; Chenhall, 2003). The contention is that an organization’s structure is not only

contingent upon contextual variables, but that for optimum performance, the different components

of an organization must fit with each other. This notion that performance is the result of the fit

between context and structure is central to contingency theory (Meilich, 2006; Burkert et al, 2014).

Management accounting researchers have drawn on this theory to investigate the ‘match’ between

MAS variables and the context and environment in which they are used. Early studies hypothesized

that a universally appropriate management accounting system which applies equally to all


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organizations in all circumstances does not exist rather, appropriate accounting systems depend on

organizational contextual variables (Otley, 2016).

Contingency theories of accounting play a unique role not found in other universal theories and

hence, have been described as the opposites of universal theories of accounting (Hartmann &

Moers, 1999). This unique role lies in the fact that ‘‘they link the impact or the optimality of the

MAS variable to the context or environment in which they are used’’ (Hartmann & Moers, 1999,

p.292). Although contingency theory has a long-standing tradition in both the organizational and

management accounting literatures, the accumulated research evidence has not consistently

supported the arguments put forward by the theory (Otley, 2016). There still remain unsettled

issues regarding the theorizing and testing of contingency hypotheses (Burkert et al, 2014). Weak

relationships between the formulation of verbal hypotheses and statistical analysis, controversy

over the fit concept under the moderation, matching and mediation forms of fit and how it is

attained, statistical methods suitability and their appropriateness in testing contingency hypotheses

etc., are some of the unresolved issues. In spite of these shortcomings, contingency-based

management accounting research contends that the design of MAS is affected by (contingent upon)

the context or situation in which an organization finds itself.

Supply chain studies and management accounting have been undertaken through the theoretical

lenses of mostly transaction cost economics (TCE) and relational theory (Dekker et al, 2013).

These studies however, focus on two dyad supply chain relationships with emphasis on risk

minimization. The TCE argues that partners in inter-firm relations need to safeguard their interest

against the potentially opportunistic behavior of others, which literally is the management of

appropriation concerns (Williamson, 2008). The essence of managing appropriation concerns has

a close relationship with the features of the transactions taking place (i.e. the specific assets that


95

have been dedicated to the relationship, the rate or frequency at which the transaction occurs, and

the level of uncertainty surrounding the environment), and the features of human beings (i.e.

opportunism and bounded rationality) (Dekker, 2004).

For example, the higher the appropriation concerns in inter-firm exchange, the more partners who

are safeguarded in a transaction need to raise their confidence that the other partner will not engage

in malfeasance, thereby influencing the application of excessive amounts of MASs which

negatively affects performance. From this perspective, and to mitigate underlying transaction risk,

the installation of MAS in the inter-firm exchanges domain has been essentially based on the

‘matching’ principle where the installation of MASs by firms aligns with the transaction context

(Reusen & Stouthuysen, 2017). For example, Reusen & Stouthuysen (2017) found that although

the transaction context is related to the extent of MAS use, significant variations which are

evidenced by performance differences across firms exist. This is because although there is a fit, it

does not reflect the optimum level (Otley, 1980, 2016). This optimality condition is what

contingency theory proclaims as its underlying principle which suggests that fit between

contextual variables and structure is important for achieving high organizational performance

(Chenhall, 2003).

Moreover, choices of MAS design in the inter-firm exchanges domain in SCM decisions have been

based on imitations of individual MAS structure which Reusen & Stouthuysen (2017) find to be

one of the root cause of misalignment in the MAS-context relationship. It has been argued that

‘‘the planning and controlling processing abilities that are fundamental to managing costs

internally is the same for managing inter-firm relationships hence, can be applied to SCM

activities’’ (Fayard et al, 2012, p.170). Hence, organizations with high levels of internal cost

management (ICM) would be able to use their knowledge and experience to model similar costing


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systems for SCM decisions. In this regard, MAS information for SCM decisions were considered

as mere extensions of the conventional intra-firm cost accounting tools of the traditional MAS,

and this has come to be known as inter-organizational cost management (IOCM). In the

subsections that follow, the theoretical foundations of each of the four dimensions of the MAS and

SCM contextual factors are discussed.

4.3 Theoretical Foundation of Constructs

Contingency fit is simply a question of alignment between three primary pieces of organizational

puzzle; 1) characteristics (or dimensions) of MAS information, 2) organizational performance, and

3) contingency factors that affect the relationship between MAS design and organizational

performance (Chenhall & Chapman, 2006). Studies in the contingency paradigm attempt to

explain the design and/or use of MAS through arguments and demonstration of the optimality or

fit within the environment context in which the MAS operates (Luft & Shields, 2003; Hartmann,

2005).

4.3.1 Management Accounting Systems (MAS)

Management accounting system has been conceptualized from different perspectives but generally

consist of formalized information systems designed and used by organizations to provide decision-

making information and support for managers (Chenhall & Morris, 1986; Bouwens, & Abernethy,

2000; Gerdin, 2005a). Hoozee and Ngo (2017) note that organizations employ MASs to influence

managerial behaviours such that they are directed towards attaining organizational objectives. This

is because empirical evidence of a positive association among accounting information, improved

decision making, and organizational performance has generally been found among management

accounting scholars (Cooper & Kaplan, 1991).


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Jermias and Gani (2004) conceptualized the management accounting function from two broad

perspectives: product differentiation strategy (Type I MAS) and low-cost strategy (Type II MAS)

based on Porter’s (1985) framework for strategic priority. The former enhances the capacity of

companies to differentiate their products as well as meeting the needs of customers by providing

information and measures that relate to key production activities, strategic planning, customer

satisfaction, quality, timely and reliable delivery of goods/services, benchmarking, and employee-

based measures. For companies that adopt low cost strategy, the latter is considered to be more

appropriate as it provides information associated with activity-based costing techniques, variance

analysis, and budgetary performance measures.

In healthcare management, and more specifically the health supply chain, both design types of the

MAS information are relevant in enhancing organizational performance. For example, hospitals

need information and measures that can be used to differentiate medical products at various stock

(or inventory) levels, track operational (or production) costs of facilities, and satisfy customers’

(patients’) needs simultaneously. In particular MASs may reveal sources of inefficiency and

ineffectiveness of hospital operations by providing detailed information about the consumption of

resources by each of the hospital’s operations (Hoozee & Ngo, 2017). A more challenging issue is

where management have to provide quality healthcare services to customers but at relatively low

cost (i.e. adopt a low-cost strategy). Based on these decision-functional roles of the MAS, many

scholars (e.g. Chenhall & Morris, 1986; Bouwens & Abernethy, 2000; Abernethy et al, 2007;

Hammad et al, 2013; Macinati & Asseni-Pessina, 2014) have conceptualized MAS design in terms

of four interrelated dimensions as shown in Fig 4.2: 1) broad scope which refers to external, non-

financial and future-oriented information, 2) timeliness which deals with the frequency and speed

of reporting accounting information, 3) integration which refers to the precise targets set for


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activities and the scope of variances calculation, and 4) aggregation which refers to various forms

of aggregation such as time period and functional areas as well as analytical or decision models.

Aggregation also specifies the dimensions of cost disaggregation according to behavior and their

interrelationships within subunits.

These four dimensions have generally been considered as critically intrinsic characteristics of the

MAS designed and used in healthcare setting (Abernethy & Lillis, 2001; Pizzini, 2006; Abernethy

et al, 2007; Hammad et al, 2013; Macinati & Asseni-Pessina, 2014). In addition, these four

attributes of the MAS information are known to be highly significant in assessing managerial

decision-making in hospitals; hence, appropriately describe the MASs designed and used in

healthcare organizations (Pizzini, 2006).

Based on Chenhall and Morris (1986), MAS is conceptualized in this study as formal systems

designed for providing information for managers in healthcare institutions. The prime objective of

this study is to examine the effect of strategic supplier partnerships (external integration), supply

chain integration (internal integration), level of information sharing (or knowledge exchange), and

supply chain risk and uncertainty on the four dimensions of the MAS design in the healthcare inter-

firm exchange domains and the decision facilitating role of the MAS information in enhancing the

performance of the health supply chain. The dimensions of the MAS information are discussed in

the subsections that follow.


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Figure 4.2: Classification of the MAS Dimensions

(Source: Adapted from the literature of Chenhall & Morris, 1986; Soobaroyen & Poorundersing,

2008; Hammad et al, 2013; Macinati & Assina-Pessina, 2014).

4.3.1.1 Broad Scope of the MAS Information

Broad scope of MAS information is viewed as a continuum where narrow MAS information is

associated with traditional MAS design which not only focuses on internal information, but also

financial and historical information. Information that is externally focused, non-financial, and

future-oriented pertains to broad-scope information. The extent to which the MAS information is

presented in various forms is represented by broad scope (i.e. both financial and non-financial) as

well as information that predicts future events. Broad scope information is needed in the health

SCM decisions such as operating cost of facilities, value of commodity throughput, procurement,

medical supplies, and inventory management, transportation, etc. The availability of information

that predicts future events depends on the level of detail (Cohen & Kaimenaki, 2011). This

suggests that valuable time that is otherwise spent by managers each time a decision on the

Broad Scope

Timeliness

Levels of

Aggregation

Integrative Nature

MAS Information

Dimensions

External information Non-financial information Future-oriented

Frequency of reporting Speed of reporting

Aggregated by time period Aggregated by functional area Analytical or decision models

Precise targets for activities and interrelationships with subunits Reporting on intra-subunit interactions


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formatting of cost data is made is saved. The extent to which decision-making information has the

appropriate level of analysis for purposes and sufficient, and is greater when the level of detail of

the MAS information is higher. It is expected that highly detailed MAS information will provide

managers with a more realistic view and clearer cost objects linked to healthcare SCM. In this

regard, the ways in which these cost objects affect performance is better understood and

contributed. The degree to which costing systems are analyzed for different purposes in the health

SC also depends on the level of detail of the MAS information and the more appropriate and useful

the information.

4.3.1.2 Timeliness of the MAS Information

The time elapsed before information is made available upon request. More specifically, the extent

of fastness or quickness the MAS responses to a requested information. This implies that the

dimension timeliness is associated with frequency as well as speed of reporting information. How

often information is made available to managers is the sub-dimension of frequency whilst the time

lag between the request for information and the time information is made available pertains to

speed of reporting. Also, frequency reflects the extent to which recent actions consequences are

quantified by information. Timeliness of the MAS information is highly essential in the health SC

to reflect a more reliable estimation of costs and up-to-date cost information as well as

identification of cost drivers and tracking of inventory. In this regard, recently made decisions are

safeguarded by a faster feedback system. Potential problems as well as opportunities are identified

in time when users’ available MAS information is frequent and timely. This offers a well-informed

and effective decisions under the attribute of frequency.


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4.3.1.3 Integration of the MAS Information

Integration simply refers to the ability of the MAS information to calculate variances that have a

link to the operation of the health supply chain. In this regard, the degree to which budgeted costs

and revenue targets in the area of say procurement and hospital fees collected from patients

respectively are realized is closely monitored. Managers are compelled to evaluate whether their

estimates are close to reality whenever the budgeted is compared to the actual. In this regard,

tangible reasons can be offered for the deviations spotted and easily modify the estimations

accordingly. An extensive variance analysis leads to a modification of the budgets to reflect reality

on frequent basis. Consequently, the MAS offers more accurate, reliable, and ultimately effective

costing decisions. Integration also has to do with information concerning departmental activities

with the organization as well as interdependences constitutes the integration dimension of MAS.

This information relates to inputs, outputs, and the operations of individual departments. In effect,

the coordination of various segments within a sub-unit is facilitated by data that crosses functional

boundaries.

4.3.1.4 Aggregation of the MAS Information

This dimension of the MAS deals with the extent to which the MAS disaggregates cost accounting

to behavior. It reflects the extent to which a summary of information provided by functional units

or managers on time period basis through decision models such as inventory models, cost-volume-

profit analysis, marginal analysis, discounted cash flow analysis etc., constitute aggregated

information. More specifically, the MAS information is categorized into functional area and time

period. It also refers to summation in formats consistent with formal decision-making.


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4.3.2 Supply Chain Integration (SCI)

Generally, the supply chain constitutes a series of interrelated and connected activities that involve

planning, controlling and coordinating materials, parts and finished goods from the raw materials

stage to end users (Stevens, 1989). The strategic collaboration of both intra-organizational and

inter-organizational processes constitute SCI (Wong et al, 2011) which is normally collapsed into

supplier, customer (patient) and internal integration (Flynn et al, 2010).

Activities of the supply chain are made up of sourcing and procurement, system management,

inventory management, warehousing, transportation, and customer service (Lambert, Cooper &

Pagh, 1998). SCI involves supplier integration, customer integration and internal integration

(Flynn et al, 2010). According to Flynn et al (2010, p.58)

‘‘it is the degree to which an organization strategically collaborates with its supply chain partners

and collaboratively manages intra-organizational and inter-organizational processes, in order to

achieve effective and efficient flows of products and services, information, money, and decisions,

to provide maximum value to the customer’’.

Integration of these activities affect each other to enhance performance. Based on these elements,

Cooper, Lambert and Pagh (1997) and Lambert et al (1998) describe SCM as the integration of

key business processes which involve end users through suppliers and offer value added products,

services and information to customers and other stakeholders. More precisely SCM constitutes a

key business process integration among a network of inter-dependent suppliers, manufacturers,

distribution centres, and retailers to enhance the flow of goods, services and information from

suppliers to customers at relatively reduced costs.


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However, different notions of the SC which demands different forms of accounting information at

different levels of sophistication and complexities exist (Burritt & Schaltegger, 2014) and this

study focuses on that of the health SC which in relation to mainstream discrete parts and consumer

goods, is complex, sophisticated and more expensive to operate (Shah, 2004). Although the SC

has become a key driver that enhances overall organizational performance in contemporary

businesses (Field & Meile, 2008; Maestrini et al, 2017), there is evidence of underdevelopment of

research that relate the specific associations between its contextual factors, MAS design and

organizational performance (Ataseven & Nair, 2017). Like the dimensions of the MAS

information discussed in the preceding subsection, the key SCM dimensions include supplier

integration, customer integration, internal integration, transfer of knowledge (or information

sharing), risk associated with the external environment (or trust), and postponement (Li, Ragu-

Nathan & Rao, 2006; Chen et al, 2013). As stated earlier, various forms of accounting information

are required for their effective management (Ittner, Larcker, Nagar & Rajan, 1999; Dekker, 2004;

Dekker, Sakaguchi & Kawai, 2013).

However, the MAS’s primary objective of minimizing supply chain cost and creating value in

SCM is in perfect alignment with the objective of SCM which aims at creating value to the

customer. Hence, empirical investigation into the relationship between the MAS information

characteristics and SCM contextual antecedents could provide valuable insights, understanding,

and far better theoretically informed evidence and explanations of the management accounting

function in SCM decisions (Dekker, 2016).

In addition, there is diversity of lack of standardization of the nomenclature that characterize the

SCM practices. This has resulted in a confused understanding of the fundamental concepts of the

area (Behesti, Oghazi, Mostaghel & Hultman, 2014). Indeed, intended results that are


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unprecedented to other management tools are provided by the supply chain. However, managing

supply chains whether as a strategic, tactical, or operational management tool must be deeply

rooted in four principles; supplier relations, sharing of information (or knowledge exchange), risks

and benefits (Kwon, Hamilton & Hong, 2011). This section explains and discusses the four

dimensions used to operationalize SCM practices in the healthcare context. As Kwon, Kim and

Martin (2016) point out, the non-availability of any one of these dimensions may result in

suboptimal performance.

4.3.2.1 Strategic Supplier Partnerships (SSP) (External Integration)

Strategic supplier partnership refers to a direct, long-term relation that exists between a focal

organization and its suppliers. In the healthcare sector, it represents the long-term relationship

between hospitals and their suppliers such as pharmaceutical manufacturers, wholesalers and

distributors of medical supplies. This relationship is such that it encourages problem-solving

efforts and mutual planning between the parties. The sharing of gains and risks among supply

chain members is the main objective of strategic supplier partnerships. Information sharing, joint

decision-making, and product and process technology transfer constitute the key attributes of

supplier partnerships (Atkinson and Waterhouse, 1996).

In this study, the definition of strategic hospital supplier is based on Vickery, Jayaram, Droge,

Calantone (2003), Barki and Pinsonneault (2005), and Chen et al (2013) as the extent to which the

business processes between a hospital and key supplier such as inter-organizational logistical

activities are strategically coupled and unified as a whole. This definition is chosen because a

major barrier cited as inhibiting the effective implementation of cost effective standardized process

(which is one of the four dimensions of supply chain performance) in the healthcare industry has


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been that non-existence of cooperation among healthcare SC partners (Nachtmann & Pohl, 2009).

Either a lack of or absence of collaborative framework between a hospital and its partners.

4.3.2.2 Internal Integration (SCI)

Integration encompasses a broad array of practices emanating from information sharing at the

operational level (e.g. stock levels) to strategic issues (e.g. collaborative efforts towards new

product development) (Behesti et al, 2014; Ataseven & Nair, 2017). A shift from managing

individual functions to integrating activities into key supply chain processes is a major requirement

for successful SCM (Lambert & Cooper, 2000). The supply chain is an integrated process that

encapsulates the transformation of raw materials to final product, and the distribution of final

products to customers (Ataseven & Nair, 2017). In this regard, supply chain integration considers

the formation of networks that encompass the basic elements that form the supply chain: the focal

organization, suppliers upstream and buyers downstream (Zailani & Rajagopal, 2005). This

implies that the integration process is made up of suppliers, producers, distributors, and customers.

4.3.2.3 Level of Information Sharing (or Knowledge Exchange)

Prior studies on SCM suggest that one attribute of SCM that has become a major driver of

competitive advantage that affect performance information sharing has been information sharing.

As an important prerequisite of SCI, hospitals are part of an environment characterized by

networks of inter- and intra-organizational relationships especially in the context of increasingly

globalized and competitive economy. Studies have shown that a prevalent way of effectively

managing SCs which seek improved performance through effective use of resources and

capabilities has been closer information-based linkages. In addition, information sharing is directly

related to the quality of medical products and services, reduce SC costs, improved SC coordination,


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and the achievement of competitive advantage. However, little research has been devoted to

information sharing and SC performance in the healthcare context (Chen et al, 2013).

The extent to which critical proprietary information is communicated to an organization’s SC

constitutes the extent or level in which information is shared (Li et al, 2006). Information sharing

can be grouped into four types: intra-organizational information sharing, inter-functional

information sharing, information sharing with suppliers, and information sharing with customers

(patients). The information can take several dimensions including strategic, tactic, market and

customers, logistic activities, inventory levels, product availability, expeditions and production

requirement status etc. (Ou, Liu, Hung & Yen, 2010; Behesti et al, 2014; Ataseven & Nair, 2017).

However, information sharing (or knowledge exchange) has been one of the crucial and

challenging elements in the health SC since the health SC deals with critical services and products

that impact human life (Kwon et al, 2016). Information sharing (or knowledge exchange) fosters

not only innovative capabilities among supply chain partners, but also a spirit of collaboration and

provides supply chain practitioners the opportunity to optimize the entire supply chain (Kwon &

Suh, 2004a). The quality of information is based on its accuracy, adequacy and timeliness.

Improvement in the coordination of supply chains, assurances on the quality, speed and flexibility

of delivery of goods and services, and reduced SC costs are embedded in information sharing

(Barrett & Konsynski, 1982; Sahin & Robinson, 2005; Zhou & Benton, 2007).

Information-based linkages are fundamental to effective SCM practices and performance

enhancements through effective use of resources and capabilities (Lee, So & Tang, 2000; Li &

Lin, 2006). In particular there is growing consensus that information sharing contributes

significantly to: 1) reduced supply chain costs, 2) speeding the material flow process, 3) customer

satisfaction through improvement in the order fulfillment process, 4) improvement in partner


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relations, 5) channel coordination enhancements, 6) speeding the delivery of goods and services,

and 7) achieving competitive advantage.

4.3.2.4. Supply Chain Risk and Uncertainty

A significant source of risk exposure and uncertainties characterized the SC as a consequence of

the development of intensified relations with suppliers (external integration) (Dekker et al, 2013).

Thus the SC has become a significant source of risk exposure for many firms. Risks relating to

lack of cooperation between exchange partners, and to performance failures even with full Co-

poration commonly exists in most inter-firm relationships (Langfield-Smith, 2008). In order to

reap collaborative benefits, these risks need to be mitigated. An effective MAS provides timely

information to mitigate these risks. Hence, a high and strong association exists between MAS

information characteristics and SCM.

A key determinant of supply chain risk has been identified by past studies (e.g. Anderson &

Dekker, 2009) to be that relating to outcome of transactions among partners; hence, properly

labelled as transaction risks. This risk underpins two main decisions: buyers’ selection of trusted

suppliers, and collaborative practices used to manage the transactions (Dekker et al, 2013).

Adapting to this assertion, supply chain risk in this study is defined as transaction risks that

essentially relate to the risk that healthcare organizations do not achieve intended or desired

outcomes of supply chain transactions they engage in.

4.3.3 Hospital SC Performance

Judging from a theoretical standpoint, a SCM system is said to have achieved its goal of cost

reduction and value creation when the system guarantees that the correct product is procured at a

reasonable price and delivered at the right location at the right time (Dooner, 2014). In relation to

health service providers, hospital SCM deals with the information, supplies and finances that


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characterize the procurement, warehousing and distribution of goods and services from suppliers

to end users with the aim of simultaneously enhancing clinical outcomes and controlling cost at

minimum levels to create value (De Vries & Huijsman, 2011). To achieve this goal, much

emphasis is placed on the integration of processes which in the healthcare setting refers not only

to physical products such as health aids, medical devices and pharmaceuticals, but also patients

related processes. Whichever the case is, the basic rational underpinning health SCM is based on

the proposition that an improved performance of the hospital SC is associated with intensive

integration and coordination of operational processes (De Vries & Huijsman, 2011).

Although different performance indicators have been proposed to capture the multi-dimensionality

measures of supply chain performance, the most frequently quoted model and widely applied has

been that of the supply chain reference model (SCOR) developed by the Supply Chain Council

(Lega, Marsilio & Villa, 2013). The SCOR model which identifies five measurement criteria for

supply chain performance assessment provides a useful framework for supply chain performance

assessment in firms (Chen et al, 2013). These metrics include cost effectiveness, responsiveness,

flexibility and speed, reliability, and efficiency in asset utilization (Zanjirani, Farahani &

Davarzani, 2009).

Various performance measures have been used in SCM studies. These range from financial

performance measures, aggregate performance and operational performance. Although, the current

study examines the relationship between variables of the supply chain and that of accounting

parameters which under normal circumstances, relate to financial performance measures, such

considerations will not address the research objectives. Considering the problem (mainly delays,

inaccessibility of drugs, higher costs, significant non-value adding steps, etc.) that this study seeks

to examine, the use of operational performance measures appropriately suits the research problem.


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To operationalize the performance effects of the variables under study, and following Chen et al

(2013) and Ataseven and Nair (2017), various facets including cost effectiveness, speed and

flexibility of delivery, asset utilization and quality of the supply chain are used to measure supply

chain performance.

4.4 Formulation of Hypotheses

A number of areas in which accounting and more precisely management accounting is involved in

the management of supply chains exist in the healthcare context. These include procurement of

drugs and medical equipment, costs involved in hospital operations, inventory management, etc.

In this section, the a priori hypotheses linking the constructs are formulated. The formulation as

presented in the subsections that follow, are based on the theoretical as well as evidence of

empirical linkages between the variables. In addition, each hypothesis is formulated based on the

theoretical requirements underlying the particular contingency model to be tested.

4.4.1 MAS Information and Hospital Supplier Partnerships

One of the most enduring concerns in management accounting literature has been the narrowing

of the interface between MAS information and strategy (Bedford, Malmi & Sandelin, 2016). To

fill out this space, a large body of literature (Abernethy, Bouwens & van Lent, 2004; Auzair &

Langfield-Smith, 2005; Dekker, Groot & Schoute, 2013) adopt the contingency approach to

document systematic relationships strategy and aspects of the MAS information. One of the

features that characterize successful SCM is an earlier involvement of suppliers in buyers’ design

process which is targeted at ensuring a substantial amount of information among the parties to

leverage cost reductions and quality improvement in supply chain output (Baiman & Rajan, 2002).


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It has therefore been established (Elmuti, Khoury, Omran & Abou-Zaid, 2013) that the use of SCM

tools reduce operational costs, reduce cycle time and lead to higher performance without

compromising quality. Documented examples of the impact of supply chain partners on an

organization’s overall operations demonstrate significant impact due to partner failure (Sutton,

Smedley & Arnold, 2008).

It has also been argued by management accounting researchers (e.g. Atkinson & Waterhouse,

1996; Gietzmann, 1996, pp. 614-616) that:

‘‘cost management efforts is enhanced by strategic supplier partnerships by accelerating the

product development process, improving product quality, and through supplier-originated ideas

and technologies, increasing process efficiency’’.

In this regard, supplier partners can contribute to a number of mechanisms through which cost

management efforts have been identified in supplier management and management accounting

studies. Among these mechanisms are reduction of costs associated with product development

through initial availability of prototypes, minimizing low quality costs through higher quality

product development. This ensures minimal defects, enhanced consistency between designs and

suppliers’ process capabilities, and improves the efficiency of products and processes by

incorporating supplier-originated ideas and innovations (Agndal & Nilson, 2007).

Studies such as Cooper and Yoshikawa (1994a, p.51) and Gietzmann (1996, pp.616-620) have

established that development of strategic partnerships with suppliers has been the basis for

Japanese manufacturers’ success in cost management efforts. Ittner, Larcker, Nagar and Rajan

(1999) showed that the performance benefits from strategic partnerships practices are contingent

on extensive use of the non-price selection aspects of MAS design, as well as regular meetings and


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interactions with suppliers, and supplier certification. On the contrary, they found organizations

that follow arms-length supplier partnerships to make minimal gains as such practices have little

impact on performance. It has also been argued that a close collaboration amongst supply chain

partners tends to reduce transaction costs to the tune of between 35% and 40% of the costs

associated with economic activities (North, 1990; Butler, Hall, Hanna, Mendonca, Auguste, Man-

yika & Sahay, 1997), and up to 50% in IT outsourcing (Rottman & Lacity, 2006). For example,

Chrysler lost USD 24 billion due to lack of collaboration between the company and its suppliers

for over a 12-year period (de Vries & Huijisman, 2011; Henke, Stallkamp & Yeniyurt, 2014).

Maintaining supply chain relationships between SC partners reduces search review costs which

eventually contributes to increased profitability for the entire supply chain partners (Kwon et al,

2016). On the basis of these arguments, the following four hypotheses are formulated:

H1a: A positive association exists between strategic supplier relations (external

integration) and MAS design as operationalized by accounting information scope in

Ghanaian hospitals.

H1b: A positive association exists between strategic supplier relations (external

integration) and MAS design as operationalized by accounting information

timeliness in Ghanaian hospitals.

H1c: A positive association exists between strategic supplier relations (external

integration) and MAS design as operationalized by accounting information

integration in Ghanaian hospitals.


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H1d: A positive association exists between strategic supplier relations (external integration)

and MAS design as operationalized by accounting information aggregation in Ghanaian

hospitals.

4.4.2 MAS Information and Hospital Supply Chain Integration (Internal Integration)

A key practice identified to facilitate the management and achievement of superior performance

in SCM has been the integration of supply chains (Wiengarten, Humphreys, Gimenez & Mclvor,

2016). Beheshti et al (2014) found that organizations that operate total supply chain integration

reported the highest level of financial performance. Three perspectives characterize the

development of supply chain integration: strategic, tactical and operational Tseng and Liao (2015).

In their comprehensive review of SCM studies from 1990 to 2011, Shi and Yu (2013) found that

SCI play a critical role in improving firm-level financial performance.

From the perspective that SCM comprises several interrelated facets of integration (Fabbe-Costes

&Jahre, 2008), various facets of the integration dimension in supply chains have been investigated

by scholars. These include external integration with both suppliers and customers and internal

integration with functional units within the organization. These three forms of integration are

defined by Schoenherr and Swink (2012, p. 100) as follows: supplier integration relates to ‘‘the

information sharing and coordination activities involving key or strategic suppliers which provide

the firm with insights related to the processes, capabilities and constraints related to suppliers that

ultimately enable more effective planning, product and process design, forecasting, and transaction

management’’.


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Internal integration on the other hand, is the ‘‘cross-functional intra-firm collaborations and

information sharing activities that occur via interconnected synchronized processes and systems’’.

It is clear from this definition that internal integration basically measures an organization’s

logistics, operations, marketing, and sales collaborations for the purposes of realising overall

supply chain objectives. Finally, Schoenherr and Swink (2012, p. 100) define customer integration

as ‘‘the close collaborations and information sharing activities with key customers which furnish

the firm with insights into market expectations and opportunities which ultimately enable more

efficient and effective response to customer needs’’.

Like the mainstream business organizations, all these facets of SCM integration apply to the health

supply chain and accounting information play a significant role in providing the required and

relevant information to achieving these objectives which ultimately enhances performance

(Dekker, 2004; Coad & Cullen, 2006; Fayard et al, 2012). Ataseven and Nair (2017) meta-

analytically examined the association between supply chain integration and its underlying

dimensions and the operational characteristics of cost, flexibility, delivery, and quality of the

supply chain and found a significant impact of the three dimensions of integration: supplier

integration, internal integration, and customer integration on the four performance attributes. It is

therefore expected that the fit between the dimensions of MAS and hospital supply integration will

positively affect performance as operationalized by cost effectiveness, utilization of hospital

assets, speed, flexibility and quality of the supply chain. on the basis of this prediction the

following hypotheses are formulated:


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H2a:

H2b:

H2c:

H2d:

There is a positive association between hospital supply chain integration (internal integration)

and MAS design as operationalized by the scope dimension of accounting information in

Ghanaian hospitals.


and MAS design as operationalized by the timeliness dimension of accounting information in

Ghanaian hospitals.


and MAS design as operationalized by the integration dimension of accounting information in

hospitals in Ghana


and MAS design as operationalized by the aggregation dimension of accounting information in

Ghanaian hospitals.

4.4.3 MAS Information and Level of Information Exchange

A key driver in SCM is the flow of information and partners’ ability to analyse information (Coad

& Cullen, 2006; Fayard et al, 2012). Information sharing enhances the channel’s capacity to

developments in the entire market. The receiving and sharing of information by a partner firm

enhances the competitive position of that firm (Chen et al, 2013; Behesti et al, 2014). It is so related

to supply chain integration that the design of the latter is normally not effective in the absence of

the former. The quality of the shared information is highly critical for effective SCM as

organizations feel insecure in giving out more than minimal information (Jamal & Tayles, 2010).

Ataseven and Nair (2017) document that information sharing (or knowledge exchange) involving

process and product designs, coordination, and joint decision making are key elements that are


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relied upon by supply chain partners to foster supply chain integration. Information sharing (or

exchange) facilitates the alignment of the interest of all firms within the supply chain which

ultimately enhances overall supply chain performance.

The quality dimensions include scope, accuracy, adequacy and timeliness which underpin the four

MAS information characteristics: broad scope, timeliness, integration, and aggregation of

accounting information (Chenhall & Morris, 1986). MAS design plays a significant role in

enhancing the quality level of information communicated among supply chain partners. The higher

the quality of shared information, the higher the sophistication level of MAS design. Based on

these arguments, the following hypotheses are formulated:

H3a:

H3b:

2H3c:

H3d:

There is a positive association between level of information sharing (or knowledge

exchange) and MAS design as operationalized by the scope dimension of accounting

information in Ghanaian hospitals


exchange) and MAS design as operationalized by the timeliness dimension of

accounting information Ghanaian hospitals.


exchange) and MAS design as operationalized by the integration dimension of

accounting information in Ghanaian hospitals.


exchange) and MAS design as operationalized by the aggregation dimension of

accounting information in Ghanaian hospitals.


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4.4.4 MAS Information and Hospital Supply Chain Risk and Uncertainty

To many firms, the supply chain has become a significant source of risk exposure following the

development of intensified relations with suppliers (Dekker et al, 2013). In particular, firms are

exposed to significant risks that need to be mitigated to enhance not only performance of

collaborative relations in supply chains, but also realized the benefits associated with such relations

(Langfield-Smith, 2008). Risks related to both failures associated with performance of full co-

operation and lack of co-operation between exchanges partners have been identified as important

elements of the supply chain.

For inter-firm relations to become successful, adequate risk management is critical (Ding, Dekker

& Groot, 2013). Transaction costs economics (TCE) has been the dominant paradigm in analysing

MAS design in inter-firm relations, including supplier partner selection choices (Anderson &

Dekker, 2009). TCE posits that the choice of controls in inter-firm relations are expected to be

aligned with the underlying exchange hazards in order to minimize transaction costs. Past studies

such as Cooper and Slagmulder (2004) suggest a stronger relational context characterized by trust

between exchange partners is largely supported. This arises from the use of cost management

practices in supply chains.

In testing hypothesis about the relationships between selection of trusted suppliers, transaction

risk, and use of SCM practices in Japanese manufacturing firms, Dekker et al (2013) found that

MAS play a significant role in mitigating the risks associated with intensified collaborations with

supply chain partners. They established that information sharing, operational reviews, contractual

contingency planning, supplier support, performance target setting, , and joint problem solving as

control practices are SCM practices in which MAS can provide information to minimize supply

chain risks. Being the key determinant of risk in supply chain transaction (Anderson & Dekker,


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2009), transaction risks are categorized into 1) relational risks, which concerns opportunistic

behaviours of self-interested partners and 2) performance risks, which concern performance

failures even at the instance where partners cooperate fully (Dekker et al, 2013).

The characteristics of transactions considered in empirical studies (e.g. Anderson & Dekker, 2005;

Ellis, Henry & Shockley, 2010) as important and antecedents of governance choices that underpin

transaction risks include size of the transaction, asset specificity, product complexity, uncertainty,

and absence of competition. Uncertainty types that result from environmental changes in market

and technology, unpredictability of technology development, and monitoring problems regarding

suppliers’ behaviours and performance have links with MAS design (Anderson & Dekker, 2005).

Beckmann, Hielscher and Pies (2014) state that supply chain associations are hedged against

complexity and uncertainty and that a decision support for managers in uncertain, globalized,

logistics-oriented and new communication settings must be provided by accounting. Burritt and

Schaltegger (2014), note that the wheels of supply chain relationships are oiled with information

provided by accounting.

Accounting systems require formalized categories for collecting and reporting information as well

as creating a common language with which members of the organization can communicate (Burritt

& Schaltegger, 2014). Financial factors such as revenues, operating costs, hedging against

uncertainties, investment planning and other corporate financial decisions are among the issues

that have strong impact on the configuration of SCNs (Melo, Nickel & Saldanha-da-Gama, 2009).

Based on the foregoing the following hypotheses are formulated:


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H4a:

H4b:

H4c

H4d:

A positive association exists between level of supply chain risk and uncertainty and

MAS design as operationalized by the scope dimensions of accounting information

in Ghanaian hospitals.


MAS design as operationalized by the timeliness dimensions of accounting

information in Ghanaian hospitals.


MAS design as operationalized by the integration dimensions of accounting



MAS design as operationalized by the aggregation dimensions of accounting


4.5. Mediating Role of MAS Information in Supply Chain Performance

Hospital-supplier integration both partially and fully mediate the impact of information sharing on

hospital supply chain performance.

4.5.1: MAS and Supplier Relations (External Integration) on Performance

Although the design and use of MASs have the ultimate goal of improving both financial and

operational supply chain performance, empirical evidence on the positive relationship between

MAS information characteristics and operation performance is relatively scarce (Macinati &

Anessi-Pessina, 2014). This is due mainly to the fact that a greater portion of MAS information


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benefits is generally qualitative and intangible in nature. On the other hand, studies such as

Christensen and Demski (2002), Ferreira (2002), and Baines and Langfield-Smith (2003) have

argued that the effect of MAS design on organizational performance depends, to a greater extent,

on the way the MAS information is used by those who receive such information. The wheels of

supply chain relationships are oiled with information provided by the MASs with a stronger focus

on logistics and information flows about corporate performance (Burritt & Schaltegger, 2014).

Accounting offers a significant input to the selection of suppliers through the auditing of supplier

environmental performance, reduction of waste and packaging materials, and health and safety

which enhance operational performance. The monitoring of performance is largely achieved by

means of accounting information with one aspect being supply chain cost. MAS considers the

information involved with external suppliers often working together across multiple supply chains,

are linked together by agreement. Timely cost information is not only a crucial element in the

strategic sourcing decisions but also influences the ongoing management of supplier relations.

In examining the relationship between managers’ beliefs about the relevance and usefulness of

cost data, cost-system functionality, and actual financial performance in US hospitals, Pizzini

(2006) found a positive association between the relevance and usefulness of cost accounting data

as evaluated by managers and the extent of the MAS in providing detailed costing systems, classify

costs according to behaviour, and frequent reporting of cost data. He however, found that out of

the three attributes of the MAS, only the cost detail attribute correlated positively with measures

of financial performance such as cash flow, operating margin, and administrative expenses.

Further, a statistically insignificant relationship between cost-system design and operating expense

per admission was confirmed. The conclusion drawn was that the use of accounting information

has not yet been successfully applied in the management of clinical costs of the sampled hospitals.


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The coordination of supply chain relationships and use of management control mechanisms to

support, plan, measure and assess supply chain activities and their results impact significantly on

supply chain performance (Van de Meer-Kooistra & Vosselman, 2000). Also, one of the key issues

in SCM has been supplier selection and more sophistication is the approach being promoted to

address supply chain interrelationships. However, studies into accounting issues that relate to

supplier selection is far limited (Burritt & Scaltegger, 2014). Particularly, in the exchange with

medical suppliers both within and across the country, a simpler costing system in the supplier

collaborative setting examined would be more effective for supply chain performance in relation

to some hospital operations such as minimization of total order cost as well as low inventory cost.

For example, ABC models available for purchasing often regard supplier evaluation and selection,

ordering, transportation, receiving, handling, quality control and storage as a set of standardized

activities carried out in a sequential fashion (Dekker & van Goor, 2000). It has been recognized

that complexity is added by the the global setting, and this affects accounting as supply chain

partners often extend to several logistics service providers, the supplier, the manufacturer, the

retail sector and the final customer. Based on these assertions, the following hypothesis is

formulated:

H5a: The relationship between strategic supplier relations (external integration) and hospital

supply chain performance is explained by an indirect effect of MAS information

whereby strategic supplier relations reduces MAS information which in turn enhances

operational performance of Ghanaian hospitals.


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4.5.2. MAS and Supply Chain Integration (Internal Integration) on Performance

Conventional MAS is largely about identifying and recording inter-firm data from which are

derived from external market transactions, intra-firm internal transformations and some events

considered as external. To a large extent, SCM also looks at intra-supply chain processes and

transactions, transformations, and events including non-market interactions (Scaltegger & Burritt,

2014). Whilst a consideration is given to MASs external to the supply chain, inter-supply chain

data from external market transactions, intra-supply chain transactions as well as some external

events of the supply chain are addressed by MAS to enhance performance (Burritt & Scaltegger,

2014). For example, few supply chain settings for accounting arise whenever there is

preponderance for vertical integration. MASs in supply chain integration place much emphasis on

the efficient use of resources and minimization of costs to enhance performance. Inter-firm cost

savings in supply chains is largely promoted by activity-based costings which is the costing system

widely used in hospitals (Schulze, Seuring & Ewering, 2012; Kwon, 2016). As noted by Dekker

and van Goor (2000), to internally integrate supply chain members, a set of MAS standards is

needed. This takes a calculation of the cost of logistics for the supply chain which is based on a

joint definition of activities and their cost drivers. This results in the aggregation of supply chain

activity based costs. In this regard, cost accounting methods such as material flow cost accounting

has been developed for supply chain accounting.

H5b: The relationship between supply chain integration (internal integration) and HSC

performance is explained by an indirect effect of MAS information whereby level of

information exchange reduces MAS information which in turn enhances HSC

performance in Ghana.


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4.5.3: MAS and Level of Knowledge Exchange on Performance

Information sharing in SCM has become a major driver that enhances organizational overall

performance (Ataseven & Nail, 2017). Where organizations, particularly healthcare institutions

belong to an environment comprising a network of intra and inter-organizational relationships,

information sharing as an important prerequisite emerges in the integration of supply chains.

However, the effect of information sharing on MAS design and the corresponding impact on

supply chain performance has had very little analysis. It has been argued that improved supply

chain coordination, reduced supply chain costs, quality of products and services, and the

achievement of competitive advantage is directly related to effective information sharing. The

function of MAS is to provide information that assists with the determination and sharing of gains

from supply chains (Burritt & Scaltegger, 2014). The link between information sharing and MAS

information is crucial in SCI as it reinforces connectedness, coordination and collaboration among

supply chain members. Studies have shown that a prevalent way of effectively managing supply

chains is to employ closer information-based linkages. This form of SC management become seeks

improved performance through effective use of resources and capabilities. However, whilst there

have been studies relating information sharing and supply chain performance, there are no studies

that link MAS information characteristics to performance. Very few studies investigate the specific

effect of information sharing (or knowledge exchange) on SC performance. On the basis of this

assertion, the following hypothesis is formulated:

H5c: The relationship between the level of knowledge exchange and HSC is explained by

an indirect effect of MAS information whereby supply chain risk and uncertainty

reduce MAS information which in turn enhances HSC performance in Ghana.


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4.5.4. MAS and Supply Chain Risk and Uncertainty on Performance

Supply chain arrangements are normally characterized with risk, complexity and uncertainty

(Beckmann et al, 2014). For example, in testing hypothesis about the relationships between

selection of trusted suppliers, transaction risk, and use of SCM practices in Japanese manufacturing

firms, Dekker et al (2013) found that MAS play a significant role in mitigating the risks associated

with intensified collaborations with supply chain partners. They established that information

sharing, operational reviews, contractual contingency planning, supplier support, performance

target setting, and joint problem solving as control practices, are SCM practices in which MAS

provides information to minimize supply chain risks. In this regard, MASs provide decision

support for managers in the uncertain, new communications settings, logistics-oriented, globalized

settings in supply chain management (Burritt & Scaltegger, 2014). Management accounting

scholars have responded with studies of how firms use MASs to mitigate the risks and uncertainties

associated with inter-firm exchanges (Langfield-Smith, 2008; Anderson & Dekker, 2005). Two

general types of risks are associated with the supply chain: relational risk and performance risk

(Anderson, Christ, Dekker & Sedatole, 2015) and are highly associated with the health supply

chain. Whereas a lack of cooperation between supply chain partners that could result in

opportunistic behaviour and appropriation of firm value by the other partner represents relational

risk, that of performance risk represents the risk of failure despite full cooperation. These risks

normally arise from the complexity and uncertainty that characterize the supply chain as well as

environmental influence. MASs are considered as primary mechanisms for mitigating these risks

through the alignment of partners’ interest and the coordination of their actions across boundaries.


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H5d: The relationship between hospital supply chain risk and uncertainty and HSC

performance is explained by an indirect effect of MAS information whereby supply

chain integration reduces MAS information which in turn enhances HSC


4.6 Contingency Effect of the SCM Contextual Dimensions

The interaction of individual characteristics of SCI to affect performance is supported by

contingency theory (Flynn et al, 2010). The environment within which an organization operates

shapes its processes and structures. This implies that in order to maximize performance,

organizations should align their structures and processes to their environment (Donaldson, 2001).

In this regard, organizations make the effort to integrate SCM characteristics with the dimensions

of MAS. To achieve better operational performance, organizations take full advantage of internal

integration. To discern the role of moderating factors in SCI, Ataseven and Nair (2017) point to

the need for investigating specific associations between SCI and performance within contingency

frameworks. This call is based on evidence of underdevelopment of contingency-based studies

that investigate the effect of SCI on organizational performance. They document the moderators

that affect either the strength or form of the relationship between an exogenous variable and a

criterion variable to enhance theory development within the SC are those contextual factors. In

this subsection the theoretical inter-linkages of the moderator between MAS design and hospital

supply chain performance is presented.

4.6.1. Supplier Relations (External Integration) to Performance

Contingency theory suggests that external fit implies that there is consistency between the

strategies an organization adopts in response its organizational structure and its external


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environment (Flynn et al, 2010). This means that to form an important element of SCI, supplier

relations are built on hospitals SC internal integration. In this regard, a change in the external

environment should be met by hospitals through the development, selection and implementation

of strategies that will maintain and keep the level of fit. For example, drugs within the Ghanaian

hospitals are either not physically or financially accessible or both. Opportunities for improving

the accuracy of demand information is offered by a close relationship between hospitals and

manufacturers of drugs, which in effect, minimizes the manufacturer’s product design and

production planning time, and allows it to be more responsive to customers (patients) needs. This

also enables manufacturers to detect demand changes more quickly, create greater value, and

reduce costs because supplier integration offers opportunities for the intelligence embedded in

collaborative processes to be leveraged. In an integrated supply chain, an understanding and

anticipation of the manufacturer’s needs in order to better meet its changing requirements is

facilitated through the development of strong strategic partnerships.

H6a: For higher levels of supplier relations, MAS will positively affect hospital supply

chain. For lower levels of supplier relations, MAS information will negatively

affect hospital supply chain performance in Ghana.

4.6.2. Relationship of Internal Integration to Performance

Like the consistency among structural characteristics within an organization which represents

internal fit (Drazin and van de vein, 1985), supply chain integration (internal integration)

recognizes that within a hospital, different functional areas and departments should operate as part

of an integration process. Since within healthcare institutions, functional barriers are broken by

internal integration and fosters cooperation in order to meet customer (patient) satisfaction. Rather


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than operating within the functional areas associated with the traditional specialization and

departmentalization, it is expected to have a link to performance. Although hospitals may maintain

a functional organizational structure, patients’ demands normally flow across activities and

functions. Studies (Germain & Iyer, 2006) have shown that the interaction between various forms

of integration (e.g. customer integration and internal integration were linked to logistics and

financial performance. In another study, Droge, Jayaram and Vickery (2004) found that the effect

of strategic supplier relations (external integration) on performance was moderated by internal

integration. Also, Davaraj, Krajewski and Wei (2007) found that the relationship between supplier

integration and performance was moderated by customer integration. No study has however,

interacted either internal integration or external integration with the MAS information

characteristics to affect performance. For the purposes of the link between MAS information and

SCM to advance theory, the following hypothesis is proposed:

H6b: For higher levels of supply chain integration, MAS information will positively

affect HSC performance. For lower levels of supply chain integration, MAS

information will negatively affect HSC performance in Ghana.

4.6.3. Relationship of Level of Knowledge Exchange to Performance

It has been argued that improved supply chain coordination, reduced supply chain costs, quality of

products and services, and the achievement of competitive advantage is directly related to

integration and effective information sharing. The function of the MAS is to provide information

that assists with the determination and sharing of gains from supply chains (Burritt & Scaltegger,

2014). The link between integration, information sharing and MAS information is crucial in SCM

as it reinforces connectedness, coordination and collaboration among supply chain members


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(Dekker, 2016). However, whilst there have been studies relating integration, information sharing

and supply chain performance, there are no studies that link MAS information characteristics to

SC performance. In addition, very few studies investigate the specific effect of integration and

information sharing (or knowledge exchange) on SC performance. Furthermore, the effect of both

SC integration and information sharing on MAS design and the corresponding impact on SC

performance has had very little analysis. Chen et al (2013) found that a positive direct relationship

exists between hospital strategic partnership, and information sharing, between information

sharing and hospital-supplier integration, and between hospital-supplier integration and hospital

supply chain performance from a sample of 117 supply chain executives in US hospitals. In

addition, hospital perceived environmental uncertainty was found to moderate the relationship

between strategic supplier partnership and level of information sharing and hospital supply chain

performance. On the basis of this assertion, the hypothesis on both the impact of internal and

external integration, information sharing, and supply chain risk and uncertainty on MAS design is

formulated.

H6c: For higher levels of information exchange, MAS information will positively affect

HSC performance. For lower levels of information exchange, MAS information

will negatively affect HSC performance in Ghana.

4.6.4. Relationship of Supply Chain Risk and Uncertainty to Performance

Literature suggests that SC integration (internal and external integration) has a positive effect on

operational performance outcomes such as cost, speed of delivery, flexibility, and quality (Wong

et al, 2011). Supply chain risk and uncertainty has been identified as a contextual factor which

may affect the effectiveness of a best practice. Whilst some recent studies (Fynes, de Buˇırca &


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Marshall, 2004) argue that both internal and external integration and performance are moderated

by supply chain risk and uncertainty. These studies are however problematic because first, they

used different approaches in the conceptualization of supply chain integration, performance and

the contingency constructs which does not permit a meaningful comparison of, or conclusion about

the contingency effects of risk and uncertainty. The following hypothesis is formulated:

H6d: For higher levels of supply chain risks and uncertainties, MAS information will

negatively affect HSC performance. For lower levels of supply chain risk and

uncertainty, MAS information will positively affect HSC performance in Ghana.

4.7 Chapter Summary

In this chapter, the study’s theoretical framework has been presented. It discusses the three main

variables underpinning the study and their sub-constructs. The justification for selecting the

dimensionalities of each of the constructs is given. It shows that each of the four main constructs

is multidimensional which can be explained by several other constructs. The chapter also shows

the theoretical as well as the empirical linkages among the constructs based on which the verbal

hypotheses are formulated.

The hypotheses formulation is based on three models of contingency fit: selection, mediation and

moderation. Hence, three models of contingency fit are to be tested. The chapter shows that

formulation of contingency hypotheses differs from those of universal theories and that the verbal

hypothesis formulated to test a particular form of contingency fit should align with its theoretical

interpretation. The formatted hypotheses also fall in line with the statistical methodology being

employed. For example, the selection form of fit is based on correlation analysis which works very


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well with covariance based structures. The same arguments hold for the mediation and moderation

forms of fit. Finally, it outlines the 12 hypotheses to be tested. Each of the three contingency fit

models being theorized has four testable hypotheses. The study’s methodology in terms of the

sample data and the statistical analysis involved are presented next in chapter five.


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CHAPTER FIVE

METHODOLOGY

5.1 Introduction

The preceding chapter presented the underlying theoretical framework and the hypotheses that

guide the study. In this chapter, the general approach and methods to the study’s total execution is

presented. The research methodology is a term used to describe the methods, procedures and

assumptions that are applied towards gathering and analysing data in a research work (Creswell,

2014). At the level of documenting a study, the methodology constitutes the specification and

justification of the most suitable methods applied, including an identification of the study’s

philosophical stance, research design, sampling approach and sample size, data collection,

instrumentation, and data analysis method. The overall objective of this study is to investigate the

hospital SC performance effects of the contingency fit between SCI, information sharing, SC risk

and uncertainty and MAS design in healthcare context using empirical data from Ghana.

The chapter has four main sections. In the first section, the philosophical dimension underpinning

the study is briefly presented. In the second section, and following Van de Stede, Young & Chen,

(2005), a discussion of the framework that contains five key elements of a well-designed survey

in management accounting research is presented. These include a description of the (1) design

strategies and purpose of the survey, (2) population and definition of the sampling, (3) research

method issues such as survey questions and others, (4) accuracy of data entry and data collection

techniques, and (5) reporting and disclosure. The modelling approach to empirically analysing and

estimating the sample data is presented in section three. This involves testing and validation of the

measuring instruments/scales for reliability and internal consistency. Finally, a detailed description


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of the empirical estimation model and its underlying statistical assumptions is presented. The

chapter ends with a summary.

5.2 The Study’s Philosophical Underpinning

Fundamentally, research in the academic domain is underpinned by some basic philosophical and

meta-theoretical assumptions, and are either explicitly or implicitly defined by the researcher.

Research paradigms underpin the basis of the researcher’s understanding of the research problem

and constitute the foundation for testing criteria as well as answering research questions. Although

paradigms are hidden in most research, their understanding help in the clarity of research designs.

They not only take into account the kind of evidence that is required and how it is to be gathered

and interpreted, but also how the evidence will provide good answers to the basic questions being

investigated. Knowledge of philosophy also assists the researcher in distinguishing the workable

designs from those that are not. Besides, understanding the difference between philosophical

assumptions and theoretical underpinnings is critical to the overall understanding of the

perspective from which the research is designed and conducted.

Philosophical paradigms refer to the basic belief system or world view that guide the study. A

paradigm, according to Kuhn (1962), consists of a cluster of substantive concepts, variables with

attached problems, and corresponding methodological approaches and tools that are integrated in

nature. Originally, Kuhn (1962) used the term to represent a conceptual framework which is shared

by a community of scientists. This provided them with a convenient model that examines and finds

solutions to problems. To this end, he labelled a paradigm as a research culture that possesses

unique values, assumptions and a set of beliefs which are common to a research community

regarding the nature and conduct of research.


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Like all other disciplines, research paradigms address the philosophical dimensions of social

sciences within which accounting research is conducted. However, there has been a concern for

increasing narrowness of accounting research in terms of its philosophical assumptions,

methodological approaches, and theoretical underpinnings. For example, Lukka (2010) noted that

the current hegemonising tendencies of mainstream accounting research have led to excessive

homogeneity in accounting research and as reported by Chua (1986) have restricted the range of

problems studied and the methods used. In the notion of paradigms Lukka (2010) states that

fundamentally, different kinds of options for conducting accounting research always exist, at least

in principle, thereby seeking to invigorate accounting researchers’ consciousness of this plethora

of possibilities.

5.3. Dimensions of Philosophical Assumptions

Paradigms are distinguished between the ontology, epistemology, methodology and axiology

dimensions. In this section, each of these individual dimension is discussed. First the philosophy

of reality represented by ontology is discussed. This is followed by epistemology which refers to

the philosophy of knowledge whilst the methodology which dictates the methods used and that of

axiology, which specifies the value of the research follows.

5.3.1. Ontology

When developing their methodologies for conducting research, both social scientists and scientists

generally draw from different ontological and epistemological assumptions. In social research, one

perceives the existence of reality as external, independent of social actors and their interpretations

of it (i.e., objectivist or realist). The other assumption is that reality is dependent on social actors


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and that individuals contribute to social phenomenon (i.e., subjectivist or nominalist). Stated

differently, a researcher who views reality as objectively given is labelled positivists and believe

that the reality can be measured by means of the properties which the researcher is independent of

as well as the instruments he/she is using. More specifically a positivist researcher views

knowledge as quantifiable and objective. Scientific methods are adopted by positivist thinkers and

with the help of quantification to enhance precision in the description of parameters and the

relationship among them, systematize the knowledge generation process. On the other hand,

researchers who view reality as consisting of individuals’ subjective experiences of the external

world are labelled interpretive and believe that reality is socially constructed. Based on an in-depth

examination of the phenomenon of interest, they derive their constructs from the field. Four

different ontologies include realism, internal realism, relativism and nominalism.

5.3.2. Epistemology

Epistemology refers to the philosophy of knowledge; that is the belief of knowledge production or

what constitutes acceptable knowledge. It shows that there are different ways into which the nature

of the physical and social worlds are inquired (Easterby-Smith, Thorpe and Jackson, 2012). Having

established the existence of reality, epistemology identifies the processes by which that reality is

known; that is it answers the why of why of how questions. It is the beliefs on the way to generate,

understand and use the knowledge that are deemed to be acceptable and valid. Positivism and

social constructionism represent two contrasting views of how social science research should be

conducted. Epistemology focuses around the respective merits of these two views.


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5.3.3. Methodology

Methodology represents a basic belief that affects the ways of investigating reality. In the context

of a particular paradigm, it refers to a model for undertaking a research process. A clear distinction

is always drawn between methodology and methods. Whilst methodology represents a domain or

map or blueprint of the study, method is used to denote the steps to be taken or the techniques to

be employed in gathering and analysing data. Methodologies dictate the methods in that they

comprise the set of beliefs that guide a researcher to select one set of research methods over the

other. Methodologies answer the why of how questions. A research method, on the other hand,

consists of a set of specific procedures, tools and techniques to gather and analyse data and answers

the how questions. A research method falls under theories different from methodologies and

philosophies. A research method refers to the practical application of doing research whereas

methodology represents the ideological and theoretical foundation of the method; hence, the

quantitative-qualitative debate is philosophical not methodological. In this regard, a research

design is required to link an appropriate set of research methods and a methodology in order to

answer research questions and/or test hypotheses that have been verbally formulated to examine a

phenomena.

5.3.4. Axiology

Like methodology, axiology represents the basic beliefs that affect the ways in which realities are

investigated. It is concerned with ethics which encompasses the role of values in research and the

researcher’s stance in connection with the subject being investigated. The essence of ontology,

epistemology, methodology, axiology and methods is summarised in Table 5.1


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Table 5.1: Ontology, Epistemology, Methodology, Axiology and Methods Distinguished

Philosophy Meaning

Ontology Philosophical assumptions about the nature of reality

Epistemology A general set of assumptions about ways of inquiring into the nature of the world

Methodology A combination of techniques used to inquire into a specific situation

Methods Individual techniques for data collection and analysis

Axiology The role of values in research and the researcher’s stance

(Source, Captured from Easterby-Smith et al, 2012)

5.4. Paradigms in Management Research

The main paradigms that characterised the philosophical debates in management research include

positivism, post positivism/critical realism, interpretivism or social constructionism and

pragmatism. These are discussed in the ensuing sub-sections.

5.4.1. Positivism

Positivists assume that there is external existence of the social world; hence, objective methods

should be employed to measure its properties rather than being inferred subjectively through

intuition, reflection or sensation. This means that positivists apply the lens of natural science to

social science. Ontologically, social reality is external and objective. Epistemologically, the use of

scientific approach by developing numeric measures to generate acceptable knowledge is

advocated. It is also assumed epistemologically that knowledge is only of significance if it is based

on observations of the external reality. Testing of theories in the form of hypotheses using

statistical tests characterised the research process. In the axiological sense, a separation of the

researcher from the researched is maintained by taking the stance of etic approach or the outsider

perspective. Positivists seek to obtain law-like generalisations by conducting value-free research


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to measure social phenomena. Table 5.2 summarises the implications of positivist philosophical

assumptions.

Table 5.2: Implications of Positivist Philosophical Assumptions

Operationalization Concepts need to be defined in ways that enable facts to be measured

quantitatively.

Reductionism Problems as a whole are better understood if they are reduced into the simplest

possible elements.

Generalisation In order to move from the specific to the general it is necessary to select random

samples of sufficient size, from which inferences may be drawn about the wider

population.

Hypothesis/Deduction Science proceeds through a process of hypothesizing fundamentals laws and

then deducing what kinds of observations will demonstrate the truth or falsity

of these hypotheses

Causality The aim of the social science should be to identify causal explanations and

fundamental laws that explain regularities in human social behaviour.

Cross-sectional

Analysis.

Such regularities can most easily be identified by making comparisons of

variations across samples.

Independence The observer must be independent from what is being observed.

Value freedom The choice of what to study, and how to study it, can be determined by objective

criteria rather than by human beliefs and interests.

(Adapted from Easterbay-Smith et al, 2012, P. 23)

5.4.2. Social Constructivism

Referred to as interpretivism, it is a paradigm developed primarily, in reaction to the application

of positivism to the social sciences and stems from the view that reality is not objective and exterior

but is socially constructed and given meaning by people. To this end, interpretivists reject

objectivism and a single truth as proposed in positivism. It focuses on the ways people make sense

of the world especially through sharing their experiences with others via the medium of language.

In this regard, a recognition of individuals with varied backgrounds, experiences and assumptions

is what interpretivists offer. These contribute, through social interaction, to the on-going

construction of reality existing in their broader social context. Interpretivist researchers prefer to

interact and to have dialogue with the studied participants in order to understand the social world


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from the subjective meanings people attach to it. Qualitative data which provides rich descriptions

of social constructs and a narrative form of analysis to describe specifics and highly detailed

accounts of a particular social reality being studied is the focus of interpretivists. Table 5.3

summarises the contrasting implications of social constructionism and positivism.

Table 5.3: Positivism and Social Constructionism Contrasted

Positivism Social Constructionism

Explanations Must demonstrate causality. Aim at increasing general understanding

of the situation.

Human Interest Is irrelevant Are the main drivers of science

The observer Must be independent. Is part of what is being observed.

Research Progression Through hypotheses and

deductions

Gathering rich data from which ideas are

induced

Sampling Requirements Large numbers selected

randomly.

Small numbers of cases chosen for

specific reasons.

Unit of Analysis Should be reduced to

simplest terms

May include complexity of whole

situations

Generalization Through Statistical probability Theoretical abstraction

Concepts Need to be defined so that

they can be measured.

Should incorporate stakeholder

perspectives.

5.4.3. Pragmatism

Pragmatists argue that objectivism and subjectivism perspectives as in positivism and social

constructionism respectively are not mutually exclusive and that one should view research

philosophy as a continuum rather than an option that stands in opposite directions. In the

pragmatists view a mixture of ontology, epistemology and axiology is acceptable as an approach

to understanding social phenomena. In this regard, emphasis is on what works best to address the

research problem at hand. Both quantitative and qualitative data are employed by pragmatists as a

way of better understanding social reality. The relationship between ontology, epistemology,

axiology and their respective paradigms are summarised in Table 5.4.


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Table 5.4: Relationship between Philosophical Assumptions and Paradigms

Research Paradigm

Fundamental Belief Positivism

(realism)

Post positivism

(critical Realism)

Interpretivism

(constructionism)

Pragmatism

Ontology External External Socially

constructed

External,

Multiple View

Realist Nominalism

Epistemology Objective Objective Subjective Objective

Subjective

Methodology Quantitative Quantitative or Qualitative Quantitative &

Qualitative Qualitative

Axiology Value-free & Value-Laden & Value – Laden & Value-Laden

Etic Etic Emic Etic 7 Emic

(Source: Developed by Author)

5.5. Paradigms in Accounting Research

Paradigms in accounting research are rooted in Burrell and Morgan’s (1979) sociological

framework. Until recently, the widely accepted view of the role of accounting as a research field

has been that of establishing general laws that encapsulates empirical events’ or objects’ behaviour

which is normally concerned with science, and facilitating the connection of the knowledge of

separable known events and to predict reliably, unknown events. This function of accounting was

supposed to have been accomplished within the framework of the natural science model, where

the adoption of careful sampling, accurate measurement, and good design and analysis of theory-

supported hypothesis has been the focus. This single world-view methodology has however, met

objections and criticisms from various schools of thought concerning the restrictive sense in which

accounting research is conducted. For example, Chua (1986) observes that grounding mainstream


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accounting in a common set of philosophical assumptions about knowledge, the empirical world

and the relationship between theory and practice has restricted the range of problems studied and

the use of research methods. Debates on the applicable methodologies in the field led to the

emergence of the three main paradigms-functionalist, interpretivists and critical

humanistic/structuralism. While the functionalist paradigm seeks to establish only laws thereby

employing only the methods and procedures that exact science permits, the understanding of some

particular event in society or in nature is endeavoured by the interpretivist sciences.

The functionalist approach is underpinned by the approach and methods used in the natural

sciences. It is pre-occupied with the construction of scientific tests and the analysis of data is

accomplished by means of quantitative methodology. Questionnaires of surveys, standardized

research instruments, and personality tests of all kinds are more pronounced among the tools that

comprise functionalist methodology. They also differ in terms of the mode of inquiry. Functionalist

inquiries from the outside whilst interpretivist inquiries from the inside. Based on these two

frameworks four paradigms in accounting research namely; the functionalist view, the interpretive

view, the radical humanist view and the radical structuralist view can be differentiated.

5.6. The Study’s Philosophical Stance

This study takes the critical realism, quantitative-positivist epistemology perspective in that, it

investigates the impacts of attributes of a phenomenon on another. This is achieved by using

empirical data, and based on a well-grounded theory (contingency theory) in accounting research

to test a priori, hypotheses and knowledge. Specifically, the study employs a hypothetico-

deductive approach to ascertain the relationship between SCM context factors, MAS design, and


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supply chain performance in hospitals. The positivist assumption allowed the key factors in the

study to be measured precisely in order to verify or falsify predetermined hypotheses (Easterbay-

Smith et al, 2012). By reducing data to a parsimonious set of variables which are tightly controlled

through statistical analysis, measures or observations for testing a theory is developed (Creswell,

2014).

Following Creswell (2014), this is categorised into research purpose, the nature of reality

(ontology), the nature of knowledge as well as the relationship between the inquirer and the

inquired (epistemology), and the methodological approach. It uses a survey cross-section data

sampled from management accountants/financial controllers and heads of procurement units of

237 hospitals in Ghana to empirically examine the relationship among the constructs.

5.7. Purpose and Design Strategies of the Survey

In this study, the potential hypothesized dimensions of SCI (internal and external), level of

information sharing, and supply chain risk and uncertainty that influence MAS design and its

implementation in hospital SCM decisions to leverage performance is examined. Given that this

study aims at testing the contingency fit relationships in the inter-firm exchanges domain, the

design methodology followed that of a cross-sectional survey research which is employed for

explanations that state the influence of the SCM variables on the MAS variables and their

combined effect on hospital supply chain performance. The use of the cross-sectional survey for

explanations examines the associations between management accounting and other variables

which are guided by theoretical expectations (Pinsonneault & Kraemer, 1993; Sapsford, 1999). In

addition, the study seeks to find answers that relate to the opinions of hospital accountants on the


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interaction effects of SCI and MAS design on hospital operational performance. Data for studies

of this nature is not available in secondary form hence, as it has been studied in prior literature

(Chenhall & Morris, 1986; Gerdin, 2005; Soobaroyen and Poorundersing, 2008; Hammad et al,

2013), a cross-sectional survey design was the only appropriate methodology. A cross-sectional

survey design as noted by Creswell (2014) provides a quantitative/calculative or numeric

description of attitudes, trends, or opinions of a population. Generalization or inferences drawn

from the population are normally derived from a study of a sample of the population under

consideration.

In this regard, the study used a cross-sectional design for testing these relationships under three

‘contingency fit’ models: selection, mediation and moderation. The hypotheses were tested under

these three contingency fit models because these models including the matching fit model (which

was not tested because of its underlying theoretical assumption not to be tested under structural

equations model) underpin contingency fit studies in management accounting research. In testing

the existence of contingency theory in the inter-firm exchanges domain, these models

appropriately explain the presence or non-presence of contingency fit relationships. They form the

various contingency fit models that are tested under contingency fit studies. Also, contingency fit

simply means the interaction of the MAS variables with other contextual variables to affect

performance (Chenhall & Chapman, 2006). These relationships were modelled using a

representative measure of activities and attributes for a cross-section of healthcare organizations

in Ghana. To obtain these measures, a quantitative survey methodology that involved Likert scales

for each of the three multidimensional constructs was designed to test the hypotheses specified in

the model. The research design proportionately represented healthcare institutions which consisted

of public and private hospitals. The design stage showed the ‘master plan’ or ‘blueprint’ of the


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study which primarily, gave insights on how the study was to be conducted. In effect, all the major

parts of the research (e.g. population and sample, sampling strategy, data and measures, estimation

models and techniques, etc.) were integrated to address each of the research questions and

associated hypothesis. These were clearly spelt out in the study design. The data were generated

using a response scale questionnaire which was self-administered to management accountants and

other accounting personnel/managers of public and private hospitals across six out of the ten

regions. These include the Ashanti, Central, Western, Eastern, Brong-Ahafo and Greater Accra

regions.

The exclusion of the other four regions (Tamale, Upper East, Upper West, and Volta regions) from

the study is based on the fact that the research is based on the Northern, Middle and Southern

Zones where Ashanti and Brong-Ahafo regions represented the Northern Zone, the Eastern and

Central regions represented the Middle Zone and the Western and Greater Accra regions

represented the Southern Zone. The exclusion however, of the other four regions is an important

shortcoming given their economic significance in national development. Consequently, the extent

to which the research was valid to extrapolate the results to all other hospitals across the country

could be subjected to error which could be significant, but the units selected were significant to

mitigate the error significance due to their composition of large proportion of healthcare

institutions. The questionnaire items were based on an adapted version of existing measuring

instruments/scales used in prior studies; hence, consistency and reliability is assured. Using a

covariance-based structural equation modelling (CB-SEM), the hypotheses linking the paths of the

structural model were tested to determine the relationships among the constructs.


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5.8 Survey Strategy

Determinism suggests that testing the association between and among variables is key to finding

answers to questions and drawing conclusions on hypotheses through experiments and surveys

(Creswell, 2014). The strategy adopted for this study comprised two main stages: the first stage

involved pertinent measures of SCM contextual factors, drivers and financial operation of SCM,

and the four dimensions of the MAS information and hospital supply chain performance from the

literature and drafted the measuring instrument. The second stage of the survey strategy involved

a preliminary testing of the instrument with 20 hospitals made up of 10 each of public and private

hospitals. This was carried out to enable a thorough revision of the instrument based on the

response from the pilot study. The outcome of the pilot survey showed that the accountants are

familiar and knowledgeable with the questionnaire items except one private hospital who

complained about the understanding of one item under external supplier relationships. This was

subsequently revised before administering to the respondents. Following Li et al (2006), the

method for instrument development was taken through four phases: 1) item generation, 2) pilot

study, 3) large-scale data collection, and 4) reliability and validity assessment of the measuring

scale. In phase four, rigorous statistical analysis was performed to ascertain the reliability and

validity of the constructs. The research framework together with the associated hypotheses was

tested using hierarchical factorial structures.

5.9. Population, Sample and Sampling Procedure

The definition of population and sample is critical in survey research because they constitute the

determinants of the possibility of drawing valid inferences from the sample characteristics (Van

de Stede, Young & Chen, 2005). It is also important because they form a central role in shaping


144

everyday opinion. The framework for a survey study requires that all relevant respondents are

included in the population and that unknowledgeable, inappropriate, or unconcerned respondents

are excluded (Federal Rules of Evidence, 1971). In addition, it emphasizes the consistency of target

population and survey population (Diamond, 2000). Whilst the target population consists of all

respondents that are under study, survey population refers to the collection of respondents

available to the researcher and which is actually sampled. This consistency is important because

the study is likely to offer results that are biased in situations where subjects not in the target

population are included in the survey population or subjects in the target population are omitted.

Guided by this framework, the study population was defined to include all healthcare institutions

in Ghana. These included hospitals (both public and private), polyclinics, pharmaceutical

manufacturers, wholesalers, and the distribution agencies of medical supplies. However, the focus

was on hospitals since they constitute the largest proportion of healthcare institutions across the

country and also the identified logistic and supply chain problems were found to be more

pronounced in hospitals. Out of this was the sampling frame which comprised the list of all

hospitals in Ghana. This approach differs from most studies in management accounting because

the luxury of obtaining a sample frame is often not a characteristic of survey researchers often (i.e.

a complete list of survey population elements that match the intended target population) and

ensuring that a probability sampling plan is developed. From the sample frame, the sample for the

study was drawn using cluster sampling. This approach was adopted because of the geographical

distribution of the sample units and to ensure that the sample is a representative of the entire

population, and also sample selection (or sampling) directly affects the generalizability of the

survey findings. The list of sampling frame was generated using the databases of the Ministry of

Health and the Ghana Health Service (GHS) respectively.


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5.9.1. Study Sample

The sampling frame comprised the list of all healthcare institutions in Ghana. Given that there are

currently 1,409 (public and private) healthcare institutions in Ghana made up of 394 general

hospitals, 32 polyclinics, 982 clinics and 3 psychiatric hospitals (Facts and Figures, GHS, 2015),

the sample size of 274 hospital accountants was initially drawn across these organizations. The

key assumption underlying the method of analysis (covariance structures) is that the sample size

is large; hence, a large sample is required to satisfy the model’s assumptions. Taking into account

non-responses and other factors (e.g. less than 3 years tenure in office) a valid sample size of 237

that meets the model’s assumptions was obtained.

Using the list of healthcare institutions in the GHS database, sample sizes of 131 and 106 were

respectively drawn from the public and private sectors although equal sample sizes were expected

to have been drawn. However, since the study’s focus was not to investigate the differences or

contrasting the MAS-SCM relationships between public and private healthcare institutions, this

had no negative impact on the results. The objective is to provide preliminary empirical evidence

of contingency theory’s application in inter-firm exchanges in the SCM domain. In this regard,

unequal sample sizes will not be an issue of much concern and does not violate the assumptions

of the model being used. Moreover, the GHS (2015) estimates the number of hospital beds to be

11, 689 and 1,145 representing 58.7% and 5.8% for public and private hospitals respectively which

is a clear indication that equal sample sizes may be difficult to obtain..

5.9.2. Sampling Procedure

The sampling procedure followed that of probability sampling. The selection of probability

samples is based on the premise that every element of the survey population (sampling frame) has


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a chance of known non-zero of being included in the sample. Also, as noted by Diamond (2000),

the representativeness of the survey results are increased if the scores of probability samples give

high response rates. This permits inferences to be ascertained from the sample to the survey

population within a calculable margin of error. The list of all healthcare institutions (hospitals,

polyclinics, clinics) on regional, district and town basis is available at the GHS database. The

cluster sampling technique was used to draw the required sample for the study by independently

selecting a random sample from the population. The study population was divided into six clusters

comprising the selected six regions (Greater Accra, Eastern, Central, Western, Ashanti, and Brong-

Ahafo) regions followed by a simple random sample drawn from each cluster. The population

dimensions included size, location, ownership, and profit status of each hospital as well as the

profile of respondents. The sampling is clustered on regional basis and does not include details of

districts. The reason is that narrowing the sampling methodology to the 216 districts in Ghana will

be problematic because unlike the regional level where each region has hospitals and clinics, the

same cannot be found at the district level. Some districts do not have hospitals and the available

clinics are for first aid only and are not included in the list in the GHS database.

5.9.3. Sample Size

Literature so far documented has not indicated any standard sample size for a research of this kind.

However, recommendations in most textbooks about sampling offer standard treatment of sample

size by determining the required precision (i.e. confidence interval) which requires an estimate of

both the sample variance and an estimate of the expected response rate. Using the formula of

2)(1 eN

Nn

(Hoyle, 2015), with a 5 percent significance level with maximum permissible error,

e value of 0.05, the final sample units for the study were obtained. (Note: n = sample size; N =


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total population of hospitals and e = the level of precision). Out of the 300 questionnaires that were

distributed, 274 responded representing a response rate of 90.13%. However, 37 of the responded

questionnaires (especially those that were sent by post from the regions) were either not completed,

lacked certain information such as number of hospital beds, or the respondent has occupied his/her

position for less than three years and so were excluded from the sample resulting in 237 valid

sample units representing 79% response rate. This approach is consistent with Maiga et al (2013),

Chen et al (2013) and Macinati and Anessi-Pessina (2014). A breakdown of the samples from the

six regions is shown in Table 5.5.

Table 5.5: Distribution of Samples by the Six Regions

Region Population Sample Responses Received

Gt. Accra 367 191 74 Ashanti 235 148 57 Eastern 142 104 40 Central 94 76 32 Western 182 125 37 Brong-Ahafo 132 99 34 Total 743 274

(Source: Sample Survey, 2017)

5.10. Approach to Data Collection

A scale-response questionnaire with a covering letter (see Appendix 1) that explains the

purpose/objectives of the study as well as providing assurance of anonymity of responses, was

distributed personally (with some assistance from some colleagues in the regions) to accountants,

managers, administrators, and departmental heads of hospitals. To address some of the challenges

encountered in the questionnaire administration by commuting on multiple occasions from one

hospital to another to follow-up on the completed questionnaire, a self-addressed envelope was


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attached to the questionnaire for especially respondents within the regions, However, for a

responded questionnaire to be included in the sample, the respondent should have held his/her

position for a minimum of three years. This was necessary to reduce errors in the responses. Prior

to the actual data collection, a pilot study involving 20 hospitals (10 public and 10 private) was

conducted within the Greater Accra Metropolis to develop and validate the questionnaire.

5.11. Scale Development and Measurements

Typically, a measurement scale is employed when the constructs involved in a study have no direct

observable measures. In this regard, the unobservable constructs and their dimensions were

measured indirectly through measurement scales of several items (or questions) in which

participants’ responses to the measurement scale questions (or items) determine the measured

value for each construct. The scales (or instruments) used in this study consisted of those developed

and used in prior literature and hence not only ensure consistency but also the reliability and

validity of the measures are guaranteed.

To initiate the scale development process, the guidelines of Jarvis, MacKenzie and Podsakoff

(2003) as well as Bisbe, Batista-Foguet and Chenhall (2007) on reflective and formative constructs

which have been used in many management accounting literatures (e.g. Fayard et al, 2012; Dekker

et al, 2013) was followed. Whether each of the modelled constructs is conceptually reflective or

formative was initially ascertained using the guidelines. For a reflective construct, participants’

responses are assumed to reflect the conditions of the latent construct. In this case changes in the

responses are caused by changes in the constructs. On the other hand, a linear combination of

participants’ responses on a given scale type of modelling reflect formative constructs. Based on


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these guidelines, a reflective measurement model in which the manifest item values are considered

to be a function of underlying latent construct value was specified for all the constructs before

administering to respondents.

5.12. Constructs and their Sources

The constructs and their literature sources are shown in Table 5.6. MAS is a multidimensional

construct consisting of four dimensions: scope, timeliness, integration, and aggregation. These

four dimensions were initially conceptualized by Chenhall and Morris (1986) and subsequently

used by Soobaroyen and Poorundersing (2008) to examine the contingency fit relationships. It has

also been used by Hammad et al (2013) and Macinati and Asseni-Pessina (2014) to examine

contingency fit relationships in the Egyptian and the US hospitals respectively. The construct SCI,

level of information sharing and supply chain risk and uncertainty as well as supply chain

performance have been used by Chen et al (2013), Ataseven and Nail (2017), Flynn et al (2010)

and Wong et al (2011).


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Table 5.6: Constructs and their Sources of Measures

Construct No.

of

Items

Literature Evidence

Strategic supplier partnership 10 (Li et al, 2006; Chen et al, 2013; Behesti et al, 2014; Ataseven &

Nair, 2017)

Level of information shearing 12 (Mahama, 2006; Dekker et al, 2013; Chen et al, 2013; Behesti et

al, 2014; Ataseven & Nair, 2017).

Supply chain integration 8 (Behesti et al, 2014; Wiengarten, Humphreys, Gimenez &

Mclvor 2016; Ataseven & Nair, 2017)

Supply chain risk and uncertainty 8 Anderson and Dekker (2005), Mahama (2006), Dekker,

Sakaguchi & Kawai (2013), Li, Fan, Lee & Cheng (2015)

Size (Hammad et al, 2013; Macinati and Anessi-Pessina, 2014)

Broad scope of MAS information 5 (Chenhall & Morris,1986; Bouwens, & Abernethy, 2000;

Abernethy & Lillis (1995; Soobaroyen & Poorundersing, 2008;

Hammad et al, 2013; Macinati & Anessi-Pessina, 2014)

Timeliness of MAS information 4 (Chenhall & Morris, 1986; Bouwens & Abernethy (2000),

Abernethy & Lillis, 1995; Hammad et al, 2013; Macinati &

Anessi-Pessina, 2014)

Integration of MAS information 4 (Chenhall &Morris, 1986; Bouwens, & Abernethy, (2000;

Abernethy & Lillis, 1995; Soobaroyen & Poorundersing, 2008;

Hammad et al, 2013; Macinati & Anessi-Pessina, 2014)

Aggregation of MAS information 7 (Chenhall & Morris, 1986; Bouwens, & Abernethy, 2000;

Abernethy &Lillis (1995), Hammad et al (2013; Macinati &

Anessi-Pessina, 2014)

Supply chain cost effectiveness 9 (Li et al, 2006; Ou et al, 2010; Chen et al, 2013; Ataseven & Nair,

2017)

Asset utilization 4 (Ou, et al, 2010; Chen et al, 2013; Behesti et al, 2014; Ataseven

& Nair, 2017)

Supply chain reliability and speed

of delivery

7 (Ou et al, 2010; Chen et al, 2013; Behesti et al, 2014; Ataseven

& Nair, 2017)

Supply chain flexibility 3 (Li et al, 2006; Ou et al, 2010; Chen et al, 2013; Behesti et al,

2014; Ataseven & Nair, 2017)


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5.13. The Modelling Process

The modelling process involved a second-order covariance based structural equation modelling

(SEM) because the underlying theory of each of the three constructs suggests that they are

accounted for by sub-multiple lower-order (or first-order) constructs. In this regard, a higher-order

of abstraction was found to be the suitable modelling approach. To this end, the three main

variables: management accounting systems, supply chain management, and hospital supply chain

performance which are second-order level of abstraction were conceptualized as composites of

four dimensions each comprising first-order levels of abstraction. The second-order model was

posited to link the manifest variables to their respective first-order latent variables which were in

turn linked to their second-order variables.

The justification for using structural equations modelling (SEM) and more specifically covariance

based (CB-SEM) is that first, the variables are latent in nature and also has multiple relationships.

That is the analysis requires a single estimate of the variables simultaneously which can be

appropriately been executed using SEM compared to other multivariate models. Second, the study

intends to test or confirm a theory in the inter-firm exchanges domain hence partial least squares

(PLS-SEM) would not be the appropriate tool since it is a non-parametric based SEM compared

to CB-SEM which confirms a theory. Third, the constructs are reflective and CB-SEM analyses

only reflective form of constructs compared to PLS-SEM which analyses both formative and

reflective form of constructs.

The initial part of the modelling process begun with the confirmatory factor analytic structure to

ascertain the reliability and validity of the regression weights; that is the strengths of the regression

paths linking the factors and the indicator variables (factor loadings). Following prior studies (Tan,

2001; Maiga et al, 2013) the two-stage model-building process where testing of the measurement


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model precedes that of the structural model was adopted for this study. This is normally done to

avoid possible interactions between the structural and measurement models. In addition, since the

structural portion consists of latent variables and specification of their relations, the extent of their

validity and attainment of psychometric soundness of each construct is critical. The measurement

model specifies how hypothetical constructs are measured in terms of observed variables while the

structural model depicts the hypothesized relationships between latent constructs. From a general

perspective, factor analysis involves the assessment of the covariation among a set of manifest

variables so that information on their underlying latent constructs can be gathered. It explains the

extent to which the manifest variables are linked to their underlying latent constructs. In this

regard, testing for the measurement model prior to evaluating the structural model is an important

preliminary step in analyzing a full SEM.

5.14. Summary of Analysis Procedure

The focal point of analysis of data was hypotheses testing to confirm or reject contingency theory

in the inter-firm exchanges domain. The SEM was therefore employed to test all hypotheses and

simultaneously control for confounding variables. Based on some studies. Table 5.7 shows a

summary of all data analysis steps taken and their respective rationale.


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Table 5.7: Summary of Analysis Procedure Analysis

procedure

Stage (of the

analysis) Description Statistical tool Rationale

Data coding Stage 1 All data were assigned a code

Data coding

sheets and SPSS

This step was necessary for avoiding the data of

error items

Data

screening Stage 2

Data was explored to remove

unwanted items and outliers. Descriptive

statistics

This could visualise missing items and potential

unwanted items. Mean scores and standard

deviation give clues about the presence of outliers

The distribution of the data was

examined to establish acceptable

skewness Z-score

This was used because it provides benchmarks for

understanding the distribution of the data

Normality

test Stage 2

Univariate normality of data was

verified and confirmed

Shapiro-Wilk's

test

Shapiro-Wilk's is the most appropriate tool when

the number of data points (n) is not more than

Byrne, 2010)

Multivariate normality of data was

verified and confirmed

Mahalanobis

test

This is a gold standard provided in CFA (Byrne,

2010)

Assumptions

test Stage 2

To ensure that the data came from

a normally distributed population.

Shapiro-Wilk's

test,

Mahalanobis

test Recommended in the literature (Byrne, 2010)

To verify whether error terms of

all predictors are independent of

each other

Durbin-Watson

statistic

Recommended in the literature (Burkert et al,

2014)

To verify homoscedasticity

Variance

Inflation Factor

value

Recommended in the literature (Maiga et al,

2013)

Scale

validation Stage 2

To compute the reliability and

validity statistics of the

measurement scales CFA

This approach is the most robust way to estimate

both the reliability and validity statistics

(Macinati & Asseni-Pessina, 2014)

Hypotheses

testing Stage 3

To test structural CFA model or

the 23 hypotheses of the study,

including the control of

confounding variables and

moderation test CFA

This approach is the most robust way to test

structural hypotheses or models to minimise Type

II error (Maiga et al, 2013)

Source: Researcher’s own construct


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6.15. Chapter Summary

In this chapter, the general approach or methodology adopted in testing the contingency fit

hypotheses among the variables has been presented. The statistical tests involved the selection,

mediation and moderation forms of fit. It initially showed the researcher’s philosophical stance

based on which the entire research design was planned. The chapter showed that the research

design is a cross section survey methodology involving management accountants, procurement

officers and hospital managers/administrators in Ghana. The survey strategy, population,

sample size, instruments development and measurement scales, and the method of data

collection have been described.

The chapter also elaborated on the statistical techniques adopted to estimating the variables

involved in the study which was based mainly on the analysis of covariance structures. Under

that section, the modelling process which involved a two-stage second-order covariance-based

structural equations modelling was given a detailed description. Testing of the measurement

CFA model (factor analytic model) and the structural model (path coefficients) have been duly

explained.


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CHAPTER SIX

SURVEY ANALYSIS AND RESULTS

6.1 Introduction

The previous chapter outlined the approach to gathering the survey data and how such data

were analyzed to test the research hypotheses and subsequent realization of the study

objectives. In this chapter, the results and findings associated with analysis of the sample data

are presented. It presents the results related to the fit between the test variables, and their joint

impact on performance. It also presents empirical evidence of the management accounting

construct as a mediator between the antecedent conditions of SCM and hospital supply chain

performance. Finally, it presents results of the moderating effect of the contingency variables

(SCM contextual antecedents) on the link between management accounting and hospital supply

chain performance. The analysis is categorized into three sections. The first section details the

preliminary analysis which captures the models underlying assumptions; i.e. reliability and

validity measures (internal consistency, composite reliability, average variance extracted,

(AVE)) of the survey instrument and related items, tests of normality and outliers of the sample

data as well as descriptive measures of the test variables. The second section presents the first

part of the covariance structural equation modelling which involves model specification and

evaluation of the measurement part of the hypothesized model. It also provides evidence on

the extent to which the measurement model adequately fits the sample data. The third section

presents findings related to the modelling and testing of the structural paths linking the latent

variables specified in the model.


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6.2 Preliminary Analysis of the Sample Data

Although the three main constructs (supply chain context factors, management accounting

design, and supply chain performance) that form this study were theoretically developed and

based on items proposed and validated in prior studies, an assessment of the sample distribution

to establish that it originated from a normally distributed population is highly important as the

research findings are seriously affected by the credibility (or validity) of the scale items. This

section presents the preliminary findings from tests of normality for the sample data as well as

reliability and validity of the scale items. The section comprises two subsections – the first

subsection presents a summary of hospital characteristics or features represented by size

(measured by the natural log of number of beds), location (urban or rural measures as

dichotomy variable taking the value of 1 if located in an urban center and 0 otherwise),

ownership (public or private measured as a categorical variable taking the value of 1 if

government owned and 0 otherwise) and the qualifications of respondents (must have held

his/her current post for at least 3 years).

The test originally involved 72 items in all for the six factors (or constructs): 9 for strategic

supplier partnership (SSP), 7 for level of information sharing (LIS), 8 for supply chain risk and

uncertainty (SCR), 6 for supply chain integration, 20 for management accounting system

(MAS) made up of 5 accounting information scope (AIS), 4 timeliness (AIT), 4 integration

(AII), 7 aggregation (AIA), and 16 for supply chain performance (SCP) also consisting of 6

supply chain cost effectiveness (SCE), 4 utilization of hospital assets (UHA), 3 supply chain

quality (SCQ), and 3 supply chain flexibility (SCF). However, during the initial analysis, it was

discovered that some items either recorded very low loading (< 0.20) or failed to load and so

were deleted. The resulting data gave 55 items in all: 6 items for SSP, 6 for LIS, 5 for SCR, 6

for SCI, 4 each for AIS, AIA, AIT, and AII, 6 for SCE, 4 for UHA, and 3 each for SCF and

SCQ.


157

It also evaluates the distribution of the sample data for normality and whether outliers are in

the dataset. More specifically, it assesses the sample data for multivariate normality criterion

and kurtotic conditions which is critical for the analysis of covariance based structures. Given

that structural equation modelling is based on the analysis of covariance structures, tests of

variances and covariance are severely affected by kurtosis compared to skewness (DeCarlo,

1997). As a consequence, the presence of kurtosis, and more importantly multivariate kurtosis

in the dataset is always a concern as it is widely known to be exceptionally detrimental to the

analysis of structural models.

Multivariate kurtosis normally refers to the situation where there are both tails and peaks of the

multivariate distribution of the manifest variables that differ significantly from those of

multivariate normal distribution, and is categorized into multivariate positive kurtosis and

multivariate negative kurtosis. The former exhibits peakeness as well as bold or thick tails in

the distribution while the latter exhibits flat distributions with light tails. In the second

subsection, results of series of tests related to basic fundamental assumptions underlying

multivariate analysis as well as tests for reliability and validity of the measuring instrument are

presented. More precisely Cronbach Alpha (CA) test for reliability, Composite Reliability (CR)

test for internal consistency, Average Variance Extracted (AVE), and Intra-Class Correlations

(ICC) are given.

6.3 Sample Characteristics

Table 6.1 presents the respondents’ characteristics as well as the organizations’ background.

The frequency distribution for the sample hospitals in terms of ownership, profit status, and

location which were categorically measured are presented. The results as reported in Table 6.1

indicate that the sample composition has the larger representation of government hospitals 131


158

(55.3%) (i.e. are owned by the government) while 106 (44.7%) are represented by

individuals, corporate organizations, religious bodies, and NGOs.

These results suggest that although healthcare delivery is largely controlled by government, an

appreciable number of private entities are involved in this business. Hence their operations can

have significant impact on the health supply chain especially the procurement and distribution

of health products. With regards to the profit status question, only privately-owned hospitals

answered as government hospitals are known to be non-profit oriented. As low as 7.5%

represented by mainly religious bodies are non-profit which further suggests the impact the

private sector healthcare organizations can have on the supply chain. Finally, close to 70% of

the sampled hospitals are located within urban centres. This suggests that costs associated with

transportation and distribution of medical products between suppliers and hospitals (a key

obligation of the MAS function) can be efficiently controlled at lower levels since a large

proportion of pharmaceutical companies (one of the key players in the health supply chain) are

located within urban centers.

Table 6.1: Distribution of Hospitals in Terms of Ownership, Profit Status and Location

(N) Frequency (%)

Ownership 237

Public 131 55.3

Private 106 44.7

Profit Status 106

Profit 98 92.5

Non-profit 8 7.5

Location 237

Urban 165 69.6

Rural 72 30.4

(Source: Author’s computations from Field Survey, 2017)


159

As these three variables play a significant role in the functioning of healthcare institutions, they

can indirectly influence performance of the hospital SC (Chen et al, 2013) and are thus

controlled for their confounding effects (discussed in subsequent sections). The descriptive

statistics for the scale items is discussed next. However, because hospital size (measured by

the natural log of number of hospital beds) and tenure of respondents (number of years

occupying present position) are continuous variables, they are included in Table 6.2 and are

presented first. The results show that an average number of log 1.70 representing 50 beds are

owned by the sampled hospitals.

Like the other three control variables, hospital size largely influences its supply chain

especially in the area of stock control and inventory management which has serious

implications for management accounting information and hence controlled as well.

Considering the number of years at present position, the results indicate that on average,

respondents have occupied their present positions for 5.7 years. This suggests that the

respondents are not only knowledgeable and experienced in their respective positions, but also

can be provide reliable perceptions based on their access to information. Based on these

findings, the respondents (who were mainly accountants of the sampled hospitals) meet the

requirements to provide the needed information.

6.4 Assessment of Data for Normality

Table 6.2 presents the descriptive statistics concerning means, standard deviations, and kurtosis

of the sample data which are continuous in nature. Raykov and Marcoulides (2000) have noted

that of particularly problematic to the analysis of CB-SEM are data that exhibit multivariate

kurtotic features. In this regard, the results for both univariate kurtosis and their critical ratios

(i.e. z-scores) and multivariate kurtosis assessment of the dataset for all the 55 items are


160

presented. A review of Table 6.2 indicates that positive values of kurtosis range from 0.046 to

1.090 and negative kurtosis values range from -0.116 to -1.018 which gives an overall average

univariate kurtosis value of 3.466. A normally distributed kurtosis under standardized

conditions has a value of 3 as the kurtosis index (𝛽) where higher values of 𝛽 signifies positive

kurtosis and lower values connotes negative kurtosis.

However, because this value is rescaled to zero in most computer programmes, the index 𝛽

takes the zero value as indicator of normality. Although a positive kurtosis or negative kurtosis

is signaled by the 𝛽 sign, Kline (2005) notes that consensus on the size of the nonzero values

based on which conclusions regarding the presence of extreme kurtosis has still not been

reached. Despite this assertion, West, Finch and Curran (1995) maintain that rescaled 𝛽 values

outside the range 0 ≤ 𝛽 ≤ 7 is an indication of departure from normality. A review of Table

6.2 and following West et al (1995) the items showed no substantial kurtotic values in the

dataset.

In the case of multivariate kurtosis index, the critical ratio value representing Mardia’s (1970)

normalized estimate of multivariate kurtosis was used. In situations where a large sample size

exhibits multivariate normality, a distribution of this index as a unit normal variate takes place

so that significant positive and negative kurtosis are reflected in large positive and negative

values respectively. It has been suggested that data that are non-normally distributed exhibit

normalized estimate of multivariate kurtosis values greater than 5 (Bentler, 2005). In Table 6.2,

the index of multivariate kurtosis and the corresponding critical ratios appear at the bottom of

the z-score and kurtosis columns have values of 4.521 and 3.466 respectively. Following

Bentler (2005) the z-statistic of 4.521 is highly suggestive of normality in the sample. On the

basis of these findings, the next stage of the preliminary analysis which involved checking for

outliers in the dataset are addressed.


161

Table 6.2: Descriptive Statistics for Scale Items

Indicator Code Mean Std. Dev Z-Score Kurtosis

Size (Measured by Number of Beds) LO10_NHB 1.70 0.344 -8.290 - 0.305

Number of Years at Post NYP 5.70 1.216 11.031 0.419

Strategic Supplier Partnerships SSP

High level of strategic relations with suppliers SSPI 5.14 1.287 3.023 0.090 Participation of suppliers in procurement process SSP2 4.82 1.370 -6.877 0.053 Quick ordering systems with suppliers SSP3 4.96 1.348 5.184 0.122 Forecast and replenish collaboratively with suppliers SSP4 3.67 0.821 8.257 0.872 Involve suppliers in planning and goal-setting process SSP5 5.24 1.553 -12.027 -0.539 Inventory management/consignment stock with suppliers SSP6 4.50 1.230 14.709 0.285

Supply Chain Integration SCI Closely coordinated interorganizational activities SCI1 5.45 1.400 -13.068 -0.261 Well-integrated logistic activities with suppliers SCI2 4.73 1.117 12.875 0.046 Excellent distribution, transportation and warehousing SCI3 5.48 1.148 4.451 -0.505 Inbound and outbound distribution with hospital suppliers

SCI4 4.63 1.170 -7.916 -0.176

Integrated software applications with suppliers SCI5 5.25 1.544 -8.009 0.716 Integrates purchasing of health products into planning SCI6 5.27 1.432 15.781 0.084 Level of Information Sharing LIS Share inventory level information with suppliers LIS1 5.27 1.354 -11.400 0.233 Share business knowledge of core business processes LIS2 4.44 1.005 6.727 0.522 Exchange information to establish business planning LIS3 5.14 1.511 9.995 0.732 Share proprietary information with suppliers LIS4 4.41 1.224 5.525 0.363 Share accurate risk information with partners/suppliers LIS5 5.28 1.371 -13.227 0.863 Willing to share real time information on demand LIS6 5.30 1.416 10.122 0.224 Supply Chain Risk and Uncertainty SCR Share medical supply problems with suppliers SCR1 5.34 1.282 -14.482 0.738 Evaluates suppliers’ achievement of target prices/costs SCR2 5.36 1.415 -9.260 0,531 Evaluates suppliers’ cost reduction efforts SCR3 4.20 1.156 4.032 0.542 Assess the quality and cost of suppliers’ products SCR4 4.33 1.128 8.277 -0.495 Hold frequent meetings with suppliers SCR5 5.31 1.488 -7.280 0.630

Management accounting System AIS

Information that relates to future possible events AIS1 4.65 1.083 11.770 -0.116 Quantify the likelihood of future events occurring AIS2 4.58 1.142 -13.288 0.164 Non-economic information AIS3 5.41 1.440 -7.813 -0.116 Information on broad factors external to hospital AIS4 5.68 1.343 6.434 0.684 Timeliness of Accounting Information AIT Immediate arrival of information upon request AIT1 5.34 1.630 -5.250 0.170 Automatic supply of information to users AIT2 4.69 1.002 10.755 -0.287 Frequent and systematic provision of reports AIT3 4.52 1.246 3.472 0.680 No delay between event and relevant information AIT4 4.76 1.184 -2.846 0.055

(Source: Author’s computations, 2017) (continued on next page)


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Table 6.2: Descriptive Statistics for Scale Items - Continued

Indicator Code Mean Std Dev Z-Score Kurtosis

Accounting Information Integration AII

Impact of information on individual decisions AII1 4.66 1.389 6.388 0.135 Influence of individual’s decision on responsibility AII2 4.72 1.182 -11.632 -0.417 Information on precise targets for all activities AII3 4.85 1.100 6.921 0.301 Impact of information on decisions on performance AII4 4.82 1.644 4.581 -1.018

Accounting Information Aggregation AIA

Information on functional areas in your hospital AIA1 5.00 1.451 -7.370 -0.333 Information on the effect of events on time periods AIA2 4.37 1.126 12.027 0.311 Processed information to influence different events AIA3 4.47 1.136 9.027 0,241 Information on summary reports for the hospital AIA4 5,45 1.363 -14.142 0.655

Supply Chain Cost Effectiveness SCE

Cost of order fulfillment reducing over the last 3 yrs SCE1 5.11 1.546 -8.072 0.936 Reduction in order fulfillment cost has improved SCE2 4.38 1.493 -9.981 0.873 Cost of order fulfillment is cost efficient SCE3 4.36 1.616 -10.976 0.474 Purchasing costs have reduced over the past 3 years SCE4 4.82 1.595 15.628 0.433 Operating costs have reduced over the past 3 years SCE5 5.05 1.490 -11.297 -0.845 Inventory costs have reduced over the past 3 years SCE6 4.92 1.261 13.158 -0.377

Utilization of Hospital Assets UHA

Growth rate in internally generated funds over 3 yrs UHA1 5.28 1.499 8.155 0.985 Growth rate in return on assets over last 3 years UHA2 5.22 1.647 -5.216 0.972 Growth rate in return on investments over 3 years UHA3 5.05 1.762 -7.046 0.302 Growth rate profit/accumulated fund over the last 3 years

UHA4 4.35 1.968 5.329 0.241

Supply Chain Flexibility SCF Flexibility of order fulfillment process getting better SCF1 5.30 1.572 -8.072 0.698 Improvement in the order fulfillment process SCF2 5.31 1.558 9.978 -0.507 Improvement in the cycle time of ordering process SCF3 4.30 1.488 13.003 0.748 Supply Chain Quality SCQ

Quality of order fulfillment process getting better SCQ1 5.27 1.517 -9.634 -.0222 Improvement in the order fulfillment process SCQ2 5.41 1.567 4.201 1.090 On-time delivery of medical suppliers from supplier SCQ3 5.33 1,598 -8.123 0.879 Multivariate 4.521 3.466

(Source: Author’s computations, 2017)

6.4.1 Assessing Data for Multivariate Outliers

To detect outliers in the sample, the squared values of the Mahalanobis distance (𝑑2) for each

observation was computed. This statistic measures the distance in standard deviation units

between a set of scores for the sample means for all the variables (centroids) and one

observation. In a particular dataset, outliers exhibit cases of scores that differ substantially from

all the others such that an extreme score in a variable is attributed to univariate outliers while


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that of multivariate outliers manifest extreme scores on two or more variables. Using the

Mahalanobis distance approach (Byrne, 2010), a typical outlying case will exhibit a 𝑑2 value

that stands distinctively apart from all the other 𝑑2 values. After reviewing the values produced

by the sample data revealed that serious multivariate outliers that could distort subsequent

analysis is minimal. Also, the 𝑝2 values for all the variables range from 0.000 to 0.001 < 0.05

confirming the absence of outliers in the dataset. Next, is the test for reliability and validity of

scale items.

6.4.2 Correlation Analysis

A review of the standardized estimates for the constructs revealed the highest correlation value

of 0.57 indicating the absence of multicollinearity among the variables. However, their

statistical significance indicates strong relations among the constructs which in contingency

theory applications measures the strength rather than form of relationship among variables. The

presence of multicollinearity signifies that the correlation between two or more variables is so

high that essentially, they are represented by the same underlying construct. In this case, the

presence of a correlation of value close to 1 or more is indicative of inadmissible solution which

is a signal of model misspecification. This situation was however not found in the correlation

matrix produced in Table 6.3.


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Table 6.3: Correlations, Reliability, Discriminant Validity and Average Variance Extracted (AVE) of Constructs

1 2 3 4 5 6 7 8 9 10 11 12

Strategic supplier partnerships 1 0.94

Level of information sharing 2 0.26*** 0.87

Supply chain integration 3 0.41*** 0.20*** 0.85

Supply chain risk and uncertainty 4 0.06* 0.13** 0.53*** 0.91

Scope of accounting information 5 0.18** 0.22*** 0.15** 0.58*** 0.94

Timeliness of accounting information 6 0.14** 0.15*** 0.05 0.47*** 0.06 0.88

Accounting information integration 7 0.33*** 0.02 0.51*** 0.29*** 0.19*** 0.16*** 0.91

Accounting information aggregation 8 0.07* 0.08* 0.14** 0.24*** 0.37*** 0.10 0.28*** 0.93

Supply chain cost effectiveness 9 0.41*** 0.12* 0.44*** 0.57*** 0.25*** 0.07* 0.40*** 0.35*** 0.88

Utilization of hospital assets 10 0.27*** 0.31*** 0.29*** 0.30*** 0.11* 0.28*** 0.08* 0.54** 0.49*** 0.83

Supply chain delivery and quality 11 0.31*** 0.22*** 0.09** 0.11* 0.01 0.13* 0.14** 0.19*** 0.17** 0.26*** 0.94

Supply chain flexibility 12 0.11** 0.37*** 0.09* 0.12** 0.40*** 0.03 0.13* 0.18** 0.04 0.12* 0.23*** 0.84

Cronbach Alpha 0.72 0.86 0.83 0.86 0.79 0.87 0.86 0.84 0.91 0.94 0.89 0.89

Average Variance Extracted (AVE) 0.88 0.76 0.73 0.82 0.89 0.78 0.80 0.69 0.77 0.68 0.84 0.71

Composite Reliability 0.73 0.66 0.81 0.76 0.67 0.79 0.84 0.89 0.75 0.69 0.77 0.86

(Source: Field Survey, 2017)


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6.5 Measures of Reliability and Validity of Scale Items

The nomological validity of the research model is established using confirmatory factor

analysis (CFA) to analyze the survey data. In this regard, an assessment of the psychometrical

properties of the scales involved an evaluation of factor loadings, internal consistency, and

discriminant validity. To be consistent with prior studies, the reliability and validity of the

measures which comprised the measurement model (CFA model) were initially evaluated

followed by an evaluation of the structural model to assess strengths of the hypothesized links

among the latent variables.

It should be noted here that each of the three constructs namely, SCM context factors, MAS

design, and hospital supply chain performance are multidimensional in nature. Hence, a

second-order model-fitting approach was used to run the CFA for each of the aforementioned

constructs based on the a priori hypothesis that the other lower-order (first-order) factors

account for the higher-order (second-order) constructs. In other words, the second-order

construct accounts for all the variances and covariance associated with the first-order

constructs. Fig 6.1(a), 6.1(b), 6.1(c) show respectively, the final output of the second-order

factor analytic models for the SCM, MAS, and SCP constructs respectively. Each second-order

factor has four first-order factors.


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Fig 6.1(a): Graphical Output of Factor Analytic Model for SCM Context Factors

(Source: Author’s own modelling based on Chen et al, 2013)

Note: SCM = supply chain management context factors; SSP = strategic supplier partnership; SCI = supply chain

integration; LIS = level of information sharing; SCR = supply chain risk and uncertainty

Fig 6.1(b): Graphical Output of Factor Analytic Model for MAS Information Dimensions

(Source: Author’s own modelling based on Chenhall & Morris, 1986)

Note: MAS = management accounting design; AIS = accounting information scope; AIT = accounting

information timeliness; AII= accounting information integration; AIA = accounting information aggregation


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Fig 6.1(c): Graphical Output of Factor Analytic Model for SCP Dimensions

(Source: Author’s own modelling based on Chen et al, 2013)

6.1(d) Graphical Output for Combined Factor Analytic Models


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The summary statistics for the scale items as presented in Tables 6.4 and 6.5 indicate that the

postulated CFA model was not supported by various fit indices at the initial stages. Evidence

of convergent as well as discriminant validity is provided by these fit indices for the three factor

analytic models along with the t-values in Table 6.4. However, the initial fit statistics of the

measurement model proved to be relatively not well-fitting between the hypothesized CFA

model and the sample data with CFI = 0.805 versus RMSEA = 0.096 for Model 1 of the SCM

construct; CFI = 0.834 versus RMSEA = 0.094 for Model 1 of the MAS construct; and CFI =

0844 versus RMSEA = 0.090, for Model 1 of the SCP construct, which fall outside the

recommended range of acceptability (< 0.05 to 0.08 ) for the RMSEA value in particular. A

review of the Modification Indices (MI) provided by the AMOS Output revealed some

evidence of misfit in all the three factor analytic models. It was discovered that if some

parameters are allowed to be estimated freely, the 𝑋2 values associated with each of the three

models will reduce considerably thereby improving the goodness-of-fit measures.

Subsequently, a post hoc analysis was initiated to improve upon the three models and this

resulted in three modifications and re-specifications of the original models. A re-specification

of Model 1 resulted in Model 2 for each construct. Further modifications resulted in Model 3

after which no further modification was possible. The next two subsections present the post

hoc analysis of the factor analytic models.


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Table 6.4: Factor Loadings of Scale Items

Indicator Code Standardized Regression Weights

t-value

Strategic Supplier Partnerships SSP 0.914 12.035

High level of strategic relations with suppliers SSPI 0.725 3.288 Participation of suppliers in procurement process SSP2 0.759 6.808 Quick ordering systems with suppliers SSP3 0.719 6.188 Forecast and replenish collaboratively with suppliers SSP4 0.650 7.512 Involve suppliers in planning and goal-setting process SSP5 0.771 11.761 Inventory management/consignment stock with suppliers SSP6 0.789 Fixed

Supply Chain Integration SCI 0.947 12.760 Closely coordinated inter-organizational activities SCI1 0.761 Fixed Well-integrated logistic activities with suppliers SCI2 0.815 7.882 Excellent distribution, transportation and warehousing SCI3 0.872 10.490 Inbound and outbound distribution with hospital suppliers SCI4 0.881 6.803 Integrated software applications with suppliers SCI5 0.746 11.920 Integrates purchasing of health products into planning SCI6 0.764 12.927

Level of Information Sharing LIS 0.927 11.276 Share inventory level information with suppliers LIS1 0.798 10.031 Share business knowledge of core business processes LIS2 0.648 9.371 Exchange information to establish business planning LIS3 0.749 10.715 Share proprietary information with suppliers LIS4 0.675 9.715 Share accurate risk information with partners/suppliers LIS5 0.726 12.037 Willing to share real time information on demand LIS6 0.709 10.791

Supply Chain Risk and Uncertainty SCR 0.880 13.173 Share medical supply problems with suppliers SCR1 0.841 Fixed Evaluates suppliers’ achievement of target prices/costs SCR2 0.749 13.056 Evaluates suppliers’ cost reduction efforts SCR3 0.794 14.376 Assess the quality and cost of suppliers’ products SCR4 0.711 12.236 Hold frequent meetings with suppliers SCR5 0.9444 10.791 Scope of Accounting Information AIS 0.968 14.286 Information that relates to future possible events AIS1 0.730 8.447 Quantifythe likelihood of future events occurring AIS2 0.767 11.112 Non-economic information AIS3 0.721 12.272 Information on broad factors external to hospital AIS4 0.809 Fixed

Timeliness of Accounting Information AIT 0.919 12.498 Immediate arrival of information upon request AIT1 0.769 12.291 Automatic supply of information to users AIT2 0.740 11.760 Frequent and systematic provision of reports AIT3 0.793 12.663 No delay between event and relevant information AIT4 0.766 Fixed

(Source: Author’s computations, 2017) (continued from next page)


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Table 6.4: Factor Loadings of Scale Items - Continued

Indicator Code Standardized Regression Weights

t-value

Accounting Information Integration AII 0.857 10.428 Impact of information on individual decisions AII1 0.820 11.413 Influence of individual’s decision on responsibility AII2 0.907 12.569 Information on precise targets for all activities AII3 0.844 11.697 Impact of information on decisions on performance AII4 0.784 Fixed

Accounting Information Aggregation AIA 0.989 12.708 Information on functional areas in your hospital AIA1 0.790 12.105 Information on the effect of events on time periods AIA2 0.810 12.794 Processed information to influence different events AIA3 0.927 14.390 Information on summary reports for the hospital AIA4 0.726 Fixed

Supply Chain Cost Effectiveness SCE 0.794 12.784 Cost of order fulfillment reducing over the last 3 yrs SCE1 0.681 11.469 Reduction in order fulfillment cost has improved SCE2 0.714 14.213 Cost of order fulfillment is cost efficient SCE3 0.709 13.693 Purchasing costs have reduced over the past 3 years SCE4 0.871 17.183 Operating costs have reduced over the past 3 years SCE5 0.867 16.230 Inventory costs have reduced over the past 3 years SCE6 0.853 Fixed

Utilization of Hospital Assets UHA 0.717 5.651 Growth in internally generated funds over the past 3 years UHA1 0.736 5.957 Growth in return on assets over the past 3 years UHA2 0.741 5.934 Growth in return on investments over the past 3 years UHA3 0.844 6.158

Growth rate profit/accumulated fund over the last 3 years UHA4 0.920 Fixed

Supply Chain Flexibility SCF 0.969 15.205 Flexibility of order fulfillment process getting better SCF1 0.871 17.903 Improvement in the order fulfillment process SCF2 0.850 15.695 Improvement in the cycle time of ordering process SCF3 0.853 Fixed

Supply Chain Quality SCQ 0.886 13.318 Quality of order fulfillment process getting better SCQ1 0.932 18.563 Improvement in the order fulfillment process SCQ2 0.812 14.946 On-time delivery of medical suppliers from supplier SCQ3 0.819 Fixed


6.6 Post Hoc Analysis of Factor Analytic Models

Following the detection and location of model misfit in the factor analytic models, the analysis was

proceeded in an exploratory mode which resulted in an improvement of the originally specified models

as enumerated in the analysis following.


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Table 6.5: Post Hoc Analysis for Factor Analytic Models

SCM Construct MAS Construct SCP Construct Combined

Fit

Index

Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model

𝑋2 491.652 436.791 414.254 233.271 227.653 175.983 135.241 288.564 247.106 223.548 401.232

GFI 0.853 0.807 0.848 0.934 0.807 0.908 0.919 0.901 0.909 0.914 0.911

NFI 0.821 0.829 0.842 0.962 0.813 0.911 0.929 0.910 0.936 0.945 0.901

CFI 0.805 0.872 0.926 0.978 0.834 0.858 0.901 0.844 0.913 0.960 0.966

RMSEA 0.096 0.087 0.071 0,063 0.094 0.089 0.075 0.090 0.076 0.058 0.067

(Source Author’s Computations, 2017)


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6.6.1 Model 2 of the Factor Analytic Models

The misfits in Model 1 as indicated in the preceding subsection involved error covariance of

substantially high values following a review of the MIs. In this regard, Model 2 involved the

addition of freely estimated parameters in a systematic manner (i.e. addition to the model, one

parameter at a time) because the MI values have the tendency to vary substantially among

series of parameterization tests. Given this background, Model 2 was built by the initial

addition of the error covariance with the highest MI value associated with each of the factor

analytic model. This parameter represented the error terms for Items 16 and 6, 2 and 3, and 5

and 15 in Model 1 of the SCM, MAS and SCP constructs respectively which from the

parameter change statistic should respectively result in approximately 0.785, 0.618 and 0.887

of parameter estimated value.

The fit indices recorded for Model 2 showed significant improvement upon Model 1 with the

𝑋2 statistic reducing from 491.652 to 436.791, the CFI = 0.872, NFI = 0.829 and RMSEA =

0.087 for the SCM construct. The MAS construct also recorded a decrease in the 𝑋2 value

from 227.653 to 175.983, CFI increased from 0.834 to 0.958 and a reduction in the RMSEA

value from 0.094 to 0.089. Similar results are obtained for the SCP construct where the 𝑋2

statistic decreased from 288.564 to 247.106, CFI increased from 0.844 to 0.953 and the

RMESA reduced from 0.090 to 0.076. Finally, a difference in 𝑋2 values between Model 2 and

Model 1 was an indication of significant improvement in the mode. However, the RMSEA

values still fell outside the acceptable range so a further modification analysis was performed.

6.6.2 Model 3 of the Factor Analytic Models

Although Model 2 showed significant improvement upon Model 1, a review of the MIs

indicated that the error covariance associated with Items 1 and 2 in the SCM factor analytic


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model signified strong misspecification parameters to the model. A further examination of

these parameters suggested that an estimated value of approximately . 391 would result if these

parameters were incorporated into the model. Like the error covariance associated with Items

6 and 16, a redundancy due to content overlap was associated with Items 1 and 2. On the basis

of the strength of this MI coupled with the obvious overlap of item content, a further

modification to the model was made by incorporating this error covariance into the model.

Modification and re-specification of Model 2 resulted in Model 3. As shown in Table 6.5, the

output for Model 3 indicated further improvement in the model with the goodness-of-fit

statistics revealing a statistically significant improvement upon Model 2 in terms of model fit

(𝑋(204)2 = 414.254; ∆𝑋1

2 = 22.53). Also, the CFI and RMSEA values improved significantly

from 0.872 to 0.926 and from 0087 to 071 respectively.

Further modifications yielded a CFI value of 0.978 whereas that of the NFI yielded 0.962 and

RMSEA value of 0.063. Also, the GFI recorded 0.934 and the ratio 𝑋2 to degrees of freedom

yielded a ratio of 7.43 and this resulted in Model 4 of the SCM factor analytic model. In spite

of this remarkable improvement in key fit indices, the MIs showed a minimum of two error

covariance with fairly large MIs (i.e. err 1 ↔ 2 and err 2 ↔ 4). However, the substantive

rationale for the addition of the parameters associated with this error covariance to the model

was found not to be strong; hence, were not considered for their inclusion in the model.

With regards to the MAS and SCM factor analytic models, a further review of the MIs showed

cross-loadings with respect to Item 12 on Factor 1 although it was initially postulated as loading

on Factor 3 related to the MAS construct. Yet, the MI indicated that in addition to loading onto

Factor 3, it should load onto Factor 1. Ideally, items on a measuring instrument should clearly

target only one of its underlying constructs (or factors). Given empirical evidence that this same

cross-loading has been documented in prior studies a modification of the model (Model 3) of

the MAS factor analytic model was specified by freely estimating this parameter. A further


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statistically significant reduction in the 𝑋2 value of the goodness-of-fit statistic was recorded.

This showed an improvement in Model 3 (𝑋(204)2 = 135.241; ∆𝑋1

2 = 37.742). In addition,

both the CFI and RMSEA values associated with model 3 improved from 0.858 to 0.901 and

from 0.089 to 0.075 respectively. SCP also improved with CFI = 0,960 and RMSEA = 0.058.

A combined model showed a CFI = 0.966 and RMESA = 0.067.

The MIs recorded in the AMOS output however showed no evidence of substantively

reasonable misspecification in Model 3 of both the MAS and SCP factor analytic models. A

review of both unstandardized and standardized factor loadings as well as factor covariance

and error covariance revealed all parameters to be statistically significant with critical ratios

values > 1.96. Given that findings associated with the validity of the three factor analytic

models fell within acceptable range of indices, Model 3 was considered to be the best-fitting

and most parsimonious models to represent the data. As a result of these measurements,

restructuring the revised hypothesized model to be tested replaced the initial hypothesized

model.

Prior literature such as Fornell and Larcker (1981), Maiga et al (2013) and Chen et al (2013)

have indicated that a generally accepted threshold for item loadings and internal consistencies

should not fall below 0.70. Following these guidelines and reviewing all factor loadings and

the significance of their t-values associated with factor to item loadings indicate that they are

above the 0.70 threshold value and beyond 0.80 in most cases. In addition, the critical values

at the 5% level of significance is exceeded by the significance of the t-values associated with

factor loadings. As shown by the factor analysis results in Table 6.4 as well as composite

reliability scores in Table 6.3, the scale used in this study largely meet these thresholds.

Chin (1998) and Hair, Black, Babin and Anderson (2015) have asserted that discriminant

validity is established if first the loadings of items on their respective constructs should be


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stronger than any other constructs in the model (or most appropriately zero loadings) and

second, the inter-construct correlations should be lower than the square of the Average

Variance Extracted (AVE). In this regard, and following these guidelines, evidence of

discriminant validity of the constructs in the model was confirmed by a comparison of the

squared correlations of two constructs with their individual AVE. The results as summarized

in Table 6.3 indicate that the AVE of the latent constructs is higher than the squared

correlations. Furthermore, the AVE and composite reliability estimates for each construct fall

beyond acceptable threshold. Based on these overall findings, the analysis proceeded with the

structural model specification and subsequent evaluation to test the hypotheses.

6.7 Structural Model Specification

Having satisfied the underlying assumptions and conditions related to the sample data and the

test items, the analysis proceeded into the real task of fitting the hypothesized model to the

sample data. Hence, the results and findings of the hypotheses tests linking the structural paths

are presented in this final section of the analysis. It has three subsections: the first subsection

specifies the hypothesized model (i.e. model specification). The second subsection identifies

the hypothesized model (i.e. model identification), and the final subsection fits the

hypothesized model to the sample data and presents results of the hypotheses tests. Since the

hypotheses involving the three main constructs – SCM, MAS, and hospital supply chain

performance are categorized into tests of fit, tests of performance, and tests of interactions as

presented under Models 1 to Model 7 in Table 6.7, the final subsection is further divided into

three parts – the first part presents the results on the ‘selection fit’ relationships between SCM

context factors and MAS design whilst the second and final parts present the results on

performance relationships on mediation and interaction effects respectively.


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6.7.1 Specification of the Hypothesized Model

The specified hypothesized model that was tested by the sample data is presented in Fig 6.2.

The input model aimed at finding a link between the SCM context factors, MAS design and

hospital supply chain performance. The theoretical foundations and empirical linkages among

the six constructs were presented in chapter five. Three scenarios that relate to model

specification (Joreskog, 1995; Byrne, 2010) include: strictly confirmatory (SC), alternative

models (AM), and model generating (MG). Strictly confirmatory is the scenario where based

on theory or empirical studies, a single model is postulated and appropriate data are collected

to test the fitness of the hypothesized model with the sample data in a single mode.

Here, re-specification of the model does not take place after it has been tested with the sample

data. Hence, further modifications to the model are not made; rather, it is either retained or

rejected based on the results of the test. In the case of the AM scenario, several alternative (or

competing) models which are grounded in theory are proposed by the researcher. The model

that most appropriately represents the sample data is selected following the analysis of a single

set of empirical data to all the models. The MG scenario deals with the situation where the

analysis proceeds in an exploratory (rather than confirmatory) mode following the postulation

and subsequent rejection of a theoretically derived hypothesized model on the basis of its poor

fit to the sample data. Here, the hypothesized model is re-specified and re-estimated after going

through some modification. The primary goal in this scenario is to trace and locate the source

of misfit in the model and produce a model that better describes the sample data.

Given that much resources in both financial and non-financial terms are associated with data

collection, the analysis followed the MG scenario since the research could not have been

abandoned or terminated on the basis of fit inadequacy between the hypothesized model and

the survey data. Joreskog (1995, p. 295) states ‘‘despite the fact that a model is tested in each


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round, the whole approach in SEM applications is MG rather than model testing’’. This

assertion suggests that if the unlikely event of a well-fitting model did not occur between the

hypothesized model and the sample data then a number of logical steps involving statistical

tests and subsequent analysis of the resulting goodness-of-fit statistics would have to be

followed and applied until the required fit is attained. Essentially, model fit evaluation is

derived from a variety of perspectives based on several criteria which in turn, assess model fit

from a diversity of perspectives. The evaluation criteria focus on the adequacy of the parameter

estimates and the model in general. Most important is the attainment of the appropriate sign

and size for the parameters that are estimated and their consistency with the underlying theory.

Fig 6.2: The Hypothesized Model

(Source: Author’s own modelling based on Chenhall and Morris, 1986, Li et al, 2006; Chen et

al, 2013)

Note: SSP = strategic supplier partnerships; SCI = supply chain integration; LIS = level of information sharing;

SCR = supply chain risk and uncertainty; MAS = management accounting systems; AIS = scope of accounting

information; AIT = timeliness of accounting information; AIA = aggregation of accounting information; AII =

accounting information integration; SCP = supply chain performance; SCM = supply chain cost minimization;

UHA = utilization of hospital assets; SCF = supply chain flexibility; SCQ = supply chain quality.


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6.7.2 Identification of the Hypothesized Model

In order to find a unique solution for values of the structural parameters, the hypothesized

model was subjected to test of identification to establish that a unique set of parameters being

tested in the structural model is consistent with the sample data. This involved a transposition

of the variance – covariance matrix of the manifest variables into the structural parameters of

the hypothesized model. Testing for model identification is a pre-requisite to estimating the

model because in situations where the model cannot be identified, it implies that attainment of

consistent estimates for all parameters is impossible, and that empirical evaluation of the

hypothesized model cannot take place. An unidentifiable model could imply that different

parameter values define the same model where the parameters are subject to arbitrariness.

Three situations arising from model identification can occur: just – identified, over-identified,

and under-identified. To further our understanding of the model identification tests, each of

these is explained briefly in the sub-sections that follow.

6.7.2.1 Just-Identified Model

A just-identified model exhibits a one-to-one mapping (or correspondence) between the dataset

and the structural parameters. In this case, the total number of parameters to be estimated equals

exactly the number of variances and covariance of the manifest variables in the dataset. Studies

in SEM analysis have however shown that in spite of the capability of the just-identified model

in responding to a unique solution for all estimated parameters, it portrays the limitation of

having no degrees of freedom leading to a non-rejection of the model in all cases so it is not

recommended as scientifically appealing.

6.7.2.2 Under-Identified Model

Where the number of variances and covariance in the dataset fall short of the number of

parameters to be estimated, the model is classified as under-identified. In this case, and for the


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purposes of attaining a determinate solution of parameter estimation, the information contained

in the model is said to be insufficient and the possibility of resulting in an infinite number of

solutions.

6.7.2.3 Over-Identified Model

In an over-identified model, the number of data points (variances and covariance) exceeds the

number of parameters to be estimated, yielding positive degrees of freedom which allows for

a rejection of the model. Since the aim in SEM analysis is to attain positive degrees of freedom

that can result in the rejection of the model, the over-identification criterion has been proven

to be scientifically usable.

To identify the hypothesized model specified in Fig 7.2 and its underlying assumptions, the 𝑡

-rule principle (Byrne, 2010; Hair et al, 2015) was followed. This is computed as =𝑝(𝑝+1)

2 ,

where 𝑝 represents the number of indicators. After reviewing the specified model, it was

discovered that the number of data points to work with (or the available information with

respect to the dataset) which constitute variances and covariance of the manifest variables (with

𝑝 variables) resulted in 2,628 data points. Given that the number of unknown parameters

(excluding those fixed at 1.00) totaled 160 made up of 63 measurement regression coefficients

(factor loadings), 17 structural regression paths, 3 factor variances, 72 error variances, 12

residual variances and 3 covariances, the model was identified as an over-identified model with

2.458 (2,628 − 170) degrees of freedom. In terms of the second-order model, there are 55

(10(10+1)

2) pieces of information: made up of 10 factor variances, with 24 (12 factor loadings

and 12 residuals) unknown parameters to be estimated resulting in 31(55 − 24) degrees of

freedom. Thus, both the general model and the second-order model were identified as

estimable. After meeting the conditions of identification, the structural parameters were

considered as estimable and a subsequent testable of the model followed.


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6.8 Fitting the Hypothesized Model

The final part of the model fitting process involved the structural model which tested and cross-

validated the linkages among the latent constructs. In fitting the structural portion of a full SEM

model which involves linkages among only the latent variables to the sample data, the four

important aspects that characterized model-fitting in full SEM (Byrne, 2010) were followed.

These include: 1) statistical significance, 2) the estimation process, 3) the model-fitting process,

and 4) the goodness-of-fit statistics. Each of these is discussed briefly in the subsections that

follow.

6.8.1 Statistical Significance

Following past studies on full SEM analysis, the test for statistical significance was based

mainly on the analysis of covariance structures which are driven by degrees of freedom (𝑑𝑓)

made up of the number of elements in the sample matrix and the number of parameters being

estimated. This approach is somewhat different from statistical significance testing involving

traditional statistical methods. In this regard, issues such as the use of null hypothesis testing

procedures, the importance of confidence intervals, practical significance etc. are captured in

the fits statistic indices. Although a plethora of criticisms have been generated on the practice

of statistical significance testing over the years, extant literature suggests that these have long

been addressed in SEM applications.

6.8.2 Model Estimation Process

The task in the estimation process (as has been the primary focus of the estimation process in

SEM) was to produce parameter values such that the discrepancy (residual) between the sample

covariance matrix and the population covariance matrix implied by the hypothesized model is

minimal. In full SEM, this task is accomplished by minimizing a discrepancy function behind

the scenes such that the point in the estimation process where the discrepancy between the


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sample covariance matrix and restriction covariance matrix implied by the model reflects its

minimal value.

6.8.3 Structural Model Fitting Process

In this part of the analysis, only the causal paths of the model are of interest and hence only the

MIs of the parameters that represent the structural (i.e. causal) paths in the model were detected

for possible misspecifications in the model. The reason is that any misfit to components of the

measurement model in the structural model are normally catered for while that portion of the

model was tested for validity in the factor analytical model. Proceeding from that point, the

extent to which the hypothesized structural model adequately describes (or ‘‘fits’’) the sample

data was assessed in a systematic manner until a well-fitting full structural model was finally

attained.

6.9 Hypotheses Tests of the Structural Model

Since the overall structural model of fits fall within the specified range of acceptability, the

analysis proceeded with testing of the causal relationships between the latent constructs. The

tests involved different modelling techniques for each contingency fit since each is associated

with a different theoretical foundation and interpretation. To evaluate the hypotheses, the

standardized parameter estimates for the model were examined using the significance of

individual path coefficients. The key goodness-of-fit statistics (CFI, GFI, RMSEA) associated

with the structural model are summarized in Table 6.7. A review of these indices showed a

relatively well-fitting model with values 0.979 , 0.906, and 0.062 represented by the CFI, GFI

and the RMSEA respectively for Model 1 and particularly the RMSEA value which fell within

the recommended range of acceptability (< 0.05 𝑡𝑜 0.08).


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However, a further review of the MIs of the structural models revealed cross-loadings of an

indicator variable onto a factor other than the one it was originally postulated to measure (MAS

← UHA3) associated with Model 5. In addition, the maximum MI flowing from management

accounting to supply chain performance (SCP ← MAS) was also detected with a value of

116.5 in Model 2. The interpretation is that if that parameter was to be freely estimated in a

subsequent model, a drop of at least this figure will occur in the overall 𝑋2 value. Turning to

the parameter change statistic associated with this parameter revealed a value of 0.572 which

represents the approximate value that would be assumed by the new parameter. specified as a

freely estimated parameter resulting in the final Model 2 with CFI = 0.964 and RMSEA =

0.057. The results of hypotheses tests among the three cases are presented next.

6.10 Selection Fit Model – Test of H1 (a – d), H2 (a – d), H3 (a – d), and H4 (a – d)

The objectives of hypotheses H1 (a – d), H2 (a – d), H3 (a – d) and H4 (a – d) is to test the fit

relationships (selection forces) between the SCM antecedent conditions and the information

characteristics of the MAS. Fig 7.3 shows the graphical view of the results for structural path

coefficients for the fit between the test variables. Each construct is treated as first-order latent

variable.

First, as summarized in Table 6.6, the results show a strong statistically significant positive and

direct association between the strategic supplier relations construct and the four dimensions of

MAS information. Consequently, the hypotheses tests, H1 (a), H1 (b), H1(c), and H1 (d) are

respectively supported at the 0.01, 0.05, and 0.1 significance levels. More precisely, the results

indicate that a strong statistically significant positive and direct relationship exists between

strategic supplier relations and broad scope MAS information (t =5.732, p-value =0.000 <

0.01), MAS information timeliness (t =6.003, p-value =0.07 < 0.1), MAS information


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integration (t = 5.273, p-value = 0.03 < 0.05) and MAS information aggregation (t = 7.153,

p-value = 0.02 < 0.05). Supply chain integration recorded statistically significant relationships

for accounting information scope and aggregation (𝑡 = 3.033, 𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0.03 < 0.05) and

(𝑡 = 4.233, 𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0.08 < 0.1) respectively. Hence H2 (a) and H2 (d) are supported.

However, the relationship between supply chain integration and accounting information

timeliness and integration recorded statistically insignificant relationships (𝑡 = 1.758, 𝑝 −

𝑣𝑎𝑙𝑢𝑒 = 0.179 > 0.05) and (𝑡 = 0.519, 𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0.129 > 0.05) respectively. This leads

no support for H2 (b) and H2 (c).

Turning to the relationship between level of information sharing and the four dimensions of

the MAS, none of the hypotheses H3 (a), H3 (b), H3 (c) and H3 (d) recorded a statistically

significant relationship with the MAS information characteristics and hence, were not

supported. In particular, the level of information sharing recorded a statistically insignificant

relationship between accounting information scope (𝑡 = 0.897, 𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0.370 > 0.05),

timeliness (𝑡 = 0.528, 𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0.597 > 0.05), integration (𝑡 = 1.048, 𝑝 − 𝑣𝑎𝑙𝑢𝑒 =

0.295 > 0.05) and aggregation (𝑡 = 1.147, 𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0.251 > 0.05). the relationship

between supply chain integration and accounting information scope, timeliness, integration and

aggregation were all statistically significant (𝑡 = 4.256, 𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0.000 < 0.01), (𝑡 =

6.597, 𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0.04 < 0.05), (𝑡 = 6.291, 𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0.000 < 0.01), and (𝑡 =

5.816, 𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0.02 < 0.05) respectively. Hence the hypotheses H4 (a), H4 (b), H4 (c)

and H4 (d) are supported.

On the whole, the results provide strong empirical support for the fit existing between strategic

supplier relationship and supply chain risk and uncertainty but partial support supply chain

integration and no support for knowledge exchange in Ghanaian hospitals. In other words, in

designing and implementing MAS information for SCM decisions involving healthcare


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institutions in Ghana, a match (or alignment) between the supplier relations and risk

dimensions of SCM and the four MAS information characteristics is crucial. Level of

information sharing was not significant with any of the four MAS dimensions. This could result

from the partially significant relationship with supply chain integration since proper integration

is evidenced by a high level of information sharing.

Table 6.6: Structural Path Coefficients and Hypotheses Tests for Fit Relationships

Fit Relationship t-values Hypotheses

Strategic supplier relationships → Accounting information scope 0.669(5.732)*** Supported

Strategic supplier relationships → Accounting information timeliness 0.816(6.003)**8 Supported

Strategic supplier relationships → Accounting information integration 0.605(5.273)** Supported

Strategic supplier relationships → Accounting information aggregation 0.748(7.153)*** Supported

Supply chain integration → Accounting information scope 0.287(3.033)** Supported

Supply chain integration → Accounting information timeliness 0.217(1.758) Not supported

Supply chain integration → Accounting information integration 0.154(0.519) Not supported

Supply chain integration → Accounting information aggregation 0.363(4.233)** Supported

Level of information sharing → Accounting information scope 0.108(0.897) Not supported

Level of information sharing → Accounting information timeliness 0.086(0.528) Not supported

Level of information sharing → Accounting information integration 0.139(1.048) Not supported

Level of information sharing → Accounting information aggregation 0.123(1.147) Not supported

Supply chain risk and uncertainty → Accounting information scope 0.416(4.256)** Supported

Supply chain risk and uncertainty → Accounting information timeliness 0.881(6.597)*** Supported

Supply chain risk and uncertainty → Accounting information integration 0.603(5.291)** Supported

Supply chain risk and uncertainty → Accounting information aggregation 0.520(5.816)*** Supported

(***), (**), (*) Significant at 1%, 5%, 10% significance levels (𝑝 − 𝑣𝑎𝑙𝑢𝑒 <

0.01, 0.05, 0.1) respectively. (Source: Author’s Computations, 2017)


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Table 6.7: Summary of Goodness-of-Fit Tests of Structural Model

Fit Indices

Test of Hypothesis 𝑋2 GFI NFI CFI RMSEA

Model 1 Selection Fit between SCI and MAS design 176.225 0.906 0.928 0.979 0.072

Model 2 Mediating effect of MAS on the relationship between SCI and operational performance

(control variables excluded)

472.451 0.891 0.873 0.917 0.072

Model 3 Mediating effect of MAS on the relationship between SCI and operational performance

(control variables included)

190.418 0.901 0.942 0.976 0.075

Model 4 Moderation effect of supplier relations (external integration), SCI (internal integration)

level of knowledge exchange, and supply chain risk and uncertainty on the relationship

between MAS information and supply chain performance (lower-order effects included)

766.544

0.853

0.847

0.905

0.077

Model 5 Moderation effect of supplier relations (external integration), SCI (internal integration),

level of knowledge exchange, and supply chain risk and uncertainty on the relationship

between MAS information and supply chain performance (lower-order excluded)

793.477

0.814

0.910

0.901

0.078

(Source: Author’s Computations, 2017)


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Fig 6.3: Structural Output for Selection Fit Relationships


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6.11 Mediating Effect of MAS Construct – Test of H5 (a) to H5 (d)

Hypotheses H 5(a), H 5(b), H 5(c), and H 5(d) test the mediating effect of the MAS construct

on the relationship between SCI and hospital supply chain performance. Two models were

tested: first without the confounding variables and second with the confounding variables. The

results are summarized in Table 6.8.

6.11.1. Confounding Variables Effect

Although the threshold for the goodness-of-fit statistics for the mediation effects as evidenced

in Models 2 and 3 in Table 6.5 were met, only the location and profit status were statistically

significant at the 5% significance level. A statistically significant test for ownership and size

was not supported by the sample data in Model 2. In other words, the operational performance

impacts of the four SCM context factors being mediated by the four MAS information

characteristics are affected by hospitals’ location and profit status but not hospital size and

ownership status in the Ghanaian context. The test for hospital size and ownership in Model 2

were statistically insignificant (t =1.163, p-value =0.245 > 0.05) and (t =1.113, p-value =

0.266 > 0.05) respectively. However, profit status and location in Model 1 were statistically

significant (t = 2.824, p-value = 0.034 < 0.05) and (t = 2.078, p-value = 0.022 < 0.05)

respectively for Model 3. The results suggest that location and profit status affect hospital

supply chain performance

6.11.2 Mediation Effect of the MAS Construct

To test for the mediation effect of the MAS construct between SCM and SCP, Baron and

Kenny’s (1986) classic four step procedure was followed. First, a test for the direct effect of

the strategic supplier partnership (external integration), supply chain integration (internal

integration), level of information sharing and supply chain risk and uncertainty on performance


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was performed. The results showed that only supply chain integration (i.e. hospital internal

integration) was statistically insignificant (t=1.792, p-value = 0.621 > 0.05); hence, H5 (b) is

not supported. All the three other SCM constructs recorded statistically significant effects on

performance at the 1% and 5% significance levels (t = 2.209, p-value = 0.027 > 0.05); (t =

4.271, p-value = 4.271 < 0.000 < 0.01) and (t = 2.017, p-value = 0.031 < 0.05) for supplier

relations (external integration), level of information sharing and supply chain risk and

uncertainty respectively, supporting H5 (a), H5 (c) and H5 (d). Second, the relationship

between the four SCM test variables and the four dimensions of the MAS information was

tested. Again, only the supply chain integration (hospital internal integration) construct

recorded a statistically insignificant effect (t = 1.149, p-value = 0.142 > 0.05). Supplier

relations (external integration) recorded a statistically significant effect at the 5% significance

level (t = 2.972, p-value = 0.024 < 0.05). A statistically significant effect on the MAS construct

was also recorded for level of information sharing at the 5% significance level (t = 3.734, p-

value = 0.036 < 0.05). Finally, supply chain risk and uncertainty recorded a statistically

significant effect on the MAS construct at the 1% significance level (t = 4.133, p-value = 0.000

> 0.01). Third, the relationship between the four dimensions of the MAS construct and supply

chain performance was tested. Here the MAS construct recorded a high statistically significant

effect on performance (t = 5.269, p-value = 0.000 < 0.01). A final test to confirm for the

existence of mediation was undertaken by testing again the relationship between the SCM

context factors and performance. This time round, only the level of information sharing

construct recorded a statistically significant effect on performance at the 5% level of

significance (t = 2.260, p-value = 0.029 < 0.05).. The remaining three SCM constructs recorded

statistically insignificant effects on performance (t = 1.854, p-value = 0.152 > 0.05), (t = 0.515,

p-value = 0.317 > 0.05) and (t = 0.351, p-value = 0.661) for supplier relations (external


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integration), supply chain integration (internal integration) and supply chain risk and

uncertainty respectively.

The results suggest that MAS information plays a significant role in the determination of

performance outcomes of SCM activities in Ghanaian hospitals. It shows that the management

accounting function is a critical element in the management of the health supply chain in

Ghana.

Table 6.8: Structural Path Coefficients for Mediation Analysis

Test of Direct Effects t-value Hypotheses

Supplier relations (external integration) → HSC performance 0.312(2.209)** Supported

SCI (internal integration) → HSC performance 0.082(1.792) Not supported

Level of information sharing → HSC performance 0.515(4.271)*** Supported

Supply chain risk & uncertainty → HSC performance 0.298 (2.017)** Supported

Test of Mediation Effect

Supplier relations (external integration) → HSC performance 0.500 (1.854) Not supported

SCI (internal integration) → HSC performance 0.086 (0.515) Not supported

Level of information sharing → HSC performance 0.662 (2.260)** Supported

Supply chain risk & uncertainty → HSC performance 0.069(0.351) Not supported

Supplier relations (external integration) → MAS information 0.482(2.972)** Supported

SCI (internal integration) → MAS information 0.019 (1.149) Not supported

Level of information sharing → MAS information 0.552 (3.734)** Supported

Supply chain risk & uncertainty → MAS information 0.486 (4.113)*** Supported

MAS information → HSC performance 0.714 (5.269)*** Supported

Profit status → HSC performance 0.159 (2.824)** Supported

Ownership status → HSC performance 0.028 (0.355) Not supported

Location → HSC performance 0.383 (2.078)** Supported

Number of Beds → HSC performance 0.001(0.030) Not supported

(***), (**), (*) Significant at the 0.01, 0.05 and 0.1 significance levels respectively


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Fig 6.4: Graphical Output of Mediation Analysis (Direct First Order Effects)


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To check the robustness of the mediation model, a second-order model was tested as shown in

Fig 6.1. The results showed that supplier relations (external integration) fits very well (loading

= 0.91), SCI (internal integration) also loads correctly (loading 0.99), level of knowledge

exchange (loading = 0.98) and supply risk and uncertainty (loading = 0.88) onto the second-

order SCM construct. However, although there was a positive direct relationship between each

of the lower-order (or first-orders) of SCM i.e., strategic supplier relations (external integration,

t = 6.423, p-value = 0.000 < 0.01), supply chain integration (internal integration, t = 4.287, p-

value = 0.014 < 0.05)), level of information sharing (t = 7.192, p = 0.000 < 0.01) and supply

chain risk and uncertainty (t = 5.483, p-value = 0.000 < 0.01), the relationship between SCM

and performance was statistically insignificant (t = 1.273, p-value = 0.336 > 0.05). The results

for the relationship between SCM and MAS information was statistically significant (t = 4.823,

p-value = 0.000 < 0.01) at the 1% level of significance, but the results between the second-

order SCM and performance were statistically insignificant as specified above. This suggests

that the constructs can load excellently unto their second-order SCM constructs but could not

combine to be statistically significant on the relationship between SCM and supply chain

performance. The results in this second-order model hold and suggest that the MAS design

serves as an intervening mechanism between inter-firm management and performance.


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Fig 6.5: Graphical Output for Mediation Effects (Second Order)


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6.13 Moderating Effects of the SCM Constructs – Hypotheses H 6(a) – H 6(d)

Hypotheses H 6(a), H 6(b), H 6(c), and H 6(d) test the contingent effect of supplier relations

(external integration), SCI (internal integration), level of information sharing, and supply chain

risk and uncertainty on the relationship between MAS information and performance. The

results are summarized in Table 6.9. As contingency theory suggests, the hypothesis in this

study tests the extent to which the design and implementation of MAS information

characteristics in the inter-firm exchanges decisions in supply chains are influenced by SCM

variables in the context of Ghanaian hospitals. As shown in Table 6.5, the fit statistics of Model

4 and Model 5 which analyses the moderating effects with the lower-order effects excluded

and included respectively, falls within the acceptable thresholds. The CFI = 0.905 and RMSEA

= 0.077 for Model 4 and CFI = 0.901 and RMSEA = 0.078 for Model 6. Two main models

were tested for interaction effects: 1) inclusion of lower-order effects, and 2) exclusion of

lower-order effects. The subsections that follow provide evidence of the moderating impact of

the SCM constructs providing contingency relationships between MAS information and

hospital supply chain performance.

6.13.2 Inclusion of Lower-Order Effects

One of the challenging and difficult aspects of SEM analysis is tests involving moderating

impacts in a relationships. Here, the test involves the product of the indicators that measure

each of the constructs to be interacted (Burkert et al, 2014). However, there is always a

redundancy of most of the product terms as the same variables are interacted over and over

again (Burkert et al, 2014). Following this arrangement, the items of each of the constructs to

be interacted were interacted but those that were redundant were eliminated. As the graphical

output shows in Fig 6.2, this test involved a second-order arrangement of the MAS information


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characteristics, SCM and the interaction of MAS information characteristics and SCM

constructs on supply chain performance. None of the control variables (i.e. hospital location,

size, ownership status and profit status) was significant. In particular, the results showed (t =

1.542, p-value = 0.123 > 0.05), (t= 0.023, p-value = 0.982 > 0.05), (t = 1.663, p-value = 0.096)

and (t = 0.531, p-value = 0.596 > 0.05) for hospital location, profit status, size and ownership

respectively.

Following prior studies (e.g. Flynn et al, 2010), this test was performed in a hierarchical order;

i.e. substitution of one interaction variable at a time. First, the moderating effect of supplier

relations (external integration) between MAS information and performance was tested. The

results showed the presence of moderating effect of the supplier relations (external integration)

construct between MAS information and performance. A statistically significant positive

relationship (t = 9.030, p-value = 0.000 < 0.01) was recorded at the 1% level of significance.

While the interaction of the supplier relations and MAS information construct recorded a

positive relationship, the lower-order effects (main effect) constructs (i.e. supplier relations and

MAS information) constructs also recorded a positive effect on performance. This shows that

there is an interaction since both are significant. Hence, H6 (a) was supported.

Second, the interaction of SCI (internal integration) and the MAS information construct was

tested. This also recorded a statistically significant positive relationship between MAS

information and supply chain performance with a reduction of the values recorded by the main

effects. Results for the interaction of SCI (internal integration) and MAS information at the 1%

significance level was (t = 17.022, p-value = 0.000 < 0.01). Thus, H6 (b) is supported. A third

test involved the substitution of the interaction between the level of information sharing

construct and the MAS information. Again the results showed a statistically significant positive

relationship (t = 13.505, p-value = 0.000 < 0.01) between the MAS information and supply

chain performance. As the moderating variables increases, the statistically significant effect of


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the main effect (lower-order effects) diminishes so there was a further reduction of the main

MAS and SCM variables. Hence, H6 (c) is supported.

Finally, the interaction between supply chain risk and uncertainty and the MAS construct was

included in the model and tested. The results showed a statistically positive relationship (t =

13.440, p-value = 0.000 < 0.01) between the MAS information and supply chain performance,

also at the 1% significance level. Thus, H6 (d) is supported. After substituting the fourth

interacting term, the statistical significance of the main effects got vanished. More precisely,

the relationship between the MAS information and supply chain performance was statistically

insignificant (t = 0.460, p = 0.646 > 0.05). Similar results were recorded for the relationship

between SCM and supply chain performance (t = 1.568, p = 0.117 > 0.05). Thus, the results

provide strong statistical support for the moderating effects of all the four SCM context factors

on hospital supply chain performance


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Table 6.9: Path Coefficients for Moderation Effects – Lower-Order Effects Included

Moderating Effect of SCM Construct t-value Hypothesis

Direct Effects

MAS → Supply chain performance .060(.460) Not supported

SCM → Supply chain performance 0.175 (1.568) Not supported

SCM X MAS → Supply chain performance 1.026 (5.508)*** Supported

Indirect Effects

Supplier relations X MAS information → HSC performance 5.541 (9.030)*** Supported

Supply chain integration X MAS information → HSC performance 10.598 (17.022)*** Supported

Knowledge exchange X MAS information → HSC performance 8.558 (13.505)*** Supported

Supply chain risk X MAS information → HSC performance 8.408(13.440)*** Supported

Control Variables

Hospital size → Supply chain performance −0.013(1.663) Not supported

Profit status → Supply chain performance 0.006(0.023) Not supported

Location → Supply chain performance 0.123(1.542) Not supported

Ownership → Supply chain performance 0.132(0.531) Not supported

Note: (***), (**), (*) Significant at 1%, 5% and 10% respectively


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Fig 6.6: Graphical Output for Moderation Effect – Lower Order Effects Included


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6.13.1 Exclusion of Lower-Order Effects

Considering the values of the various measures tested in this study, the results provide strong

support for the presence of moderating contextual variables in the association between hospital

supplier relations (external integration), hospital supply integration (internal integration),

information sharing (or knowledge exchange), hospital supply risk and uncertainty and various

aggregate dimensions of hospital supply chain performance. This suggests that contingency

theory is strongly supported in the inter-organizational relation domains in supply chains. That

is the relationship between MAS information characteristics and hospital supply chain

performance is contingent upon supplier relations (external integration), SCI (internal

integration), level of knowledge exchange, and supply chain risk and uncertainty.

Unlike the results obtained in the preceding section, the control variables for hospital size and

ownership status were not statistically significant. The other two control variables (location

and profit status) were found to be statistically significant at the 5% significance level when

the main effects were excluded from the model (t = 4.132, p = 0.023 < 0.05) and (t = 2.761,

p = 0.042 < 0.05) for profit status and location respectively. As shown in Table 5.9(a), the

hypotheses linking all the path coefficients are statistically significant at the 1%, 5% and 10%

significance levels respectively and hence supported. In particular, the moderating effect of

strategic supplier partnership (external integration) on the relationship between MAS design

and hospital supply chain performance is statistically significant at the 5% significance level (t

= 5.841, p-value = 0.022 < 0.05). This suggests that a high level of supplier relationship tends

to increase hospital supply chain performance and vice versa. Similar results hold for supply

chain integration at the 1% significance level (t = 14.217, p-value = 0.000 < 0.01), level of

information sharing (or knowledge exchange) at the 1% significance level (t = 6.768, p – value

= 0.000 < 0.01), and finally, supply chain risk and uncertainty at the 5% significance level (t =


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2.592, p – value = 0.034 < 0.05). The results for the knowledge exchange dimension suggest

that making supply chain information available to supply chain partners enhances supply chain

performance. Similar findings are assigned to supply chain risk and uncertainty. The

implication is that supply chain performance is more enhanced in an unfavourable environment

than in a favourable environment.

Table 6.10 (a): Path Coefficients for Moderation Effects – Lower-Order Effects

Excluded

Moderating Effect of SCM Construct t-value Hypothesis

Strategic supplier partnerships X MAS information → HSC performance 0.567(5.841)** Supported

Hospital supply chain integration X MAS information → HSC performance 0.849 (14.217)*** Supported

Information sharing level X MAS information →HSC performance 0.433(6.768)*** Supported

Supply chain risk and uncertainty X MAS design → HSC performance 0.112(2.592)** Supported

Control Variables

Hospital size → HSC performance −0.113 (0.969) Not supported

Profit status → HSC performance 0.136 (4.132)** Supported

Location → HSC performance 0.142 (2.761)** Supported

Ownership → HSC performance 0.059(1.271) Not supported


Note (***), (**), (*) Significant at 1%, 5% and 10% respectively


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Fig 6.7. Graphical Output for Moderation Effect of SCM Variable without main Effects


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6.14 Chapter Summary

In this chapter, the results from the survey have been presented. Whilst a detailed discussion

and interpretation of the findings are presented in the next chapter, the overall results suggest

that a direct and positive relationship exists between supply chain context factors and

management accounting system design in the SCM of Ghana’s healthcare context, and that the

management accounting function fully mediates the relationship between supply chain

management and performance. For effective and efficient management of the health supply

chain, a consideration of the fit between the four MAS information characteristics (broad scope,

timeliness, integration, and aggregation) and SCM contextual antecedents namely, strategic

supplier partnerships, supply chain integration, levels of information (or knowledge) sharing,

and the risk and uncertainty dimensions (or environmental uncertainty) are highly critical.

It has also been established from the results that the performance impacts of the management

accounting function in the health supply chain is moderated by the four supply chain

management context factors. In this regard, the performance impacts of the management

accounting function are expected to be high for hospitals that maintain a high level of supplier

relations and knowledge dissemination among members in the supply chain but experience

shortfalls in performance for low levels of supplier relationships and information availability.

With regards to risk and uncertainty in the supply chain, high performance was found to be

associated with unfavourable environments and hence hospitals in unfavourable environments

are likely to have high levels of supply chain integration compared to those in favourable

environments.


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CHAPTER SEVEN

DISCUSSION AND INTERPRETATION OF FINDINGS

7.1 Introduction

This study provides preliminary empirical evidence on the contingency fit relationships

between SCI (external and internal integration), level of information sharing, supply chain risk

and uncertainty and four dimensions of MAS information that can leverage various dimensions

of hospital supply chain performance. The study uses empirical data from healthcare

institutions in Ghana. In the preceding chapter, the survey analysis and results relating to the

study variables were presented. In this chapter, the interpretation and discussion of the findings

associated with the survey results are presented. The chapter is made up of three sections. The

first section discusses and interprets the findings associated with the contingency fit

relationships between the constructs. The second section discusses the effect of the MAS

construct as a mediating variable. The final section discusses the interacting effect of the SCM

contextual antecedents and the MAS information characteristics on hospital SC performance.

All discussions and interpretations are expressed within contingency’s theory applications in

organizational design. The chapter ends with a summary.

7.2 Recap of Research Objectives, Questions and Hypotheses

As stated in chapter one, this study design seeks to achieve three main objectives; that is to:

1. Investigate the ‘selection fit’ relationships between SCM contextual antecedents and

MAS information characteristics among hospitals in Ghana.

2. Examine the mediation effect of the MAS construct to leverage hospital supply chain



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3. Examine the performance implications of the moderation effect of the SCM contextual

antecedents on the relationship between MAS design and HSC performance.

7.2.1. Research Questions

The three research questions that underpin the study are as follows.

1) Do the MASs designed and used by healthcare institutions in Ghana ‘fit’ (or align with)

the SCM contextual antecedents that characterize Ghana’s health supply chain?

2) To what extent do the MAS information used in Ghana’s healthcare management

systems offer a mediating role between the antecedent conditions and performance

effects of the hospital supply chain?

3) To what extent do the health SCM context factors offer a contingency effect on the

relationship between the MAS and hospital supply chain performance

7.3 Fit between SCM and MAS Information Characteristics

This section discusses and interprets the findings associated with the fit between the contextual

factors of SCM and MAS information characteristics. The study finds a direct and strong

positive association between the supplier integration and each of the four dimensions of the

MAS information. The findings which support hypotheses H1(a), H1(b), H1(c), and H1(d)

suggest that the information characteristics (dimensions) of the MAS (i.e. scope, timeliness,

aggregation, and integration) are useful and highly relevant to supplier relation decisions in

healthcare context. In other words, strategic supplier relations (external integration) strongly

influence the design and implementation of MAS information in hospital SCM decisions. The

findings support that of Ou, Liu, Hung, and Yen (2010) who examined the interactions among

SCM practices and their effect on firm financial performance. Specifically, they reported

findings of the relationship between SCM contextual factors such as external supplier,


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customer integration and various dimensions of supply chain performance. They found external

supplier relations to positively impact the internal contextual factors of a firm which in turn

impact positively on firm financial performance. In the following four subsections, a discussion

of the findings related to each of the four SCM dimensions is given.

7.3.1 Strategic Supplier Partnerships (External Integration) and MAS Information

With respect to the fit between strategic supplier partnerships (external integration) and MAS

information, the results show a perfect alignment between the test variables. This suggests that

supplier relations influence MAS design in Ghanaian hospitals. MAS design in healthcare

should consider aligning the characteristics of supplier relationships with the MAS information

characteristics for optimal performance. This is because an effective supply chain strategy

aligns an organization’s performance priorities and objectives and those of its suppliers

(Selviaridis & Spring, 2018). This finding is consistent with that of Harlez and Malagueno

(2016) who documented that the benefits of the alignment between strategic priority choices

of partnerships in managing a hospital’s boundary-spanning activities and the application of

performance management systems (i.e. MAS) in hospitals are positively related. They further

established that a higher emphasis on partnerships or governance strategic priorities leads to

greater positive effect on the interactive use of performance management systems on hospitals.

It is less associated with managers with administrative background than top-level managers

with clinical background.

In another dimension, Guimaaraes and de Carvalho (2013) who examined the notion of

strategic outsourcing in the context of lean thinking inside and beyond healthcare

organizations’ boundaries in extended supply chains found a perfect alignment (or fit) between

SCM notions in healthcare and the application of lean tools. Although not the same SCM


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features as in the current study, the finding on supplier strategic relations obtained in the current

study supports and falls in line with that of Guimaaraes and de Carvalho (2013).

The findings also complement the findings of the intra-strategic fit studies conducted by Pizzini

(2006) and Macinati & Anessi-Pessina (2014) in US Private and Italian National hospitals

respectively. Both studies found a direct positive association between strategy and MAS

design. Pizzini (2006) found that cost containment strategies encourage more sophisticated

MAS design while Macinati and Anessi-Pessina (2014) found organizational strategy to

directly influence MAS design. Bouwens and Abernethy (2000), Abernethy and Lillis (2001)

investigated the relationship between strategy and MAS design in hospitals where the MAS

was operationalized by the four dimensions: scope, timeliness, aggregation and integration and

found that the four dimensions of the MAS information are highly suitable for hospital

operations.

Relating the current findings to these prior literatures suggests that the successful design and

implementation of MAS information in hospital SCM systems is affected by the MAS-strategy

fit relationships or the alignment between the MAS information and organizational strategy,

and that organizational strategies (whether internal or external) are critical to the design of

MASs in hospital supply chains. The findings also suggest that the contingency-structure

combinations on strategy can be extended from the intra-organizational domain to the inter-

organizational partnership domain in healthcare setting. It shows that any misfit (or

misalignment) between the MAS information and strategic choices of supply partners within

the SCM context could result in lower outcomes normally some aspects of performance in

terms of inefficient allocation of resources, delay or extended delivery, interruptions or supply

shortages, high costs, etc. For example, the findings are in perfect line with those of Bedford,

Malmi and Sandelin (2016) who found that accounting control (i.e. MAS) and structural control

choices’ effectiveness are determined not only by their fit with strategic context but also by the


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extent to which they fit with each other. In general, the findings extend previous works by

providing empirical evidence on the use of MAS information in the hospitals and strategic

choices within the hospital SCM context.

7.3.2 Supply Chain Integration (Internal Integration) and MAS Information

The study finds a statistically significant strong positive support for hospital SCI (internal

integration) with accounting information scope and aggregation but finds no statistical

relationship with timeliness and integration of the accounting information. This finding

suggests that hospital internal integration partially fits the MAS information characteristics. In

particular, there is a misalignment between hospital SCI (internal integration) and MAS design.

There are weak collaborations of procurement, warehousing and distribution of medical

products as well as weak cross-functioning and collective responsibility across functions. This

finding is consistent with the findings of Asamoah et al (2011) who found SCI (internal

integration) as a key challenge within the pharmaceutical supply chain in Ghana. Internal

integration integrates supplier relations, information sharing and risks; hence, an important

dimension of the SCM construct.

This finding is highly inconsistent with existing SCM studies. For example, Ataseven, and Nair

(2017) found in their meta-analytic study, a perfect match between the three dimensions of

integration and various dimensions (cost, speed, flexibility and quality) of supply chain

performance. Although not in line with prior literature, internal integration is critical to the

efficient functioning of the hospital supply chain. Supply chain managers in Ghanaian hospitals

can minimize costs, improve speed of delivery, enhance quality assurance and operate a much

more flexible supply chain for well-integrated SCM components. The findings also did not

support that of Chen et al (2013) who found a positive association between supply chain

integration and various facets of supply chain performance measures. The findings also fall


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outside the findings of Behesti et al (2014) who investigated the financial performance impacts

of supply chain integration on Swedish manufacturing firms and found that a strong and

positive relationship exists between supply chain integration and firm financial performance.

They established that any level of supply chain integration in an organization results in

performance enhancements and is highly beneficial to the financial well-being of the firm.

They reported further that the highest level of financial performance was reported by

companies with total supply chain integration. Hospitals in Ghana must consider aligning their

MASs with internal integration for optimal performance.

7.3.3 Level of Information Sharing and MAS Information

Like internal integration, the results for the level of information sharing show no statistically

significant effect on any of the four dimensions (scope, timeliness, integration and aggregation)

of the MAS design. This is true because information sharing is highly associated with internal

integration. The results suggest that hospitals is Ghana might either be using too many MASs

(e.g. too many performance measurement indicators or systems) or too few MASs (e.g. too few

measurement diversity); and it supports contingency theory that either excessive or insufficient

use of the MAS relative to the contextual factor will result in misalignment and negatively

affect performance. That is either the sharing of SC information is excessive or insufficient

among healthcare institutions in Ghana. This result is again inconsistent with prior studies

although decisions on information sharing strongly align with the MAS information

characteristics to positively affect performance. For example, the findings of Mahama (2006)

showed that a direct and positive association between the MAS information represented by

performance measurement systems and information sharing exists in a study that investigated

the relationship between two MAS information and cooperation in strategic supply

relationships. He also found information sharing to be indirectly associated with performance

measurement systems. Information sharing is a critical aspect of hospital SCM because of the


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complexity and sophisticated nature of clinical operations. Hospitals have weak information

linkages possibly due to lack of IT integration. However, whilst this finding does not support

most existing literature, it is in perfect alignment with Asamoah, Abor and Opare (2011) who

find weak SCI and information sharing to be the main threat of the pharmaceutical SC although

they measured integration as one construct suggesting that both external and internal

integration are weak in Ghana’s healthcare management. The reason for the findings not

supporting most existing works could possibly be due to the fact that those studies were based

on samples drawn from the developed countries where they have well-structured SCM

environment. Hospitals in Ghana need to put in place mechanisms that will enhance

information sharing to leverage performance.

7.3.4 Supply Chain Risk and Uncertainty and MAS Information

The results show a statistically positive relationship with all the four dimensions (scope,

timeliness, integration and aggregation) of MAS. The risk and uncertainty dimensions of the

supply chain has mostly been examined in management accounting literature in the context of

trust existing between supply chain partners (e.g. Langfield-Smith, 2008; Tsamenyi, Qureshi

& Yazdifar, 2013; Dekker et al, 2013). Hence, the terms risk, trust and environmental

uncertainty are used interchangeably. This makes the findings on the fit between MAS

information and supply chain risk fall in line with a number of management accounting –

supply chain risk studies. Dekker et al (2013) investigated the extent to which firms employ

MAS information to manage risks associated with supply chain partners engaged in intensified

collaborations. More specifically, they examined the extent of the MAS information as well as

multiple interrelated SCM control practices impact (through the selection of supplier partner

in the context of perceived goodwill and competent trust), on inter-firm transactions risk.


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The study finds a statistically significant positive association between the very basic

fundamental risks associated with transaction characteristics and collaborated trusted suppliers

and the application of management control systems (MCS) to manage relationships. The

findings also add to the literature of Grotsch, Blome, and Schleper, (2013). They examined the

antecedents that foster proactive supply chain risk management implementation from a

contingency theory perspective and found a positive association between a mechanistic MAS

and proactive supply chain risk management implementation suggesting a alignment between

the MAS information characteristic and the antecedent condition of SCM risk.

7.4 Interpretation of the MAS Mediation Effect

In this section, the mediating effect of the MAS on hospital SC performance is presented. First,

the findings relating to the performance effects of the confounding variables (size, location,

profit status, and ownership) on hospital SC performance are discussed.

7.4.1 Confounding Variables and Hospital Supply Chain Performance

As the results indicated, the geographical location and profit status of the sample hospitals were

found to be statistically significant and hence influence hospital supply chain performance. The

other two variables (ownership and size) were found not to be statistically significant both

under the mediating and non-mediating conditions of the MAS construct. The findings are

consistent with that of Chen et al (2013) who controlled for several confounding variables in

their structural model including hospital size (measured by a variety of proxies: number of

beds, employees, discharges, patients, and patient revenue), purchasing department size,

location (urban/rural), and profit status for a sample of 117 US hospitals.

Only the location and profit status of the 117 hospitals sampled were found to be statistically

significant. The findings also support the findings of Macinati and Anessi-Pessina (2014) who


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controlled for the effect of hospital size on hospital financial performance in their model but

found the results to be statistically insignificant. Based on the current findings and those of

prior literature, it can be concluded that location and profit status play critical roles in hospital

supply chain performance. In SCM, the siting or location of a facility is a strategic decision

which impacts significantly on its supply chain activities and hence performance. In hospital

settings, location may positively or negatively affect the flow of customers (patients) to the

hospital as well as distribution of medical products.

One of the key variables of performance measurement is profit hence, organizations with profit

status which comprised the private sector are likely to outperform non-profit organizations

which comprised mainly the public sector. This is because government hospitals are labelled

as non-profit oriented although they generate revenue. This could be one of the reasons for the

high costs associated with the procurement, warehousing, and distribution activities of the

health supply chain resulting in high costs of health products at the pharmacy level for the end

user which is under the oversight responsibility of the MOH. The notion of non-profit status

does not allow for cost-containment strategies to be adopted by the MASs employed in these

organizations compared to private hospitals.

7.4.2 Mediating Effect of MAS on Performance

Although SCI (internal and external integration) was statistically insignificant, the MAS

recorded a partially mediating effect on performance. The findings suggest that the

characteristics of the MAS information is useful for hospital SCM decisions. Consistent with

Abernethy and Lillis (2000), Pizzini (2006), Hammad et al (2013), and Macinati and Anessi-

Pessina (2014) who found a statistically positive association between hospital organizational

context factors and MAS information scope, timeliness, integration and aggregation, the results

suggest that the four characteristics of MAS information are relevant to hospital SCM. Pizzini


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(2006) documents a positive association between perceived usefulness of cost data and the

dimensions of MAS information in a survey of 277 US hospitals. These dimensions are

operationalized by the extent to which the system can provide greater cost detail, classification

of costs according to behavior, and the frequency in which cost information are reported. This

operationalization exactly falls in line with Chenhall and Morris’s (1986) conceptualization of

MAS information characteristics of scope, timeliness, aggregation and integration. As in past

studies, the findings contribute to further development of integrative contingency framework

that explains the dimensions of MAS investment in the SCM field.

7.4.3 Joint Effect of MAS and SCM on Hospital SC Performance

The findings related to the joint effects of the MAS information dimensions and the SCI, level

of information sharing, and supply chain risk and uncertainty on hospital SC performance are

now considered. The empirical results highlight the value of the SC context factors and the

dimensions of MAS information as severely underpinning hospital external integration,

internal integration, information sharing and uncertainty to leverage hospital supply chain

performance. The results suggest that the design and use of MAS information is central to

achieving greater hospital SC performance in terms of costs, flexibility and speed, quality and

asset utilization.

The results show some consistencies with contingency theory since the findings indicate that

hospital-supplier partnerships, level of information sharing and risk and uncertainty have direct

and positive influence on MAS design. The findings reveal several sets of interesting mediating

relationships from strategic supplier partnerships, level of information sharing, and supply

chain risk and uncertainty to hospital SC performance through MAS information. In all these

mediating relationships, the study finds MAS design to partially mediate the influence of

hospital SCM on hospital SC performance. These findings are consistent with the findings of

Chen et al (2013) who showed that a direct and positive relationship exists between hospital-


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supplier knowledge exchange and the level of hospital-supplier integration from their sample

of 117 US hospitals. In addition, they observed interesting mediating relationships flowing

from knowledge exchange to hospital SC performance through hospital-supplier integration.

In the further analysis, the results indicated positive indirect effects flowing from each of the

supplier relations, internal integration, information sharing and risk and uncertainty through

the second-order SCM construct to hospital SC performance. The study finds the strategic

supplier partnership variable to be statistically significant and positively associated with SC

performance. This finding provides important insights of the supplier partnership as a strategic

factor in enhancing hospital SC performance in terms of reduced cost, flexibility, and quality

of health products. Lee, Lee, and Schniederjans (2011) in their analysis of the impact of SC

innovations on hospital performance among Korean hospitals showed a direct positive

association between supplier partnerships and organizational performance. They show

empirically that supplier cooperation (or strategic supplier partnerships) is an important

element that underpins the innovations related to better value to customers (patients) through

reduced costs and improved product quality and services along the SC. They show further that

a statistically significant relationship exists between hospital supplier cooperation, SC

innovation, and hospital performance in terms of waste reduction, quality, and cost

minimization.

7.4.4 Mediating Role of the MAS Information Dimensions

The study finds the MAS information characteristics to partially mediate the relationship

between supplier relations (external integration), information sharing, risk and uncertainty and

hospital SC performance but an insignificant relationship between internal integration. This

suggests that the results provide some support for an indirect effect of the three SCM constructs

on hospital supply chain performance, acting through the use of optimal amounts of MAS


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information for decision making. These findings strongly align with the findings of Hammad

et al (2013) who found the four dimensions (scope, timeliness, integration, and aggregation) of

the MAS information to significantly play an intervening role in the relationship between

decentralization and perceived environmental uncertainty on managerial performance in the

context of Egyptian hospitals.

Their study design which was a contingency-based intervening model showed that the type of

MAS information designed and used by hospitals in Egypt is strongly influenced by the

environment in which they operate. In addition, the findings strongly fall in line with that of

Soobaroyen and Poorundersing (2008) who, by investigating the availability and effectiveness

of MAS information for functional managers in manufacturing firms in Mauritius, found the

available MAS to play a significant intervening role in the relationship between task

uncertainty and decentralization on managerial performance. Like the current study, their study

design was also based on the four dimensions (scope, timeliness, integration, aggregation) of

the MAS developed by Chenhall and Morris (1986) and used a contingency-based intervening

model to examine the intervening role of the MAS information characteristics between task

uncertainty and decentralization on managerial performance.

The findings in a way also complement the findings of Macinati and Anessi-Pessina (2014)

who used covariance based structures to investigate the extent to which contextual variables

influence the design and use of MAS, and the financial performance effects of their

relationships in the Italian National Health Service. They found both MAS design and its usage

to be respectively, directly and indirectly influenced by organizational, contingency, and

behavioral variables. In addition, they found the use of MAS in pursuance of cost containment

strategies in the Italian hospitals to be encouraged by the mediating role of MAS design. They

however, found the existence of a statistically significant weak positive relationship between

MAS use and financial performance.


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Linking the current findings to those findings of prior studies suggests that both the design and

use of MASs mediates the performance effects of contextual variables in healthcare setting. It

can be theorized in health inter-organizational relations in supply chains that effective design

and implementation of MASs in healthcare management enhance SCM variables’ impact on

supply chain performance. In this regard, the technical advancement of MAS design as well as

thorough understanding of its usage by users in healthcare inter-organizational management is

critical if the performance effects of the MAS designed and used in such institutions are to be

realized. An appreciation of the qualities and full exploitation of the potentials of the MASs

designed and used in healthcare institutions by managers is critical. Turning to the study by

Gerdin (2005a), the findings confirm that there is an indirect association between inter-

dependence and subunit performance through a greater amount of MAS information use for

decision making using a survey of 132 production managers.

7.5. Contingency Effect of SCM on Performance

Positioning external integration, internal integration, information sharing and risk and

uncertainty as contingency variables, the sample data confirmed the moderating effects on

hospital SC performance, hence supporting contingency theory. The study also offers empirical

evidence for statistically significant positive associations of aggregate performance with each

of the four SCM context factors. The results show the SCM dimensions and the four

dimensions of hospital SC performance having positive associations, hence supporting H6a,

H6b, H6c, and H6d. This result matches that of Ataseven and Nair (2017) who found a

statistically significant positive association between supply chain aggregate integration and

aggregate performance, where aggregate integration relates to the three facets of integration


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(supplier integration, internal integration, and customer integration) and aggregate

performance relates to cost, flexibility, speed, and quality of the supply chain.

The findings as obtained in the current study are based on tests conducted in two separate

scenarios. In the first scenario, all main effect variables were included in the model and tested.

The results showed a statistically significant moderating effect for the cross-product term but

showed a statistically insignificant effect of the main effects signifying that the SCM context

factors strongly interact with the dimensions of the MAS information to affect hospital SC

performance. In the second scenario, only the cross-product terms were tested. The results

showed a statistically significant moderation effect on the relationship between SCM context

factors and hospital SC performance. The results support the findings of Grotsch et al (2013)

who found that the relationship between mechanistic MAS information and SC proactiveness

is moderated by SC risk management implementation. Wong et al (2011) also found perceived

environmental uncertainty to moderate the relationship between SCI (external and internal

integration) and performance. The findings support several prior literatures on the moderating

role of SCM on performance hence supporting contingency theory (Flynn et al, 2010; Wong et

al, 2011; Chen et al, 2013; Ataseven & Nail, 2017; Qi et al, 2017).

7.6 Implications of Findings

Beginning with contingency theory’s claim that the product of the relevant contextual

dimensions determines effective managerial performance this study examined MAS

information characteristics in relation to the contingency factors of hospital supply chain

management. The findings suggest several theoretical and practical as well as policy

implications for researchers, practitioners, hospital managers (or administrators) and policy-

makers. This section discusses the implications of the findings and is classified under three


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subsections comprising the theoretical, practical and policy implications with respect to

researchers, practitioners (hospital managers/administrators) and policymakers.

7.6.1 Theoretical Implication

Judging from the combined findings from the SCM dimensions on the four dimensions of

performance, it can be theorized that a perfect alignment of the hospital SC with the MAS

information is more likely to provide a broad range of performance benefits for hospitals in

Ghana. That is a high level of SCI (external and internal integration), information sharing, and

high knowledge about risk and uncertainty among healthcare institutions in Ghana is likely to

reduce supply chain cost, improve speed, flexibility, and quality of the supply chain as well as

result in the efficient usage of hospital assets. This theorization holds as these dimensions of

the SC encapsulates all the other SCM contextual dimensions.

Also, compared to the other contextual dimensions, supply chain integration is

multidimensional consisting of supplier integration, customer (patient) integration and internal

integration. Further division of each of the three dimensions of integration shows that

information sharing between suppliers and customers play a significant role in enhancing

supply chain performance. Also, the findings have important implications for management

accounting research. The findings of this study suggest the observance of carefulness and

special attention in the development and testing of contingency hypotheses by researchers.

7.6.2 Practical Implications

Like the case of researchers, the results offer important practical implications for practitioners

who are engage in MAS design in healthcare institutions. From an inter-organizational

perspective, the four dimensions of the MAS information were found to strongly align with (or

fit) the contextual dimensions of hospital SCM. The findings suggest the essence for

practitioners and designers of MAS information in Ghana’s healthcare institutions to focus on


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the four attributes of scope, timeliness, integration and aggregation of the MAS information.

Based on the intervening mechanism role exhibited by the dimensions of the MAS information,

the results indicate that effective implementation of SCM decisions requires the use of optimal

amounts of MAS information.

Designers of MAS information in hospitals should ensure that optimal amounts of MAS

information are not only available to SC managers and practitioners, but also accessible to

managers in the SCM department for performance enhancements. The findings highlight the

importance of hospital SC managers maintaining strong ties with key suppliers to ensure

proactive SCM. This is true for relational governance which fosters close information (or

knowledge) exchange as well as building trust. Information sharing has been found to be a key

enabler of proactive SCM. In addition, and from an inter-organizational viewpoint, this study

finds a statistically significant connection between the existence of supply chain risk and

uncertainty and MAS information characteristics.

These results demonstrate that SCM departments in hospitals will be eager to get rid of a

supplier becoming insolvent once a full commitment has been made to that supplier. There is

also the risk of losing specific investments in supplier – relations and the danger of losing

important capabilities and subsequent competitive advantage by switching suppliers. This

reinforces the importance of pursuing proactive supply chain risk management practices by

SCM departments. Careful supplier selection, and risk-reward sharing contracts by hospitals

are some of the proactive measures that can be instituted to mitigate supply chain risks. On the

whole, the findings suggest the need for hospital managers to target appropriate dimension(s)

of hospital SC performance that suits the competitive priority. The results suggest further that

by focusing on the relevant SCM contextual dimensions, the identified dimensions of the

hospital SC performance can be improved via the SCM dimensions with which they are

associated with so that the resources of the organization can be effectively utilized.


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7.6.3 Policy Implications

Although the study finds a fit (or match) between the contextual dimensions of SCM and the

four dimensions of the MAS information, these relationships are based on the selection,

moderation and mediation models of fit which in comparison to the other contingency models

of fit (matching fit model), are theoretically incompatible (Luft & Shields, 2003; Chenhall,

2003; Gerdin & Greve, 2004; Hartmann, 2005; Burkert et al, 2014). What this suggests to mean

is that the design and use of MAS information is predicted and explained in response to the

MAS context where causal relations between contextual variables of SCM and MAS

information characteristics are sought.

The implication is that the design and use of particular MAS information characteristics is more

effective if accomplished by the so-called selection forces that characterized the selection fit

concept. In other words, the causality of the SCM context variables – MAS information

characteristic relationship is theoretically argued to root in an optimization process (i.e.

selection forces) that results in MAS – context ‘fit’. Rather than imposing a particular MAS

design and using it in the healthcare sector particularly those in the public sector, policymakers

should allow practitioners in these settings to both theoretically and empirically select their

MAS design or use to ‘fit’ their level of (or congruence with) supply chain complexities and

sophistication. In other words, hospitals should be allowed to adapt their MAS to the full

demands of their contexts.

7.7 Chapter Summary

In this chapter, the discussions and interpretations associated with the survey analysis have

been presented. The discussions show that the sample data supports three models of

contingency fit – the selection, mediation and moderation forms of fit. It highlights the


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consistencies and alignments or complementarities of the study’s findings with those of prior

literature, thereby adding to the body of knowledge and understanding of contingency-based

management accounting studies. It also demonstrates the extent to which the contingency

paradigm fits in the external domains of the Ghanaian healthcare industry as a significant

contribution to literature. The chapter also highlighted the theoretical, practical, and policy

implications of the findings as they relate to management accounting researchers, practitioners

and designers of MAS information in healthcare institutions and policy-makers respectively. It

concludes that both researchers and practitioners as well as policymakers have specific roles to

play in testing of contingency hypotheses, designing of MAS information and the selection of

MAS information dimensions respectively in the healthcare context. In the chapter that follows,

a summary of key findings, conclusions and contributions of the thesis are presented.


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CHAPTER EIGHT

SUMMARY, CONCLUSIONS AND CONTRIBUTIONS

8.1 Introduction

Following the survey results presented in chapter seven and subsequent discussion and

interpretation of the findings in the preceding chapter, the summary, conclusions and

contributions arising from the findings are presented in this last chapter of the thesis. The

chapter presents the summary of the key findings, conclusions drawn from the findings, and

the contributions arising from the study. Like any empirical work, this research design is not

without limitations. Subsequently, the limitations and weaknesses associated with the study are

presented next whilst avenues for further studies or future research directions draw the curtains

on this chapter.

8.2 Summary of the Study

In this section, the summary of the thesis in terms of the research framework (or main thrust)

and the key findings is presented. It summarizes the thesis in terms of the research problem,

underlying motivation through methodology and key findings.

8.2.1 The Study’s Main Thrust

The SC is being increasingly recognized as a key driver that contributes to overall operational

and organizational performance of both profit and non-profit-oriented organizations (Maestrini

et al, 2017; Jin, Jeong & & Kim, 2017). This wide recognition is based on the significant impact

it has on a company’s financial cycle; hence, interacts with accounting information to leverage

performance. SC performance has become one of the most important and critical areas for the

CEOs and executive leaderships of hospitals (Barlow, 2010c) since improvement upon


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efficiency and cost reduction strategies has been a challenging issue in hospital management

(Abernethy & Brownell, 1999; Abernethy & Lillis, 2001; Pizzini, 2006; Abernethy et al, 2007).

Hence, improvements in hospital SC performance has increasingly gained importance as

healthcare organizations strive to improve operational efficiency and to reduce costs (Chen et

al, 2013). In this regard, the design of accounting information systems to enhance SC

performance has become a contemporary issue in management accounting research since the

MAS information not only reduces SC cost, but also creates value to maximize shareholder

wealth and economic sustainability. Several calls have been made by management accounting

scholars to: 1) develop new forms of the MAS information that extends beyond organizational

boundaries in supply chains, 2) depart from a manufacturing corporate focus to the notion of

SC as an entity and, 3) establish relevance within SCM and networking (Burritt & Schaltegger,

2014; Dekker, 2016; Otley, 2016).

Given that there has been large amounts of research on the relationship between MAS

information characteristics and SCM attributes which were predominantly informed by the

TEC, these studies failed to link the MAS information characteristics to SC performance

although the link between SCI and SC performance has been explored extensively in the

literature. There is thus, little systematic evidence on studies that examine the performance

impacts of MAS design on SC performance. This thesis fills this gap using contingency theory

since the TEC emphasizes fit in the transactional context (or external), and neglects the

dimensions of internal fit (e.g. internal integration) of the supply chain which relates to cost of

inventory management and warehousing, product quality and production cost. Contingency-

based research underpins the design and implementation of MAS information effectively in

organizations, and the contingency implications for organizational performance. In addition,

whereas prior works researched the organizational contextual variables that influence the MAS

information characteristics to leverage performance, they focused exclusively on the intra-


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organizational domains of the contingency application in MAS design. Consequently, studies

that examine the contingency-MAS application in the inter-firm exchanges domain in the

supply chain remain largely underdeveloped. Also, research on the MAS-SC relationship has

received much attention in mainstream discrete parts manufacturing and consumer goods

entities with little attention given to service-oriented organizations where more sophisticated

MAS information is required for managerial decisions.

The sophisticated level (or nature) of the MAS information has also been noted to be relatively

more associated with (or pronounced) in healthcare institutions due to the complex, multi-

functional, and information-intensive environments that characterize their operations, and the

significant proportion of operation costs that are absorbed by supply and logistic-related

expenditures (Schneller & Smeltzer, 2006). However, whereas cost-containment strategies

even encourage more sophisticated MAS design and use in the healthcare context, there is little

systematic evidence on its use and benefits (Macinati & Anessi-Pessina, 2014). To this end,

prior research in management accounting have called for research that provides more

understanding and insights into MAS design and use in healthcare institutions (Bai et al, 2010;

King & Clarkson, 2015). To contribute to bridging these gaps, this study empirically

investigated the contingency fit relationships between MAS information characteristics and

SCI (external and internal integration), level of information sharing, and supply chain risk and

uncertainty that can leverage various operational dimensions of hospital supply chain


Ghana was selected as the research site because its healthcare SCM system faces challenges of

misfit (or misalignment) hence, experiencing negative performance impacts of its supply chain.

The system has already undergone significant managerial reforms between 2008 and 2012 with

the key objective of improving overall performance of the supply chain in the area of

procurement, warehousing, transportation, inventory management, and distribution of health


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products (MOH, 2012). However, the identified gaps as inefficient, ineffective and weaknesses

remain unaddressed and have widened rather than have been bridged. The system currently

reveals ineffective, sub-optimal and significant non-value-added steps in the procurement,

warehousing and distribution of medical products which ultimately results in higher delivery

cost for the end user (Asamoah et al, 2011; Denkyira, 2015). These weaknesses and

inefficiencies have serious implications for the design and use of MAS information. In this

regard, the study provided empirical evidence on the extent to which the design and use of

MAS information relate to hospital SCM contextual conditions and the resulting implications

for hospital managers and health policymakers in Ghana.

Hence the study had three objectives as follows:

1) Provide empirical evidence on the extent to which the design and use of MAS

information ‘fit’ (or aligned) with the boundary-spanning management decisions of

health inter-organizational relations in supply chains to leverage hospital SC


2) Examine the mediation effect of the MAS construct to leverage hospital SC operational


3) Examine the performance implications of the moderation effect of SCI (external and

internal integration), level of information sharing, and supply chain risk and uncertainty

on the relationship between MAS design and hospital SC operational performance.

The study which comprised both theoretical and empirical sections was structured into eight

chapters. Chapter one introduced the study with an outline of the research background and

associated problem identification which has contributed to the study’s underlying motivation.

To this end, the research objectives and questions which were answered through hypotheses

tests were derived. Chapter one also gave a brief definition and discussion of the variables


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underpinning the study which comprised the fit relationships between three intersecting

constructs: management accounting systems information, SCM represented by SCI (external

and internal integration), level of information sharing, SC risk and uncertainty, and hospital

supply chain performance. Analysis of the relationship among these variables resulted in the

identification of a number of gaps which motivated this study. Finally, the relevance of the

study and an outline of the logical sequence of the remaining chapters were clearly spelt out.

Chapters two and three were devoted to systematic literature reviews. In chapter two, the

study’s context as well as concept of the health SC in comparison to that of the mainstream

discrete parts manufacturing and consumer goods entities were discussed. Also, the design and

implementation of the MAS information in the SC with a more focus discussion of the

application of the performance implications of the MAS information in hospital supply chain

management was presented.

Chapter three reviewed the theoretical underpinnings of SCM as well as theoretical and

empirical literature on contingency-based management accounting studies. Given that

contingency theory constitutes a major theoretical lens in management accounting research,

and the most widely theoretical approach to the design of MAS information coupled with the

fit concept as its key concept, the study was hooked to this theory as its theoretical lens to

explain the fit relationships between the study variables. However, the review found serious

distortions and non-conformance to the theoretical requirements associated with the various

types of contingency fit models (selection, matching, moderation fit etc.) employed by prior

contingency-based management accounting study designs. In particular, existing studies lack

consistency (or a mismatch) between the formulation of verbal hypotheses, the statistical test

employed and the interpretation of results. Such contingency hypotheses tests seriously

impaired the advancement of theory-consistent management accounting studies.


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Chapter four presented the theoretical model and development of the research hypotheses. In

this chapter, a justification for the selection of the study variables and their operationalization

was given. The research model was a modified version of Chen et al (2013) and Macinati and

Anessi-Pessina (2014) which examined the effect of the SCM context factors on hospital

supply chain performance and the MAS information characteristics on hospital supply chain

performance respectively. As a basis to the formulation of the testable hypotheses, the chapter

highlighted the theoretical linkages or showed evidence of empirical linkages between the

study variables. To contribute to the advancement of theory-consistent management accounting

research, it was ensured that the formulated verbal hypotheses align with the contingency fit

models to be tested. Finally, the chapter spelt out the hypotheses that were tested.

Chapter five presented the research methodology. In this chapter, the research design which

was rooted in a quantitative-positivist epistemology was explained. After giving a brief

background of the philosophical assumptions that underpin academic research, the research

design which showed the general approach (or blueprint) to the conduct of the study as well as

the methods and techniques to the collection and analysis of data was captured. The chapter

also highlighted details of the study population, sample composition, and the sampling strategy

adopted. Also, a discussion detailing the data collection instrument and the sources and

development of the adapted scales was presented. The requirements of the construct items or

indicators which comprised the questionnaire that was administered to respondents

(accountants, hospital managers/administrators) was assessed in terms of being reflexive rather

than formative. The chapter gave a further discussion to the techniques of data analysis and the

estimation model which involved mainly covariance-based structural equation modelling.

Included in this discussion was a detailed analysis of the modelling process and the assessment

of model fit between the structural model and the sample data based on goodness-of-fit

statistics.


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It was shown that the modelling process which was based on AMOS graphics comprised a

measurement part and a structural part. The measurement part tested the adequacy of reliability

and internal consistency of the observed variables while the structural part tested the

relationship between the constructs.

Chapter six presented the survey analysis and results while chapter eight discussed and

interpreted the findings. In chapter seven, the results of the survey data were presented. First,

the findings relating to the preliminary analysis of the sample data were documented. This

involved tests of normality, outliers, skewness and multivariate kurtosis. Other preliminary

analysis includes tests of reliability, composite reliability, internal consistency, and average

variance extracted (AVE). The chapter also presented as part of the preliminary analysis,

sample means and standard deviations for continuous variables and frequency distribution for

the nominal variables. Second, the results relating to the factor analytic models for all the test

variables and their factor loadings signifying reliability were presented. Included in these

results is the goodness-of-fit indices (or statistic) which assess the adequacy of model fit

between the sample data and the hypothesized model. Finally, the results involving the fit

relationships, performance, mediating and moderating effects of the study variables were

captured.

In chapter seven, a detailed discussion and interpretation of the findings was given. The

discussions were done in the context of the findings relating to existing studies as to whether

the findings support or deviate from those of prior literature. The conclusions, summary of

findings and potential contribution to literature are presented in chapter eight (this chapter). In

this chapter, the key findings of the survey analysis are presented. The survey analysis relates

to the fit relationships, mediating role of the MAS information and the moderating effect of the

SCM context factors on Hospital SC performance.


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8.2.2 The Study’s Main Findings

First, the findings revealed the importance and relevance of the contingency fit relationship (or

alignment) between SCI (external and internal integration), level of information sharing, and

supply chain risk and uncertainty of healthcare SCM practices and the four dimensions of the

MAS information in enhancing hospital SC performance. Specifically, decisions on supplier

relations, information sharing (or knowledge exchange), risk and uncertainties associated with

the supply chain, and hospital supply chain integration were found to strongly correlate (or

align with) the four dimensions namely, scope, timeliness, integration and aggregation of the

MAS information.

The findings suggest the strong and positive influence of the four dimensions of SCM practices

on the design, implementation as well as performance implications of MAS information in the

Ghanaian healthcare SCM decisions. As strong positive associations were found to exist

between these SCM context factors and the MAS information dimensions, it supports the

selection, mediation and moderation fit models of contingency theory although SCI and level

of information sharing was not statistically supported in the selection and mediation fit models.

The findings suggest that the scope dimension of the MAS design which relates to external,

non-financial, and future-oriented information strongly support decisions on hospital SC

management practices in Ghana. It further suggests that such information must fit (or align

with) the decision needs of supplier relations, information dissemination and risk-taking in

uncertain environments to integrate the entire health SCN. Such information must also be well-

aggregated in terms of time and frequency of delivery, and interrelationship with subunits and

possess precise targets for activities. Second, the study establishes the fact that hospital

geographical location and profit status do play significant roles in the management of HSCs.

On the other hand, ownership in terms of government-owned or private-owned do not play any

role in enhancing performance of the hospital SC.


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Third, the study documents performance impacts of the hospital SC by SCI (external and

internal integration), level of information sharing, and supply chain risk and uncertainty in two

separate scenarios: 1) the presence and 2) absence of the MAS variable as a mediator. In the

former scenario, the significance of SCI was not supported by the sample data. The remaining

three SCM context factors were found to have a positive impact on hospital SC performance

which consists of cost effectiveness, utilization of hospital assets, speed and flexibility of

delivery, and supply chain quality. The non-significant results of the SCI widely support the

findings of Asamoah et al (2012) who found SCI to be weak in Ghana’s pharmaceutical SCM

system. In the latter case, none of the context factors were found to be statistically significant

and hence had no relationship with hospital SC performance. From these results, the study

establishes the fact that the four dimensions of the MAS information serve as strong intervening

mechanisms between the four SCM context factors and hospital SC performance. Stated

differently, the MAS information partially mediates the relationship between the four

dimensions of the supply chain and HSC performance. This finding also supports the mediation

fit model of contingency theory.

Fourth, the study finds the establishment of a MAS-contingency interaction framework within

the inter-organizational domains in supply chains, where the performance effects of the

interaction between SCI, level of information sharing, and SC risk and uncertainty and the

MAS information characteristics on hospital SC is established. These findings strongly support

the moderation fit model of contingency theory in inter-firm exchanges. Given that both the

main effects and the cross-product term in an interaction can result in a statistically significant

coefficient for interaction effect to be supported and interpreted, it has been argued (Hartmann

& Moers, 1999) that all main effects comprising an interaction must be included in the model

when testing for interaction.


229

This is a necessary condition to satisfy in order to avoid the situation where a statistically

coefficient of interaction is interpreted even though the interaction is due to a significant

relationship between the main effect variables. As noted in chapter three, this argument is

stressed in Stone and Hollenbeck (1984, p. 201) which states that the cross-product term may

not be the interaction even though it carries the interaction term in a regression equation. This

suggests a ‘partialed out’ of the lower-order (or main) effects when an interaction effect is

being tested by including them in the model.

Following these arguments, two models of interaction were tested: one that excludes the lower-

order effects and the other that includes the lower-order effects. In the former, a statistically

significant coefficient was found for all the cross-product terms suggesting that the SCM

context factors interact with the dimensions of the MAS information to affect HSC

performance. In the latter case where all lower-order effects were included in the model, only

the cross-product terms were found to be statistically significant although there was a reduction

in the coefficient values. The main effect variables were however found to be statistically

insignificant, again suggesting that the MAS information characteristics strongly interact with

the SCM contingent variables to affect HSC performance.

Based on these empirical evidence, three key findings were found to be associated with the

moderation tests. First, the study finds that the design and use of the MAS information

operationalized by the four dimensions (scope, timeliness, integration and aggregation) will

have a positive/greater impact on hospital SC performance in hospitals that maintain high levels

of supplier partnerships. The reverse will reduce performance of the hospital SC. Second, the

dimensions of the MAS information designed and used will have a greater positive effect on

hospital SC performance in hospitals that share (or exchange) more information or knowledge.


230

The reverse will lead to lower performance of the hospital SC. Third, the dimensions of the

MAS information designed and used will have a greater positive effect on hospital SC in

hospitals that experience more risk and an uncertain environment. On the contrary, it will lead

to a greater negative effect on hospital SC performance for hospitals that experience low risk

and a certain environment. The study thus finds a non-monotonic interaction to exist between

the SCM context factors, the dimensions of the MAS information and hospital SC performance.

On the whole, the findings suggest that the selection fit model which falls under the

configuration school of thought, and both the mediation and moderation fit models which fall

under the Cartesian school of thought are strongly supported by the sample data. Hence,

contingency applications of the MAS information characteristics can be extended from the

intra-organizational domains to the inter-organizational domains in supply chains in the

Ghanaian healthcare setting.

8.3 Conclusions

This study investigated the contingency fit relationships between SCI (external and internal

integration), level of knowledge exchange, supply chain risk and uncertainty and four MAS

information dimensions on hospital supply chain performance using empirical data from

healthcare institutions in Ghana. This section presents the conclusions arising from the research

findings and the resulting implications. Based on the findings, the following conclusions can

be drawn.

First, the study provides empirical evidence and documents that the MAS-contingency

combination fits in the inter-organizational relations domain in supply chains and highly

applicable in the external environment of healthcare institutions. The sample data confirms that

SCI made up of strategic supplier relations and internal integration, information sharing (or


231

knowledge exchange), and supply chain risk and uncertainty should be considered as an added

external variable in the contingency theory paradigm. Perhaps, this conclusion can be extended

to organizations in the mainstream discrete parts manufacturing and consumer goods domains

as well. It concludes that the four antecedent conditions of the supply chain namely, supplier

relations, information sharing (or knowledge exchange), risk and uncertainties, and supply

chain integration are critical for the design and implementation of MAS information in hospital

managerial decisions that extend beyond organizational boundaries.

That is to state that hospitals in Ghana should consider aligning their SCM decisions with the

four dimensions of MAS information (scope, timeliness, integration and aggregation) by

finding answers to the following questions. Does the current MAS information provide timely

and adequate decision-support information for supplier relation decisions (e.g. supplier

selection, switching supplier etc.) or readily make relevant information available to supply

chain members or provide adequate logistic-related information (e.g. tracking of

inventory/stock levels, procurement, warehousing and distribution related information) for

effective SCM decisions? Does the MAS information currently used by our hospital provide

supply chain information that is forward-looking or ahead of time (i.e. future-oriented) or

enable managers to foresee risks measures associated with the supply chain (e.g. supplier

failure, supply shortages, supply chain disruptions, etc.)? Do the MAS information dimensions

aggregate relevant information in terms of time periods to functional units, and provides

information targeted precisely at activities and interrelationships with subunits and external

partners? Does the accounting information provide analytical and decision models or integrated

into the SCM system?

If concrete answers cannot be found for these questions, then it could mean that the MAS

information being used in Ghanaian hospitals have become obsolete and dysfunctional in the

context of SCM and needs to be revised and redesigned. It means that the MAS information is


232

still based on the intra traditional management accounting system which merely estimate costs

and does not provide information that is non-financial, aggregated and future-oriented in nature

for boundary-spanning managerial decisions. It could suggest that healthcare institutions may

not have incorporated into their MAS information the evolutionary changes in the processes

and procedures of hospital supply chain operations which has rendered the current system to

have little value for managerial decisions in supply chains. As contingency theory suggests,

the design and use of MAS information in organizations should evolve with changing

idiosyncratic circumstances that are both internal and external to the firm. Failure to achieve

this could result in the absence of fit which in turn lowers performance.

Second, the study concludes that the four dimensions of the MAS information could pose as

intervening mechanisms in the effective management of health SC in the Ghanaian context.

This suggests that for enhancements in the hospital SC, the four dimensions of the MAS

information play an indirect role. The most critical element of SCM (i.e. SCI) was found to be

statistically insignificant in both the selection and the moderation model. This suggests that

hospitals in Ghana have weak relationships with their suppliers as well as weak internal

linkages. More precisely, there is weak SCI among hospitals in Ghana. It could mean that either

there is over-fit or under-fit of the use of the MAS information in managing SCM decisions.

The intervening role played by the MAS suggests that hospitals in Ghana will benefit from the

optimal use of the MAS information in enhancing SC performance.

Third, although SCI was statistically insignificant in both the selection and mediation fit

models, it was statistically significant in the moderation fit model. This suggests that SCI

(external and internal integration) combines with the MAS information to derive the best SCM

decisions that will yield high SC performance. The positive effect suggests that a high

performance will be attained for hospitals that have strong SCI (i.e. having strong supplier

relationships and strong internal linkages). The opposite will be an adverse effect on


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performance. The same interpretation applies to the level of knowledge exchange and supply

chain risk and uncertainty. Sharing of more information will enhance SCM decisions and have

a positive impact on performance. Furthermore, hospitals that face high levels of supply chain

risk and environmental uncertainties have the tendency to improve on performance than those

in low risk and uncertain environment. The opposite will result in low output.

8.4 Contributions of the Thesis

This thesis which is one of the few studies conducted within the African continent adds to the

management accounting literature by theorizing and testing the existence of fit relationships

between SCI (external and internal integration), level of information sharing, supply chain risk

and uncertainty, MAS design, and supply chain performance in healthcare setting. It provides

useful insights on how hospitals in Ghana should benefit from the MAS-contingency

framework so as to improve hospital SC performance. It shows the joint effect of the

contingency-structure combinations in the supply chain field on MAS design and performance.

First, it fills a gap in both the management accounting and SCM literatures on the link between

MAS information and supply chain performance which has been neglected in MAS-CSM

studies. Theoretically, the relationship between MAS information and SC performance needs

to be advanced since both have similar objective of creating value and reducing SC costs. The

study has thus, provided preliminary empirical evidence on the MAS impacts of SCI, level of

knowledge exchange, and supply chain risk and uncertainty by developing a workable

theoretical framework and hypotheses that links SCM, MAS design, and supply chain

performance in a healthcare setting. Empirical examination of the interaction effects of these

three variables hardly exists in prior management accounting – supply chain literature.


234

Second, the thesis demonstrates the benefits of conceptualizing the MAS, SCM and SCP

constructs which permits the comprehensive understanding of the details of MAS, SCM and

SCP at the dimension levels. More precisely, the use of multidimensional MAS, SCM and SCP

constructs (Wong et al, 2011) permit the development of a comprehensive model and theory

of the contingency effects of SCI, level of information sharing, and supply chain risk and

uncertainty. This theory is strengthened by the use of a second-order structural equation

modelling which offers a unique contribution as such modelling approaches hardly exists in

the management accounting literature. No study was identified from the extensive reviews, that

has used a second-order SEM in testing contingency hypothesis although there are studies that

have used SEM. Thus, the study presents a paradigm for assessing the multidimensionality

feature of the MAS construct in relation to other organizational constructs.

Third, this study has empirically examined the reverse impacts of the SCM contextual

dimensions on the MAS information characteristics (the selection form of fit) that can leverage

hospital supply chain performance. Compared to existing studies on the MAS-supply chain

interaction, this is an area that lacks empirical investigation in the management accounting

literature. The findings extend contingency theory’s application from the intra-organizational

domain to the inter-organizational domain in supply chains thereby expanding the contingency

variables from existing ones to include SCI (strategic supplier partnerships and internal

integration), level of information sharing (or knowledge exchange), and supply chain risk and

uncertainty. Perhaps, the most significant contribution of this thesis is the development and

testing of a novel theoretical model on the mediating and moderating effects of SCI (external

and internal integration), level of information sharing, and supply chain risk and uncertainty

on MAS design and hospital SC performance relationships. Hospital managers can develop

their MAS based on contingency theory’s lenses given its optimum choices.


235

Finally, it responds to the several calls for studies that investigate multiple contingency

endogenous variables on multiple endogenous variables for the advancement of theory

consistent with management accounting research.

8.5. Recommendations

Based on the conclusions, the following have been recommended. These recommendations are

in line with the study objectives. First, integration of the health SC is highly crucial to enhance

the operational performance of the SC of healthcare institutions in Ghana. For optimal

performance of the health SC, integration of the required resources in the right place and at the

right time is needed to ensure the availability of supplies to enable healthcare institutions serve

their patients more effectively and efficiently. Second, both public and private healthcare

institutions should give more importance to the SCM principles in relation to MAS design and

apply optimal amounts of the MAS to SCM decisions in order to attain the highest

performance. This should be inculcated into the strategic plans of hospitals. Third, for optimal

performance, hospitals in Ghana should consider aligning (or matching) their SCM activities

with the four dimensions (scope, timeliness, integration and aggregation) of the MAS. This is

because as the results indicated regarding objective one and two, the optimal selection of the

MAS (selection fit model) and mediation effect (mediation fit model) of the MAS dimensions

enhances hospital SC performance since the results suggest that the MAS provides decision

support for effective SCM decisions. That is hospital SCM decisions in terms of operating costs

that are taken outside the MAS dimensions are likely to result in misalignment and negatively

affect performance.

In objective three, the results suggest that a positive (or greater) impact on hospital SC

performance can be attained in hospitals that:


236

- maintain high levels of supplier partnerships. The reverse would lead to lower

performance

- share (or exchange) optimal MAS information or knowledge relative to their

SCM activities. The excess or insufficient sharing of the MAS information

relative to SCM decisions would lead to lower performance.

- experience high risk and uncertain environment but will lead to negative effects

for hospitals that experience low risk and certain environment.

This suggests that to achieve the benefits in terms of SC optimization, an enterprise-wide-item-

level visibility must be made possible through the SC information structure – which is

underpinned by MAS design and supported by wide area network technologies.

8.6 Limitations of the Study

First, the empirical results are based on sample data relating to only one country (Ghana) given

the number of developing countries hence, possible generalization of the findings to include

the health supply chain in all developing countries is limited. Additionally, it focused only on

healthcare institutions, and more precisely hospitals; hence, its findings may not be applicable

to other organizational contexts. Notwithstanding, the results can be generalized within the

Ghanaian context. Second, not all the contextual variables of the supply chain were examined.

For example, customer relationship, postponement and the separate examination of information

technology (IT) performance impacts on the hospital supply chain are important dimensions of

the SCM practices. Hence, their exclusion from the study could be an important shortcoming

given especially the role of IT in enhancing supply chain integration.


237

Third, although the use of SEM as a statistical tool for examining fit relationships offers

effective and better insight, meaning and understanding of the fit concept compared to other

statistical methods (e.g. MRA), it cannot be used under the matching form of fit model which

happens to be the most classical form of contingency fit (Burkert et al, 2014). This is due to

the curvilinear dimension (i.e. inverted U-shaped) that underpins the relationship between the

MAS variable and performance. The meaning is that given a certain level of the contingency

variable, only one optimal MAS variable can be attained. On the other hand, linear relationships

are associated with the covariance based SEM methodology and is a key assumption underlying

this approach (Edwards, 2009) as against the curvilinear relationship which is a key feature of

the matching form of fit. Also, the matching form of contingency fit is based on the iso-

performance assumption as its key underlying theoretical concept. Under this concept as

discussed in chapter three, several optimal MAS-contingency combinations are predicted with

the same level of performance produced by each. This form of analysis is not attainable in SEM

as the issue of hetero-performance effect on the fit-line and/or asymmetric effects of misfit on

performance which are highly associated with matching fit models cannot be addressed. The

other forms of fit models (selection, mediation, moderation, etc.) are however effectively and

appropriately tested under SEM than other statistical techniques (Burkert et al, 2014). Another

limitation is the mitigation of the likelihood of common methods bias. This is because the

sections of the questionnaire which was tested on performance were answered solely by

hospital accountants and that they could have answered the questions to their advantage and

this will result in common methods bias. Using the same sample, future studies can test for

common methods bias to see whether there is any significant difference in the responded

sample.


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8.7 Future Research Directions

Several avenues for future research can be identified. First, this work only considers the

extension of the MAS-contingency framework from the intra-organizational domain to the

inter-organizational domains relating healthcare institutions, and more precisely hospitals. The

application of the fit concept in the boundary-spanning relationships that can leverage

performance in supply chains involving the discrete parts manufacturing and consumer goods

industry provides avenues for future studies. Second, empirical evidence on the contingency

theory’s application in the SCM field can be extended to include the other dimensions (e.g.

customer relations, postponement, information technology, etc.) of SCM practices which were

not considered in this study. In addition, this study examines supply chain integration as a

composite variable yet, integration is a multidimensional construct consisting of supplier

integration, customer integration, and internal integration. Future studies can examine each of

these dimensions on their own in relation to aggregate performance or each performance

dimension. Third, this work has focused exclusively on the MAS characteristics involving the

four dimensions of scope, timeliness, integration and aggregation in examining its design in

the SCM field. Other numerous dimensions of the MAS design which have been used in prior

studies can be examined in relation to SCM.

For example, the dimensions of the MAS information can be classified under long-term

planning, budgeting, decision support systems, financial and non-financial information, and

others. Fourth, and in addition to the dimensions specified above, the management accounting

techniques (or concepts) (e.g. ABC, value chain analysis, target costing, kaizen costing, open

book accounting, etc.) which have been proposed as SCM tools in inter-organizational relations

are avenues for future research within the contingency framework in relation to organizational

performance.


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8.8 Chapter Summary

In this chapter, a summary of the key findings and the conclusions arising from the main

findings have been presented. It also elaborated on the contributions to literature which is

mainly theory-based. The chapter also provided the limitations that are likely to inflict this

study and finally the directions for future research.


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Appendix A

Questionnaire for Hospital Accountants

Introduction

Dear Respondent,

This PhD research project proposes to investigate the impact of supply chain management

(SCM) contextual factors (or its antecedents) on management accounting system (MAS) design

among Ghana’s healthcare institutions, and the impact of their relationships on performance.

The essence of this research project is to contribute to finding answers to the numerous

challenges that confront the SCM system of the health sector. Issues bothering on procurement

arrangements, inventory management, high variations in the pricing of health products,

inaccessibility of drugs either physically or financially etc., are some of the challenges

mitigating against the efficient operation of the health supply chain. These challenges as noted

in past literature, have major implications for the design and use of MAS. Management

accounting systems play significant role in SCM practices. The findings I believe, will have a

wide range of policy implications for health policy makers and health administrators.

Over 300 government and private hospitals across the country are being sampled and your

hospital has been selected to be part of the sample. I would therefore appreciate it most if you

could take some time off your busy schedule to diligently complete this questionnaire. I trust

that your honest opinion (there is no right or wrong answer) would be provided. Be assured

that this research is solely for academic purposes hence any information given will be treated

as strictly confidential.

Please take note: The information provided will be treated as confidential and the identity

of respondents as it has always been the case, is kept anonymous.

Thank you.

Directions to Respondents

The questionnaire is made up of two sections. Section A requires specific responses whilst

section B is based on ranking.

Section A

Please supply information where applicable

1. Designation/Status of Respondent (e.g. accountant, financial controller, administrator,

etc.)

…………………………………………………………………………………….

2. Years of being at this post (please provide in the box)

3. Hospital type (please tick) public Private

4. If private, is it profit-oriented? Yes No


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5. Number of beds in your hospital (please provide)

6. Your hospital is located in (please tick) Urban center Rural area

Section B

This section is made up of 4 parts: A, B, C, and D. Part A concerns SCM context factors, part

B relates to organizational contextual factors and the external environment, part C concerns

management accounting system design in your hospital, and part D relates to supply chain

performance in your hospital.

Please indicate the extent to which your organization (hospital) perform the following supply

chain management (SCM) activities by checking the appropriate box/column where 1 =

strongly disagree/never/not at all 2 = disagree/hardly occur/sometimes; 3 = fairly

disagree/seldom/once a while; 4 = neutral; 5 = fairly agree/often/occasionally; 6 =

agree/regularly/most of the time; 7 = strongly agree/to a greater extent/all the time

Part A – Health Supply Management Context Factors

Strategic supplier partnerships 1 2 3 4 5 6 7

1. Our hospital maintains a high level of strategic relations with key suppliers

2. We encourage a high level of suppliers’ participation in our procurement processes

3. Our hospital establishes quick ordering systems with key vendors/suppliers

4. Our hospital solves logistic problems jointly with key vendors/suppliers

5. We include our key vendors/suppliers in our planning and goal-setting activities

6. Our hospital on frequent basis, arrange for delivery with vendors/suppliers

7. We undertake vendor inventory management or consignment stock with suppliers

8. We plan, forecast and replenish collaboratively with key vendors/suppliers

9. Our hospital dedicates capacity for key vendors/suppliers


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Health Supply Chain Integration 1 2 3 4 5 6 7

1. Interorganizational logistics activities between our hospital and major key vendors/suppliers are closely coordinated

2. Our logistics activities are well integrated with the logistics activities of key vendors/suppliers

3. We have seamless integration of logistics activities with key vendors/suppliers

4. Our logistics integration is characterized by excellent distribution, transportation and/or warehousing facilities

5. Our inbound and outbound distribution of hospital supplies with key vendors/suppliers is well integrated

6. Our health organization integrates purchasing of health commodities and other medical equipment into strategic planning.

Level of Information Sharing 1 2 3 4 5 6 7

1. We inform vendors/suppliers in advance of changing needs

2. Our key vendors/suppliers share business knowledge of core business processes with us

3. We and our key vendors/suppliers exchange information that helps establish our business planning

4. Our partners share proprietary information with us

5. We share accurate risk related information with our supply chain members

6. We are willing to share real time information on demands with our suppliers

7. Information is actively shared between functional teams in our hospital

8. Members in the supply chain keep each other informed about events or changes that may affect the other party

9. We share inventory level information with key/strategic suppliers


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Health Supply Chain Risk and Uncertainty 1 2 3 4 5 6 7

1. Our hospital establishes target costs/prices for health commodities with suppliers

2. Our hospital evaluates whether suppliers have achieved the target price (cost) for health products.

3. Our hospital evaluates suppliers’ cost reduction efforts for medical supplies.

4. Our hospital assesses the quality and costs of suppliers’ products

5. Our hospital holds frequent meetings with suppliers.

6. Our hospital shares with suppliers’ problems associated with medical supplies

7. Our hospital solves problems associated with medical supplies in conjunction with suppliers,

8. Our hospital compares different suppliers’ products

Part B – Management Accounting System Design

Please indicate the extent to which the following accounting information are generated for

supply chain management decisions.

Scope of Information Provided by Accounting System 1 2 3 4 5 6 7

1. Information that relates to possible future events

2. Quantification of the likelihood of future events occurring

3. Non-economic information

4. Information on broad factors external to your hospital

5. Non-financial information that relates to the efficacy, output rates, employee absenteeism

Timeliness of Accounting Information 1 2 3 4 5 6 7

1. Requested information to arrive immediately upon request

2. Information supplied to users automatically upon its receipt into information systems or as soon as processing is complete


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3. Reports are provided frequently on a systematic, regular basis

4. There is no delay between event occurring and relevant information being reported to users

Integration of Accounting Information 1 2 3 4 5 6 7

1. Information on the impact that your decision will have throughout your department,

2. The influence of the individuals’ decision on your area of responsibility

3. Information on precise targets for the activities of all sections within your department

4. Information that relates to the impact that your decisions have on performance of your department

Aggregation of Accounting Information 1 2 3 4 5 6 7

1. Information provided on your different sections or functional areas in your hospital

2. Information on the effect of events on particular time periods

3. Information that has been processed to show the influence of events on different functions

4. Information on the effect of different sections’ activities on summary reports for your department and the hospital as a whole

5. Information in forms that enable you to conduct ‘‘what-if’’ analysis

6. Information in formats suitable for input into decision models

7. Costs separated into fixed and variable components


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Part D – Supply Chain Performance

Minimization of Health Supply Chain Costs 1 2 3 4 5 6 7

1. The cost associated with the order fulfillment process in our hospital is getting better (has been reducing) over time

2. We have seen an improvement (reduction) in the cost associated with order fulfillment process over time

3. Based on our knowledge of the order fulfillment process we think it is cost efficient

4. The following costs have been reducing in our hospital over the past three years

a) Purchasing costs

b) Operating costs

c) Inventory costs

d) Warehouse costs

e) Distribution/Transportation costs

Utilization of Hospital Assets 1 2 3 4 5 6 7

1. Our hospital has been experiencing a growth rate in internally generated funds

2. Our hospital has been experiencing a growth rate in return on assets

3. Our hospital has been experiencing a growth rate in return on investments

Supply chain quality and speed of delivery

1. On-time delivery of drugs/medical supplies from our suppliers

2. The length of the order fulfilment process in our hospital is getting shorter with time


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3. We have seen an improvement in the cycle time of the order fulfilment process with time

4. Based on our knowledge of the order fulfilment process we think it short and efficient

Health Supply Chain Flexibility 1 2 3 4 5 6 7

1. The flexibility of the order fulfilling process in our hospital is getting better with time

2. We have seen an improvement in the flexibility of the order fulfilment process with time

3. Based on our knowledge of the order fulfilling process, we thing it is flexible

Health Supply Quality 1 2 3 4 5 6 7

1. The quality of the order fulfilment process in our hospital is getting better with time

2. We have seen an improvement in the quality of the order fulfilment process with time

3. Based on our knowledge of the order fulfilment process, we think it is of high quality


management accounting and supply chain performance of

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