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Risk, risk management practices, and the success of supply chain integration

Frank Wiengarten, Paul Humphreys, Cristina Gimenez

www.elsevier.com/locate/ijpe

PII:DOI:Reference:

S0925-5273(15)00088-2 http://dx.doi.org/10.1016/j.ijpe.2015.03.020 PROECO6039

To appear in: Int. J. Production Economics

Received date: 12 February 2013Accepted date: 18 March 2015

Cite this article as: Frank Wiengarten, Paul Humphreys, Cristina Gimenez, Risk, risk management practices, and the success of supply chain integration,Int. J. Production Economics, http://dx.doi.org/10.1016/j.ijpe.2015.03.020

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Frank Wiengarten1

ESADE School of Business, Ramon Llull UniversityAv. de Pedralbes, 60-62, 08034 Barcelona, Spain

Phone: (34) 932 806 162, e-mail: [email protected]

Paul HumphreysUniversity of Ulster

Jordanstown campus, Shore Road Newtownabbey, Co. Antrim, United KingdomPhone: (44) 28 90368410,e-mail: [email protected]

Cristina GimenezESADE School of Business, Ramon Llull University

Av. de Pedralbes, 60-62, 08034 Barcelona, SpainPhone: (34) 932 806 162, e-mail: [email protected]

1 Corresponding Author

Title:


Abstract:

Companies have reacted to the apparent opportunities and threats of globalization through various global production practices that have increased supply chain complexity and various forms of risk. Through increasing supply chain integration, companies have attempted to manage this increased level of complexity. Supply chain integration has been identified as a key practice to manage supply chains and achieve superior performance. The intent of this paper is to explore the role of risk and risk management practices in the success of supply chain integration in terms of their impact on cost and innovation performance. By applying the relational view and through cross-country survey and secondary country data we explore differences in supply chain integration efficacy based on the risk of conducting business (measured in terms of the strength of a country’s rule of law) and the mitigating effect of supply chain risk management practices. One of the main conclusions suggests that supplier integration is also effective in weak rule of law (i.e., high risk) environments. Furthermore, companies can complement and strengthen the performance impact of their supplier integration practices through supply chain risk management practices in risky environments.

Key words:

Supply chain integration, rule of law, risk management, operational performance, relational view

1. Introduction

The current trend towards globalization has not only provided companies with various

opportunities, but also a number of challenges. On a global basis, companies have

established warehouse facilities, production plants and distribution centers across countries

for various reasons, such as cost advantages, access to raw material sources or specialist

skills and capabilities (Choi et al., 2012). However, globalizing supply chains also results in

many challenges, that may include, increased complexity and various associated risks

(Chopra and Sodhi, 2004; Blackhurst et al., 2005; Tang, 2006). Companies have been

managing supply chain complexity through tightly integrated supply chains (Schoenherr and

Swink, 2012). Supply chain integration (SCI) can be defined as the extent to which a

company strategically interconnects and aligns its supply chain with its partners, upstream

and downstream (Jayaram et al., 2010; Schoenherr and Swink, 2012).

It is generally accepted that tighter integration leads to improved performance (e.g., Forrester,

1961; Kim, 2009). Integrating supply chain processes with customers and suppliers enables

companies to improve and streamline information and data exchange, which may lead to the

improvement of product and material flows throughout the supply chain (Wiengarten et al.,

2013). In addition, SCI may enable companies to access various resources and capabilities in the

form of knowledge embedded within other supply chain members and subsequently increase a

company’s innovativeness (Craighead et al., 2009; Cao and Zhang, 2011).

Whilst the general consent is that SCI leads to improved performance, some studies have

failed to show this link (see for example, Flynn et al., 2010; Schoenherr and Swink, 2012).

During the recent years, a contingency view of SCI has been adopted, showing that the

relationship between integration and performance depends on different contingency factors

(Wong et al., 2011; van der Vaart et al., 2012). Most of the studies have considered

contingency factors at the firm level, such as product complexity, complexity of business

conditions, uncertainty. Recently, Wiengarten et al. (2014) extended the SCI literature

including country-level factors, such as the country’s logistical capabilities. We aim to

contribute to this recent stream of literature by analysing the moderating role of the strength

of a country’s legal system (i.e., the rule of law), which can be viewed as a proxy for the risk

of firms facing opportunistic behaviour.

The advantages and positive effects of globally integrated supply chains may be

threatened through various forms of risk, such as the opportunistic behavior of supply chain

members, geopolitical risk, sovereign risks or exchange rate risks (Aron et al., 2005). All of

these forms of risk may hinder companies to experience the full performance capabilities of

their integrated supply chains. For example, the outsourcing literature has identified that

various forms of risk have a significant impact on the success of outsourcing (Ellram et al.,

2008; Handley and Benton, 2009; 2012). Additionally, a recent study by Wong et al. (2011)

identified that the impact of SCI on operational performance is contingent on varying levels

of environmental uncertainty.

To manage the various forms of risk that supply chains are exposed to companies are

increasingly investing in risk management tools such as mitigation practices and

contingency planning (Kleindorfer and Saad, 2005; Ellis et al., 2011). In applying mitigation

practices, companies have relied on supplier involvement, selection evaluation and supplier

development activities (Krause, 1999; Krause et al., 2000; Kannan and Tan, 2002; Fawcett

et al., 2006; Ellis et al., 2011). Contingency planning practices, on the other hand, include

securing excess capacity or holding inventory at strategic positions within the supply chain

(Zsidisin and Ellram, 2003; Tomlin, 2006).

Building on the concept of SCI and applying the arcs of integration framework along

with the relational view, our objective is to explore how risk impacts on the success of SCI.

Specifically, we explore how risk, in the form of the “rule of law”, at the country level

affects the impact of SCI on operational performance in terms of cost and innovativeness. In

other words, we use the strength of a country’s legal system (i.e., the rule of law) as a proxy

for the likelihood of supply chain members to conduct opportunistic or ill-behaviour. In

addition, we explore how supply chain risk management practices can complement SCI

under different risk scenarios. Subsequently, this research is set out to investigate the

following research questions:

RQ1: To what extent does the risk associated with a weak rule of law affect the impact of

SCI on operational performance (i.e., cost and innovation)?

RQ2: To what extent can supply chain risk management practices complement SCI under

different risk scenarios?

We have chosen the dimensions of cost and innovativeness to represent the two ends of

the operations strategy spectrum in the context of the overall organizational strategy in terms

of cost leadership and differentiation strategy (through innovation) (Porter, 1998; Craighead

et al., 2009). In doing so we attempt to further develop and explore theory and literature in

terms of SCI and the relational view. Additionally, we will explore how SCI and risk will

behave and interact differently under these two dimensions.

2. Literature Review and hypotheses development

2.1. Supply chain integration and performance

SCI can be categorized along multiple dimensions and aspects (van der Vaart and van

Donk, 2008; van der Vaart et al., 2012; Ahmed and Pagell, 2012). Van der Vaart and van

Donk (2008) noted that integration covers a broad array of practices, from sharing of

operational information such as stock levels to strategic activities such as collaboration for

new product development. Integration can also be studied from a company boundary

perspective (Frohlich and Westbrook, 2001; Schoenherr and Swink, 2012). In that sense,

integration can be defined from an internal perspective if it refers to the integration among

different functional areas within the firm’s boundaries and externally if it refers to how the

firm integrates with its upstream and downstream supply chain partners (i.e., customers and

suppliers) (Flynn et al., 2010).

Research assessing the impact of SCI on firm performance has been extensive (Schoenherr

and Swink, 2012). Frohlich and Westbrook’s (2001) ‘arcs of integration’ framework can be

viewed as a seminal article that conceptualized and measured the performance implications of

SCI. They conceptualized integration through its direction (towards customers and/or suppliers)

and extent. Frohlich and Westbrook (2001) identified that an outward-facing strategy

characterized by a high level of customer and supplier integration leads to higher levels of firm

performance compared to companies with lower levels of integration.

Many researchers have linked the potential performance benefits of SCI through the

theoretical foundations of the resource-based view and its relationship specific view, the

relational view (Dyer and Singh, 1998; Chen et al., 2004; Mesquita et al., 2008). Dyer and

Singh (1998) argue that organisations engaging in alliances can gain supernormal profit

(relational rents) through the following four sources (1) relation-specific assets, (2)

knowledge-sharing routines, (3) complementary resources/capabilities, and (4) effective

governance. In a case study of Japanese automakers, Dyer and Singh (1998) empirically

verified that close supplier relationships lead to more relationship specific assets

(investments), lower transaction costs, and ultimately superior operational performance.

Applying the relational view to our research setting, SCI can be viewed as an alliance as

opposed to an arm’s length relationship. Subsequently, from a theoretical viewpoint higher

levels of integration might lead to higher performance outcomes because of increased

knowledge exchange, finding complementarities and lower transaction costs.

Therefore, from a cost perspective, increasing the degree of supplier and customer

integration may benefit companies in terms of reducing manufacturing cost, increased

inventory turnover or even an increase in labour productivity. However, the longer-lasting

benefit, in the form of sustainable competitive advantage, could be achieved through the

exchange and joint development of expertise and capabilities leading to the development of

new and innovative products. Subsequently, by increasing SCI, companies may gain cost

and innovation advantages as stated in the following hypotheses:

Hypothesis 1. Customer integration is positively associated with (a) cost and (b) innovation

performance.

Hypothesis 2. Supplier integration is positively associated with (a) cost and (b) innovation

performance.

2.2. Supply chain integration and performance: A contingent view

Whist previous research has generally concluded that having close and integrated supply

chain relationships is a means to achieve superior performance (e.g., Lee et al., 1997; Chen

et al., 2004; Vereecke and Muylle, 2006) a large body of research has also identified mixed

or negative results (Stank et al., 2001; Cousins and Menguc, 2006; Flynn et al., 2010;

Narasimhan et al., 2010). Devaraj et al. (2007) identified that supplier integration does

significantly contribute to operational firm performance in terms of cost, quality, flexibility,

and delivery performance, whereas customer integration does not. Similarly, Flynn et al.

(2010) identified that whilst internal integration was directly related to business and operational

performance, customer integration was only related to operational performance. Furthermore,

supplier integration was neither directly related to operational nor to business performance.

Flynn et al. (2010) further investigated these results through various contingency and

configuration factors. Moreover, Narasimhan et al. (2010) identified that customer and supplier

integration had no significant impact on cost performance and that supplier integration even had

a negative effect on quality performance. Recently, Schoenherr and Swink (2012) also

highlighted these mixed findings through retesting and extending the arcs of integration

framework by investigating the moderating role of internal integration on the relationships

between arcs of integration and performance. They identified that internal integration strengthens

the impact of supplier and customer integration on delivery and flexibility performance;

however, not on quality and cost performance.

Although internal integration has been one of the moderating factors considered in the

literature (Flynn et al., 2010; Schoenherr and Swink, 2012), other authors have considered

additional types of contextual factors. For example, Fynes et al. (2004) considered supply chain

relationship dynamics; Wong et al. (2011) environmental uncertainty (considering change in

customer orders, unpredictability in suppliers performance, change in production technology,

etc.) and van der Vaart et al. (2012) the DWV3 (Duration of the life cycle, time Window for

delivery, Volume, Variety, and Variability) of Childerhouse et al. (2002). The main

characteristic of all these studies is that the contingent factor was measured at the supply chain

relationship level (i.e., it refers to the specific context of the buyer-supplier relationship, such as

the level of uncertainty, the complexity of the product, etc.). Recently, Wiengarten et al. (2014)

extended the SCI literature by adding a contextual factor at the country level. The authors show

the moderating role of a country’s level of logistical capabilities on the SCI-performance

relationship. In particular, they show that plants located in countries with high logistical

capabilities do not gain the same performance improvements from SCI as the plants located in

countries with low levels of logistical capabilities.

In the following section, we will argue that other type of factors such as risk need to be

incorporated in the study of the SCI-performance relationship. These factors are also at the

country level.

2.3. Supply chain integration and risk

Risk research, in general, has been given increased attention by supply chain researchers

and practitioners (Speckman and Davis, 2004; Knemeyer et al., 2009; Tang and Musa, 2011;

Sodhi et al., 2012). Mitroff and Alpaslan (2003) have identified three general types of

threats or risk that can impact the supply chain and affect SCI in particular: natural accidents

(fires, earthquakes, etc.), normal accidents (technology failure/breakdowns), and abnormal

accidents (ill-will by insiders/outsiders). As highlighted in the introduction we are

specifically interested in the third potential risk category, the abnormal accidents,

characterized through ill-will by insiders/outsiders.

We could study risk at the buyer-supplier relationship level but our study would not

differ much from the existing studies that consider environmental uncertainty or business

conditions (e.g. Wong et al., 2011; van der Vaart et al., 2012). By considering risk at the

country-level we will join Wiengarten et al. (2014) in their search for aspects that help

explain the differences in the SCI-performance relationship across countries. In the

following, we provide a discussion as to why a country’s rule of law can be considered as an

appropriate indicator of opportunistic behaviour.

We adopt the strength of a country’s legal system (i.e., the rule of law) as a proxy for the

likelihood of supply chain members to conduct opportunistic behaviour. We argue that in countries

with weak rules of law the likelihood or the risk of opportunistic behaviour is higher than in countries

with strong rules of law (Kaufmann et al., 2011). Williamson (1975, p. 9) defined opportunism “as a

lack of candor or honesty in transactions, to include self-interest seeking with guile”. The term

“guile” was used to refer to “lying, stealing, cheating, and

calculated efforts to mislead, distort, disguise, obfuscate, or otherwise confuse”

(Williamson, 1985, p. 47). Opportunistic behaviour is expected to be higher in countries

with weak rules of low, as companies and individuals in these countries are more likely, for

example, to be exposed to cheating and stealing, and they cannot rely on the courts of law or

the police to be impartial.

It is argued that the success of SCI depends on the behaviour of the buyer-supplier dyad

and therefore on the country specific rule of law. Countries with weak rules of law can be

characterized as having low levels of abiding society, poor contract enforcement, weak

property rights, corrupt police and court systems, as well as an increased likelihood of crime

and violence resulting in high levels of environmental uncertainty (Kaufman et al., 2011).

Subsequently, the strength of the host country’s legal system (i.e., the rule of law) can be

viewed as a proxy for the likelihood of opportunistic behaviour at the country level.

Therefore, the focal company’s supply chain may be more exposed to opportunistic

behaviours if based in a country characterized by weak rules of law. Companies and

individuals in countries with weak rules of law are more likely, for example, to be exposed

to crime and do not trust the courts of law or the police to be impartial. From a supply chain

perspective this may imply that SCI might not be as successful in environments

characterized by weak rules of law compared to strong rule of law environments. Giving

individuals and companies the opportunity to behave opportunistically may actually lead to

ill-behaviour in supply chain relationships, which has an adverse impact on performance

(Mitroff and Alpaslan, 2003).

As an example, the literature on contract management has identified that the less complete or

specified a contract is, the less successful outsourcing practices may be (Poppo and Zenger,

2002; Goo et al., 2009). Handley and Benton (2009) have highlighted that effective contracts

which are reflected in clear service level agreements, with established penalty and reward

structures, guard against opportunistic behavior. Reflecting this on the more intangible

aspects at the country level implicitly suggests that the less rules are specified, followed and

enforced the less efficient SCI may become.

This can also be theoretically further explored and underpinned by the relational view. We

have highlighted that significant performance benefits or determinants of interorganizational

competitive advantage fall into the categories of relation-specific assets, knowledge sharing

routines, complementary resources and capabilities and effective governance (Dyer and Singh,

1998). However, the risk inherent with weak rules of law may prevent companies gaining

benefits from their SCI initiatives in terms of supernormal profits. Firstly, weak rules of law may

require companies to invest in additional safeguards to prevent ill-behavior. This would require

effective governance tools in the form of enforcement and governance practices (Poppo and

Zenger, 2002). In terms of knowledge-sharing routines, weak rules of law may make these

sources of relational rents less effective. Supply chain members may not only experience higher

levels of ill-behavior in weak rule of law environments, but because of these experiences may

also not share and transfer specific knowledge that would otherwise permit the creation of new

specialized knowledge. In addition, false information may have been deliberately transferred in

order to gain short-term advantages or benefits over other supply chain members. Based on these

premises, potential complementarities between the member firms’ resources and capabilities may

not have been identified or explored. All these factors may even lead to a negative effect of SCI

on performance, if practiced in environments with weak rules of law. Subsequently, the

following hypotheses are stated:

Hypothesis 3. Weak rules of law reduces the strength of the positive relationship

between customer integration and (a) cost and (b) innovation performance.

Hypothesis 4. Weak rules of law reduces the strength of the positive relationship

between supplier integration and (a) cost and (b) innovation performance.

2.4. Supply chain risk management practice, risk and supply chain integration

In terms of our first research question, it has been argued that SCI might not be as

effective in companies situated in nations with weak rules of law as compared to being

situated in countries with strong rules of law. In this section we discuss our second research

question through exploring the importance of risk management practices in order to

complement SCI under different risk scenarios.

Companies implement risk management practices depending on their risk strategy and the

severity and likelihood of risk events (Brindley, 2004; Knemeyer et al., 2009). Various risk

events have been observed and researched, such as the disruption of supply, breakdown of

transportation, production, or warehousing, procurement failures and forecast inaccuracies

(Johnson, 2001; Chopra and Sodhi, 2004; Speckman and Davis, 2004). Whilst these events

might be very severe in terms of their impact on supply chain performance, the likelihood might

be somewhat predictable (Brindley, 2004). However, due to constantly focusing supply chain

strategy on cost objectives, these risk events might have increased in terms of likelihood and

severity (Speier et al., 2011). Tightly coupled and synchronized supply chain processes make

these disruptions even more likely and severe. Companies are attempting to strike a balance and

increase their process slack or buffering of product/people/etc. to manage risk, but at the same

time taking into consideration the added costs (Speier et al., 2011).

Supply chain risk management is the integrated process of identification, analysis and

either acceptance or mitigation of uncertainty and risk in the supply chain (Manuj and

Mentzer, 2008; Blome and Schoenherr, 2011). Risk identification include practices vendor

and supplier rating programs, contingency programs or early warning systems whereas risk

mitigation include practices such as rethinking and re-evaluating their supply and

distribution strategy (for example, through the use of postponement, changing the location

of some facilities, etc.) and supplier development (Manuj and Mentzer, 2008; Blome and

Schoenherr, 2011).

Conducting business in countries with weak rules of law may require additional

managerial effort to gain the full benefits of SCI. In hypotheses three and four we proposed

that the rule of law has an adverse effect on the efficacy of SCI and in this section we

discuss the possibilities to compensate for these negative effects through managing these

inherent associated risks in practicing SCI in weak rule of law environments. The relational

view underpins the importance of effective governance for the success of interorganizational

relationships (Dyer and Singh, 1998). Companies could ensure the success of their SCI

efforts through rethinking and re-evaluating their supply and distribution strategy (e.g.,

relocating inventories and using postponement) whilst considering potential risk scenarios

(Manuj and Mentzer, 2008). Also, other risk management practices could be employed such

as vendor and supplier rating programs, contingency programs or early warning systems

(Blome and Schoenherr, 2011).

However, these systems require managerial and monetary investments, which could also

have a negative effect on the efficacy of SCI. Subsequently, we propose that companies

need to choose and align their investments in risk management practices with the risk

environment (Brindley, 2004). This would mean that companies practicing SCI in weak rule

of law environments (i.e., high risk environments) could complement their SCI efforts

through investing in supply chain risk management practices.. Subsequently, the following

hypotheses are proposed:

Hypothesis 5. Higher levels of supply chain risk management practices

implementation complements customer integration when being situated in weak rule of

law environments and subsequently strengthen the positive relationship between

customer integration and (a) cost and (b) innovation performance.

Hypothesis 6. Higher levels of supply chain risk management practices implementation

complements supplier integration when being situated in weak rule of law environments

and subsequently strengthen the positive relationship between supplier integration and (a)

cost and (b) innovation performance.

3. Research Method

3.1. Sampling and data collection

Data collected through the International Manufacturing Strategy Survey (IMSS) was used

to explore the importance of a country’s logistical capabilities on the SCI and its efficacy. The

IMSS is a research network of business schools and assembly manufacturing firms, designing a

common database and collecting data for the study of manufacturing management strategies and

practices on both a global and national scale. The network was originally set up in 1992 by a

group of 20 business schools led by the London Business School, UK and Chalmers

University of Technology, Sweden. In this study we utilize data collected from the 5 th round

of the survey collected in 2009. The companies were contacted multiple times through

emails and telephone calls. The final combined response rate of the companies in the

different countries was 24.18%.

---Insert Table 1 here---

We also included a secondary data source by The World Bank. In order to measure the

risk of opportunistic behaviour we use the rule of law index developed and measured by The

World Bank. The rule of law index measures the level of confidence that citizens have in

their legal / regulatory system as well as their likelihood to abide by the rules of the system

in the specific country context. For consistency purposes we also used scores provided for

2009 (Kaufmann et al., 2011).

The final sample selected for the purpose of this study consisted of 637 plants situated in

19 countries situated in Europe, Asia and North America. Table 1 and 2 provide overviews

of our sample in terms of country, rule of law score and industry.


Before starting with the analyses we tested our sample for common method bias or variance.

We assessed common method bias through the Harman’s one factor test (Sanchez and Brock,

1996). Results indicate that the single factor model produced a significantly worse

model fit compared to our proposed and confirmed five-factor model (χ2/d.f = 16.64; RMSEA

= 0.167; AGFI = 0.59; CFI = 0.75; GFI = 0.66; IFI = 0.79; NFI = 0.78; RFI = 0.78). Furthermore, we

assessed potential issues regarding response bias. Unfortunately, we could not obtain information regarding the

date of response to compare the responses across early and late respondents for each country.

However, we could compare responses from individuals that provided answers to all survey

questions to those that only partially completed the questionnaire. We utilized the latter

group as a proxy for non-respondents that have been included in our final sample. We

conducted independent samples t-tests that indicated non-significant differences between

complete and incomplete questionnaires suggesting that non-response bias is not a serious

concern (Schoenherr and Narasimhan, 2011).

3.2. Measures

SCI was conceptualized through customer and supplier side integration. Respondents were

asked multiple identical questions with regards as to how they coordinate planning decisions

and flow of goods with their key/strategic suppliers and customers. Customer and supplier

integration were each measured through six items ranging from one (none) to five (high)

indicating the level of adoption (Frohlich and Westbrook, 2001). The customer and supplier

items are listed in Table 3 and Appendix A.

Supply chain risk management practice was measured through various indicators

assessing the level of effort that companies have invested in action programs within the

previous 3 years. Items representing supply chain risk management practice focus on the

risk identification and mitigation phases (Manuj and Mentzer, 2008; Blome and Schoenherr,

2011). More specifically, regarding risk identification we include vendor monitoring and

rating programs and early warning systems and contingency programs (Blome and

Schoenherr, 2011). To measure the level of adoption of risk mitigation strategies we include

practices such as the restructuring and rethinking of the supply and distribution strategy

(Manuj and Mentzer, 2008) and supplier development programs (Blome and Schoenherr,

2011). All items were also measured on a five point likert scale ranging from one (none) to

five (high).

Operational performance was measured across the dimensions of cost, and

innovativeness (Garvin, 1987; Shin et al., 2000; Rosenzweig and Roth, 2004; Craighead et

al., 2009). Respondents were asked to address multiple items for each dimension indicating

their performance relative to their main competitors’ performance on a five point Likert-

scale where one indicates much worse, three equal, and five much better. Again, the

performance items are listed in Table 3 and Appendix A.

Rule of law is defined by the World Bank as the extent to which the agents have

confidence in and abide by the rules of society, and in particular the quality of contract

enforcement, property rights, the police, and the courts, as well as the likelihood of crime

and violence (Kaufmann et al., 2011). The indicator in Table 1 is a perception of rule of law

within a country. It is a proxy for how confident the citizens are in the legal / regulatory

system as well as their likelihood to abide by the rules of the system in the specific country

context. The rule of law measure ranges from -2.5 (weak) to 2.5 (strong) on a continuous

scale. The associated values that are listed in Table 1 are provided by The Word Bank2.

In addition, we employed three control variables to ensure the generalizability of our

results in terms of industry, level of globalization and location. Level of globalization was

conceptualized as the percentage of sourcing and sales outside the plant’s country.

Furthermore, we included the variable geographical focus to control for the location of the

plant.

3.3. Reliability and validity

We conducted confirmatory factor analysis (CFA) to validate our measures and to confirm

our proposed factor structure. In the following we analyse and discuss validity in terms of

content validity, convergent validity, discriminant validity and reliability (Nunnally, 1978;

Anderson and Gerbing, 1988). Firstly, content validity is assured through the several

development and design stages of the IMSS survey. Secondly, we used our CFA results to test

for convergent validity as suggested by O’Leary-Kelly and Vokurka (1998). Our proposed

structure of the items measuring the two dimensions of SCI, supply chain management risk

practices and two dimensions of performance resulted in a reasonably good

fitting model (χ2/d.f = 2.60; RMSEA = 0.049; AGFI = 0.91; CFI = 0.97; GFI = 0.93; IFI = 0.98;

NFI = 0.96; RFI = 0.95) indicating convergent validity (Bollen, 1989). Furthermore, all factor

loadings exceeded the value of .50 and the t-values were all greater than 2.0 (see Table

3) (Vickery et al., 2003). Finally, the factor loadings all exceeded twice the value of their associated

standard error, which further indicates for convergent validity (Flynn et al., 2010).

2 The rule of law indicator provides aggregate views on the quality of governance provided by a large number of enterprise, citizen and expert survey respondents across countries. The data has been gathered by a number of survey institutes, think tanks, non-governmental organizations, and international organizations (Kaufmann et al., 2011).

To test for discriminant validity we conducted CFA using a constrained model with every

possible pair of latent constructs and set the correlations between the paired constructs to 1.0

(Flynn et al., 2010). We compared the obtained results with the original unstrained model.

Results regarding χ2 differences indicate discriminant validity (O’Leary-Kelly and Vokurka,

1998; Flynn et al., 2010).


Finally, Cronbach’s alpha (α) has been used to test for the reliability. The Cronbach’s

alpha values listed in Table 3 are all above the commonly excepted level of 0.70, which

indicates that reliability is relatively high.

3.4. Measurement equivalence

Since we are using cross-country data it is also important to assess the equivalence of the

measures across countries. To do so we assessed the following properties of measurement

equivalence: calibration, translation, and metric equivalence (Douglas and Craig, 1983;

Wiengarten and Pagell, 2012).

Calibration equivalence in our dataset is ensured since we are using standardized likert

scales items across countries. The scales can be regarded as being standardized and

universally understandable. Furthermore, we controlled for translation equivalence through

calculating Tucker’s (1951) coefficient of factor congruence. Our calculated values all

exceeded the commonly referred threshold of 0.90. Finally, we assessed metric equivalence

through calculated individual Cronbach’s alpha for each country. The maximum individual

difference of alpha values across country was below 0.09.

Subsequently, through confirming all three dimensions of measurement equivalence we

conclude that this sample can be interpreted and utilized across countries. We conclude that

the sample exhibits the required characteristics of measurement equivalence.

4. Results

We seek to address the research questions as to what extent does risk associated with a

weak rule of law affect the impact of SCI on operational performance and to what extent can

supply chain risk management practices complement SCI under different risk scenarios. We

carried out a series of ordinary least square (OLS) regression analyses using the mean

composites for supplier and customer integration, risk management practices and cost and

innovation performance. To do so we calculate the mean scores for our constructs.

Furthermore, we use the z-scores to standardize our measures for the OLS analysis. Table 4

presents the correlation matrix.


Before carrying out the OLS analysis, we tested the data for linearity, normality and

multicollinearity (Kennedy, 1999). We assessed linearity and equality of variance through

plotting standardized residuals against the standardized predicted values. Tests for normality

also indicated that none of the assumptions of OLS regression were violated. To pre-analyze

the data, we calculated the variance inflation factors (VIFs) to detect any possible threats

(Aiken and West, 1991). The results indicate that VIFs are all well below 2 suggesting that

multicollinearity is not a serious concern. In addition, we calculated the Belsley-Kuh-

Welsch indices, which confirm that multicollinearity is not of significance.

All variables including the interaction terms for hypotheses three to six were introduced in

one model for each of the two operational performance dimensions (i.e., cost and innovation)

following a stepwise approach. In the first step we introduced the control variables, in the

section the independent variables and moderators, in the third step the two-way interaction

terms between supplier integration and customer integration and rule of law and the fourth

step the three-way interaction terms between supplier integration and customer integration

and rule of law and supply chain risk management practices. Table 5 presents the results of

the final model including all variables and interaction effects.


In hypotheses H1 and H2 we proposed that customer and supplier integration are

positively associated with the performance dimensions of cost and innovation. Results in

Table 5 cannot confirm the general propositions of these hypotheses. In model 1, using cost

as the dependent variable, supplier integration has a significant positive impact on cost

performance (B=.150; p=.008). However, customer integration does not significantly

improve cost performance (B=-.016; p=.759). In the second model, using innovativeness as

the dependent variable, supplier integration does also significantly improve innovation

performance (B=.103) but only at the p<.1 level. However, customer does also not

significantly improve innovation performance (B=-.066; p=.235). Subsequently, considering

the direct effect of SCI on performance, only supplier integration significantly improves cost

and innovation performance.

In hypotheses 3 and 4 we propose that weak rules of law reduces the strength of the

positive relationship between customer and supplier integration and cost and innovation

performance. To test these hypotheses we calculated the interaction terms between supplier

and customer integration and rules of law. In the cost model our results indicate that none of

the interaction effects were statistically significant.

Furthermore, in the innovation model our results also indicate that the 2-way interaction

terms were non-significant. Subsequently, we conclude that rule of law does not directly

moderate the integration – performance relationship.

In hypotheses 5 and 6 we propose a 3-way interaction effect between SCI (supplier and

customer integration), rule of law and risk management practices. Specifically, these

hypotheses test our second research question. We propose that higher levels of investments

in supply chain risk management practices may enable companies to complement their SCI

efforts when being situated in weak rule of law environments and thus strengthening the

positive relationship between customer and supplier integration and (a) cost and (b)

innovation performance.

In the cost model, the 3-way interaction effect between customer integration, risk and

risk management practices was insignificant (B=-.004; p=459), thus rejecting H5a.

However, in the cost model the 3-way interaction effect between supplier integration, rule of

law and supply chain management risk practices was significant (B=-.177; p=.004) and

added a significant weight of F (10.940; .000), thus confirming H6a. Supplier integration

has a significant positive impact on cost performance when the rule of law is low (i.e., high

risk) and companies have implemented supply chain risk management practices. In addition,

supplier integration has a significant negative impact on cost performance when the rule of

law is high and when companies are practicing supply chain risk management practices,

indicating a substitution effect between risk and risk management practices. A none-

significant slope was identified for the impact of supplier integration on cost performance

when companies are situated in low risk countries practices low levels of supply chain risk

management. Additionally, a non-significant slope was also identified for the impact of

supplier integration on cost performance when companies are situated in high-risk countries

practicing low levels of supply chain risk management.

Results in the innovation model somehow mirror our results from the cost model. The 3-

way interaction effect between customer integration, risk and risk management practices was

insignificant (B=.008; p=.806) thus rejecting H5b. However, in the innovation model the 3-way

interaction effect between supplier integration, rule of law and supply chain management risk

practices was significant (B=-.135; p=.031) and added a significant weight of F (5.271;

.000), thus confirming H6b. Supplier integration has a significant positive impact on

innovation performance when the rule of law is low (i.e., high risk) and companies have

implemented supply chain risk management practices. In addition, supplier integration has a

significant negative impact on innovation performance when the rule of law is high and

when companies are practicing supply chain risk management practices, indicating a

substitution effect between risk and risk management practices. A non-significant slope was

identified for the impact of supplier integration on innovation performance when companies

are situated in low risk countries practices low levels of supply chain risk management.

Additionally, a non-significant slope was also identified for the impact of supplier

integration on innovation performance when companies are situated in high-risk countries

practicing low levels of supply chain risk management.

5. Discussion

With this paper we intended to explore the role risk and risk management practices for

the success of SCI on cost and innovation performance. Our first two hypotheses tested the

general impact of customer and supplier integration on cost and innovation performance.

Previous studies provide mixed evidence regarding the direct impact of SCI on performance

and so does our study (e.g., Narasimhan et al., 2010; Schoenherr and Swink, 2012). We

somewhat confirm these mixed results. Our results indicate that whilst supplier integration

does significantly improve cost and innovation performance, strong integration with

customers does not pay off in terms of cost and innovation performance (see Table 6 for

summary of results). We have supported the development of these direct effect hypotheses

through the relational view but could not identify unifying confirmation.


Subsequently, we followed numerous calls within the literature to consider contextual

factors. The impact of risk, measured in terms of the rule of law, was evaluated in terms of

its influence on the SCI – performance relationship (Flynn et al., 2010). In hypotheses 3 and

4 we have proposed that SCI has a stronger impact on performance in environments

characterized by low levels of risk (i.e., strong rules of law), taking a focal company

perspective. We found no support that risk directly moderates the impact of customer and

supplier integration on and innovativeness.

In hypotheses 5 and 6 we then continued through exploring the possibility as to whether or

not companies can complement their supply chain integration efforts through implementing

supply chain risk management practices. We have identified that in the customer integration

model companies cannot gain significant performance benefits from complementing their

integration efforts through additional practices. Supply chain risk practices as proposed in this

paper are not effective to complement customer side integration practices in weak rules of law

environments. However, implementing supply chain management risk practices does compensate

for opportunistic behaviour in risk environments for supplier integration. This has been

confirmed for the cost and innovation model.

These results have various theoretical and managerial implications that will be discussed in

the following. From a theoretical perspective our research extends the work of Flynn et al.

(2010) and Schoenherr and Swink (2012) who urged researchers to consider other contextual

factors that influence SCI. In addition, both research teams suggested the need to consider

regional differences. The research reported in this paper utilises the findings from the World

Bank Report on Governance (Kaufmann, 2011) and looks specifically at one contextual

factor, the influence of the rule of law at the country level on SCI.

We have identified mixed results in our direct effects hypotheses. Thus these results

show that customer integration does not weight the same importance as supplier integration

when it comes to cost and innovation performance. It seams that the more supply chain

integration the higher performance relationship that has already been questioned by previous

researchers such as (Wiengarten et al., 2014) does also not hold in our research setting.

Supply chain integration comes at a cost. This needs to be taken into consideration when

identifying the “right” level of integration to gain performance benefits. This, as it seems, is

even more so true when it comes customer integration. Whilst not being the focus of our

study it could be of interest to study the interaction effects between supplier and customer

integration to identify the right mix of supply chain integration.

There are a number of practical implications that emerge from the research. Given the

mixed results, organisations need to be selective in terms of the SCI strategies and the extent

of their adoption. It seems that as a practice in itself customer integration is not as effective

as supplier integration when it comes to cost and innovation performance consideration. It

could be that customer integration might be more beneficial for other performance

dimensions such as flexibility.

We have also tested whether SCI practices are more or less effective depending on the

contextual risk environment. Our results could not find support for this proposition. Results

indicate that neither the effect of supplier nor customer integration on cost or innovation

performance differs depending on the risk environment. These results are important for

managers of global supply chains that implement standardised integration practices across

countries with varying rule of law levels. Managers can expect the same level of efficiency

from SCI across different risk scenarios.

However, from a supply side integration perspective the efficacy of these practices can be

even improved in terms of cost and innovative performance. If managers complement their

supplier side integration efforts with risk management practices in low rule of law environments

their efficacy increases. However, from a customer side perspective implementing risk

management practices does not yield any significant performance enhancing effects in low rule

of law environments. Thus, once more indicating that managers need to take a more selective

approach to implement supply chain management practices.

6. Conclusion

This study extends previous research on SCI, by adding to the body of literature on the

relationship between SCI and performance. The inclusion of the rule of law as a contextual

factor and incorporating the mitigating effect of supply chain risk management practices

builds upon previous research. Our findings suggest that the strength of the legal

environment, in terms of a country’s rule of law, per se is not a factor to be considered when

practicing SCI. Furthermore, we identified that supply chain risk management practices can,

in selected contexts; increase the efficacy of supplier integration.

There are some limitations with the existing research that should be considered in future

studies. The current study uses a cross-sectional design. Since integration between customers,

suppliers and focal companies takes place over time, it would be useful to investigate SCI

practices longitudinally. Secondly, the data was collected from the perspective of the focal

organization, future work could broaden the scope by considering customers and suppliers as

well, who potentially could be operating in different rule of law environments. Thirdly, the data

was only collected from single respondents and the sampling was carried out not randomly.

However, this large and multinational dataset does provide valuable insights on the aggregate

level. Fourthly, risk management practices were considered in general, not

distinguishing between different types of suppliers/customers. Further research should consider

risk management practices for specific types of suppliers/customers (e.g. strategic

suppliers/customers, local suppliers/customers, etc.). Finally, other contextual factors could be

considered, such as cross-cultural differences, specific industry sectors and company size.

Global supply chains form the backbone of the global economy, promoting trade,

consumption and economic growth. Trends such as globalisation, lean processes and the

geographical concentration of production have made supply chain networks more efficient,

but have also changed their risk profile. Many global organisations rely on companies who

operate in environments were the rule of law is weak and so it is important to have in place

contingencies to assess the potential impact of such legal risks.

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Table 1: Sample overview by country and rule of law score

Country FrequencyRule of law

Score (2009)

Belgium 33 1.382

Brazil 37 -.208

Canada 17 1.790

China 51 -.345

Denmark 18 1.896

Estonia 27 1.110

Germany 35 1.648

Hungary 69 .785

Italy 56 .354

Japan 20 1.290

Korea, Rep. 33 .978

Mexico 14 -.592

Netherlands 44 1.802

Romania 30 .0480

Spain 36 1.154

Switzerland 31 1.762

Taiwan 27 .924

UK 15 1.732

USA 44 1.540

Total 637

Table 2: Sample overview by industry

Industry Frequency

Manufacturer of metal products 233

Manufacturer of machinery and equipment 183

Manufacturer of office, accounting and computing machinery 12

Manufacturer of other electrical machinery/ apparatus 91

Manufacturer of TV, radio and communication 42machinery/apparatus

Manufacturer of medical, precision and optical instruments, 39watches and clocks

Manufacturer of motor vehicles, trailers and semi-trailers 52

Manufacturer of other transport equipment 33

637

Table 3: Confirmatory factor analysis results

Construct Mean S.D.Stand.

t-valueStd.

R2

Loading error

Customer Integration α = .827 2.97 .998

Share inventory level information with.71 19.00 .050 .51

key/strategic customers

Share production planning and demandforecast information with key/strategic .74 20.11 .045 .55customersAgreements on delivery frequency with

.68 17.85 .046 .46key/strategic customers

Dedicated capacity for key/strategic .71 18.75 .048 .50

customers

Vendor managed inventory orconsignment stock with key/strategic .69 17.93 .048 .47customers

Plan, forecast and replenishcollaboratively with key/strategic .76 20.54 .044 .57customers

Supplier Integration α = .862 3.07 .850

Share inventory level information with.66 17.15 .046 .53

key/strategic suppliers

Share production planning and demandforecast information with key/strategic .59 15.13 .042 .44suppliers

Agreements on delivery frequency with.63 15.91 .040 .31

key/strategic suppliers

Dedicated capacity for key/strategic.73 19.49 .044 .39

suppliers

Vendor managed inventory orconsignment stock with key/strategic .65 16.53 .045 .42suppliers

Plan, forecast and replenishcollaboratively with key/strategic .75 20.04 .044 .56suppliers

Risk Management Practices α = .768 2.82 .894

Rethinking and restructuring supplystrategy and the organization and .63 15.64 .047 .39management of supplier portfolio

Implementing supplier development and.69 17.60 .046 .48

vendor rating programs

Rethinking and restructuring distributionstrategy in order to change the level of .66 16.53 .047 .43intermediation

Implementing practices including early

warning system, effective contingency.72 20.04 .046 .52

programs for possible supply chaindisruptions

Cost performance α = .777 3.21 .651

Unit manufacturing cost .71 16.45 .032 .50

Labour productivity .70 16.04 .030 .49Inventory turnover .68 15.28 .033 .46

Manufacturing overhead costs .67 14.97 .031 .44

Innovation performance α = .742 3.43 .703

Product customization ability .62 14.26 .034 .37

Time to market .73 16.39 .035 .54

Product innovativeness .74 16.60 .034 .54

Table 4: Correlations

(8)(1) (2) (3) (4) (5) (6) (7)

Supplier Integr. (1) 1

Customer Integr. (2) .562** 1

Supply Chain Risk.512** .432** 1

Management Pract. (3)

Cost Performance (4) .318** .235** .348** 1

Innovation Performance.225** .147** .304** .499** 1

(5)

Rule of Law (6) .247** .248** .250** .195** .190** 1

Industry (7) .164** .087* .098* .031 .011 -.041 1

Level of Globalization (8) -.050 -.023 -.026 -.029 -.098* 235** .038 1

** Correlation is significant at the 0.01 level (2-tailed)

* Correlation is significant at the 0.05 level (2-tailed)

Table 5: OLS analysis of the Moderation Model

Moderation Model (1) (2)

Cost Model Innovation Model

Variable Standardized estimate/ Standardized estimate/

p-value p-value

Intercept 3.140/ 3.445/

.000 .000

Control Variables:

Industry -.013/ -.046/

.767 .302

Level of Globalization .037/ -.042/

.461 .422

Geographical Focus -.086/ -.103

.089 .046

Independent Variables:

Supplier Integration .150/ .103/

.008 .081

Customer Integration -.016/ -.066/

.759 .235

Moderating Variables:

Rule of Law .055/ .022/

.260 .668

Supply Chain Management Risk .205/ .144/Practices

.000 .008

Interaction Terms:

2-way Interaction

Supplier Integration X Rule of Law -.084/ -.110/

.141 .060

Customer Integration X Rule of Law -.063/ -.039/

.242 .482

3-way Interactions

Supplier Integration X Rule of Law X -.177/ -.135/Supply Chain Management Risk

.004Practices .031

Customer Integration X Rule of Law X -.004/ .008/Supply Chain Management Risk

.459Practices .906

Model Summary

Step 1:Adjusted R2/ .100/ .035/

Sig. F Change .000 .000

Step 2:Adjusted R2/ .146/ .059/

Sig. F Change .000 .001

Step 3: Adjusted R2 (incl. 2-way.172/ .081/

interaction term)/

Sig. F Change.000 .001

Step 4: Adjusted R2 (incl. 3-way.188/ .089/

interaction term)/

Sig. F Change.004 .057

Highest VIF/ 2.453/ 1.806/

Highest Condition Index 4.447 4.408

Table 6: Hypotheses discussion

Direct Effects: • H1a (rejected): Customer Integration does notSupply Chain significantly improve cost performance.integration & • H1b (rejected): Customer Integration does notPerformance significantly improve innovation performance.

• H2a (confirmed): Supplier Integration doessignificantly improve cost performance.

• H2b (confirmed): Supplier Integration doessignificantly improve innovation performance (only atthe <.1 level).

2-Way Interactions: • H3a (rejected): Rule of law does not moderate theSupply Chain relationship between customer integration and costIntegration & Risk performance.(Rule of Law) • H3b (rejected): Rule of law does not moderate the

relationship between customer integration andinnovation performance.

• H4a (rejected): Rule of law does not moderate therelationship between supplier integration and costperformance.

• H4b (rejected): Rule of law does not moderate therelationship between supplier integration and innovationperformance.

3-Way Interactions:

Supply chain• H5a (rejected): Companies cannot complement supplier

integration, Risk (Ruleintegration through supply chain risk management

practices when situated in high-risk environmentsof Law) & supply chain

(weak rule of law) through implementing, thusrisk management

strengthening the impact of customer integration on costpractices

performance.• H5b (rejected): Companies cannot complement supplier

integration through supply chain risk managementpractices when situated in high-risk environments

(weak rule of law) through implementing, thusstrengthening the impact of customer integration oninnovation performance.

• H6a (confirmed): Companies can complement supplier integration through supply chain risk management practices when situated in high-risk environments (weak rule of law) through implementing, thus strengthening the impact of supplier integration on cost performance.• H6b (confirmed): Companies can complement supplier integration through supply chain risk management practices when situated in high-risk environments (weak rule of law) through implementing, thus strengthening the impact of supplier integration on innovation performance.

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