ORGANIZATIONAL CONFIGURATIONS AND STRATEGIES RELATED TO FINANCIAL PERFORMANCE IN MEDICAL GROUP PRACTICES: A TEST OF

PORTER’S GENERIC STRATEGIES

by

TODD B. SMITH

S. Robert Hernandez, DrPH, Committee Chair

Richard M. Shewchuk, PhD Jeffery H. Burkhardt, PhD Thomas L. Powers, PhD Douglas J. Ayers, PhD

A DISSERTATION

Submitted to the graduate faculty of The University of Alabama at Birmingham, in partial fulfillment of the requirements for the degree of Doctor of Philosophy.

BIRMINGHAM, ALABAMA

2011

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Copyright by Todd B. Smith

2011

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ORGANIZATIONAL CONFIGURATIONS AND STRATEGIES RELATED TO FINANCIAL PERFORMANCE IN MEDICAL GROUP PRACTICES: A TEST OF

PORTER’S GENERIC STRATEGIES

TODD BRENTON SMITH

HEALTH SERVICES ADMINISTRATION

ABSTRACT

Research in the field of organizational configurations (OC) involves the formation

of groups of firms that are similar to each other on certain characteristics, and dissimilar

from other groups, and explores organizational performance differences between the

groups (Ketchen & Shook, 1996; Short, Payne, and Ketchen, 2008). However, OC is

replete with literature lacking in common key terms, measurement methods, and

specification of variables, and too few empirical articles with a strong theoretical basis

have been published (Short, Payne, & Ketchen, 2008). Porter’s (1980, 1985) generic

strategies are a specific typology within the field of OC that have been used extensively

in the literature. Through a review of the major academic literature databases, however,

only one empirical study (Payne, 2001) has been published that specifically uses Porter’s

generic strategies with a sample of medical group practices. Therefore, following a call

from Shortell and colleagues (2005) for theory-driven research regarding predictors of

high-performing versus low-performing medical groups, this paper used data from the

MGMA 2009 Cost Survey to explore the financial performance differences between

medical practices that were classified into one of several strategy groups, based on

Porter’s generic strategies. This study also used an inductive methodology, a cluster

analysis, to divide the sample into six distinct groups, which were then compared with the

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features and performance of the groups created with deductive methodology, Porter’s

generic strategies.

The findings of both the inductive and deductive methodologies suggest that

medical group practices using a differentiated strategy were the best performers, with

cost leaders, hybrids, and mixed strategy groups having similar performance levels.

Specifically, medical group practices that provided a greater number of ancillary services,

had at least one branch office, and spent more on advertising and furniture/equipment,

were more likely to have higher profit ratios than other practices. However, the inductive

technique indicated that having a low accounts receivable ratio, a cost leadership strategy,

may also be related to a high profit ratio. Overall though, while this paper partially

supports Porter’s generic strategies typology, the inductive methodology was relatively

more efficient in explaining the variation in the dependent variable based on the eight

strategy indicator measures developed within the study. The findings of this study

provide additional support for Porter’s generic strategies, as well the overall field of

organizational configuration research, and will advance the knowledge base within the

field of health care regarding the strategy-performance links within medical group

practices.

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DEDICATION

I dedicate this dissertation to my wife and parents. My wife has endured many

years of weekends and evenings without a husband during my time as a doctoral student.

Her unwavering support and overlooking of my “honey-do” list for many years is very

much appreciated. Additionally, my parents have always been available to listen to the

ordeals of my academic endeavors and have been understanding about my absence from

many family affairs these past several years. I cannot overstate the appreciation I have

for my family’s support of, and interest in, my doctoral studies.

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ACKNOWLEDGEMENTS

I wish to thank Drs. Shewchuk and Haiyan Qu for their extensive work with me

over many months regarding the methodology section of this dissertation. Their help was

invaluable and I learned so much from each of them. I also wish to thank my brother and

sister-in-law, Bryan and Misty Smith, for their review and feedback of the manuscript in

regards to the overall flow and grammar.

The University of Alabama at Birmingham (UAB) and my supervisors have been

instrumental regarding my doctoral studies during my employment at UAB. The tuition

reimbursement from UAB and the support of my supervisors was vital in the successful

completion of my studies. Specifically, Dr. David Kimberlin and Mr. Rich Pierce have

provided me with the occasional flexibility in my work schedule as I completed my

doctoral studies.

Mr. David Gans and MGMA were extremely gracious in providing me with the

data for this study and I will forever be thankful to them for their assistance.

Additionally, I want to also thank Dr. Hernandez for providing me with the link between

UAB and MGMA, as well as his guidance in the initial phases of my research, including

the identification of an applicable theory and subsequent theory development.

Finally, I wish to thank all of my committee for their continued support and many

other UAB faculty members who have guided me through my doctoral studies. As a

part-time student, I have taken somewhat longer than normal to complete my studies and

dissertation. However, the faculty and my committee have never wavered in their

support of my efforts to complete this degree.

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TABLE OF CONTENTS

Page

ABSTRACT........................................................................................................................iii DEDICATION ..................................................................................................................... v ACKNOWLEDGEMENTS ................................................................................................ vi LIST OF TABLES .............................................................................................................. ix LIST OF FIGURES ............................................................................................................ xi CHAPTER 1: INTRODUCTION ........................................................................................ 1 Purpose and Research Questions ............................................................................. 6 CHAPTER 2: LITERATURE REVIEW ............................................................................. 7 Foundations of Organizational Configuration Research ......................................... 7 Organizational Configuration Research ................................................................ 15 Porter’s Generic Strategies - Typology ................................................................. 25 The Outcome of a Successful Strategy - Performance .......................................... 33 Medical Group Practices ........................................................................................ 38 CHAPTER 3: METHODS ................................................................................................. 44 Research Questions ................................................................................................ 44 Study Population, Variables, and Operational Definitions .................................... 46 Statistical Methods in Organizational Configuration Research............................. 64 Hypotheses ............................................................................................................. 67 CHAPTER 4: RESULTS AND FINDINGS ..................................................................... 73 Preliminary Data Cleaning and Preparation for Data Analysis ............................. 73 General Practice Demographics ............................................................................ 78 Descriptive Analyses for the Variables Included in the Model ............................. 80 Grouping the Practices ........................................................................................... 85 Empirical Analyses ................................................................................................ 87 Hypothesis 2 – Performance Differences ............................................................ 100

Hypothesis 3 – Performance Differences Between the Theoretical and Empirical Models ................................................................................................. 102

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Table of Contents (continued) CHAPTER 5: SUMMARY AND CONCLUSIONS ....................................................... 104 Discussion of Study Findings .............................................................................. 104 Limitations ........................................................................................................... 106 Future Research ................................................................................................... 111 Final Conclusion .................................................................................................. 113 LIST OF REFERENCES ................................................................................................. 115 APPENDICES ................................................................................................................. 128 A Total RVUs and Total Number of FTE Physicians ...................................... 128 B Practices Grouped By Size ........................................................................... 129 C Comparison of Practices Included and Excluded ......................................... 130

D Descriptive Statistics and Discussion of Selected Variables ........................ 131

E Winsorization................................................................................................ 144 F Imputation ..................................................................................................... 145 G Post-Hoc Analysis – Hypothesis 1 ............................................................... 146 H IRB Approval ............................................................................................... 147

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LIST OF TABLES

Table

1 Measures of the Independent and Dependent Variables........................................ 72

2 Descriptive Statistics – Total Number of FTE Physicians .................................... 76

3 Practice Type (N = 1,413) ..................................................................................... 77

4 Legal Organization Type (N = 1,413) ................................................................... 78

5 Majority Owner (N = 1,413) .................................................................................. 78

6 Population category (N = 1,413) ........................................................................... 79

7 Descriptive Statistics – All Variables Included in Model (N = 1,413).................. 80

8 Specialty Services (N= 1,413) ............................................................................... 81

9 Branches for Each Practice (N = 1,413) ................................................................ 81

10 Measures for Each of the Strategy Indicator Variables & the Outcome

Variable (Profit Ratio) ........................................................................................... 82

11 Preliminary Differentiator and Cost Leader Groupings (N = 1,413) .................... 86

12 Final Categorization of All Practices (N = 1,413) ................................................. 86

13 Descriptive Statistics for the Four Groups (N = 1,413) ......................................... 88

14 One-Way Analyses of Variance for Effects of Group Membership on

the Profit Ratio (N = 1,413) ................................................................................... 88

15 Probabilistic Weighted Means of Each of the Six Groups by Each of the

Eight Strategy Indicator Ratio Measures & The Size of Each Group ................... 91

16 Mean Profit Ratio for Each Group (N = 1,413) ................................................... 101

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List of Tables (continued)

17 One-Way Analysis of Variance for Effects of Group Membership on

the Profit Ratio (N = 1,413) ................................................................................. 102

18 Variation Explained by the Inductive and Deductive Methodologies ................. 103

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LIST OF FIGURES

Figure

1 Hypothesis Model: Differentiator, Cost Leader, Hybrid, and Mixed

Groups – Broad Focused Medical Groups (Multispecialty) .................................. 70

2 Hypothesis Model: Differentiator, Cost Leader, Hybrid, and Mixed

Groups – Narrow Focused Medical Groups (Single Specialty) ............................ 71

3 Mean Profit Ratio of Practices by Organizational Strategy Configuration

(N = 1,413) ............................................................................................................. 88

4 Normalized Means for Each of the Eight Indicator Variables, Plotted

by Group Membership ........................................................................................... 91

5 Group 1 – Stuck in the Middle #1 ......................................................................... 92

6 Group 2 – Stuck in the Middle #2 ......................................................................... 93

7 Group 3 – Cost Leader ........................................................................................... 94

8 Group 4 – Stuck in the Middle #3 ......................................................................... 95

9 Group 5 – Stuck in the Middle #4 ......................................................................... 96

10 Group 6 - Differentiator ......................................................................................... 97

11 Strategy Dimension - Differentiator ...................................................................... 98

12 Strategy Dimension - Costs Leadership ................................................................ 99

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CHAPTER 1

INTRODUCTION

In 2008, national health care expenditures in the United States were

approximately $2.3 trillion, a figure that is expected to grow to $4.5 trillion (19.3% of

GDP) by 2019 (Truffer et al., 2010). Medical group practices, defined as three or more

physicians sharing business, clinical, and administrative functions, fall within the

physician and clinical services segment of health care, which comprises about one-fifth

of total national health expenditures (Sisko et al., 2009). However, very little is known

about medical groups as compared with other segments of health care (e.g., health

insurance and hospitals) (Casalino, Devers, Lake, Reed, & Stoddard, 2003). Thus, to

further our knowledge of medical group practices and benefit numerous stakeholders in

the U.S. health care system, this study will analyze the data from hundreds of medical

groups to identify groups of medical practices with similar organizational configuration

strategies and determine if financial performance varies between the groups.

At the present time, major factors in the health care and private industry are

putting a financial strain on medical group practices in the U.S., which also leads to the

pertinent nature of this study regarding the efficiency of medical group practices. For

example, large companies with purchasing power and influence on health care decisions

are increasingly placing health care providers under scrutiny (Shortell et al., 2005). Some

of these organizations are offering their employees scorecards indicating the cost

effectiveness of specific physicians within their individual health care plans (Thibadoux,

Scheidt, & Luckey, 2007). Also, new methods of reimbursement for physician services

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are being deployed. For example, the pay for performance (P4P) model rewards

physicians for quality care and may potentially decrease reimbursement for those

physicians who demonstrate lower quality care (Locke & Srinivasan, 2008).

Additionally, major future cuts in Medicare physician payments are expected due to the

2010 Patient Protection and Affordable Care Act, which established the Independent

Payment Advisory Board to issue recommendations for reducing the growth in Medicare

spending (Ebeler, Neuman, & Cubanski, 2011).

As with traditional for-profit firms, and unlike physician groups in many of the

health care systems in other countries with large population-based public or government

health insurance programs, U.S. medical group practices are often entrepreneurial and

profit-seeking (Relman, 2007) and they must generate sufficient profit to maintain

viability (Shortell, et al., 2005). Thus, most medical group practices have placed a

significant emphasis on efficiency and cost control - strategies which will become more

relevant in future years with the increasing pressures from the large purchasers of health

care services (Shortell, et al., 2005). Unlike most traditional for-profit firms, however,

medical group practices operate in an environment with many unique constraints: third-

party payers, capitation, very strong governmental influences, and geographic limitations

(Porter & Teisberg, 2004). Further, although many traditional firms often integrate with

other firms and make attempts to create economies of scale, physician practices have

typically remained as relatively small, entrepreneurial-style firms. Thus, with these

increasing cost and revenue pressures, many physician practices are implementing

specific strategies to enhance their operations and profitability (Locke & Srinivasan,

2008; Shortell, et al., 2005). To date though, very little empirical or theoretical research

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has been published that explores the various organizational configuration (OC) strategies

that a medical group practice employs or the outcomes of these strategies on the

profitability of an individual medical group practice.

Research in the field of organizational configurations, which comprises an

amalgamation of strategic research methods and techniques with a common focus on the

strategy, goals, and structures of similarly grouped firms, contains theories that may aid

in this exploration of the strategy-performance link in medical group practices.

Typologies, strategic groups, archetypes, and other organizational classification methods

have been placed under the broad configurational research subset of strategic

management and are commonly used to describe similar groups of firms based on their

configuration and strategy, explain the success or failure of a firm based on group

membership, and make predictions about which groups of firms may perform better than

others. However, the field of configurational research is replete with literature lacking in

common key terms, measurement methods, and specification of variables, and too few

empirical articles with a strong theoretical basis have been published (Short, Payne, &

Ketchen, 2008).

Michael Porter (1980, 1985) has developed a generic strategies typology which

falls within the field of configurational research and has been used extensively to

categorize firms by the strategies they employ. Using Porter’s generic strategies to

classify firms and study firm performance has been widely accepted in the academic

literature (Allen & Helms, 2006; Chrisman, Hofer, & Boulton, 1988; A. Miller & Dess,

1993). Porter states that firms either explicitly or implicitly adopt one of several

strategies to defend against and outperform their competitors. The most notable of these

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strategies are cost leadership and differentiation, in addition to the market scope (broad or

narrow market segment) strategy. Porter posits that successful firms will typically adopt

no more than one of these specific strategic configurations, as each strategy requires total

commitment from the firm. Further, Porter theorizes that firms that fail to adopt one of

these specific strategies, or focus on more than one strategy simultaneously, will

ultimately become “stuck in the middle” (a.k.a. a mixed strategy). Firms employing a

mixed strategy are expected to exhibit subpar performance compared with other firms in

their industry due to their lack of focus and commitment to a single, overarching strategy

for their firm (Parnell, 2006; Porter, 1980). In addition to the stuck in the middle

category though, many authors (Buzzell & Wiersema, 1981; Cross, 1999; Hambrick,

1981; Helms, Dibrell, & Wright, 1997; Hill, 1988; Hlavacka, Bacharova, Rusnakova, &

Wagner, 2001; Karnani, 1984; D. Miller, 1992; D. Miller & Friesen, 1986b; Murray,

1988; Phillips, Chang, & Buzzell, 1983; White, 1986) have introduced another category

to Porter’s generic strategies, the hybrid group, which includes strong characteristics of

both the cost leadership and differentiator constructs. The hybrid strategy is distinct from

the mixed strategy in that firms with a mixed strategy do not exhibit strong characteristics

of either a cost leader or differentiator.

Through searches of several major literature databases, no published works in the

academic literature have been found that specifically use Porter’s generic strategies

model to explore the financial performance differences that may exist between medical

group practices. However, through a review of the academic literature related to Porter’s

generic strategies in other industries, and literature related specifically to medical group

practices and health care in general, this author believes it will be possible to derive

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strategic characteristics found within medical group practices to develop groups that are

similar to those described within Porter’s generic strategies. Further, using literature

from numerous fields as a guide and data from medical group practices, this study will

build and test a model based on Porter’s generic strategies.

From a review of the literature on strategic typologies and organizational

configurations, this author theorizes that successful medical group practices will typically

conform to one of Porter’s pure generic strategies or the hybrid strategy. The proposition

is that medical group practices employing an organizational configuration strategy based

upon one of these pure generic strategies, or the hybrid strategy, will exhibit superior

financial performance compared with those medical groups practices that demonstrate the

characteristics of a mixed strategy (Powers & Hahn, 2004).

As there is a dearth of research examining the performance of medical group

practices, Shortell and colleagues (2005) have called for theory-driven research regarding

predictors of high-performing versus low-performing medical groups. Through a review

of the major academic literature databases, however, no research specifically using

Porter’s generic strategies regarding the organizational configurations, strategies, or

financial performance variations in medical group practices, and only one empirical

research work related to studies of medical group strategies using an objective financial

performance measure (Payne, 2006), has been discovered. Thus, this study will initially

lead to a further understanding of how one may categorize medical group practices by the

generic strategies they employ. Additionally, this study will advance the body of

knowledge regarding financial performance differences between medical group practices

based on their organizational configuration type and strategy. Finally, as Porter’s generic

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strategies are known as a generalizable typology, this research study will lead to a greater

understanding of the strategy-performance link in the general field of business and further

the literature in regards to methods, terms, and measurements in the field of

configurational research.

To guide the theory development, the operationalization of the variables, and the

specification of the model within this study, the business and health care academic

literature in the fields of strategic management and configurational research will be

utilized. To validate the theoretical model, the study will employ both empirical (i.e.,

cluster analysis) and theoretical (i.e., Porter’s generic strategies) classification methods to

categorize the medical group practices within the sample. Subsequently, the study will

compare the characteristics and performance differences that may be found between the

groups that will be created from the empirical and theoretical classification methods.

Purpose & Research Questions

1. Identify specific organizational configurations and strategies used in medical group practices that can be linked specifically to one of Porter’s generic strategies.

2. Using a deductive methodology, do medical groups that exhibit the characteristics of one of Porter’s pure generic strategies (i.e., target scope and then differentiation or cost leadership) perform better financially than medical groups that use a hybrid strategy or groups that exhibit a mixed strategy (stuck in the middle)?

3. Using an inductive methodology (cluster analysis), do specific groups of organizations with similar characteristics perform better financially than others?

4. Comparing the inductive and deductive methodology, will one methodology lead to the formation of groups that will better predict the financial performance of certain medical group practices?

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CHAPTER 2

LITERATURE REVIEW

The Foundations of Organizational Configuration Research

Research regarding organizational configurations (OC) has foundations in the

fields of both organizational theory and strategic management, as well as other popular

and historical fields of management. The configurational stream of research has

furthered our understanding of organizational configurations and performance differences

between organizations through a parsimonious method of grouping firms by similar

characteristics and then studying the different characteristics and performance differences

between the groups. The key concept of configurational research is that individual firms

can be viewed as clusters of firms with similar practices and strategies, and that key

organizational features and strategies can shape a firm’s performance, with performance

often measured as firm’s return on assets (Short, Ketchen, Palmer, & Hult, 2007).

Configurational research, which typically focuses on the similar patterns within

identified groups of firms as well as the performance outcome differences between the

different groups of firms, has many underpinnings from other fields of management

research. Research in the field of strategic management typically posits that specific

strategies can be matched to the structure and environment of a given firm and proper

deployment of these strategies can lead to superior performance compared with firms

using other strategies. In contrast, in the field of organizational theory, researchers

typically focus on the design and behavior of firms, sometimes through the use of

strategic choice and decision-making models, but without a central focus on an outcome

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based on performance (Fiss, 2007). In the fields of structural contingency and industrial

organization, the focus is typically related to how environmental factors guide a firm’s

strategic decisions and impact a firm’s financial performance. Organizational

configuration research combines facets from all of the aforementioned fields of

management research in an effort to understand the performance differences between

firms belonging to distinct groups based on common attributes (Dess, Newport, &

Rasheed, 1993; Ketchen et al., 1997).

Structural Contingency Theory - Categorizing Firms and Their Strategy

Structural contingency theory is a major foundation of OC. Weber (1947) was

one of the first authors to describe various configurations of organizations and ascribe

specific structural differences between, and attributes to, these organizations. As a

historian and sociologist though, Weber’s works did not lead to practical managerial or

academic implications within the various configurations he found. In later years though,

Woodward (1958) took Weber’s work a step further by identifying numerous categories

of production firms with differing strategies, finding that small batch production

companies focused on meeting the customer’s need while large batch production

companies focused on efficiency. The focus of Woodward’s (1958) organizational work

was on the type and quantity of products produced, and how the technology enabling the

production affects various aspects of the structure of the organization. Woodward (1958)

also used empirical methods to describe the performance differences between specific

firms and their peers on a number of structural dimensions.

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Many other structural contingency theorists have laid the foundation for the

current research field of organizational configurations. Burns and Stalker (1961) studied

the differences between firms with either a mechanistic or organic structure, determining

that within dynamic economic sectors, firms with an organic structure would perform

better. Lawrence and Lorsh (1967) studied the differences between six organizations and

the impact the environment had on their structures and performance, finding generally

that firms able to achieve both high integration and differentiation will perform better

than other firms. In a study of hospital operating rooms, Galbraith (1973) used

contingency theory to explore how firms should organize in uncertain environments,

finding that there was no one best way to organize, but not all ways of organizing are

equally effective (thus exploring the issue of equifinality, which will be discussed later).

Filley and Aldag (1978) created a taxonomy of organizations based on contingency

theory and identified patterns within a number of firms, creating craft, promotion, and

administrative groups. Subsequent contingency theory research by Filley and Aldag

(1980) determined that the product selection varied between firms with an efficiency

strategy and firms with a “made-to-order” strategy.

In summary, structural contingency theorists typically view the unit of analysis as

the firm, as do OC theorists. However, unlike OC theorists, those in the field of

structural contingency typically limit their research to structural and situational concepts

related to firms. Building on structural contingency theory, however, organizational

configuration theorists study organizations as open-systems and in a multi-dimensional,

non-linear context. Thus, OC theorists believe organizations determine their adaptive

needs to an environment, rather than the environment determining the characteristics of

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an organization, and situations of equifinality often exist in the view of OC theorists

(Meyer, Tsui, & Hinings, 1993).

Organizational Ecology

The widely cited organizational ecology works of Hannan and Freemen (1977,

1984) have also laid the foundation for organizational configuration theory. These

authors state that firms will develop certain structural characteristics allowing them to

adapt to their environment in order to become or remain successful. Thus, certain firms

through collective, rational action will succeed while others will not. The debate in the

organizational configuration versus ecology literature relates to whether the structure

leads to strategy or vice-versa. Organizational configuration theorists emphasize

strategic choice and thus tend to downplay the structural elements, while organizational

ecologists posit that the structure or environment lead to the strategy of an organization

and certain organizations will fail because they are unable to adapt to a certain

environment (Ketchen, Combs, Russell, Shook, Dean, et al., 1997).

Industrial Organization Theory

Organizational configuration theory also has strong links to industrial organization

theory. Concepts from industrial organization (IO) theory lead one to believe that macro

industry-wide factors are the primary drivers of performance of an individual firm (Bain,

1956; Barney, 1986a; Mason, 1939; Thornhill & White, 2007). This view takes the

external environment of a firm and explores these environmental factors as they relate to

a firm’s internal performance. The traditional model in industrial economics is the

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structure-conduct-performance paradigm: firm performance depends on its conduct

related to pricing, research and development, and investments, and firm conduct is based

on an industry’s structure – concentration levels, barriers to entry, and degree of product

differentiation (Spanos, Zaralis, & Lioukas, 2004). Thus, the success of the industry as a

whole is thought to be primarily influenced by “barriers to entry, the number and relative

size of firms, the existence and degree of product differentiation in the industry, and the

overall elasticity of demand for the industry” (Barney, 1986b, p. 792).

In the IO view of business, organizations tend to position themselves within an

industry so that they can enhance their productivity and ultimately their profitability

(Parnell, 2006). However, the industrial organization theory of the firm cannot explain

the wide variations in firm performance within a specific industry (Parnell, 2006). The

original work regarding Porter’s five-forces model (1980), which deals with a firm’s

opportunities and threats, is based on the IO economics view.

A number of researchers (Caves & Porter, 1977; Porter, 1980) have studied the

opportunities and threats that exist for a firm in a competitive environment using

industrial organization theory. However, this type of analysis typically assumes that a

firm’s resources are similar to those of its competitors (Barney, 1991). Thus, the focus in

IO theory is more on the attractiveness of a given industry, rather than on the individual

strategies that a firm may use to improve its performance in comparison with its

competitors.

In industrial organization theory, specific industries are often seen as more attractive

than others based upon certain environmental structural characteristics such as the

competitive intensity, barriers to entry, bargaining power of suppliers and buyers, and the

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threat of substitutions within a given industry (Bishop & Megicks, 2002). Researchers

(Caves, 1980; Fiegenbaum, McGee, & Thomas, 1988; Hatten & Schendel, 1977; Spence,

1977) have also used IO theory to study how individual firms within an industry can

modify the structural characteristics of their industry to enhance typical profit margins by

increasing product differentiation, reducing demand elasticity, or other strategies

(Barney, 1986b). As the industry/firm performance relationship is a narrow view of the

concept though, some authors (Porter, 1981) began to look at the strategic group level to

further our understanding of the competitive strategy/performance link. Strategic group

research, which is discussed in more detail below, was derived from IO theory and is a

component of OC theory. Strategic groups are defined as clusters of firms within a

specific industry that exhibit similar characteristics (Parnell, 2006). However, strategic

group research has been noted as lacking in generalizability across industries (Short, et

al., 2008).

Organizational Theory

Organizational theory focuses on the characteristics of organizations, though often

absent the detailed analyses related to firm performance, as with the more specific

organizational configuration research. Jensen (1983, p. 320) describes organizational

theory as the study of “why organizations take the form they do and why they behave has

they do.” Astley and Van de Ven (1983) used organizational theory and a model of

strategic choice to find that organizations are autonomous, proactive, and self-directing.

The individuals within these organizations act within their structural constraints, but they

may shape the structure of their organizations.

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Daft (1993) says that organizational size is often the most widely used variable in

organizational theory, with size often used to predict changes in the structural design of

organizations. Historically, organizational theories have studied broad issues within

organizations such as the coordination of work and goal conflict (March, Simon, &

Guetzkow, 1958). In more recent years, organizational theory has shifted from

paradigm-driven work to problem-driven work, with several works focusing on the

impact and changes of organizations over time through historical economical and societal

shifts (Davis & Marquis, 2005). However, the overall focus of organizational theorists

typically remains broad, without a specific focus on the prediction of organizational

performance as it relates to strategy, as with studies involving organizational

configuration research.

Strategic Management and Competitive Advantage

Organizational configuration research is derived from the previously discussed

theories, which focus more on the structure of organizations, and combines these theories

with the field of strategic management and its focus on the performance of a firm.

Strategic management is defined as a field of research that centers on the relationships

between a firm’s strategies, leadership, organization, environment, and performance

(Hoskisson, Hitt, Wan, & Yiu, 1999; Ketchen & Shook, 1996). Early strategy writers

(Barnard, 1938; Penrose, 1959; Selznick, 1957) made attempts to define internal

competitive resources that made firms successful. These researchers argue that a firm’s

success is a function of its unique set of competitive resources (Hoskisson, et al., 1999),

which is similar to some of the more modern research using the resource-based view

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theory. Strategic management is in contrast to researchers (Bain, 1956; Mason, 1939) in

the field of industrial organization economics, who are more interested in the structure of

a specific industry and competitive positions within that industry. In the field of strategic

management, however, researchers typically focus on realized strategy, as opposed to

intended strategy (Spanos, et al., 2004), and individual firm level strategies and

performance are typically the units of analysis.

Hofer and Schendel (1978) describe an organization’s strategy as the characteristics

a firm creates to achieve its desired objectives and goals though matching its skills and

resources to its environment. “A firm’s competitive strategy varies along two main

dichotomous dimensions: (1) a method of developing competitive advantage and (2)

breadth of operations” (Ketchen, Thomas, & Snow, 1993, p. 1286). The strategy that

encompasses matching a firm’s resources to its goals can enable a firm to achieve its

desired competitive advantage, which is noted as the outcome of the choice of an

effective competitive strategy (Chrisman, et al., 1988). Hofer and Schendel (1978)

identify several types of organizational strategies, but these authors believe that

competitive strategies are the most vital strategies for most firms to employ in order to

achieve a competitive advantage in the marketplace. To measure this competitive

advantage, “performance differences between configurations should be based on (1) a

firm’s commitment to prior strategic choices and (2) differing levels of environmental

benevolence across an industry” (Ketchen, et al., 1993, p. 1286).

Barney (1991) has distinguished between a sustained competitive advantage and

merely a competitive advantage. He states that the sustained competitive advantage

strategy employed by a firm is one that is not currently being implemented by a firm’s

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competitors and cannot be easily replicated by the firm’s competitors. Others, however,

use the term to signify that a firm has created a strategy that enables it to generate above

average returns over time when compared with that firm’s competitors (Barney, 1991;

Schoemaker, 1990).

Organizational Configuration Research and Strategy

Miller (1986) states that certain organizational structures and strategies, though

related in complex ways, often coalesce into several common, identifiable, and relatively

stable organizational configurations. The study of the differing elements and strategies

that are found between different sets of firms, as well as performance differences that

may exist between the groups, leads one to the field of organizational configuration

research. Organizational configuration research differs from the aforementioned

organization theory and strategy research streams in how it defines and utilizes three

main concepts related to deductively or inductively defined groups of firms: description,

explanation, and prediction (Short, et al., 2008). The descriptions of a firm in OC

research are based on the similarities and differences between the defined groups.

Explanations of a firm typically involve an analysis of the factors contributing to the

successfulness of the specified groups. And finally, the prediction typically offers an

analysis of which firms or groups of firms will be more successful under a certain set of

conditions (Short, et al., 2008). Short and colleagues (2008) recently identified 110

articles since 1993 published in the field of OC. However, albeit the numerous articles

published in this field, Short and colleagues (2008), as well as others (Dess, et al., 1993;

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Mintzberg, 1990), have elucidated a number of issues with OC research, including many

definitional and methodological conundrums.

Organizational configuration research suggests that organizations can be viewed

as interconnected groups, rather than in isolation, and that performance variations

between these groups can often be found (Fiss, 2007). Typically, OC research (e.g.,

Mintzberg’s (1979) theory of organizational structure and Miles & Snow’s (1978) theory

of strategy structure, and process) posits a higher degree of performance in groups of

organizations with a specific type of a theoretically generated configuration – based on

strategic, contextual, and structural characteristics – as opposed to other groups of firms

with an oppositional or less than ideal configuration (Doty, Glick, & Huber, 1993).

Organizational configurations are generally described using mutually exclusive and

collective exhaustive categories and the methodological approach to generate these

categories is typically through a taxonomy or typology. A typology typically refers to a

“conceptually driven classification scheme whereas a taxonomy refers to an empirically

generated classification” (Short, et al., 2008).

Organizational configuration research is a term that is often used in a broad sense in

the field of strategic management to cover a number of techniques that describe the

structure and strategies of organizations. Research in the field of OC often involves

specific methods used to divide firms into groups with “conceptually distinct

characteristics that commonly occur together” (Meyer, et al., 1993, p. 1175) –

specifically, grouping firms with similar profiles together and distinguishing each group

from other groups in the same sample (Ketchen & Shook, 1996). In describing four

specific OC methods used to classify organizations, Short (2008) clarifies several major

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OC terms for future researchers, as these terms are often garbled in the OC literature

(Meyer, et al., 1993; Reger & Huff, 1993), and thus less useful to the field of OC

research. Strategic groups are defined as methods to explore the differences between

groups of intra-industry firms with similar competitive strategies and archetypes describe

organizational features of firms within the same industry. As both of these methods are

confined to context-specific settings, they are not generalizable. On the other hand,

generic strategies describe a firm using broadly defined general strategies and

organizational forms delineate organizational features of firms across industries. Both

generic strategies and organizational forms are based on methodologies that create a

generalizable outcome (Short, et al., 2008). Beyond these broad classification terms,

researchers may then use either an empirical (taxonomy) or theoretical (typology)

methodology to classify organizations within their sample, and these classification

schemes are described in detail below.

Organizational Configuration Research in the Field of Health Care

Although the field of OC research is relatively new, the health care industry has been

particularly overlooked by many OC researchers. However, there are a limited number

of health care specific studies in the field OC. Ketchen and colleagues (1993) used

configurational research methods to longitudinally study a sample of hospitals, grouping

them using both inductive and deductive methods to offer predictions of performance

based on configurational types. Bierly and Chakrabarti (1996) used configurational

research as a basis for grouping pharmaceutical firms based on their knowledge

strategies, exploring profit differences between the groups. Jiang and colleagues (2006)

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used a strategic adaptation method to classify hospitals into four quadrants based on

quality, cost, business operations, and structure, finding that hospitals in the low-

mortality/low-cost quadrant were better performers than those in the other three groups.

Forte and colleagues (2000) divided a number of hospitals into distinct groups based on

configurational research and measured the performance differences between the groups.

And finally, Payne (2006) studied performance differences using organizational

configuration research by grouping medical group practices into several configurations

based on a number of strategy and structure variables. While each of the aforementioned

studies were based on configurational research methods and used a sample from the field

of health care, the specific methods by which they grouped the firms were based on a

variety of inductive and deductive methodologies, which will be described in more detail

below.

Taxonomy-Based (Inductive) Configurational Research

In general, taxonomy is defined as the science and theory of classification methods.

In configurational research, taxonomies are the quantitative methods used to classify

subjects within a specific sample, often with an inductive classification technique

followed by an empirically generated cluster analysis. Typically, the researcher will

follow the quantitative technique with a deductive test of the classes against a set of

theoretical propositions (Short, et al., 2008). Following a prescribed theory of taxonomy,

researchers build a specific classification system to develop a model they will use to

arrange each specific entity within a sample into a group, based upon its similar

characteristics to others in that group, as well as differences in characteristics from

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entities in others groups (Chrisman, et al., 1988). Thus, inductive configurational

research methods (i.e., taxonomies) use “configurations from empirical procedures as the

basis for performance comparisons” (Ketchen, et al., 1993).

Taxonomies have been used by a number of authors (Hambrick, 1984; Hawes &

Crittenden, 1984; Inamdar, 2007; Payne, 2006) in the field of strategic management, but

they are often deemed as an exploratory analysis of configurations, lacking in theoretical

underpinnings. Taxonomies explore different organizational features between sets of

firms and specifically include archetype (Greenwood & Hinings, 1993; Kang, Morris, &

Snell, 2007; Kikulis, Slack, & Hinings, 1995; D. Miller & Friesen, 1978) and strategic

group (Dranove, Peteraf, & Shanley, 1998; Hatten & Hatten, 1987; M. S. Hunt, 1972;

Leask & Parker, 2007) models that have been used extensively in the academic literature.

Strategic Groups – Taxonomy

Hunt (1972) introduced the concept of strategic groups – certain clustering of

firms within a given industry that have similar characteristics – through research in the

appliance industry. Newman (1978) also contributed to the early beginnings of strategic

group research through a study of firms within the chemical process industry. Porter

(1980) furthered Hunt and Newman’s work by stating that strategic groups were a

strategic posture, and only indirectly related to specific strategic tactics, and that there

were inherent mobility and entry barriers that prevented firms’ entry and/or movement

into and between groups.

Strategic groups are used frequently in the literature and are typically based on

the industrial organization paradigm (Ketchen, 1993; Bain, 1956; Mason, 1939; Hunt,

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1972). In the strategic group stream of research, each industry is viewed as somewhat

unique and thus different numbers of configurations can be derived when exploring

configurations in different industries. Additionally, with strategic groups, a specific

firm’s performance is posited to be more related to industry-specific conditions, rather

than a firm’s organizational attributes. Compared with a model using a conceptual theory

to divide firms within an industry into similar groups, the strategic groups’ method relies

on identifying statistical homogeneity to identify groups with an industry and then

compares the performance differences between the groups (Ketchen, et al., 1993). As

such, Wiggins and Ruefli (1995, p. 1636) used the strategic group methodology and

defined performance groups as those that are “statically indistinguishable from those of

other firms in the group but distinguishable from the performance levels of firms in other

performance groups.”

In strategic group research, groups of firms within a given industry are identified

as having a specific set of similar competitive approaches (Short, et al., 2007). These

strategic groups are characterized by differing performance levels and specific tactics are

then identified within the different groups to provide predictive validity for a given

group’s superior or sub-par performance (Wiggins & Ruefli, 1995). Ketchen (1997) has

found that group membership can account for a significant portion (8%) of performance

variance in firms, but Ketchen (1993) has also noted that strategic group research is

fundamentally an intra-industry concept, and is thus limited in its generalizability.

Leask and Parker (2007), using strategic group theory in an analysis of the

pharmaceutical industry in the U.K., identified patterns of strategic activities across

firms, as well as differing performance levels between the specific groups. Other

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researchers (Dess & Davis, 1984; Hambrick, 1983; Hatten & Schendel, 1977) have also

found relationships between strategic group membership and performance within specific

industries. In other studies of strategic groups, however, Cool and Shendel (1987) found

no performance differences between groups in the pharmaceutical industry and

Fiegenbaum and Thomas (1990) found mixed results in the insurance industry.

Archetypes – Taxonomy

Archetypes are distinguishable from strategic groups in that archetypes describe

organizational features while strategic groups describe a firm’s competitive strategies

(Short, et al., 2008). Greenwood and Hinings (1993) presented a classic example of the

archetype method in a study of organizational configurations when they described

archetypes in terms of overall patterns in the structures and systems of municipalities.

Specially, these authors longitudinally studied the organizational designs of 422

municipal organizations, focusing on the ideas and values within these organizations, to

determine if specific archetypes existed. They found two specific archetypes within this

industry, but the authors did not attempt to generalize these findings to other industries or

explore the performance differences between the two groups they described. In the field

of human resources, Kang and colleagues (2007) developed a descriptive archetype

classification scheme to further elucidate the links between organizational learning, social

relations, and human resources management amongst firms. As a category though, Short

(2008) describes the archetype model as a context-specific model based solely on

organizational features and thus studies of archetypes do not lead to generalizable

findings.

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Typology-Based (Deductive) Configurational Research

Typologies, which are theoretically generated classification schemes, as opposed to

the empirically generated taxonomy models discussed above, have been used by

numerous authors (Hofer & Schendel, 1978; D. Miller, Friesen, & Mintzberg, 1984;

Mintzberg, 1979; Weber, et al., 1947; Woodward, 1958) to classify organizations by their

competitive strategies. Typologies are typically grouped into either generic strategy

models, which describe a firm’s strategies, and organizational form models, which

delineate organizational features (Short, et al., 2008). Typologies can be context-specific

or generalizable and often a factor analysis method is used to determine which variables

group together. Typology models use a deductive, theory-driven methodology, with

specific examples including the Miles and Snow (1978) typology and Porter’s (1980,

1985) generic strategies typology. To determine the groupings, a priori decisions must

be made regarding the groups, and this technique will lack the empirical referents and

cutoff points of taxonomic techniques (Meyer, et al., 1993).

Typologies are based on structural contingency theory (Weber, 1947; Burns &

Stalker, 1961; Woodward, 1958; Lawrence & Lorch, 1967; Galbraith, 1973) and are

typically used to analyze the organizational and environmental attributes of a firm and

then determine subsets of organizational configurations that may lead to superior firm

performance (Ketchen, et al., 1993). Generally, deductive configurational research uses

methods to sort firms into specific strategy-structure configurations based on a theory or

conceptualization and then tests the relative performance of each group (Ketchen, et al.,

1993). In one example of a specific typology, Ketchen and colleagues (1993) built on

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environmental conditions using structural contingency theory, along with strategic

choice, and determined that firms will not only adapt to a given environment, but also

will attempt to influence their environment. Other researchers (Miles & Snow, 1978;

Hambrick, 1983; Zajac & Shortell, 1989) have developed specific typological methods

within the field of configurational research that have arguably been found to make better

predictions of superior firm performance than other typological methods within the field

of configurational research. Overall, the deductive approach to predicting a firm’s

performance based on its configuration has been found to be better than research using

inductive methods (Ketchen, et al., 1993).

Miles & Snow – Typology

The Miles and Snow (1978) typology has been used extensively in the literature to

group firms into one of four categories based on the set of business strategies a specific

firm employs (Shortell & Zajac, 1990). The categories of the Miles and Snow typology

include prospector, analyzer, defender, and reactor. A prospector is typically the

innovator, a firm that may be introducing a new product or service to the market. An

analyzer is a firm that is developing ideas and exploiting new opportunities. The

defender is attempting to maintain a stable, status quo market. And finally, the reactor

has a strategy that is typically erratic and unstable, and reactors are often following the

lead of others within their industry or the current status of the industry environment.

Each of these types of firms can be defined by general characteristics; for example, its

level of formalization, control, and efficiencies (Miles & Snow, 1978).

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Ketchen (1993) used the basic Miles & Snow (1978) and Zammuto (1988)

typologies to divide hospitals in a specific market into four groups. First, Ketchen

divided the hospitals by their focus on either innovation (r-strategy) or efficiency (k-

strategy). Hospitals with larger numbers of patients in non-routine services were placed

in the innovative group while those in the efficiency group performed a higher percentage

of routine services. Next, Ketchen subdivided the hospitals in each group based on their

breadth of operations – narrow or broad – which was measured by the total number of

services offered. Thus, each hospital was placed into one of four groups: entrepreneurs/r-

specialists (pursue new opportunities in a narrow domain), prospectors/r-generalists

(pursue new opportunities across a broad domain), defenders/K-specialists (efficiently

exploit existing opportunities in a narrow domain), and analyzers/K-generalists

(efficiently exploit existing opportunities across a broad domain) (Ketchen, et al., 1993).

For the outcome variables in his study using the Miles and Snow typology, Ketchen

(1993) chose a number of performance measures including net patient revenue per bed,

return on equity, return on assets, profit-per-discharge, and occupancy. Ketchen (1993)

found that the defenders and analyzers did not perform as well as the other groups, while

the entrepreneurs and innovators performed equally well. However, Ketchen (1993)

noted that environmental uncertainty, as well industry specific characteristics, may have

limited the generalizability of his findings.

Many other authors have also used the Miles and Snow typology, including Shortell

and Zajac (1990) who used the Miles and Snow typology in a study of performance

differences between groups of hospitals and found validity and support for the Miles and

Snow typology. In another study, Segev (1989) directly compared the Miles and Snow’s

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typology with Porter’s generic strategy typology (discussed below), finding that while the

groups from each typology were not completely compatible, there was a distinct

congruence between the two typologies.

Porter’s Generic Strategies - Typology

Like Miles and Snow’s typology, Porter’s generic strategies are a configurational

research model based on a deductively based classification scheme. Porter’s generic

strategies, which were introduced in his books Competitive Strategy (1980) and

Competitive Advantage (1985), have become the most widely accepted typology method

for studying organizational strategies (Akan, Allen, Helms, & Spralls, 2006; Allen,

Helms, Takeda, & White, 2007), as evidenced by the diverse coverage of Porter’s generic

strategies in numerous prominent strategic management textbooks (David, 1999; Miller,

1998; Dess, Lumpkin & Taylor, 2004; David, 2003; Wheelen & Hunger, 2004;

Thompson & Strickland, 2003) and in widely cited articles related to strategic typologies

and organizational strategies (Chrisman, et al., 1988; C. Galbraith & Schendel, 1983; A.

Miller & Dess, 1993; D. Miller & Friesen, 1986a). As a theoretical approach to the study

of organizational configurations, Porter’s generic strategies have the advantage over the

inductive configurational methods as they can be generalized to other industries and

populations (Short, et al., 2008).

However, the evidence to support Porter’s generic strategies is quite equivocal and

the specific methods utilized to analyze firm performance have been less than precise.

Several studies (Buzzell & Wiersema, 1981; Spanos, et al., 2004; Wright, 1987) have

directly contradicted the assertions Porter makes about specific generic strategies leading

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to superior firm performance compared with groups using another generic strategy or no

specific generic strategy at all. On the other hand, many authors (Dess & Davis, 1984;

Hambrick, 1981, 1982; Hawes & Crittenden, 1984; Nayyar, 1993; Parker & Helms,

1992; Powers & Hahn, 2004; Torgovicky et al., 2005) have found that firms effectively

employing one of Porter’s four explicit generic strategies (cost leadership, differentiation,

focus cost leadership, or focus differentiation) will exhibit superior performance relative

to those firms not choosing an explicit generic strategy. As an outcome of a firm’s

choice in strategy, Porter measures performance based on the returns of an individual

firm, though noting that a firm’s returns may be derived to some degree on the industry in

which it operates (Porter, 1980).

Porter believes that cost leadership and differentiation strategies are at opposite ends

of the spectrum, and thus these two strategies cannot occur simultaneously in a successful

firm (Borch, Huse, & Senneseth, 1999). Specifically, Porter states that the benefits of

optimizing a firm’s strategy cannot be gained if a firm is simultaneously pursuing more

than one strategy (Porter, 1985), and thus successful businesses should compete

exclusively on one of the four specific generic strategies (Wright, 1987). Firms that are

not completely committed to one of Porter’s explicit generic strategies will utilize no

strategy, or one referred to as a “stuck in the middle” strategy (a.k.a. the mixed strategy).

These “stuck in the middle” firms are expected to exhibit inferior performance relative to

firms that are committed to one of the explicit generic strategies. Miles and Snow’s

typologies (1978) are similar to Porter’s generic strategies (Porter, 1980), with the

prospector often compared with firm utilizing a cost leadership strategy, the defender

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often compared with a firm utilizing the differentiation strategy (Segev, 1989), and the

reactor often compared with the “stuck in the middle” firms (Borch, et al., 1999).

However, to further obfuscate the explicit strategy categories prescribed by Porter,

some literature (Hill, 1988; Spanos, et al., 2004) has suggested another strategy,

sometimes referred to as a hybrid strategy, to supplement the four explicit generic

strategies as well as the mixed generic strategy. The hybrid strategy is exhibited when a

firm is successfully and simultaneously pursuing both the cost leadership and

differentiation generic strategies (Hill, 1988).

Many authors (Chrisman, et al., 1988; Porter, 1980) have acknowledged that firms

typically use specific strategies to maximize their profitability and achieve success.

However, Porter’s generic strategies deviate from models outside of the configurational

research literature stream, such as industrial organizational economics, in that the focus

on strategy also includes the configuration of activities within a firm that create a

competitive advantage (Spanos, et al., 2004). Specifically, Porter’s generic strategies

include several competitive positions that a firm may take to create a superior position

over the long-term compared with the firm’s competitors (Porter, 1980). Thus, Porter’s

concept of generic strategies serves as a guide to categorize firms by their competitive

advantage - market scope (a.k.a. target scope or broad/narrow focus), differentiation, or

cost leadership. The market scope strategy categories include grouping firms first by

their focused or broad strategy (targeting a narrow or expansive market segment) and

then defining their source of competitive advantage through either a cost leadership or

differentiation strategy (Campbell-Hunt, 2000).

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A cursory review of Porter’s generic strategies may lead one to believe that all firms

have a choice in which strategy they choose. However, depending on the structure of an

industry’s environment, and the resources available to any given firm, the choice in

strategy may be limited for certain firms in specific industries (Wright, 1987). Thus, to

classify a firm into one of Porter’s generic strategies, one of the first assumptions one has

to make is that a firm’s strategy is the outcome of its strategy formulation process. This

is in opposition to a linear type approach in strategic research, in which the strategy is

integrated with the firm’s plans and actions to achieve its goals, or falls within the

interpretive view in which the strategy is merely a frame of reference (Chrisman, et al.,

1988).

“Stuck in the middle” (mixed) strategies

One critique of Porter’s generic strategies is that Porter’s classification scheme is not

collectively exhaustive as it does explicitly include a category for firms employing a

mixed strategy (Chrisman, et al., 1988). However, other authors (Chrisman, et al., 1988;

Hambrick, 1983; Hawes & Crittenden, 1984; Kumar, Subramanian, & Yauger, 1997)

believe that Porter’s generic strategies are mutually exclusive and include the mixed

strategy as a distinct category – thus, all firms fall into one, and only one, of the four pure

generic strategies or the mixed strategy, making the taxonomy both mutually exclusive

and collectively exhaustive by covering all possibilities with distinct categories.

In a work specifically discussing the mixed category related to Porter’s generic

strategies, Cronshaw and colleagues (1994) define firms as stuck in the middle if they are

not pursing one of the pure generic strategies – cost leadership, differentiation, or focus

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market scope. These authors view firms in this category in a positional view in

relationship to their competitors, finding that in certain circumstances, a firm in the

mixed category can be successful. For example, Miller (1992) states that the stuck in the

middle strategy may be the most appropriate strategy for high performance for those

firms in a hyper-competitive industry. Specifically in the hospital industry though,

Kumar (1997) classified 11% of hospitals in his sample in the stuck in the middle

category and found that this group performed poorest in revenue and cost control

measures, and only average in measure of new services and success in retaining patients,

as compared with hospitals employing one of the four pure generic strategies.

Simultaneous (hybrid) strategies

Wright (1987) finds that the pure strategy concept is more complex than Porter

presents and may not fit all industries and firms. For example, large firms may have

more resources that will enable them to employ a purer version of the cost leadership

strategy through controlling costs more widely through various ends of the value chain.

On the other hand, these same large firms may be able to successfully employ a hybrid

strategy through the addition of a differentiated strategy. Stonehouse and Snowdon

(2007) state that there is considerable evidence that the hybrid strategy, simultaneously

employing the strategies of both cost leadership and differentiation, may be the most

effective business model. Other authors (Buzzell & Wiersema, 1981; Cross, 1999;

Hambrick, 1981; Helms, et al., 1997; Hill, 1988; Hlavacka, et al., 2001; Karnani, 1984;

D. Miller, 1992; D. Miller & Friesen, 1986b; Murray, 1988; Phillips, et al., 1983; White,

1986) also believe that a hybrid strategy may offer a firm the best competitive position.

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The hybrid strategy is also distinctly different from the stuck in the middle strategy, in

which firms have neither the characteristics of cost leadership nor differentiation.

However, Wagner and Digman (1997) found no significant performance differences

between firms using multiple (hybrid) strategies and those using a single, pure strategy.

Due to the debate about the mutual exclusivity of Porter’s generic strategies, several

researchers (A. Miller & Dess, 1993; Payne, 2001; Spanos, et al., 2004) have treated

Porter’s strategies as dimensions. For example, a firm may “score” low, medium, or high

in one of Porter’s categories based on a specific variable, and several variables combined

will create an overall dimension for that firm, with varying strengths in each of Porter’s

categories (Spanos, et al., 2004). However, Hambrick (1983) uses a pure strategy model

absent the dimensions and found no firms employing a mixed strategy in a specific set of

mature capital goods industries. Additionally, Dess and Davis (1984) used descriptive

responses from executives and normative recommendations from the academic

community to develop a non-dimensional classification method of firms with similar

strategic orientations based on Porter’s generic strategies. Their research supported the

proposition that higher performing firms employed a pure generic strategy rather than a

mixed strategy.

Additional debates revolve around the use of the mixed strategy versus a hybrid

strategy. Spanos and colleagues (2004), using a regression model with strategy and

industry structure variables to predict a firm’s profitability, differentiated between the

mixed strategy, one that is characterized by the lack of a distinct emphasis on one of the

four pure generic strategies, and a hybrid strategy, which denotes a firm employing both a

cost leadership and differentiation strategy. Miller and Dess (1993) deployed a

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dimensional model based on Porter’s generic strategies and found that firms employing a

hybrid strategy were no less successful than firms employing a pure strategy, but that

those firms employing a mixed strategy were less successful than the others. Miller and

Dess (1993) determined the success of firms based on two measures of profitability and

two measures of growth.

In a meta-analysis (17 studies) of generic strategies’ research, Campbell-Hunt (2000)

found that in general, Porter’s generic strategies were too broad, and that a more fine-

grained descriptive system was necessary, one with dimensions rather than distinct

groups. Specifically, in the results using their meta-analysis, Campbell-Hunt (2000)

found that using the strategies of cost leadership, differentiation, mixed, and hybrid led to

very little explanation in the variance in financial or growth performance, with neither the

pure strategy groups nor the hybrid group demonstrating superior financial performance.

Using Campbell-Hunt’s (2000) dimensional suggestion, Thornhill and White (2007)

sought to answer the question of whether or not a pure strategy would lead to superior

financial performance, measured by operating margin, relative to a mixed or hybrid

strategy, using a sample of 2,351 firms across a wide range of industries. These authors

found, in general, a positive relationship between those firms deploying a pure strategy

and enhanced business performance, though their results differed somewhat by industry.

Additionally, Thornhill and White (2007, p. 559) found that “pursuit of a pure strategy is

always equal or preferable to hybrid strategic positioning and that, on average, pure

strategies are often better and never worse than being in the middle.”

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Taxonomies versus Typologies

Although OC certainly has its limitations, it has played a significant role in

furthering our understanding of organizational configurations and the OC-performance

link (Short, et al., 2008). Short and colleagues (2008) note that using an inductive,

empirical method of classification without a theoretical basis will not be useful for

generalization purposes. Meyer (1993), on the other hand, states that the debate between

typology and taxonomy research methods is divisive and non-productive in the field of

OC, as most researchers using taxonomies select variables for inclusion in their model

based on a theoretical foundation, and thus taxonomy research can still be useful in

exploring and furthering the field of organizational configurations. To provide additional

data for the typology/taxonomy debate, Ketchen and colleagues (1997) performed a

meta-analysis of organizational configuration research by using 33 primary research

studies including 40 independent samples and found no significant difference in the

performance variance explained between those studies using a typology (deductive

methodology) or taxonomy (inductive methodology) approach. Overall, the two

approaches yielded an average of 27.6% in the prediction of performance differences

between groups developed along organizational configuration models.

Bacharach (1989) has also criticized much of the past OC research, stating that

many of the typologies that have been used are abstract in nature, and thus not

theoretical. To counter this limitation, Bacharach suggests clearly defining the

assumptions and boundaries of the applicable theory, using common language across

levels with constructs and variables, and distinguishing between propositions and

hypotheses. Strong criticism of OC research has also come from Barney and Hoskisson

33

(1990), who state that OC research lacks in its specification of key concepts from theory

and over relies on purely empirical methods, such as cluster analysis. Additionally, these

authors suggest that researchers should abandon strategic group research and take an

approach that relies on theories viewing strategies at the firm level.

However, to counter much of the criticism of OC research and the debate between

typologies and taxonomies, Ketchen (1993) has suggested using both the taxonomy and

typology approaches when studying organizational configurations. To put it simply, he

suggests grouping the firms based on a theoretically based typology model and then

performing a cluster analysis to determine if the typology is effective. Ketchen’s (1997)

meta-analysis study specifically states that there is certainly merit in future studies

including both techniques in their analyzes of the strategies and performance of firms in a

single sample, and this would also increase the criterion-related validity for the study of

that specific sample.

The Outcome of a Successful Strategy - Performance

Cool and Schendel (1987) describe performance as a multi-dimensional concept,

which implies that multiple performance indicators should be used to measure overall

firm performance. Researchers have used profit, turnover, return on investment, return

on capital employed, and inventory turnover to measure financial performance. Non-

financial measures such as innovativeness and market standing have also been used

(Allen & Helms, 2002). Dess & Robinson (1984) have also studied both financial and

non-financial measure of performance in the field of strategic management.

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Numerous researchers have used organizational configuration theory to

demonstrate differences in performance between definable groups based on the strategy

and configuration of the groups (Ketchen, 1997; Payne 2006; Inamdar, 2007; Meyer, et

al., 1993; etc.), with researchers often using ANOVA or MANOVA as techniques to

examine the performance differences between groups based on strategy constructs. A

meta-analysis of 32 studies by Ketchen and colleagues (1997) determined that 8% of

performance variance can be explained by configurational membership. Although

“Porter discusses performance almost exclusively in terms of return on investment” (A.

Miller & Dess, 1993, p. 569), researchers have used a wide variety of both

subjective/objective and financial/non-financial measures to quantify a firm’s

performance. Research using organizational configuration models typically uses one of

five primary outcome measures – sales, equity and investment, assets, margin and profit,

and market share, or a combination of these constructs (Ketchen, 1993). Non-financial

measures such as innovativeness and market standing have also been used (Allen &

Helms, 2002). However, Allen and Helms (2002) state that performance is often

measured in three dimensions – effectiveness, efficiency, and adaptability, but the

measurements of these constructs, and which construct is best, has led to very little

consensus in the literature regarding the linkages between firm strategy to performance.

Short (2007) agrees with a multi-dimensional approach and used return on assets,

a market measure (Tobins Q), and a measure of default risk (Altman’s Z) in a study of the

performance outcomes between inductively and deductively defined groups. Short

(2007) used the theories of strategic choice and organizational ecology in this study, and

included an analysis of the differences in groups based on firm-level characteristics.

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Using MANOVA techniques, Short (2007) found significant differences in performance

based on the defined groups.

Allen and Helms (2002) used a five-item subjective scale of perceived

performance compared with a firm’s peers, and had respondents complete a number of

these scales for each firm after reviewing individual objective financial measures for each

firm. In other measures of performance, Cronshaw and colleagues (1994) used return on

investment as a measure of successful companies, dividing the firms into three categories

– high, medium, and low – based on the firm’s ROI over a four year period. Cronshaw

and colleagues stated that ROI is well correlated with most measures of financial and

strategic achievement. To develop the groups on which to measure performance, these

researchers divided their sample based on a number of strategic postures that were

grounded in Porter’s cost leadership, differentiation, and mixed strategy constructs.

Dess and Davis (1984) used two measures – sales growth and profitability

(average after tax return on total assets) over a four year period – to define performance

of individual firms. Through a review of strategy variables by executives and academic

experts, firms were first divided into strategic groups (clusters) based somewhat loosely

on Porter’s generic strategies. Significance of the performance differences found

between the groups was tested with an ANOVA method. Ultimately using subjective and

objective measures to divide the groups, while still staying close to Porter’s outlined

generic strategies, these researchers found significant differences between several of the

groups, consistent with Porter’s generic strategy’s constructs and propositions.

Likewise, Miller and Dess (1993) also used profitability and growth constructs as

outcome measures of performance related to strategy in their study of 200 corporations in

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a variety of industries. Using a dimensional approach to group corporations based on

Porter’s generic strategies, these authors developed seven distinct groups. The authors

found differences in performance between the groups, though software constraints

limited a rigorous exploration of the significance in the performance differences that were

found between the strategy groups.

In the banking industry, Powers and Hahn (2004) used return on assets (ROA) to

operationalize performance in a study of differences between groups that were defined

using Porter’s generic strategies. Using ANOVA, these authors found a significantly

higher ROA in the group with a cost leadership strategy compared with the mixed

strategy group. The various strategy groups were defined based on a number of

competitive methods that were subjected to a principal components factor analysis

initially, followed by a cluster analysis, to derive the generic strategy groups.

In the health care setting, Hlavacka and colleagues (2001) used a number of

constructs to determine performance – ability to retain patients, ability to control

operational expenses (efficiency), growth in overall revenue, and return on new

services/facilities - in a study of 81 Slovak hospitals. However, a subjective respondent

survey of the hospitals’ chief administrators was used to determine the levels of success

within each of these performance measures. After grouping the sample of hospitals from

subjective measures based on the responses from the chief administrators and Porter’s

generic strategies, Hlavacka and colleagues (2001) used a MANCOVA method and

found mixed support for Porter’s generic strategies.

Kumar and colleagues (1997) used return on new services/facilities, profit margin,

success in retaining patients, and success in controlling expense to measure hospital

37

performance differences between groups based on Porter’s generic strategies. Hospital

performance and strategy were measured subjectively and an analysis of the performance

differences between the groups’ performance was performed using MANCOVA and

based on the four performance criteria. Although significant findings in differences in

the percentage of for-profit and not-for-profit hospitals falling into one of Porter’s generic

strategies were discovered, Kumar and colleagues (1997) did find that on average,

hospitals using a focused cost leadership strategy led in many performance measures and

that hospitals using a mixed strategy were, on average, poorer performers.

Dess and Robinson (1984) have added to the performance literature with a study

of the use of objectively obtained versus subjectively obtained financial performance

measures. Specifically, Dess and Robinson (1984) discuss several occasionally

troublesome issues in the measurement of performance in firms: the use of subjective

performance measures and measuring financial performance in privately-held firms. In

regards to subjective measures, Dess and Robinson (1984) found that certain subjectively

obtained financial measures were strongly correlated with objectively obtained measures.

For privately held firms though, Dess and Robinson (1984) state that with these firms,

there is an increased risk of accounting errors and that ownership forms (sole

proprietorships, partnerships, corporations, etc.) may create anomalies in the

measurement of financial performance across various firms within a sample.

Porter’s Generic Strategies in the Health Care Industry

In an application of Porter’s generic strategies typology in the health care industry,

Torgovicky and colleagues (2005) studied the performance of two of Israeli’s four sick

38

funds (not-for-profit institutions providing ambulatory care and paying for hospital care

when required). Objective performance measures included patient satisfaction, transfer

rates between funds, financial balance, and cost containment. Torgovicky (2005)

surveyed managers’ subjective perceptions of business practices within their individual

fund, applied generic strategy labels to each group, and determined that the sick fund

employing a mixed strategy performed more poorly than the other fund, which employed

a focused differentiated strategy. In U.S. hospitals, Baliga and Johnson (1986) used

Porter’s generic strategies model to analyze how they use specific competitive methods to

improve performance. Overall though, the empirical use of Porter’s generic strategies is

relatively limited in the field of health care.

Medical Group Practices

Health care has been noted as a specific industry within which lies the physician

services industry, and the physician services industry includes the segments of both solo

practitioners and partnerships (i.e., medical group practices) (Blair & Buesseler, 1998).

To generate an overall view of medical group practices, a number of general

organizational characteristics of medical group practices will be discussed below.

Number of Medical Group Practices and Solo vs. Group Practices.

Hing and colleagues (2008) estimated that between the years 2005 and 2006, there

were 163,800 office-based medical practices in the U.S. involving 308,900 physicians.

Both the Medical Group Management Association (MGMA) (2004) and the National

Ambulatory Medical Survey (NAMCS) have estimated the number of physician group

39

practices (3 or more physicians) in the U.S. to be approximately 31,000 (excluding the

single specialty practices of radiology, anesthesia, and pathology). More than one-third

(38.6%) of office-based physicians are organized in solo or single partner practices, a

number that trended downward in the last decade, and about 9.1% of physicians are in

practices with 11 or more physicians (Hing & Burt, 2008). A group practice, as opposed

to a solo practice, can provide a wider range of services, enhance leverage in negotiations

with third-party payers and managed care entities, permit shared facilities and resources,

enable better work schedules, lessen on-call responsibilities, and spread the financial risk

(Ittner, Larcker, & Pizzini, 2007; Lin, Chen, Liu, & Lee, 2006). Additionally, in a study

of physician incomes in Taiwan, Lin and colleagues (2006) found that physicians in

group practices had significantly higher incomes than those in solo practices.

In a survey from the American Medical Group Association, it was found that the

vast majority of physicians believe that group practices enhance their quality of life

(86%), improve patient care (92%), and provide more opportunities for professional

development (84%) (Romano, 2001). However, about 52% of the respondents thought

that participating in a group would lower their earnings, even though data from the

American Medical Association indicates that physicians in any size group, from small to

large, earned more than solo practitioners (Romano, 2001). In a study of larger practices

(20 or more physicians), Shortell and colleagues (2005) found that the largest groups

within their sample had significantly better financial performance than smaller groups.

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Employment and Practice Ownership, and Practice Patterns

Hing and Burt (2008) report that 70% of physicians are the owners or part owners of

their firms, with primary care physicians (29.8%) being more likely than surgical

specialists (19.0%) to be employees rather than owners. In a large telephone survey of

office-based physicians, Reschovsky and colleagues (2006) found 44% of physician

respondents to be employees, rather than owners, within their practice. This survey also

reported on the practices with which these physicians were affiliated, finding that 62%

were independently owned, 3.8% were owned by HMOs, 8.4% were within medical

schools, and 12% were owned by hospitals. Additionally, the vast majority (82%) of

ambulatory care visits occur in physician offices, as opposed to a hospital’s outpatient or

emergency department. Of the ambulatory care encounters occurring in physician

offices, approximately 61% occur in primary care offices, with the remaining visits split

relatively evenly between surgical or medical specialty offices (Schappert & Burt, 2006).

An Application of Porter’s Generic Strategies in Medical Group Practices

Medical group practices are typically defined as groups of physicians who have

formed a legal entity and share clinical, administrative, and business functions (Casalino,

et al., 2003). Overall, these medical group practices demonstrate a number of

configurational types based on management structures, ownership, compensation

practices, physician specialty mix (or lack thereof), number of physicians, etc. (Casalino,

et al., 2003; Ittner, et al., 2007). The health care industry, including medical group

practices, is much different from many other industries due to factors such as regulatory

issues, demand, and payment (insurance vs. self-pay). Additionally, firms in the

41

physician services industry are often described as having much greater organizational

complexity than firms in other industries (Blair & Buesseler, 1998; Robinson, 2001;

Thibadoux, et al., 2007; Torgovicky, et al., 2005) . Nevertheless, as with nearly any

industry, medical group practices must maintain or seek a competitive advantage in order

to generate superior performance compared with their peers (S. D. Hunt, 1997).

However, in the medical group practice segment of health care, many have noted the lack

of information and research regarding the financial performance of medical group

practices (Casalino, et al., 2003; Shortell, et al., 2005).

Thus, as the scrutiny of the U.S. health care system is extremely heightened at the

present time, and with efforts of health care reform underway across our country,

research regarding the performance of medical group practices is extremely timely and

warranted. In the last several decades, the U.S. health care system has become

increasingly integrated as many free-standing entities, such as hospitals and physician

practices, have merged, aligned, and created networks to generate efficiencies and

improve performance (Blair & Buesseler, 1998). This increased integration has left the

U.S. health care system with very few (35.8%) solo office-based physician practitioners

(Hing & Burt, 2007). The medical groups of office-based physicians (which “excludes

federally-employed physicians, specialists in anesthesiology, radiology, or pathology;

and physicians who typically do not see patients in an office – such as emergency

medicine physicians” ) with three or more physicians account for one-fifth of all medical

practices but contain about one-half of the total number of office-based physicians (Hing

& Burt, 2008, p. 2). It is believed that a study of the performance of medical group

practices is highly warranted at this time and will enhance the general knowledge

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regarding Porter’s generic strategies, as well as the specific performance differences that

may be found between medical group practices grouped using Porter’s generic strategies

typology.

However, despite the wide use and acceptance of Porter’s generic strategy model,

and the existence of obvious characteristics within medical group practices that could be

utilized to study medical group practices based on Porter’s generic strategies model, only

one published article (Payne, 2006) of medical groups using Porter’s generic strategies

has been found, as well as a related unpublished dissertation (Payne, 2001). In Payne’s

dissertation (2001), he divided the medical group practices into sets of firms with similar

characteristics and examined the financial performance of each distinguishable unit.

Payne’s clusters of medical group practices were based on Ketchen’s (1993)

operationalization of Porter’s generic strategies and each practice’s strategic methods

were measured with the following specific variables:

1) promotion and marketing – advertising intensity

2) research and development – CME intensity

3) production and operations capacity – procedural intensity

4) scope of activities – practice type

5) distribution – fee-for-service ratio

6) production and operations capabilities – nonmedical revenue ratio

Payne (2001, 2006) also calculated structure variables based on organization size

(physician FTEs), physical size (square footage of the buildings), and geographic

dispersion (total number of separately operating clinics), and each firm’s financial

performance based on 1) sales, 2) equity and investment, 3) assets, and 4) margin and

43

profit. Data was gathered from the Medical Group Management Association’s 1998 Cost

Survey as well as control variables from U.S. Census data and the HCFA Medicare

Managed Care Data. Overall, Payne (Payne, 2001) found that medical groups following

a focused-differentiation strategy performed best financially. The cost leadership firms,

particularly those with a broad target scope, performed worse than the other groups.

As a further contribution to the body of organization configurational research, this

specific research endeavor will be a study of the characteristics and performance of

medical group practices using Porter’s generic strategies. As suggested by several

authors (Ketchen, Combs, Russell, Shook, Dean, et al., 1997; Thornhill & White, 2007),

the initial methodology will be guided by the organizational configuration research

literature, as well as literature from related fields, to develop the model and

operationalize the variables. However, after the theoretical approach to the classification

of the medical practices is finalized, an empirical, inductive methodological analysis of

the sample will ensue. Following the analyses of the data using each of the two

methodologies, theoretical and empirical, a comparison of the groups formed by each

method, as well as any performance differences that may exist between the groups

formed by each method, will be completed. This sequence of analyses will not only

provide the study with a measure of validity for the formation of the theoretically

generated groups, but it may also elucidate any performance differences that may be

found between the groups of medical group practices generated by each methodology.

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CHAPTER 3

METHODOLOGY

Research Questions

Through a series of research questions, this study will attempt to categorize medical

group practices using Porter’s generic strategies, and if distinguishable groups are found,

it will explore any performance differences between the groups. The following

operationalization of strategic and configurational variables are proposed, based on

Porter’s generic strategies, as well as two hypothesis models (see Figures 1 and 2).

Ultimately, this work will provide information that may lead to a better understanding of

the types of strategies that physician practices utilize and will explore any differences in

performance that may exist between the groups.

Porter’s generic strategies (1980), and subsequent literature related to theoretical and

empirical research of these strategies (Ketchen & Shook, 1996), will guide the

categorization of each firm by their strategy and configuration, as well as the

development of a model to aid in the exploration of the performance differences that may

exist between groups of medical practices categorized by strategy type. It is believed that

either implicitly or explicitly, successful medical group practices will employ one of

Porter’s four pure generic strategies – broad-cost leadership, broad-differentiated, focus-

cost leadership, or focus-differentiated – or the strategies of either a hybrid or mixed

group, as defined by previous research. Through the data and the operationalization of

the chosen variables, constructs will be developed to define which of the six strategies is

being utilized by each of the medical group practices. A subsequent analysis of the

45

financial performance of each group will aid in the determination of whether or not the

pure strategy groups perform better than the other groups, as predicted by Porter’s theory

of generic strategies.

Based on the literature review, it is expected that on average, cost leaders will have

low ratios of support staff FTEs, bad debt, and square footage of office space, as well as

greater efficiency levels as measured by a ratio of total costs to total physician

productivity. Differentiated practices are expected, on average, to have a greater number

of ancillary/supplemental services, a higher ratio of advertising, a wider range of market

segments, and a greater focus on their image. For the outcome/performance variable in

this analysis, this study will utilize one of the same performance variables used in

Payne’s (2001) study, a ratio of each practice’s operating income per FTE physician.

It is posited that medical groups will deploy strategies similar to those in traditional

industries in order to maintain competitive and sustainable positions within their segment

of the health care industry. Thus, the initial review of the literature regarding Porter’s

generic strategies leads to the first research question:

1. Using a deductive methodology, do medical groups that exhibit the characteristics of

one of Porter’s pure generic strategies (i.e., market scope and then differentiation or

cost-leader) perform better financially than medical groups that use a hybrid strategy

or groups that exhibit a mixed strategy?

As discussed earlier in the literature review section, the taxonomy versus typology

issue will be explored with the second and third research questions:

46

2. Using an inductive methodology, do specific groups of organizations with similar

characteristics perform better financially than others?

3. Will the inductive or deductive methodology lead to groups that will better predict the

financial performance of certain medical practice groups?

Study Population, Variables, and Operational Definitions

This study will use data from the Medical Group Management Association’s

(MGMA) “Cost Survey: 2009 Questionnaire Based on 2008 Data,” which includes

information collected from 1,797 medical group practices in the United States. Sets of

variables from the survey instrument will be operationalized to denote the strategy for

each physician practice - costs leadership, differentiation, hybrid, or mixed. Additionally,

as supported by Payne’s (2001) study, the decision of a medical group to accept a single

specialty or multispecialty model will be operationalized to represent Porter’s market

scope strategy (broad or narrow target scope).

Operationalization of these variables to match Porter’s strategies and the

performance outcome measure will admittedly be somewhat subjective, as there is very

little empirical or theoretical literature in this specific industry to guide our selection of

variables, but it will be based on Porter’s definitions and the literature using Porter’s

generic strategies model to study other industries. Regarding the outcome measure –

financial performance, researchers (Day & Wensley, 1988) have found both the

definition and operationalization of performance challenging. Often, objective measures

of performance in many industries are impossible to obtain and thus much of the work in

the field of competitive analysis has remained at the conceptual level (Allen & Helms,

47

2006). However, using previous literature in the general OC and health care literature,

variables from our sample of medical group practices will be operationalized to measure

performance.

Defining Porter’s Strategies in Medical Group Practices

For Porter’s market scope strategy, there is a clear distinction in medical group

practices, as detailed below, which easily enables the classification of focused and broad

strategies employed by each individual medical group practice. For the taxonomy of the

cost leadership and differentiator constructs, and the variables related to each construct, a

frequent approach taken by several authors (D. Miller & Friesen, 1986b; Spanos, et al.,

2004) classifies each firm as high, average, or low for each measure. However, as

Ketchen (1997) notes in a review of the configurational literature, there is little depth in

regards to specific sets of configurational variables that should be used for a particular

construct. Therefore, many researchers have developed unique, sample specific

configurational constructs (in 33 of 40 independent samples) rather than building on

specific sets of variables from previous literature, probably due to the limited and specific

variables available to each researcher (Ketchen, Combs, Russell, Shook, Dean, et al.,

1997).

Market scope strategy (a.k.a. broad vs. focused/target market scope strategies)

Certain firms may target the entire market segment and will thus employ a broad

market scope strategy. Other firms, however, may choose a specific customer group,

product range, or segment of a given market. Firms choosing this latter strategy are

48

typically designated as employing Porter’s focus generic strategy (Akan, et al., 2006;

Wright, 1987). A firm choosing a focused market segment can develop strategies to

target that specific market “more effectively or efficiently than competitors who are

competing more broadly” (Porter, 1980, p. 38). A focus strategy can include targeting a

specific subset of customers, geographic location, or set of services (Allen & Helms,

2006; Hlavacka, et al., 2001; Hyatt, 2001; Porter, 1979a; Porter, 1996). The type of firm

utilizing a focused strategy will attempt to grow its market share and profit by catering to

its market more effectively, as compared with its larger competitors (Akan, et al., 2006).

In the health care industry, a medical group practice may choose to include only a

single type of medical specialty within their overall practice (e.g., a practice with only

cardiologists or only pediatricians) or it may choose a practice model that includes more

than one specialty. This choice exemplifies Porter’s choice of a broad or narrow target

scope, with the practices choosing whether to include a large segment of the patient

population (multispecialty practices) or one that only targets a specific segment (single

specialty practices).

In the overall population of physicians in the U.S., about 78.8% of physicians

practice in a solo or single-specialty group and another 20.3% practice in multispecialty

group practices (Hing & Burt, 2008). About one half of these physicians practice in a

primary care specialty, with another 28.6% in medical specialties and 21.5% in surgical

specialties. General and family practices comprise the greatest overall group of

physicians (18.5%), followed by internal medicine (14.3%), pediatrics (10.1%), and

obstetrics and gynecology (7.7%). These specialties, or lack thereof, will define whether

49

a medical group practice is employing a broad (e.g., multispecialty practice) or focused

(e.g., single specialty practice) market scope strategy.

The instrument used to collect our data includes a choice of four medical group

practice types: 1) Single specialty, 2) multispecialty with primary and specialty care, 3)

multispecialty with primary care only, and 4) multispecialty with specialty care only.

Payne (2001) divided a similar sample of medical group practices into two groups, single

specialty (68.3% of respondents) and multispecialty practices (31.7% of respondents),

denoting the single specialty practices as focused. Our model will use a method similar

to Payne’s (2001) and designate all single specialty practices as using a focused strategy,

with the remaining multispecialty practices falling into the broad market strategy. These

two groups (broad and focused) will be analyzed separately, as noted below and in our

hypothesis models.

Market-scope strategy is not a stand-alone strategy though, according to Porter

(1980). To succeed in a targeted or broad market, a firm must additionally choose either

the differentiation or cost leadership strategy. Thus, within either the focused or broad

market scope strategy, a successful firm will also specifically utilize either a

differentiated or cost leadership strategy, or fall into either the hybrid or mixed category,

in order to effectively compete in the overall market (Porter, 1980).

Composite Values for Differentiator/Cost Leadership Constructs

In a study using Porter’s generic strategies, Dess and Davis (1984) used a subjective

survey instrument to collect variables related to differentiation, cost leadership, and

focused strategies for firms. A factor analysis of these variables followed, with specific

50

strategies loading +/- 0.50 designed as significant for a given construct (and thus included

in the construct) and those strategies loading at +/- 0.30 designated as not significant for a

given construct (and thus being dropped from the construct). A K means clustering

algorithm was then used to develop a typology based on Porter’s generic strategies (Dess

& Davis, 1984). More recent studies (Allen & Helms, 2006; Powers & Hahn, 2004) have

also used a factor analysis method to match a set of variables to Porter’s generic strategy

model. However, Spanos (2004) used a simple division of each strategy variable into the

thirds, with only firms having values in the top third meeting the criteria for further

inclusion in a given construct. Due to the limited literature on specific variables

applicable to the medical group practice industry, once a descriptive analysis of the data

is performed, several methods will be explored to determine the best technique to divide

the sample into groups based on the differentiator, cost leadership, hybrid, and mixed

strategy categories. However, in many cases, the designation of a firm exhibiting the

characteristics of a cost leadership or differentiator strategy will be based on a firm

falling in the top (or bottom, as applicable) third of the distribution for a given variable.

Differentiation strategy

Firms that employ a strategy of differentiation create something that is perceived

industry wide as being unique – “design or brand image, technology, features, customer

service, dealer network, or other dimensions” (Porter, 1980, p. 37). Others describe

differentiation as a strategy in which a firm provides a unique product or service to its

customers, setting the firm apart from its competitors (Akan, et al., 2006; Cross, 1999;

Hlavacka, et al., 2001). While these firms do not ignore costs, the cost leadership

51

strategy is not a primary strategy for differentiated firms that are successful. Approaches

to differentiation may include tactics such as providing superior customer service,

focusing on brand image, or employing enhanced technology. Miller (1986, 1988) used

two primary forms of differentiation – one based on marketing and the other based on

innovation and technology – though his research was related more to the inter-

relationships among the variables and the environment, rather than the performance

differences between the firms based on the generic strategies that the firms employed.

While the differentiated firm must still address cost issues, low cost is not of primary

concern for the firm using a differentiation strategy, and differentiated firms may often

charge a premium price for their product or service (Allen & Helms, 2006). Ultimately,

customer loyalty to the differentiated firm will lead to higher returns and can enable the

differentiated firm to focus less on price and efficiencies. Also, due to the uniqueness of

the firm or their product, the entrance of competitors to the firm’s specific market is

somewhat inhibited (Porter, 1980).

Through a factor analysis of numerous firm strategy tactics and a subsequent

analysis of the relationships of these tactics to the firm’s overall performance, Akan and

colleagues (2006, p. 46) identified three statistically significant critical practices of the

differentiated firm: “innovation in marketing technology and methods, fostering

innovation and creativity, and a focus on building high market share.” Further, in a study

of specific strategic tactics that firms use to differentiate themselves from their

competitors, Allen and Helms (2006) found that differentiation characteristics related to

innovation were most significantly related to firm performance. Allen and Helms (2006,

p. 447) also found that among a number of strategic tactics firms use, “producing

52

products or services for high price market segments and providing specialty products and

services” were most significantly correlated with the firm performance for organizations

in the focused differentiation category. However, firms in the narrow focused market

segment used similar tactics to those differentiated firms who were not focused. The

following variables from the instrument will be used to operationalize the differentiator

construct for this sample of medical groups.

Advertising Intensity. The MGMA 2006 Performance Report Based on 2005 Data

(MGMA, 2006) found that in nearly every category of advertising spending, medical

group practices who advertised or marketed more were better performers than those with

less advertising or marketing. Using Ketchen’s (1993) constructs and previous work

from other researchers (Akan, et al., 2006; Oster, 1982), higher level of advertising

typically indicates a greater degree of differentiation. Payne (2001), using a very similar

MGMA survey instrument from 1996, used the variable Promotion and Marketing,

divided by the variable Total Operating Costs, to reflect a level of differentiation within a

firm. Approximately 56% of the medical groups in Payne’s (2001) sample spent less

than 1% of their total operating costs on promotion and marketing activities. Thus,

medical groups allocating more than 1% of their total operating costs to advertising will

be designated as differentiators.

Providing specialty products/services. Akan (2006) indicates that a broader range of

new products or services, or providing unique specialty products or services, will indicate

a higher level of differentiation. Pham and colleagues (2004) describe ancillary services

53

such as imaging and laboratory testing as a major strategy of differentiation for medical

groups, often allowing them to raise prices for certain services. For example, lab testing

is included in only 30.2% of practices and x-ray imaging in only 16.1% of practices

(Hing & Burt, 2008). The MGMA instrument includes a question asking the

respondents about the ancillary/supplementary services provided by their medical group

practice, including radiology, laboratory tests, durable medical equipment, etc. The total

number of unique ancillary/supplemental services includes 16 choices, plus one category

for Other services, for a total of 17 possible unique services provided by each group.

Following Spanos’ (2004) method of the top third in a category denoting group

membership for a specific strategy, medical groups providing a number of ancillary

services representing the top third of all practices in the sample will be denoted as

differentiators.

Image. Allen and Helms (2006) state that image and customer perception of a

firm are characteristics that define a differentiator. This strategy enables a firm to

provide a unique or superior value compared with other firms. Wirtz (2007, p. 298) calls

image a “company’s uniqueness caused by psychological and attitudinal positioning” and

found that companies with high levels of image differentiation performed better than

other firms. In health care a firm’s uniqueness is tied in part to its brand image, which

may be based on the medical group’s acquisition of high-tech equipment and services

(Westbrook, 1997). In this study, the total dollars that a medical practice pays for

furniture and equipment in their offices will be noted as a reflection of the image they

wish to pursue, with firms having higher operating costs in this category designated as

those wanting to create a differentiated image as compared with other medical group

54

practices. Thus, firms with a higher ratio, the top one third following the Spanos (2004)

methodology, of furniture and equipment costs to overall operating costs will be deemed

differentiators.

Range of market segments. Several authors (Allen & Helms, 2006; Chan-Olmsted &

Jamison, 2001) have found that a greater presence in different geographical areas, or

geographical reach/breadth, is a sign of differentiation. Payne (2006) measured

geographic dispersion in medical group practices by the total number of separately

operating clinics. The MGMA instrument includes the following question: “How many

branch or satellite clinics did your practice have?” Thus, firms with a greater number of

satellite clinics, those in the top third following the Spanos (2004) methodology, will be

deemed as differentiators.

Cost leadership & RVUs

“Cost leadership requires aggressive construction of efficient-scale facilities;

vigorous pursuit of cost reductions from experience; tight cost and overhead control;

avoidance of marginal customer accounts; and cost minimization in areas like R&D,

service, sales force, advertising, and so on,” which will lead to above-average returns for

a firm within a given industry (Porter, 1980, p. 35). Thus, a firm employing the cost

leadership strategy will create internal policies, whether implicit or explicit, to create

efficiencies and minimize overall costs (Allen & Helms, 2006), often through the

production of a standardized product (Wright, 1987). The cost leadership strategy is

employed to yield higher than average yields compared with the firm’s competitors,

55

which also inhibits the entry of new competitors (Hyatt, 2001; Porter, 1980). The cost

leadership position often requires higher than average market share (Helms, et al., 1997),

a wide product line, increased capital to purchase the latest technology and modern

facilities, and aggressive pricing. These techniques typically lead to greater efficiencies

which will enhance the cost leadership position of a firm (Allen & Helms, 2006).

Maintaining the cost leadership position can then become cyclical, as lower costs inhibit

new competitors due to the cost leader’s efficiencies, and provides for enhanced

profitability that the cost leadership firm can then use to reinvest in additional

technologies, facilities, or workers to sustain their competitive advantage (Porter, 1980).

Bharadwaj and colleagues (1993) state that successful cost leadership strategies are

more difficult to implement in service industries, as economies of scale are much more

difficult due to the generally decentralized nature of the locations where customer contact

occurs. However, economies of scope can often occur with service firms when they are

able to broaden their number services offered, and add additional services for their

customers beyond what may have traditionally been offered. However, while this tactic

may increase revenue, it may also crossover into the differentiation strategy (Bharadwaj,

et al., 1993).

In the banking industry, Powers and Hahn (2004) found that firms implementing a

cost leadership position realized a significant performance advantage over firms with a

mixed, focused, or differentiation strategy. In other literature, Rangone (1999), through a

case study of 14 smaller firms, found that innovation, production, and market

management capability are key to a firm obtaining a sustained competitive advantage in

an industry. On the contrary though, Borch and colleagues (1999) found that few small

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firms favored Porter’s cost leadership strategy. Borsh’s sample was in the field of

dentistry, where the vast majority of the firms were small, as compared with an industry

with many large firms where this finding may not be as relevant. In addition though,

entrepreneurial firms typically take on more risk, are more aggressive and proactive, and

often use higher levels of technology than non-entrepreneurial firms (Borch, et al., 1999),

which may inhibit the use of a cost leadership strategy for smaller firms.

For service firms, Bharadwaj and colleagues (1993) have created a conceptual model

specifically related to various firm strategies and their techniques for maintaining a

sustained competitive advantage. Their model includes a review of a firm’s equipment

versus people intensity, complexity of assets, intangibles/tangibles ratio, experience,

service delivery process, size, and business portfolio composition (Bowman & Faulker,

1997).

In the focused cost leadership category of firms, the most highly correlated

strategic tactics associated with firm performance were those related to quality and

service issues, in addition to those tactics of reducing costs. Namely, firms in this

category having a greater focus on providing extensive training for front-line personnel,

improving operational efficiency, and controlling the quality of services or products,

were found to be significantly better performers than their competitors (Allen & Helms,

2006).

In an exploratory analysis of Japanese firms, Allen and colleagues (2007) found that

most (41%) use the cost leadership strategy. In the hospital industry in the Slovak

Republic though, Hlavacka (2001) found only 5% of hospitals using the cost leadership

strategy. However, in U.S. hospitals, Kumar and colleagues (1997) found 45% of for-

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profit and 75% of the not-for-profit hospitals were pursuing a cost leadership strategy

(combining both the broad and focused market scope categories), with the focused cost

leadership category of firms leading in performance over other groups and the broad cost

leadership group demonstrating only average performance. Kumar defined the cost

leadership construct through a number of variables – achieving lower costs of services

than competitors, making services/procedures more cost-efficient, improving utilization,

etc. – through subjective responses from 171 hospital chief administrators. Payne

(2001), however, found that medical group practices using a cost leadership strategy

(approximately 39% of his sample) performed worse financially than groups employing

one of Porter’s other generic strategies.

Physician Output – Resource-Based Relative Value Unit (RVU). Many of the

variables for the components of the cost leadership construct will use the resource-based

relative value unit (RVU) as a denominator to quantify and weight the volume of

physician services produced by each practice. In most medical group practices

(anesthesiology is excluded), the RVU is used as a uniform measure of quantifiable

output and is typically the prevailing method of measuring physician output (Johnson &

Newton, 2002). The total RVU variable establishes relative weighs which set the

“relative costliness of the inputs used to provide physician services” (Fleming et al.,

2009, p. 112) and is then adjusted with a modifier to account for area price differences.

Relative value units, established by Medicare in 1992, are part of the resource-based

relative value scale (RBRVS), which sets the physician fee schedule. The inputs for total

RVU include “physician work, physician practice expenses, and professional liability

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insurance expenses” for each physician service (Fleming, et al., 2009, p. 112). “On

average, work represents 52% of total physician payments, practice expenses represent

44%, and liability insurance represents 4%” (Maxwell & Zuckerman, 2007, p. 65). The

work RVU (less the practice and professional liability insurance expenses) reflects the

time, effort, skill, and stress associated with providing each service and can be used to

create a common scale by which one may evaluate the productivity of a physician or

medical group practice across various specialties and organizations (Fleming, et al.,

2009). The total RVU, with the inclusion of the practice and professional liability

insurance expenses in addition the work RVUs, creates a variable that is thought to

redistribute payments towards evaluative-oriented services and is better aligned with

actual resource costs (Maxwell & Zuckerman, 2007).

Fleming and colleagues (2009) used the Total RVU variable as a denominator for a

number of performance variables when assessing the financial performance of 33 medical

group practices prior to implementation of an electronic health record system. In other

research, Alexandraki and colleagues (2009) used the Total RVU variable as a measure of

physician productivity in a study of teaching and nonteaching physician services, stating

that the Total RVU variable allowed for comparisons of physicians in different work

settings and regions of the country. In the practitioner setting, Sides (2000) states that

Total Costs divided by Total RVUs is an important measure of physician productivity and

this variable can also be used in cost-allocation methods.

However, there is support for using the Work RVU variable, and not the Total RVU

variable. Sunshine and Burkhardt (2000) used only the Work RVU value in their

research, as these authors believed that this will be a more stable value over time, as

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compared with the professional and liability components which may vary somewhat

more from year to year. Specifically, Sunshine and Burkhardt (2000) measured annual

workload in radiology practices by the Work RVUs divided by the total number of FTE

physicians in each practice. Johnson and Newton (2002) find issues with the practice

expense component of the Total RVU value as it did not receive the initial rigorous study

as a component within the Total RVU value as did the work component, and the practice

expense value is not resource based. Also, Johnson and Newton (2002) note that political

influences can often impact how the practice expense value is modified. However, as this

study is one measuring the output of the overall practice, and not the individual

physicians, Total RVUs will be used to measure physician productivity within each

medical group practice. Therefore, to measure cost leadership and to eliminate the

variation in several pieces of the overall cost leadership construct, it is expected that the

Total RVU variable can be used to normalize specific measurements. The following

variables, normalized with the value for Total RVUs, will comprise the overall cost

leadership construct for the medical group practices.

Efficiency (total operational costs / RVUs and FTEs / RVUs). Miller and colleagues

(1986b) relate capacity utilization to cost leadership by stating that high capacity

utilization contributes to efficiency. Ketchen (1993) used length of stay in hospitals to

denote the capabilities of a firm to efficiently meet the needs of the customers. In a

medical practice, Vonderheid and colleagues (2004) used FTEs and Work RVUs as an

efficiency measure. These authors state that in a traditional medical practice, financial

measures - including revenues and costs - are often analyzed based on a “per physician

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FTE” basis. Based on this previous literature, this study will employ two measures of

medical group practice efficiency - Total Operational Costs divided by Total RVUs and

Total Number of FTE Physicians divided by Total RVUs.

Tight Control of Costs - Physical Size. In a study using a modification of Porter’s

typology, Spanos and colleagues (2004) state that a cost leader will emphasize efficient-

scale facilities, though these authors were unable to include a physical size variable in

their model due to the lack of data. Other authors also deem cost leaders as those who

use space efficiently: Parnell (2006) states that the physical size of a firm can be related

to the value a firm creates; Porter (1997) uses gross revenue per square foot as a measure

of performance in a study of inner-city retailers; and Sandino (2007) notes sales per

square foot as a measure to monitor for cost control in the retail sector. However, none

of these authors explicitly used square footage as a specified variable in their research

model to denote a cost leader. With support from the theoretical literature though, it is

believed that the physical size in proportion to the Total RVUs in a specific medical

practice will be an indicator of a medical group practice’s control of overhead costs, with

firms exhibiting lower gross square footage as a ratio to Total RVUs deemed as cost

leaders.

Tight Control of Costs (Support Staff FTEs). The literature related to cost leadership

and levels of support staff, those individuals not performing the core function of a firm, is

mixed. Hlavacka and colleagues (2001) found significant positive item-to-total

correlations between the low-cost-based competitive strategy variables “making

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services/procedures more cost-efficient, improving the time and cost required for

coordination of various services, and improving the utilization of various staff.” Other

literature (Langland-Orban, Gapenski, & Vogel, 1996; Tennyson & Fottler, 2000; Watt et

al., 1986) related to hospitals has indicated lower staffing levels are positively associated

with higher financial returns. However, Mark (1999) found that increases in per capita

income were associated with increases in all staffing ratios, except within the category of

total nurses, which was negatively correlated. Additionally, the MGMA 2006

Performance Report Based on 2004 Data found that better performing medical groups

had higher levels of total support staff FTEs per FTE physician (MGMA, 2006).

However, following Porter’s theoretical definition of a cost leader – cost minimization

and tight control of overhead costs – this study posits that lower levels of total support

staff ratios to total RVUs will indicate a cost leadership strategy (Porter, 1985).

Total Accounts Receivable (A/R). Porter (1985) states that avoidance of marginal

customer accounts is an indicator of a cost leadership strategy. The MGMA 2006

Performance Report Based on 2005 Data found 17% of all multispecialty groups with a

total A/R (in dollars) greater than 120 days, but that the better performing medical

practices had just over 10% of total A/R greater than 120 days (MGMA, 2006). Thus, it

is expected that medical groups with a cost leadership strategy will exhibit a lower

percentage of total A/R in the greater than 120 days’ category. Specifically, firms will be

designated as using a cost leadership strategy if they fall in the bottom one third of Total

A/R greater than 120 days (in dollars).

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Performance/Outcome Variables: Operating Income Ratio (Operating Income Per FTE

Physician).

Numerous researchers (Bierly & Chakrabarti, 1996; Helms, et al., 1997; Thornhill &

White, 2007) studying organizational performance related to organizational

configurations have used net operating margin as a measure of a firm’s financial

performance. Specifically in health care, McDonagh (2006) used a ranking system of

hospitals to gauge organizational performance, with the ranking system including net

operating margin as one contributor to a hospital’s overall ranking. Jiang and colleagues

(2006) also used operating margin as a measure of a hospital’s profitability. However,

Payne (2001) used a slightly different performance variable - net practice income as a

ratio to the total number of FTE physicians - as one measure of organizational

performance in a study of medical group practices.

In the only published study that was found to specifically use data from medical

groups, an MGMA sample from 1996, Payne (2006) used “net revenue prior to

disbursement” as a performance measure of physician practices due to Payne’s

assumption that medical groups would often disburse profits to the owners or partners in

order to minimize the profits of the group as a whole. After defining the groups using a

K-means cluster analysis technique, Payne (2006) then used MANOVA and ANOVA

and found significant differences in performance related to the four outcome variables

used in his analysis – return on sales, return on equity, return on assets, and profitability.

While contingency theory supports a single ideal profile for groups that are higher

(or lower) performers than other groups (Drazin and Van de Ven, 1985; Gresove, 1989),

organizational configuration theory often supports more than one optimal profile for a

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successful firm. This creates an issue of equifinality though, which has been explored

more extensively in recent years (Payne, 2006). However, albeit the controversy related

to the equifinality and performance variables, based on the literature and with the data

available within the MGMA sample, this study’s outcome variable will be based upon the

organizational performance (the profit ratio) of each physician practice.

Porter defines margin (Porter, 1985, p. 38) as “the difference between total value and

the collective cost of performing the value activities.” Porter frequently refers to

profitability as a performance variable (Porter, 1979b; Porter, 1996), but often the return

on investment (ROI = [(gain from investment – cost of investment) / cost of investment])

accounting method is implied (A. Miller & Dess, 1993). Gapenski (2005) defines

operating income as firm’s revenue minus expenses. This study’s sample however,

includes mostly financial variables related to revenue and cost, with the investment

variable missing from the sample data (which is necessary to compute ROI). Most

measures of productivity in medical groups use variables with the full-time equivalent

physicians as the denominator (Abouleish et al., 2002). Therefore, this study will use

operating income (the total medical revenue after operating costs) divided by the total

number of FTE physicians in a given practice – as a measure of a firm’s profitability, and

thus a performance indicator via Porter’s generic strategies model. Specifically, the ratio

of net practice income to the total number of FTE physicians will be utilized as the

overall performance measure of the medical group practices.

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Statistical Methods in Organizational Configuration Research

Recently, Fiss (2007) developed an extensive paper on the most widely used

methods in the field of organizational configurations, noting the limitations of, and

differences between, many of the most frequently employed empirical methods in OC

research. Fiss (2007) begins with a discussion of the use of linear regression in OC,

finding that regression presents issues with its focus on the relative importance of the

variables in the equation and is problematic in viewing the different configurations and

combinations of variables in the groups. Thus, while linear regression excels at

explaining the relationship of one variable on the performance outcome while holding the

other variables constant, it does little to help one understand the interaction between the

variables. And, while interaction effects may overcome some of the limitations of

regression, this method is very limited in the number of variables that may be used to

adequately interpret the equation. Fiss (2007) also explored the general issue of

equifinality in OC research. Porter’s generic strategy model indicates that several types

of OCs can lead to superior performance over other configurations, but regression

methods employ a unifinality method and thus will not allow for multiple routes to

superior performance.

Fiss (2007) next discusses cluster analysis techniques and notes that is it often an

empirical method of choice in much of the OC literature, as the issue of equifinality can

be overcome with this method. Cluster analysis methods typically use ANOVA or

MANOVA to explore the differences in performance between the groups created by the

clustering algorithm. However, this method has several major limitations. First, the

cluster analysis technique does not lead to any details about the impact of the individual

65

variables on the performance outcome variable. Next, the nature of the clustering

algorithms omits the theoretical underpinnings of the causal relationship between the

independent and dependent variables, as well as to how the predictor variables are related

to one another. Thus, research using clustering algorithm methods leaves one with

questions in regards to how the various strategies a firm may employ work in tandem.

Finally, cluster analysis requires a great deal of subjectivity on behalf of the researcher

when determining cutoff points for the independent variables (Fiss, 2007).

Other researchers have also commented on the clustering methods in OC research.

Ketchen and Shook (1996, p. 442) state that some authors have suggested that the

frequent use of cluster analysis, in addition to the equivocal group membership-

performance link that has been found with OC methods such as strategic group

membership, “is an embarrassment to strategic management.” However, Ketchen and

Shook state that “cluster analysis can be valuable to future of strategy research because of

the technique’s unparalleled ability to classify a large number of observations along

multiple variables” (1996, p. 453). A major assertion from Ketchen and Shook (1996) is

that research designs including a cluster analysis technique should include additional

methodological techniques to reduce researcher subjectivity and control the limitations of

cluster analysis through the triangulation of multiple techniques.

Others have also discussed methods to overcome the criticisms of cluster analysis.

Thornhill and White (2007) have suggested using a purely conceptual classification

scheme using Porter’s generic strategies through an exploratory factor analysis to identify

groups of firms with similar strategies. Thornhill and White (2007) followed this method

by first splitting their sample into two, and then performing an exploratory factor analysis

66

on the first sample, followed by a confirmatory factor analysis on the second sample.

Thus, Thornill and White (2007) were able to follow-up their initial results of the

relationship between the variables in their sample with a subsequent test of the latent

construct.

Another quantitative method, deviation scores, has been used more recently by

several authors (Hult, Ketchen, Cavusgil, & Calantone, 2006; Vorhies & Morgan, 2003;

Xu, Cavusgil, & White, 2006). This method has the researcher define an ideal type with

an empirical profile and then each case is compared with the ideal profile. Using this

method, along with an appropriate theoretical basis, researchers may test the fit of various

profiles. However, this method lacks in the ability to determine whether any causal

relationships exist between the independent variables and which variables may be related

to the performance differences between groups. Additionally, the deviation scores’

method is less than idyllic as it requires a great deal of researcher subjectivity when

creating the ideal profile (Fiss, 2007).

To counter these methods, Fiss (2007, p. 1183) says that a “set-theoretic approach

using Boolean algebra to determine which combinations of organizational characteristics

combine to result in the outcome in question” is the best method for empirical research

using the organizational configuration theory. With this method, the researcher develops

a “truth table” listing all possible organizational configurations and then a determination

is made as to whether each outcome leads to the desired outcome. However, no

published articles using this methodology were found in the empirical literature of

organizational configurations.

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Although the previous discussion of statistical methodology in OC demonstrates that

a number of methods have been utilized in the literature to study performance differences

between group, cluster analysis is still the primary method used in organizational

configuration research (Short, et al., 2008). However, research using the cluster analysis

with only inductive methods may receive serious criticism for lacking a theoretical basis.

Additionally, in a meta-analysis of 40 empirical configurational studies, Ketchen and

colleagues (1997) found no difference in effect size between the inductive and deductive

methods. However, these researchers did find that studies using a broader range of

organizational configurations yielded stronger effect sizes than those studies with a more

narrow range. Ketchen and colleagues (1997) also found unequivocal evidence from the

40 articles reviewed that configurational research methods were able to predict the

performance of a firm. In summary, as clustering analysis is a widely used method in OC

research, and to further the explanatory power of the results for both the medical group

population as well as the overall research stream, this study will utilize a clustering

algorithm based initially on a deductive methodology using Porter’s generic strategies,

followed by a clustering algorithm using a purely inductive methodology.

Phase I: Deductive, Theoretically Based Model/Typology

1. Using a deductive methodology, do medical groups that exhibit the characteristics of

one of Porter’s pure generic strategies (i.e., target scope and then differentiation or

cost leadership) perform better financially than medical groups using a hybrid

strategy or mixed strategy?

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Broad-focused:

H1a: On average, broad-focused (multispecialty) medical group practices exhibiting a

pure generic strategy (cost leader or differentiated) will have higher levels of financial

performance compared with groups exhibiting a hybrid or a mixed strategy.

Narrow-focused:

H1b: On average, narrow-focused (single specialty) medical group practices exhibiting a

pure generic strategy (cost leader or differentiated) will have higher levels of financial

performance compared with groups exhibiting a hybrid or a mixed strategy.

Phase II: Inductive Taxonomy – An Empirical Model (Cluster Analysis)

2. Using an inductive methodology, do specific groups of organizations with similar

characteristics perform better financially than others?

Broad-focused:

H2a1: Discrete clusters will not form based on a cluster analysis technique using the

same variables used in the deductive analysis for broad-focused medical groups.

H2b1: Clusters that emerge from the cluster analysis technique (inductive technique) will

not exhibit significant performance differences between groups of broad-focused medical

groups.

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Narrow-focused:

H2a2: Discrete clusters will not form based on a cluster analysis technique using the

same variables used in the deductive analysis for narrow-focused medical groups.

H2b2: Clusters that emerge from the cluster analysis technique (inductive technique) will

not exhibit significant performance differences between groups of narrow-focused

medical groups.

Phase III – Comparing Taxonomy and Typology Models (Goodness of Fit test)

3. Will the inductive or deductive methodology lead to groups that will better predict the

financial performance of certain medical practice groups?

Broad-focused:

H3a: Using a goodness of fit test, the variation in the performance between the broad-

focused groups using the inductive approach will be greater than that of the deductive

approach.

Narrow-focused:

H3a: Using a goodness of fit test, the variation in the performance between the narrow-

focused groups using the inductive approach will be greater than that of the deductive

approach.

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Figure 1. Hypothesis Model: Differentiator, Cost Leader, Hybrid, and Mixed Groups – Broad Focused Medical Groups (Multispecialty)

BROAD FOCUS: MULTISPECIALTY

COST LEADER

DIFFERENTIATOR

FINANCIAL PERFORMANCE

HYBRID

MIXED

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Figure 2. Hypothesis Model: Differentiator, Cost Leader, Hybrid, and Mixed Groups – Narrow Focused Medical Groups (Single Specialty)

NARROW FOCUS: SINGLE SPECIALTY

COST LEADER

DIFFERENTIATOR

FINANCIAL PERFORMANCE

HYBRID

MIXED

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Table 1

Measures of the Independent and Dependent Variables

Variables Included in the Model as Measures of Strategy Type Independent Variables

• Market Scope Strategy Variables: Focused vs. Broad - Type of medical group practice (single or multispecialty)

Variables and Measures of the Differentiation Strategy 1. MARKETING (Advertising Intensity): promotion &

marketing costs / total general operating costs

2. SPECIALTY SERVICES (Providing Specialty Products/Services): Number of ancillary / supplementary services provided

3. IMAGE: furniture and equipment costs + furniture and equipment depreciation/ total general operating costs

4. BRANCHES (Range of Market Segments): number of

branch or satellite clinics

Variables and Measure of the Cost Leadership Strategy

5. EFFICIENCY (Production and Operations Efficiency): a. Total operational costs / total RVUs b. Total Number of FTE Physicians / total RVUs

6. COST CONTROL (Control of Costs): – physical size: gross

square footage of all practice facilities / total RVUs

7. OVERHEAD (Tight Control of Overhead Costs) – front office support staff FTEs / total RVUs

8. ACCOUNTS RECEIVALBE (Tight Control of Marginal Accounts) – total accounts receivable > 120 days (in dollars)

Dependent Variable 9. PROFIT RATIO - total medical revenue after operating

costs / total number of FTE physicians

Note: Variables are from the MGMA 2009 Survey (Based on 2008 Data)

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CHAPTER 4

RESULTS AND FINDINGS

The 2008 MGMA Cost Survey included a number of variables from a total of 1,797

medical group practices. This chapter will include a discussion regarding the initial

review of the applicable variables from the dataset, a summary of the descriptive statistics

for the applicable variables, an explanation regarding necessary modifications of the

originally proposed methods, the results from the inferential analysis (i.e., an ANOVA

and a cluster analysis), and the results of the hypothesis testing. The preliminary review

of the applicable variables in the MGMA dataset revealed numerous issues including

duplicate cases, numerous cases with missing data, and wide variability. Along with

these issues, several variables that were included in the original model had to be replaced

with alternative variables. Due to these factors, which will be discussed briefly in this

chapter and in greater detail in the appendix, the final analyses of the MGMA data

included only 1,413 of the 1,797 practices included in the complete dataset.

Preliminary Data Cleaning and Preparation for Data Analysis

Duplicate Cases and Missing Data

An initial review of the specific variables included in the originally proposed model,

as well as several other variables, led to the identification of nine cases that were obvious

duplicates in the dataset. These practices were eliminated from the dataset, leaving 1,788

cases for further analyses. For subsequent analyses that describe the data using all

practices, the duplicated practice data were excluded.

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Normalizing the Variables

As practice size and volume will obviously vary between the practices, the data

analysis plan included methods to normalize many of the variables in the model. In the

original model, the total physician RVUs for each practice was to be used to normalize

three of the four cost leadership strategy indicator variables (i.e., RVUs was proposed as

a denominator for the strategy indicator ratios that were to be created using these three

variables). However, only 684 of the 1,788 original cases reported a value for total

physician RVUs in their practice. Thus, after a review of the data for possible alternatives

for the RVU variable, a decision was made to include the total number of FTE physicians

in each practice as a proxy for the total physician RVUs variable. Total number of FTE

physicians was also selected as it had been proposed in the original methods to normalize

the dependent variable, the mean profit ratio for each practice. The correlation between

these two variables, total physician RVUs and total number of FTE physicians,

demonstrated a significant (p < 0.01), yet weak (r = 0.185) relationship (see Appendix

A). However, there were no other variables present within the MGMA dataset that were

intuitively related to physician practice size or volume, other than several variables that

also included a large number of cases with missing data.

The variable total general operating costs within each practice was initially

proposed as the variable that would be used to normalize two of the four differentiator

strategy variables, advertising intensity and providing specialty products/services.

However, to maintain consistency within the model to the greatest degree possible, total

number of FTE physicians was replaced as the normalizing variable for both advertising

intensity and providing of specialty products/services within each practice. The inclusion

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of the total number of FTE physicians variable to normalize many of the strategy

indicator variables also led to the elimination of this variable as one of the cost leadership

indicator variables, as it could not be used as both a distinct strategy indicator variable as

well as a variable to normalize many of the other strategy indicator variables.

As discussed in greater detail below, two of the remaining strategy indicator

variables - total ancillary services provided by each practice and total number of

branches within each practice - were recoded as binary variables, thus negating the need

for normalization of these two variables. The last of the eight indicator variables,

accounts receivable greater than 120 days (in dollars), was normalized independently of

the total number of FTE physicians in each practice. For this variable, the total accounts

receivable value (in dollars) reported by each practice was used to normalize each

practice’s value for their accounts receivable greater than 120 days. Table 10 provides

the revised strategy indicator measures, as well as the outcome variable, and the specific

variables used to create each new strategy indicator measure.

Full-time Equivalent Physicians - Inclusion of Only Cases with 1 – 35 FTE physicians

Within the original overall sample (n = 1,788), after the duplicate cases were

removed, 13 cases included a missing value for the total number of FTE physicians

variable and 3 cases were deemed as extreme outliers (> 1, 000 total number of FTE

physicians). Overall, the mean of the total number of FTE physicians for the remaining

1,772 practices was 17.21. One hundred and forty-eight (8.4%) practices had less than

1.0 physician FTEs and 203 (11.5%) practices had greater than 35 FTEs, after the 3

outlier cases were eliminated (see Appendix B). The comparison of the variability of the

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total number of FTE physicians between the two groups was stark, with a standard

deviation of 7.56 for the group including only the practices with between 1 – 35 total

FTE physicians (n = 1,421), 47.35 for the group with all 1,772 cases, and 108.54 for the

group with only those practices with more than 35 FTE physicians (with the three cases

with extreme outliers and missing values excluded). Thus, to create a more

homogeneous sample for our inferential analysis, only those practices that had between 1

and 35 total FTE physicians (n =1,421 or 80.2% of the overall sample) were included in

the subsequent data analyses (see Appendix B for the frequencies within selected size

groups of practices).

Table 2

Descriptive Statistics – Total Number of FTE Physicians

Total Number of FTE Physicians N Min Max M SD All Practices 1,772 .00 968 17.21 47.35 Practices with 1 – 35 FTEs 1,421 1.00 35 7.38 7.56 Practices with > 35 FTEs 203 35.40 968 98.18 108.54 Note: The three extreme outlier cases were excluded from this analysis.

Total General Operating Costs and Profit Variables

Two of the 1,421 remaining practices did not report a value for their practice’s total

general operating costs, two other cases reported a negative value, and three reported a

value of zero. These seven cases were eliminated from further analysis, leaving 1,414

cases for further analyses. One practice did not report a value for the model’s outcome

variable, the profit of each practice (i.e., medical revenue less operating expenses). This

single case was also eliminated from further analyses, leaving 1,413 cases as the final

sample size for the subsequent descriptive analyses and the inferential analyses.

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Single specialty vs. Multispecialty Practices

Of the 1,413 practices that remained, 81.7% (1,154) were single specialty practices,

17.1% (242) were multispecialty practices, and 1.2% (17) reported “other” as their

practice type. The initial data analysis plan included grouping the cases within the

sample by either single or multispecialty groups, which would relate to Porter’s broad

and narrow target market scope construct. However, with the relatively small number of

cases represented as multispecialty practices (n = 242), and the proposed methods that

would lead to the division of the multispecialty practices into four separate groups, it was

determined that the small sample size for the multispecialty practices would be

inadequate for a separate inferential analysis. Therefore, the methods were altered to

combine both the single and multispecialty practices (n = 1,413) into a single sample and

eliminate the separate analyses using each these practice types.

Table 3

Practice Type (N = 1,413)

Practice Type Frequency Percent Single Specialty 1,154 81.7 Multispecialty 242 17.1 Other 17 1.2

As is evident from the preceding pages in this chapter, a significant amount of

effort ensued during the data cleaning and preparation stage of the analysis. The

following sections of this chapter will detail the results from the descriptive and

inferential analyses that were completed after the initial data cleaning and preparation.

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General Practice Demographics

Regarding the legal organization of the practices (N = 1,413), 27.6% (390) reported

their practices as not-for-profit; 50.3% (709) were for-profit businesses, LLCs,

partnerships, professional corporations, sole proprietorships, or other; and 22.2% (314) of

the practices did respond to this question.

Table 4

Legal Organization Type (N = 1,413)

Legal Organization Type Frequency Percent Not-for-profit corporation/foundation 390 27.6 For-profit entity 709 50.3 Other 22 1.6 Missing 314 22.2

Fifty percent (709) of the practices were owned by an integrated system or

hospital, 46.4% (655) of the practices were owned by physicians, and the remaining 3.5%

(49) reported another type of ownership.

Table 5

Majority Owner (N = 1,413)

Majority Owner Frequency Percent IDS or hospital 709 50.2 Physicians 655 46.4 Other 49 3.5

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The population category of the metropolitan areas for the practices was split

relatively evenly within the sample (n = 1,413), with 18.4% (260) residing within a

nonmetropolitan area of fewer than 50,000 residents, 23.4% (331) residing within a

metropolitan area between 50,000 – 250,000 residents, 19.5% (276) residing within a

metropolitan area between 250,001 – 1,000,000 residents, 16.1% (227) residing within a

metropolitan area greater than 1,000,000 residents, and 22.6% (319) of the practices did

not respond to this question.

Table 6

Population category (N = 1,413)

Population Category Frequency Percent Nonmetropolitan (fewer than 50,000) 260 18.4 Metropolitan (50,000 to 250,000) 331 23.4 Metropolitan (250,001 to 1,000,000) 276 19.5 Metropolitan (more than 1,000,000) 227 16.1 Missing 319 22.6

Overall, the cases included in the model did not vary significantly from the excluded

cases based on legal organization type (p = 0.196) and majority owner type (p = 0.57).

However, when comparing those practices included in the final analysis with all practices

within the MGMA dataset, those included were significantly (p = 0.006) more likely to

reside in smaller metropolitan areas. Additionally, the effect size was also very small (<

0.01) for each of these three variables (see Appendix C for the ANOVA and Effect Size

results).

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Descriptive Analyses for the Variables Included in the Model

The model, after elimination of the second variable to measure one dimension of

production and operational efficiency (total number of FTE physicians), now includes

four variables that indicate whether or not a practice exhibits the characteristics of a cost

leader and four variables that indicate the designation of a practice as a differentiator, in

addition to the dependent variable, the profit ratio. However, many of the practices did

not respond to one or more of the questions that were included as variables in our model.

The number of missing responses varied from 100 – 500 for each of the strategy indicator

measures. The descriptive statistics for the variables, prior to normalization, are detailed

below. A further discussion of each variable included in the model, including the

descriptive statistics for each variable, is included in Appendix D.

Table 7

Descriptive Statistics – All Variables Included in Model (N = 1,413)

Variables N Min Max M SD

Promotion and marketing (1) 995 $0 $873,594 $36,562 $68,471

Furniture and equipment (3a) 880 $0 $1,357,256 $48,896 $121,625

Furniture and equipment dep (3b) 926 $(167,081) $6,501,692 $102,734 $305,583

Total general operating cost (5) 1,413 $2,160 $79,274,718 $1,860,000 $3,629,000

Total square feet (6) 877 587 500,000 18,605 27,059

Total FTEs of Support Staff (7) 1,317 .01 346.88 32.53 44.69

AR > 120 (8) 1,294 .00 $10,632,000 $326,341 $724,400

Total accounts receivable (8) 1,315 0 $61,581,000 $1,585,979 $3,083,723

Profit (9) 1,413 $(16,733,495) $38,127,495 $2,860,000 $4,359,000 Note: The number in parentheses after each subheading represents the strategy dimension measured by each variable, as detailed in Table 10.

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After the initial review of the data, two of the indicator variables, total number of

ancillary services and the number of reported branches for each practice, were recoded

into binary variables. Binary recoding was necessary as numerous practices reported no

ancillary services provided by their practice and/or no additional branches beyond their

primary practice site. Thus, a binary variable to measure these two dimensions of

differentiation was deemed more practical. The frequencies for each of these binary

variables are detailed in the tables below, and further details regarding these two

variables are included in Appendix D.

Table 8

Specialty Services (N= 1,413)

Practices Reporting One or More Ancillary Services (2) Frequency Percent No 820 58.0 Yes 593 42.0 Note: The number in parentheses represents the strategy dimension measured by this variable, as indicated in Table 10. Table 9 Branches for Each Practice (N = 1,413)

Branches (4) Frequency Percent No Branches 948 67.1 1 or More Branches 465 32.9 Note: The number in parentheses represents the strategy dimension measured by this variable, as indicated in Table 10.

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Table 10 Measures for Each of the Strategy Indicator Variables & the Outcome Variable (Profit Ratio) Independent (Strategy Indicator) Variables & the Outcome Variable Differentiation Variables

1. MARKETING - Advertising Intensity: promotion & marketing costs / total number of FTE physicians

2. SPECIALTY SERVICES - Providing Specialty Products/Services: the provision

of ancillary services (binary variable – yes/no)

3. IMAGE - Image: furniture and equipment costs (3a) + furniture and equipment

depreciation (3b) / total number of FTE physicians

4. BRANCHES - Range of Market Segments: number of branch or satellite clinics

(binary variable – yes/no)

Cost Leadership Measures (Variables) 5. EFFICIENCY - Production and Operations Efficiency: Total general operating

costs / total number of FTE physicians

6. COST CONTROL - Control of Costs: – physical size: gross square footage of all

practice facilities / total number of FTE physicians

7. OVERHEAD - Control of Overhead Costs – front office support staff FTEs / total number of FTE physicians

8. ACCOUNTS RECEIVABLE - Control of Marginal Accounts – total accounts receivable > 120 days / total accounts receivable

Outcome Variable 9. PROFIT - Profit (total medical revenue after operating costs) / total number of

FTE physicians

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Creation of Ratios - Winsorized Ratios & Outliers

Once the data cleaning and descriptive analyses were finalized, the ratios for the

applicable variables were computed. However, as discussed previously, the significant

variability with many of the variables, as well as numerous outliers, presented possible

issues. Nenide and colleagues (Nenide, Pricer, & Camp, 2003) have challenged the

validity in predicting firm performance through traditional methods when using financial

ratio data calculations. These authors contend that many researchers who utilize ratio

data to predict firm performance do not test for sample assumptions (e.g., normal

distribution, negative denominators, and outlier influence) and do not address potential

errors within the data (Nenide, et al., 2003). To reduce the effect of these issues, Nenide

and colleagues propose the use of the Winsorizing technique. Winsorizing the data is

accomplished by deeming values beyond a certain threshold for a given variable as

outliers. These outlier values are then replaced with a value that is one point above the

outlier threshold value.

Thus, based on recommendations from Nenide and colleagues (Nenide, et al., 2003),

each of the six non-binary strategy indicator variables were Winsorized. Specifically, an

outlier threshold value was generated by adding the value of the 75th percentile for each

variable to the interquartile range (IQR) value multiplied by 1.5 (i.e., 75th percentile +

(IQR)*1.5). In addition to the previous reviews of the data to correct for data errors,

Winsorizing the data led to the inclusion of many cases that might have otherwise been

eliminated (i.e., the extreme outliers) and dramatically reduced the variability of the

strategy indicator ratios in our model. A comparison of the values of the Winsorized and

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non-Winsorized variables, as well as the changes in variability for each strategy indicator

ratio variable, is included in Appendix E.

Imputation of Values

As mentioned previously, many of the strategy indicator variables had numerous

cases with missing data, including one variable with 536 (38%) missing values. Only

711 cases would have remained for further analysis if a simple listwise deletion of the

cases was utilized. A review of the patterns of missing data ensued via SPSS and the

missing data were verified as missing completely at random. Several methods for

overcoming the missing data issue were reviewed, including mean substitution,

indicator/dummy variables adjustment, and imputation methods. Overall, multiple

imputation was found to be the best alternative. “Multiple imputation allows pooling of

the parameter estimates to obtain an improved parameter estimate,” thus incorporating

the uncertainty into the standard errors, which is an improvement over single imputation

methods (Acock, 2005, pp. 1,019). Compared with others method that would eliminate

cases with missing data (e.g., listwise deletion), multiple imputation enabled the inclusion

of many more cases in the final analyses. Five maximum likelihood multiple imputation

methods followed, and the method of providing the best fit for the variables was used to

impute the missing values of each six strategy indicator ratios, as well as the two binary

indicator variables. Imputation of these values enabled all 1,413 practices to remain in

the sample for further analyses. Residuals from the imputation process are included in

Appendix F.

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Grouping the Practices

Differentiator Construct and Cost Leader Constructs

The steps in the data cleaning and data preparation process detailed above ultimately

left 1,413 cases for further analyses and led to the creation of eight strategy indicator

variables, four for cost leadership and four for differentiation. Two of these variables

were binary (i.e. yes/no) and the six other strategy indicator variables were ratios. The

individual values for each of the strategy variables were then reviewed, and using the

Spanos (2004) methodology, a cutoff point for each variable was computed. Thus, for

each of the eight strategy indicator variables, approximately one-third of the sample was

designated as a differentiator for each four measures of differentiation and approximately

one-third of the sample was designated as a cost leader for each of the four measures of

cost leadership. Subsequently, the four variables in each dimension, cost leadership and

differentiation, were summed together to create two separate composite strategy variables

for each practice. Initially, the overall composite values from each dimension led to the

designation of each practice as a cost leader, a differentiator, or both. Next, those

practices deemed as both a cost leader and differentiator were designated as hybrids (and

thus eliminated from the cost leader and differentiator groups), and those practices not

exhibiting the cost leader or differentiator traits were labeled as having a mixed strategy.

Again using the methods of Spanos and colleagues (2004), a best fit for dividing the

sample roughly into thirds was derived for the two composite strategy indicator variables,

cost leader and differentiator. The results indicated that 23.4% (n = 330) of the sample

were initially deemed as exhibiting the traits of a differentiator and 37.2% (n = 525) of

the practices were deemed cost leaders.

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Table 11 Preliminary Differentiator and Cost Leader Groupings (N = 1,413)

Differentiator Cost Leader

Inclusion? Frequency Percent Frequency Percent

No 888 62.8 1,083 76.6 Yes 525 37.2 330 23.4

Overall Construct

After the practices had been categorized into the cost leader and differentiator

constructs, those practices that were deemed as both costs leaders and differentiators

were identified as hybrid practices (2.5%; n = 36). Additionally, those practices deemed

as neither cost leaders nor differentiators (42%; n = 594) were labeled as having a mixed

strategy. The remaining groups were those exhibiting only one of the initial two traits -

cost leadership (34.6%; n = 489) or differentiation (20.8%; n = 294).

Table 12

Final Categorization of All Practices (N = 1,413)

Strategy Group Frequency Percent Stuck In The Middle 594 42.0 Differentiator 294 20.8 Low Cost Leader 489 34.6 Hybrid 36 2.5

87

Empirical Analyses

Hypothesis 1 - Theoretical Groupings (Typology)

After the sample had been divided into four distinct groups based on the exhibited

organizational configuration strategy of each practice, an ANOVA was used to test the

theoretical performance hypothesis. To account for the previously discussed combination

of both the multispecialty and single specialty groups into a single group, Hypothesis 1

was revised as follows:

H1: On average, medical group practices exhibiting a pure generic strategy (cost

leader or differentiator) will have higher levels of financial performance as compared

with groups exhibiting a hybrid or a mixed strategy.

Overall, we find support for part of Hypothesis 1. The ANOVA produced a

significant (p < .001) finding of a difference between the mean profit ratios of the four

groups. The Sheffe post hoc analysis (see the Appendix G) led us to reject the null

hypothesis and find support for our research hypothesis, thus determining that the cases

categorized within the differentiator group produced a significantly (p < 0.05) higher

mean profit ratios than the other three groups. However, Hypothesis 1 was not fully

supported, as the cost leader group was not found to be significantly different, based on

the mean profit ratio, from the hybrid or the mixed strategy group. Also, it should be

noted that the hybrid group, those practices exhibiting the traits of both a cost leader and

differentiator strategy, was quite small (n=36) when compared with the other three

groups, which were relatively similar in size. The mean and standard deviation for each

of the four groups are listed in the table below. The large standard deviation values

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relative to the group means, especially for the differentiator group, demonstrate the high

degree of variability for many of the indicator measures in this model.

Table 13

Descriptive Statistics for the Four Groups (N = 1,413)

Groups M SD

Stuck In The Middle 218,884 310,921 Differentiator 585,928 836,923 Low Cost Leader 254,077 284,039 Hybrid 352,835 230,110 Table 14 One-Way Analyses of Variance for Effects of Group Membership on Profit Ratio (N = 1,413)

Source df SS MS F Between Groups 3 2.891E13 9.637E12 44.697*** Within Groups 1409 3.038E14 2.156E11 Total 1412 3.327E14

***p < .001.

Figure 3. Mean Profit Ratio of Practices by Organizational Configuration Type (N =

1,413)

$0$100,000$200,000$300,000$400,000$500,000$600,000$700,000

Stuck In TheMiddle

Differentiator Low CostLeader

Hybrid

Mea

n Pr

ofit

Per F

TE

Phys

ican

Practices Categorized by Organizational Configuration

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Hypothesis 2 – Empirical Groupings

To test the second set of hypotheses, an inductive methodology was used to

determine if groups of medical practices with similar characteristics would form, based

on all eight of the strategy indicator measures used to categorize the practices for

Hypothesis 1. If unique groups were established, a further investigation would ensue to

determine if one or more of the groups performed better than the other groups based on

the outcome variable, the mean profit ratio. As with Hypothesis 1, the original two

inductive hypotheses were combined to account for the elimination of the initially

proposed separate analyses of the narrow and broad focused medical group practices.

To test Hypothesis 2a, a cluster analysis using a probabilistic model was utilized

to create the mutually exclusive groups. This technique simultaneously maximized the

within-group similarities and between-group differences. Using this technique in SPSS,

initial cluster centroids were created and with multiple passes through the data, an

optimal solution of cluster membership was created. Hypothesis 2a is listed below:

H2a: Discrete clusters will not form based on a cluster analysis technique using

the same variables used for the medical groups in the deductive analysis.

The cluster analysis revealed six groups based on the eight strategy indicator

measures. Thus, Hypothesis 2a was rejected. Overall, the sizes of five of the six groups

formed by the cluster analysis were relatively very similar, ranging from 154 to 277, with

only one group having many more practices (419) than the other six groups. While

significant differences are present across the clusters, the latent class variable did not

explain significant variation in R2 for the Accounts Receivable variable.

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The table below (Table 15) provides the means for each strategy indicator

variable for each of the six groups created through the cluster analysis. The first four

strategy indicator variables are related to the differentiator dimension and the last four

variables are related to the cost leadership dimension. Comparing each group to the other

groups, groups with higher values for the differentiator dimension exhibit the traits of a

differentiator and groups with lower values for the cost leadership dimension exhibit the

traits of a cost leader.

A detailed review of the various strategy traits attributed specifically to each of

the six groups created by the cluster analysis will be discussed in the following pages.

Figure 4 depicts all six groups simultaneously in a single figure, with the strengths of

each of the eight strategy indicator measures, relative to the other groups, plotted for each

individual group. The first four variables in the figure relate to the differentiation

strategy dimension and the last four variables relate to the cost leadership strategy

dimension. To clearly illustrate the rankings of each group for each strategy indicator

variable, the values for the four variables in the cost leadership dimension were reflected.

Thus, the higher rankings of a group on a number of variables in the cost leadership

dimension connote a cost leadership strategy and the higher rankings on the

differentiation dimension connote a differentiated strategy. Although Figure 4 is

somewhat difficult to analyze with all six groups depicted simultaneously, one can begin

to see the differing patterns in organizational strategy configurations that emerge between

the six inductively created groups.

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Table 15

Probabilistic Weighted Means of Each of the Six Groups by Each of the Eight Strategy

Indicator Ratio Variables & The Size of Each Group

Indicator Variables Group 1 (n = 419)

Group 2 (n = 277)

Group 3 (n = 231)

Group 4 (n = 181)

Group 5 (n = 151)

Group 6 (n = 154)

Marketing ($) 4,848 9,873 1,311 14,647 1,571 13,248 Image ($) 12,430 15,325 3,212 26,997 3,725 24,256 Specialty Services (%) 0.67 0.05 0.24 0.02 0.65 0.81 Branches (%) 0.61 0.03 0.25 0.02 0.12 0.76 Efficiency ($) 244,432 191,930 84,871 353,723 168,092 411,894 Cost Control ($) 2,296 1,617 1,259 2,429 2,185 3,264 Accounts Receivable ($) 0.17 0.26 0.20 0.25 0.22 0.17 Overhead ($) 5.21 2.15 1.64 3.75 4.18 7.60

Figure 4. Normalized Means for Each of the Eight Indicator Variables, Plotted by Group Membership

Marketing Image Specialty Services

Branches Efficiency Cost Control

Accounts Receivable

Overhead

1.0

0.8

0.6

0.4

0.2

0.0

Group 1 Group 2 Group 3 Group 4 Group 5 Group 6

Cost Leader Strategy Variables Differentiation Strategy Variables

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Group 1 – Stuck in the Middle #1 (n = 419). Comparing Group 1 to the other five

groups, as well as the four theoretically created groups, Group 1 appears to most

resemble the stuck in the middle, or mixed strategy, group. Of the eight strategy

measures, this group of practices ranks second on two of the differentiator strategy

measures (Specialty Services and Branches) and second on one cost leader strategy

measure (Accounts Receivable). However, they rank fourth on two other differentiator

measures (Marketing and Image) and fourth on two cost leadership measures (Efficiency

and Cost Control). This group is also the largest of the six groups, with nearly 30% (n =

419) of the total number of practices, which also coincides with the relatively large size

of the mixed strategy group that was created by using the theoretical/deductive

methodology. The other five groups created through the inductive methodology were

more similar in size to each other, ranging from 151 – 277 practices in each group.

Figure 5. Group 1 – Stuck in the Middle #1

1.0

0.8

0.6

0.4

0.2

0.0 Marketing Image Specialty

Services Branches Efficiency Cost

Control Accounts

Receivable Overhead

Cost Leader Strategy Variables Differentiation Strategy Variables

93

Group 2 – Stuck in the Middle #2 (n = 277). Group 2 is somewhat more difficult to

interpret as compared with most of the other groups. This group of practices has the third

highest values for two of the differentiation strategy measures, Marketing and Image, and

fifth highest for the other two differentiation indicator measures – Specialty Services and

Branches. For the cost leadership strategy measures, these practices are third highest in

Efficiency and second highest in Cost Control and Overhead, but rank the lowest of all

six groups in regards to Accounts Receivable. This variability in the rankings for these

eight strategy indicator measures does not coincide well with any of the theoretically

created Porter groups or the hybrid category.

Figure 6. Group 2 – Stuck in the Middle #2

1.0

0.8

0.6

0.4

0.2

0.0 Marketing Image Specialty

Services Branches Efficiency Cost

Control Accounts

Receivable Overhead

Cost Leader Strategy Variables Differentiation Strategy Variables

94

Group 3 – Cost Leader (n = 231). Group 3 exhibits many of the traits of a cost leader.

For the differentiation strategy dimension, this group of practices is lowest among the six

groups for Marketing and Image, fourth in Specialty Services, and third in Branches.

Related to the cost leadership measures, this group of practices ranks third for Accounts

Receivable, but has the highest values among the six groups for the three remaining cost

leadership strategy measures. Thus, this group of practices is clearly is somewhat

analogous to Porter’s cost leadership strategy group.

Figure 7. Group 3 – Cost Leader

1.0

0.8

0.6

0.4

0.2

0.0 Marketing Image Specialty

Services Branches Efficiency Cost

Control Accounts

Receivable Overhead

Cost Leader Strategy Variables Differentiation Strategy Variables

95

Group 4 – Stuck in the Middle #3 (n = 181). Group 4 may also be viewed as somewhat

analogous to practices exhibiting the stuck in the middle strategy. In the differentiation

strategy dimension, this group of practices ranks highest among the six groups for

Marketing and Image, but lowest for Specialty Services and Branches. For the cost

leadership characteristics, this group ranks second from the bottom on Efficiency, Cost

Control, and Accounts Receivable, but had the third highest value for Overhead. These

mixed rankings for each of the strategy dimensions may be viewed as the traits of a group

of practices with a stuck in the middle strategy, though not quite as clearly as with Group

2.

Figure 8. Group 4 – Stuck in the Middle #3

1.0

0.8

0.6

0.4

0.2

0.0 Marketing Image Specialty

Services Branches Efficiency Cost

Control Accounts

Receivable Overhead

Cost Leader Strategy Variables Differentiation Strategy Variables

96

Group 5 – Stuck in the Middle #4 (n = 151). The practices within Group 5 also resemble

practices that employ a stuck in the middle strategy, though again, the strategy traits

within this group were not quite as similar to a stuck in the middle categorization as with

Group 2. For the differentiator strategy measures, this group of practices ranks fifth on

Marketing and Image, third on Specialty Services, and fourth on Branches. In the cost

leadership dimension, this group ranks second on Efficiency, third on Cost Control, and

fourth on Overhead. Thus, this group of practices does not excel in either of the two

strategy dimensions.

Figure 9. Group 5 – Stuck in the Middle #4

1.0

0.8

0.6

0.4

0.2

0.0 Marketing Image Specialty

Services Branches Efficiency Cost

Control Accounts

Receivable Overhead

Cost Leader Strategy Variables Differentiation Strategy Variables

97

Group 6 – Differentiator (n = 154). Group 6 is clearly most analogous to those practices

demonstrating the traits of a differentiator. This group of practices ranks first or second

on each of the four measures related to the differentiation strategy dimension and below

each of the other five groups on three of the four cost leadership strategies. However,

Account Receivable for this group does not follow what would be predicted from a group

with the strong traits of a differentiator, as this group has an Accounts Receivable value

that was highest among the six groups. Ignoring the value for this group’s Accounts

Receivable measure, this group bears a very strong resemblance to a differentiator.

Figure 10. Group 6 - Differentiator

1.0

0.8

0.6

0.4

0.2

0.0

Marketing Image Specialty Services

Branches Efficiency Cost Control

Accounts Receivable

Overhead

Cost Leader Strategy Variables Differentiation Strategy Variables

98

Differentiator Dimension. The figure below represents all six groups, but only includes

the four indicator variables for the dimension measuring differentiation. In this figure,

Group 6 is clearly depicted as the group most resembling a group of practices with the

traits of a differentiator. Group 3 is also depicted as the near polar opposite of Group 6,

as Group 3 ranks below average in all four measures of differentiation, and last for two of

these measures. The remaining four groups do not exhibit patterns that would clearly

identify them as a differentiator or exclude them from the cost leadership category.

Figure 11. Strategy Dimension - Differentiator

1.0

0.8

0.6

0.4

0.2

0.0 Marketing Image Specialty

Services Branches

Differentiation Strategy Variables Group 1 Group 2 Group 3 Group 4 Group 5 Group 6

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Cost Leader Dimension. Only the four measures of the cost leadership strategy are

depicted in the figure below, but all six groups are simultaneously included in this figure.

Group 3 clearly stands out as the cost leader, ranking highest of all six groups in three of

the four measures of cost leadership, and Group 6 clearly stands out as nearly a mirror

image of Group 3, ranking the lowest of the six groups on three of the three costs

leadership strategy variables. The other four groups are much less well defined with

regards to the costs leadership dimension.

Figure 12. Strategy Dimension - Costs Leadership

1.0

0.8

0.6

0.4

0.2

0.0 Efficiency Cost

Control Accounts

Receivable Overhead

Cost Leader Strategy Variables Group 1 Group 2 Group 3 Group 4 Group 5 Group 6

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Hypothesis 2 – Performance Differences

Hypothesis 2b utilized an ANOVA to test for performance differences between the

six groups. The analysis of the results incorporated an evaluation of both the magnitude

and range of the F values to determine if there were significant performance differences

between the groups, as well as whether there was significant discrimination between the

clusters.

H2b: Clusters that emerge from the cluster analysis technique (inductive

technique) will not exhibit significant performance differences between groups of medical

group practices.

Hypothesis 2b was not supported, as significant (p < 0.001) differences between the

mean profit ratios of the groups were found (see Table 17 below). The mean profit per

physician within each group ranged from $105,288 to $725,732 (see Table 16 below).

Group 6 has the highest profit ratio (profit per physician in each practice), with nearly

twice the average profit of the next highest group. Groups 2 and 4 have the lowest profit

ratios.

As noted previously, Group 6 exhibits many of same characteristics as the

theoretically generated group of practices that were categorized as differentiator. Group

6 has high values for the four differentiation strategy variables, and low values for all but

one of the cost leadership strategy measures, Accounts Receivable. Group 3, the subset

of practices that are most analogous to the Cost Leadership group created from the

theoretical model, had the third highest profit ratio among the six groups.

Group 1, the group deemed most analogous to those practices with a stuck in the

middle strategy, had the second highest profit ratio amongst the six groups. This group

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was also the largest of the six groups, with 419 practices, and was relatively similar in

size to the group categorized as the stuck in the middle group in the theoretical model.

The remaining three groups - Groups 2, 4, and 5 – exhibited a range of high to

low values on each of the eight strategy indicator variables, but none of these groups

corresponds very well with one of Porter’s groups or with a group exhibiting the hybrid

strategy, though they could be interpreted as practices employing the stuck in the middle

strategy. It is noteworthy that none of the six groups exhibited relatively high values on

all eight indicator variables, which would indicate a group of practices with the traits of a

hybrid.

Table 16

Mean Profit Ratio for Each Group (N = 1,413)

Group # N M ($/FTE)* SD

Group 1 419 369,121 244,545

Group 2 277 105,288 243,461

Group 3 231 367,902 309,609

Group 4 181 112,006 340,060

Group 5 151 254,157 228,482

Group 6 154 725,732 1,122,002

Total 1,413 310,846 485,402

*Profit Ratio = Profit / Total Number of FTE Physicians

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Table 17 One-Way Analysis of Variance for Effects of Group Membership on the Profit Ratio (N = 1,413)

Source df SS MS F Between Groups 5 4.803E13 9.606E12 47.479*** Within Groups 1407 2.847E14 2.023E11 Total 1412 3.327E14

***p < .001.

Hypothesis 3 – Performance differences between the theoretical and empirical model

Hypothesis 3 is related to whether the inductive or deductive methodology would

lead to groups that will better predict the financial performance of certain medical group

practices. To test Hypothesis 3, the performance differences among all of the groups

were compared and a determination was made regarding which methodology generated

groups that were superior in explaining performance differences between the groups - the

empirical or theoretical model.

H3: Using a goodness of fit test, the variation in the performance between the

medical group practices using the inductive approach will be greater than that of the

deductive approach.

Hypothesis 3 was rejected as it was found that the inductive methodology was

relatively more efficient in explaining the variation in the dependent variable based on

the eight indicator measure. The Eta squared value for the inductive methodology, the

cluster analysis, indicates that approximately 14.4% of the variation in the profit ratio of

each practice can be explained by inclusion in one of the six groups. Using the deductive

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methodology, only 8.7% of the variation in the profit ratio was accounted for by group

membership (see Table 18).

Table 18

Variation Explained by the Inductive and Deductive Methodologies

Methodology η2

Inductive .144 Deductive .087

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CHAPTER 5

SUMMARY & CONCLUSION

Discussion of Study Findings

Overall, Porter’s generic strategies model was found to be useful in the

categorization of physician practices by the various organizational strategy configurations

they employ and in determining if differences in financial performance vary between the

groups. Using Porter’s model, the results indicate that practices conforming to a

differentiated organizational configuration strategy perform significantly better that those

practices with another organizational configuration. However, the analysis did not fully

support Porter’s theory, as the cost leadership group did not produce a significantly

higher mean profit ratio as compared with the other three groups. These results are

further supported by Payne’s (1998) dissertation, in which he used Porter’s generic

strategy labels for groups that formed following a cluster analysis technique, as his

differentiator group also performed better than the other groups.

Additional support of Porter’s theory can be derived from the small size of the

hybrid group in the analysis using the deductive methodology and the absence of this

group after using the inductive methodology. While certain authors (Buzzell &

Wiersema, 1981; Cross, 1999; Hambrick, 1981; Helms, et al., 1997; Hill, 1988;

Hlavacka, et al., 2001; Karnani, 1984; D. Miller, 1992; D. Miller & Friesen, 1986b;

Murray, 1988; Phillips, et al., 1983; White, 1986) have supported a hybrid group in

addition to Porter’s original groups, Porter does not acknowledge the presence of hybrid

groups in his theory. Rather, Porter believes that the differentiator and cost leader are

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thought to be at opposite extremes, thus eliminating the ability for an organization to

perform well as both a differentiator and cost leader. The limited number (2.5%) of

medical group practices exhibiting the strategy traits of the hybrid classification supports

the difficulty of a practice employing the strategies of both a cost leader and a

differentiator. It may be beneficial to eliminate the hybrid category from future

theoretical models exploring the financial performance and strategies within medical

group practices as it does appear that in general, the strategies of cost leadership and

differentiation are at opposite extremes, and that few medical group practices attempt to

deploy both strategies simultaneously.

While the theoretical, a priori, classification model was posited to generate groups

that would better predict the financial performance of the medical groups, this was not

supported. Rather, the inductive classification scheme explained much more of the

variation in the profit ratio, nearly twice that of the inductive methodology, for each

practice. However, it is interesting that the inductive classification technique created

several groups that were very similar to the cost leader, differentiator, and mixed strategy

groups that were generated by the theoretical methodology. Further, the similarities in

the groups formed by each methodology provide validation for the a priori, theoretical

classification of the medical group practices using Porter’s generic strategies.

The composite strategy indicator variable that created the construct for the groups

noted as having a differentiated strategy included the number of branches, the dollars

spent on marketing and furniture/equipment, and the number of ancillary services for

each practice. The construct for the cost leadership strategy included a number of

measures related to efficiency, with practices designated as a cost leader having smaller

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practice sizes (measured by square feet), fewer support staff, lower account receivable

ratios, and lower operating cost ratios. With the support from both the inductive and

deductive methodologies of the differentiator as the group with the highest mean profit

ratio, those overseeing the management of medical group practices may want to focus

their efforts on the differentiation strategies, and less on efficiency. However, in the

cluster analysis, the group with the highest mean profit ratio, as well as the first or second

highest rankings on the four differentiator measures, had the lowest accounts receivable

ratio (i.e., fewer dollars in accounts receivable greater than 120 days compared to the

total dollars in accounts receivable). This may indicate that medical group practices

should implement not only the strategies of differentiation, but should also focus on

lowering their total days in accounts receivable in order to achieve superior profit

margins.

Limitations

This study has numerous limitations that will be discussed in the following pages.

First, equifinality holds that multiple paths may be possible to reach the same outcome,

while strict OC theorists believe that there is a single optimal organizational

configuration. Next, the somewhat unique characteristics of the health care industry may

have somewhat confound the analysis regarding the cost leadership strategy, as demand

and payment issues are typically quite different in health care, as compared with other

industries. Additionally, the various types of physician specialties within the sample may

have obfuscated the analysis if certain specialties were more likely to pursue one strategy

over another (e.g., a cardiology practice may be more likely than a pediatric practice to

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pursue the differentiator strategy). Other limitations include the exclusion of several

variables (e.g., total RVUs) proposed in the original methodology plan due to missing

data, the elimination of the separate analysis of the single and multi-specialty practice

types, and the inclusion of the hybrid group in the model, which was found to be

extremely small.

Equifinality, an open systems concept introduced by von Bertalanffy (1949), is a

concept which finds that multiple paths may be possible to reach the same outcome. In

OC, researchers use the concept of equifinality to denote that multiple types of OCs may

be employed to create high performing firms. Thus, strict configurational theories that

espouse that one specific OC form is better than another may be problematic in the view

of equifinality. Gresov and Drazin (1997) state that many researchers point to

equifinality when their results do not find specific performance differences between

groups. In Payne’s (2001) study of medical group practices, he employed the concept of

equifinality in a sub-optimal environment, finding that contextual situations in the

medical group environment led to conflicts when employing specific strategies, and that

multiple types of medical group configurations had superior performance. However, as

noted previously, Porter’s generic strategies model has been widely used in the literature

and strategy textbooks and offers a parsimonious model to explore the different practice

configurations that performed as well as those practices that attempted to differentiate

themselves from other medical group practices. Additionally, the data supports the

contention that practices with organizational configurations that are aligned with both the

cost leader and differentiator constructs, the hybrids, are a small segment of the sample.

While these hybrid practices did not exhibit higher profit ratios than the other three

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groups, the small sample size for this group limited a discussion about the profit ratios

found in this group.

The lower average profit ratio found in the cost leadership group may be due to

the somewhat unique traits of the health care industry. For example, most health care

consumers are somewhat insulated by medical practice costs through third-party payers.

The higher profit ratios in practices exhibiting the traits of a differentiator may indicate

that consumers are drawn to those practices that are able to differentiate themselves from

other practices, rather than the lower costs that might be present in practices with a cost

leadership strategy. Additionally, most consumers do not demand health care as with

traditional commodities. Health care is typically viewed as something required when ill,

and thus is very dissimilar to a service or product that one would purchase in a store.

Therefore, the price of the service, or searching for a lower price, may not be as relevant

for consumers of the services provided by a medical group practice.

There is another plausible explanation for the Hypothesis 1 finding that medical

groups exhibiting differentiator behavior experienced significantly higher mean profit

ratios than the other three groups. Since most medical groups in this sample were single

specialty, it is possible that the differentiator group with the higher profit ratios was

comprised of specialties which generate higher fees, such as cardiology or cardiovascular

surgery. The cost leadership group may have been comprised of specialty groups which

generate lower fees such as pediatrics and family practice. In this case, specialties which

generate higher fees may have slack resources. These slack resources may allow these

groups to pursue differentiator strategies such as investing in marketing or spending more

on furniture and equipment. However, groups which traditionally generate lower fees

109

(e.g., pediatric practices) may have fewer resources and be restricted to cost leadership

activities such as controlling overhead costs, lowering square footage per FTE physician,

and hiring fewer front office personnel.

The variable total RVUs produced by each practice was also excluded from the

analysis due to the limited number of practices that responded to this question. However,

as this variable is widely used to measure a practice’s productivity, an inferential analysis

using this variable to normalize many of the indicator variables would be beneficial.

Additionally, the variable measuring the total number of FTE physicians in each practice

was used as a proxy for total RVUs in each practice. However, these two variables

demonstrated a relatively low correlation value, which may indicate that the decision to

replace the total RVUs variable with the total FTE physicians’ variable may be

questionable.

As discussed in the results section, and in much greater detail in the appendices,

problems with the MGMA data were plentiful. Variables with missing data and/or

obvious errors confounded the data analysis to a certain degree and necessitated an

alteration of the originally planned methods, as well as the imputation of the values of

many of the variables for a number of practices. The analysis provides strong support for

changes in the data collection methods for this instrument, including a computer-based

survey that would include stipulations regarding specific questions which must be

answered by the respondent, as well as parameters based on pre-defined thresholds for

many of the questions (e.g., the respondent would not be able to include a value of

greater than 1,000 total FTE physicians in a single practice). Further, MGMA may want

110

to refine the instrument to clarify the instructions for specific variables that were found to

have a large number of extreme, dubious, or missing values.

Another limitation of this study includes the lack of an analysis of any potential

differences that may be present in the average profit ratios in each of the various types of

single specialty practices. This study did include a process to normalize the profit of each

practice by dividing the profit value by the total number of physicians in each practice

(i.e., profit / total number of FTE physicians). However, the analysis did not include

grouping the practices by specialty type (e.g., cardiology practices, pediatrics practices,

anesthesia practices, etc.) and then determining if the average profit per physician was

different across the various types of specialty groups. It would be useful if further

research using this sample included a review of whether the ratios of specific specialty

types of practices varied significantly among the groups created through Porter’s theory

and the groups created through the cluster analysis. For example, one might expect that

the average profit per physician of a cardiology practice would be significantly higher

than a pediatric practice. Thus, the group designated as differentiators might have a

higher percentage of certain types of specialty practices with higher average profit ratios

than other specialty groups. While it is not posited that the specialty group type drives the

strategy of a practice, one might expect that certain specialty practices with a higher

average profit per physician may have greater resources, which may enable those high

profit specialty practices to employ the differentiation strategy.

111

Future Research

Further analysis of this data should include a comparison of the overall sample,

including the excluded cases (those practices with less than 1 FTE physician or greater

than 35 FTE physicians), to determine if any differences in the outcome might vary based

on the excluded practices. Including the very small and large practices in future studies,

in addition to the medium-sized practices, may be beneficial in furthering the knowledge

base regarding how organizational strategies impact profit ratios, as well in determining

if differences exist between the three groups. The practices excluded from the analysis

were relatively small in number, and either very large or small practices. However, it

would be beneficial to explore whether or not any differences may occur in the outcome

if these practices are included in the analysis. Also, an exploration of the differences in

the outcome between the three groups – small, medium, and large – may aid in our

understanding of the organizational strategies used by different sizes of practices.

An analysis of the ratios of various types of specialty practices (e.g., cardiology,

pediatrics, etc.) may lead to a better understanding of the organizational strategies used

by different types of medical group practices and whether certain types of specialties are

more likely to pursue the cost leadership or differentiator strategy.

Rather than analyzing a single year of MGMA data, as was completed with this

study, a longitudinal study would with the same medical group practices may lead to

details about whether or not medical group practices may change their strategies over

time and in which practices this may occur (e.g., Leask and Parker’s 1997 study of firm-

level performance in the pharmaceutical industry over time). A longitudinal study may

112

also provide evidence regarding whether specific performance factors may predict which

firms will alter their strategies over time.

Due to the small sample size of the multispecialty practices, both the single and

multispecialty groups were combined for this study. While the small number of

multispecialty practices may limit the applicability of any findings from a study of only

this group of practices, it still may be beneficial to perform the inferential analysis

separately and compare the results of the two separate samples. This separate analysis

could provide support for practices choosing either the single or multispecialty practice

type and would lead to details regarding any differences in the strategies used by each of

the different practice types.

Further research related to how the accounts receivable ratio impacts a highly

profitable medical group practice would be beneficial as well. While Porter (1985)

specifically stated that avoidance of marginal customer accounts is an indicator of a cost

leadership strategy and MGMA (MGMA, 2006) found that the better performing medical

practices had a lower percentage of total accounts receivable greater than 120, the

outcome of this study provided a bit of a conundrum. The best performing group created

from the inductive cluster analysis technique had the highest values for the each of the

four differentiator variables and most closely resembled the differentiator group from the

theoretical model. However, this group also had the highest ranking for the accounts

receivable variable (the lowest ratio of A/R < 120 days to Total A/R), which indicated a

cost leadership strategy, and relatively low rankings on the other three cost leadership

variables. Thus, while certain organization configuration strategies related to

differentiation were found to be positively related to a medical group practice’s

113

profitability, maintaining a low A/R ratio, and not necessarily the other traits of a cost

leader, may also play an important role the profitability of medical group practices.

Although Porter did not include a hybrid group in his original theory, as discussed

previously, many researchers have found evidence that this group does exist in many

populations. However, the inclusion of the hybrid group in the theoretical model slightly

limited the power of the analysis, as those included in the hybrid group were excluded

from other groups (i.e., if the hybrid classification were eliminated, practices previously

assigned to the hybrid group would be classified as another group). As the hybrid group

was quite small in this study, the inclusion of this group in future studies with medical

group practices may limit the applicability of any inferential analyses.

Final Conclusion

Porter’s generic strategies have been used for nearly 30 years to categorize firms by

the strategies they employ and study the performance differences between the different

groups. In the field of health care, this author found no previous empirical research

specifically using Porter’s generic strategies as a theoretical grouping model for medical

group practices and little empirical research related to financial performance and strategy

differences between physician practices. Therefore, this paper will be a contribution to

the field of strategic management, organizational configurations, and medical group

practice management as it elucidates the various strategies medical group practices

employ and compares the performance levels of firms employing different organizational

configurations. It also supports Porter’s generic strategy typology in general as it

demonstrated performance differences between medical group practices grouped by

114

Porter’s typology, with the differentiator group performing significantly better than those

medical group practices conforming to another organizational configuration type. This

paper also supports the exclusion of the hybrid group in further studies of medical group

practices. For practitioners and physician practice managers, this research provides

valuable information in regards to the specific strategies and organizational

configurations that are typically associated with the most highly profitable medical group

practices. Overall, this research will provide a contribution to the literature related to

Porter’s generic strategies and the field of organizational configurations, as well as to the

field of health care and the specific strategy-performance links within medical group

practices.

115

REFERENCES

Abouleish, A. E., Prough, D. S., Whitten, C. W., Zornow, M. H., Lockhart, A., Conlay, L. A., et al. (2002). Comparing Clinical Productivity of Anesthesiology Groups. Anesthesiology, 97(3), 608-615.

Acock, A. C. (2005). Working with missing values. Journal of Marriage and Family, 67(4), 1012-1028.

Akan, O., Allen, R., S. , Helms, M., M. , & Spralls, S. A. I. (2006). Critical tactics for implementing Porter's generic strategies. [Generic strategies; specific tactics; empirical; detailed definitions of each strategy; practitioner]. The Journal of Business Strategy, 27(1), 43.

Alexandraki, I., Palacio, C., House, J., Catalano, C., & Mooradian, A. (2009). Resource-Based Relative Value Scale Analysis Between Teaching and Nonteaching Hospitalist Services. The Health Care Manager, 28(1), 81.

Allen, R. S., & Helms, M. M. (2002). Employee perceptions of the relationship between strategy, rewards and organizational performance. [Generic strategies; empirical; employee/managerial reward related to strategy and firm performance]. Journal of Business Strategies, 19(2), 115.

Allen, R. S., & Helms, M. M. (2006). Linking strategic practices and organizational performance to Porter's generic strategies. [Generic strategies; empirical; tactics leading to performance]. Business Process Management Journal, 12(4), 433.

Allen, R. S., Helms, M. M., Takeda, M. B., & White, C. S. (2007). Porter's Generic Strategies: An Exploratory Study of Their Use in Japan. [Generic strategies; empirical; international (Japan); survey only on attitudes of strategy (no performance outcome variable)]. Journal of Business Strategies, 24(1), 69.

Astley, W. G., & Van deVen, A. H. (1983). Central Perspectives and Debates in Organization Theory. Administrative Science Quarterly, 28(2), 245-273.

Bacharach, S. B. (1989). Organizational Theories: Some Criteria For Evaluation. Academy of Management. The Academy of Management Review, 14(4), 496.

Bain, J. S. (1956). Barriers to new competition, their character and consequences in manufacturing industries. Cambridge,: Harvard University Press.

Baliga, B., & Johnson, B. (1986). Analysis of an industry in transition. Health care strategic management, 4(12), 5.

Barnard, C. I. (1938). The functions of the executive. Cambridge, Mass.,: Harvard University Press.

116

Barney, J. B. (1986a). Types of Competition and the Theory of Strategy: Toward an Integrative Framework. The Academy of Management Review, 11(4), 791-800.

Barney, J. B. (1986b). Types of competition and the theory of strategy: Toward an integrative framework. Academy of Management Review, 791-800.

Barney, J. B. (1991). Firm Resources and Sustained Competitive Advantage. [Competitive advantage, sustained; firm resources; RBV theory]. Journal of Management, 17(1), 99.

Barney, J. B., & Hoskisson, R. E. (1990). Strategic Groups: Untested Assertions and Research Proposals. Managerial and Decision Economics, 11(3), 187-198.

Bharadwaj, S. G., Varadarajan, P. R., & Fahy, J. (1993). Sustainable competitive advantage in service industries: A. [Competitive advantage; theoretical and prescriptive - great article with lots of strategy tactics, possible operationalization of variables]. Journal of Marketing, 57(4), 83.

Bierly, P., & Chakrabarti, A. (1996). Generic Knowledge Strategies in the U.S. Pharmaceutical Industry. Strategic Management Journal, 17, 123-135.

Bishop, P., & Megicks, P. (2002). Competitive strategy and firm size in the estate agency industry. Journal of Small Business and Enterprise Development, 9(2).

Blair, J. D., & Buesseler, J. A. (1998). Competitive forces in the medical group industry: a stakeholder perspective. [Stakeholders - competitive advantage; healthcare - physician practices]. Health Care Management Review, v23(n2), p7(21).

Borch, O. J., Huse, M., & Senneseth, K. (1999). Resource Configuration, Competitive Strategies, and Corporate Entrepreneurship An Empirical Examination of Small Firms.(Statistical Data Included). [Porter - small firms]. Entrepreneurship: Theory and Practice, 24(1), 49.

Bowman, C., & Faulker, D. O. (1997). Competitive and Corporate Strategy.

Burns, T., & Stalker, G. M. (1961). The management of innovation. [London]: Tavistock Publications.

Buzzell, R. D., & Wiersema, F. D. (1981). Successful Share-Building Strategies. Harvard Business Review, 59(1), 135.

Campbell-Hunt, C. (2000). What Have We Learned about Generic Competitive Strategy? A Meta-Analysis. [Generic Strategy - Meta-analysis]. Strategic Management Journal, 21(2), 127-154.

Casalino, L. P., Devers, K. J., Lake, T. K., Reed, M., & Stoddard, J. J. (2003). Benefits of and Barriers to Large Medical Group Practice in the United States. Arch Intern Med, 163(16), 1958-1964.

117

Caves, R. E. (1980). Industrial Organization, Corporate Strategy and Structure. [Industrial Organization Economics]. Journal of Economic Literature, 18(1), 64-92.

Caves, R. E., & Porter, M. E. (1977). From Entry Barriers to Mobility Barriers: Conjectural Decisions and Contrived Deterrence to New Competition. [Industrial Organization Economics]. The Quarterly Journal of Economics, 91(2), 241-262.

Chan-Olmsted, S., & Jamison, M. (2001). Rivalry through alliances::: Competitive Strategy in the Global Telecommunications Market. European Management Journal, 19(3), 317-331.

Chrisman, J. J., Hofer, C. W., & Boulton, W. R. (1988). Toward a System for Classifying Business Strategies. [Strategy Typology - Porter and Abbell; a revised, new typology; theoretical - great article; need to revisit]. The Academy of Management Review, 13(3), 413-428.

Cool, K. O., & Schendel, D. (1987). Strategic Group Formation and Performance: The Case of the U.S. Pharmaceutical Industry, 1963-1982. Management Science, 33(9), 1102-1124.

Cronshaw, M., Davis, E., & Kay, J. (1994). On Being Stuck in the Middle or Good Food Costs Less at Sainsbury's. [Generic Typology - Stuck in the Middle]. British Journal of Management, 5(1), 19-32.

Cross, L. (1999). Strategy drives marketing success. Graphic Arts Monthly, 71(2), 96.

Daft, R., & Lewin, A. (1993). Where are the theories for the" new" organizational forms? An editorial essay. Organization Science, 1.

Davis, G., & Marquis, C. (2005). Prospects for organization theory in the early twenty-first century: Institutional fields and mechanisms. Organization Science, 16(4), 332.

Day, G. S., & Wensley, R. (1988). Assessing Advantage: A Framework For Diagnosing Competitive. Journal of Marketing, 52(2), 1.

Dess, G. G., & Davis, P. S. (1984). Porter's (1980) Generic Strategies as Determinants of Strategic Group Membership and Organizational Performance. Academy of Management Journal, 27(3), 467-488.

Dess, G. G., Newport, S., & Rasheed, A. M. A. (1993). Configuration research in strategic management: key issues and suggestions. Journal of Management, v19(n4), p775(721).

Dess, G. G., & Robinson, R. B., Jr. (1984). Measuring Organizational Performance in the Absence of Objective Measures: The Case of the Privately-Held Firm and Conglomerate Business Unit. Strategic Management Journal, 5(3), 265-273.

118

Doty, D. H., Glick, W. H., & Huber, G. P. (1993). Fit, equifinality, and organizational effectiveness: A test. [Configurational Theory]. Academy of Management Journal, 36(6), 1196.

Dranove, D., Peteraf, M., & Shanley, M. (1998). Do strategic groups exist? an economic framework for analysis. Strategic Management Journal, 19(11), 1029-1044.

Ebeler, J., Neuman, T., & Cubanski, J. (2011). Medicare Policy. The Kaiser Family Foundation, 1 - 24.

Fiegenbaum, A., McGee, J., & Thomas, H. (1988). Exploring The Linkage Between Strategic Groups And Competition. International Studies of Management & Organization, 18(1), 6.

Fiegenbaum, A., & Thomas, H. (1990). Strategic Groups and Performance: The U.S. Insurance Industry, 1970-84. Strategic Management Journal, 11(3), 197-215.

Filley, A., & Aldag, R. (1980). Organizational growth and types: Lessons from small institutions. Research in organizational behavior, 2, 279-320.

Filley, A. C., & Ramon, J. A. (1978). Characteristics and Measurement of an Organizational Typology. The Academy of Management Journal, 21(4), 578-591.

Fiss, P. C. (2007). A Set-Theoretic Approach to Organizational Configurations. Academy of Management. The Academy of Management Review, 32(4), 1180.

Fleming, N. S., Becker, E. R., Culler, S., Cheng, D., McCorkle, R., & Ballard, D. J. (2009). Financial performance of primary care physician practices prior to electronic health record implementation. Baylor University Medical Center Proceedings, 22(2), 112-118.

Forte, M., Hoffman, J. J., Lamont, B. T., & Brockmann, E. N. (2000). Organizational form and environment: an analysis of between-form and within-form responses to environmental change. Strategic Management Journal, 21(7), 753-773.

Galbraith, C., & Schendel, D. (1983). An Empirical Analysis of Strategy Types. Strategic Management Journal, 4(2), 153-173.

Galbraith, J. R. (1973). Designing complex organizations. Reading, Mass.,: Addison-Wesley Pub. Co.

Gapenski, L. C. (2005). Healthcare finance : an introduction to accounting and financial management (3rd ed.). Chicago: AUPHA/HAP.

Greenwood, R., & Hinings, C. R. (1993). Understanding strategic change: The contribution of archetypes. Academy of Management Journal, 36(5), 1052.

119

Gresov, C., & Drazin, R. (1997). Equifinality: Functional equivalence in organization design. Academy of Management. The Academy of Management Review, 22(2), 403.

Hambrick, D. C. (1981). Strategic Awareness within Top Management Teams. Strategic Management Journal, 2(3), 263-279.

Hambrick, D. C. (1982). Environmental Scanning and Organizational Strategy. Strategic Management Journal, 3(2), 159-174.

Hambrick, D. C. (1983). High Profit Strategies in Mature Capital Goods Industries: A Contingency Approach. The Academy of Management Journal, 26(4), 687-707.

Hambrick, D. C. (1984). Taxonomic Approaches to Studying Strategy: Some Conceptual and Methodological Issues. Journal of Management, 10(1), 27-41.

Hannan, M. T., & Freeman, J. (1977). The Population Ecology of Organizations. The American Journal of Sociology, 82(5), 929-964.

Hannan, M. T., & Freeman, J. (1984). Structural Inertia and Organizational Change. American Sociological Review, 49(2), 149-164.

Hatten, K. J., & Hatten, M. L. (1987). Strategic Groups, Asymmetrical Mobility Barriers and Contestability. Strategic Management Journal, 8(4), 329-342.

Hatten, K. J., & Schendel, D. E. (1977). Heterogeneity within an industry: firm conduct in the U.S. brewing industry, 1952-71. The Journal of Industrial Economics, 26(2), 97.

Hawes, J. M., & Crittenden, W. F. (1984). A Taxonomy of Competitive Retailing Strategies. Strategic Management Journal, 5(3), 275-287.

Helms, M. M., Dibrell, C., & Wright, P. (1997). Competitive strategies and business performance: evidence from the adhesives and sealants industry. Management Decision, 35(9), 689.

Hill, C. W. L. (1988). Differentiation Versus Low Cost or Differentiation and Low Cost: A Contingency Framework. [Porter - Cost, Differentiation; contingency approach]. Academy of Management Review, 13(3), 401-412.

Hing, E., & Burt, C. W. (2007). Office-based medical practices: methods and estimates from the national ambulatory medical care survey. Adv Data(383), 1-15.

Hing, E., & Burt, C. W. (2008). Characteristics of office-based physicians and their medical practices: United States, 2005-2006. Vital and health statistics. Series 13, Data from the National Health Survey(166), 1.

120

Hlavacka, S., Bacharova, L., Rusnakova, V., & Wagner, R. (2001). Performance implications of Porter's generic strategies in Slovak hospitals. Journal of Management in Medicine, 15(1), 44-66.

Hofer, C. W., & Schendel, D. (1978). Strategy formulation : analytical concepts. St. Paul: West Pub. Co.

Hoskisson, R. E., Hitt, M. A., Wan, W. P., & Yiu, D. (1999). Theory and research in strategic management: swings of a pendulum (Yearly Review of Management). [Strategic management - review]. Journal of Management, 25(3), 417(414).

Hult, G., Ketchen, D., Cavusgil, S., & Calantone, R. (2006). Knowledge as a strategic resource in supply chains. Journal of Operations Management, 24(5), 458-475.

Hunt, M. S. (1972). Competition in the major home appliance industry, 1960-1970 : a thesis.

Hunt, S. D. (1997). Resource-advantage theory: An evolutionary theory of competitive firm behavior? Journal of Economic Issues, 31(1), 59.

Hyatt, L. (2001). A simple guide to strategy. Nursing Homes, 50(1), 12.

Inamdar, N. S. (2007). Examining the Scope of Multibusiness Health Care Firms: Implications for Strategy and Financial Performance. Health Services Research, 42(4), 1691-1717.

Ittner, C. D., Larcker, D. F., & Pizzini, M. (2007). Performance-based compensation in member-owned firms: An examination of medical group practices. Journal of Accounting and Economics, 44(3), 300-327.

Jensen, M. (1983). Organization theory and methodology. Accounting Review, 319-339.

Jiang, H. J., Bernard, F., & James, W. B. (2006). Sustaining and Improving Hospital Performance: The Effects of Organizational and Market Factors. Health Care Management Review, 31(3), 188.

Johnson, S., & Newton, W. (2002). Resource-based relative value units: a primer for academic family physicians. FAMILY MEDICINE, 34(3), 172-176.

Kang, S.-C., Morris, S. S., & Snell, S. A. (2007). Relational Archetypes, Organizational Learning, and Value Creation: Extending the Human Resouce Architecture. Academy of Management Review, 32(1), 236-256.

Karnani, A. (1984). Generic Competitive Strategies-an Analytical Approach. Strategic Management Journal, 5(4), 367-380.

121

Ketchen, D. J., Combs, J. G., Russell, C. J., Shook, C., Dean, M. A., Runge, J., et al. (1997). Organizational configurations and performance: A meta-analysis. Academy of Management Journal, 40(1), 223.

Ketchen, D. J., Combs, J. G., Russell, C. J., Shook, C., & et al. (1997). Organizational configurations and performance: A meta-analysis. Academy of Management Journal, 40(1), 223.

Ketchen, D. J., & Shook, C. L. (1996). The Application of Cluster Analysis in Strategic Management Research: An Analysis and Critique. Strategic Management Journal, 17(6), 441-458.

Ketchen, D. J., Thomas, J. B., & Snow, C. C. (1993). Organizational Configurations and Performance: A Comparison of Theoretical Approaches. Academy of Management Journal, 36(6), 1278.

Kikulis, L. M., Slack, T., & Hinings, C. R. (1995). SECTOR-SPECIFIC PATTERNS OF ORGANIZATIONAL DESIGN CHANGE. Journal of Management Studies, 32(1), 67-100.

Kumar, K., Subramanian, R., & Yauger, C. (1997). Pure versus hybrid: performance implications of Porter's generic strategies. Health Care Management Review, v22(n4), p47(14).

Langland-Orban, B., Gapenski, L., & Vogel, W. (1996). Differences in characteristics of hospitals with sustained high and sustained low profitability. Hospital & health services administration, 41(3), 385.

Lawrence, P. R., & Lorsch, J. W. (1967). Differentiation and Integration in Complex Organizations. Administrative Science Quarterly, 12(1), 1-47.

Leask, G., & Parker, D. (2007). Strategic groups, competitive groups and performance within the U.K. pharmaceutical industry: Improving our understanding of the competitive process. Strategic Management Journal, 28(7), 723-745.

Lin, H.-C., Chen, C.-S., Liu, T.-C., & Lee, H.-C. (2006). Differences in practice income between solo and group practice physicians. [solo vs. group practice]. Health Policy, 79(2-3), 296-305.

Locke, R., & Srinivasan, M. (2008). Attitudes Toward Pay-for-Performance Initiatives Among Primary Care Osteopathic Physicians in Small Group Practices. JAOA: Journal of the American Osteopathic Association, 108(1), 21.

March, J., Simon, H., & Guetzkow, H. (1958). Organizations: John Wiley & Sons Inc.

Mark, T. L. (1999). Analysis of the rationale for, and consequences of, nonprofit and for-profit ownership conversions. Health Services Research, 34(1), 83(81).

122

Mason, E. S. (1939). Price and Production Policies of Large-Scale Enterprise. [Industrial Organization Theory - historical]. The American Economic Review, 29(1), 61-74.

Maxwell, S., & Zuckerman, S. (2007). Impact of Resource-Based Practice Expenses on the Medicare Physician Volume. Health Care Financing Review, 29(2), 65.

McDonagh, K., J. , & Umbdenstock, R., J. . (2006). Hospital Governing Boards: A Study of Their Effectiveness in Relation to Organizational Performance. Journal of Healthcare Management, 51(6), 377.

Meyer, A. D., Tsui, A. S., & Hinings, C. R. (1993). Configurational approaches to organizational analysis. Academy of Management Journal, 36(6), 1175.

MGMA. (2006). Performance and Practices of Successful Medical Groups: 2006 Report Based on 2005 Data. Englewood, CO: Medical Group Management

Association (MGMA).

Miles, R. E., & Snow, C. C. (1978). Organizational strategy, structure, and process. New York: McGraw-Hill.

Miller, A., & Dess, G. G. (1993). Assessing Porter's (1980) model in terms of its generalizability, accuracy and simplicity. The Journal of Management Studies, 30(4), 553.

Miller, D. (1986). Configurations of Strategy and Structure: Towards a Synthesis. Strategic Management Journal, 7(3), 233-249.

Miller, D. (1988). Relating Porter's Business Strategies To Environment And St. Academy of Management Journal, 31(2), 280.

Miller, D. (1992). The Generic Strategy Trap. [Generic strategies - mixed strategies; theory]. The Journal of Business Strategy, 13(1), 37.

Miller, D., & Friesen, P. H. (1978). Archetypes of Strategy Formulation. Management Science, 24(9), 921-933.

Miller, D., & Friesen, P. H. (1986a). Porter's (1980) Generic Strategies and Performance: An Empirical Examination with American Data -- Part II: Performance Implications. Organization Studies, 7(3), 255.

Miller, D., & Friesen, P. H. (1986b). Porter's (1980) Generic Strategies and Performance: An Empirical Examination with American Data Part I: Testing Porter. Organization Studies, 7(1), 37-55.

Miller, D., Friesen, P. H., & Mintzberg, H. (1984). Organizations : a quantum view. Englewood Cliffs, N.J.: Prentice-Hall.

123

Mintzberg, H. (1979). The structuring of organizations : a synthesis of the research. Englewood Cliffs, N.J.: Prentice-Hall.

Mintzberg, H. (1990). 5 STRATEGY FORMATION Schools of Thought. Perspectives on strategic management, 105.

Murray, A. I. (1988). A Contingency View of Porter's "Generic Strategies". Academy of Management Review, 13(3), 390-400.

Nayyar, P. R. (1993). On the measurement of competitive strategy: Evidence from a large multiproduct U.S. firm. Academy of Management Journal, 36(6), 1652.

Nenide, B., Pricer, R. W., & Camp, S. M. (2003). The Use of Financial Ratios for Research: Problems Associated With and Recommendations for Using Large Databases. Paper presented at the Fourth Annual Conference of the National Business and Economics Society, Virgin Islands.

Newman, H. (1978). Strategic groups and the structure-performance relationship. The Review of Economics and Statistics, 417-427.

Oster, S. (1982). Intraindustry structure and the ease of strategic change. The Review of Economics and Statistics, 376-383.

Parker, B., & Helms, M. M. (1992). Generic Strategies and Firm Performance in a Declining Industry. [Generic strategies; empirical; survey]. Management International Review, 32(1), 23.

Parnell, J. A. (2006). Generic strategies after two decades: a reconceptualization of competitive strategy. [Generic strategies - review of literature]. Management Decision, 44(8).

Payne, G. T. (2001). Strategy and Structure Configurations: An Examination of Fit and Performance. Texas Tech.

Payne, G. T. (2006). Examining Configurations and Firm Performance in a Suboptimal Equifinality Context. Organization Science, 17(6), 756.

Penrose, E. T. (1959). The theory of the growth of the firm. New York,: Wiley.

Pham, H. H., Devers, K. J., May, J. H., & Berenson, R. (2004). Financial Pressures Spur Physician Entrepreneurialism. Health Affairs, 23(2), 70.

Phillips, L. W., Chang, D. R., & Buzzell, R. D. (1983). Product Quality, Cost Position and Business Performance: A Test of Some Key Hypotheses. [Generic strategies; support of mixed]. Journal of Marketing, 47(2), 26-43.

Porter, M. E. (1979a). How competitive forces shape strategy. Harvard Business Review, 57(2), 137-145.

124

Porter, M. E. (1979b). The structure within industries and companies' performance. The Review of Economics and Statistics, 214-227.

Porter, M. E. (1980). Competitive strategy : techniques for analyzing industries and competitors. New York: Free Press.

Porter, M. E. (1981). The Contributions of Industrial Organization To Strategic Management. Academy of Management Review, 6(4), 609-620.

Porter, M. E. (1985). Competitive advantage: Creating and sustaining superior performance: Free Press.

Porter, M. E. (1996). What Is Strategy? Harvard Business Review, 74(6), 61-78.

Porter, M. E. (1997). New strategies for inner-city economic development. Economic Development Quarterly, v11(n1), p11(17).

Porter, M. E., & Teisberg, E. (2004). Redefining competition in health care. Harvard Business Review, 64-77.

Powers, T. L., & Hahn, W. (2004). Critical competitive methods, generic strategies, and firm performance. [Generic strategies - performance and competitive methods; empirical]. The International Journal of Bank Marketing, 22(1), 43.

Rangone, A. (1999). A resource-based approach to strategy analysis in small-medium sized enterprises. Small Business Economics, 12(3), 233.

Reger, R. K., & Huff, A. S. (1993). Strategic Groups: A Cognitive Perspective. Strategic Management Journal, 14(2), 103-123.

Relman, A. S. (2007). A second opinion: rescuing America's healthcare: a plan for universal coverage serving patients over profit: Public Affairs.

Reschovsky, J., Hadley, J., & Landon, B. (2006). Effects of compensation methods and physician group structure on physicians' perceived incentives to alter services to patients. Health Services Research, 41(4 Pt 1), 1200.

Robinson, J. C. (2001). Physician organization in California: Crisis and opportunity. Health Affairs, 20(4), 81.

Romano, M. (2001). United we do quite well, thank you. Modern Healthcare, 31(12), 34-34.

Sandino, T. (2007). Introducing the First Management Control Systems: Evidence from the Retail Sector. The Accounting Review, 82(1), 265.

125

Schappert, S. M., & Burt, C. W. (2006). Ambulatory care visits to physician offices, hospital outpatient departments, and emergency departments: United States, 2001-02. Vital and Health Statistics(159), 1-66.

Schoemaker, P. J. H. (1990). Strategy, Complexity and Economic Rent. Management Science, 36(10), 1178.

Segev, E. (1989). A Systematic Comparative Analysis and Synthesis of Two Business-Level Strategic Typologies. Strategic Management Journal, 10(5), 487-505.

Selznick, P. (1957). Leadership in administration; a sociological interpretation. Evanston, Ill.,: Row.

Short, J. C., Ketchen, D. J., Palmer, T. B., & Hult, G. T. M. (2007). Firm, strategic group, and industry influences on performance. Strategic Management Journal, 28(2), 147-167.

Short, J. C., Payne, G. T., & Ketchen, D. J. (2008). Research on Organizational Configurations: Past Accomplishments and Future Challenges. [Feature]. Journal of Management, 34(6), 1053-1079.

Shortell, S. M., Schmittdiel, J., Wang, M., Li, R., Gillies, R., Casalino, L., et al. (2005). An empirical assessment of high-performing medical groups: results from a national study. Medical Care Research and Review, 62(4), 407.

Shortell, S. M., & Zajac, E. J. (1990). Perceptual and Archival Measures of Miles and Snow's Strategic Types: A Comprehensive Assessment of Reliability and Validity. The Academy of Management Journal, 33(4), 817-832.

Sides, R. W. (2000). Cost analysis helps evaluate contract profitability. Healthcare Financial Management, 54(2), 63.

Sisko, A., Truffer, C., Smith, S., Keehan, S., Cylus, J., Poisal, J., et al. (2009). Health spending projections through 2018: Recession effects add uncertainty to the outlook. Health Affairs, 28(2), w346.

Spanos, Y. E., Zaralis, G., & Lioukas, S. (2004). Strategy and industry effects on profitability: evidence from Greece. Strategic Management Journal, 25(2), 139-165.

Spence, A. M. (1977). Entry, Capacity, Investment and Oligopolistic Pricing. [Industrial Organization Economics]. The Bell Journal of Economics, 8(2), 534-544.

Stonehouse, G., & Snowdon, B. (2007). Competitive Advantage Revisited: Michael Porter on Strategy and Competitiveness. Journal of Management Inquiry, 16(3), 256.

126

Sunshine, J., & Burkhardt, J. (2000). Radiology Groups' Workload in Relative Value Units and Factors Affecting It. Radiology, 214(3), 815.

Tennyson, D., & Fottler, M. (2000). Does system membership enhance financial performance in hospitals? Medical Care Research and Review, 57(1), 29.

Thibadoux, G. M., Scheidt, M., & Luckey, E. (2007). Accounting and Medicine: An Exploratory Investigation into Physicians' Attitudes Toward the Use of Standard Cost-Accounting Methods in Medicine. Journal of Business Ethics, 75(2), 137.

Thornhill, S., & White, R. E. (2007). Strategic purity: A multi-industry evaluation of pure vs. hybrid business strategies. [Generic strategies - mixed strategies; theory]. Strategic Management Journal, 28(5), 553-561.

Torgovicky, R., Goldberg, A., Shvarts, S., Dayan, Y. B., Onn, E., Levi, Y., et al. (2005). Application of Porter's Generic Strategies in Ambulatory Health Care: A Comparison of Managerial Perceptions in Two Israeli Sick Funds. [Porter - healthcare]. Health Care Management Review, 30(1), 17.

Truffer, C. J., Keehan, S., Smith, S., Cylus, J., Sisko, A., Poisal, J. A., et al. (2010). Health Spending Projections Through 2019: The Recession's Impact Continues. Health Aff, hlthaff.2009.1074.

von Bertalanffy, L. (1949). Problems of organic growth. Nature, 163(4135), 156-158.

Vonderheid, S., Pohl, J., Schafer, P., Forrest, K., Poole, M., Barkauskas, V., et al. (2004). Using FTE and RVU Performance Measures to Assess Financial Viability Of Academic Nurse-Managed Centers. Nursing Economics, 22(3), 124.

Vorhies, D., & Morgan, N. (2003). A configuration theory assessment of marketing organization fit with business strategy and its relationship with marketing performance. Journal of Marketing, 67(1), 100-115.

Wagner, B., & Digman, L. (1997). The relationships between generic and time-based strategies and performance. Journal of Managerial Issues, 9(3), 334.

Watt, J., Derzon, R., Renn, S., Schramm, C., Hahn, J., & Pillari, G. (1986). The comparative economic performance of investor-owned chain and not-for-profit hospitals. New England Journal of Medicine, 314(2), 89.

Weber, M., Henderson, A. M., & Parsons, T. (1947). The theory of social and economic organization. London: W. Hodge.

Westbrook, K. W. (1997). Assessing the strategy-performance paradigm: Implications for organizational adaptation in discontinuous environments for home health care providers. Health Marketing Quarterly, 14(4), 27 - 44.

127

White, R. E. (1986). Generic Business Strategies, Organizational Context and Performance: An Empirical Investigation. Strategic Management Journal, 7(3), 217-231.

Wiggins, R. R., & Ruefli, T. W. (1995). Necessary conditions for the predictive validity of strategic groups: Analysis without reliance on clustering techniques. Academy of Management Journal, 38(6), 1635.

Wirtz, B. W., Mathieu, A., & Schilke, O. (2007). Strategy in High-Velocity Environments. Long Range Planning, 40(3), 295-313.

Woodward, J. (1958). Management and technology. London: H. M. Stationery Office.

Wright, P. (1987). A Refinement of Porter's Strategies. [Porter - Theory]. Strategic Management Journal, 8(1), 93-101.

Xu, S., Cavusgil, S., & White, J. (2006). The impact of strategic fit among strategy, structure, and processes on multinational corporation performance: a multimethod assessment. Journal of International Marketing, 14(2), 1-31.

Zammuto, R. F. (1988). Organizational Adaptation: Some Implications of Organizational Ecology for Strategic Choice. The Journal of Management Studies, 25(2), 105.

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APPENDIX A

Total RVUs and Total Number of FTE Physicians

Table A1 Correlation Between Total Number of FTE Physicians (N = 1,775) and Total RVUs (N = 684) Correlation Coefficients Pearson’s Correlation Kendall’s tau b Spearman’s rho Total Physician FTEs & Total RVUs

.185** .488** .582**

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

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APPENDIX B

Practices Grouped By Size Table A2 Descriptive Statistics - Total Number of FTE Physicians By Groups – All Cases (N = 1,788)

Number of FTEs Frequency Percent 0 to .99 148 8.3 1 to 4.99 743 41.6 5 to 9.99 327 18.3 10 to 14.99 139 7.8 15 to 19.99 89 5.0 20 to 35 123 6.9 36 to 50 68 3.8 51 to 100 83 4.6 101 to 250 40 2.2 251 to 1,000 12 .7 Greater than 1,000 3 .2 Missing 13 .7

Table A3 Total Number of FTE Physicians By Groups – 1 Physician FTE to 35 Physician FTEs (N = 1,421) Number of FTEs Frequency Percent 1 to 4.99 743 52.3 5 to 9.99 327 23.0 10 to 14.99 139 9.8 15 to 19.99 89 6.3 20 to 35 123 8.7

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APPENDIX C

Comparison of Practices Included and Excluded Table A4 Comparison of Practices Included and not Included in the Final Analysis Practice Characteristics Inc. In Final Analysis? Legal organization Majority owner Population category

No M 3.54 3.33 2.64 N 247 375 245 SD 1.50 1.60 1.04

Yes M 3.40 3.50 2.43 N 1099 1413 1094 SD 1.52 1.58 1.07

Total M 3.43 3.46 2.47 N 1346 1788 1339 SD 1.52 1.58 1.06

Table A5 One-Way Analysis of Variance Summary for Practice Characteristics Practice Characteristic df SS MS F Legal organization Between Groups 1 3.85 3.85 1.68

Within Groups 1,344 3089.22 2.30 Majority owner Between Groups 1 9.08 9.08 3.63

Within Groups 1,786 4463.56 2.50 Population category Between Groups 1 8.86 8.59 7.62**

Within Groups 1,337 1506.75 1.13

**p<0.01.

Table A6

Effect Size for Practice Demographic Variables Practice Characteristics η2 Legal organization .001 Majority owner .002 Population category .006

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APPENDIX D

Descriptive Statistics and Discuss of Selected Variables

Descriptive Analysis for the Independent Variables - Differentiator Indicators

Marketing. The promotion and marketing costs reported by each practice were a

proxy for their advertising intensity and an indicator of whether or not the practice was

deemed a differentiator. Out of the sample of 1,413 practices, 418 respondents did not

provide a response to this question. Of those responding (n = 995) with the total dollars

spent by their practice on promotion or marketing, the practices had a mean of $36,562

and a range between $0 and $873,594. To account for varying sizes of the practices, this

variable was normalized with the total number of FTE physicians in each practice. Thus,

a new variable was created as a ratio to measure the advertising intensity in each practice,

with promotion and marketing costs as the numerator and the total number of FTE

physicians in each practice as the denominator.

Specialty Services. The total number of ancillary services provided by each practice

was used as a proxy for whether or not a practice offered specialty products/services and

was an indicator of whether or not a practice was a labeled as a differentiator. Each

respondent answered “yes” if their practice offered one of 17 specific ancillary services

(e.g, general radiology, drug administration, durable medical equipment, etc.). A new

variable was computed to sum the total number of ancillary services available in each

practice. In the sample used for the final model (n = 1,413), 37.1% (524) of the cases

included missing values one or more of the 17 ancillary services.

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Table A7

Specialty Services: Number of Practices Providing Ancillary Services – 17 Possible

Services (N=1,413)

Number of Ancillary Services Frequency Percent 0 313 22.2 1 220 15.6 2 133 9.4 3 109 7.7 4 53 3.8 5 33 2.3 6 18 1.3 7 4 .3 8 2 .1 9 2 .1 10 2 .1 Missing 524 37.1

As the questionnaire included a box requiring the respondent to check “yes” if their

practice had one of the 17 ancillary services, a determination was made that the presence

of missing data for the ancillary services questions would indicate that a practice did not

provide that particular ancillary service. After the variables with missing values were

recoded as cases not having each applicable ancillary services, 59.2% (837) of the cases

reported no ancillary services, 15.6% (220) reported 1 ancillary service, 20.9% (295)

reported between 2 – 4 ancillary services, and the remainder (4.2% or 61 cases) reported

between 5 and 10 ancillary services out of 17 total ancillary services available as a

choice.

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Table A8

Total Ancillary Services With Missing Data Coded as Zero (N=1,413)

Number of Ancillary Services Frequency Percent 0 837 59.2 1 220 15.6 2 133 9.4 3 109 7.7 4 53 3.8 5 33 2.3 6 18 1.3 7 4 .3 8 2 .1 9 2 .1 10 2 .1 Missing 0 0

The most frequently reported types of available ancillary services were advanced

radiology (10.6%), health education/counseling (11.3%), clinical laboratory services

(13%), drug administration (13.5%), and general radiology (16.7%). The remainder of

the categories had fewer than 10% of the cases reporting the applicable service.

Table A9

Percent of Practices with Selected Ancillary Services

Provide Service?

Advanced Radiology

Clinical Laboratory

General Radiology

Advanced Radiology

Health Ed/Counseling

No 54.1 50.0 52.7 48.5 54.9 Yes 10.6 13.0 13.5 16.7 11.3 Missing 35.3 37.0 33.8 34.7 33.8

Due to the findings in the descriptive analysis for the total ancillary services

available in each practice, a determination was made to recode this variable into a binary

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variable, with 58% (820) of the practices reporting no ancillary services and 42% (593)

of the practices reporting one or more ancillary services. Comparing the final sample

with the original sample, the two samples were similar, with the original sample of 1,788

having 59.2% (1,059) of the practices reporting no ancillary services and 40.8% (729) of

the practices reporting one or more ancillary services.

Image. Each respondent was asked to provide the costs of general furniture and

equipment, as well as the depreciation costs of the general furniture and equipment, in

their practice. These values were used as a proxy for each practice’s “Image” and were

an indicator of whether or not a practice was included as a differentiator. The average

value for furniture and equipment for the practices was $48,896, with a range between $0

and $1,357,256. The average value for furniture and equipment depreciation was

$102,734, with a range between a negative $167,081 and a positive $6,501,692. Many

respondents (n = 533 and n=477, respectively) did not report a value for their general

furniture and equipment costs or the depreciation value for the general furniture and

equipment category.

The values for these two variables were summed together and created the numerator

for the “Image” indicator ratio variable. To account for the missing values with one or

the other variables in the numerator, it was necessary to address the missing data. Thus,

74 missing values for the furniture and equipment variable and 120 missing values for the

furniture and equipment and depreciation variable were recoded as a zero, resulting in a

total of 607 practices reporting a missing value for this variable. Once this was

completed, the “Image” variable was created by summing these two variables. The

resulting variable included 1,000 cases without missing data. The mean was $138,160

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with a range of $0 to $6,634,036. To account for the varying sizes of each practice in the

sample, a ratio for this variable was created with the summed furniture and equipment

variables as the numerator and a denominator including the number of FTE physicians in

each practice.

Table A10

Descriptive Statistics for the Image Ratio Variable (Differentiator) (N=1,000)

Min Max M SD Image ($) 0 6,634,036 138,160 339,056

Branches. The total number of branches reported by each practice was a proxy for

their range of market segments and was an indicator or whether or not a practice would

be labeled as a differentiator. Of the initial 1,788 practices, 27.5% (492) of the

respondents did not report the number of branches for their practice. Thus, an analysis

was completed of the practices not reporting the number of branches compared with

those practices reporting the number of branches, based on the total number of FTE

physicians in each of these two groups. The intention of this analysis was to determine if

an assumption could be made that the practices not reporting a branch typically did not

have any braches beyond their primary location. The analysis excluded data from 13

respondents who did not include data regarding the total number of branches for their

practice, as well as the three cases previously identified as outliers (reporting > 1,000

total FTE physicians in their respective practices).

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Table A11

Descriptive Statistics for Total Number of Branches (N=1,788)

Practices Reporting Number of Branches Valid or Missing Total Percent Valid 1,296 72.5 Missing 492 27.5

The results of this analysis indicated that those practices not reporting the number of

branches were significantly smaller in total number of physician FTEs as compared with

all practices, with a mean of 3.8 physicians per practice. Even those practices reporting

zero or one branch, reported a higher average total physician FTEs per practices as

compared with a missing values for the question regarding their number of branches. In

fact, the other practices significantly increased in number of FTE physicians when

grouped by number of branches. Due to these results, for those with missing data for the

total number of branches, a determination was made to recode this variable to zero for

their reported branches value. The tables below details the results of this analysis.

Table A12

Comparison of Total Physician FTEs vs. Number of Branches (N=1,772)

Total Number of Physicians in Each Practice

Number of Branches N M SD Min Max 0 644 10.0893 41.93252 .00 968.00 1 157 12.9248 26.22903 .00 222.00 2 118 13.2851 13.72947 .00 63.00 3 to 5 145 19.0412 18.81416 .00 138.51 5 or More 217 72.7080 92.11816 1.00 733.60 Missing 491 3.8011 8.04773 .00 97.67 Total 1772 17.2118 47.34691 .00 968.00 Note: All Cases, Excluding 13 with missing Total Physicians FTEs and 3 Outliers

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Table A13 One-Way Analyses of Variance for Total Number of Physician FTE and Number of Branches (N=1,772)

Source Df SS MS F Between Groups 5 794486.73 158897.35 88.37*** Within Groups 1,766 3175616.90 1798.20 Total 1,771 3970103.62 *** p < 0.001

Once the practices with missing values for their number of branches were changed to

zero, the descriptive results from the sample included in the model (n = 1,413) indicate

that 67.1% (948) of the practices reported no branches (the primary facility/clinic did not

count as a branch), 26.4% (373) reported 1 – 5 branches, and 6.5% (92) reported 6 or

more branches. Due to this finding, a determination was made to recode the number of

branches for each practice into a binary variable, with the results indicating that 67.1%

(948) of the cases having no branches and 32.9% (465) of the branches having 1 or more

branches.

Table A14

Practices with No Missing Values for Number of Branches (N=1,413)

Number of Branches Frequency Percent 0 948 67.1 1 145 10.3 2 105 7.4 3 to 5 123 8.7 6 to 10 64 4.5 11 or more 28 2.0

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Descriptive Analysis for the Independent Indicator Variables – Cost Leader

Production and Operations Efficiency – Cost Leader. The survey respondents

reported the total general operating costs for their practices. This value served as a proxy

for our production and operations efficiency variable, and was an indicator of whether a

practice was deemed a cost leader. As discussed earlier, two of practices did not report a

value for their practice’s total general operating costs, two other cases reported a negative

value, and three reported zero. These seven cases were eliminated from further analysis.

Of the 1,413 cases in our final sample, total general operating costs ranged from $2,160

to $79,274,718, with a mean of $1,860,000. To normalize this value, a ratio was created

using the total general operating costs as the numerator and each practice’s total number

of FTE physicians as the denominator.

Our initial model included a second ratio to measure of the production and

operation efficiency of each practice – total FTE physician in each practice divided by

total RVUs. As the variable for total FTE physicians in each practice has been used as

the denominator for many of our new construct’s variables, and our sample size of cases

including the total RVUs in each practice was small (n = 692 for the overall sample and

n=551 for those cases included in the final sample), this specific indicator variable was

eliminated from the final model.

Control of Costs – Cost Leader. Each respondent reported the value for gross

square footage of all of their practice facilities, which was a proxy for how well their

practice controlled costs as indicated by the physical size of each practice. This variable

was used as an indicator within the cost leader construct and was normalized by using the

original square footage as the numerator and total number of physician FTEs as the

139

denominator of a newly created ratio. Our initial descriptive analysis indicated 50

practices responded with “zero” for their square footage value and another 13 practices

responded with very low values (42 – 587). For this variable, the values for these 63

cases were recoded as missing data. Adding these two subsets to the originally missing

data for this variable, there were a total of 536 cases with missing data. Of the 877

practices reporting data, the average square footage was 18,605, with a range between

587 – 500,000 total square feet. As with the other four cost leader indicator variables, a

ratio was created with the square footage as the numerator and the total number of FTE

physicians as the denominator.

Table A15

Descriptive Statistics – Square Footage

Square Feet N Min Max Mean SD

Only Cases Inc. In Final Model (N=1,413)* 877 587 500,000 18,605 27,059 All Cases (N=1,772)** 1,053 587 1,646,582 41,909 97,322 * Not including cases with missing values ** Not including cases with missing values or outliers Tight Control of Overhead Costs – Cost Leader. Respondents were asked to provide

the total number of support staff employed by their practice, which was a proxy for how

well each practice controlled their overhead costs. This variable was used to determine

whether or not a practice was designated as a cost leader. Seventy-two practices reported

a zero as the value for the total number of support staff in their practice. As it was

determined that is was unlikely that a practice could operate without any support staff,

the total number of support staff variable was recoded as a zero for these practices. Of

the remaining 1,317 cases with reported values, the average total number of support staff

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reported in each practice was 32.5, with a range between 0.01 and 346.88. As with the

other four cost leader indicator variables, a ratio was created with the total number of

support staff FTEs in each practice as the numerator and the total number of FTE

physicians as the denominator.

Table A16

Descriptive Statistics - Total Number of Support Staff FTEs

Total Number of Support Staff FTEs N Min Max M SD

Only Cases Inc. In the Final Model (N=1,413)* 1,317 .01 347 32.56 44.69 All Cases (N=1.772) 1,643 .01 9,545 81.07 304.51 * Not including cases with missing values ** Not including cases with missing values or outliers

141

Tight Control of Marginal Accounts – Cost Leader. In our analysis, a ratio for each

practice’s accounts receivable value was created (measured in dollars) by dividing the

total accounts receivable greater than 120 days by each practice’s overall accounts

receivable value. This ratio was an indicator of the practice’s tight control of marginal

costs and part of the construct that determined whether each practice was deemed a cost

leader. Sixteen cases with a negative value for this variable were recoded as a zero to

prevent potential issues with a negative numerator value for the subsequent ratio that will

be created for this variable.

For each practice’s accounts receivable greater than 120 days value, the average was

$326,340 with a range between $0 and $10,632,000. The variable measuring accounts

receivable greater than 120 days had 119 missing values. The total accounts receivable

for each practice was an average of $1,585,979, with a range between $0 and

$61,581,000. The variable measuring total accounts receivable had 98 missing values.

Once the ratio was computed, the values varied from 0 to 1.0, with an average of 0.2113,

and resulted in 121 practices with a missing value for their accounts receivable ratio.

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Table A17 Descriptive Statistics – Accounts Receivable

Variable N Min Max M SD

AR > 120 Days ($) Only Cases Inc in Final Model* All Cases**

Total Accounts Receivable ($) Only Cases Inc In Final Model* All Cases** Accounts Receivable Ratio Only Cases Inc In Final Model* All Cases**

1,294 1,642

1,315 1,666

1,292 1,636

0.00

0

0 0

0.00 0.00

10,632,000

378,44,715

61,581,000 106,885,913

1.00 1.00

326,341 662,390

1,585,979 3,120,629

.2113 .2213

7.24E5 2.06E6

3,083,723 8,069,586

.14709 .17328

* Not including cases with missing values ** Not including cases with missing values or outliers

143

Descriptive Analysis for the Dependent/Outcome Variable – Profit.

In the inferential model, the dependent variable is a measure of the profit ratio for

each practice. This variable is measured by the total medical revenue after operating

costs for each practice divided by the total number of FTE physicians within each

practice. The mean profit for our sample is $2,860,000, with a range between a negative

$16,733,495 and a positive $38,127,495. As with many of the independent variables in

the model, this value was normalized by using the total number of FTE physicians in

each practice as the denominator for the profit ratio, and the total medical revenue after

operating costs as the numerator.

Table A18

Descriptive Statistics – Profit (Total Medical Revenue Less Operating Costs)

Profit N Min Max M SD

Only Cases Inc. In Final Model* 1413 $-16,733,495 $38,127,495 $2.86E6 $4.359E6 All Cases** 1783 $-49,607,581 $430,085,103 $6.08E6 $1.767E7 * Not including cases with missing values ** Not including cases with missing values or outliers

144

APPENDIX E

Winsorization

Table A19

Strategy Ratios – Before and After Winsorizing

Strategy Ratios* Min Max M SD Marketing (n = 995) Non-Winsorized .00 178,436 4,929 1,1296 Winsorized .00 11,423 3,302 3,620 Image (n = 806) Non-Winsorized .00 285,384 19,089 33,567 Winsorized .00 43,343 13,274 14,106 Efficiency (n = 1,413) Non-Winsorized 2,160 5,880,695 269,163 340,074 Winsorized 2,160 557,281 232,151 134,735 Cost Control Non-Winsorized .00 34,483 2,317 2,436 Winsorized .00 4,785 2,113 1,242 Overhead (n = 1,381) Non-Winsorized .00 117 4.07 4.42 Winsorized .00 10.00 3.84 2.49 Accounts Receivable (n = 1,292) Non-Winsorized -.90 1.00 .21 .15 Winsorized -.90 .53 .21 .14

* The Two Binary Variables Were Not Winsorized

145

APPENDIX F

Imputation

Table XYZ

Imputation of Values - Residuals for Eight Indicator Variables via Cluster Analysis

Indicators Marketing Image Specialty Services Branches Efficiency Cost Control AR Overhead

Marketing .

Image 0.22 .

Specialty Services 0.21 0.53 .

Branches 0.20 0.64 2.19 .

Efficiency 0.08 0.52 1.50 0.36 .

Cost Control 0.00 0.00 0.44 2.45 1.89 .

AR 0.65 2.43 0.99 1.18 0.19 0.00 .

Overhead 0.05 0.79 1.04 0.00 2.44 0.00 0.93 .

146

APPENDIX G

Post-hoc Analysis – Hypothesis 1

Table XYZ

Post-Hoc Analysis for Hypothesis 1 - Scheffe

Porter’s Groups N 1 2

Stuck In The Middle 594 218,884 Low Cost Leader 489 254,077 Hybrid 36 352,835 Differentiator 294 585,928 Sig. .190 1.00

Table G2

Hypothesis 1 –Post Hoc Analyses - Bonferroni Comparisons for Strategy Groups

Comparisons

Mean

Difference($)

Std.

Error

95% CI Lower Bound

Upper Bound

Differentiator vs. Stuck In The Middle 367,044* 33,110 279,567 454,521 Differentiator vs. Cost Leader 331,851* 34,267 241,318 422384 Differentiator vs. Hybrid 233,093* 81,988 16,479 449,707 Cost Leader vs. Stuck in the Middle 35,193 28,352 -39,174 110,099 Hybrid vs. Stuck in the Middle 133,951 79,698 -76,611 344,513 Hybrid vs. Cost Leader 98,759 80,186 -113,091 310,609

* p < 0.05

147

APPENDIX H

IRB Approval