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Sustaining Superior Performance through an Entrepreneurial Boom and Bust: Inter-Firm Differences in the E-Consulting Industry (1997-2001) and the Investment Management Industry (1927-1931)
A thesis presented by Maria Julia Prats Moreno to The Graduate School of Business Administration George F. Baker Foundation
in partial fulfillment of the requirements for the degree of Doctor in Business Administration
Harvard University Cambridge, Massachusetts June - 2004
© 2004 — Maria Julia Prats Moreno All rights reserved
Ashish Nanda Paul Healy Paul Gompers Howard Stevenson
Maria Julia Prats Moreno
Sustaining Superior Performance through an Entrepreneurial Boom and Bust: Inter-Firm Differences in the E-Consulting Industry (1997-2001) and the Investment Management Industry (1927-1931)
ABSTRACT
This study explores the effect of strategic choices and resource combinations on new-venture survival during an Entrepreneurial Boom and Bust (EBB) period. An EBB is a period of rapid expansion of an industry in terms of number of players, fueled by expectations of high returns occasioning a considerable infusion of resources, followed by a sudden change in industry prospects and consequent reallocation of those resources. The result is that in an EBB a large proportion of new business organizations fail shortly after being formed. Although considerable research has been conducted at the industry level as to why the boom-and-bust phenomenon exists, we do not yet understand the mechanisms by which some firms deal successfully with this acute environmental shift while others fail. An in-depth study of 104 e-consulting firms from 1997 to
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2001 and 85 investment management firms from 1927 to 1931, combining range of data sources and analytical tools—archival and interview-based data, cluster analysis, multilogit models, probit regression models—yields insights on newventure strategies at industry inception and the effects of the strategies and initial endowments on venture success. First, the study identifies a typology of new-venture entry strategies. In the e-consulting industry, it finds that firms followed four strategy archetypes— Conservative Growers, Focused Consultants, Expansionists, and Aggressive Acquirers. Further, differences in strategic behavior reflect differences in resource endowments, and these differences are systematically associated with differences in performance. Whereas Conservative Growers and Focused Consultants were most successful in weathering the contraction phase, Expansionists and Aggressive Acquirers were the most susceptible to failure. A parallel analysis of the investment management industry during the 1920s yields strikingly similar results; the few differences may reflect structural differences between the two industries. Second, the study shows that resource configuration and strategic patterns in both the expansion and decline phase of the industry influence a firm's ability to adapt to the sudden environmental change. Strategies that offer a wide range of services ensure a better chance for survival; moreover, industry knowledge rather than entrepreneurial experience is crucial to the survival of a firm in such turbulent times.
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TABLE OF CONTENTS
ACKNOWLEDGMENTS
CHAPTER 1:
INTRODUCTION
1.1.
Background and Motivations
1
1.2.
Research Questions Overview
3
1.3.
The E-Consulting and the Investment Management Industries as Research Settings
5
1.4.
Results Overview
8
1.5.
Contributions of the Research
10
1.6.
Limitations of the Research
11
1.7.
Structural Overview of the Dissertation
13
CHAPTER 2:
THE ENTREPRENEURIAL BOOM AND BUST PHENOMENON
14
2.1.
The Entrepreneurial Boom and Bust Described
14
2.2.
Relevant Literature and Theoretical Development
18
2.2.1. Previous Relevant Literature
18
2.2.2. Theoretical Framework. Firm Survival in an EBB Context
20
2.2.2.1.
22
Strategies in an EBB
v
2.2.2.2.
Initial Endowments and Firm Resources in an EBB
CHAPTER 3:
29
ENTRY STRATEGY GROUPS IN A BOOM AND BUST PERIOD: THE CASE OF THE E-CONSULTING INDUSTRY
3.1.
33
Relevant Literature
35
3.1.1. Entry Strategies
35
3.1.2. Strategic Groups
37
3.2.
Conceptual Framework
38
3.3.
Setting, Sample, Data Collection, Variables, and Methods
40
3.3.1. The E-Consulting Industry
40
3.3.2. Sample
43
3.3.3. Data Collection
46
3.3.4. Variables and their proxies
47
3.3.5. Methodology
52
Results
54
3.4.1. Data Description
54
3.4.2. Correlation among Independent Variables
66
3.4.3. Cluster Analysis to Identify New Venture Strategies
67
3.4.
3.4.4. Identification of the Resource Base Specific to Each Cluster
71
3.4.5. Performance Differences among Strategic Groups
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75
3.5.
Discussion from the Cluster Analysis
79
3.6.
Additional Analysis
82
3.6.1. Variables and their Proxies
83
3.6.2. Methodology
89
3.6.3. Results
91
3.6.4. Discussion from the EBB Cycle Analysis
102
CHAPTER 4:
ENTRY STRATEGY GROUPS IN A BOOM AND BUST PERIOD: THE CASE OF THE INVESTMENT MANAGEMENT INDUSTRY
4.1.
4.2.
108
Setting, Sample, Data Collection, Variables, and Methods
110
4.1.1. Industry Background
110
4.1.2. Sample
118
4.1.3. Data Collection
122
4.1.4. Variables and Their Proxies
123
4.1.5. Methodology
131
Results
132
4.2.1. Data description
132
4.2.2. Cluster Analysis to Identify New Venture Strategies
138
4.2.3. Identification of the Resource Base Specific to Each Cluster
4.3.
141
4.2.4. Performance Differences among Strategic Groups
144
Additional Analysis
147
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4.3.1. Analysis of Investment Management Firm Strategies, and Resources on firm Performance 4.4.
148
Discussion from the Investment Management Industry Analysis
CHAPTER 5:
151
DISCUSSION, LIMITATIONS, AND FUTURE RESEARCH
154
5.1.
Summary of Findings and Implications
154
5.2
Research Limitations and Future Research
157
Appendix 1 The Entrepreneurial Boom and Bust in the E-Consulting Industry
161
A1.1. Introduction
161
A1.2. Defining the Boom and Bust in the E-Consulting Industry
170
A1.3. The Challenge of delivering E-Consulting Services
184
A1.4. Competition in E-Consulting
191
Appendix 2 List of E-Consulting Firms
202
Appendix 3 Characteristic Origins of Investment Companies
203
Appendix 4 List of Investment Management Firms
208
Appendix 5 Portfolio Imputation Criteria
209
References
211
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LIST OF TABLES AND FIGURES
Table 3.1
E-Consulting Sample Selection
Table 3.2
Variables Used in the E-Consulting Strategic Group Analysis
Table 3.3
45
48
Summary Statistics for E-Consulting Strategic Group Analysis
56
Table 3.4
Correlation Matrix for E-Consulting
57
Table 3.5
Summary Statistics for E-Consulting Cluster Analysis
68
Table 3.6
Multilogit Regression Model of Resources on E-Consulting Clustered Strategies
Table 3.7
72
Results of Regression Analysis of E-Consulting Strategic Clusters and Resources on Firm Performance
76
Table 3.8
Variables Used in the E-consulting EBB Analysis
84
Table 3.9
Summary Statistics for E-consulting EBB Analysis
85
Table 3.10
Correlation Matrix for E-Consulting EBB Analysis
86
Table 3.11
Results of Probit Regression Model Analysis of E-Consulting Strategy and Resources on Firm Performance 92
Table 4.1
Distribution of Types and Survival Rates in the Investment Management Industry in 1936
Table 4.2
Variables Used in the Investment Management Industry Analysis
Table 4.3
120
125
Summary Statistics for Investment Management
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Firm Analysis Table 4.4
133
Correlation Matrix for Investment Management Strategic Firm Analysis
Table 4.5
134
Summary Statistics for Investment Management Cluster Analysis
Table 4.6
139
Multilogit Regression Model of Resources on Investment Management Clustered Strategies
Table 4.7
Results of Regression Analysis of Investment Management Strategic Clusters and Resources on Firm Performance
Table 4.8
142
145
Results of Regression Analysis of Investment Management Strategies and Resources on Firm Performance
149
Table A1.1
E-Consulting Market Forecasts
163
Table A1.2
Evolution of Worldwide Consulting Industry Revenues 1997-2004
165
Table A1.3
Capital Availability for Internet companies
174
Table A1.4
The Growing Importance of Internet commerce, Circa 1999 176
Table A1.5
E-Consulting Firms that were Unable to Go Public
179
Table A1.6
The E-Consulting Market Downturn
182
Table A1.7
Summary of Services and Skills Necessary to Provide E-Consulting Services
Table A1.8
187
Service Methodologies of Selected sample of E-Consulting Companies
189
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FIGURE 1
Evolution of the E-Consulting Variables
58
FIGURE 2
New Investment Management Firms (1920-1931)
115
FIGURE 3
Evolution of Investment Management Firm Variables
135
FIGURE A1 Equity Financing for Venture Backed Internet Companies (in $ million)
174
FIGURE A2 Evolution of the E-Consulting Index
183
FIGURE A3 Competition in Providing E-Consulting Services
192
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ACKNOWLEDGMENTS
I would like to thank all those who generously shared their time and provided me with the support I needed to write this dissertation. This research never could have succeeded without their help. I start with a number of firm founders, managers, and industry experts who allowed me access to their organizations and expertise. Professionals at Accenture, DiamondCluster International, Digitas, iXL, Mainspring, Sapient, Scient, Lehman Brothers, and the Kennedy Information Research Group facilitated my research. I will always be indebted to each of them. I also express gratitude to individual faculty members for their assistance throughout my doctoral program. Special mention goes to Professor Joe Bower for his encouragement and support from the beginning. I also extend my thanks to other faculty members who contributed to my development as a researcher: to Professors Teresa Amabile, Clayton Christensen, Tom Eisenmann, David Garvin, Myra Hart, Walter Kuemmerle, Jay Lorsch, Thomas Piper, Mary Tripsas, Michael Tushman, and Belen Villalonga. I want to especially thank Professor Asis Martinez-Jerez and his wife Maria Jose. Their friendship has been invaluable. I am grateful to the Harvard Business School, IESE Business School, and the Ewing Marion Kauffman Foundation for providing the financial support I needed to research this dissertation. I am also grateful for all the assistance and encouragement I received from different support groups at Harvard Business School. Marguerite Dole, just retired, has offered patient support to all of the doctoral students in the Business Policy Unit. Special mention also goes to Laura Malacuso and Bobbie Brown, for their continuous support as I sent drafts to be revised, scheduled numerous meetings over the years; they provided access to my advisors on urgent issues. I thank in addition Kathleen Cohrs and Peggy Moreland. Without their understanding and patience, it would have been difficult to communicate effectively with my advisors. Kathleen Ryan of HBS research services and Bill Simpson, senior statistician at HBS, provided invaluable data and suggestions. In the doctoral program office, Maria Curcio, LuAnn Langan, and Janice McCormick were always encouraging and facilitated the administrative process. I am very grateful to my doctoral colleagues and friends Clark Gilbert, Boris Groysberg, both now successful HBS faculty members, David Ager, Dennis Campbell, Chaikat Chaudhuri, Amanda Cowen, Laura Donohue, John Donovan, Adam Kleinbaum, Niels Ketelholm, Elisabeth Long, Marcelo Pancotto, Tatiana Sandino, Jasjit Singh, Suraj Srinivasan, Spela Trefalt, and Ann Winslow for their
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feedback and support. Also, in particular, Anita Tucker has been a friend through our years together in the doctoral program and this last year when we were both on the faculty of the Wharton Business School. I am forever indebted to my dissertation committee, Professors Ashish Nanda, Paul Healy, Paul Gompers, and Howard Stevenson, in many ways. They have inspired me and set a rigorous standard that will benefit me throughout my career. Professor Paul Gompers offered guidance and provided me valuable encouragement and insight. Professor Paul Healy rigorously challenged my ideas as he read my work closely and provided invaluable detailed comments. Professor Howard Stevenson was a relentless supporter from my first day at HBS. All along he always provided honest, broad, stimulating, and insightful comments and suggestions that will serve me well in my professional career. His generous attitude in the midst of his own demanding projects is something I would like to emulate in my future work with students and colleagues. The help and example Ashish, my committee chair, provided is difficult to summarize. During these years I witnessed him working endless one-on-one hours with doctoral students, conducting research seminars, writing articles and cases, as he taught doctoral students, MBA students, business school professors, and executives. In every activity I witnessed his rigor, passion, and dedication. I could not have had a better mentor. He has stimulated me and enhanced my learning experience with his wealth of sincere generosity. Special mention goes to Pedro Nueno, IESE professor and mentor. Throughout my years in Boston, he always found the time, wherever he was around the world, to encourage and stimulate my research, also allowing me to participate in and learn from his numerous fascinating projects. Without his faith, patience, and generous help, I would have never entered academic life. I also gratefully acknowledge former IESE Dean Carlos Cavalle, IESE Dean Jordi Canals, and Professor Juan Roure for their mentorship and support. Finally, I thank my numerous friends and family. To my friends at Bayridge residence, especially Irene D., Irene P., Karen B., Marie O., and Annette B., thank you for always facilitating my work and providing a familial atmosphere to support my rigorous schedule all these years. Many other friends that I cannot name here have also contributed to make this time unforgettable. I have shared both joys and sorrows with them as we enjoyed sports and stimulating conversations. I want to especially thank my siblings Angi, Jose Manuel, Jordi, Rosamaria, and Xavier for sharing with me their projects and successes. Being part of their lives during this challenging time, despite the geographical distance, has eased the journey. Finally, I am infinitely indebted to my parents Josep and Angelines. Their attentive care to every small detail has been a source of strength and peace. Without their sacrifices and generous and exemplary life, I could never have started and persevered in this rewarding endeavor.
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CHAPTER 1:
1.1.
INTRODUCTION
BACKGROUND AND MOTIVATIONS This study explores the effect of strategic choices, initial endowments, and
resource combinations on new-venture survival during an Entrepreneurial Boom and Bust (EBB) period, with a focus on two industries: the e-consulting industry during the 1990s, and the investment management industry during the 1920s. An EBB is a period of rapid expansion of an industry in terms of number of players fueled by expectations of high returns (occasioning a considerable infusion of resources), followed by a sudden change in industry prospects (and consequently the reallocation of those resources). Such industries are characterized by both the development of “promising” startups 1 (Bhide, 2000) and large proportions of business failure (Freeman et al., 1983; Carroll, 1984; Cooper et al., 1991; Bhide, 2000). The emerging phase of an industry is usually accompanied by the largest proportion of newly formed companies that the industry will ever experience 1
For example, Steve Wozniak and Steve Jobs created the first commercially successful personal computer and pioneered the PC industry by founding Apple Computer in 1983. As thousands of new PC users emerged, scores of new PC firms were founded, and the PC industry itself was firmly launched. By 1995, from an initial base of zero, the PC industry had amassed more than $250 billion in net shareholder value (Hannan & Freeman, 1989; Klepper & Graddy, 1990; Meeker et al., 1995; Geroski & Mazzucato, 2001; Schoonhoven & Romanelli, 2001). Another example is founded in the development of the investment management industry in the 1920s. Successful mutual fund firms, which subsequently became important in the 1940s, were born during the boom of the 1920s (United States. Securities and Exchange Commission, 1942).
1
(Klepper et al., 1990), but several of new business organizations fail shortly after being formed (Freeman et al., 1983; Carroll, 1984; Singh et al., 1986) or achieve only marginal performance (Cooper et al., 1991; Bhide, 2000). Investors’ inflated expectations about new business opportunities trigger an overflow of resources into the industry and a large number of firms are created. But the resources dissipate once the expected promise is not realized. Many of the ventures started during the boom period then face a sudden environmental turnaround that tests their ability to survive. This pattern —Entrepreneurial Boom and Bust— has been observed in a wide range of industries at their inception, 2 including newspapers (Delacroix & Carroll, 1983), railroads, air transportation operators, commercial banks (Carroll, 1984), disk drives (Sahlman & Stevenson, 1986; Lerner, 1997), savings and loan banks (Rao, 1989), the United States beer brewing industry between 1880 and 1890 (Horvath et al., 2001), and the US automobile and tire industries (Horvath et al., 2001). Although considerable research has been conducted on why the boomand-bust phenomenon exists, previous research on entrepreneurship has not explained the mechanisms by which some new ventures deal successfully with such acute environmental shift whereas others fail to do so. Neither studies at the population level —which show what many industries undergo a boom and 2
Emerging industries are newly formed or re-formed industries that have been created by technological innovations, shifts in relative cost relationships, emergence of new consumer need, or other economic and sociological changes that elevate a new product or service to the level of a potentially viable business opportunity (Porter, 1980).
2
bust (Gort & Klepper, 1982) but cannot explain the phenomenon (Geroski et al., 2001)— nor strategy and entrepreneurship scholars have examined what leads to a firm’s survival in the context of an EBB. Disjointed findings in survival studies (Thornhill & Amit, 2003) —even at odds with one another— suggest partial research designs or/and a lack of careful identification of contextual dynamics to explain firm outcomes. Because no general theory exists to specify the characteristics that are most important to young firms’ performance under these environmental conditions, this research is exploratory. Combining strategy and entrepreneurship theory with in-depth archive and field data insights (Glaser & Straus, 1967), this study builds a framework to examine systematically firm survival in the context of an Entrepreneurial Boom and Bust. The intersection of these literatures helps provide insight into venture survival and build a basis for some rudimentary prescriptions.
1.2.
RESEARCH QUESTIONS OVERVIEW There is a basic question surrounding an Entrepreneurial Boom and Bust
episode: What distinguishes the winners and the losers under such environmental pressures? Even in the short period of time than an EBB lasts, do managerial decisions have impact on firm survival? This dissertation examines
3
how initial resource3 endowments, and strategies4 affect new-venture performance in the context of an EBB. Previous research has suggested differences in firm behavior are attributable to the environment (Carrol, 1984; Covin & Slevin, 1989; Romanelli, 1989). Following this insight, the exploration of new-venture strategic patterns has been divided into two steps. The study first explores whether a typology of entry strategies exists and, if so, its impact on firm survival at the end of the EBB cycle. Specifically, it answers the following research questions: Do different competitive strategies exist among new ventures in the same industry at industry inception? If so, what is the nature of each archetype of competitive strategy? What resources are associated with each archetype? Do archetypes differ in their impact on firm survivability? And finally, how do resource differences affect strategic choices and firm survivability? A second related analysis looks at the impact of initial endowments, organization resources, and strategies on firm survival for both the expansion and decline phases of the EBB cycle. Specifically, it answers the following research questions: How do new-venture strategies and resources affect a firm’s ability to adapt to a sudden change in the environment, and hence its survival? 3
Through the paper, the term resources is used to denote the firm’s stock of tangible and intangible assets, including employees’ individual skills (Lieberman & Montgomery, 1998). 4
The term strategy is used in a generic sense, to refer not necessarily to consciously articulated plans of actions, but rather to a consistent stream of actions that are intended to further an entrepreneur’s objectives (Mintzberg, 1978).
4
That is, controlling for organizational resources and initial endowments, do strategies during the expansion phase of the EBB period affect firm survival? Do strategies during the contraction phase of the EBB period affect firm survival? Is there any space for managerial action?
1.3.
THE E-CONSULTING AND THE INVESTMENT MANAGEMENT INDUSTRIES AS RESEARCH SETTINGS The exploratory nature of the research and the complexity of the cause-
effect relationship of variables call for more in-depth analysis of individual industries as a pilot “case” study of the phenomenon (Yin, 1994).5 To redefine solid constructs and variables for future research, we chose a pattern-matching approach. “Such logic compares an empirically based pattern with a predicted one (or with several alternative predictions). If patterns coincide, the results can help a case study strengthen its internal validity.” (Yin, 1994) pp.106. To explore these research questions, we first chose the e-consulting industry for several reasons. First, it experienced the phenomenon of interest. The industry shifted from dramatic growth to sudden collapse in a short period (the e-consulting industry from 1997 to 2001). Resources allocated to develop the new industry, service demand expectations, and the population of firms went
5
The case study strategy may be used to explore those situations in which the intervention being evaluated has no clear, single set of outcomes (Yin, 1994) pp.15.
5
through a rapid cycle of boom and bust, providing an opportunity to focus on a particularly intense environmental change. Second, the industry fits our research questions and controls for technology effects. In the e-consulting industry, firm success does not depend upon any technology-based decision, a variable that previous research has identified as key to firm failure at industry inception.6 The study identifies and explores only the effects of strategy and resources on a venture’s performance. In addition, the e-consulting industry allows an in-depth follow-up of the EBB episode effects on firms through interviews of managers, founders, and key opinion leaders in the industry. Finally, the e-consulting industry is an attractive setting to study the boom and bust phenomenon because not much research has been conducted on entrepreneurial professional service firms.7 This exploratory research allowed the construction of a preliminary theoretical framework to examine strategies and resources of firms in the context 6
For instance, Suarez and Utterback (1995) show that firms that adopt the dominant design —“a specific path, along an industry’s design hierarchy, which establishes dominance among competing design paths” pp.416— were more likely to survive. 7
The subject has been explored previously only in related topics focused on physicians (Wholey et al., 1993), accountants (Pennings et al., 1998), biotechnology scientists (Zucker et al., 1998), and university technology inventors (Di Gregorio & Shane, 2003). Wholey et al. (1993) study organization formation among physicians when interests of corporate clients are strong and professional diversity leads professional groups to expand their jurisdiction by organizing. Pennings et al. (1998) examine the effect of human and social capital upon firm dissolution with data from a population of Dutch accounting firms. Zucker et al. (1998) find that the timing and location of start-up biotech enterprises are determined primarily by the local number of highly productive “star” scientists actively publishing genetic sequence discoveries. Di Gregorio and Shane (2003) find that universities’ entrepreneurial eminence and licensing policies influence such start-up activity.
6
of an EBB episode. The behavior of the e-consulting industry is a specific instance of the more general case of strategic behavior in emerging industries where environmental munificence and high expectations at inception are followed by a sudden turnaround. By observing growth strategy and choice of scope of each firm entering into the e-consulting market, its resource base and performance consequences, researchers can learn about the effectiveness of various strategies in a market characterized by this boom and bust phenomenon.8 The other selected setting for analysis was the Investment Management industry inception in the 1920s. Although this industry differs in terms of chronological time, product/service offered, and other characteristics to be discussed later, it coincides in key dimensions with the e-consulting industry. The Investment Management industry shifted from dramatic growth to sudden collapse in a short period (the investment management industry from 1927 to 1931); firm success does not depend upon any technology-based decision, and as a professional service firm, its human capital is a key component of the firm competitive strategy.
8
Although the EBB dynamic may be bolder for industries with low entry barriers such as service- based industries, it is not exclusive of them. Asset-intense industries such as the disk drive industry or semiconductors, among others, have experienced the same effect (Sahlman et al., 1986; Romanelli, 1989). Therefore, this industry dynamic is not so much a function of industry structure as resource providers’ willingness to endure risks for the sake of expected highreturn opportunities.
7
1.4.
RESULTS OVERVIEW In the context of the e-consulting industry, the analysis of 104 new
ventures shows that firms followed four strategy archetypes —Conservative Growers, Focused Consultants, Expansionists, and Aggressive Acquirers. Further, differences in strategic behavior reflect differences in resource endowments and these differences are systematically associated with differences in performance. Whereas Conservative Growers and Focused Consultants were most successful in weathering the contraction phase, Expansionists and Aggressive Acquirers were the most susceptible to failure. A parallel analysis in the context of the investment management industry during the 1920s yields strikingly similar results. An analysis of the performance drivers of 85 investment management firms from January 1927 through December 1931 suggests that initial endowments shape firm strategy during the expansion phase of the EBB, and both predict firm survival at the end of the EBB period. We find that firms followed three archetypes of strategy— Conservative Growers, Focused Investors, and Expansionists— that started from different resources bases. Conservative Growers were best able to withstand the contraction period. Although investment management firms and e-consulting firms differ in many dimensions—for instance, investment firms are more scalable— results from both populations at industry inception (the investment management industry in the 1920s and the e-consulting industry in the 1990s) are comparable.
8
We infer that limitations to new-venture growth are not outside the firm (lack of resources, demand level) but are internal. Both studies support the argument that under EBB conditions, service strategies based on a wider range of services lead to better results, and industry knowledge rather than entrepreneurial experience is crucial to effective leadership of a firm in such turbulent times. Finally, in-depth archival analysis of 104 e-consulting firms complemented by personal interviews of founders, managers, and equity analysts in the econsulting industry suggest that firms with the greatest chances of survival shared the following features: they grew during the expansion phase sufficiently to capture and retain resources, but moderately enough so that the firms could deal with both increased organizational complexity and external shock; they acquired small firms, if at all, during the expansion phase and/or acted as consolidators during the decline phase of the EBB. They had flexible service offerings and retreated faster from international markets. Thus, although previous research recommended that firms in a hostile environment must remain largely innovative and entrepreneurial in terms of product and markets (Covin et al., 1989), our findings suggest that when the environmental shock is as intense as in an EBB, firms that adapt their service to firm capabilities and contract fast are more likely to survive. Successful firms had higher percentages of specialized human capital and founders able to manage complexity, to
9
understand the industry, and to choose the right resources to exploit the opportunity and to adapt their offering to the new demand.
1.5.
CONTRIBUTIONS OF THE RESEARCH This study contributes to entrepreneurship and strategy theory and offers
valuable insights to practitioners. First, the precise identification of the environmental factors in which new ventures are born acknowledges previous research that identified context as crucial to their development (Carroll & Hannan, 1989; Hannan et al., 1989; Carroll & Hannan, 1992), and fills a gap in the present literature investigating firm survival. Difficulties in accessing firm data at industry inception (Aldrich, 1999) have limited firm-level explorations. Usually new ventures are too small and private and often the industry is not recognized as such until it increases its economic significance, which results in little available public data. Moreover, relationships between firms’ actions and the complex environmental pressures to which new ventures are subjected are not yet apparent and thus previous studies had largely focused on mechanisms at the population level —for instance, population legitimacy problems (Delacroix et al., 1983; Aldrich, 1999), population density (Carroll, 1985), or other population dynamics (Hannan & Freeman, 1977). Our data set allows the examination of new-venture strategies and resources during industry growth. Second, the framework proposed to analyze entry strategies advances theory on strategic groups and the empirical analysis increases our
10
understanding of them. Strategic groups correspond to different resource bases and yield different firm-performance outcomes. In identifying entry strategies that better position new ventures for severe environmental conditions, this research extends our limited knowledge of strategies at industry inception. Third, the identification of the distinct nature of the expansion and decline period of an EBB context points out the inaccurate assumptions embedded in previous research regarding the context in which firms are created. Without analytically eliminating the factor of population mechanisms in explaining firm survival, it offers alternative mechanisms based on entrepreneurial choices in gathering resources, and in taking strategic action. Specifically, we account for firm survival through the boom and bust episode with a combination of factors including initial endowments, organizational resources, and strategic decisions made during the expansion and the decline period of the EBB. Finally, in terms of industry formation, we describe the early years of two professional service industries: the e-consulting industry that was born in the mid-1990s under high expectations and that today may be considered history and the investment management industry in the 1920s that conversely gave rise to today’s multibillion-dollar mutual fund industry.
1.6.
LIMITATIONS OF THE RESEARCH We acknowledge several limitations. First, this study focuses its
exploration on two industries. Although it is convenient for building grounded
11
theory, an industry-specific study may not be applicable to broader categories. Future research must find new EBB episodes to refine the framework. For instance, successful growth and scope strategies may differ in physical assetintensive industries where economies of scale may be easier to realize than in human-capital–based industries. Similarly, as previous research has shown, technology-based industries may also present other key variables related to firm survival. Second, we acknowledge that entrepreneurs cannot anticipate boom periods or predict the environmental turnaround limiting the usefulness of normative recommendations. Although the timing for a shakeout may not be predicted, a munificent environment sets the conditions for speculative behavior and increases their likelihood. Previous studies show that these episodes are never sustainable and that specific warnings may be identified before a shakeout sweeps the industry. Finally, our research suggests that resource and strategy configurations led to different types of business models (or architectures for doing business) that turned out to have different degrees of success. An interesting venue for future research is to explore the industry structural characteristics that force the industry to evolve as a whole in such a different way. For instance, our preliminary research on the interplay of incumbent and new ventures in the econsulting industry indicates that incumbent firms —strategy consultants, system integration firms, accounting firms— enjoyed huge advantages. Firm reputation
12
played a critical role during the decline phase in retaining and attracting new clients and employees, therefore facilitating the re-entry and rapid positioning of those firms after the shock.
1.7.
STRUCTURAL OVERVIEW OF THE DISSERTATION The remaining chapters have been organized as follows. Chapter 2
explores the Entrepreneurial Boom and Bust phenomenon and develops the basic framework to analyze firm survival under these environmental conditions. Chapter 3 presents relevant literature on entry strategies and strategic groups, and builds the theoretical framework to explore —in the context of the econsulting industry— the existence of strategic groups. Subsequently it expands the analysis to the complete EBB cycle and disentangles the effect of strategy dimensions on firm survival. The case of the investment management industry is presented in Chapter 4, where we identify a typology of entry strategies. Chapter 5 concludes with a discussion of the study and its implications for entrepreneurs and others.
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CHAPTER 2:
THE ENTREPRENEURIAL BOOM AND BUST PHENOMENON
This chapter describes the Entrepreneurial Boom and Bust (EBB) phenomenon and develops a theoretical framework to explore the EBB episode. Drawing on the strategy and entrepreneurship literatures, the chapter identifies EBB's unique features and suggests a multidimensional model to explore the mechanisms by which some new ventures deal successfully with such an acute environmental shift while others do not. 2.1.
THE ENTREPENEURIAL BOOM AND BUST DESCRIBED Empirical research has shown that soon after their inception, many
industries have undergone a cycle of rapid growth followed by severe contraction in the number of firms despite continued growth in output (Gort et al., 1982; Hannan et al., 1989; Klepper et al., 1990). This pattern has been observed in a wide range of industries, (see pp.2 for a list of industries). Initially, the number of firms in each of these industries grew, only to decline rapidly. The shakeout is then followed by a period during which the number of firms stabilizes (Gort et al., 1982; Klepper et al., 1990). In a study of 46 new industries, Klepper et al. (1990) found that the evolution of the number of firms in those new industries followed a similar pattern though the length and severity vary across industries. For instance, Klepper et al. (1990) found that the modal shakeout involved a decline of about 50 percent in the number of
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producers, and in extreme cases the number of firms declined by roughly 80 percent in 10 to 15 years. A number of theories have been proposed to explain the pattern of rapid growth and shakeouts across industries. Some theories emphasize the role of precipitating events, such as major technological changes (Utterback & Suarez, 1993; Jovanovic & MacDonald, 1994); others interpret shakeouts as part of a gradual evolutionary process shaped by technological change (Klepper et al., 1990; Klepper & Miller, 1995), or determined by the carrying capacity of the environment (Brittain & Freeman, 1980). Institutional theory argues that this pattern exists because firms initially lack external legitimacy due to their small numbers (Scott & Meyer, 1983), whereas other theories posit multiple factors (Aldrich, 1999). However, Geroski and Mazzucato (2001) suggest that models used to explain population movements across many industries seem not to provide a satisfactory account because one or more of them is particularly appropriate for explaining one particular phase of the evolution. Further, different models are necessary even to account for industry-inception development patterns across industries.9
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For instance, one salient feature of some populations is the large rise in the number of firms operating in the very early years of the industry, a phenomenon which cannot be accounted for using either standard economic models or standard density dependence models (Geroski et al., 2001). Geroski and Mazzucato (2001) propose that the contagion model seems to be particularly useful in the early phase of evolution, largely because it helps to account for the rapid colonization of the market. The contagion model predicts that random shocks set off speculative bubbles.
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It is not the purpose of this section to discriminate among different theories regarding the boom and bust phenomenon. However, we believe that a categorization of contextual factors can bring precision to the analysis of causal mechanisms relevant to new-venture survival. Thus, we identify a boom and bust episode category —the Entrepreneurial Boom and Bust— as the first building block to analyze venture performance. Building upon Geroski and Mazzucato’s (2001) insight, we conducted a closer historical observation of the evolution of 12 industries at inception,10 and found that industry inceptions that have suffered acute changes in the number of participants in their early years —the disk drive industry (Sahlman et al., 1986; Lerner, 1997), biotechnology (Pisano, 1990), e-consulting industry, and the investment management industry among others— follow consistent contextual patterns. Although more systematic research is necessary, similar cycle features suggest an idiosyncratic congruent category. The exploration suggests that patterns arise as a consequence of collective actions of multiple actors under impacted information (Azoulay & Shane, 2001; Schoonhoven et al., 2001).11 The cycle starts with an exogenous event —the commercialization of a new technology, industry deregulation, or the spread of a new business model— that triggers a new opportunity for profits. 10
Examined industries include Railways (1870s), Investment Management (1920s), Leasing Services (1960s), Savings and Loans Banks (1960s), Disk Drives (1980s), Real State (1980s), Video Games (1980s), Microcomputer Software (1980s), Biotechnology (1990s), Fiber Optics (1990s), and e-Consulting (1990s). 11
Entrepreneurs, venture capitalists, public capital markets, media, lawyers, and industry professionals, who together actively create and sustain legitimate market spaces for new products, services, and technologies.
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Initially the opportunity is identified and exploited by a few insightful entrepreneurs who are able to gather resources and, in many cases, start a new firm. Capital markets, potential entrepreneurs, and other constituencies observe the initial success of those individuals. Social construction mechanisms create a shared expectation that the opportunity will quickly expand. Projected growth triggers a racing behavior among entrepreneurs and investors, and valuation of firms that are already exploiting the opportunity increases. The illusion of munificent availability of resources acts as an isolating mechanism helping new ventures, although for a short period of time, to enjoy a protected prosperity. Furthermore, many firms push their expansion based on the market expectations. Even as many new projects are initiated and the valuation of existing businesses rises, new information begins to limit the size of the opportunity and question the socially constructed enthusiasm. Expectations are revised and prospects for growth lowered, leading investors to rush to cut financing and public markets to trade down the valuations of existing firms. Firms then face a double challenge: on the one hand, a cutback in resources, making it difficult to sustain previous commitments, and on the other hand, a decline in product/service demand. As a consequence, the industry experiences a shakeout, and several of the entrepreneurial firms exit the industry.
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We have called this cycle “the Entrepreneurial Boom and Bust (EBB) phenomenon.” This phenomenon belongs to a larger category of economic phenomena, collectively called speculative bubbles (Stiglitz, 1990).12 Theoretical models and empirical studies from finance (Minsky, 1977; Garber, 2000) as well as from sociology (Abolafia & Kilduff, 1988; Aldrich & Fiol, 1994) have characterized this empirical regularity.13
2.2.
RELEVANT LITERATURE AND THEORETICAL DEVELOPMENT
2.2.1.
Previous Relevant Literature
Although considerable research has been conducted on why the boomand-bust phenomenon exists, previous research on entrepreneurship has failed to explain the mechanisms by which some new ventures deal successfully with such acute environmental shift and others fail to do so.
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Speculative bubbles have arisen numerous times in history across geographical borders. Underlying reasons put forward for the phenomenon are diverse – i.e., lack of information about the underlying value of assets (tulip mania in the 17th century), uncertainty introduced by government manipulations (the Mississippi Bubble and the South Sea Bubble in 1720), new financial developments (the crisis of 1929), or major sectorial shifts. For a complete review, see (Kindleberger, 2000). 13
Minsky (1977; Kindleberger, 2000) constructs a model of speculative bubbles undergoing four phases. First, there is an exogenous shock to the macroeconomic system. Second, there is a period of mania where new opportunities for profits are sought in an atmosphere of collective irrationality. Once the excessive character of the upswing is realized, the financial system experiences distress. Finally, the rush to reverse the expansion process becomes so precipitous as to degenerate into panic. Abolafia and Kilduff (1988) reframed the model to include social construction mechanisms to account for how self-interested market participants contribute to an environment that first moves through mania and then panic. Similarly, Aldrich (1994) suggests cognitive and sociopolitical legitimization as the mechanism that underlies the process.
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Studies at the population level derived from ecological approaches (Aldrich, 1979; Hannan et al., 1989) have shown that a firm’s survival in a population depends, in part, on environmental factors at the time of founding, such as demand, population dynamics, and population density (Carroll & Delacroix, 1982; Carroll, 1985; Aldrich, 1999). Yet the evolution of the EBB cycle and, by extension, its effects on firm survival cannot be accounted for using either standard economic models or standard density-dependence models (Geroski et al., 2001). Strategy and entrepreneurship scholars have explored factors that directly or indirectly influence venture survival. Firms’ strategic (Abell, 1980; Sandberg, 1984; Covin et al., 1989; Romanelli, 1989; Eisenhardt & Schoonhoven, 1990), technological (Utterback & Abernathy, 1975; Utterback et al., 1993; Suarez et al., 1995), managerial (Cooper et al., 1986; Roure & Maidique, 1986; Christensen et al., 1998), and competitive forces (McDougall & Robinson, 1990; Christensen & Rosenbloom, 1995), initial endowments (Evans & Leighton, 1989; Dunn & HoltzEakin, 2000; Holtz-Eakin & Rosen, 2001), and founder’s characteristics14 have been identified as relevant variables for explaining new-venture survival.
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Studies on the founder’s effect on venture performance have taken several approaches. Research based on the idea that entrepreneurial status has a permanent nature embedded in individuals has failed to establish an empirical link between individual characteristics and organizational outcomes (Say, 1830; Knight, 1921; Schumpeter, 1934; McClelland, 1961). More fruitful research has been developed using human and social capital theory as the basis to guide empirical research regarding the effects of founder characteristics on performance and more concretely on venture survival. Founder’s education, career history, family background, and status have proven to affect firm survival (Bates, 1990; Bruderl et al., 1992; Gimeno et al., 1997). Similarly, founder’s beliefs about information and the value of resources (Shane, 2000), different structural positions (Aldrich & Zimmer, 1986; Aldrich, 1999;
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However, disjointed findings —even at odds with one another— suggest partial research designs or/and a lack of careful identification of contextual dynamics to explain firm outcomes. Indeed, few studies (Romanelli, 1989; Christensen et al., 1998) have examined these strategic factors at industry inception or tested a model including the effects on firm survival of factors on more than one level of analysis.15 In addition, we argue that unique features of EBB’s contradict certain general assumptions of previous research on new-venture strategic behavior, making them inapplicable to this particular context.
2.2.2.
Theoretical Framework. Firm Survival in an EBB Context
Research on new ventures has often assumed that new firms entering an industry face limited financial and human resources, have no reputation (Venkataraman et al., 1990; Lussier, 1995; Lin et al., 2000), and that against Dunn & Holtz-Eakin, 2000) or different ties, social contacts, and prestige (Larson, 1992; Higgins & Gulati, 1999; Stuart et al., 1999) also explain firm success. 15
Relevant studies follow. For a good review, see Thornhill and Amit (2003). In their longitudinal study on new banks, Bamford et al. (1999) find that initial founding conditions and decisions are significantly related to the growth potential of new ventures. Romanelli (1989) studied the joint effects of environmental conditions and organization characteristics. She explored strategies that an organization uses during its early years to exploit environmental conditions as a predictor of firm survival, indicating that both environmental determinism and strategic choice operate on young firms. Eisenhardt and Schoonhoven (1990) related three levels of analysis to organization growth. In the context of U.S. Semiconductor ventures between 1978 and 1988, characteristics of the founding top-management team, strategy, and environment were related to sales growth of the newly founded firms. Finally, Baum et al. (2001) tested a multilevel model to predict venture growth in the context of the architectural woodworking industry. They found that the CEO’s specific competencies and motivations and the firm’s competitive strategies were direct predictors of venture growth, while the environment and the CEO’s traits and general competencies had a significant indirect effect.
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great odds founders of new ventures must seek opportunities and implement ways of competing in industries usually served by established, often large businesses. However, during a boom and bust period these assumptions do not hold. New ventures configure the new industry (Aldrich, 1999). Opinion leaders actively promote a new industry’s necessary legitimization (Aldrich, 1999; Hitt et al., 2001; Shane & Cable, 2002a; Shane & Stuart, 2002b). Competition is not yet settled, and if incumbents don’t exist in related industries for a period of time the new ventures are the only relevant players. Further, firms are born in a munificent environment. Hence, in the first few years, financial resources are abundant, human resources are easy to attract, and the market is created along with the creation of new firms. Under these circumstances founders may choose different courses of action than when facing resource constraints and competitive pressures. The sudden change in the environment —from munificence to constraints in demand and resources— tests in a poignant manner managers’ ability to sustain their firms’ existence. Previous research on small firms has suggested different strategic orientations for successful firms under benign and hostile environments. Strategy researchers suggest that among other important factors, the entrepreneur’s early strategic decisions help shape the firm’s future development and performance (Sandberg & Hofer, 1987). Further, resource-based
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perspectives on strategic management suggest that resources are the foundation for strategy, and that unique bundles of resources generate the competitive advantages that lead to firm success (Wernerfelt, 1984; Andrews, 1987). Previous research has also shown that resources —and a firm’s initial endowments in particular— influence venture survival. Specifically, it showed that resources and capabilities acquire greater importance when the external environment is in a state of flux (Brush et al., 2001; Makhija, 2003). Combining strategy and entrepreneurship theory with in-depth insights derived from archives and field data, (Glaser et al., 1967), the following section establishes a framework for the study.
2.2.2.1.
Strategies in an Entrepreneurial Boom and Bust
Strategy researchers suggest that among other important factors, the entrepreneur’s early strategic decisions help shape the firm’s future development and performance (Sandberg et al., 1987). More concretely, Abell (1980) suggested that in the initial stages firms’ strategies focus on 1) how strongly to enter the market (rate and type of growth), and 2) how broad a segment of the market to serve (scope). Growth Strategy. Based on archival analysis and personal interviews of company founders, we assume that during the expansion phase of an EBB firm
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growth may be considered a managerial decision.16 Under these circumstances, entrepreneurs must deal with the tension of rapidly reaping the benefits of their new venture’s growth while avoiding its perils. Firms entering a munificent environment —the first condition for what may evolve into an EBB— find a market in need of their services/products and enjoy many resource opportunities. There are fresh ideas, new investors, and motivated employees eager to be part of the next generation of winners. Firms have flexibility in accessing resources and are pressed to expand. Fast-growing firms experience a double effect. First, prospects for firm growth make it easier to obtain commitments from outsiders to organizational goals and priorities (Pfeffer & Salancik, 1978). Specifically, growth facilitates access to capital and financial markets are willing to pay a high premium for the stock of high-growth companies (Gompers & Lerner, 1999; Bhide, 2000). Simultaneously, firm growth increases the chances of attracting and retaining talent by increasing opportunities for employees, providing greater challenge for managers, and satisfying their desires for higher salaries and prestige (Baum et al., 2001). Besides, the challenges of increasing the mobility of labor and capital, and aggressively pursuing high returns, pressure firms to grow fast to secure 16
In the realm of e-consulting, demand was rampant and entrepreneurs had the choice of pacing their growth. “There is a real demand for technical and business-functional expertise in the marketplace and a supply that is not keeping up with that demand” said Knotts, one of Answerthink’s founders (Carter et al., 1994; News, 1997; Bamford et al., 1999). “Internet consultancies are undersized for their enormous market.”(Orenstein, 2000) For example, “One of the battles in our briefings with Wall Street analysts was to convince them about the righteousness of our growth policy. Mel [Bergstein] did not want to grow faster even at the expenses of suffering a market discount.” Interview with Julia Wallace Potter, head of investor relations at Diamond Cluster (March, 2003).
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access to those resources. Entrepreneurs and resource providers know that good opportunities quickly attract competition from other new organizations, as well as older and more resource rich competitors. Thus, the spiral of growing expectations and fear of exclusion from the opportunity push firms to grow rapidly to secure resources and withstand the increase in competition. Although entrepreneurship research has pointed to growth as the crucial indicator of venture success (Low & MacMillan, 1988; Covin & Slevin, 1997), rapid growth can also create many challenges (Hambrick & Crozier, 1985). Young organizations’ processes are not yet robust, and thus the likelihood of experiencing coordination problems increases (Penrose, 1959; Barnett et al., 1994; Covin et al., 1997; Bhide, 2000). These problems may affect both service quality (Cusumano & Yoffie, 1998; Oliva et al., 2002), and employees’ satisfaction (Hambrick et al., 1985). Further, while in an environment of euphoria with easy access to resources, firms may cope with these problems by allowing for process redundancies, overspending on employee benefits, and even client turnover.17 When expectations change and investors and clients reassess 17
Several examples from e-consulting firms during the expansion phase may illustrate the point. “Some high profile clients fired IXL (including, OAG, Mid-West Express Airlines)… but there were so many people waiting for our services that we did not care…” Tom Fishburne. IXL management team, interview (April 2002). “When you’re very successful, you grow very fast, and you’re well respected by your clients; you do tend to try a lot of things even if you know that you are not very good at it,” says Jean-Philippe Maheu, COO of Razorfish.” (Robb, 2001) “As a proactive move to keep morale high and to retain the people who are the future of our firm, on October 16, 1998 we re-priced up to 1.7 million options.” Mel Berstein, Founder of DiamondCluster Investors Conference Q2 F99. “Last February Scient Corp. moved into gleaming new San Francisco headquarters in the prestigious One Market Street office building, overshadowed by the Bay Bridge and a stone's throw away from the city's reconstructed
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their commitments, firms face severe challenges. These new market conditions are more prone to value operational effectiveness over entrepreneurial drive.18 Competition among industry participants gets more intense. Further, if firms have been pursuing high growth, then financing requirements are at their peak, frequently amplified by the increasing cost of resources and higher than expected coordination costs. Consequently, organizations with limited experience must deal with the pressures of both adjusting to the rush of expansion and running the firm under especially adverse conditions. In summary, during the expansion phase entrepreneurs are pushed to pursue rapid growth to secure resources and to gain market share while assuring coordination to deliver products/services. During the contraction phase, they must focus on efficiency in a down market. Simultaneously, managers determine the path toward growth. In the context of an entrepreneurial boom, high munificence, pressures to grow, and attempts to preempt scarce assets to build unique combinations may induce new ventures to pursue different strategies to shorten the process of achieving scale
waterfront. The building, the landmark former headquarters of the once mighty but now extinct Southern Pacific Railroad, had undergone a year-long total renovation and seismic retrofitting to get it ready for the arrival of its information economy tenants. On moving day, Scient executives welcomed employees with balloons, streamers, confetti, noisemakers and a catered waffle breakfast.” (Pallatto, 2001). 18
“The pressure to meet the numbers was incredible. Monthly, we were receiving information from the controller asking us to pressure our account managers. We literally spent days in meetings only discussing how to meet the numbers. At the same time, our engineers were redoing projects that did not meet client’s expectations…” Tom Fishburne. IXL management team, interview (April 2002).
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and scope and building capabilities. For instance, Penrose (1959) suggested, and empirical studies agreed (Levie, 1997), that organic growth is typically associated with smaller, younger firms in emerging industries, whereas acquisition growth is more likely in older and larger firms in mature industries.19 However, under the EBB circumstances, we expect to find variation among new ventures’ growth paths. Scope Strategy. Two key strategic dimensions define the scope of a new venture. First, the decision on local and/or international expansion and second, the scope of product/services offered. Firms in their initial stages must decide which geographic markets to serve. New ventures during a boom period might have several reasons to rapidly expand to international locations. First, international expansion is a source of rapid growth (Feeser & Willard, 1990; Zahra et al., 2000), thus an alternative or complementary path to achieve results when firms are under growth pressures. Second, anecdotal evidence suggests that the race for capturing and retaining resources forces new ventures to include early in their portfolio big, well-known clients to signal quality and quickly acquire visibility and reputation.20 Those clients may force new
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In fact, prior empirical research on acquisitive strategies pursuing diverse goals such as the entry into a new market (Caves & Mehra, 1986), firm growth (Amit et al., 1989; Hitt et al., 1991; Bruton et al., 1994; Hitt et al., 1998), firm knowledge (Vermeulen & Barkema, 2001), and corporate restructuring and renewal (Johnson, 1996) center the attention on established firms. 20
“Bergstein founded Diamond in 1994. From the start, Bergstein searched for a way to distinguish Diamond from rivals in the consulting arena. Bergstein decided that the nation’s largest companies needed advice on developing strategies to succeed in the digital age. By 1998, he had pinned Diamond’s approach on helping companies develop “killer apps”— applications and businesses like e-mail and online exchanges that redefine a market entirely.
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ventures to become international from inception to serve them. Finally, previous empirical research has shown that organizational routines and capabilities that create competitive advantages in the domestic arena are not the same as those that create competitive advantages internationally (Ghoshal, 1987; Barkema & Vermeulen, 1998). Insightful entrepreneurs, taking into account the accelerated industry maturation process to which new firms in the boom period are subject, and the well-known difficulties in changing organizational routines (Nelson & Winter, 1982; Hannan & Freeman, 1984), may try to position their firms from inception on the international path to quickly develop routines for coordinating resources located in different nations, for targeting customers in multiple geographic locations simultaneously, and for managing multicultural workforces. Further, although there are costs associated with new ventures internationalization,21 international firms may be better positioned to access resources and grow out if
Since then, Diamond has become a go-to company for businesses transforming themselves into players in the e-world. Before the auto industry’s Big Three —Ford, General Motors, and Daimler Chrysler— announced their business-to-business online exchange earlier this year, they called in Diamond for advice on how best to define and coordinate the technology architecture. Other clients come from the energy, financial services, and health-care fields, and include names such as Goldman-Sachs, First Data, Sears, and buyout specialist Clayton, Dubilier, & Rice.” (BusinessWeek, 2000). “Viant's clients include J.P. Morgan, Kinko's, Ralph Lauren, American Express, the Tandem Computers division of Compaq, General Motors, Deutsche Bank, and Standard & Poor's.” (Grove, 1998) 21
International expanded new ventures may suffer from increased coordination problems (Brush, 1995), increased logistical costs, and the costs of acquiring knowledge of foreign markets and institutions (Autio et al., 2000).
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the demand falls in selective markets, and as a consequence they may be better positioned to sustain overall success.22 Finally, new ventures must define the scope of their product/service strategy. It has been documented that successful entrepreneurs may use specialist strategies to enter a new industry, including decreased expenditure and controlled competition (Romanelli, 1989; Bhide, 2000). However, this prescription may not hold in a nascent industry where resources are abundant and competing forces are not established yet. Customer demands in emerging markets are not settled; they evolve when more information about the product or service is available, suggesting that firms configure their portfolios and adapt them as clients know better what to expect from the product/service (Porter, 1980; Aldrich, 1999). Therefore, as client needs evolve, firms must be able to adapt to changes in demand.23
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“Technology continues to disrupt business; however the macro-economic environment in North America has significantly dampened the demand for our services. We believe this is purely an economic issue. The weakness is focused on North America. In Europe and Latin America, our demand remains strong. This exceptional growth in Europe and Latin America will partially offset the shortfall in North America. We will continue developing skills and credentials to be poised to take advantage of the wireless revolution when it does reach North America—which it will do.” (Briefing to Analyst Diamond Cluster, March 5th 2001.) 23
“Today building pretty Websites is not where the action is. ‘No longer is relatively simple front-end Web development the project du jour’, says Pooneh Fooladi, an analyst with IDC’s Internet Services research program. Instead, businesses are creating complex ecommerce sites that can handle lots of transactions. ‘The increased requirements of these projects are causing more companies to seek the assistance of outside service providers,’ says Fooladi.” (Fortune, 2000) “We’re moving from pure information to transacted content, and dynamic trade and real-time links to legacy systems become key.” (Pinault, 2000)
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2.2.2.2.
Initial Endowments and Firm Resources in an Entrepreneurial Boom and Bust
Previous research has shown that resources —and a firm’s initial endowments in particular— influences venture survival. Specifically, it shows that resources and capabilities acquire a greater importance when the external environment is in a state of flux (Brush et al., 2001; Makhija, 2003). Relevant for an EBB context are founder and organizational human capital and the firm’s legitimacy.24 In the context of an EBB, characterized by high uncertainty in product and market configurations paired with rapid venture development, resource gathering and organizational configuration do not allow for trial-and-error learning processes.25 The founder’s skills in gathering the right resources —leading to the firm’s ability to spot market changes and adapt their offering— together with quickly developing the firm’s reputation, that is, facilitating external interactions to gather resources and clients (Singh et al., 1986), increase the odds of achieving a competitive position (Pfeffer et al., 1978). Human Capital. At a firm’s inception, resource gathering and capability development depend on the founding team (Drucker, 1985). New ventures do not 24
“Legitimacy is a generalized perception or assumption that the actions of an entity are desirable, proper, or appropriate within some socially constructed system of norms, values, beliefs, and definitions. […] Sociopolitical legitimacy refers to the acceptance by key stakeholders, the general public, key opinion leaders, and government officials of a new venture as appropriate or right” (Aldrich, 1999) pp.229-230. 25
The most common learning process followed by successful entrepreneurs is “learning by doing” (Bhide, 2000).
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yet have a legacy of resource strengths; it must be built. Constructing an initial resource base in a new venture requires that resources be identified and assembled to meet a perceived opportunity, before they are allocated to fit a product market strategy (Penrose, 1959; Brush et al., 2001). Each entrepreneur (or team of entrepreneurs) starts with a personal resource endowment, which guides the ensemble of the first key resources to build a resource platform that will yield distinctive capabilities (Boeker, 1989; Brush et al., 2001). Founders transfer their individual strengths to organizational capabilities that can lead to a unique advantage (Brush et al., 2001). Previous literature has shown that different forms of a founder’s human and social capital increase the likelihood of obtaining financial resources.26 Similarly, other founding team characteristics —such as industry-specific knowhow (Cooper et al., 1994)27, their own beliefs, biases and past practices (Boeker, 1989)28, management skills (Castanias & Helfalt, 1991; Coff, 1997; Teece et al.,
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For instance, Bates (1990) found that higher levels of human capital, such as educational attainment, are correlated with the munificence of received financial resources. Fried and Hisrich (1994; Aldrich, 1999) suggest that VCs tend to fund entrepreneurs they learn about through referrals from entrepreneurs in their portfolio companies, fellow VCs, and close friends and family. Similarly, Shane and Stuart (2002b) and Shane and Cable (2002a) find that entrepreneurs with pre-existing or indirect ties with a venture capitalist have a higher likelihood of receiving venture funding in the early stages. 27
Cooper, Gimeno-Gascon and Woo (1994) noted that “providing a tacit understanding of the key success factors in an industry, specialized knowledge of the product or technologies, or accumulated good will with customers and / or suppliers” will increase firm survival (Cooper et al., 1994) pp. 374-5. 28
For example, Boeker (1989) found that entrepreneurial characteristics influence the relative importance of various functional areas in the firm.
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1997; Holbrook et al., 2000; Castanias & Helfalt, 2001)29, or size of the team (Eisenhardt et al., 1990)— have been found relevant to the development of the venture. Similarly unique knowledge —of intangible resources that are valuable, rare, and inimitable (Barney, 1991)—is also likely to generate competitive advantage (Pfeffer et al., 1978). In new ventures, a firm’s knowledge resides mainly in its human capital,30 with special relevance given to its importance in specialized human resources such as technical staff (Hellmann & Puri, 2002). This suggests that firms with more valuable knowledge embedded in their human resources may have a better competitive position. Legitimacy. New ventures pioneering the inception of an industry lack the necessary legitimacy to develop. These organizations must establish ties with an environment that might not understand the existence of such a new form (Aldrich, 1999). Inadequate track records increase the transactional risks that a firm’s reputation helps mitigate (Stinchcombe, 1965; Singh et al., 1986). Indeed, researchers have identified legitimacy as a valuable asset (Fombrun, 1996; Shapiro & Varian, 1999). Therefore, for highly uncertain environments —such as
29
For example, Holbrook et al. (2000) showed that the success or failure of new U.S. semiconductor firms could be traced in part to the prior managerial experience and knowledge of company founders, which influenced the strategic choices they made. Specifically, formal managerial training may give founders greater insights in identifying market signs, and higher flexibility in solving problems in the context of a highly uncertain and turbulent environment. 30
It may be argued that firm knowledge is also embedded in a firm’s systems and routines. New ventures are usually in the process of transforming individual knowledge into explicit systems.
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EBB episodes— legitimating strategies play a vital role in speeding up industry development as well as firm differentiation (Aldrich, 1979).
In summary, we build a model to explore firm survival in the context of an Entrepreneurial Boom and Bust episode which contains four main constructs in two categories: strategies —growth and scope; resources —human capital, and firm legitimacy.
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CHAPTER 3: ENTRY STRATEGY GROUPS IN A BOOM AND BUST PERIOD: THE CASE OF THE E-CONSULTING INDUSTRY
This chapter develops and empirically tests a theoretical framework that identifies new-venture strategies that are robust to a severe change in environmental conditions. Specifically, it develops a typology of entry-strategy options among new ventures in an emerging industry that experiences an Entrepreneurial Boom and Bust period. An additional analysis shows the effect on firm survival of strategic options during the expansion and decline phases, controlled by initial endowments and organizational resources. Overall the chapter suggests that some strategic options are more flexible than others in the context of a firm's ability to adapt to the sudden environmental change and therefore survive.
Pioneering ventures in emerging industries face unique challenges in choosing a course of action, gathering the right resources, and dealing with uncertain environments. Yet previous research on entry strategies failed to validate successful entry strategies (Teplensky, 1993). Further, no studies have explored strategic patterns among new ventures in the context of EBB. However, EBB unique features contradict certain general assumptions of
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previous research on new-venture strategic behavior, making them inapplicable to this particular context, therefore presenting an opportunity for new research. This chapter proposes a typology of entry strategies based on growth and scope of entry and postulates that differences in strategic behavior reflect differences in resource endowments—specifically, firm knowledge and skills, founder’s background, and financial resources— and that these differences are likely to be systematically associated with differences in performance. Specifically, we answer the following research questions: Do different competitive strategies exist among new ventures in the same industry at industry inception? If so, what is the nature of each archetype of competitive strategy? What resources are associated with each archetype? Do archetypes differ in their impact on firm performance? And finally, how do resource differences affect strategic choices and firm performance? The rapid rise and sudden decline of the e-consulting industry in the United States well exemplify the varied strategies and performance of firms as an industry expanded quickly and then contracted sharply. The rest of the chapter is organized as follows. Section 3.1 presents relevant literature on entry strategies and strategic groups, and section 3.2 builds the theoretical framework to explore the existence of strategic groups. Section 3.3 describes the setting, sample selection, data collection, variables, and methods. Results are presented in section 3.4. Section 3.5 discusses the
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results from the cluster analysis, and section 3.6 extends the analysis to the complete EBB cycle.
3.1.
RELEVANT LITERATURE
3.1.1.
Entry Strategies
Previous research on entry strategies has focused on 1) established firms entering new markets, and 2) new ventures entering established markets. In the first domain, three main research questions have been explored: first, the risks and benefits associated with market entry (Porter, 1980; Tushman & Anderson, 1986; Lieberman & Montgomery, 1988; Mitchell, 1989; Mitchell, 1991; Shamsie et al., 2004); second, the motives for entry at various times and under a variety of conditions (Teece, 1987; Mitchell, 1989; Tegarden et al., 1999; Robinson & Chiang, 2002); and finally, the dimensions of entry strategies such as productline breadth and market coverage (Lambkin, 1988). Research on entry strategies of entrepreneurial ventures into established markets has mainly explored the performance consequences of a onedimensional view of strategy— that is, niche versus aggressive coverage— with contrasting results attributable to sample biases, missing variables, and differing definitions of construct (see McDougall and Robinson (1990)] for a good review). Later contributions improved those designs (Sandberg & Hofer, 1987) —although
35
still using a small sample—beginning to understand the conditions under which a niche strategy worked better than an aggressive one. 9 Finally, McDougall and Robinson (1990) advanced this line of research by identifying 9 patterns of multidimensional strategic behavior identified across 247 new ventures entering a single industry and clustered them in eight different strategic groups. But the study did not test performance consequences of the identified clusters. Few empirical studies have focused on entry strategies of an emergent industry where for a period of time new ventures are the only relevant players and competition is not yet settled (Romanelli, 1989; Teplensky et al., 1993).31 Further, new-venture strategic behavior in the context of an EBB has not yet been explored. As outlined in chapter 3, its unique features demand special attention given that some general assumptions of previous research on newventure strategic-entry behavior are inapplicable to this particular context. Under such circumstances founders may choose strategies different from those that they use when confronted with resource constraints and competitive pressures. In addition, the sudden change in expectations and resource availability tests the endurance of the strategic options chosen during the expansion period. Hence, 31
In a study of manufacturers of MRI at industry inception, Teplensky et al. (1993) identified a typology of firms based on timing and scope of a firm’s entry into the market, strategic adjustments over time, and the impact of these decisions on the firm’s performance. Although they proposed a testable hypothesis based on this typology, their sample was too small for them to test the hypothesis. Romanelli (1989) studied the joint effects of environmental conditions and organization characteristics. She explored strategies that an organization uses during its early years to exploit environmental conditions, as a predictor of the organization’s rate of survival, and concluded that both environmental determinism and strategic choice influence young firms.
36
to explore entry strategies in this particular setting, a different framework is necessary.
3.1.2.
Strategic Groups
Strategic group theory is a popular tool for analyzing the competitive structure of industries (Hunt, 1972; Porter, 1980; McGee & Thomas, 1986).32 It hypothesizes that firms are homogeneous within strategic groups and heterogeneous between groups and hence firm performance depends —at least partly—on which group a firm is in. Those who research new-venture strategy recommend identifying strategy groups to specify the conditions that contribute to the success of one strategy over others and, it is hoped, ways to move the field of entrepreneurship forward with greater clarity in identifying and discussing newventure strategies (Hambrick, 1980; Dess & Davis, 1984; Robinson & Pearce II, 1988; McDougall et al., 1990).33 Specifically, we propose that to explore strategy groups among entry strategies of firms developing a new segment or industry in a highly munificent environment that suddenly dissipates helps strategy researchers identify more robust new-venture strategies —that is, the pattern of actions that best resists adverse environmental conditions.
32
Initially developed by Hunt (1972) in his doctoral dissertation, the concept of strategic groups has become one of the dominant areas of empirical research in the strategic management literature. See Barney (1990) for a good review. 33
While several archetypes of start-up strategy have been developed (e.g., (McDougall et al., 1990; Carter et al., 1994), the debate concerning type of strategy and survival seems unresolved (e.g., Romanelli, 1989 vs. Feeser and Willard, 1990).
37
However, an unabated stream of empirical research has reported mixed findings in attempting to prove that strategic groups exist at all (Cool & Schendel, 1987) and, if they do exist, in assessing their relation to venture survival (McDougall et al., 1990; Carter et al., 1994). This provokes skeptics to question the value of the whole research program (Barney et al., 1990). In response to these criticisms, we develop a theoretical framework for both choosing which strategic variables to include in the analysis of entry strategies in an EBB environment, and for judging the quality of the results of this analysis.
3.2.
CONCEPTUAL FRAMEWORK To build a conceptual framework to identify and validate strategic groups,
we draw from strategy and entrepreneurship literature —especially from the Resource-Based View of the firm— in saying that strategies are a firm’s patterns of actions supported by its resources. Specifically, in the context of firms creating a new segment or industry, entry strategies are the new venture’s pattern of decisions supported by its initial endowments. We argue that entrystrategy groups are explained by initial new-venture endowments and that both predict strategic-group performance. We build a framework in three steps. First, we explore the strategic groups based on the relevant dimensions for the special context of an EBB developed in chapter 2. Thus, we define variables to capture a firm’s growth
38
strategy and scope to explore the existence of a typology of strategies among firms entering the industry. Second, if strategic groups exist in entering an EBB, we should be able to find a relationship between strategic groups and the initial endowments of group participants as defined in chapter 2. This could be interpreted as testing the external validity of the clusters, since if the clusters do not differ in terms of variables not used in the cluster analysis, they are unlikely to represent distinct empirical categories (Barney et al., 1990). Finally, we claim that these resources in conjunction with entry strategies will lead to the variation of strategic-group performance. Previous literature shows that initial endowments and strategy affect new-venture performance (Romanelli, 1989; Eisenhardt et al., 1990). The assertion that strategy-group membership affects—to some extent—the performance of individual firms depends on the existence of mobility barriers (Barney et al., 1990). These barriers are structural attributes of a strategic group that make it very costly for firms not already in that group to move into it (Caves & Porter, 1977). For new ventures, we argue, those attributes are their initial endowments that are the base from which the newly created firm supports its initial strategy. Although it may be contend that new resources may be acquired changing those mobility barriers, the brevity of an EBB allows us to state without concern that initial endowments serve as mobility barriers among strategic groups for the expansion phase of the EBB. Hence, if strategic groups exist, we should find different
39
resource endowments for each strategic group, and consequently differences in strategic-group performance. To identify whether a typology of strategic groups at industry inception exists, we apply the developed framework to the e-consulting industry Entrepreneurial Boom and Bust episode.
3.3.
SETTING, SAMPLE, DATA COLLECTION, VARIABLES, AND METHODS
3.3.1.
The E-Consulting Industry
To empirically explore our framework, we chose the e-consulting industry for several reasons. First, the e-consulting industry shifted from dramatic growth to sudden collapse in a short period (1999–2000). Resources allocated to develop the new industry, service-demand expectations, and the population of firms together went through a rapid cycle of boom and bust, providing an opportunity to focus on a particularly intense environmental change. Second, this industry is of special interest because it isolates strategic decisions from technological choices.34 Finally, the e-consulting industry is an attractive setting for the study of the boom and bust phenomenon because not much research has 34
Previous research has shown that technology choices may affect a firm’s strategy and survival. For instance, Suarez and Utterback (1995) show that firms that adopt the dominant design —“a specific path, along an industry’s design hierarchy, which establishes dominance among competing design paths” pp. 416— were more likely to survive.
40
been conducted on entrepreneurial professional service firms. For a detailed description of the e-consulting industry, its inception and evolution, its services and competition dynamics see Appendix 1. A new consultant segment was born in the mid-1990s as firms began seeking management advice as to how to capitalize on the new business opportunities made possible by the proliferation of the Internet. This segment consisted of the services that consulting firms provided to help conceive and launch e-commerce business models and to integrate them, if necessary, with existing businesses. Practitioners argued that the Internet was “too different,” requiring a mix of skills, business models, and advice that was distinct from those available from traditional consulting firms.35 The business opportunity led to the birth of numerous firms ranging from “pure play” firms to system integrators that engaged in consulting with firms on their Internet strategy.36
35
“The people who understand the Web are in the boutiques. The people who provide high-end strategy don’t understand the technology well enough yet” (Fisher, 1999b). “Like many other industries, the consulting industry is experiencing a radical change in the competitive landscape as a result of the Internet. As the focus and urgency of theses Internet-related solutions expand, we think the traditional consulting skill sets and delivery model clearly must change” (Morgan Stanley Dean Witter, Internet Consulting and Application Services , April 2000). 36
“Clients are looking for solutions to their problems rather than purchasing any particular service. Not only do consultancies need the ability to provide advice on a wide range of subject areas, they also have to integrate their advice with implementation and often maintenance and support services. To be able to integrate multiple services is critical because, by its very nature, a solution must consist of services that are tightly aligned and connected with each other” (Hedin, 2000). “By the 1990s, IT had become a significant part of most business operations, and was starting to be used for more strategic purposes. In response to these developments, we created or acquired complementary business units in an attempt to provide a single source for all consulting needs of a business” (S1 Diamond Technology Partners, 1996). We believe successful IT services firms should be able to understand business processes and implement the technology that increases operating performance. This requires integrating large, sophisticated, and sometimes previously separate, technologies. Firms must have strong skills in business
41
Several e-consulting companies were born and many of them went public in a bull market. In 1999 Kennedy Information Group expected the global market for e-consulting to reach $37.5 billion by 2003; Forrester Research estimated that the U.S. market alone would reach $47.7 billion in 2002; International Data Corporation estimated that within 4 years the worldwide market for e-consulting would grow 753 percent versus an increase of 83 percent in the traditional systems integration market (Kennedy Information Research Group, 2000). Encouraged by such predictions, although before 1991 few firms— Cambridge Technology Partners (CTP) was among the first e-consulting firms— fit what would later become the e-consulting business model, hundreds of firms were founded in that space.37 By January 2000 more than 260 companies were considered e-consultants; of these, more than 100 had gone public by 1996, and in 1999 their combined revenues accounted for 60 percent of the total market for e-consulting engagements (Kennedy Information Research Group, 2000).38 However, from mid-March to late May 2000, the high technology sector experienced a substantial decline in market value, with the NASDAQ dropping 34.7 percent and the Internet Stock Index (ISDEX) falling 55.3 percent. The stock prices of most of the newly public e-consulting firms declined at the same consulting, package implementation, technology, and interactive integration and must know how to help clients build strong business cases for their initiatives (S1 Answerthink, 1998). 37
BBB Technologies is the firm that pioneered the Internet. Since implementing and operating the ARPANET (1969), the forerunner of today's Internet, they were also responsible for a number of networking firsts: the first packet switch, the first router (1977), and the first personto-person network email (1971). 38
This estimate varies depending on the definition of an e-consulting firm. See Section 3.3.2 for the criteria used in this study to identify e-consulting firms.
42
time as the correction in tech stocks pushed several e-consultancies into bankruptcy. Others watched their stocks trade below $1 for weeks, leading to their delisting from NASDAQ. Yet others were acquired. By December 2001, more than 60 percent of the new e-consulting ventures had disappeared. The expansion phase of the e-consulting EBB extended from the first quarter of 1998 (when venture capital firms invested in the industry at an increasing rate, and service demand grew at double digits per quarter) to the collapse of NASDAQ in April 14, 2000. This triggered the reallocation of funds to different opportunities as well as the fall in service demand. The decline phase followed. According to Lehman Brothers Global Equity research (April 9, 2002), the last quarter of 2001 marked the bottom of the market and the end of the bust period for the e-consulting industry.
3.3.2.
Sample
Industry analysts differ in their definitions of the e-consulting industry. Further, traditional classifications are not informative for nascent industries such as e-consulting. We define the e-consulting industry as firms that help conceive and launch an e-commerce business model and integrate it, if necessary, with an existing business. An e-consulting firm is a company whose main source of revenue in June 1999 was consulting services including branding, information technology, and/or strategy oriented to the Internet.
43
To identify the industry participants, we used the publications of 12 different industry experts (Jupiter, KIRG, Fortune, InfoTrends, Varbusiness, Upside, Red Herring, Stephens Analysts, Forrester Inc., Morgan Stanley, Lehman Brothers, The Industry Standard) over four years (1998, 1999, 2000, and 2001) to build a list of 269 firms that were cited at least once as e-consulting firms.39 After narrowing this set by selecting firms that were cited as e-consulting firms by two or more sources or for two or more years, we visited web-sites and examined SEC documents of the firms that were cited twice or less to ascertain whether their main activity in June 1999 was e-consulting. Analysts from three investment banks following the e-consulting market and five industry executives were asked to review the selected set of companies to check for missing firms or inappropriate entries. This selection process produced a set of 209 firms. Interviews with industry experts suggested that the dynamics of evolution among public firms differed from those of private firms.40 As a result, we centered the analysis on public firms, reducing the previous set to 113 firms. During the period when we identified industry participants, both new venture and 39
One of the challenges of an emerging industry is to define its boundaries. To identify industry participants we follow previous authors’ methodology. For instance, see Pisano (1990) for his sampling on the biotechnology industry in the 1980s. 40
Although there has been no systematic research into their differences, we decided to confine our study to one type of firm. Publicly traded firms have the advantage of data accessibility. Including only public companies in our sample biases the study toward successful firms with high growth prospects. According to a survey done by The Industry Standard, the rate of failure among private firms in July 2000 was 44 percent (Helft, 2001). At that point, the rate of failure for public firms was only 16 percent. Therefore we believe that the test is stronger. Our study is based on firms that a priori had conditions to succeed. We believe that implications of our results for a firm’s strategy and resource management in the context of an EBB would not change.
44
incumbent firms entered the market.41 Following the literature, we decided to focus our attention only on new ventures. Incumbents and newcomers differ in a number of critical dimensions in the evaluation of performance at industry inception (resources, organizational routines, brand awareness, etc.). Mitchell (1991) argues that there are two relevant time scales in market entry problems, one for incumbents and another for firms that are new to a specific subfield. Further, firms need to be compared to the relevant cohort. In our industry, incumbents entered on average later in time when some of the uncertainties were resolved. We excluded them from the sample. Thus we included only firms born after 1990. This reduction led us to the final population of the sample for our study, comprising 104 firms. See Table 3.1 for a summary on sample selection. See Appendix 2 for a list of firms, founding data, and ownership status as of December 2001.
TABLE 3.1 E-Consulting Sample Selection Selection Criteria - Firms identified as e-consulting firms from 12 pronouncements of industry experts (over four years) - Less: firms that were cited only twice or once (39) - Less: firms where e-consulting not main business (21) - Less: private firms and firms born before 1990 (105) Final Sample 41
Number of Firms 269 230 209 104 104
A firm is defined as a “new” venture if it was eight years old or less at the beginning of the period considered in this study (1998) (Biggadyke, 1979; Miller & Camp, 1985; McDougall et al., 1990).
45
3.3.3.
Data Collection
Multiple data collection methods were used (Eisenhardt, 1989). To explore the effects of strategies, resources, and initial endowments on firm performance, quarterly data on the founder, organization characteristics, and firm demographics were collected for the 104 companies from birth (founding conditions) through December 2001 (or until the firm ceased to exist as an independent entity).42 The founding date corresponds to the initiation of formal operations and the establishment of a regular location of business.43 Data sources included SEC filings, S-1, 10-K, and 10-Q forms, firm web sites, firm press releases, analysts’ and industry reports by brokerage and consulting firms (Investext-Thompson Financial), press articles (Fortune, The Economist, The Wall Street Journal), specialized press (Consulting News, Red Herring, The Standard, VARBusiness) and market-research reviews (Kennedy Information Research Group, IDC), as well as Compustat, Datastream, OneSource, IBES, and Hoover’s. After collecting archive data, founders of 7 firms, 18 managers and board members from different e-consulting firms, and 3 equity analysts were
42
Quarterly data are necessary to capture sharp changes in a small time window. Imputation-missing data methods are used to complete some data series. For instance, an autoregressive moving average model (Greene, 2000) was applied to forecast for some missing quarterly employee data (in 11 firms we had yearly data in 2 out of 5 years). 43
Several firms changed their name during the time of the study. We considered the date of founding the time of the first organization. If the first organization was founded before 1990, the firm was discarded from the sample.
46
interviewed.44 Interviews had an open-ended question format and lasted for 90 minutes approximately.
3.3.4.
Variables and their Proxies
This section describes the strategic variables, initial endowments, and firm performance variables. Table 3.2 presents the definition for the complete set of variables.
44
Founders Ted Fernandez (Answerthink), Mel Berstein (Diamondcluster), John Donovan (i-Cube, Primix, Rare Medium), Ralph Armijo (Navidec), ), John M. Connolly (Mainspring – IBM); Managers Tom Fishburne. (IXL), Andrew P. Klump (IXL), Thomas Davenport (Accenture), John J. Sviokla (Diamondcluster), Julia W. Potter (Diamondcluster), Anita McGahan (Digitas), Loren D. Weinberg (Mainspring), Glenn Meyers (RareMedium), Steven J. Hoffman (Sapient), Christopher M. Format (Scient), and Ingrid B. Bekkers (Scient), among others.
47
TABLE 3.2 Variables Used in the E-Consulting Strategic Group Analysis Class
Variable
a
Definition
Type
Source
Firm Performance Outcome
Market to Book
Good outcome (1) if firm was independent in 2001 or was acquired in Dummy Company press good conditions for stakeholders; 0 if the firm was acquired on releases, Factiva distress or went bankrupt Market value to book value at the end of quarter t Continuous Compustat
Strategies Growth
Rate of Growth
Yearly growth in employees in quarter t
Continuous S-1, 10K, 10Q
Type of Growth
Number of firms acquired since quarter t -4
Continuous S-1, 10K, 10Q, SDC
International Scope
Scope
Service Scope
Number of countries firm i had offices in quarter t divided by sales
Continuous S-1, 10K, 10Q
Range of services offered: 1-pure econsulting, 2-econsulting software, 3-econsulting+outsourcing
Categorical S-1, 10K, 10Q, Analyst reports (Investext), One Source
Any of the founders has previous experience on the information technology industry : 0-no, 1-yes Any of the founders have founded other firms before: 0-no, 1-yes
Dummy
Venture Capital backed firm: 0 non VC-backed, 1 VC-backed
Dummy
Cash and all securities readily transferable to cash at the end of quarter t divided by number of employees in quarter t Percentage of technical employees at the end of quarter t
Continuous Compustat
Initial Endowments Industry Knowledge Founding team Entrepreneurial Experience Venture Capital Financial Cash& Equivalents per employee Organizational
Technological Focus
Organization's Experience Scale Macroeconomic Performance Cycle phase Year a
Dummy
OneSource, S-1, Hoover's, firm's website OneSource, S-1, Hoover's, firm's website Thompson Financial
Number of quarters from founding to quarter t
Continuous S-1, 10K, 10Q, Analyst reports (Investext) Continuous S-1
Quarterly number of employees at the end of quarter t
Continuous S-1, 10K, 10Q
Take value of 1 for the expansion phase of the industry; the contraction phase is 0 1998 1999 2000 2001
Dummy Categorical
Quarterly data have been collected for 104 e-consulting firms.
3.3.4.1.
Strategic variables. We define two constructs—growth and
scope. Growth strategy is defined by two variables: the rate at which the firm enters the industry during the expansion period, and the way it does it. Firms may grow organically and by acquisitions. Penrose (1959) suggested, and empirical studies agreed, (Levie, 1997) that organic growth is typically associated with smaller, younger firms in emerging industries, whereas acquisition growth is more likely in older and larger firms in mature industries. However, in the context of an entrepreneurial boom, high munificence, pressures to grow, and attempts
48
to preempt scarce assets to build unique combinations may induce new ventures to also pursue acquisition strategies to shorten the process of achieving scale and scope, and building capabilities. The Rate of Growth variable is defined as the percentage change in number of employees over the same quarter one year earlier.45 Type of Growth variable is the number of acquisitions made by a firm in the previous year. Two variables define the construct of scope. International Scope is defined as the number of countries in which the firm has offices in one phase divided by the average number of employees in that period. Service Scope is a categorical variable that takes the value of 1 for firms that offer a service focused exclusively on e-consulting services, 2 if the firm combines those services with the development of some proprietary software, and 3 if, in addition, the firm adds outsourcing services. See Appendix 1 section A1.3 for a detailed description of the e-consulting services. Classification is based on revenue information and on the firm’s written description of its business model at the pick of the sample window, that is, at the end of the expansion period.46 3.3.4.2.
Resource variables. We use three constructs to classify
firm resources as founders’ background, organizational resources, and financial resources.
45
Sales and employment measures are the most widely used in empirical growth research (Delmar, 1997). In a human-capital–intensive industry, employee growth better captures the nature of the industry. 46
When classifications are ambiguous, we conduct sensitivity analyses. These analyses indicate that our results are not sensitive to alternative interpretations of the classifications.
49
To assess a founder’s contribution to the firm, we define two variables. Industry Knowledge as a dummy taking value 1 if any of the founders had previous experience in firms related to the information technology industry; similarly, Entrepreneurial Experience is defined as 0 if the firm is the first that the founder has started, and 1 otherwise. Organizational resources are defined by three variables. Technological Focus is the percentage of information technology consultants and technical employees of the firm i had in the expansion or decline period. Scale is the average number of employees for firm i in a phase. Organization’s Experience is firm age defined as number of quarters since founding to quarter t (Carroll et al., 1982; Freeman et al., 1983). Finally, we defined two variables representing economic endowments. Venture Capital is a dummy variable that takes value 1 if the firm received venture capital and 0 otherwise; and Cash and Equivalents per Employee is defined as cash and all securities readily transferable to cash for firm i in quarter t divided by the number of employees in quarter t.47 3.3.4.3.
Measuring firm performance. To answer our research
question, we must measure performance at the end of the EBB as well as at the end of the expansion period. To that purpose we define two variables to measure firm performance: firm survival and Tobin’s q. Business survival is especially relevant in dynamic industry settings, in which entrepreneurs must overcome initial crisis before reaching stability. Long 47
Demers and Lev (2001) provide some support for the importance of cash availability as predictor of survival for Internet firms, particularly after the Spring 2000 downturn.
50
recognized as an important indicator of commercial performance, it complements financial measures of performance, consistent with the recognition that managers must consider many criteria when evaluating the long-term potential of the businesses. Further, business survival is relevant to the entrepreneur and employees who must consider the stability of their jobs and to economic policymakers who must be concerned with the long-run ability of firms to provide jobs and taxes (Birch, 1979). Different definitions of business failure lead to different results (Watson & Everett, 1996).48 Further, Freeman et al. (1983) emphasize the importance of distinguishing between failure and acquisition of successful firms.49 We therefore, define a firm as having survived if it remained an independent organization or was acquired under good conditions for stakeholders—that is, if at the time of the acquisition, the target firm 1) had higher market value than the valuation achieved the quarter after the IPO and 2) did not downsize in the two quarters previous to the acquisition.50 Otherwise, the acquisition is considered a
48
Previous research in entrepreneurship has used four alternative definitions of failure: (1) discontinuance of the business for any reason (Cochran, 1981; Berryman, 1983); (2) bankruptcy and a resulting loss to creditors (Dun and Bradstreet); (3) disposal of the business to prevent further losses (Ulmer & Nielsen, 1947); and (4) failing to make a go of it (Cochran, 1981). 49
To code acquisitions as a failure skews results because many acquisitions are pursued for strategic purposes. Furthermore, at industry inception business models are largely undefined, with bases for competition unsettled and probably changing, making an acquisition a positive outcome for the future of the organization. 50
This proxy fits the purpose of this research because we want to distinguish factors that lead to potential long-term value from those that lead to distress or failure. Interviews with econsulting equity analysts reveled that companies that were forced to layoff professionals during the bull market had fundamental flows in their strategy and/or structure. This was reflected on their market valuation. For acquisitions during the decline phase, an individual account of them shows that acquired firms sold as a unique viable solution before going bankrupt. We
51
failure. Following this definition, among the twenty acquisitions that occur in this period, five were considered a success and fifteen a failure. Hence, the Outcome variable is a dummy variable that takes the value of 1 if the company had a good outcome, and 0 if it was acquired in distress or went bankrupt. Finally, to get a performance measure at the end of the expansion period, we follow Montgomery and Wernerfelt (1988) and use Tobin’s q as a measure of rents.51 We define Market to book as Tobin’s q for firm i for the period of expansion (decline).
3.3.5.
Methodology
We use cluster analysis procedures to develop a taxonomy of entry strategy patterns. We cluster the 104 e-consulting firms by the variables Rate of Growth, Type of Growth, International Scope, and Service Scope during the phase of expansion. Hence, we seek to group observations into clusters such that each cluster is as homogeneous as possible with respect to firm strategies followed during the time of expansion (from January 1998 to second quarter of
acknowledge that some of the survivors had lower market valuations in December 2001 than the quarter after the IPO. However, while the firm is alive, potential for long-term value creation exists. 51
“By combining capital market data with accounting data, q implicitly uses the correct risk-adjusted discount rate, imputes equilibrium returns, and minimizes distortions due to tax laws and accounting conventions.” (Montgomery et al., 1988) pp. 627. Here q is defined as the market value of equity divided by the book value.
52
2000).52 We select the Euclidean distance as a similarity measure and the average-linkage method to obtain a hierarchical clustering.53 To explore cluster demographics we focus on three categories of resources or endowments at the beginning of the expansion period: founder’s background, organizational capabilities, and financial resources. We carry out a multivariate analysis to explore common characteristics of initial endowments associated with each cluster. For that purpose we estimate a multinomial logit model (1). The multinomial logit model is used in cases where (a) the dependent variable consists of more than two outcomes and (b) there is no natural ordering of these outcomes. It takes the following form:
(1)
ln (pi/p0) = β0,i/0 +β1,i/0 x1 + …+βn,i/0 xn +ε,
i=1,2,3
where pi is the probability of sorting into the ith category and p0 is the probability of sorting into category 0. The β coefficients have the added subscript i/0, which indicates that they are estimates of the logit for category i versus the base category of 0. In this set of regressions, each cluster of strategies is represented as a set of binomial variables. Most interesting are the resource estimates,
52
The assumption behind clustering firms using only variables that define their strategy during the expansion period is that this is what strictly speaking is the firm’s entry strategy and we hypothesized that will affect their fate at the end of the EBB. 53
The distance between cluster k and cluster l is given by the average of the (nk * nt) Euclidean distances, where nk and nt are the number of subjects in clusters k and l, respectively (Sharma, 1996).
53
which reflect patterns in the overall sorting of resources into each of the categories relative to the first one. Finally, we relate each cluster to firm performance using a probit model (2) to test for the probability of firm survival at the end of the decline period. Similarly, an ordinary least square regression model (3) is used to explain variance onto Market to book ratio at the end of the expansion and the decline period. The models tested follow.
(2) P (Outcomei,2) = β1 x Cluster + β2 x TechnicalFocusi,1 + β3 x IndustryKnowledgei +β4 x EntrepreneurialExperiencei + β5 x Scalei,1 + β6 x OExperiencei,1 + β7 x VentureCapitali + β8 x C&Eemploy i,1 + εi
(3) Market to booki,2 = β1 x Cluster + β2 x TechnicalFocusi,1 + β3 x IndustryKnowledgei +β4 x EntrepreneurialExperiencei + β5 x Scalei,1 + β6 x OExperiencei,1 + β7 x VentureCapitali + β8 x C&Eemploy i,1 + εi
3.4.
RESULTS
3.4.1.
Data Description
The final population set of the sample for our study comprised 104 public firms. On average, firms were three years old at the beginning of the expansion period (January 1998). Of the firms 31 percent had gone public before 1998. All
54
of them were public before the second quarter of 2000. Firms had an average size of 238 employees in March 1998 (median of 88), and yearly average sales ranging from $38.8 million in 1998 to $102 million in 2000. Table 3.3 and Table 3.4 present summary statistics and the correlation matrix, respectively. Data are presented and analyzed per phase to reflect variable differences between the expansion and decline phases.54 Figure 1 shows the evolution of the main variables.
54
Through this paper, when we report different values for the expansion and decline phase of a given variable, it is implicit that the difference is statistically significant at the 1 percent level.
55
TABLE 3.3 Panel A: Summary Statistics E-Consulting Complete EBB a
Number of Observations
Standard 25th 75th Mean Deviation Minimum quartile Median quartile Maximum
Class
Variable
Firm Performanc
Outcome Market to Book
1537 1074
0.979 5.942
0.145 29.668
0 -556.656
1 0.912
1 2.420
1 6.431
1 612.146
Rate of Growth Type of Growth Scope International Scope
1537 1537 1537
1.558 1.305 2.652
7.502 2.640 31.980
-2 0 0
0.023 0 0
0.448 0 0.053
1.200 2 0.407
51.600 25 100
Service Scope
1537
2.196
0.775
1
2
2
3
3
Strategies Growth
Initial Endowments Founding team Industry Knowledge
1537
0.697
0.460
0
0
1
1
1
Entrepreneurial Experience Financial Venture Capital Cash & Equivalents per empl Organizational Technological Focus
1537 1537 1537 1537
0.454 0.350 10.386 53.763
0.498 0.477 20.402 20.856
0 0 0 10
0 0 0.940 40
0 0 3.886 50
1 1 11.720 70
1 1 297.900 99
Scale Organization's Experience
1537 1537
497.236 19.405
770.053 9.479
1 0
100 12
260 19
525 26
9400 44
Macroeconomic Performance Cycle phase Year
1537 1537
0.350
0.477
0 1998
0
0
1
1 2001
Panel B: Summary Statistics for the Expansion Period Outcome Market to Book
999 555
0.998 8.030
0.045 30.617
0 -556.656
1 1.916
1 4.771
1 10.177
1 190.343
Rate of Growth Type of Growth
999 999
2.278 1.373
9.180 2.935
-1 0
0.273 0
0.726 0
1.600 2
51.600 25
Scope International Scope Service Scope
999 999
3.854 2.167
39.596 0.783
0 1
0 2
0.077 2
0.642 3
100 3
999 999 999
0.679 0.466 0.464
0.467 0.499 0.499
0 0 0
0 0 0
1 0 0
1 1 1
1 1 1
Scale
999 999 999
8.154 53.408 447.117
17.086 20.809 754.351
0 10 2
0.744 40 90
2.705 50 220
9.913 70 440
273.927 99 9400
Organization's Experience
999
16.900
8.756
0
10
16
24
38
Firm Performanc Strategies Growth
Initial Endowments Founding team Industry Knowledge Entrepreneurial Experience Financial Venture Capital Cash & Equivalents per empl Organizational Technological Focus
Macroeconomic Performance Year
999
1998
2000
Panel C: Summary Statistics for the Declined Period Firm Performanc Strategies Growth
Outcome
538
0.942
0.233
0
1
1
1
1
Market to Book
519
3.708
28.478
-44.063
0.548
1.132
2.625
612.146
Rate of Growth
538
0.222
1.291
-2.000
-0.346
-0.058
0.400
14.000
Type of Growth Scope International Scope Service Scope
538 538 538
1.178 0.417 2.249
1.974 1.855 0.758
0 0 1
0 0 2
0 0 2
2 0.231 3
14 27.397 3
538 538
0.732 0.431
0.443 0.496
0 0
0 0
1 0
1 1
1 1
Cash & Equivalents per empl Organizational Technological Focus
538 538 538
0.450 14.532 54.422
0.498 24.930 20.949
0 0.000 10
0 1.637 40
0 7.246 50
1 16.507 70
1 297.900 99
Scale Organization's Experience
538 538
590.301 24.056
790.709 9.009
1 4
150 18
350 23
737 31
7895 44
Initial Endowments Founding team Industry Knowledge Entrepreneurial Experience Financial Venture Capital
Macroeconomic Performance Year 538 a Quarterly data collected for 104 e-consulting firms.
2000
56
2001
TABLE 3.4 Panel A: Correlations for the Quarterly Panel Data Set for the Complete E-Consulting EBB Cycle* Class Performance
Variables 1 Outcome
Strategies Growth 2 Rate of Growth 3 Type of Growth Scope 4 Intl Scope 5 Service Scope
1
2
0.038
1
-0.001
0.031
0.008 0.087
Initial Endowments Founding team 6 Industry Knowledge 0.118 7 Entrepreneurial Exp-0.071 Financial 8 Venture Capital 9 Cash per Employ Organizational 10 11 12 Macro Econ 13 Class Performance
3
4
5
6
7
8
9
10
11
12
Variables 1 Outcome
5 Service Scope
0.001
1
0.011 -0.031
1 -0.130 -0.142 0.035
1
-0.049 -0.093 -0.028 0.161 -0.033 0.106 0.044 -0.071 0.040 0.147 -0.031 -0.179 -0.026 0.020 -0.021 0.108
1 1
-0.281
1 0.132 -0.004 0.001 1 0.119 0.036 0.114 Technical Focus 0.081 -0.038 -0.013 0.020 0.188 1 0.148 -0.102 0.027 0.102 Scale -0.043 -0.036 0.226 -0.043 -0.269 0.027 0.070 0.230 -0.071 -0.049 1 Org Experience -0.077 -0.213 -0.104 -0.078 0.101 0.053 -0.021 -0.035 0.025 -0.035 0.215 1 Cycle Phase 0.056 -0.034 -0.014 0.149 0.023 0.089 0.360 -0.181 -0.131 0.026 -0.051 0.049 Panel B: Correlations for the Quarterly Panel Data Set for the Expansion Phase
Strategies Growth 2 Rate of Growth 3 Type of Growth Scope 4 Intl Scope
1
2
Financial 8 Venture Capital 9 Cash per Employ Organizational 10 Technical Focus 11 Scale
3
4
5
6
7
8
9
10
11
12
1 0.011 -0.035 0.005 0.013
Initial Endowments Founding team 6 Industry Knowledge 0.041 7 Entrepreneurial Exp-0.022 -0.022 -0.027
1 0.040 1 0.005 -0.036
1
-0.151 -0.233 0.044
1
-0.054 -0.153 -0.027 0.162 -0.056 0.082 0.054 -0.085 0.040 0.119 -0.037 -0.181 -0.015 -0.033 -0.017 0.081
Class
Variables 1 Outcome
Strategies Growth 2 Rate of Growth 3 Type of Growth Scope 4 Intl Scope 5 Service Scope
0.035 -0.071
1 1
-0.293
1 0.134 0.002 0.085 0.043 0.120 1 1 0.169 -0.119 0.034 0.065 0.003 0.086 0.193 -0.059 -0.046
-0.044 -0.032 0.026 0.188 -0.047 0.314 -0.047 -0.239 1 -0.015 -0.213 -0.116 -0.079 0.097 0.038 0.014 -0.047 0.018 -0.049 0.234 12 Org Experience Panel C: Correlations for the Quarterly Panel Data Set for the Decline Phase
Performance
13
1
1
2
Organizational 10 Technical Focus 11 Scale 12 Org Experience
4
5
6
7
8
9
10
11
12
1 0.122 0.023 -0.089 0.175
Initial Endowments Founding team 6 Industry Knowledge 0.221 7 Entrepreneurial Exp-0.130 Financial 8 Venture Capital 9 Cash per Employ
3
1
0.006 0.049 0.138 -0.018 -0.017
1 0.046 1 -0.029 -0.061
1
-0.008 0.035 0.040
1
0.039 0.020 -0.120 0.151 0.122 0.153 -0.069 -0.040 0.104 0.201 -0.130 -0.172 0.048 0.080 -0.076 0.136 0.013 0.020 -0.010 0.186 0.193 0.067 -0.067 -0.343 -0.168 -0.133 -0.054 0.074
* In bold significant values at p chi2
0.000
Pseudo R2
38%
Prob > F
0.058
R-squared
14%
a
Model 1 is a Probit Regression with robust standard errors. Dependent variable is firm Outcome . Model 2 is an Ordinary Least Square Regression with robust standard errors. The dependent variable is the qvalue at the end of the second quarter of 2000.
b
c
dF/dx is the marginal increase due to a given variable controlling for every other variables. It is for the discrete change of the dummy variable from 0 to 1. *p < 0.1
**p < 0.05 ***p < 0.01
76
Model 1 in Table 3.7 presents the result of a Probit Regression model with Outcome as a dependent variable and strategic clusters, resources, and controls as independent variables. The model has a good explanatory power (pseudo R2 of 38 percent). Conservative Growers (S1) have a positive relation with firm survival. Specifically, firms belonging to this cluster increase their likelihood of survival with a marginal effect of 32 percent (p chi2
0.000
0.000
0.000
Observed P
0.663
0.663
0.663
Predicted P 2 Pseudo R
0.764 38.5%
0.530 67.9%
0.740 76.2%
a
The dependent variable takes the value of 1 if the firm has a positive outcome at the end of the EBB and 0 otherwise. Robust standard errors are in parentheses. b
dF/dx is the marginal increase due to a given variable controlling for every other variable. It is for discrete change of the dummy variable from 0 to 1. †
p < 0.10
*p < 0.05 **p < 0.01
***p < 0.001
92
***
Model 1 includes new venture’s initial endowments and controls. The overall model has good explanatory power (pseudo R2 of 39 percent p chi2
-0.085 -0.0033
(1.138) 2.642 0.0043 *** (0.844) -1.931 -0.0001 (1.725) -5.014 -0.0002 *** (1.802) 1.929 0.0008 ** (0.902) 5.099 0.2878 *** (1.320) 3.371 0.0103 *** (1.132) 0.052 0.0000 *** (0.020) 0.147 0.0000
(0.128)
(0.216)
-0.327 (0.864)
-0.563 (1.236)
85
85
0.847
0.847
0.985
0.990
-15.766
-11.237
27.180
31.230
0.001
0.000
Pseudo R2 57% 69% a Model is a Probit Regression with robust standard errors. Dependent variable is firm Outcome . b dF/dx is the marginal increase due to a given variable controlling for every other variables. *p < 0.1
**p < 0.05 ***p < 0.01
145
Model 1 in Table 4.7 presents the result of a Probit Regression model
with Outcome as a dependent variable and firm initial endowments as independent variables. The model has a good explanatory power (pseudo R2 of 57 percent). Firms started by individuals with industry background significantly (p
