For many years, researchers have studied the characteristics associated with entrepreneurship in order to find out about the differences between entrepreneurs and non-entrepreneurs (Gartner, 1989). While this work has moved the field forward, the results have often been confounded by questions of cause and effect, ambiguous definitions, and conflicting or inconclusive results. Experimental game theory offers the opportunity to isolate specific variables, thereby clarifying results. In the present study, subjects play a multi-period game where they are asked to choose whether or not to enter simulated markets. There were 20 subjects in each of two conditions with the variant being market volatility. Each of the 40 subjects was also asked to complete a questionnaire designed to test entrepreneurial characteristics of locus of control, risk-taking, and ambition. When controlling for condition, both ambition and locus of control were significant predictors of market-entry. These results suggest that game theory can be a useful mechanism for studying entrepreneurship and entrepreneurial characteristics.

For many years, researchers have been interested in understanding the characteristics of entrepreneurs. Many have studied the characteristics associated with entrepreneurship in order to find out about the differences between entrepreneurs and non-entrepreneurs (Gartner, 1989). Much of the prior research has utilized trait theory or demographic information, or more recently, attitudes, as the methodology for studying these relationships (Robinson et al. 1991). While insightful, much of the prior research suffers from ambiguous results caused by inconclusive causal relationships and muddied definitions. Gartner (1988) has called for a behavioral approach to studying entrepreneurship. The present study provides a fresh look at entrepreneurial characteristics by observing entrepreneurial behavior in a simulated market-entry game and by testing the relationship between this behavior and player traits.

Market Entry

In a typical year, over 15,000 new products are brought to market in the United States, with nearly 80% of consumer product entries and 33% of industrial product entries leading to early failure (Narasimhan and Zhang, 2000). While failure may be statistically likely, there are ample opportunities for success and the occasional story of a Bill Gates or Michael Dell to drive new entrants toward the market. So, in spite of the dangers, a new batch of entrepreneurs and firms will submit another batch of entries the following year.

Entry behavior has been studied from a variety of perspectives, including organization theory, economics, strategy, marketing, and entrepreneurship. In the organization theory literature, market entry has focused on the relationship between population density, the number of organizations in a given market, and organizational performance. In their seminal work, Hannan and Freeman (1977) theorized how an organizational population evolves and how that evolution affects performance for the firms within the population. Research on the dynamics of organizational populations, and hence industries, has for some time focused on the twin processes of organizational founding and mortality (Hannan and Freeman, 1989; Stinchcombe, 1965; Davis and Powell, 1992). More recently, work has focused on combining ecological and institutional theories to explain how the context influences market-entry and exit strategies in richer ways (Baum and Oliver, 1991, 1992; Haveman, 1993). These studies of organizational populations have been primarily concerned with determining the effects on (and implications for) organizational founding and market-entry of such environmental factors as population size, legitimation threshold, carrying capacity, heterogeneity, uncertainty, and interfirm coordination (Aldrich, 1979; Davis and Powell, 1992). For instance, there is a lot of interest, and some debate, at present surrounding issues that arise in the emergence of new industries or markets.

In the field of economics, entry is viewed as a key mechanism in the link between supply and demand as with Adam Smith’s invisible hand. Examples of recent work include Nti (2000), who tested the effects of contests to coordinate entry decisions, Amel and Liang (1997) who examined the effects of entry using dynamic market models and Selten and Guth (1982) who tested equilibrium point selection.

The strategy literature on market entry often focuses on the environmental conditions surrounding entry such as the barriers that potential entrants may face (Prahalad and Hamel, 1990; Bakema, Bell and Pennings, 1996) and first or early mover advantages (Lieberman and Montgomery, 1988; Robinson, Fornell and Sullivan, 1992). Other recent studies have looked at the strategic market entry and exit decisions of managers and entrepreneurs on which births and deaths are premised (Mitchell, 1989; Delacroix and Swaminathan, 1991; Singh and Lumsden, 1990; Haveman, 1992). Previous to this, research on market entry and exit focused primarily on as aspects of competitive strategy (Porter, 1981). This line of work, conducted mostly within schools of business, highlighted the contingent nature of market-entry decisions on the stage of evolution in any particular industry. That is, the appropriate use of market entry or exit for a firm’s strategy was found to depend on whether the industry was newly-emerging, established (mature), or declining.

While the marketing field has also put forward interesting research, its literature on market entry has been somewhat fractious and disconnected from the other disciples. In one study, Moorthy and Png (1992), introduced a model of sequential product introduction that balances against producer impatience in order to minimize market cannibalization. Another study (Sullivan, 1992) developed a model for determining whether to enter a market through brand extension or new product introduction.

While each of these literatures has largely ignored the others (Leibenstein, 1987), entrepreneurship has done more to try to integrate market entry research across disciplines. For instance, Gartner (1985) suggested that entrepreneurship research should rely on the work of the other disciplines and others have followed up on multidisciplinary market entry research (Kouriloff, 2000). Furthermore, much of the entrepreneurship literature has drawn on psychology and organizational behavior as it studies the individual characteristics of those who enter markets (Brockhaus, 1980; Gartner, 1989; Hull, Bosley and Udell; Robinson; Stimpson, Huefner and Hunt; McGrath, MacMillan and Scheinberg.

Each discipline has added a dimension to the understanding of market entry while pointing out the extensiveness of interest in the subject. Furthermore, they have provided a confusing and oftentimes confounding array of results. Overall, this informs both the critical importance and complex nature of the entry decision. There is general consensus that market entry is an important topic but also widespread criticism that the existing research is either too complex or not applicable in a broader context. Buckley and Casson (1998) state that most market entry theories are “too much of a paradigm or framework and too little of a model to provide detailed advice on research design and hypothesis testing.”

It is felt here that much of the problem lies in the design of the research. To date, most market entry research has been designed around field studies or cases. Although these techniques are valid, they present several weaknesses. First, markets are rarely homogeneous. For instance, what may be considered a single market using one Standard Industry Classification (SIC) code can also be considered several different industries when using a more narrow code. Even in the most narrow codes, there is still the possibility of unobservable heterogeneity (Petersen and Koput, 1991). A second weakness of field studies is that they tend to biased toward certain types of markets and potential entrants. For instance, if an industry never, or only briefly, gets off the ground, it is highly unlikely that any reliable research can be conducted on that market. In a similar fashion, market entry field studies is biased toward those who enter a market and rarely considers those who opted out from considering entry. Finally, field research is confounded by the effects of extraneous variables that are impossible to isolate from the variables of interest.

Market-Entry Games

Game theory is a science that reduces complex problems into the structure of simplified games in order to predict and test how people will behavior. Game theory was first introduced to the public by John von Neumann and Oskar Morgenstern (1944). It was soon realized that this is a very powerful tool for studying and predicting the behavior of humans as they interact. Since its introduction, game theory has been used extensively in the fields of economics, political science, biology, and social philosophy (Binmore, 1992). It has also found favor in everything from business strategy (Dixit and Nalebuff, 1992) to biblical interpretation (Brams, 1980). In spite of its value and broad acceptance, until quite recently game theory has been utilized very little in the entrepreneurship literature. In one of the few studies to date, Seale and Olson (2001) developed a simple game to test coordination in a market entry experiment. In that study, they point out the need to step back from the narrowed focus of much of the research in market entry in order to come to terms with some of the general characteristics of market entry relationships and behaviors. The current research is an answer to this call to use game theory to study market entry behavior.

Personality Characteristics

Over the years, many researchers have studied the relationship between personality characteristics and entrepreneurship. In looking at the personality characteristics of entrepreneurs, it is assumed that the set of characteristics found in entrepreneurs tends to be different from those found in non-entrepreneurs. Some of the earlier work, conducted by McClelland and Atkinson (1953, 1961), looked at the needs of achievement, affiliation, and power in entrepreneurs versus others. Others have focused on risk-taking (Palmer, 1971; Brockhaus, 1980; McGrath et al, 1992) and locus of control (Borland, 1975; Timmons, 1978), to name just a few of the studies. In the present study, risk-taking, locus of control, and ambition have been chosen as characteristics to test in relation to the behavior in the market-entry simulation.

Risk-Taking

As early as the 18th century, Richard Cantillon was calling the entrepreneur a risk-taker. Ever since, the idea of taking risks appears in many of the definitions of the term “entrepreneur.” Ronstadt (1984), defines entrepreneurship as, “the dynamic process of creating incremental wealth . . . by individuals who assume the major risks in terms of equity, time, and/or career commitment.” Timmons (1994) writes, “Entreprenuership . . . requires a willingness to take calculated risks-both personal and financial.” Dictionary.com (2000), defines the entrepreneur as, “A person who organizes, operates, and assumes the risk for a business venture.” While risk-taking is supported in the definitions of entrepreneurship, research on the relationship between the two is less clear. Hull et al (1980), found that business owners and business starters scored higher in their assessment of propensity to take risks than did non-owners or non-starters. Others, meanwhile, have found no significant difference in the risk-taking propensity of managers and entrepreneurs (Brockhaus, 1980). While ambiguity exists, there is reason to believe that those who enter new markets have a higher tolerance for risk than those in the general public.

Hypothesis 1: There is a positive correlation between risk-tolerance score and market entry play.

Locus of Control

Locus of control, a construct developed by Rotter (1966), concerns the belief one has about whether the outcomes of ones actions are contingent on what s/he does (an internal control orientation) or are based on events outside the personal control of the individual (an external control orientation). It is commonly thought that individuals who have a high internal control orientation are more likely to accept the personal responsibilities of operating their own business or entering a new market. Robinson et al (1991), found that entrepreneurs had higher personal control expectations than non-entrepreneurs.

Hypotheses 2: There is a positive correlation between internal control orientation score and market entry play.

Ambition

Ambition, or the need for achievement has also long been thought to be associated with entrepreneurship. An individual with high ambition competes against a self-imposed standard and looks for challenging goals with immediate feedback. Hull et al (1980), found that business school alums who judged themselves as likely to start a business had a significantly higher need for achievement score based on survey results.

Hypothesis 3: There is a positive correlation between the level of ambition score and market entry play.

A computer simulated market entry game was created in order to test market entry behavior under controlled conditions. A single questionnaire was then devised by drawing on common scales used to measure a person’s locus of control, level of personal ambition, and risk taking propensity.

The objectives of this research are two-fold. First, it is important to find out how people behave under simplified market entry conditions. Second, the research affords the opportunity to connect this behavior to personality characteristics commonly associated with entrepreneurship. Experiments of this kind cannot tell us the complete story because they will always lack some features of the markets they intend to simulate.  Nevertheless, the key features studied by entrepreneurship researchers lend themselves to study using experimental games since they can be readily and meaningfully manipulated.  Furthermore, experiments allow one to (1) observe and record individual choices as they occur, (2) observe behavior in which no entries occur, (3) control the levels, and combinations, of population parameters, and (4) in many cases, establish the equilibrium behavior. Hence, market-entry and exit experiments are needed to resolve disagreements or uncertainties about the microdynamics that underlie inconsistent, controversial, missing, or otherwise inconclusive field studies. With this in mind, the intent here is to demonstrate the importance and promise of experimental games to developing a disciplinary-based model of entry in emerging industries.  The attractiveness of market entry experiments is that they are simplifications of real entry decisions that can be complicated in a controlled manner, to potentially uncover the factors that make coordination in real entry so difficult.

The market-entry paradigm is formalized using a modification of the terminology of Selten and Guth (1982). Market-entry experiments are n-person games in strategic form where each player i (i Î 0 [1, 2, n]) has two pure strategies: enter the market (d_i = 1) or stay out (d_i = 0). Volatility is introduced by multiplying the results by a factor of either 1 (low volatility) or 5 (high volatility). The formulas used to compute a subject’s payoff (H_i(d)) are:

    v,                                            if d_i = 0 or

    H_i(d) = k[ r (c –  m + 1)        if d_i = 1

d represents the decision to enter the market or stay on the sidelines; v is a constant ($.15) and represents a cash payment or “interest” earned by staying out of the market; k is also a constant ($.25) and is used to provide a reasonable cash incentive for entry behavior; r = either 1 or 5 and represents a factor change in the consequences of market play; c represents market capacity or the demand for entry. For each of 5 blocks of trial, c is selected randomly without repeat from amongst the odd integers between 1 and 19 for each level of r; m represents the market or the number of entrants to the market,. Note that the incentive to enter increases linearly in the difference between c and m (see Table 1a and Table 1b).

The experiment was run with 20 subjects. Subjects were recruited by announcement in management courses and general posting at a midsize state university. They were told that they would receive a $5 show up fee, a $6 endowment to help prevent negative earnings effects, plus the opportunity to earn an additional amount likely to be between $0 and $25 dependent on their performance and other variables. In return, they would be expected to participate in a 2-hour computer game session and complete a questionnaire. All subjects were asked to arrive at a computer lab at a prespecified time and location. The lab consisted of a bank of 20 terminals that were connected to a master computer. The design of the experiment allowed all participants to understand that they were involved in the experiment together but did not allow for them to communicate pertinent information between themselves. All subject participation was completely voluntary and was in no way related to course requirements.

Upon arrival in the lab, each student was given the questionnaire to complete. Upon completion, each was given instructions on how to play the game. When all subjects were ready and any questions had been answered, the experiment was begun. The experiment consisted of a total of 100 trials. Each trial had the same order: subjects were informed of the value of c (market capacity) and r (risk factor) then asked to make a binary decision of whether to enter the market or stay out. On each trial, each subject could guarantee themselves $.15 by not entering the market. Conversely, they could enter the market and earn a return conditioned on the market capacity, the number of other entrants, and the market’s risk factor. There was no advantage to being first mover or for waiting until all other subjects had made their decisions. After all subjects had entered their decision, a central computer informed each subject of the total number of market entrants and the payoff he or she earned for their behavior. After all subjects reviewed this feedback, the next trial began. When all trials were completed, the subjects were thanked, debriefed, and told that their earnings could be collected within one week either by mailed check of from the university bursar office.

Aggregate Entry Decisions

It is first important to determine if the game adequately represented market conditions. To do so requires reviewing aggregate level data. Table 2 maps out the relationship between market capacity and the number of entrants for each of the 100 trials. The first column shows the capacity of the market. The next 5 columns display the number of entrants for each market capacity across each of the 5 blocks of 20 trials. The last 3 columns provide the grand total of entrants at each level of market capacity, the average number of entrants at each market capacity, and the average difference between market capacity and the number of entrants. In each case, the data is divided into 2 sections for each risk factor.

As can be seen, correlations between capacity and entry are very strong, varying from a low of .92 to a high of .99, with slight improvement over time. The only observable pattern of over or under entry occurred when market capacity was 7 and the risk factor was 5. Here, entry was 2.8 higher than market capacity, leading to heavy losses from those who entered. However, this disequilibrium seemed to straighten itself out in the latter half of the trials. There was a slight underentry with a risk factor of 1 and corresponding overentry with a risk factor of 5. In both cases, the disequilibrium occurred within the first two blocks of trials, with near perfect equilibrium in the final two blocks. It is noteworthy that, overall, equilibrium would predict 1000 entries and the game produced 999.

One other measure of aggregate entry is the number of violations to monotonicity. Monotonicity simply requires that levels of entry rise and fall with market capacity. Looking at Table 2, there are 10 cases of monotonicity violation. It is also noteworthy that only one of these occurred in the final 2 blocks of the game.

Individual Performance and Characteristics

Looking at Table 3, the average number of entries was 49.95 per subject with a standard deviation of 19.00 entries. The lowest number of recorded entries was 22 out of 100 while the highest was 99. The average payout to the subjects, including show up fee and a starting endowment, was $28.77 with a standard deviation of $7.07. The median was $28.50. Payouts varied from a low of $15.00 to a high of $41.20.

Table 4 provides the output from regressing the personality characteristics onto entry behavior. The first column, B, provides the directional relationship between the variables while the last column provides the level of significance. It can be seen that each of the variables were significant predictors of entry. Row 2 provides support for hypothesis 1, showing that those who scored high on risk-taking were more likely to entry the markets. Likewise, row 3 provides support for hypothesis 2, as internal locus of control was positively related to entry. However, row 4 runs counter to hypothesis 3, with level of ambition negatively related to entry.

Table 5 is a correlation matrix to exhibit relationships between entry behavior, payout, and personality characteristics. The first two columns show average entry and an entry variable that takes into account the risk factor, respectively. After a column for payout, the final three columns feature measurements for risk, ambition, and locus of control, respectively. As to be expected, there is a very high correlation between the two entry behavior factors. Of greater interest are the correlations between average entry and risk (.427 at .03 significance) and average entry and ambition (-.599 at .003 significance). There is also a highly significant relationship between ambition and locus of control (-.523 at .009). In conducting a stepwise regression between these two variables, locus of control became an insignificant predictor of entry. No other relationships show significance.

This research provides a new look at the important issue of market entry. One of the contributions of this research is that it provides a research design for looking at entry behavior without the problems of market heterogeneity and confounding variables. Furthermore, it has the advantage of viewing both entry and non-entry behavior from an equal footing. There are several important findings obtained from the results. First, coordination between supply and demand was strikingly high, with correlations running from .92 to .99.

A second related finding is that some learning occurred. While coordination was strong throughout the game, disequilibrium positions and violations to monotonicity both decreased over time. This suggests a learning effect that may have important implications in the real world. For instance, if rules are well-known and remain relatively stable across time, potential market entrants will better coordinate their efforts toward equilibrium. It is noteworthy here that they did not move toward a pareto optimal solution that would have benefited each player (entrant) at the expense of the experimenter (society).

The research also provides a new perspective in the evaluation of how the personality characteristics of risk-tolerance, locus of control, and ambition affect entry behavior. As expected, those with a higher risk-tolerance score were more likely to enter the market. However, higher levels of ambition were associated with lower entry behavior. This may stem from the fact that there was no apparent gain to be made from entering the market. A possible implication is that high ambition individuals need to see results from their actions or they may become passive and look for other outlets for their ambition.

In all, it is hoped that this paper begins rather than ends a line of inquiry. There are numerous additional effects that may be fruitful to examine in relation to the puzzles that surround market entry in emerging industries. As an example, demographic and questionnaire information could be gathered by subject to determine relationships between entry decisions and entrepreneurial characteristics. This could lead to greater understanding of an entrepreneurial model of individual decision-making. In addition, the model can be made more realistic by adding market characteristics such as heterogeneity in entry costs, uncertainty in the true capacity of the market, and first or early mover advantages.

CONTACT: David Olson, California State University–Bakersfield, 9001 Stockdale Hwy, Bakersfield CA 93311; (T) 661-664-2284; (F) 661-664-2438; dolson@csub.edu

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