Profiling the Growth Oriented Nascent Entrepreneur in the US Evidence from. Representative Samples

Transcription

1 Profiling the Growth Oriented Nascent Entrepreneur in the US Evidence from Representative Samples Maija Renko, Florida International University Paul Reynolds, Florida International University Paper presented at Academy of Management Annual Meeting 2006, Atlanta, Georgia ABSTRACT High growth new firms are of considerable interest, in no small part due to their disproportionate contribution to overall job growth; among the 13 million nascent entrepreneurs present in the US in 2001, the 675,000 that aspire to provide 50 or more jobs five years after the firm birth would account for 40% of all new firm jobs. Estimating the impact of demographic (entrepreneur-related) and organizational variables that influence firm growth expectations at the time of business start-up is a major step toward understanding the unique features of high growth new firms. This assessment makes use of two publicly available, harmonized datasets, namely GEM 2001 data from the US and the first PSED data set, assembled from Gender, start-up team size, and features of the opportunity recognition process are statistically significant factors affecting projected job growth in the first five years of new firm operation; educational attainment, age, and a focus on manufacturing did not have a significant impact. Comparison of answering patterns to opportunity recognition items in GEM vs. PSED suggests that extreme caution is needed when wording questionnaire items; individuals would rather describe themselves as planning entrepreneurs instead of necessity entrepreneurs. 1 Electronic copy available at:

2 INTRODUCTION Nascent entrepreneurs with high growth ambitious should, all other things being equal, be associated with new firms with higher growth trajectories. Thus, knowledge of the determinants of growth expectations during the venture creation phase may be central in understanding the growth of newly founded firms (Delmar and Davidsson, 1999).The central research question of the following assessment is: What demographic (entrepreneur-related) and organizational variables at the time of business start-up influence or are related to firm growth expectations? As will be described in the following, previous research has established links between a large number of explanatory variables and firm growth (or growth expectations) as a dependent variable. In this study, the choice of micro level explanatory variables is somewhat limited by data constraints. On the other hand, the assessment takes advantage of two large scale random samples of the US population to develop a solid empirical base for the conclusions regarding those factors associated with aspirations for higher levels of new firm growth. Once it became clear that new firms were a major source of new job creation, it was determined that most of this growth was from firms with high growth trajectories, often referred to as gazelles (Birch, 1987). A small proportion of high growth firms is usually the source of a major proportion of total job growth. The data from the Global Entrepreneurship Monitor 2001 assessment of the United States can be used to illustrate the importance of the contributions of high growth aspiration firms. The extrapolation from the sample to the full adult population is illustrated in Table 1. Nascent entrepreneurs are classified by the expected size of their firm in terms of jobs created, five years following the birth of an operational business. There are five future job aspiration 2 Electronic copy available at:

3 categories : 0-1, 2-4, 5-14, 15-49, and 50 and more. As shown in column 1, the 2001 GEM survey of the adult population involved a representative sample of 2,440 individuals years old, representing a total population of 199 million individuals. In the sample, 162 respondents appeared to be active in a start-up effort where they would expect to own part of the business but had not paid any salaries and wages for more than 3 months the operational definition of a nascent entrepreneur. This sample represented about 13 million in the total population, as shown in column 4. Those expecting jobs five years after start up represented 1.3 million in the population, about 10% of all nascent entrepreneurs, and those expecting 50 or more jobs accounted for almost 700 thousand in the population or 5% of all nascent entrepreneurs. The expected job contributions are adjusted by taking account of the average team size, as shown in column 6; 13 million nascents appear to be working on about 7.8 million new firms. Longitudinal studies (Reynolds and Curting, 2004) suggest that about 30% of the start-up efforts would be realized, reducing the number of active start-ups that would become operational to about 2.4 million. The expected employment is calculated by multiplying the number of firms to be realized by the average jobs projected in five years (column 9) by the number of active firms (column 8). The total number of jobs created is about 13 million, but very much skewed toward the high growth aspiration start-ups. Those expecting to create 50 or more jobs are 5% of the nascents but will provide 39% of the new jobs. Those expecting to create 15 or more jobs are 15% of the nascents but will account for 63% of the new jobs. It is clear that the new firms with high growth aspirations will, if realized, make a substantial contribution to economic growth. Table 1 about here 3

4 Understanding new firm growth is a central focus of a large number of conceptual and empirical research studies. Although researchers have been able to show the importance of resources for firm growth, it is not clear what other factors may have a major role to play at various phases of new firm development. This assessment will emphasize the connection between certain firm- and entrepreneur related variables at the time of company formation and entrepreneurs expectations of their firms future growth. The paper is organized as follows: First, there is an overview of the concept of firm growth and growth orientation, i.e. the dependent variable of the empirical study. This is followed by a discussion of the prior work and theoretical background that has led to the selection of six independent variables as predictors of growth expectations; summarized by presenting six explicit hypotheses. The sources of the empirical data, two representative samples of US entrepreneurial activity, i.e. Global Entrepreneurship Monitor (GEM) 2001 and first Panel Study of Entrepreneurial Dynamics (PSED). The research results are presented hypothesis by hypothesis, and finally the last section of the paper presents conclusions and some suggestions for future research. FIRM GROWTH AND GROWTH ORIENTATION Firm growth is often considered to be indicative of success and future performance of the firm (Pukkinen et al., 2005, Baum and Locke, 2004). Venture growth causes valued economic and social gains, including job creation (Aldrich, 1999), and it is a measurable and wellunderstood venture goal (Kirzner, 1985). According to Covin and Slevin (1997), venture growth is the essence of entrepreneurship. In previous studies, firm growth and related performance has been conceptualized and measured in over thirty different ways (Brush and Vanderwerf, 1992). Because of this variety in 4

5 conceptualizations and measures used, comparing different research results is a challenging task. It has even been suggested that the chosen concept of growth and the related operationalizations influence the very research results (Delmar 1997, Heshmati 2001, Roper 1999). For example, using personnel growth as an indicator of firm growth may lead to different kinds of conclusions than employing the measure of sales turnover growth. (Robson and Bennett, 2000). Because growth is a dynamic process that takes place over time, reliable measurement of growth requires multiple observations over time of whichever indicators chosen. Just as much as the measures of growth vary, so do the independent variables that have been suggested to influence venture growth. Personality traits, organizational factors, and environmental factors have been studied by entrepreneurship researchers as causes of new venture success (Baum and Locke, 2004). For example, entrepreneurial climate in a society as well as role models have been linked to firm growth (Davidsson and Henrekson, 2002, Reynolds et al., 2003). However, this paper focuses on selected firm- and entrepreneur related variables and their influence on firm growth expectations. Growth is often predicated on managerial perceptions and expectations about specific resources (Penrose, 1959). Researchers have studied managers evaluations of resources and their relationships to performance over time, finding that certain types of resources and strategies can lead to above average performance and growth (Mosakowski, 1993). Early firm growth has been predominantly explained by the individual characteristics of the founder or founders of the business; age, sex, and experience (Stuart and Abetti, 1990), cognitive constructs such as perceived competence (Chandler and Jansen, 1992) and personal goals (Birley and Westhead, 1994). However, even though much effort has been directed e.g. to understanding the influence of motivational make-up of the entrepreneur on growth of a new 5

6 firm (e.g. Smith and Miner, 1984), this research direction has never been able to establish strong influences (Autio, 2000). For example, Stuart and Abetti (1990) found only a very slight correlation between objectives pursued by entrepreneurs and the performance of their businesses (p = 0.21). Evidence showing that resources have an important impact on growth and performance of young firms is more established. For example, Chandler and Hanks (1994) found that companies with broad capabilities grew faster than those companies that did not have such capabilities. Interestingly, however, when the relationship between entrepreneurial intentions (like growth intentions) and resource environment is concerned, it has been shown in several studies (Bruno and Tyebjee, 1982, Krueger and Brazeal, 1994, Krueger and Dickson, 1994) that perceptions of resource availability are more significant than actual resource availability (subjective vs. objective resource availability). As mentioned above, growth is a dynamic process (e.g. Wiklund and Shepherd, 2003). Thus, reliable measurement of firm growth needs to follow the phenomenon, i.e. firm development, over a period of time. However, in practice, researchers resources seldom allow longitudinal research designs. In the current study, instead of actual firm growth, entrepreneur s growth intentions (i.e. growth orientation) are in focus. Intentions are a predictor of activity in a given environment. It has been attested in many studies (Boyd and Vozikis, 1994, Krueger and Brazeal, 1994, Krueger et al., 2000) that entrepreneurial intentions correlate significantly with entrepreneurial behavior. Following similar line of reasoning, many research studies show that growth intentions of an entrepreneur and the real growth of a firm may correlate positively (Bellu and Sherman, 1995, Kolvereid and Bullvåg, 1996, Wiklund and Shepherd, 2003). 6

7 DEMOGRAPHIC TRAITS, ORGANIZATIONAL FACTORS, AND GROWTH ORIENTATION Previous research has established links between numerous entrepreneur- and firm related variables and growth intentions as well as actual growth of the firm. In the following, gender, human capital, source of business opportunity, as well as type of business (service vs. manufacturing) are considered as potential explanatory variables for nascent entrepreneurs differing growth aspirations. Table 2 provides a simple summary of key concepts, their definitions, as well as operationalizations in the empirical data collection. As described above, the study subjects are nascent entrepreneurs, and the phenomenon of interest is their growth orientation. Definitions for both concepts are provided in Table 2. Table 2 about here Human Capital Human capital theory maintains that knowledge provides individuals with higher cognitive abilities, leading to more productive and efficient activity (Becker, 1964, Mincer, 1974). Knowledge, again, may be defined as being either tacit or explicit (Polanyi, 1967). Tacit knowledge refers to the often non-codified components of activity, whereas explicit knowledge consists of information normally conveyed in procedures, processes, formal written documents, and educational institutions. Making entrepreneurial decisions utilizes an interaction of both tacit and explicit knowledge, as well as social structures and belief systems. Individuals with more or higher quality human capital should be better at perceiving entrepreneurial opportunities, and they should also have superior ability in successfully exploiting opportunities (Davidsson and Honig, 2003). Thus, nascent entrepreneurs with more human capital can be expected to have higher growth aspirations for their firms. 7

8 The relationship between human capital and entrepreneurial activity and success may be confounded by a number of factors. For example, education seems to be particularly important for female entrepreneurs (Bates, 1995). Also, different types of human capital may be more important at different stages of the entrepreneurial process. For example, the findings of Davidsson and Honig (2003) support the role of formal education and previous start-up experience in predicting who, among a cross-section of the general population, would attempt to engage in nascent entrepreneurship. However, formal education did not appear to be a factor in determining the frequency of gestation activities over time nor in predicting those who succeeded with a first sale or a profitable venture. Level of education. Education is often treated as a proxy for human capital. Empirical research has demonstrated a range of results regarding the relationship between education, entrepreneurship, and success, with education frequently producing nonlinear effects on the probability of becoming an entrepreneur, or in achieving success (Bellu et al., 1990, Gimeno et al., 1997, Reynolds, 1997, Davidsson and Honig, 2003, Arenius and Minniti, 2005). More specifically, formal education is one component of the explicit knowledge part of human capital, which, again, may provide skills useful to entrepreneurs (Davidsson and Honig, 2003). Thus, it is possible to propose: Hypothesis 1: Higher level of education of a nascent entrepreneur has a positive effect on his / her firm growth expectations. Age. Human capital (general human capital, management know-how, industry specific know-how) that entrepreneurs provide to their firms accumulates throughout their careers. Human capital is not only the result of formal education, but includes experience and practical learning that takes place on the job, as well as non-formal education. Thus, broad labor market 8

9 experience, as well as specific vocationally oriented experience, has been theoretically predicted to increase human capital (Becker, 1964). Although empirical results have been mixed (Davidsson and Honig, 2003), there are studies showing that work life experience is significantly related to entrepreneurial activity (Bates, 1995, Gimeno et al., 1997, Robinson and Sexton, 1994). It has been suggested that the relationship between age and the likelihood of starting a new business picks at a relatively early age and decreases thereafter (Reynolds et al., 2003). For growth expectations, it is proposed: Hypothesis 2: Older age of the nascent entrepreneur has a positive effect on his / her firm growth expectations. Start-up team size. Human capital of a new, starting venture, is mainly available from individuals in the startup team. The larger this startup team, the more human capital there is. The important role of the management team for the success of a start-up firm has been confirmed in several studies (Roure and Maidique, 1986, Delmar and Davidsson, 1999, Eisenhardt and Schoonhoven, 1990, Doutriaux 1992). Bollinger et al. (1983) reviewed the then-existing knowledge on factors contributing to the success of technology-based new firms. The only empirical finding that they could cite was that the faster growing technology-based new firms were started by greater management teams, and that more technology had been transferred from the incubating organization to the more successful new firms. Accordingly, the following hypothesis is developed: Hypothesis 3: Larger startup team size has a positive effect on firm growth expectations. Gender Gender differences in entrepreneurial behavior have been a subject of a significant amount of attention. Men and women entrepreneurs differ very little with respect to demographic 9

10 and psychological variables (Brush, 1990, 1992), while more pronounced differences seem to exist in business goals and management styles; women tend to pursue intrinsic goals (intangible, psychological in nature) rather than financial gain (Brush, 1992, Rosa et al., 1994). Based on Danish Global Entrepreneurship Monitor data, Bager and Schott (2004) found that expected growth correlates positively with a number of personal characteristics, in particular being male, having entrepreneurial competence, and having a network encompassing other entrepreneurs. To some extent, the gender difference maybe attributable to a confound effect with differences in human capital; women are less likely to track into technical disciplines like engineering, which would give them skills for launching businesses in manufacturing or high technology sectors. Businesses in largely male-dominated sectors, like telecommunications or pharmaceuticals, are typically larger and have greater growth potential. (Carter and Brush, 2004) Finally, Anna et al. (2000) suggest that systemic social, cultural, and work structure barriers may cause women s intentions to differ from those of men. Taken this previous empirical evidence on gender differences in entrepreneurship, it is hypothesized that: Hypothesis 4: Male nascent entrepreneurs have higher growth expectations for their firms than female nascent entrepreneurs. Opportunity Recognition Process An increasing number of entrepreneurship scholars agree that opportunity perception is the most distinctive and fundamental characteristic of entrepreneurial behavior (Kirzner, 1973, 1979). Opportunity recognition is the beginning of the entrepreneurship process (Christensen, Madsen, and Peterson, 1994), and Bygrave and Hofer (1991) propose a definition of the entrepreneur as someone who perceives an opportunity and creates an organization to pursue it. 10

11 Hills and Singh (2004) cite Bhave (1994) as a seminal piece of work identifying two types of opportunity recognition, again, based on Cyert and March: externally stimulated and internally stimulated opportunity recognition. Externally stimulated opportunities are those, where the decision to start a business comes before opportunity recognition. In general terms, this compares to Kirznerian entrepreneurship (Kirzner, 1973, 1979), where entrepreneur recognizes opportunities differently from his / her peers because of his / her sophisticated understanding of customers, markets, and ways to serve those markets (Shane, 2000). A more Schumpeterian view of opportunity recognition is the internally stimulated recognition, where entrepreneur has a business idea first and only later decides to create a venture. Davidsson (1991) finds that objective measures of ability, need, and opportunity can explain a substantial share of the variation in actual growth of young firms, and that objective and subjective measures of these three factors can explain a substantial share of the variation in growth motivation. However, Hills and Singh (2004) conclude that the performance implications of the opportunity recognition process are largely unexplored in the opportunity recognition literature. Specifically, they state that future research should address the question Is there some link between performance and internally versus externally stimulated opportunities? (Hills and Singh, 2004, 270). Typically, Schumpeterian types of entrepreneurs that shift markets to a disequilibrium state are viewed as growth entrepreneurs, whereas those that feel an urge or need to start a business and then search for business opportunities are referred to as necessity entrepreneurs and can be expected to have lower growth aspirations. Inspired by the research gap identified by Hills and Singh (2004), it is proposed that: Hypothesis 5: Internally stimulated opportunity recognition leads to higher growth orientation than externally stimulated opportunity recognition. 11

12 On a macro level, Wong et al. (2005) do not find support for the contribution of opportunity- or necessity entrepreneurship towards economic growth of a country. Even though these results on a macro level are somewhat discouraging, the dynamics on micro level can be different. Business Sector Pukkinen et al. (2005) point out that the choice of growth measurement (e.g. personnel growth, turnover growth, growth orientation) has a significant influence on the results of the relationship between business sector (industry) and firm growth. Overall, their data from Finland suggests that both service and manufacturing sectors correlate with firm growth more strongly than other sectors when subjective and future oriented growth measures are employed. When looking at actual growth in terms of number of employees and sales turnover, service firms have grown fastest (Pukkinen et al., 2005). Typically, industry sector data available for new ventures comes at a high level of aggregation. Thus, it is difficult to study the influence of industry sector on venture growth. Based on Swedish data, Delmar and Davidsson (1998) find that super growers were typically high-technology manufacturing firms, technology-based services, education & health care, and other knowledge-intensive service firms (advertising agencies, business consultants, and such). Delmar and Davidsson (1998) describe these industries as new ; they are either the result of newly created markets (as information technology) or typical for the Swedish context - are earlier state monopolies being deregulated (education and health care). Business sector and other demographic features of new start-ups may have a direct bearing on the speed and success of completing the start-up process and future success of the firm (Reynolds, 2004a). In this study and given the data available from GEM 2001 and PSED, it 12

13 is hypothesized that nascent entrepreneurs starting manufacturing firms expect greater firm growth than entrepreneurs in other business sectors. Ideally, it would be possible to distinguish the kinds of new growth sectors Delmar and Davidsson (1998) were able to identify and hypothesize on faster growth in those sectors, but given the aggregation level of the PSED as well as GEM 2001 data this is not possible. Manufacturing businesses typically require larger initial investments than e.g. service- or retail businesses. Logically, one would expect that the owners of these firms would aim at faster growth in order to compensate the initial investments and to gain rents. Also, manufacturing as a sector comprises high technology manufacturing firms, that represent the largest share of super growers in Delmar and Davidsson (1998) data. Hypothesis 6: Nascent entrepreneurs starting businesses in manufacturing sectors expect faster growth for their businesses than entrepreneurs in other business sectors. METHODOLOGY Sources of Data Over the past years, concentrated data collection efforts have brought entrepreneurship researchers together in order to collect large scale, reliable data on entrepreneurial activities. One of these efforts, the Global Entrepreneurship Monitor (GEM), has grown into a substantial international project that engages researchers from dozens of countries to collect internationally comparable data on entrepreneurial activities. Another data collection project, Panel Study of Entrepreneurial Dynamics (PSED), was established to collect large scale, reliable data on entrepreneurial activities within the US population (Gartner et al., 2004). The current study makes use of both of these publicly available research databases, namely GEM and PSED. The goals of these two data collection efforts are rather different, and the depth of data in the GEM database is much more limited than the PSED data. Despite the 13

14 limitations that arise from the use of secondary data, there are interesting research questions that can be addressed using combined data from the PSED and GEM databases to expand the number of nascent entrepreneurs in the analysis. Combining data from the GEM 2001 and PSED projects provides a total data base of 1,129 US nascent entrepreneurs. Both of the original datasets, GEM as well as PSED, contain cases that represent (1) individuals not active in starting up businesses (control group in PSED) and (2) individuals active in starting up businesses, i.e. nascent entrepreneurs. Using the definition and operational criteria for nascent entrepreneur presented in Table 2, nascent entrepreneurs have been screened from the original data files. This screening has resulted in a dataset, which includes 1,129 individuals (cases, nascent entrepreneurs) that have been interviewed. Out of these total cases, 845 are derived from the PSED dataset and 284 cases from the GEM 2001 database (US data only). These are the cases used for empirical analyses in this current study. The nature of the data used in the current study is cross-sectional. From the GEM data, only data from the year 2001 is used. The more recent data is currently available only to members of the national GEM teams. Year 2001 was the first year when an item tapping the sources of opportunity recognition was included in the GEM interview schedule. This is an important item for the current study and precludes use of GEM data from early years. GEM data was obtained from the project website: and the original data file used in the current study is GEM 2001 Adult Population Survey Data.por. PSED data was accessed through the University of Michigan website: and the original data file used in the current study is called ercw14q.sav. Both PSED and GEM databases are representative of the US adult population. In both studies, random, structured data collection procedures (phone interviews) have been completed 14

15 to collect information on individual- level variables. The data collection is described in detail elsewhere (See e.g. Gartner et al., 2004; Reynolds et al., 2003; Reynolds, et al 2005), and an interested reader is referred to those original sources. Variables Growth orientation. The growth orientation of a nascent entrepreneur is captured by asking him / her about the number of employees expected to be working in the startup firm in five years (continuous variable). For cases in the PSED dataset this is computed as a sum of two questions, i.e. By the end of the fifth year of operation, about how many full time employees, not counting owners, do you expect to be working for pay at this new business? and By the end of the fifth year of operation, about how many part-time employees do you expect to be working for pay at this new firm?. For cases in the GEM 2001 dataset, this value is the answer to the following question How many people will be working for this business, not counting owners but including all subcontractors, when it is five years old? By subcontractors, we mean only people or firms working exclusively for this business, and not working for others as well. Ideally, growth orientation should be measured with a scale comprising multiple, future oriented items. However, in the dataset used for this study, such items are not available. In the PSED dataset, a number of items were employed to measure expected business volume and growth. In that dataset, the reliability statistic (Cronbach s alpha) for four items 1 inquiring about employment growth is.644 (n=446), which is less than preferred but can be considered satisfactory (Nunnally, 1967). Human capital. Human capital in the current study is captured through three measures. First, each interviewee was presented a question about his / her age (continuous variable). 1 Expected full-time jobs, 1st year; Expected part-time jobs, 1st year; Expected full-time jobs, 5th year, Expected part-time jobs, 5th year. 15

16 Second, interviewees of PSED and GEM 2001 studies were asked to provide information on their education. Because the response categories in the two data collection efforts were not similar, the education variable was recoded for the current study. As described in Table 3, the education variable employed here has five categories, four standing for highest level of education. Age and level of education reflect human capital from the point of view of the nascent entrepreneur interviewed for the studies. Table 3 about here The third human capital variable, namely size of start up team, is a firm level variable. It is a continuous variable of the start up team size (persons only), and when the question was asked in the GEM data collection it was specified that these people in the start up team should expect to share ownership. Gender. Gender is coded as a dummy variable (1=male; 0=female) based on interviewees answers. From PSED data, NCGENDER variable is the one used in the current study, as suggested by Carter and Brush (2004, 20). Opportunity recognition process. The PSED dataset includes multiple items and scales that have been designed to tap the opportunity recognition process (Hills and Singh 2004). However, as we are combining data from two separate sources, the analysis is limited by the kind of data that is common to the two sources. In the case of GEM 2001 data, opportunity recognition is captured with one question that has a similar counterpart in the PSED dataset. These opportunity recognition questions were recoded for the current study according to the following scheme (Table 4): Table 4 about here 16

17 Response categorized as 1 represents internally stimulated opportunity recognition. The wording of the opportunity recognition question in the two datasets is different and can potentially lead to different kind of interpretation by the interviewees. Especially have no better choices of work (in the GEM questionnaire) is a negative wording and social desirability may bias the answers more towards the take advantage of business opportunity category. Hence, I looked at the chi-square measures to test the hypothesis that the source of data (GEM 2001 vs. PSED) and opportunity variable are independent. It turned out that the significance values were low (.00), which indicates that there is a relationship between two variables. In the PSED data, 23 per cent of nascent entrepreneurs indicate that business idea or opportunity came first, whereas in the GEM 2001 data, 55% of nascent entrepreneurs said they were starting a business to take advantage of business opportunity. This discrepancy in the response pattern is obviously a great concern for the validity of this question as a proxy for internally stimulated opportunity recognition. Type of business. For the current study, sector was dichotomized into two groups, manufacturing and all other sectors. Hence, a dummy variable (1=manufacturing; 0=other) is used as a proxy for manufacturing startups. Obviously, these simple operationalizations are neither perfectly valid nor perfectly reliable. However, in order to collect data from a large sample of nascent entrepreneurs, depth of the data has to be sacrificed. As pointed out previously e.g. by Bager and Schott (2004), the personal business related characteristics should be regarded as proxies for the variables as they only rely on the answering of one question. Table 5 below presents the descriptives of variables used in the analysis. The dependent variable (growth orientation) is highly positively skewed. This is not surprising in the light of 17

18 previous empirical analyses. Cabral and Mata (2003) present evidence that the size distribution of a cohort of surviving firms shifts to the right and approaches a log-normal distribution over time, after being highly skewed at birth. Also, size of start-up team is positively skewed, as well as the dummy variable for manufacturing business. Within the dataset of 1129 nascent entrepreneurs, there are only 34 manufacturing businesses (3%). The distribution of the variables has been carefully analyzed, and three cases were identified clearly as outliers for the dependent variable; three companies expected to employ more than 1000 people in five years. These three outliers were filtered from the dataset, after which the dependent variable was log transformed in order to normalize its distribution. Five cases were identified clearly as outliers for the startup team size; these cases had a value of 20 or more for this variable. These five cases were filtered from the dataset, after which the startup team size variable was log transformed. After these procedures, the dataset comprised 593 cases for which value of the dependent variable was available. Out of these cases, 432 come from the PSED dataset and 161 from the GEM 2001 dataset. Table 5 about here Analysis and Results Before testing the hypotheses, it is useful to consider the patterns of growth orientation within the sample. In total, the nascent entrepreneurs in this sample is expected to create 20,650 new jobs within five years. However, this figure includes estimates from three nascent entrepreneurs who expected to employ more than a thousand people within five years. Excluding these three optimistic estimates reduces the total jobs expected within five years from this sample to 12,350. Furthermore, missing data reduces the number of cases on which job projections are available to 611 of the 1,129 nascent entrepreneurs in the sample. 18

19 The (more cautious) total of 12,350 jobs represents jobs per each of the 608 individuals who gave estimates. When the individuals in the dataset are weighted so that the original weights assigned to them in the GEM and PSED data collection efforts are used as a basis for weighting and then adjusted within the current sample, the average job count expected within five years is per entrepreneur. As pointed out by Autio (2005), estimates like this should be read with some caution, as they represent expectations, not actual job creation. Not all nascent entrepreneurial activity leads to the actual creation of a new firm, and even when a new firm is started, the realized job creation often falls short of expectations. Still, even with these reservations, the statistics reported in Table 6 underline the potential of nascent entrepreneurial activity in general, and growth oriented entrepreneurship in particular, for job creation. (See also Autio 2005 on growth oriented entrepreneurship) Table 6 about here As the dataset includes cases from two different data collection efforts, the actual analysis started by comparing the distribution of variables between the sources. T-tests were conducted for continuous variables. The results show that in terms of age distribution as well as for startup team size, there is no significant difference between cases from PSED dataset vs. cases from GEM 2001 dataset. For education, the mean value for the PSED cases is significantly higher (2.93) than the mean of GEM cases (2.62). This is most likely attributable to the different coding schemes in the original datasets (see Table 3). Also, for the dependent variable, i.e. expected number of employees in five years, the mean value for PSED cases is significantly higher (2.62) than for GEM cases (1.63). This is most likely due to the level of precision when asking interviewees about their estimates. In the GEM questionnaire, one item was used to capture a 19

20 value for the dependent variable, whereas in the PSED questionnaire interviewees were asked separately about full-time and part-time employees in five years (two separate questions, which were summed to get a value for total employment estimate in five years). For categorical variables, chi-square tests were conducted in order to detect differences in answering patterns in the PSED vs. GEM data. No significant differences were found for gender and type of business (manufacturing). The significant difference for opportunity recognition variable was already discussed above. Multiple linear regression was used to detect the effects of explanatory variables on the dependent variable. Table 7 below presents the correlations between variables as well as mean values and standard deviations after the necessary data transformations that were described above. Table 7 about here Explanatory variables were introduced to the regression in two steps. First, the human capital variables (age, education, and startup team size) were included in the model. At the second step, the remaining variables (gender, opportunity recognition, and business type) were added to the model. Table 8 below presents the results of the analysis. Table 8 about here Hypothesis 1 predicted that higher level of education of a nascent entrepreneur has a positive effect on his / her firm growth expectations. In the light of the current data, however, there seems to be no relationship between the level of education and growth orientation. As pointed out by Arenius and Minniti (2005), the prevailing uncertainty surrounding education as a predictor of entrepreneurial behavior and success is due in part to the fact that education levels have primarily a contextual significance. 20

21 Hypothesis 2 predicted that older age of the nascent entrepreneur has a positive effect on his / her firm growth expectations. This hypothesis is not supported by the data either. If anything, the linear relationship between age and growth orientation seems to be a negative one. However, a closer look at the relationship between age and growth orientation reveals that an inverse model (b1 = ; significance.003) of age may be a better predictor of growth expectations than a linear (b1=-.0118; significance.014), logarithmic (b1=-.4970; significance.007), or quadratic (b1=-.0469; significance.024) model. Hypothesis 3 (larger startup team size has a positive effect on firm growth expectations) gets strong support from the current data; there is a strong positive relationship between startup team size and growth orientation. Also hypothesis number four about male nascent entrepreneurs having higher growth expectations for their firms than female nascent entrepreneurs is supported. Results concerning the internally stimulated opportunity recognition are interesting. Contrary to the way hypothesized, internally stimulated (i.e. Schumpeterian, opportunity driven) entrepreneurship has a negative relationship with growth orientation. Remembering that the explanatory variable is based on one question only and that the interpretation of that one question may have been different in the different data collection efforts, this result should be interpreted cautiously. However, this result seems to give grounds for further hypothesizing in subsequent studies that externally stimulated nascent entrepreneurs actually have a higher growth orientation. Perhaps entrepreneurs that plan for a business startup and evaluate multiple business opportunities in the process have chosen the entrepreneurial career and aim at growing their businesses. Also, the current data shows a weak negative correlation between startup team size and internally stimulated opportunity recognition. It may be that opportunity driven 21

22 entrepreneurs are more often solo entrepreneurs that exploit business opportunities without further aspirations for business growth and continuity. The final hypothesis about the relationship between business type (manufacturing) and growth orientation is not supported. This is not surprising taken the data constraints; there is only a very small number of cases with a positive value for the manufacturing business variable. The R square value of the full model, indicating that 10 per cent of the variance is accounted for, suggests that there is plenty of space for additional explanatory variables. Even though the explanatory power of the model is not great, the model does show that a careful selection of few key explanatory variables and their operationalization in the form of well thought out items makes it possible to explain one aspect of a complex phenomenon. Discussion and Conclusions This research has made use of two harmonized datasets, namely GEM 2001 data from the US and PSED 1 data. Both of these original datasets are publicly available to all researchers; any interested scholar can verify this analysis. Because both data sets are relatively new and complicated to asses, there have been few attempts to harmonize data from the GEM and PSED research programs for a consolidated assessment. While these two data sets are not completely isomorphic, it is possible to harmonize certain parts of data after careful analysis of items. The ultimate benefit of this kind of a procedure is the increased confidence in the research results from two sources; slightly different data collection procedures improve confidence that the phenomena is robust and the larger sample facilitates more precise estimates of model parameters. The key results of the study further support previous studies that have established a relationship between startup team size and growth orientation (Delmar and Davidsson, 1999, 22

23 Eisenhardt and Schoonhoven, 1990, Doutriaux 1992) and gender (male) and firm growth orientation (e.g. Brush, 1992, Anna et al. 2000). A rather speculative, but very interesting result about the negative association between internally stimulated opportunity recognition and growth expectations provides interesting ideas for future research. For example, the PSED dataset comprises numerous items that tap the opportunity recognition of a nascent entrepreneur as well as multiple items that grasp future expectations of the entrepreneur. A more detailed analysis of those items as well as an analysis of the actual growth pattern that is made possible by the longitudinal nature of the PSED data collection effort should provide further insights into the opportunity recognition performance relationship that has been identified as a research gap (Hills and Singh, 2004). Moving beyond the results that concern the actual hypotheses that were tested in the study, we would like to point out some issues about the quality of the study as well as ideas for future research. First of all, the fact that most constructs in this study were operationalized through single item measures raises concerns about construct validity of the study. However, the paper shows that even though the two major data collection efforts on entrepreneurial dynamics have very different goals, there is still some overlap in the datasets and through a careful combining of items from GEM and PSED, a researcher can increase the sample size and statistical validity of the research. As data from post-2001 GEM studies will become publicly available and as PSED 2 data collection is already under way, the opportunities for entrepreneurial scholars to work with comprehensive, harmonized representative samples will be expanded. All items used in the study have been self reported by nascent entrepreneurs. Even though there is no reason to suspect that nascent entrepreneurs would be untruthful in their 23

24 estimates of their firms future growth, evidence from the Danish GEM data, for example, shows that the expected future size of the firm is substantially higher for nascent entrepreneurs than for the owner-managers of existing businesses (Bager and Schott, 2004). Bager and Schott (2004) data show that about one third of the nascent entrepreneurs expect their firm to grow to a size larger than 10 persons in 5 years, while only about one fifth of the manager-owners expect to reach such size. A potential explanation for this phenomenon is that entrepreneurs modify expectations as they gain experience. Experience-based learning process may either increase or reduce entrepreneurs ambitions and expectations, but as Bager and Schott (2004) speculate - perhaps the first option is less frequent than the latter, resulting in an overall reduction in growth expectations. Thus, only extremely cautious predictions on the actual future size of businesses can be made based on the kind of growth expectation data reported in this paper. Age variable turned out to be only marginally significant in the current study, but future research should take a closer look at the relationship between age and growth orientation without the assumption that this relationship should be linear. Even though age in this study was used as a proxy for human capital, an individual s age is both a reflection of past work experiences and current situation; older individuals may have reduced career aspirations (Reynolds, 2004b). Thus, the fact that age does not have a linear relationship with growth orientation may not be that surprising after all. Finally, as the different answering patterns to the opportunity recognition item in PSED vs. GEM data collection show, entrepreneurship researchers have to be extremely careful when it comes to wording of questionnaire items. Even though both opportunity recognition questions, i.e. the one in PSED and the other in GEM, give interviewees similar answer categories, the wording of the question in GEM most likely urges people to describe themselves as opportunity 24

25 driven entrepreneurs. It is socially more desirable to start a business because of an opportunity, not because of a necessity. The PSED question, again, presents a planning entrepreneur as an alternative to the opportunity entrepreneur. Overall, individuals answers to these questions show that they would rather describe themselves as planning entrepreneurs than necessity entrepreneurs. 25

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