Success Factors in New Ventures: A Meta-analysis

Michael Song, Ksenia Podoynitsyna, Hans van der Bij, and Johannes I. M. Halman


Technology entrepreneurship is key to economic development. New technology ventures (NTVs) can have positive effects on employment, and could rejuvenate industries with disruptive technologies. However, NTVs have a limited survival rate. In our most recent empirical study of 11,259 NTVs established between 1991 and 2000 in the United States, we found that after four years only 36 percent, 4,062 companies with more than five full-time employees, had survived. After five years, the survival rate fell to 21.9 percent, leaving only 2,471 firms still in operation with more than five full-time employees. Thus, it is important to examine how new technology ventures can better survive. In the academic literature, a number of studies focus on success factors for NTVs. Unfortunately, empirical results are often controversial and fragmented.

To get a more integrated picture of what factors lead to the success or failure of new technology ventures, we conducted a meta-analysis to examine the success factors in NTVs. We culled the academic literature to collect data from existing empirical studies. Using Pearson correlations as effect size statistics, we conducted a meta-analysis to analyze the findings of 31 studies and identified 24 most-widely researched success factors for NTVs.

After correcting for artifacts and sample size effects, we found that among the 24 possible success factors identified in the literature, 8 are homogeneous significant success factors for NTVs (i.e., they are homogeneous positive significant meta-factors that are correlated to venture performance). They are supply chain integration, market scope, firm age, size of founding team, financial resources, founders’ marketing experience, founders’ industry experience, and existence of patent protection.

Of the original 24 success factors, 5 were not significant: the success of technology ventures are not correlated with founders’ R&D experience, founders’ experience with start-ups, environmental dynamism, environmental heterogeneity, and competition intensity.

The remaining 11 success factors are heterogeneous. For those heterogeneous success factors, we conducted a moderator analysis. Of this set, 3 appeared to be success factors and 2 were failure factors for subgroups within the NTVs’ population. To facilitate the development of a body of knowledge in technology entrepreneurship, this study also identifies high-quality measurement scales for future research. We conclude the article with future research directions.

Keywords: meta-analysis, technology, entrepreneurship, performance


Technology entrepreneurship is key to economic development. New Technology Ventures (NTVs) can have positive effects on employment, and could rejuvenate industries with disruptive technologies (Christensen and Bower, 1996). Unfortunately, the survival rate of NTVs is the lowest among new ventures in general. To examine the survival rates of new ventures, we conducted a longitudinal analysis of 11,259 new technology ventures established between 1991 and 2000 in the United States. The empirical results reveal that after four years only 36 percent (or 4,062 companies) with more than five full-time employees, had survived. After five years, the survival rate fell to 21.9 percent, leaving only 2,471 firms with more than five full-time employees still in operation.

Why is this research important?

Given the high failure rate of NTVs, it is important to identify what factors lead to the success and failure of these ventures. Current academic literature, however, does not offer much insight. Numerous studies focus on success factors for new technology ventures, but the empirical results are often controversial and fragmented. For example, the data on R&D investments alone yield ambivalent conclusions. While Zahra and Bogner (2000) found no significant relationship between R&D expenses and NTV performance, Bloodgood, Sapienza, and Almeida (1996) found a negative relationship and Dowling and McGee (1994) found a positive relationship between R&D investments and NTV performance. Similarly, although NTVs often develop knowledge-intensive products and services (OECD, 1997), the research results on product innovativeness have been ambiguous. More than two-thirds of the empirical studies have found a positive relationship between product innovation and firm performance, while the remaining studies have found a negative relationship or none at all (Capon, Farley, and Hoenig, 1990; Li and Atuahene-Gima, 2001).

The inconsistent and often contradictory results can stem from methodological problems, different study design, different measurements, omitted variables in the regression models, and noncomparable samples. To help resolve this problem, we looked for a method that would operate independently of model composition. Meta-analysis provides a solution (Hunter and Schmidt, 1990, 2004) and a lens through which we can evaluate the success factors that contribute to NTVs’ performance. We based our meta-analysis on studies that explicitly focus on antecedents of NTV performance.

This paper attempts to make several contributions to technology entrepreneurship literature: (1) our integrated quantitative evaluation of the success factors of new technology ventures provides one step toward developing an integrated theoretical foundation for technology entrepreneurship, (2) it identifies universal success factors, (3) it identifies success factors that are controversial and, by moderator analysis, offers some tentative reasons for those controversies, (4) it reports existing high-quality scales of constructs that are important for NTV performance, and (5) it proposes and provides a new theoretical framework for studying success factors of technology ventures and a road map for future research in technology entrepreneurship.

Organization of the paper

This paper is organized in the following manner. First, we explain our data collection and methodology. We then present the results of our research, including the results of the meta-analysis, examples of high-quality scales, and the conclusions and implications. We conclude the paper with a description of its limitations and future research directions.


Meta-analysis is a statistical research integration technique (Hunter and Schmidt, 1990). One aspect that clearly differentiates it from narrative reviews is its quantitative character. Unlike primary research, in a meta-analysis the data analyzed consist of the findings from previous empirical studies (Camisón-Zornoza et al., 2004). Just as empirical research requires the use of statistical techniques to analyze its data, meta-analysis applies statistical procedures that are specifically designed to integrate the results of a set of primary empirical studies. This allows meta-analysis to pool all the existing literature on a given topic, not only the most influential and best-known studies (Stewart and Roth, 2001, 2004). At the same time, meta-analysis compensates for quality differences by correcting for different artifacts and sample sizes (Hunter and Schmidt, 1990, 2004).

There are two main types of meta-analytic studies in the literature. The first focuses on a relationship between two variables or a change in one variable across different groups of respondents. In general, this type of meta-analysis is strongly guided by one or two theories (e.g., Palich, Cardinal, and Miller, 2000; Stewart and Roth, 2001, 2004). The second type of meta-analytic studies examines a large number of meta-factors related to one particular focal construct, such as performance. Such meta-analyses aim to integrate all the existing research on that focal construct and are largely atheoretical because the research they combine rests on heterogeneous theoretical grounds (e.g., Gerwin and Barrowman, 2002; Montoya-Weiss and Calantone, 1994). Because the current literature teems with numerous theoretical streams where only the setting (new firms) is the common denominator (Shane and Venkataraman, 2000), we chose the second type of meta-analysis to study the potential success meta-factors of NTV performance. We selected independent ventures and collected studies that explicitly focused on antecedents of NTVs’ performance.

In our study, we explore—rather than define ourselves—what “new technology venture” means in the literature. Primary studies use such terms as “new,” “adolescent,” “young,” or “emergent“ to define the “new” axis; and “high technology,” “technology-intensive,” and “technology-based” to describe the technology domain. We examined past research studies where the majority of the sample represented such “new” “technology” ventures. In general, the primary studies set the maximum age for NTVs at 15 years, yet most primary studies selected cut-off values of 6 and 8 years. Another important selection criterion was the publication of the correlation matrix in the paper, because the correlation matrices serve as the main input for the meta-analysis. All the collected studies investigated surviving NTVs; consequently, we do not consider failures in our meta-analysis.

Meta-analysis allows the comparison of different empirical studies with similar characteristics, and thus lets researchers integrate the results. To conduct a meta-analysis it is important to select studies as input for the analysis and follow a meta-analytical protocol to arrive at those results.

Select Studies as Input for the Analysis

First, we combed the literature for research that discussed the success factors of NTVs, using the ABI-INFORM system and the Internet. We used keywords—“new,” “adolescent,” “young,” “emerging” and “high-tech,” “technology,” “technology-intensive,” “technology-based”—to limit our sample’s age and domain. Finally, to assess the type of firm, we applied the keywords “firm,” “venture,” and “start-up.” We intentionally did not limit the studies to those recognized as the best in the field, as usually done in a narrative review: this would have betrayed the spirit of meta-analysis (Hunter and Schmidt, 1990). Instead, we collected as much research as possible, corrected later for any quality differences and controlled for missing studies.

After we gathered papers from ABI-INFORM and the Internet, we added cross-referenced studies from them. In total, we collected 106 studies that met our search criteria. Next, we ensured that the articles on our list (1) represented the correct level of analysis, (2) significantly reflected NTVs, and (3) reported a correlation matrix with at least one antecedent of performance and one performance measure. This procedure reduced the number of appropriate research studies to 31 due to the absence of correlation matrices. Appendix A details our study sample by countries of origin, industries, performance measures, the minimum and maximum ages of the ventures, and their sample sizes. In addition, we provide two other features. First, “sample type” indicates the particular characteristics of the sample. This may be NTVs that went through initial public offering (IPO), ventures funded by venture capitalists (VC), ventures from a general database of NTVs, NTVs involved in a governmental support program, internationalizing NTVs that have activity abroad, or combinations of these types. Second, “venture origin” indicates whether the venture was actually independent. Although our meta-analysis focused primarily on independent ventures, it also included mixed samples of independent and corporate ventures, where most were independent, and samples where the type of venture was not specified. Appendix B lists the journals from which the 31 papers originate.
When coding the studies, we took care to refer to the scales reported in the primary studies, so that dissimilar elements would not be combined inappropriately, and conceptually similar variables would not be coded separately, to compensate for the slightly different labels that authors use to refer to similar constructs (Henard and Szymanski, 2001).

Protocol for Meta-analysis

We used Hunter and Schmidt’s protocol (1990) for our meta-analysis. Our most important consideration was to the ability to make comparisons across research studies. To do this, we could draw on Pearson correlations between a meta-factor and the dependent variable or the regression coefficient between the meta-factor and the dependent variable. Because regression coefficients depend on the particular variables included into the model and because the models vary across studies, we followed the suggestions of Hunter and Schmidt (1990). Hunter and Schmidt strongly encourage using Pearson correlations as the input, because correlations between two variables are independent of the other variables in the model (Hunter and Schmidt 1990). Other meta-analytic studies have made this choice, including Gerwin and Barrowman (2002) and Montoya-Weiss and Calantone (1994).

Another advantage of Hunter and Schmidt’s method (1990) is their use of random effects models instead of fixed effects models (Hunter and Schmidt, 2004; p.201). The distinction is as follows: fixed effects models assume that exactly the same “true” correlation value between meta-factor and dependent variable underlies all studies in the meta-analysis, while random effects models allow for the possibility that population parameters vary from study to study. Given the differences in how NTVs were defined in the selected primary studies, the choice for random effects models was appropriate.

Following the procedure of Hunter and Schmidt (1990), our second step was to correct meta-factors for dichotomization, sample size differences, and measurement errors.

1) To correct dichotomized meta-factors: we made a conservative correction by dividing the observed correlation coefficient of the sample by 0.8, because dichotomization reduces the real correlation coefficient by at least 0.8 (Hunter and Schmidt, 1990, 2004).
Thus, individual correction of observed correlations for dichotomization is as follows:

ad:= correction for dichotomization; ad=0.8 if variable is dichotomized and ad=1 if it is not;
rooi:= observed correlation of the primary study i.

2) To correct sampling error: we weighted the sample correlation by sample size (Hunter and Schmidt, 1990, 2004).
The formula for the weighted average of correlations corrected for sample size is:

where Ni:= sample size of the primary study i.

3) To remedy measurement errors: we used Cronbach’s alphas. We divided the correlation coefficient by the product of the square root of the reliability of the meta-factor and the square root of the reliability of performance. Since reliabilities were not always reported, we reconstructed them by using the reliability distribution (Hunter and Schmidt, 1990, 2004).
Thus, the formula for real population correlation is:

= compound reliability correction factor;
:= average of the square roots of reliabilities of independent variables composing a
given meta-factor;
: average of the square roots of reliabilities of dependent variables composing a
given meta-factor.

The third step in the meta-analysis protocol was to determine whether a meta-factor was a success factor. To accomplish this, we assessed three conditions. First, the studies should have, in essence, the same correlation. Other meta-analysis procedures often use a Chi-square test to reveal this homogeneity. However, Hunter and Schmidt (1990, 2004) argue against it and state that this test will have a bias because of uncorrected artifacts. They suggest a variance-based test. The total variance in the correlation coefficient has three sources: variance due to artifacts (dichotomization and measurement errors), variance due to sampling error, and real variance due to heterogeneity of the meta-factor. The meta-factor is assumed to be homogeneous, if the real variance is no more than 25 percent of the total variance. According to Hunter and Schmidt (1990, 2004), in that case unknown and uncorrected artifacts account for these 25 percent, so that the real variance is actually close to zero. We describe the used formulas in Appendix D.

For homogeneous meta-factors, we applied two significance tests. First, we determined whether the whole confidence interval (based on the real standard deviation) was above zero. Second, if it was above zero, we calculated the p-value for the real correlation to estimate the degree of significance. Both of these significance tests are necessary, because the p-value is misleading when part of the confidence interval of the real correlation is below zero. Only when all three conditions held did we consider a given meta-factor to be a success meta-factor for NTVs.
For those heterogeneous meta-factors, we conducted a moderator analysis. We divided the data into subgroups according to various methodological characteristics (see Appendix A). Then, we conducted a separate meta-analysis for each subgroup, hoping to find homogeneous meta-factors in the subgroup in two steps. First, we conducted moderator analysis to deal with different performance measures. Second, we checked whether country, industry, sample type, venture origin, or maximum age of the NTVs in the sample were possible moderators. Third, we conducted moderator analysis for different meta-factor measures.

Finally, we reviewed the “file drawer” in an attempt to assess any publication bias. Because there is a general tendency to publish only significant results, insignificant results are often abandoned in researchers’ file drawers (Hunter and Schmidt, 1990; Rosenthal, 1991). This “file drawer” technique provides a number, XS, indicating the number of null-result studies that when added, would make the total significance of a meta-factor exceed the critical level of 0.05. Thus, the higher the value of XS, the more stable and reliable the results are. If XS, is 0, it indicates that the meta-factors are already insignificant according to the p-value criterion.


Success Factors of Technology Ventures

Our meta-analysis revealed 24 meta-factors related to the performance of NTVs. We present the definitions of these meta-factors in Table 1.


Table 2 (below) reports the meta-analytic results on the antecedents, or the success meta-factors of NTVs’ performance. To be concise and limit the sensitivity of the results to studies not included in our meta-analysis, Table 2 presents only the meta-factors found in three or more research studies. The table presents r, an estimate of the real population correlation; total N, the aggregate sample size; and K, the number of correlations that build a given meta-factor. Both N and K are conservative: we counted each study only once. Ninety-five (95) percent confidence interval is the spread of the real correlation variance. XS is the critical number of null-results studies.


To make the analysis of the meta-factors more transparent and interpretable, we generate appropriate categories grounded in the literature’s existing frameworks (Chrisman, Bauerschmidt, and Hofer, 1998; Gartner, 1985; Timmons and Spinelli, 2004). These categories are: (a) Market and Opportunity, (b) the Entrepreneurial Team, and (c) Resources. After three researchers reviewed those categories for completeness and appropriateness, we conducted content analysis, a classification technique that assigns variables to a particular category. Two researchers independently assigned each variable to a category. The two researchers agreed on variables' categorizations in 91.2 percent of the cases across 306 variables. A third researcher resolved any disagreements, making the final categorization. At the same time, variables were combined to form meta-factors.

Reflecting the primary studies, theMarket and Opportunity category typically described either the market characteristics, such as environmental dynamism, environmental heterogeneity, and competitive strategies based on Porter’s (1980) typology. The Entrepreneurial Team category included characteristics of the NTV team, including experience and capabilities, both as individuals and as a team. The Resources category united a broad scope of factors, comprising resources, capabilities, and characteristics of the NTVs as firms. Such resources included financial resources, firm size, patents, and university partnerships.

The meta-factors were unevenly distributed across the three categories. The majority fell into the Resources category; the smallest number, into the Entrepreneurial Team category. The Resources category consisted of heterogeneous meta-factors for 55 percent and the Market and Opportunity category for 56 Percent. Only the Entrepreneurial Team category was completely homogeneous.

Results in Table 2 reveal eight universal success factors (i.e., they are homogeneous positive significant meta-factors that are correlated to venture performance):

  • supply chain integration (r = 0.23, p<0.001)

  • market scope (r = 0.21, p<0.001)

  • firm age (r = 0.16, p<0.001)

  • size of founding team (r = 0.13, p<0.01)

  • financial resources (r = 0.12, p<0.01)

  • marketing experience (r = 0.11, p<0.05)

  • industry experience (r =0.11, p<0.05)

  • patent protection (r =0.11, p<0.05)

One success factor represented Market and Opportunity, five success factors represented Resources, and two success factors were part of the Entrepreneurial Team category.

Results in Table 2 also suggested that the following five factors have no significant effects on technology venture performance: 1) R&D experience, 2) prior start-up experience, 3) environmental dynamism, 4) environmental heterogeneity, and 5) competition intensity. Three of these meta-factors represented Market and Opportunity and two represented the Entrepreneurial Team category.


As Table 2 indicates, 11 of the 24 meta-factors had heterogeneous correlations (i.e., the importance of the factors depend on situations). Therefore, we conducted moderator or subgroup analysis for differences in performance measures, meta-factor measures, venture origin, maximum age of venture in the sample, sample type, country, and industry.

Table 3 presents those results from the moderator analysis, including r, an estimate of the real population correlation; total N, the aggregate sample size ; K, the number of correlations that build a given meta-factor; the 95 percent confidence interval of the real variance; and XS, the critical number of null-results studies.
Table 3 also presents the variance explained by dichotomization of meta-factors, measurement, and sampling error. This variance must be more than 75 percent to yield a homogeneous factor. In that case, the real variance is less than 25 percent of the total variance of correlations from the primary studies. The remaining variance is likely due to other unknown and uncorrected artifacts, and therefore it can be neglected (Hunter and Schmidt, 1990, 2004). To keep overview, for the moderator, or subgroup analysis, we only report the meta-factors with at least two subgroups that have no overlapping confidence intervals; each subgroup consists of at least two studies.


The results reported in Table 3 suggest that of the 11 heterogeneous factors, 3 meta-factors (firm type, R&D alliances, and product innovation) had distinct moderator subgroups (i.e., the effect of these factors on venture performance depends on situation). The relationship between firm type and performance depended on the way performance was measured. Firm type was insignificantly related to the profits of NTVs, but significantly and positively related to the sales of NTVs. No other (methodologically oriented) moderators affected the firm type.
R&D alliances were negatively associated with performance for independent ventures. However, for ventures of a mixed origin,R&D alliances were positively associated with performance.

Product innovationwas moderated by venture origin. For independent NTVs, product innovation has a significantly negative association with performance. However, for samples with mixed firm type, product innovation has a significantly positive association with performance.

By examining the results in Table 2, eight meta-factors proved inconclusive: internationalization, low-cost strategy, market growth rate, marketing intensity, R&D investments, firm size, non-governmental financial support, and university partnerships. Of these eight meta-factors, market growth rate and non-governmental financial support have only one subgroup with two or more studies when differences in meta-factor measurements are considered. We also found only one suitable subgroup for internationalization when looking at sample type, for marketing differentiation when looking at the country, and for university partnerships when either looking at the sample type or the industry. Further research is needed to validate or disprove these potential moderators. Finally, no methodological moderators were found for R&D investments, low cost strategy, and firm size.

Identification of High-Quality Measurement Scales

Our high-quality scale is either a ratio/interval measure or a Likert-type scale with a Cronbach’s alpha of at least 0.7 (Nunnally 1978) that consists of at least three items. The last condition ensures that Likert-type scales will be reliable and that they will still hold a certain reserve for future studies in case one of the items does not load. Identification of such scales can assist the work of future researchers in the technology entrepreneurship and alert them to poor operationalization practices. Consequently, one of our study goals was to report on scales from meta-factors that were stable and reliable success factors for NTVs.

We selected only significant homogeneous (unmoderated) meta-factors from Table 2 or homogeneous subgroups from Table 3. This selection resulted in 11 strongly supported NTV success factors. To ensure that individual scales would perform well in further studies, within each meta-factor, we selected only scales with an observed correlation significant at the 0.05 level. Marketing experience did not have a significant high-quality scale in the previous studies. Therefore, we report high-quality scales found for 10 NTV success factors in Appendix C. Further research should be conducted on other potentially significant success factors (see moderated meta-factors from Table 2) before valid conclusions can be drawn.


Major Research Results

To the best of our knowledge, this is the first systematic, quantitative effort to integrate research on antecedents of new technology ventures (NTVs). The results of our study are summarized in Figure 1. In the spirit of meta-analysis, we present the results in four main blocks: significant and insignificant homogeneous factors, heterogeneous factors with moderators and heterogeneous factors without moderators. The latter two blocks are shown by the dotted lines. We also show within each block from which category a given meta-factor originates.


The results of our meta-analysis are compelling: only eight of the 24 meta-factors are homogeneous and significant, suggesting that they are the only universal success factors for the performance of NTVs. The majority of them belong to the Resources group (5), two to the Entrepreneurial Team category, and one belongs to the Market and Opportunity category. Five of the 24 meta-factors were homogeneous, but not significant. Two meta-factors are success factors for sub-groups in the population of NTVs and one works only for sales and not for profit-based performance. Eight of the 24 meta-factors remain heterogeneous even after we searched for methodological moderators. They are evenly distributed across Market and Opportunity, and Resources categories. Therefore, more research is necessary on the heterogeneous, moderated meta-factors listed in Table 2.

Theoretical and Managerial Implications

An essential implication from our meta-analysis for future regression studies is that when results are contradicting or non-significant it may be due to our heterogeneous factors. In that case, a detailed study to significant differences in correlation coefficients for various sub samples between factor and dependent variable may explain the deviating results.

In the Market and Opportunity category nine success factors were represented in our meta-analysis. Market scope clearly enhances NTV performance, as well as product innovation for corporate ventures. However, product innovation is detrimental for independent NTVs. Apparently a radical innovation strategy is too risky for independent ventures, while corporate ventures can share risks with their parent companies. Entrepreneurs may keep these findings into consideration.

Five success factors were heterogeneous in this category, while three were insignificant. Examining the number of heterogeneous meta-factors, one might conclude that the NTV population is generally too heterogeneous to examine its success factors. This idea was supported by the fact that for most of these factors no clear methodological moderators were found, suggesting that there may be other moderators that have not been reported in published research studies (e.g., educational background of the entrepreneur).

Until now, in contingency research scholars have focused on product differentiation strategy and its interactions with different environmental characteristics, such as competition intensity and environmental dynamism (Li, 2001; Li and Atuahene-Gima, 2001; Zahra and Bogner, 2000). Other competitive strategies have received considerably less attention in studies of environmental contingencies.

Finally, existing meta-factors in this category describe opportunity in a rather indirect way. A direct focus on the opportunity concept, the key concept of entrepreneurship (Shane and Venkataraman, 2000) is missing. A range of opportunity dimensions may be considered. For example, the source of an opportunity; sources vary in the amount of uncertainty and thus have different degrees of success predictability (Drucker, 1985; Eckhardt and Shane, 2003).
In the Entrepreneurial Team category the characteristics of the entrepreneurial team were described by four types of experience: marketing, R&D, industry, and start-up experience. Experience in marketing and industry were homogeneous, significant success factors. Both prior start-up experience and R&D experience were insignificant at the 0.05 level. The former finding may be further evidence of overestimation of the role of prior start-up experience, ironically one of the most profound venture capitalist evaluation criteria (Baum and Silverman, 2004). It should be noted that the latter finding might have been caused by lack of variance in the samples of NTVs, since NTVs are often defined by having a certain amount of R&D expenses. Our findings suggest that acquiring more experience in marketing and industry may lead to higher NTV performance.

The weak results of the entrepreneurial team factors can be explained in several ways. First, findings may be due to the tendency to limit experience to the number of years the founder(s) spent in a certain area, without measuring the quality, variety, and complementarity of both joint and individual experiences (Eisenhardt and Schoonhoven, 1990; Lazear, 2004). Moreover, certain aspects of the Entrepreneurial Team may have been overlooked in the literature on NTVs. In particular, researchers have identified a variety of cognitive characteristics that make entrepreneurs distinctive, such as psychological traits (Gartner, 1985; Stewart and Roth, 2004), cognitive biases, and thinking styles (Baron, 1998, 2004).

Another explanation is that the influence of the meta-factors in this category manifests itself through a more subtle, indirect mechanism. Researchers have concentrated their efforts on direct links between personality characteristics of entrepreneurs and the performance of NTVs. However, recent research has found support for their indirect influence on the performance of ventures (Baum, Locke, and Kirkpatrick, 1998); for example, human capital factors influence performance by directing the competitive strategies entrepreneurs choose (Baum, Locke, and Smith, 2001) or channeling the opportunities they recognize (Shane, 2000). Future research should investigate these alternative explanations.

The Resource category consists of more than half of the identified success factors in the meta-analysis. Although a significant amount of research has been conducted within this category, results have not been conclusive. We found five success factors within this category: supply chain integration, firm age, size of founding team, financial resources, and patent protection. So investing in supply chain integration seems to yield higher returns. However, except for supply chain integration, most factors may not be fully controllable ones. One may control the size of the founding team and collect more experience in the team (indicating that this factor is close to the Entrepreneurial Team factors), while enlarging communication requirements and facing power problems. In any case, the meta-analysis results indicated that enlarging the team may improve NTV performance. The financial resources, however, may be more difficult to control. Even though our study results suggest that more financial resources may improve performance, not all firms can absolutely control their financial resources. Nevertheless, setting up NTVs may need to wait until required financial resources have become available. Finally, when a possibility of patent protection exists firms should take the opportunity.

Our analysis also found six heterogeneous meta-factors within this category. In our moderator analysis, we showed that firm type has a positive influence on sales performance. Moreover, in ventures of mixed origin, R&D alliances improved performance, while for independent ventures these alliances worsened performance. Perhaps equity conditions could better be negotiated in corporate ventures, having more power than independent ventures.
A remarkable finding of our study was that the R&D investments were not a success factor (much like product innovation, mentioned earlier). Generally, when looking at all resource factors, we did not find any particularly technological resource factors. Within the population of NTVs, these factors generally have a high level and there was insufficient variation in these factors. However, in line with a resource-based view of the firm, the focus may need to be on the quality of the resources rather than the quantity. Barney (1991) posited that the value, rareness, non-imitability, and non-substitutability of resources—instead of the amount of resources—led to competitive advantage. We advise future research consider that direction.



As with all research, this meta-analysis had several limitations. First, the Pearson correlations we used are primarily intended for measurement of the strength of a linear relationship between two variables. In the case of zero correlation, a chance existed of observing a vivid curvilinear relationship between variables. Second, the primary studies used in the meta-analysis based their samples on surviving NTVs because of the difficulties in accessing NTVs that failed. Therefore, any meta-analysis in this topical area must be inherently biased toward more successful, surviving firms. This bias has two implications: (1) meta-factors that influence the success and mortality of a NTV could conceivably be substantially different (Shane and Stuart, 2002) and (2) strategies (meta-factors) that seem to deliver the best performance can be misleading. The greater the potential a particular strategy has, the greater the risks associated with it. Finally, the last limitation of the study was the sample size of the meta-analysis itself, which included 31 studies reflecting the emerging nature of this research domain as well as the generally poor standards of descriptive statistics publication. However, the 31 studies provided a sufficient sample size for a preliminary meta-analysis (Gerwin and Barrowman, 2002; Montoya-Weiss and Calantone, 1994).

Future Research Directions
Our meta-analysis should not and must not preclude future research, but rather stimulate and direct it. Based on our results and implications and current literature (e.g., Gartner, 1985; Timmons and Spinelli, 2004), we suggest the following theoretical framework for future research.


The theoretical framework consists of five elements, Entrepreneurial Opportunities, Entrepreneurial Team, Entrepreneurial Resources, Strategic and Organizational Fit, and Performance. The dotted lines represent the fit. In general, we suggest to take this framework as a basis for future research and to examine its factors and, in particular, the linkages into more detail in future research. Below, we will define the categories in the framework, list some factors and give future research directions following from our meta-analysis.

Entrepreneurial Team

Entrepreneurial Team is defined as the management team of the new venture (Timmons and Spinelli, 2004). Entrepreneurial Team is a core element of the entrepreneurship phenomenon. Shane and Venkataraman (2000) characterize entrepreneurship as the nexus between the individual and the opportunity. Researchers identified the following factors in this category:

  • Members' characteristics (age, attributes, biases, thinking styles, etc.)

  • Experience, knowledge, and skills

  • Values and beliefs

  • Behaviors and leadership styles.

According to our meta-analysis, future research should include cognitive biases and thinking styles, the quality, variety and complementarity of team member experiences, as well as the mediating and moderating influences of the team factors on other antecedent performance relationships. In this research, industry and marketing experience may be considered as control variables.

Entrepreneurial Opportunity

Entrepreneurial Opportunities are those situations in which new goods, services, raw materials, and organizing methods may be introduced and sold at greater price than their cost of production (Shane and Venkataraman, 2000). The contemporary definitions of entrepreneurship emphasize that it is opportunity-driven, therefore, Entrepreneurial Opportunity is an essential part of the entrepreneurship framework (Eckhardt and Shane, 2003; Shane and Venkataraman, 2000; Timmons and Spinelli, 2004). Researchers distinguish the following factors in this category:

  • Opportunity dimensions (type of opportunity, form of opportunity, source of opportunity, etc.)

  • Environmental characteristics (environmental dynamism, environmental heterogeneity, internationalization, etc.)

  • Market characteristics (market growth rate, competition intensity, entry barriers, buyer and supplier power, etc.).

Based on our meta-analysis, future research may include the direct examination of opportunity dimensions, as well as search for moderators of the internationalization performance and the market growth rate performance relationship. In this future research, market scope may be considered as a control variable.

Entrepreneurial Resources

Entrepreneurial Resources include all tangible and intangible assets that a firm may possess and
control (Chrisman et al., 1998; Timmons and Spinelli, 2004). Gartner (1985) has identified the resources accumulation process as an essential part of the entrepreneurial functions, while Timmons and Spinelli (2004) consider Entrepreneurial Resources as an important building block of their venture creation framework. Important factors within this category are:

  • Financial means and investments (financial resources, non-governmental financial support, R&D investments, etc.)

  • Intellectual property (patent protection, licensing, etc.)

  • Partnerships and networks (R&D alliances, supply chain integration, university partnerships, etc.)

  • Institutional characteristics (firm age, firm size, firm type, size of the founding team, etc.).

From our meta-analysis we suggest to include more qualitative measures of resources into future research, like the value, rareness, non-imitability, and non-substitutability of resources (Barney, 1991). Moreover, we advice more moderator research on the non-governmental financial support performance and the R&D investment performance relationship, as well as the relationships between university partnerships and performance, and firm size and performance. In future research financial resources, patent protection, supply chain integration, firm age and size of the founding team may be considered as control variables.

Strategic and Organizational Fit

Strategic and Organizational Fit is defined as the congruence between strategy and organization of the new venture and the driving forces Entrepreneurial Team, Entrepreneurial Opportunity, and Entrepreneurial Resources (Chrisman et al., 1998; Timmons and Spinelli, 2004). Fit regards an important uniting aspect of the various elements of the framework. Gartner (1985) refers to a new venture as a gestalt of individuals, environment, organization, and process dimensions, indicating that all elements in a new venture must be balanced. We consider the following factors in this category:

  • Competitive strategy (low cost strategy, market scope, marketing intensity, product innovation, etc.)

  • Structure

  • Processes

  • Systems.

Our meta-analysis suggests more interaction research between competitive strategies and
environmental characteristics, such as environmental dynamism and competition intensity. In particular, other competitive strategies than product innovation may be examined.


Our framework suggests that the better the fit between the driving forces and the strategy and organization of the venture, the better the performance. In our meta-analysis we found a broad scope of performance measures. Once, we found the difference in performance measures to be a moderator of the antecedent performance relationship. Therefore, we suggest to have a broad set of performance measures in future new venture research and to experiment with different subsets of performance measures.



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Since some studies used multiple measures of performance, sum of performance moderator subgroups sample sizes may be greater than total N of a meta-factor.

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