Profitability as a measure of success

This section discusses the results obtained when using profitability as a measure of success. This outcome can be measured either as profitability type I (the monthly revenue exceeds monthly expenses) or II (the monthly revenue also covers the owner’s salaries). To do this, logistic regression was used to measure the impact of NET composition on achievement of these outcomes, and multiple linear regression was used to measure the impact of NET composition on the time taken to achieve the relevant outcome. All the assumptions that correspond to these two techniques were first tested and confirmed.19

6.3.3.1 Profitability type I

The results of the logistic regression using profitability type I as the outcome are reported in Appendix 33 to 35. Models 11 to 14 present the results when studying demographic

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diversity as a compositional predictor, and Models 15 to 18 illustrate the relationship between NET composition studied by HC measures and outcome. Resource heterogeneity and familiarity were included in Models 19 and 20 respectively, to study their effect on the achievement of profitability type I. None of these 10 models were statistically significant, meaning that the models are not a good representation of the study of NET composition and its effect on success.

All the compositional variables except education report negative coefficients. This means that six out of the eight variables support that heterogeneous team compositions are negatively related to nascent entrepreneurial success when measured by profitability type I. Likewise, NETs categorised as ‘others’ are less likely to succeed compared to copreneurs, but these findings are also not statistically significant.

The linear regression models were used to investigate if NET composition had any effect on the time taken to achieve profitability type I. Appendix 40 to 42 report the results from the analyses. Overall, the models are statistically significant at least at 𝑝 < 0.1.

Of the four constructs, only demographic diversity when studied by ethnic demographics reported a significant result. Model 13a shows that ethnicity was related positively to profitability type I (b = 0.38, p < 0.10), which means that the time taken to achieve this outcome was longer for ethnically heterogeneous NETs compared to ethnically homogeneous NETs. However, when other variables from the same construct are added to the model (gender and age), it is no longer statistically significant (see Model 14a in Appendix 40).

NET diversity in terms of age, industry experience and resource heterogeneity reduced the time taken to achieve profitability type I, and ‘others’ type teams reduced the time compared to copreneurial teams. In contrast, heterogeneous compositions in terms of gender, education and start-up experience seemed to prolong the time to succeed. However, none of these seven variables were statistically significant.

From Models 11a to 20a, two controls were consistently significant. The average time invested by the NET members extend the time to achieve this outcome. The second control identified as significant was opportunity. This also reported a weak (𝛽 < 0.2) but

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still positive significant effect, meaning that opportunity entrepreneurs take more time to make revenues that would cover the business expenses.

6.3.3.2 Profitability type II

The results of the logistic regression performed by using profitability type II as a measure of success are presented in Appendix 36 to 38. Unlike profitability type I, profitability type II reports two significant findings. First, Model 22 in Appendix 36 presents the results when analysing age demographics. The model was statistically significant at 𝑝 < 0.01, and shows that age was statistically significant at (𝑏 = 0.50, 𝑝 < 0.01), meaning that NETs with higher diversity in terms of age were 65.1% more likely to achieve Profitability Type II compared to age-homogeneous teams. This effect remains significant when the other two demographic variables are included in Model 24. In this case, age diversity reported a positive and statistically significant coefficient (𝑏 = 0.51, 𝑝 < 0.01) with an odds ratio of 1.666.

Model 30 presents the second significant finding (see Appendix 38). The overall model was statistically significant at 𝑝 < 0.05. It also reports that familiarity has a positive coefficient (𝑏 = 0.90, 𝑝 < 0.01), meaning that NETs formed by family, friends and colleagues were more likely to achieve profitability type II compared to copreneurs. According to the odds ratio, ‘others’ teams are 2.5 times more likely to achieve profitability type II compared to copreneurs.

Model 21 to 30 shows that when the nascent business develops in the trading industry, it seems to be less likely to achieve profitability type II compared to those in the manufacturing industry. This is significant when gender, ethnicity, human capital, resources or familiarity are included in the models. However, when the model includes age, this effect is no longer significant.

Multiple linear regression results representing the time to make the profitability type II milestone offers a different explanation to that of the logistic models. In relation to the full models, Models 21a to 30a (see Appendix 44 to 46) are statistically significant. The models related to the demographic diversity construct are presented in Appendix 44. Gender and age, when studied separately, showed a negative non-significant effect, meaning that diversity in terms of these two variables reduced the time taken to achieve

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profitability type II compared to homogeneous compositions. In contrast, ethnicity showed a positive non-significant effect, suggesting that ethnically heterogeneous teams extend the time taken to achieve the third milestone.

The three human capital measures returned a positive effect when studied separately (see Appendix 45, Models 25a to 27a) or included in the same model (see Appendix 45, Model 28a). NETs with heterogeneous compositions in terms of education, industry experience, or start-up experience extended the time taken to achieve profitability type II compared to homogeneous compositions. Model 29a shows that, the higher the level of resource heterogeneity, the less time needed to achieve this outcome (𝑏 = 0.10, 𝛽 = −0.13, 𝑛. 𝑠. ). Nonetheless, this finding was also not statistically significant. Model 30a includes familiarity as the compositional predictor variable. It presents a positive, weak, and nonsignificant effect (𝑏 = 0.14, 𝛽 = 0.07, 𝑛. 𝑠. ).

The type of industry appeared as significant in each of the models regardless of which compositional construct was observed. NET efforts in the trading industry achieved profitability type II in less time than those in the manufacturing industry. This effect was consistently significant at 𝑝 < 0.05 and 𝛽 > 0.20.

Like the results from the other two outcomes, the average time spent in the business by the members seems to increase the time taken to achieve success instead of reducing it. This result is observed in Models 21a to 30a, with a significant level of 𝑝 < 0.05 in most of the cases.