Prediction based on entrepreneurship-prone personality profiles: sometimes worse than the toss of a coin

Abstract

The human personality predicts a wide range of activities and occupational choices—from musical sophistication to entrepreneurial careers. However, which method should be applied if information on personality traits is used for prediction and advice? In psychological research, group profiles are widely employed. In this contribution, we examine the performance of profiles using the example of career prediction and advice, involving a comparison of average trait scores of successful entrepreneurs with the traits of potential entrepreneurs. Based on a simple theoretical model estimated with GSOEP data and analyzed with Monte Carlo methods, we show, for the first time, that the choice of the comparison method matters substantially. We reveal that under certain conditions the performance of average profiles is inferior to the tossing of a coin. Alternative methods, such as directly estimating success probabilities, deliver better performance and are more robust.

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Fig. 1
Fig. 2

Notes

  1. 1.

    Similar approaches, as discussed here, are also used to provide advice, for instance, to young adolescent individuals seeking guidance on occupational choice.

  2. 2.

    For a review of the recent literature on entrepreneurship-related personality characteristics, see Kerr et al. (2017).

  3. 3.

    For an overview of studies analyzing the effect of risk attitudes on entrepreneurial activities, see also Kerr et al. (2017).

  4. 4.

    This kind of fit measure is widely used in psychology, and not only in entrepreneurship; see, inter alia, Chapman and Goldberg (2011).

  5. 5.

    Thus, this simplifying assumption can be relaxed by using a vector of personality variables instead of univariate Γ.

  6. 6.

    Blanchflower (2000), Hundley (2001), and Benz and Frey (2008a, 2008b) show that entrepreneurs experience higher job satisfaction than wage workers such that non-monetary benefits of entrepreneurship might play a significant role with respect to occupational choices.

  7. 7.

    There are two possible interpretations of π with respect to risk consistent with the model setting. First, entrepreneurship is not associated with any risk such that π is the deterministic relative income (relative to alternative income). Second, entrepreneurship is associated with risk and π is the average relative income but the client is risk-neutral and only cares about averages.

  8. 8.

    Typically, even if π can be measured without any problem, we would only have reliable historical data on it for a subset of individuals because it is a counterfactual for those who were never entrepreneurs.

  9. 9.

    Note that \(p_{\text {COIN}}=\mathbb {E}[\mathcal {S}_{\text {\textit {COIN}}}]=\mathbb {P}(\textbf {a}_{\text {\textit {COIN}}}= 1\wedge \textbf {t}= 1)+\mathbb {P}(\textbf {a}_{\text {\textit {COIN}}}= 0\wedge \textbf {t}= 0)\). The coin completely ignores historical and client data such that \(\mathbb {P}(\textbf {a}_{\text {\textit {COIN}}}=a)\) and \(\mathbb {P}(\textbf {t}=t)\) are independent. Hence, we get

  10. 10.

    For instance, Δ(ε,ε) = 1/2 implies that changing the similarity criterion by a certain amount would change the expected recommendation success rate by 50 percentage points in the same setting indicating that approach performance is unstable.

  11. 11.

    Self-employment and wage work can take place at the same time or at different time points.

  12. 12.

    Note that the relatively low number of observations is due to the fact that we need sufficient information about individuals who generated incomes from self-employment and wage work. This is also why we had to use the willingness to take risk as information instead of the Big Five. We show below that this choice does not affect the reasoning of our approach.

  13. 13.

    The code is provided upon request.

References

  1. Anscombe, F., & Glynn, W. (1983). Distribution of Kurtosis statistic for normal statistics. Biometrika, 70, 227–234.

    Google Scholar 

  2. Astebro, T., & Chen, J. (2014). The entrepreneurial earnings puzzle. Journal of Business Venturing, 29, 88–105.

    Article  Google Scholar 

  3. Begley, T., & Boyd, D. (1987). Psychological characteristics associated with performance in entrepreneurial firms and smaller businesses. Journal of Business Venturing, 22, 147–173.

    Google Scholar 

  4. Benz, M., & Frey, B. (2008a). Being independent is a great thing. Economica, 75, 362–383.

  5. Benz, M., & Frey, B. (2008b). The value of doing what you like. Journal of Economic Behavior and Organization, 68, 445–455.

  6. Bernardo, A., & Welch, I. (2001). On the evolution of overconfidence and entrepreneurs. Journal of Economics & Management Strategy, 10, 301–330.

    Article  Google Scholar 

  7. Blanchflower, D. (2000). Self-employment in OECD countries. Labour Economics, 7, 471–505.

    Article  Google Scholar 

  8. Bono, J., Boles, T., Judge, T., Lauver, K. (2002). The role of personality in task and relationship conflict. Journal of Personality, 70, 311–344.

    Article  Google Scholar 

  9. Borghans, L., Duckworth, A., Heckman, J., ter Weel, B. (2008). The economics and psychology of personality traits. Journal of Human Resources, 43, 972–1059.

    Google Scholar 

  10. Bosma, N., Hessels, J., Schutjens, V., Van Praag, M., Verheul, I. (2012). Entrepreneurship and role models. Journal of Economic Psychology, 33, 410–424.

    Article  Google Scholar 

  11. Caliendo, M., Fossen, F., Kritikos, A. (2009). Risk attitudes of Nascent entrepreneurs. Small Business Economics, 32, 153–167.

    Article  Google Scholar 

  12. Caliendo, M., Fossen, F., Kritikos, A. (2010). The impact of risk attitudes on entrepreneurial survival. Journal of Economic Behavior and Organization, 76, 45–63.

    Article  Google Scholar 

  13. Caliendo, M., Fossen, F., Kritikos, A. (2014a). Personality characteristics and the decision to become and stay self-employed. Small Business Economics, 42, 787–814.

  14. Caliendo, M., Kritikos, A., Künn, S., Loersch, C., Schröder, H., Schütz, H. (2014b). Evaluation der Programme Gründercoaching Deutschland. IZA Research Report 61.

  15. Chapman, B., & Goldberg, L. (2011). Replicability and 40-year predictive power of childhood ARC types. Journal of Personality and Social Psychology, 101, 593–606.

    Article  Google Scholar 

  16. Costa, P., McCrae, R., Holland, J. (1984). Personality and vocational interests in an adult sample. Journal of Applied Psychology, 69, 390–400.

    Article  Google Scholar 

  17. Cronbach, L., & Gleser, G. (1953). Assessing the similarity between profiles. Psychological Bulletin, 50, 456–473.

    Article  Google Scholar 

  18. D’Agostino, R. (1970). Transformation to normality of the null distribution of G1. Biometrika, 57, 679–681.

    Google Scholar 

  19. Fischer, R., & Boer, D. (2014). Motivational basis of personality traits: a meta-analysis of value-personality correlations. Journal of Personality, 83, 491–510.

    Article  Google Scholar 

  20. Gartner, W. (1989). ‘Who is an entrepreneur?’ Is the wrong question. Entrepreneurship: Theory and Practice, 12, 47–68.

    Google Scholar 

  21. Green, D., Müllensiefen, D., Lamb, M., Rentfrow, P. (2015). Personalty predicts musical sophistication. Journal of Research in Personality, 58, 154–158.

    Article  Google Scholar 

  22. Heckman, J., Stixrud, J., Urzua, S. (2006). The effects of cognitive and noncognitive abilities on labour market outcomes and social behavior. Journal of Labor Economics, 24, 411–482.

    Article  Google Scholar 

  23. Heller, D., Ferris, D., Brown, D., Watson, D. (2009). The influence of work personality on job satisfaction: incremental validity and mediation effects. Journal of Personality, 77, 1051–1084.

    Article  Google Scholar 

  24. Helmers, C., & Rogers, M. (2010). Innovation and the survival of new firms in the UK. Review of Industrial Organization, 36, 227–248.

    Article  Google Scholar 

  25. Holland, J. (1997). Making vocational choices: a theory of vocational personalities and work environments (3rd edn.) Odessa: Psychological Assessment Resources.

    Google Scholar 

  26. Holmes, T., & Schmitz, J. (1990). A theory of entrepreneurship and its application to the study of business transfers. Journal of Political Economy, 98, 265–294.

    Article  Google Scholar 

  27. Hundley, G. (2001). Why and when are the self-employed more satisfied with their work? Industrial Relations, 40, 293–316.

    Google Scholar 

  28. Jokela, M. (2009). Personality predicts migration within and between U.S. States. Journal of Research in Personality, 43, 79–83.

    Article  Google Scholar 

  29. Kalleberg, A. L., & Leicht, K. T. (1991). Gender and organizational performance: determinants of small business survival and success. Academy of Management Journal, 34, 136–161.

    Google Scholar 

  30. Kerr, W., Nanda, R., Rhodes-Kropf, M. (2014). Entrepreneurship as experimentation. Journal of Economic Perspectives, 28, 25–48.

    Article  Google Scholar 

  31. Kerr, S., Kerr, W., Xu, T. (2017). Personality traits of entrepreneurs: a review of recent literature. Harvard Business School Working Paper, 18–047.

  32. Kihlstrom, R., & Laffont, J.-J. (1979). A general equilibrium entrepreneurial theory of firm formation based on risk aversion. Journal of Political Economy, 87, 719– 748.

    Article  Google Scholar 

  33. Koellinger, P., Minniti, M., Schade, C. (2007). I think I can, I think I can. Journal of Economic Psychology, 28, 502– 527.

    Article  Google Scholar 

  34. Kösters, S., & Obschonka, M. (2011). Public business advice in the founding process: an empirical evaluation of subjective and economic effects. Environment and Planning C: Government and Policy, 29, 123–138.

    Article  Google Scholar 

  35. Levine, R., & Rubinstein, Y. (2017). Smart and illicit: who becomes an entrepreneur and do they earn more? Quaterly Journal of Economics, 132, 963–1018.

    Google Scholar 

  36. Magnusson, D., & Törestad, B. (1993). A holistic view of personality: a model revisited. Annual Review of Psychology, 44, 427–452.

    Article  Google Scholar 

  37. Manso, G. (2016). Experimentation and the returns to entrepreneurship. Review of Financial Studies, 29, 2319–2340.

    Article  Google Scholar 

  38. Mardia, K. (1974). Applications of some measures of multivariate skewness and kurtosis for testing normality and robustness studies. Sankhy A, 36, 115–128.

    Google Scholar 

  39. Mayer, J., & Skimmyhorn, W. (2017). Personality attributes that predict cadet performance at west point. Journal of Research in Personality, 66, 14–26.

    Article  Google Scholar 

  40. Obschonka, M., & Stuetzer, M. (2017). Integrating psychological approaches to entrepreneurship: the entrepreneurial personality system (EPS). Small Business Economics, 49, 203–231.

    Article  Google Scholar 

  41. Obschonka, M., Silbereisen, R., Schmitt-Rodermund, E. (2010). Entrepreneurial intentions as development outcome. Journal of Vocational Behavior, 77, 63–72.

    Article  Google Scholar 

  42. Obschonka, M., Schmitt-Rodermund, E., Silbereisen, R., Goslin, S., Potter, J. (2013). The regional distribution and correlates of an entrepreneurship-prone personality profile in the United States, Germany, and the United Kingdom: a socioecological perspective. Journal of Personality and Social Psychology, 105, 104–122.

    Article  Google Scholar 

  43. Quatraro, F., & Vivarelli, M. (2015). Drivers of entrepreneurship and post-entry performance of newborn firms in developing countries. The World Bank Research Observer, 30, 277–305.

    Article  Google Scholar 

  44. Rauch, A., & Frese, M. (2007). Let’s put the person back into entrepreneurship research. European Journal of Work and Organizational Psychology, 16, 353–385.

    Article  Google Scholar 

  45. Rodionova, Z. (2015). The personality test that could one day decide your credit score. The Independent, 2. September.

  46. Schmitt-Rodermund, E. (2004a). Pathways to successful entrepreneurship: parenting, personality, entrepreneurial competence, and interest. Journal of Vocational Behavior, 65, 498–518.

  47. Schmitt-Rodermund, E. (2004b). Pathways to successful entrepreneurship: parenting, personality, entrepreneurial competence, and interests. Journal of Vocational Behavior, 65, 498–518.

  48. Schumpeter, J. (1934). The theory of economic development. Cambridge: Harvard University Press.

    Google Scholar 

  49. Shaver, K., & Scott, L. (1991). Person, process, choice: the psychology of new venture creation. Entrepreneurship Theory & Practice, 16, 23–45.

    Article  Google Scholar 

  50. Stewart, W., & Roth, P. (2001). Risk propensity differences between entrepreneurs and managers. Journal of Applied Psychology, 86, 145–153.

    Article  Google Scholar 

  51. Stuetzer, M., Goethner, M., Cantner, U. (2012). Do balanced skills help nascent entrepreneurs to make progress in the venture creation process? Economics Letters, 117, 186–188.

    Article  Google Scholar 

  52. Zhao, H., & Seibert, S. (2006). The big five personality dimensions and entrepreneurial status. Journal of Applied Psychology, 91, 259–271.

    Article  Google Scholar 

  53. Zhao, H., Seibert, S., Lumpkin, G. (2010). The relationship of personality to entrepreneurial intentions and performance: a meta-analytic study. Journal of Management, 36, 381–404.

    Article  Google Scholar 

Download references

Acknowledgments

We are indebted two anonymous referees as well as to the participants of the Entrepreneurship Residence Week, in particular to Moren Levesque, Simon Parker, and Mirjam van Praag for their helpful comments.

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Correspondence to Alexander S. Kritikos.

Appendices

Appendix A: Proof of consistency

To establish consistency with the profile-based approach, we must essentially answer the following question: What happens to the distribution of the individual trait if we condition on entrepreneurial abilities? Let μΓ|E denote the mean of Γ for entrepreneurs and let \(\mu _{\Gamma |E^{\textsf {c}}}\) denote the mean of the personality trait for non-entrepreneurs. Similarly, denote the variance of personality trait, Γ, by \(\sigma _{\Gamma |E}^{2}\), respectively \(\sigma _{\Gamma |E^{\mathsf {c}}}^{2}\). It is straightforward to derive that

$$ \mu_{\Gamma|E}=\mu_{\Gamma}+\rho W(\kappa),\qquad\mu_{\Gamma|E^{\textsf{c}}}=\mu_{\Gamma}+\rho V(\kappa) $$
(A.1)
$$ \sigma_{\Gamma|E}^{2}= 1-\rho^{2}w(\kappa),\qquad\sigma_{\Gamma|E^{\mathsf{c}}}^{2}= 1-\rho^{2}v(\kappa) $$
(A.2)

where κ = τμπ, W(κ) = ϕ(κ)/[1 −Φ(κ)] > 0 where ϕ(⋅) is the density and Φ(κ) the distribution function of the standard normal distribution; V (κ) = −ϕ(κ)/Φ(κ) < 0; w(κ) = W(κ)[1 − W(κ)]; and v(κ) = V (κ)[1 − V (κ)].

As the correlation, ρ, determines how strong the connection is between the personality trait and entrepreneurial abilities, we focus on the role of this parameter. If trait and abilities are independent, the correlation between them is zero such that \(\mu _{\Gamma |E}=\mu _{\Gamma |E^{\textsf {c}}}=\mu _{\Gamma }\) and \(\sigma _{\Gamma |E}^{2}=\sigma _{\Gamma |E^{\mathsf {c}}}^{2}= 1\). In such a setting, we cannot construct a distinct personality profile of an entrepreneur. However, if traits and abilities depend on each other with non-zero correlation, there will be a personality profile of an entrepreneur given by μΓ|E. To see this, note that ρ > 0 implies

$$ |\mu_{\Gamma|E}-\mu_{\Gamma|E^{\textsf{c}}}|=\rho[W(\kappa)-V(\kappa)]>0 $$
(A.3)

such that there is a difference between the average trait of an entrepreneur and the average trait of a non-entrepreneur. The difference in Eq. A.3 increases in the correlation between trait and entrepreneurial abilities. Furthermore, the variance of the personality trait conditional on being an entrepreneur, \(\sigma _{\Gamma |E}^{2}\), decreases if the correlation between trait and abilities increases, as can be clearly seen in Eq. A.2.

A personality- or profile-oriented approach has the following strategy. It takes the client’s personality trait, γ, and compares it to the typical trait, μΓ|E, of an entrepreneur. If Γ and π are sufficiently correlated, the Γ-values of entrepreneurs will be concentrated in one place and Γ-values of non-entrepreneurs in another. Hence, similarity between the client’s γ and profile μΓ|E is an indication that the client is an entrepreneur. If the correlation is weak, all Γ-values will be located in roughly one place independent from π such that similarity between the client’s trait, γ, and profile μΓ|E has little meaning.

To show consistency with the variable-oriented approach, let \({\Psi }\in \mathbb {R}\) denote a normally distributed variable with mean μΨ and variance \(\sigma _{\Psi }^{2}\). Ψ is assumed to capture all factors affecting entrepreneurial abilities that are not related to the personality trait, represented by Γ, such that we can assume that Ψ and personality trait, Γ, are independent. The variable-oriented approach is consistent with the following model of entrepreneurial abilities:

$$ {\Pi}=a{\Gamma}+b{\Psi} $$
(A.4)

where a and b are nonzero constant scalars. For instance, let Γ represent extraversion (one of the Big Five personality traits). If a > 0, more extraversion will increase entrepreneurial abilities, which is in line with previous research (Costa et al. 1984; Zhao and Seibert 2006; Zhao et al. 2010). The difference between the variable-oriented perspective and entrepreneurship-prone profiles is that in the model in Eq. A.4 there is no specific reference profile of an entrepreneur. The assumption underlying Eq. A.4 is that, given a > 0, the trait Γ simply positively relates to entrepreneurial abilities, i.e., a higher score in Γ is associated with higher abilities. The model in Eq. A.4 generates a joint distribution of personality trait and abilities that is consistent with our recommendation model. Notice that π in Eq. A.4 is normal (it is the sum of two normal independent variables) with the following parameters:

$$\mu_{\Pi}=a\mu_{\Gamma}+b\mu_{\Psi},\qquad\sigma_{\Pi}^{2}=a^{2}+b^{2}\sigma_{\Psi}^{2} $$

The covariance between π and personality trait Γ is given by

$$\sigma({\Pi},{\Gamma})=a(1 + 2\mu_{\Gamma}^{2})+ 2b\mu_{\Psi}\mu_{\Gamma} $$

Furthermore, it can be demonstrated that π and Γ are jointly normal according to the model in Eq. A.4. The joint distribution of π in model (A.4) and personality trait Γ is bivariate normal if and only if Y = απ + βΓ is normal for any constant \(\alpha ,\beta \in \mathbb {R}\). It is obvious that Y is normal if either α = 0 or β = 0, as π and Γ are both normal. If α = β = 0, Y = 0 with probability 1, which corresponds to a normal distribution with mean and variance zero. Hence, we must demonstrate that Y is normal if α and β are both nonzero. Note that π and Γ are dependent and correlated. Furthermore, note that

$$ Y=\alpha{\Pi}+\beta{\Gamma}=\alpha(a{\Gamma}+b{\Psi})+\beta{\Gamma}=\delta_{\Gamma}{\Gamma}+\delta_{\Psi}{\Psi} $$
(A.5)

where δΓαa + β and δΨαb. Using independence and normality of Γ and π, the moment-generating function of Y is given by

$$ M_{Y}(t)=M_{\Gamma}(\delta_{\Gamma}t)M_{\Psi}(\delta_{\Psi}t)=\exp\left\{ t\delta_{\Gamma}\mu_{\Gamma}+\frac{1}{2}\delta_{\Gamma}^{2}t^{2}\right\} $$
(A.6)
$$\times\exp\left\{ t\delta_{\Psi}\mu_{\Psi}+\frac{1}{2}\delta_{\Psi}^{2}\sigma_{\Psi}^{2}t^{2}\right\} $$

such that

$$M_{Y}(t)=\exp\left\{ t\left[\delta_{\Gamma}\mu_{\Gamma}+\delta_{\Psi}\mu_{\Psi}\right]+\frac{1}{2}\left[\delta_{\Gamma}^{2}+\delta_{\Psi}^{2}\sigma_{\Psi}^{2}\right]t^{2}\right\} $$

Equation A.6 is the moment-generating function of a normal distribution with mean δΓμΓ + δΨμΨ and variance \(\delta _{\Gamma }^{2}+\delta _{\Psi }^{2}\sigma _{\Psi }^{2}\). As Y is normal for any constant α and β, π and Γ must be bivariate normal. Without loss of generality, we can normalize a such that \(a^{2}= 1-b^{2}\sigma _{\Psi }^{2}\) obtaining \(\sigma _{\Pi }^{2}= 1\) and ρ = σ(π,Γ). Hence, as our recommendation model, the model in Eq. A.4 generates a bivariate normal distribution for [Γ,π] with mean m and covariance Q.

Appendix B: Robustness

To check the robustness of the results obtained with GSOEP data, this appendix provides additional simulation results using 1,620 combinations of parameter values.

B.1 Simulation setup

Let l = 1,…,L denote all parameter combinations. Let Sl denote the recommendation performance of an arbitrary approach given parameter combination l. We consider L = 1, 620 combinations. As before, for every parameter combination, we compute 10,000 simulations with sample sizes n(Θ) = 1, 000 and n(Ω) = 100. Given a sample of historical and client data, we apply three approaches to the same simulated data:

  • general average scores (GAS);

  • average scores with an optimized similarity criterion, given that m and Q are known (OAS); and

  • the probability-based approach (PBA).

Parameters, which are given in Table 3, are selected in a way such that a high number of different conditions is covered. Correlation between personality trait and entrepreneurial abilities ranges from weak, ρ = 0.1, to strong, ρ = 0.9.

Table 3 Variations in parameters

𝜖 is the non-optimized similarity criterion of average scores. Following the general logic of entrepreneurship-prone personality profiles, we assume that to receive a recommendation for entrepreneurship the client’s personality trait must be sufficiently similar to the profile, and that sufficient similarity promises good recommendation results. Hence, we use a rather strict (small) similarity criterion for average scores. However, we consider two different similarity criteria to examine the effect of changes in similarity criteria on average success rates (to test the second requirement). In particular, if 𝜖 = 0.01, we say that the similarity criterion is strict, whereas 𝜖 = 0.15 is interpreted as a tolerant similarity criterion.

To compute 𝜖 for the optimized version of average scores, we numerically maximize Eq. 6 for every parameter combination. Given the assumption on μπ and τ, we cover a wide range of population shares of entrepreneurs, which is demonstrated in Fig. 3.

Fig. 3
figure3

Simulated population shares of entrepreneurs

B.2 Performance analysis using 1,620 parameter combinations

Benchmarking success probabilities

To get an overview over average performance, we compute the simulation average, approximating \(\mathbb {E}[\mathcal {S}^{l}]\), for every parameter combination l and every approach.

In Fig. 4, we plot the distribution of average recommendation. Figure 4 reveals that the general average-scores approach (GAS) substantially underperforms compared to all other approaches.

Fig. 4
figure4

Distribution of average success rates, \(p=\mathbb {E}[\mathcal {S}]\)

In Fig. 5, we only show distributions of average success rates for a high correlation between personality trait and entrepreneurial abilities (ρ = 0.9). Still, even when correlation between personality trait and entrepreneurial abilities is high, general average scores underperform in comparison to all other approaches.

Fig. 5
figure5

Distribution of average success rates, \(p=\mathbb {E}[\mathcal {S}]\), if correlation is high

In contrast to general average scores, optimized average scores (OAS) exhibit high average success rates, which are slightly inferior to the upper boundary of recommendation performance represented by the probability-based approach (PBA). The results on relative performance are consistent with those obtained with the GSOEP calibrated model.

Testing requirements

To test the first requirement, in Fig. 6, we plot average recommendation success rates of (a) the optimized average-scores approach and (b) the general average-scores approach as a function of the parameter combinations index, l. Optimized average scores (Fig. 6a) always fulfill the first requirement. In case of general average scores (Fig. 6b), average success rates are smaller than 50%, the approach is inferior to the coin, in about 44% of all parameter combinations. More specific, only if the population share of entrepreneurs is low (about 19% on average, ranging between about 2% and 50%), general average scores outperform the coin with respect to average recommendation success rates.

Fig. 6
figure6

Recommendation success of average scores

To test the second requirement, let \(l_{\varepsilon ,\varepsilon ^{\prime }}=(l_{\varepsilon },l_{\varepsilon ^{\prime }})\) denote a pair of parameter combination where all parameters besides the similarity criterion are exactly the same. Our simulation-based measure of robustness, the simulation counterpart of Δ(ε,ε), is

$$\hat{\Delta}(l_{\varepsilon,\varepsilon^{\prime}})=\frac{1}{M}\left|\sum\limits_{m = 1}^{M}\mathcal{S}_{m}^{l_{\varepsilon}}-\sum\limits_{m = 1}^{M}\mathcal{S}_{m}^{l_{\varepsilon^{\prime}}}\right| $$

In Fig. 7, we present robustness measures for average scores. Changing from a strict (𝜖 = 0.01) to a tolerant (𝜖 = 0.15) similarity criterion, or vice versa, changes average recommendation success rates by about 11 percentage points at maximum. The results become more striking when we compare the strict and the tolerant criterion to the optimized similarity criterion 𝜖. The difference in average success rates between the strict and the optimized criterion is 95 percentage points at maximum, while the success rate difference between the tolerant and the optimized criterion is approx. 84 percentage points at maximum. The results indicate that average scores are not robust—mistakes of the adviser can generate high costs (e.g., a loss in average recommendation success rates of 95 percentage points).

Fig. 7
figure7

Similarity-criterion-induced changes in average success rates

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Konon, A., Kritikos, A.S. Prediction based on entrepreneurship-prone personality profiles: sometimes worse than the toss of a coin. Small Bus Econ 53, 1–20 (2019). https://doi.org/10.1007/s11187-018-0111-8

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Keywords

  • Advice
  • Personality
  • Entrepreneurship
  • Profiles

JEL Classification

  • C15
  • D81
  • L26