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Growth mindsets: defining, assessing, and exploring effects on motivation for entrepreneurs and non-entrepreneurs

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Abstract

Motivated by the poorly understood nature of the term “mindsets” in the domain of entrepreneurship, we embarked on an exploration encompassing three research goals: a) defining and assessing growth mindsets in entrepreneurship, b) investigating how growth mindsets in entrepreneurship correlate with personality constructs, and c) exploring how growth mindsets predict motivation related to being an entrepreneur. Overall, findings from a sample of entrepreneurs (n = 264) and non-entrepreneurs (n = 330) reveal evidence consistent with the inference that a unidimensional, ‘growth mindset in entrepreneurship’ (GME) construct underlies five distinct mindset measures closely related to entrepreneurship: mindsets of leadership, mindsets of creativity, person mindsets, mindsets of intelligence, and mindsets of entrepreneurial ability. This GME construct correlated positively with conscientiousness and openness (albeit with small effects), but did not consistently correlate with extraversion, agreeableness, or neuroticism. We also found significant and positive relations for the GME with resilience and need for achievement, but a significant (and unexpected) negative correlation with risk-taking. With respect to motivation (operationalized via expectancy-value theory), GME predicted self-efficacy, but only for individuals who did not identify as entrepreneurs. GME exhibited limited utility in predicting enjoyment, utility, or identity evaluations related to value, but was robustly linked to cost evaluations. We discuss the implications of these findings and suggest directions for future research.

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Data Availability

We have numerous files available on Open Science Framework (OSF; anonymized for review). Our data and measures can be found here: https://osf.io/gu4sc/?view_only=d3b1f3f480dd4a22ab8e6b74c35411ea.

Notes

  1. Please see Table 1 for a breakdown of the demographics of our sample. Based on a total sample of 594 participants, the study had 80% power to detect an r of .114, with a two-tailed alpha of .05 (Faul et al., 2007).

  2. Two items—one from the leadership scale and one from the intelligence scale—were deleted because their wording was directionally opposite that of the other 13 items, substantially reducing scale reliability (and in the case of the intelligence item, not exhibiting metric invariance across entrepreneurs vs. non-entrepreneurs).

  3. OSF repository: https://osf.io/gu4sc/?view_only=d3b1f3f480dd4a22ab8e6b74c35411ea

  4. Statistical significance for the difference in indirect effects was determined by examining the index of moderated mediation (Hayes, 2018) in all three models. For each dimension of value, the index of moderated mediation significantly differed from zero based on bootstrapped confidence intervals (for Identity Value it was −0.13, 95% CI [−0.24, −0.01]; for Interest/Enjoyment: −.09, 95% CI [−0.17. -0.003]; for Cost: 0.05, 95% CI [0.01, 0.10].

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Author information

Authors and Affiliations

  1. Department of Psychology, Tulane University, New Orleans, LA, 70118, USA

    Joseph Billingsley

  2. Department of Psychology, Penn State University at Mont Alto, Mont Alto, PA, 17237, USA

    Nikolette P. Lipsey

  3. College of Humanities and Social Sciences, North Carolina State University, Raleigh, NC, 27695, USA

    Jeni L. Burnette

  4. Poole College of Management, North Carolina State University, Raleigh, NC, 27695, USA

    Jeffrey M. Pollack

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  1. Joseph Billingsley
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Appendices

All items used the same 1–7 scale (1 = strongly disagree, 7 = strongly agree).

Appendix A. Measures

All items used the same 1–7 scale (1 = strongly disagree, 7 = strongly agree).

Mindsets of Entrepreneurship

  1. 1.

    I have a certain amount of entrepreneurial ability, and I can’t really do much to change it.

  2. 2.

    My entrepreneurial ability is something about me that I can’t change very much.

  3. 3.

    To be honest, I can’t really change my entrepreneurial ability.

Mindsets of Leadership

  1. 1.

    I have a certain amount of leadership ability, and I can’t really do much to change it.

  2. 2.

    To be honest, I can’t really change my ability to lead.

  3. 3.

    Becoming a good leader takes time, effort, and energy.

Mindsets of Creativity

  1. 1.

    I have a certain amount of creativity and I really can’t do much to change it.

  2. 2.

    You either are creative or are not—even trying very hard you cannot change much.

  3. 3.

    Some people are creative, others aren’t—and no practice can change it.

Mindsets of Intelligence

  1. 1.

    I don’t think I personally can do much to increase my intelligence.

  2. 2.

    To be honest, I don’t think I can really change how intelligent I am.

  3. 3.

    With enough time and effort, I think I could significantly improve my intelligence level.

Mindsets of People

  1. 1.

    People can do things differently, but the important parts of who they are can’t really be changed.

  2. 2.

    The kind of person someone is is something very basic about them that can’t be changed very much.

  3. 3.

    Everyone is a certain type of person, and there is not much that can be done to really change that.

Big Five

“ How well do the following statements describe your personality? I see myself as someone who...”

  1. 1.

    ...is reserved.

  2. 2.

    ...is generally trusting.

  3. 3.

    ...tends to be lazy.

  4. 4.

    ...is relaxed, handles stress well.

  5. 5.

    ...has few artistic interests.

  6. 6.

    ...is outgoing, sociable.

  7. 7.

    ...tends to find fault with others.

  8. 8.

    ...does a thorough job.

  9. 9.

    ...gets nervous easily.

  10. 10.

    ...has an active imagination.

  11. 11.

    ...is considerate and kind to almost everyone.

Risk Taking

“Using the scale below, please indicate the extent to which you agree or disagree with the following statements.”

  1. 1.

    I enjoy being reckless.

  2. 2.

    I take risks.

  3. 3.

    I seek danger.

  4. 4.

    I know how to get around the rules.

  5. 5.

    I am willing to try anything once.

  6. 6.

    I seek adventure.

  7. 7.

    I would never go hang-gliding or bungee-jumping.

  8. 8.

    I would never make a high-risk investment.

  9. 9.

    I stick to the rules.

  10. 10.

    I avoid dangerous situations.

Resilience

“Please indicate how accurately that trait describes you...”

  1. 1.

    I tend to bounce back quickly after hard times.

  2. 2.

    I have a hard time making it through stressful events.

  3. 3.

    It does not take me long to recover from a stressful event.

  4. 4.

    It is hard for me to snap back when something bad happens.

  5. 5.

    I usually come through difficult times with little trouble.

  6. 6.

    I tend to take a long time to get over set-backs in my life.

Need for Achievement

“To what degree do you agree with the following four statements?”

  1. 1.

    I need to meet the challenge.

  2. 2.

    I need to continue learning.

  3. 3.

    I need personal growth.

  4. 4.

    I need to prove that I can succeed.

Self-Efficacy

“I am confident that I can…”

  1. 1.

    Identify new business opportunities

  2. 2.

    Create new products

  3. 3.

    Think creatively

  4. 4.

    Commercialize an idea or new development

Value- Enjoyment/Utility

“Please indicate the extent to which you agree or disagree with the following statements.”

  1. 1.

    Being an entrepreneur is enjoyable.

  2. 2.

    Being an entrepreneur is interesting.

  3. 3.

    Being an entrepreneur could help me achieve other important goals in my life.

  4. 4.

    Being an entrepreneur provides more opportunities than other career options.

Value- Identity

“Please indicate the extent to which you agree or disagree with the following statements.”

  1. 1.

    Being an entrepreneur is an important part of my identity.

  2. 2.

    Being an entrepreneur is important to who I am.

Cost

“Please indicate the extent to which you agree or disagree with the following statements.”

  1. 1.

    Being an entrepreneur demands too much time.

  2. 2.

    Being an entrepreneur is too much work.

  3. 3.

    I have so many other responsibilities that I am unable to put in the effort necessary to be an entrepreneur.

  4. 4.

    Being an entrepreneur is too stressful.

Appendix B. Data Analysis

Goal 1

Research Question 1: Taxometric Analysis

Taxometrics is a quantitative analysis that assesses whether a set of observed scores reflects an underlying categorical latent variable or an underlying continuous latent variable (Borsboom et al., 2016; Meehl, 1992; Ruscio et al., 2006; Ruscio & Ruscio, 2004). It answers the question of whether observed scores are likely the product of latent discrete classes or profiles, on the one hand, versus continuous latent factors or dimensions, on the other. This conclusion, in turn, offers researchers guidance on the empirical techniques best suited for subsequent empirical investigation—that is, should researchers undertake latent class analysis (for categorical structures), or factor analysis (for continuous structures).

Taxometric analysis works by determining the fit of both latent categorical and latent continuous models to the observed data, then formally comparing these degrees of fit. This approach provides a quantitative index of how much better (or worse) a continuous measurement model captures the data, relative to a discrete measurement model. Specifically, and as implemented in the R package used here (RTaxometrics; Ruscio & Wang, 2017), the software simulates data assuming an ideal latent categorical structure, then simulates data assuming an ideal latent continuous structure, and then compares the fit of the observed data to each of the two simulated datasets. Fit of the observed data to each simulated dataset is measured using a variant of the Root Mean Squared Residual (RMSR). These two fit measures are then combined into a single index of relative fit, referred to as the Comparative Curve Fit Index (CCFI). The CCFI ranges from 0 to 1, with values substantially greater than .50 (.55 or higher) representing support for a latent categorical model, values substantially less than .50 (.45 or lower) representing support for a latent continuous model, and values near .50 indicating unclear results (Sakaluk, 2019). In practice, the above procedure is performed using three different specifications of “ideal” categorical and continuous latent structure (for details, see Ruscio & Wang, 2017), with a CCFI value generated for each approach (termed “MAMBAC,” “MAXEIG,” and “L-Mode,”, respectively), as well as an overall CCFI that averages all three outputs. In this way, researchers can determine whether multiple approaches converge on the same conclusion—either categorical or continuous latent structure (Ruscio et al., 2006).

In accordance with conventional recommendations (Ruscio & Wang, 2017), data were first checked to gauge their suitability for taxometric analysis (for data analytics overview). Data checks indicated no issues with skew or with item validities (discriminatory ability). Although some within-group correlations were above recommended levels for taxometrics, we proceeded with analyses.

For the present investigation, we entered composite scores for each of the five mindset scales—Entrepreneurship, Leadership, Creativity, Intelligence, and Personality—as continuous indicators into the taxometric analysis. In accord with the suggestions of Sakaluk (2019), a researcher-provided initial estimate of the taxonic base rate (a parameter necessary to simulate data under the assumption of idealized categorical structure) was used—in this case .25. This initial estimate allows CCFI values to be generated efficiently, during which process the software generates an empirically estimated taxonic base rate. The analysis was then re-run using the empirical estimate of the base rate.

Research Question 2: Factor Analysis

To determine the number of dimensions underlying growth mindsets of entrepreneurship, we conducted both exploratory and confirmatory factor analysis. In order to improve generalizability of results, we randomly split the data into training and test datasets (N = 297 in each case), conducting exploratory factor analysis with the training data, and confirmatory factor analysis with the test data.

Exploratory factor analysis was conducted using the R package psych (Revelle, 2018). Number of factors was determined using a variety of criteria, including parallel analysis, root mean square residuals, root mean square error of approximation (RMSEA), and interpretability of resulting factor loadings, in addition to Kaiser’s criterion and the scree plot. When multi-factor solutions were estimated, oblimin rotation was used, on the assumption that resulting factors were likely to correlate.

Confirmatory factor analysis was conducted using the R package lavaan (Rosseel, 2012). Model fit was assessed with the Chi-squared test, Comparative Fit Index (CFI), RMSEA, and Standardized Root Mean Residual (SRMR). Robust versions of these criteria were employed when deviations from multivariate normality were indicated.

Exploratory Factor Analysis

Exploratory factor analysis was conducted on the training dataset. Given that each of the five mindset scales was had been previously validated, we entered composite scores for each scale (Flora, 2018). The strongest correlations occurred between entrepreneurship and leadership (.74) and between personality and creativity (.70). Inspection of a Normal Q-Q plot in conjunction with results of Mardia’s test suggested that the data could not be assumed multivariate normal. Accordingly, unweighted least squares estimation was used where possible.

Parallel analysis, inspection of a scree plot, and examination of eigenvalues in light of Kaiser’s criterion suggested the presence of either one or two factors (see Table 2). Therefore, we ran both a one-factor and a two-factor model for further examination. Because any two factors would likely be correlated (given than all variables represent facets of mindsets), oblimin rotation was used to interpret loadings.

Factor loadings for the one-factor model are provided in Table 3, for the two-factor model in Table 4. Additional criteria for distinguishing between the two models—beyond parallel analysis, a scree plot, and Kaiser’s criterion—were based upon Flora (2018), and included root mean square residuals (RMSR), visual inspection of the residual matrix, an exact-fit test, Root Mean Square Error of Approximation (RMSEA), and interpretation of factor loadings. Results are summarized in Table 2.

The one-factor solution accounted for 60% of variance, with all factor loadings greater than .50. RMSR was .04 and inspection of residual matrix revealed that all residuals were less than .10—both of which are consistent with good model fit. However, the exact-fit test was significant (p < .001), and the lower bound of the 90% confidence interval for RMSEA was > .10, with the latter finding in particular suggesting poor fit. A two- factor solution was estimated using unweighted least squares, but produced a Heywood case. A two-factor solution using maximum likelihood was then estimated and converged normally. Collectively, the two factors accounted for 69% of the variance, and overall exhibited notably better fit. The hypothesis of exact fit was not rejected (p = .67), RMSR was < .01, and the lower bound of the 90% confidence interval for RMSEA was 0. Interpretability, however, was not entirely clear. As Table 4 shows, mindsets of entrepreneurship and leadership clustered together—which makes theoretical sense. But the loading for entrepreneurship was suspiciously high (and indeed was out-of-bounds using other forms of rotation). More worryingly, the remaining variables—intelligence, creativity, and personality—did not uniquely and strongly specify a single factor. Specifically, mindset of leadership showed signs of cross-loading (.39 on the “personality” factor, as well as .47 on the “entrepreneurship” factor). Additionally, the two factors correlated strongly (.70), but not above the threshold of .85, which is generally considered sufficient to conclude that the latent structure is unidimensional (Brown, 2015).

Altogether, the general better statistical fit of the two-factor model led us to slightly favor a two-factor structure in which mindsets of entrepreneurship and leadership clustered together, but parsimony and interpretability kept us open to the possibility of a one-factor solution.

Confirmatory Factor Analysis

Confirmatory factor analysis was conducted on the test dataset. As with the EFA, composite scale scores for each mindset domain were used. Results of Mardia’s test again suggested that the data could not be assumed multivariate normal. Accordingly, models were estimated using robust maximum likelihood estimation (“MLM”).

A two-factor model was estimated, with the first factor consisting of mindsets of entrepreneurship and leadership, and the second factor consisting of mindsets of creativity, intelligence and personality. For both factors, the metric of the latent was set by the mindset scale that loaded most strongly on the factor during the exploratory stage of analysis. Thus, mindset of entrepreneurship set the metric for Factor 1, and mindset of personality set the metric for Factor 2. Model estimation terminated normally. Fit of the model was good based on multiple major indices: robust χ2 (4) = .705, p = .951; robust CFI = 1.00; robust RMSEA = 0.00; SRMR = .008. All indicators loaded strongly on their assigned factors (> .70), but the two factors exhibited an extremely high correlation of .94. This correlation was well above the recommended threshold of .85 for concluding that two factors represent a single dimension.

Accordingly, a one-factor model was estimated with mindset of entrepreneurship setting the metric of the latent. Model estimation terminated normally. Fit of the one-factor model was also good based on the same major indices: robust χ2 (5) = 3.059, p = .691; robust CFI = 1.00; robust RMSEA = 0.00, 90% CI [0.00, 0.09]; SRMR = .015. All indicators loaded strongly on the single factor (≥ .70).

The good fit of the one-factor model, together with the very high correlation of factors in the two-factor model, provided strong support for unidimensional latent structure, and additional evidence boosted this support. First, we estimated an alternative two-factor confirmatory model with a different pattern of loadings. Specifically, we assigned mindset of personality to load with the mindsets of entrepreneurship and leadership, rather than with creativity and leadership. Fit of this model was also excellent, χ2 (4) = 1.545, p = .819, indicating that the initial two-factor specification did not seem to be highlighting a particularly meaningful pattern of clustering. Second, using the test dataset, we repeated the exploratory factor analyses that we conducted earlier on the training dataset. Parallel analysis, inspection of a scree plot, and Kaiser’s criterion all suggested one rather than two factors. But most importantly, a two-factor EFA using the test data failed to yield the same pattern that was observed with the training data, in which entrepreneurship and leadership clustered together in one factor, while creativity, intelligence, and personality clustered in another. Indeed, with the test data, there was no interpretable second factor at all—factor loadings for the second factor were uniformly below .30. Thus, EFA results for a two-factor solution from the test dataset did not replicate those from the training dataset.

Goal 2

Research Questions 3 & 4: Correlation Analyses

To examine discriminant validity, we first report correlations of mindsets with each of the items in the Big Five. For convergent validity, we report correlations of mindsets with the three traits associated with successful entrepreneurship—namely, risk-taking, resilience and need for achievement.

Goal 3

Research Questions 5–8: Regression Analyses

All linear regression models were reported using unstandardized parameter estimates. These estimates were denoted with “b,” and because both predictor and outcome variables were assessed on 7-point scale, “b” indicates the expected change in outcome (on the 7-point scale) for a 1 unit increase in growth mindset. Statistical inference was made using a two-tailed alpha of .05. For moderation analyses, dummy coding was used to distinguish entrepreneurs (“1”) from non-entrepreneurs (“0”). Mediation and moderation analyses were conducted using the “Process” macro for R Version 3.5, beta 0.1 (Hayes, 2018). Prior to regression analyses, weak metric invariance of mindset items was established between entrepreneurs and non-entrepreneurs, ensuring that any significant interactions observed in the data were not due to differences in measurement properties of mindset between the two groups.

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Billingsley, J., Lipsey, N.P., Burnette, J.L. et al. Growth mindsets: defining, assessing, and exploring effects on motivation for entrepreneurs and non-entrepreneurs. Curr Psychol 42, 8855–8873 (2023). https://doi.org/10.1007/s12144-021-02149-w

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