1. The study builds upon recent papers that explore personality and entrepreneurship.2. Structural equation modelling is used to test hypotheses.3. Extraversion and Agreeableness predict entrepreneurial outcomes.4. Narrow personality traits are better predictors of entrepreneurial outcomes.

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The use of image-based testing to assess individual differences has increased substantially in recent years, with proponents arguing that they offer a more engaging alternative to text-based psychometric tests. Yet research examining the validity of these tests is near to non-existent. Traditional image-based formats have been little more than an adaptation of self-reports, with images replacing questions but not response options. The current study develops a novel image-based creativity measure, where images replace conventional response scales, and scores on the measures are obtained using a linear regression scoring algorithm to predict three self-reported creativity measures. Using sequential forward selection on a set of 77 image-based items, an optimal solution of 14 items that were valid predictors of self-reported creativity scores were identified. The image-based measure had good test-retest reliability. Implications are discussed in terms of the usefulness of image-based testing for practitioners seeking engaging and short test formats.

Selection methods are commonly used in talent acquisition to predict future job performance and to find the best candidates, but questionnaire-based assessments can be lengthy and lead to candidate fatigue and poor engagement, affecting completion rates and producing poor data. Gamification can mitigate some of these issues through greater engagement and shorter testing times. One avenue of gamification is image-based tests. Although such assessments are starting to gain traction in personnel selection, few studies describing their validity and psychometric properties exist. The current study explores the potential of a five-minute, forced-choice, image-based assessment of the Big Five personality traits to be used in selection. Study 1 describes the creation of the image pairs and the selection of the 150 best-performing items based on a sample of 300 respondents. Study 2 describes the creation of machine-learning-based scoring algorithms and tests of their convergent and discriminate validity and adverse impact based on a sample of 431 respondents. All models showed good levels of convergent validity with the IPIP-NEO-120 (openness r = .71, conscientiousness r = .70, extraversion r = .78, agreeableness r = .60, and emotional stability r = .70) and were largely free from potential adverse impact. The implications for recruitment policy and practice and the need for further validation are discussed.