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This research examines the interplay of opportunity evaluation and emotions as determinants of entrepreneurial exploitation using affect-as-information theory and the affective processing principle as conceptual bases. Three central assumptions are confirmed across two studies. The first is that the effects of opportunity characteristics on exploitation are mediated by evaluation. The second is that emotions influence exploitation decisions in addition to evaluation. Fear reduces exploitation, whereas joy and anger increase it. The third is that fear, joy, and anger influence evaluation's effect on exploitation with higher levels of fear reducing and higher levels of joy and anger increasing the positive impact of evaluation on exploitation.

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The current paper aims to identify the antecedents of social entrepreneurial intention formation. Applying the theory of planned behavior on an international sample of 159 entrepreneurial volunteers in a corporate framework, we find positive relationships between empathy, perceived social norms, self-efficacy, perceived collective efficacy, and social entrepreneurial intentions with mediation by perceived desirability and perceived feasibility. Overall, we contribute to the upcoming domain of social entrepreneurship research by investigating the individual and environmental antecedents of social entrepreneurial action in a corporate setting.

This study investigates how family commitment moderates whether and how financial knowledge, positive experience with debt suppliers, and economic goal orientation affect owner–managers' attitudes toward debt financing in family firms. Using a sample of 280 German family firms, we find significant relationships between both financial knowledge and positive experience with debt suppliers and owner–managers' financial attitudes toward debt. Our findings show that family commitment moderates these relationships such that high family commitment increases the impact of prior experience with debt suppliers, though the effect of economic goal orientation is lowered and reversed. Overall, we contribute to research on financial decision making, capital structure, and social capital in family firms.

Investors increasingly use machine learning (ML) algorithms to support their early stage investment decisions. However, it remains unclear if algorithms can make better investment decisions and if so, why. Building on behavioral decision theory, our study compares the investment returns of an algorithm with those of 255 business angels (BAs) investing via an angel investment platform. We explore the influence of human biases and experience on BAs’ returns and find that investors only outperformed the algorithm when they had extensive investment experience and managed to suppress their cognitive biases. These results offer novel insights into the role of cognitive limitations, experience, and the use of algorithms in early stage investing.

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