Human resource management: the need for theory and diversity

Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte

The Role of Prior Entrepreneurial Exposure in the Entrepreneurial Process: A Review and Future Research Implications

Despite considerable research, the current state regarding how and in which context prior entrepreneurial exposure impacts the entrepreneurial process is unclear. The present paper's goal is to systemize and discuss extant quantitative-empirical research on the role of prior entrepreneurial exposure in the entrepreneurial process to clarify the current state, identify research gaps, and offer future research directions. Results from the systematic literature review of 69 quantitative-empirical journal articles suggest that prior findings are ambiguous and theoretical shortcomings exist. We contribute to the literature by clarifying the current state and by offering directions for entrepreneurship research and practitioners promoting entrepreneurial activity

Prospect for Knowledge in Survey Data: An Artificial Neural Network Sensitivity Analysis

Policy making depends on good knowledge of the corresponding target audience. To maximize the designated outcome, it is essential to understand the underlying coherences. Machine learning techniques are capable of analyzing data containing behavioral aspects, evaluations, attitudes, and social values. We show how existing machine learning techniques can be used to identify behavioral aspects of human decision-making and to predict human behavior. These techniques allow to extract high resolution decision functions that enable to draw conclusions on human behavior. Our focus is on voter turnout, for which we use data acquired by the European Social Survey on the German national vote. We show how to train an artificial expert and how to extract the behavioral aspects to build optimized policies. Our method achieves an increase in adjusted R² of 102% compared to a classic logistic regression prediction. We further evaluate the performance of our method compared to other machine learning techniques such as support vector machines and random forests. The results show that it is possible to better understand unknown variable relationships