Frédéric DELMAR

Does terrorism influence entrepreneurial entry? We investigate if people use entrepreneurship to cope with terrorism. Contrary to current research on disruptive contexts and entrepreneurial entry, we argue based on human capital theory that terrorism– a man-made disruptive context– changes labor markets and pushes people towards employment over entrepreneurship. Human capital allows people to cope differently with these changes. The empirical test of peoples’ how people cope in the labor market in response to terrorism is challenging because terrorists tend to select people with specific demographics (e.g., occupational, political, and religious affiliations), which leads to selection bias. To overcome the resulting endogeneity, we use a nationally representative random sample and an exhaustive list of terror attacks in Egypt in 2017 to identify inherent randomness in the success of terror attacks. We find that terror attacks negatively influence entrepreneurial entry, and that human capital mitigates this negative influence. Our findings contribute to theoretical advancements on entrepreneurship in disruptive contexts, urging caution against generalizing across different types of disruption. We emphasize variation within and between types of disruptive contexts and the role of human capital in coping.

Theories and models in management build on the prediction of future states and outcomes. Yet, management scholars often focus on model fit metrics, while giving less attention to prediction and forecasting methods. The underlying assumption is that predictions and forecasting are byproducts of good model fit. In this paper, we argue and find that when researchers focus solely on fitting data, they risk creating models and theories that fail to generalize to other similar datasets, limiting their forecasting, practical value, and applicability. Model fit metrics are subject to overfitting and underfitting. Overfitting compromises prediction by overlearning from the data, while underfitting sacrifices prediction by failing to capture key patterns. We apply newly developed forecasting metrics to show how forecasting metrics can complement model fit metrics, enhance model evaluation, and address model fit limitations. We emphasize the importance of using multiple metrics to comprehensively assess and improve both predictive performance and theoretical development.

We argue that the standard statistical models commonly used by management scholars to investigate the relationship between prior entrepreneurial experience and the probability of switching into entrepreneurship from wage employment are less capable of unveiling the true relationship, especially in the context of big data. In particular, because the immense volume of data means that almost everything can be significant, the statistical significance relying on p-values may not imply economic significance. In addition, in the context of big data, more flexible relationships than simple linear relationships (linear, curvilinear, cubic, etc.) are possible, yet the standard statistical models that pre-specify the linear relationships between the independent and dependent variables lack the capability of detecting such relationships. To illuminate these concerns, we re-visit this important relationship using two different models: logistic regression – a standard statistical model commonly used by management scholars, and random forests – a powerful machine learning tool for analyzing big data. Through comparing the discrepant findings of these two models, we assert the benefits of using contemporary approaches to handle big data in re- visiting fundamental questions in management.

Building from human capital theory, we investigate the relative financial payoffs to prior entrepreneurial experience inside versus outside the entrepreneurial context. Testing from the sample of 26,235 entrepreneurs who were at risk of making a career choice between serial entrepreneurship or wage employment, we find that greater prior entrepreneurial experience leads to a higher financial payoff in wage employment than in serial entrepreneurship, implying that the financial payoffs to prior entrepreneurial experience can be extended, and much higher, outside the entrepreneurial context. Our findings hold a host of novel implications for understanding the motivation of entrepreneurship and also add to the research of serial entrepreneurship.

Even though many new ventures are started by teams, we know surprisingly little about how members in these teams work together. In this paper we therefore develop a definition of new venture teams and we propose a research agenda for theorizing entrepreneurship as teamwork. Our definition emphasizes the systemic and contextual properties of new venture teams. We explain why their success is dependent on the quality of team members interactions – a quality that is endogenous to the process itself – and we detail the social and economic conditions that makes new venture teams function differently from other professional teams. On the basis of this definition, we outline a research agenda that shifts the focus of analysis in new venture team’s research. From a matter of team composition, to a study of emergence and tipping points in the process of new venture creation. In all, we hope to further entrepreneurship theory and practice by specifying how new venture teams organize new ventures.

This research aims to investigate what types of passion of the founder would have a positive or negative impact on potential co-founders’ decision to join the venture team, and how individual differences of co-founders affect such decision-making. Two important limitations exist in the current literature: First, rather than focusing on entrepreneurs’ general passion, we need a better understanding of the specific targets that entrepreneurs are passionate about and how they influence stakeholders’ decision-making (Warnick, Murnieks, McMullen, & Brooks, 2018). Second, it remains unclear what kinds of obsessive passion have a positive or negative impact on the decision-making of important venture stakeholders, especially co-founders who contribute significantly to venture progress (Klotz, Hmieleski, Bradley, & Busenitz, 2014). Accordingly, despite the importance of co-founders, little research investigates their decision-making in the entrepreneurial process (Shepherd, Williams, & Patzelt, 2015). It remains elusive what factors drive or restrict individuals to join venture teams in the first place.