Frédéric DELMAR

Management theories and models aim to predict future states and outcomes. Yet, as management scholars, we often tend to prioritise model fit metrics over prediction and forecasting, assuming that strong model fit inherently leads to accurate predictions. We challenge this assumption, arguing that an exclusive focus on model fit can yield theories that fail to generalise to new datasets, thereby limiting their forecasting accuracy and practical relevance. In a systematic review of 6,514 studies, we find a pronounced dominance of model fit approaches. Model fit metrics are susceptible to overfitting, where models capture noise rather than patterns, and underfitting, where key relationships are overlooked. Both problems undermine predictive performance. Drawing on insights from operations research, we apply newly developed forecasting metrics to address these limitations. Empirically examining the gender gap and motherhood penalty in returns from employment and entrepreneurship, we demonstrate how these metrics can complement traditional fit measures. By integrating multiple assessment metrics, we offer a comprehensive framework for improving both predictive accuracy and theoretical development in management research. We provide the Stata syntax that scholars can download and use to assess the forecasting ability of their models.

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 introduce a new model to address three methodological biases in research on new venture growth and survival. The model offers entrepreneurship scholars numerous benefits. The biases are identified using a systematic review of 96 papers using longitudinal data published over a period of 20 years. They are: (1) distributional properties of new ventures; (2) selection bias; and (3) causal asymmetry. The biases make the popular use of normal distribution models problematic. As a potential solution, we introduce and test an event magnitude regression model approach (EMM). In this two-stage model, the first model explores the probability of four events: a firm staying the same size, expanding, contracting, or exiting. In the second stage, if the firm contracts or expands, we estimate the magnitude of the change. A suggested benefit is that researchers can better separate the likelihood of an event from its magnitude, thereby opening new avenues for research. We provide an overview of our model analyzing an example data set involving longitudinal venture level data. We provide a new package for the statistical software R. Our findings show that EMM outperforms the widely adopted normal distribution model. We discuss the benefits and consequences of our model, identify areas for future research, and offer recommendations for research practice.

We investigate how potential co-founders' perceptions of a founder's obsessive passion (OP) influence the decision to join a venture team. Using a conjoint experiment with a primary sample of 116 founder-entrepreneurs and validating it with an additional sample of 59 founder entrepreneurs, we found that potential co-founders were more likely to join if they perceived that the founder had OP for developing ventures. Potential co-founders were less likely to join if they perceived OP for founding ventures. Further, we found significant interactions between perceived OPs, as well as interactions between perceived OP and potential co-founders' own OP.

This chapter present a research project dedicated to better understand how new venture teams work together to achieve desired outcomes. Teams, as opposed to an individual, start a majority of all innovative new ventures. Yet, little research or theory exists in new venture settings about how members interact with each other over time—teamwork—to produce innovative technologies, products, and services. We believe a systematic study of social and psychological processes that underlie new venture teamwork and venture outcomes is timely and important. Unique features of our research project include: (1) a team level focus on social and psychological processes, to assess relations to proximal (e.g., innovation, first sales and team satisfaction), and distal value creation outcomes (e.g., sales growth, raised capital and profits); (2) Combined qualitative and quantitative research methodologies to provide both theory building and theory testing for the relations of interest; and (3) A time-sequential design with data collection every three months over one year to allow us to investigate the relations of interest for new ventures.

The 2019 Global Award for Entrepreneurship Research has been awarded to Professor Boyan Jovanovic at New York University in the USA. Boyan Jovanovic has developed pioneering research that advances our understanding of the competitive dynamics between incumbent firms and new entrants, entrepreneurial learning and selection processes, and the importance of entrepreneurship for the economy. Key perspectives in his research are that the entrepreneur makes employment choices based on the comparative advantage of his or her skills and that entrepreneurial firms are vehicles of technological change and knowledge diffusion that influence industry dynamics and, in turn, economic growth.

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.