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.

Understanding variability is a key focus in many areas of psychological research, with growing interest in modeling individual- and group-level variability. Although multilevel models such as heterogeneous variance models (HVMs) and Mixed-Effects Location-Scale models have been used to capture these dynamics, they typically rely on linear assumptions and restrict temporal changes in variability to a single level. Recent calls for nonlinear approaches in psychology highlight the need for more flexible models that can better account for complex, dynamic processes. This article introduces the use of Gaussian processes (GPs) within the framework of HVMs to address these limitations. By incorporating GPs in HVMs, we allow for the modeling of nonlinear variability across multiple levels, including temporal dynamics at both the individual and group levels. We demonstrate the benefits of this approach in two empirical applications. Our findings show that using GPs provides an improved model fit compared with traditional linear methods and highlight the utility of GPs in variance modeling, offering new possibilities for studying dynamic and emergent processes in psychological and social science research.

Significant events represent essential shifts in the new venture's development. These events may increase, decrease, or have no effect on team members' cognitions, decisions, and behaviors. Although this is a distinct possibility, research has yet to fully elucidate the specific impacts on new venture team members following significant events. To address this gap, we adapt Event System Theory to the new venture context to develop a theory of significant events and their effects on team members' cognition and behavior. The theory describes how significant events first capture members’ attention and then undergo a distinctive psychological interpretation, which affects cognitive and behavioral outcomes, such as leaving the team. By describing the processes that precede and follow significant events, our theory provides a needed explanation of how events may change team members’ engagement with their teams or lead to their exit. We derive specific propositions.

Digital platforms are increasingly important spaces where entrepreneurs vent frustrations and seek social support. Yet women entrepreneurs often report a paradox: despite receiving substantial engagement when venting online, they frequently do not feel supported. We address this disconnect by developing a dynamic, interactional theory of gender congruity grounded in identity management. Rather than focusing on whether social support is offered, we examine how different forms of social support, such as emotional versus informational, carry distinct identity implications. Using large-scale observational data from Reddit, we show that women entrepreneurs’ venting elicits systematic variation in social support provision depending on venting topic and listener gender. When women vent about business difficulties, they receive more emotional support overall, largely from female listeners, but less informational support, especially from male listeners, despite informational support being most strongly associated with feeling supported in this context. When women vent about work-life balance, emotional support declines while informational support increases, driven primarily by female listeners. Overall, perceived supportedness depends on whether social support aligns with the identity meaning of the vented issue, explaining why engagement alone is an incomplete indicator of social support in online entrepreneurial communities.

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.