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.

Some of the most influential theories in psychology describe multilevel processes based on dynamic concepts, often with emerging properties. This article aims to provide a precise mathematical definition of consensus emergence, a central concept in such theories. The definition can close the gap between the theoretical definition of consensus emergence and the quantities measured in empirical studies. Empirical studies on the emergence of consensus in the team and group literature can produce new insights when researchers explicitly consider several critical conceptual and methodological issues. Using this definition, we can clarify several issues in modeling consensus emergence in multilevel models. We illustrate this by three potential pitfalls (identifiability, inappropriate statistics, and model misspecification) in estimating consensus emergence in empirical studies and give recommendations on how to avoid them.

In this study, we examine how gender stereotype biases affect the social support given to entrepreneurs by members of the digital entrepreneurial community. While entrepreneurship is a socially embedded activity, extant social support research in entrepreneurship remains scant. We hypothesize that in the digital entrepreneurial community, when entrepreneurs’ conversation topics match their gender role (i.e., being a male and struggling with work-life balance or being a female and struggling with business difficulties), social media commenters’ perceived gender role congruity tends to be higher, which in turn leads to a higher likelihood of providing social support. We also hypothesize that social media commenters’ gender will moderate the relationship between entrepreneurs’ gender and social media commenters’ perceived gender role congruity. The results from three studies confirm our hypotheses.

There is a widespread recognition that child labor during adolescence has both detrimental, and developmental consequences for the labor market, which raises an important unanswered question: Does child labor during adolescence play a role in the labor market through entrepreneurial entry? We propose that child labor during adolescence plays a positive role in the engagement of formerly working adolescents in the labor market through entrepreneurial entry, rather than the employment route. We combine two longitudinal datasets from the UK with census data to test our proposition. We account for endogeneity using coarsened exact matching, Mundlak Chamberlain estimator, and a within twin fixed effects estimation. Our findings indicate that child labor during adolescence positively correlates with entrepreneurial entry. This finding, however, is observed among people with limited human capital, particularly people with no education qualification. Critically, our results present some differences when disentangling sole-entrepreneurial entry, and job-creating entrepreneurial entry. We explain two channels: education, and health.

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.

The study of emergent, bottom-up, processes has long been of interest within organizational and group research. Emergent processes refer to how dynamic interactions among lower-level units (e.g. individuals) over time form a new, shared, construct or phenomena at a higher level (e.g. work group). To properly study emergence of shared constructs one needs models, and data, that both take into account variability across individuals and groups (multilevel), and variability over time (longitudinal). This article makes three contribution to the modelling and theory of of consensus emergence. First, we formulate two separate patterns of consensus emergence; homogeneous and heterogeneous. Homogeneous consensus emergence is characterized by gradual and almost deterministic adjustments of the individual trajectories, whereas heterogeneous consensus emergence show more randomly oscillating trajectories towards consensus. Second, we introduce a model-invariant statistic that measures the strength of the consensus; and allows for comparisons between different models and patterns of consensus emergence. Third, we show how Gaussian Processes can be used to further extend the consensus emergence models, allowing them to capture nonlinear dynamics, on both individual and group level, in emergent processes. Using an established data set, we show that conclusions on the pattern of consensus emergence can change depending on whether the nonlinear group mean change over time is adequately modelled or not. Thus it is crucial to correctly capture the group dynamics to properly understand the consensus emergence.

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.

Analyzing big data requires the use of more powerful big data analytical techniques. The risk of not employing such techniques is that we misunderstand the true relationships in fundamental issues. In the study, we replicate and extend Evans and Leighton (1989) to revisit the entrepreneurial entry problem using a traditional logistic regression and one type of big data techniques – random forests. Through comparing the discrepant findings of these two models, we assert the benefits of using contemporary approaches to handle big data in revisiting fundamental questions in entrepreneurship.

We investigate how perceived obsessive passions (OPs) influence potential co-founders’ decision to join a venture team. Using a conjoint experiment with two samples: 116 founder entrepreneurs and 177 master business school students (supplementary sample), we find that potential co-founders are more likely to join when they perceive that the founder has OP for inventing and developing. Potential co-founders are less likely to join when they perceived OP for founding. Further, we also find significant interactions between perceived OPs, and interactions between perceived OP and potential co-founders’ own OP. These findings provide important theoretical implications.

This paper investigates the impact of perceived passion of a founder on potential co-founders’ decision to join or not a venture team. Employing a conjoint experiment, we find that co-founders are less likely to join when they perceive that the founder has obsessive passion for: (1) the founder’s individual vision, (2) the engagement in multiple unrelated ventures, and (3) the achievement of high performance at all costs. Co-founders are more likely to join, when they perceive that the founder has obsessive passion for (4) improving team members’ capabilities increases. Further, co-founders’ self-esteem moderates the impact of perceived obsessive passion. These findings provide important theoretical implications for the role of passion in team formation.

The establishment of a new organization is a tumultuous process where out of the initial chaos, some entrepreneurial groups succeed in creating order, but many do not. Given the importance of creating new organizations, emergence – the interactive process of lower level independent elements converging, leading to the creation of novel, self-reproducing processes a higher, systems level – has not been sufficiently addressed in entrepreneurship literature. To offer a fresh perspective on this topic, we incorporate the assumptions of complex adaptive systems – a non-equilibrium system; members’ self- organization; nonlinear system behavior; and non-reducible emergent entities – to better illustrate systemic conditions necessary to drive emerging processes that act as foundations for new organizations. Complexity science frameworks have proven to be useful for modeling what occurs in rapidly changing systems, such as the emergence of a new organization from entrepreneurial groups. To better illustrate this emergence process from an entrepreneurial group, we incorporate Tuckman’s four stage framework of team development: Forming, storming, norming and performing and overlay them with the characteristics of emerging processes. In closing, we suggest several avenues and methodological extensions for future research in entrepreneurship. We consider the entrepreneurial group as the first formative stage of new organization within a complex system.

We argue that the matching model, which suggests individuals self-select themselves into a career in which they have relative advantages, may explain serial entrepreneurship. To test this theoretical argument, we propose to investigate the qualitative differences of prior entrepreneurial experience. In particular, we believe that prior entrepreneurial experience can be decomposed into three dimensions: (1) venture success experience (i.e., the extent to which an individuals’ previous venture was financially successful), (2) venture managerial experience (i.e., managerial expertise individuals have developed through leadership experience in their previous entrepreneurial spell) and (3) venture industry experience (i.e., venture industry expertise individuals have developed specific to the target industry in their previous entrepreneurial spell). While some experience dimensions are more transferrable to wage employment (i.e., venture industry experience), leading to higher financial payoffs, some are more specific and useful in entrepreneurship (i.e., venture success experience and venture managerial experience). Individuals with more transferable experience may prefer to become wage-employed, but those with more specific experience may self-select to return to entrepreneurship. Testing from the sample of 16,888 entrepreneurs who were at risk of making a career choice between serial entrepreneurship or wage employment partially confirms this conjecture. Our study then provides a fine-grained view for the motivation of serial entrepreneurship.

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.

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.

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.

Konkurrens, selektion och entreprenoriellt larande – Global Award for Entrepreneurship Research till Boyan Jovanovic

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.

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.