The Effects of Self-Reinforcing Mechanisms on Firm Performance

This study empirically investigates the influence of the market-bound (i.e., interaction and network effects) on the firm-bound (i.e., scale and learning effects) self-reinforcing mechanisms, and their combined effect on product and organizational performance. The findings from a sample of 257 manufacturing firms reveal that interaction effects have a positive effect on network effects. Network effects have a positive impact on the potential for firms to realize scale and learning effects, which in turn, is positively related to their actual realization of these effects. The actual realization of scale and learning effects has a positive effect on product performance, which in turn positively influences organizational performance. These effects are robust across industries and provide ample opportunities for future research.management;economics;increasing returns;self-reinforcing mechanisms

A Managerial Perspective on the Logic of Increasing Returns

The focus of this study is on the challenges faced by managers in effectively dealing with the new management logic of increasing returns as the information and knowledge intensity of their transformation processes rises. Dealing with these challenges is especially relevant for companies currently making the transition from capital and physical labor intensive transformation processes (old economy) to information and knowledge intensive transformation processes (new economy). For these companies, this study provides a definition of increasing returns, explains the sources of increasing returns and develops tools for monitoring the realization of increasing returns. These tools are applied at the Randstad Group, a leading temporary employment agency based in the Netherlands, currently making the transition from the old to the new economy. The study concludes with a discussion of the management implications of the new logic of increasing returns and suggestions for further research.management;economics;interaction effects;increasing returns;network effects

The dynamics underlying the rise of star performers

Across different domains, there are ‘star performers’ who are able to generate disproportionate levels of performance output. To date, little is known about the model principles underlying the rise of star performers. Here, we propose that star performers' abilities develop according to a multi-dimensional, multiplicative and dynamical process. Based on existing literature, we defined a dynamic network model, including different parameters functioning as enhancers or inhibitors of star performance. The enhancers were multiplicity of productivity, monopolistic productivity, job autonomy, and job complexity, whereas productivity ceiling was an inhibitor. These enhancers and inhibitors were expected to influence the tail-heaviness of the performance distribution. We therefore simulated several samples of performers, thereby including the assumed enhancers and inhibitors in the dynamic networks, and compared their tail-heaviness. Results showed that the dynamic network model resulted in heavier and lighter tail distributions, when including the enhancer- and inhibitor-parameters, respectively. Together, these results provide novel insights into the dynamical principles that give rise to star performers in the population

Conceptualizing and measuring psychological resilience:What can we learn from physics?

The number of resilience conceptualizations in psychology has rapidly grown, which confuses what resilience actually means. This is problematic, because the conceptualization typically guides the measurements, analyses, and practical interventions employed. The most popular conceptualizations of psychological resilience equate it with the ability to (1) resist negative effects of stressors, (2) “bounce back” from stressors, and/or (3) grow from stressors. In this paper, we review these three conceptualizations and argue that they reflect different concepts. This is supported by important lessons from engineering physics, where such concepts are clearly differentiated with precise mathematical underpinnings. Against this background, we outline why psychological resilience should be conceptualized and measured in terms of the process of returning to the previous state following a stressor (i.e., bouncing back). By establishing a clearer language of resilience and related processes, measurements and interventions in psychological research and practice can be targeted more precisely

Injury Prediction in Competitive Runners with Machine Learning

Purpose: Staying injury-free is a major factor for success in sports. Although injuries are difficult to forecast, novel technologies and data science applications could provide important insights. Our purpose is to use machine learning for the prediction of injuries in runners, based on detailed training logs. Methods: Prediction of injuries was evaluated on a new data set of 77 high-level middle and long distance runners, over a period of seven years. Two analytic approaches were applied. First, the training load from the previous seven days were expressed as a time series, with each day’s training being described by ten features. These features were a combination of objective data from a GPS watch (e.g., duration, distance), together with subjective data about the exertion and success of the training. Second, a training week was summarized by 22 aggregate features, and a time window of three weeks before the injury was considered. Results: A predictive system based on bagged XGBoost machine learning models, resulted in Receiver Operating Characteristic curves with average Areas Under the Curves of 0.724 and 0.678 for the day and week approach, respectively. Especially the results of the day approach reflect a reasonably high probability that our system makes correct injury predictions. Conclusions: Our machine learning-based approach predicts a sizable portion of the injuries, in particular when the model is based on training load-data in the days preceding an injury. Overall, these results demonstrate the possible merits of using machine learning to predict injuries and tailor training programs for athletes

Psychological Momentum

Psychological Momentum (PM) is a positive or negative dynamics of cognitive, affective, motivational, physiological, and behavioral responses to the perception of movement toward or away from either a desired or an undesired outcome. Such a perception can be fostered by any event or series of events that alters the perceived rate at which one is moving regarding the outcome in question. The history and the context in which such events are embedded determine the occurrence and intensity of PM more determining than the events per se. Therefore, PM is a process of extrapolation that builds upon experiences and extends to anticipated future outcomes (e.g., Hubbard, 2015). PM should not be confused with the “hot/cold hand” phenomenon, which refers to the belief that streaks of success/failure breed future success/failure. The occurrence of streaks is neither sufficient nor necessary to entail a perception of movement toward or away from a final outcome