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A case‐based research approach is used to illustrate the power of modeling manufacturing performance measures and to help develop insights into the nature and problems evident when attempting to use simulation across different enterprise sectors. The research methodology used considered three specific companies, each with distinctive characteristics and attributes; a small to medium enterprise (SME), a medium to large enterprise (MLE) and a large enterprise (LE). From a cross‐case analysis of the use of discrete‐event simulation when applied in these instances, the research attempts to develop policy implications that will provide a better understanding of how simulation studies should be approached across different manufacturing enterprises and how simulation can help organizations achieve organizational excellence.

Organizational crises are complex events for researchers to assess. However, research in this domain remains fragmented, and advanced empirical techniques remain underutilized. In this chapter, we offer an integrated approach to assessing crises. We first specify a behavioral process model of crisis management comprised of three stages: interpretations, responses, and outcomes. Within each stage, we identify areas of opportunity and provide methodological recommendations that enhance our understanding of crises and crisis management. We also provide recommendations that could be applied across stages of the model. Taken together, we present a framework by which researchers can more effectively measure and analyze critical crisis dimensions.

No one acquainted with the facts that gave rise to the packing‐house scandals of the year 1906 believed that the belated promises of reform then made by certain American meat packers were to be relied upon. Many of these people were threatened with a serious loss of trade, and it was evidently their best policy at the time not too strongly to oppose legislation that was apparently devised to permanently better the conditions in the slaughtering establishments and packing‐houses.

This paper examines and discusses the need for micro-level analyses of academic entrepreneurship and outlines a micro-level research agenda for the study of academic entrepreneurship.

Based on a review of academic literature on academic entrepreneurship, this study focuses on individual actors and suggests some future research agendas.

The authors highlight that more studies dealing with academic entrepreneurship need to take a micro-level perspective, thereby outline several fruitful avenues of research: (1) star scientists and principal investigators, (2) TTO professionals, (3) graduate entrepreneurs, (4) university administrators, (5) policy makers and funders as well as (6) micro-level organisational routines.

This paper derives three main implications for management practice and policy. First, there is a real need to develop the managerial skills, competencies and capabilities of scientists and individuals. Second, policy makers need to ensure the necessary resources to pursue a paradigm shift towards more entrepreneurial thinking and action and create adequate incentives. Third, firms need to offer support and guidance on how to best commercialise and transfer scientific knowledge and ideally complement support structures of universities and research institutes.

This paper provides an organising framework for the study of micro-level academic entrepreneurship and emphasises the need to focus further on individual actors and how their actions, behaviours and approaches contribute to academic entrepreneurship in different institutional, environmental and cultural contexts.

Machine learning (ML) has recently gained momentum as a method for measurement in strategy research. Yet, little guidance exists regarding how to appropriately apply the method for this purpose in our discipline. We address this by offering a guide to the application of ML in strategy research, with a particular emphasis on data handling practices that should improve our ability to accurately measure our constructs of interest using ML techniques. We offer a brief overview of ML methodologies that can be used for measurement before describing key challenges that exist when applying those methods for this purpose in strategy research (i.e., sample sizes, data noise, and construct complexity). We then outline a theory-driven approach to help scholars overcome these challenges and improve data handling and the subsequent application of ML techniques in strategy research. We demonstrate the efficacy of our approach by applying it to create a linguistic measure of CEOs' motivational needs in a sample of S&P 500 firms. We conclude by describing steps scholars can take after creating ML-based measures to continue to improve the application of ML in strategy research.