Which current trends characterize quantitative research in management? Reviewing 170 articles published in Administrative Science Quarterly, The Academy of Management Journal and Organization Science in 2020, I found that quantitative analysis remained the most frequently used method in management research; 115 of the reviewed articles used quantitative methods to analyze empirical data. The reviewed studies employed a diverse array of statistical models, with method choices depending on research questions and data characteristics. Im addition, most articles, particularly those analyzing organization-level phenomena, explicitly account for endogeneity, providing further evidence that such consideration is now a necessary condition for publication in top-tier journals today.

While the Eisenhard method, one of the most recognized templates for qualitative research in management, is a method for building theory from multiple-case study research by utilizing the logic of replication and theoretical sampling, over half of the articles in Organizational Science citing Eisenhardt (1989) to justify their case study approaches were single-case research. Against the background of this misunderstanding and ritualistic citations of Eisenhardt (1989), this paper reviews four core elements of the Eisenhardt method with illustrations from her case study research articles.

This paper points out that simulation is one of the best methods to elaborate theories and that agent-based simulation, in particular, has the feature of going back and forth between micro and macro levels. Simulation is complementary to such other methods as case studies, survey research and experiments. The paper presents examples of research combining these methods. Finally, the paper discusses what can be considered the first step toward management organization research using simulation.

How can we apply machine learning to advance the social sciences? To examine this question, this paper reviews existing researches from two perspectives: machine learning for explaining reality and machine learning for controlling reality. In particular, the paper organizes previous researches on controlling reality using machine learning in terms of optimization and augmentation as the control direction of objects.

In this study, we focus on outsourced management of a wide range of product development. We analyze user's management behavior, which is crucial to realizing a high degree of project satisfaction, through a case study of railroad car development. Furthermore, we consider the necessary knowledge management. A result of the analysis indicates that the users who manage outsourcing effectively are deeply involved in design integration by utilizing architectural knowledge and component-specific knowledge, which the suppliers traditionally retain. The result also shows that these users secure the initiative during product development.