Mapping the landscape of research findings: Generalization across contexts in strategic management research
指出战略管理研究常忽视发现能否推广到其他情境，建议用抽象概念（如不确定性、相互依赖）而非名义类别（如行业、国家）来刻画研究情境，以整合已有发现并指导未来研究。
Abstract Research Summary Knowledge accumulation requires that we understand whether and when relationships identified in any research setting generalize to others—that is, suggesting domains where results hold (or not). Strategy scholars carefully identify how theoretical mechanisms operate in their chosen research contexts, but attend less to whether their findings apply in other contexts. Accordingly, we recommend reframing empirical contexts, typically described in terms of nominal categories (e.g., industries, countries, or time periods), by highlighting contextual attributes of the nominal settings (e.g., industry concentration or technological modularity), which in turn reflect more abstract conceptual categories (e.g., uncertainty, interdependence, and variance) across potential research contexts. This approach can help integrate prior findings and suggest future study contexts to better enhance our understanding of the research landscape. Managerial Summary Academic research in strategic management tends to derive findings in very specific nominal settings—particular industries, years, and regions. Since strategy practitioners operate across a wide variety of industries and regions, they need to assess whether available research findings are applicable in their own settings. We suggest that understanding whether and when research findings apply to unstudied settings can be facilitated by categorizing research settings using more abstract conceptual constructs (such as environment uncertainty, variance across firms, or interdependence between firms), rather than by the traditional emphasis on nominal settings. We discuss a variety of research setting attributes (such as industry concentration and technological modularity) that can aid the translation of extant research findings to settings where practitioners are operating.