Research summary: The use of Heckman models by strategy scholars to resolve sample selection bias has increased by more than 700 percent over the last decade, yet significant inconsistencies exist in how they have applied and interpreted these models. In view of these differences, we explore the drivers of sample selection bias and review how Heckman models alleviate it. We demonstrate three important findings for scholars seeking to use Heckman models: First, the independent variable of interest must be a significant predictor in the first stage of a model for sample selection bias to exist. Second, the significance of lambda alone does not indicate sample selection bias. Finally, Heckman models account for sample‐induced endogeneity, but are not effective when other sources of endogeneity are present. Managerial summary: When nonrandom samples are used to test statistical relationships, sample selection bias can lead researchers to flawed conclusions that can, in turn, negatively impact managerial decision‐making. We examine the use of Heckman models, which were designed to resolve sample selection bias, in strategic management research and highlight conditions when sample selection bias is present as well as when it is not. We also distinguish sample selection bias, a form of omitted variable (OV) bias, from more traditional OV bias, emphasizing that it is possible for models to have sample selection bias, traditional OV bias, or both. Accurately identifying the type(s) of OV bias present is essential to effectively correcting it. We close with several recommendations to improve practice surrounding the use of Heckman models. Copyright © 2015 John Wiley & Sons, Ltd.

PurposeDecades of research offer mixed results regarding the relationship between green product strategies and corporate financial performance. On the one hand, many scholars put forward green product strategies as a source of competitive advantage and in turn enhance financial performance. On the other hand, some studies suggest the opposite – that green product strategies may encounter managerial difficulties or are too costly, consequently leading to meager, if any, financial gain. This study explores cross-country contextual differences as a contingency to resolve this inconsistency. Thus, the research question is, “Do stakeholders of a country affect the link between green product strategies and financial performance?”Design/methodology/approachUsing a meta-analytic approach, the authors examine three country-level contingencies related to stakeholders: the impact of regulatory (stringency of environmental regulators), economic (consumer economic wealth) and political conditions (democratic vs. authoritarian governments) of a country in which the effects of a green product strategy on financial performance may vary.FindingsConsistent with our predictions, the meta-analysis of 26 studies published over a 20-year period reveals that green products positively relate to financial performance in countries with lax environmental regulation, low consumer economic status and authoritarian regimes.Originality/valueThe authors applied both (natural) resource-based and resource dependence theories by focusing on the interactions between firms' internal resources/capabilities and the external resources that firms can access. By doing so, the study adds to our understanding of stakeholders as resource providers to enhance financial benefits of green product strategies and provide insight into key boundary conditions of the link.

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PurposeWe examine whether domestic firms react differently to foreign direct investment (FDI) entry modes –mergers and acquisitions (M&A) versus greenfield. Specifically, we ascertain whether the entry mode of foreign competition motivates different corporate social responsibility (CSR) responses from domestic firms and when such relationships hold.Design/methodology/approachWe employ fixed-effects models using 1,331 US firm-year observations for 2015–2018. Furthermore, we examine the interactive effects of industry concentration to examine a key boundary condition.FindingsForeign entry via greenfield mode has no effect on domestic firm CSR. Entry through M&A has a significantly positive effect. We attribute these findings to the increased threat to domestic firms from foreign M&A whereas foreign entry through greenfield mode is less threatening as entrants face significantly more challenges in host countries. We identify industry concentration as a boundary condition of our findings. The effect of foreign M&A entries on domestic firms' CSR becomes weaker as industries are more concentrated.Originality/valueThis study offers novel insights on FDI by parsing out different reactions to entry mode by domestic firms. We add to our understanding of CSR as a mechanism to stave off foreign competition, offer insights into a key boundary condition of such actions and demonstrate the robustness of our findings.

The use of Heckman models by strategy scholars to resolve sample selection bias has increased by more than 700 percent over the last decade, yet significant inconsistencies exist in how they have applied and interpreted these models. In view of these differences, we explore the drivers of sample selection bias and review how Heckman models alleviate it. We demonstrate three important findings for scholars seeking to use Heckman models: First, the independent variable of interest must be a significant predictor in the first stage of a model for sample selection bias to exist. Second, the significance of lambda alone does not indicate sample selection bias. Finally, Heckman models account for sample-induced endogeneity, but are not effective when other sources of endogeneity are present. Managerial summary: When nonrandom samples are used to test statistical relationships, sample selection bias can lead researchers to flawed conclusions that can, in turn, negatively impact managerial decision-making. We examine the use of Heckman models, which were designed to resolve sample selection bias, in strategic management research and highlight conditions when sample selection bias is present as well as when it is not. We also distinguish sample selection bias, a form of omitted variable (OV) bias, from more traditional OV bias, emphasizing that it is possible for models to have sample selection bias, traditional OV bias, or both. Accurately identifying the type(s) of OV bias present is essential to effectively correcting it. We close with several recommendations to improve practice surrounding the use of Heckman models. Copyright