Explore the vast range of titles available.

Marketing researchers are increasingly taking advantage of the instrumental variable (IV)-free Gaussian copula approach. They use this method to identify and correct endogeneity when estimating regression models with non-experimental data. The Gaussian copula approach’s original presentation and performance demonstration via a series of simulation studies focused primarily on regression models without intercept. However, marketing and other disciplines’ researchers mainly use regression models with intercept. This research expands our knowledge of the Gaussian copula approach to regression models with intercept and to multilevel models. The results of our simulation studies reveal a fundamental bias and concerns about statistical power at smaller sample sizes and when the approach’s primary assumptions are not fully met. This key finding opposes the method’s potential advantages and raises concerns about its appropriate use in prior studies. As a remedy, we derive boundary conditions and guidelines that contribute to the Gaussian copula approach’s proper use. Thereby, this research contributes to ensuring the validity of results and conclusions of empirical research applying the Gaussian copula approach.

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.

Marketing academics and practitioners alike remain unconvinced about the chief marketing officer's (CMO's) performance implications. Whereas some studies propose that firms benefit financially from having a CMO in the C-suite, other studies conclude that the CMO has little or no effect on firm performance. Accordingly, there have been strong calls for additional academic research regarding the CMO's performance implications. In response to these calls, the authors employ model specifications with varying identifying assumptions (i.e., rich data models, unobserved effects models, instrumental variable models, and panel internal instruments models) and use data from up to 155 publicly traded firms over a 12-year period (2000-2011) to find that firms can indeed expect to benefit financially from having a CMO at the strategy table. Specifically, their findings suggest that the performance (measured in terms of Tobin's q) of the sample firms that employ a CMO is, on average, approximately 15% greater than that of the sample firms that do not employ a CMO. This result is robust to the type of model specification used. Marketing academics and practitioners should find the results intriguing given the existing uncertainty surrounding the CMO's performance implications. The study also contributes to the methodology literature by collating diverse empirical model specifications that can be used to model causal effects with observational data into a coherent and comprehensive framework.

Endogeneity in empirical marketing research is an increasingly discussed topic in academic research. Mentions of endogeneity and related procedures to correct for it have risen 5x across the field’s top journals in the past 20 years, but represent an overall small portion of extant research. Yet there is often substantial difficulty in reconciling issues of endogeneity with many of the substantive questions of interest to marketing strategy for both theoretical and/or practical reasons. This paper provides an overview of main causes of endogeneity, approaches to addressing it, and guidance to marketing strategy researchers to balance these issues as the field continues to move towards more methodological sophistication, potentially at the expense of managerial tractability.

Propensity score matching (PSM) has become a popular technique for estimating average treatment effects (ATEs) in accounting research. In this study, we discuss the usefulness and limitations of PSM relative to more traditional multiple regression (MR) analysis. We discuss several PSM design choices and review the use of PSM in 86 articles in leading accounting journals from 2008–2014. We document a significant increase in the use of PSM from zero studies in 2008 to 26 studies in 2014. However, studies often oversell the capabilities of PSM, fail to disclose important design choices, and/or implement PSM in a theoretically inconsistent manner. We then empirically illustrate complications associated with PSM in three accounting research settings. We first demonstrate that when the treatment is not binary, PSM tends to confine analyses to a subsample of observations where the effect size is likely to be smallest. We also show that seemingly innocuous design choices greatly influence sample composition and estimates of the ATE. We conclude with suggestions for future research considering the use of matching methods.

Endogeneity in regression models is a key marketing research concern. The Gaussian copula approach offers an instrumental variable (IV)-free technique to mitigate endogeneity bias in regression models. Previous research revealed substantial finite sample bias when applying this method to regression models with an intercept. This is particularly problematic as models in marketing studies almost always require an intercept. To resolve this limitation, our research determines the bias's sources, making several methodological advances in the process. First , we show that the cumulative distribution function estimation's quality strongly affects the Gaussian copula approach's performance. Second , we use this insight to develop an adjusted estimator that improves the Gaussian copula approach's finite sample performance in regression models with (and without) an intercept. Third , as a broader contribution, we extend the framework for copula estimation to models with multiple endogenous variables on continuous scales and exogenous variables on discrete and continuous scales, and non-linearities such as interaction terms. Fourth , simulation studies confirm that the new adjusted estimator outperforms the established ones. Further simulations also underscore that our extended framework allows researchers to validly deal with multiple endogenous and exogenous regressors, and the interactions between them. Fifth , we demonstrate the adjusted estimator and the general framework's systematic application, using an empirical marketing example with real-world data. These contributions enable researchers in marketing and other disciplines to effectively address endogeneity problems in their models by using the improved Gaussian copula approach.

Research summary: This paper posits adaptive capability as a mechanism through which a firm's prior growth influences the exhibition of future entrepreneurial action. Defined as the firm's proficiency in altering its understanding of market expectations, increased adaptive capability is a consequence of the new resource combinations that result from expanding organizational boundaries. Increased adaptive capability in turn corresponds to expansion of entrepreneurial activity, as firms increase their entrepreneurial orientation as the strategic mechanism to capitalize on their improved understanding of market conditions. We find support for our research model in a two-study series conducted in South Korea and the United Kingdom. Managerial summary: Most would agree that entrepreneurially oriented firms—being innovative, entering new markets, and taking risk—grow faster. But how a firm becomes entrepreneurial is a complicated question. In this study, we flipped the growth relationship around and found support for growth contributing to a firm's entrepreneurial orientation. But between growth and being more entrepreneurial is the firm's ability to recognize changes in market expectations. We argue that as a firm grows, it acquires new resources and new knowledge of how to use those resources. These new resource combinations increase its ability to recognize changes in market expectations—its adaptive capability. This capability uncovers new entrepreneurial opportunities for value creation. To capture this potential value, firms expand their entrepreneurial orientation.

This study empirically examines the relationship between wide-ranging board diversity and the quality of corporate social responsibility (CSR) disclosure variables in Malaysia. We extend prior literature covering broader dimensions of board diversity (e.g., gender, education level, education background, age, tenure, nationality and ethnicity) and their impact on CSR after controlling for board and audit committee characteristics. Using 200 listed firms in Bursa Malaysia during 2009-2013 and applying both OLS and 2SLS instrumental variables (IV) approaches, we document significant positive effect of board education level and board tenure diversity on the quality of CSR disclosure. Further analysis using robust regression also shows positive association between gender diversity and CSR disclosure. Our findings also demonstrate that the quality of CSR disclosure is significantly negatively associated with board age and nationality diversity. These results remain consistent with using alternative measures for board diversity, and characteristics for board of director and audit committees as well as split samples between large and small firms. Additional tests exhibit complementary relationship of education level and nationality with gender, while substitutive relationship of age and tenure with gender in influencing CSR. These findings provide useful insights into the policy makers in setting regulations in respect of board diversity in Malaysia and other emerging economies in the Asian region. Our evidence is also useful for listed companies in setting the criteria to identify directors who can support their strategic decisions.

In this paper we use simulations to examine how endogeneity biases the results reported by ordinary least squares (OLS) regression. In addition, we examine how instrumental variable techniques help to alleviate such bias. Our results demonstrate severe bias even at low levels of endogeneity. Our results also illustrate how instrumental variables produce unbiased coefficient estimates, but instrumental variables are associated with extremely low levels of statistical power. Finally, our simulations highlight how stronger instruments improve statistical power and that endogenous instruments can report results that are inferior to those reported by OLS regression. Based on our results, we provide a series of recommendations for scholars dealing with endogeneity.

Purpose This study aims to conduct a comprehensive methodological review, exploring the strategies used to address endogeneity within the realms of corporate governance and financial reporting. Design/methodology/approach This research reviews the application of various methods to deal with endogeneity issue published in the 10 journals covering the corporate governance discipline included in the Web of Science’s Social Sciences Citation Index. Findings With a focus on empirical studies published in leading journals, the author scrutinizes the prevalence of endogeneity and the methodologies applied to mitigate its effects. The analysis reveals a predominant reliance on the two-stage least squares (2SLS) technique, a widely adopted instrumental variable (IV) approach. However, a notable observation emerges concerning the inconsistent utilization of clear exogenous IVs in some studies, highlighting a potential limitation in the application of 2SLS. Recognizing the challenges in identifying exogenous variables, the author proposes the generalized method of moments (GMM) as a viable alternative. GMM offers flexibility by not imposing the same exogeneity requirement on IVs but necessitates a larger sample size and an extended sample period. Research limitations/implications The paper sensitizes researchers to the critical concern of endogeneity bias in governance research. It provides an outline for diagnosing and correcting potential bias, contributing to the awareness among researchers and encouraging a more critical approach to methodological choices, recognizing the prevalence of endogeneity in empirical studies, particularly focusing on the widely adopted 2SLS technique. Originality/value Practitioners, including corporate executives and managers, can benefit from the study’s insights by recognizing the importance of rigorous empirical research. Understanding the limitations and strengths of methodologies like 2SLS and GMM can inform evidence-based decision-making in the corporate governance realm.