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A growing body of research outlines how to best facilitate and ensure methodological rigor when using dictionary-based computerized text analyses (DBCTA) in organizational research. However, these best practices are currently scattered across several methodological and empirical manuscripts, making it difficult for scholars new to the technique to implement DBCTA in their own research. To better equip researchers looking to leverage this technique, this methodological report consolidates current best practices for applying DBCTA into a single, practical guide. In doing so, we provide direction regarding how to make key design decisions and identify valuable resources to help researchers from the beginning of the research process through final publication. Consequently, we advance DBCTA methods research by providing a one-stop reference for novices and experts alike concerning current best practices and available resources.

Social entrepreneurs often make public appeals for funding to investors who are motivated by nonfinancial considerations. This emerging research context is an opportunity for researchers to expand the bounds of entrepreneurship theory. To do so, we require appropriate research tools. In this chapter, we show how computer-aided text analysis (CATA) can be applied to advance social entrepreneurship research. We demonstrate how CATA is well suited to analyze the public appeals for resources made by entrepreneurs, provide insight into the rationale of social lenders, and overcome challenges associated with traditional survey methods.

We illustrate the advantages of CATA by examining how charismatic language in 13,000 entrepreneurial narratives provided by entrepreneurs in developing countries influences funding speed from social lenders. CATA is used to assess the eight dimensions of charismatic rhetoric.

We find that four of the dimensions of charismatic rhetoric examined were important in predicting funding outcomes for entrepreneurs.

Data collection and sample size are important challenges facing social entrepreneurship research. This chapter demonstrates how CATA techniques can be used to collect valuable data and increase sample size. This chapter also examines how the rhetoric used by entrepreneurs impacts their fundraising efforts.

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.

Research in entrepreneurship is increasingly exploring how archetypes, taxonomies, typologies, and configurations can help scholars understand complex entrepreneurial phenomena. We illustrate the potential for set-theoretic methods to inform this literature by offering best practices regarding how qualitative comparative analysis (QCA) can be used to explore research questions of interest to entrepreneurship scholars. Specifically, we introduce QCA, document how this approach has been used in management research, and provide step-by-step guidance to empower scholars to use this family of methods. We put a particular emphasis on the analytical procedures and offer solutions to dealing with potential pitfalls when using QCA-based methods and highlight opportunities for future entrepreneurship research.

This chapter provides an article-by-article annotated bibliography of the extant social entrepreneurship literature from the top management and entrepreneurship journals. Special emphasis is given to the methods used in empirical studies, providing a one-stop reference to scholars interested in conducting social entrepreneurship research.

Forty-three social entrepreneurship articles from ten top management and entrepreneurship journals were selected and summarized.

In the early days of the COVID-19 pandemic, an area could only report its first positive cases if the infection had spread into the area and if the infection was subsequently detected. A standard probit model does not correctly account for these two distinct latent processes but assumes there is a single underlying process for an observed outcome. A similar issue confounds research on other binary outcomes such as corporate wrongdoing, acquisitions, hiring, and new venture establishments. The bivariate probit model enables empirical analysis of two distinct latent binary processes that jointly produce a single observed binary outcome. One common challenge of applying the bivariate probit model is that it may not converge, especially with smaller sample sizes. We use Monte Carlo simulations to give guidance on the sample characteristics needed to accurately estimate a bivariate probit model. We then demonstrate the use of the bivariate probit to model infection and detection as two distinct processes behind county-level COVID-19 reports in the United States. Finally, we discuss several organizational outcomes that strategy scholars might analyze using the bivariate probit model in future research.