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Machine learning (ML) has recently gained momentum as a method for measurement in strategy research. Yet, little guidance exists regarding how to appropriately apply the method for this purpose in our discipline. We address this by offering a guide to the application of ML in strategy research, with a particular emphasis on data handling practices that should improve our ability to accurately measure our constructs of interest using ML techniques. We offer a brief overview of ML methodologies that can be used for measurement before describing key challenges that exist when applying those methods for this purpose in strategy research (i.e., sample sizes, data noise, and construct complexity). We then outline a theory-driven approach to help scholars overcome these challenges and improve data handling and the subsequent application of ML techniques in strategy research. We demonstrate the efficacy of our approach by applying it to create a linguistic measure of CEOs' motivational needs in a sample of S&P 500 firms. We conclude by describing steps scholars can take after creating ML-based measures to continue to improve the application of ML in strategy research.

A proposed typology of moral exemplars in business highlights instances selected to illustrate standards for inclusion. The typology distinguishes among champions, heroes, and saints as different kinds of business exemplars. The typology reflects variations in both specific decision conditions and moral value emphases of business actors. The typology also differentiates moral exemplars from moral neutrals (i.e., amoral actors) and moral sinners (i.e., moral value scofflaws). The objective is to advance understanding of moral character and moral courage in business settings.

The methodology combines original conceptual argument and brief case summaries taken from available literature. The chapter is not a systematic survey of literature but cites key works. Construction of the typology involved iteration between conceptual development and case interpretation.

The chapter separates business cases into private business and public business, and applies Adam Smith’s distinction between citizenship and good citizenship. An additional distinction is made between extreme conditions and normal conditions. Moral heroism in business is restricted to life-and-death or strongly analogous situations in extreme conditions such as hazardous whistleblowing. Moral sainthood in business involves extreme maximization of a single value going far beyond simple compliance with legal requirements and typical ethical norms – Smith’s definition of citizenship. Moral championing in business concerns some degree of lesser self-sacrifice in defense of important values reflecting Smith’s definition of good citizenship.

The chapter is a selection of literature undertaken in iteration with the conceptual development effort. The original research aspect of the chapter is thus quite limited. The author is not positioned to judge the accuracy of published information, for or against a particular instance. The classifications thus depend on whether the instance would, if the generally reported facts are basically accurate, serve as a reasonable illustration of standards for inclusion. Criticisms have been made concerning some of the instances discussed here.

The emphasis is on providing standards for defining moral exemplars for business to suggest how much can be accomplished in business through moral influence.

The conceptual contribution is original, although drawing on the philosophical literature debate about saints and heroes. The chapter treats exemplar as the overarching construct, separated into three kinds: heroes, saints, and champions. Sinner is implicit in the notion of saint. The chapter adds moral champions and moral neutrals to isolate moral heroism. The cases exist in the literature, but have been combined together here for the first time.

The authors examined the impact of the Market Facilitation Program (MFP) and Coronavirus Food Assistance Program (CFAP) payments to United States agricultural producers on non-real estate agricultural loans.

The authors used quarterly, state-level commercial bank data from 2016–2020 to estimate dynamic panel models.

The authors found MFP and CFAP payments not associated with the percentage of non-real estate agricultural loans with payments over 90 days late. However, these payments associated with the percentage of non-real estate agricultural loans with payments between 30 and 89 days late. The available data utilized cannot consider when producers received the actual payment and what they specifically did with those funds.

The contribution of this study is for US policymakers and agricultural lenders. The findings could be helpful in designing and implementing future ad hoc payment programs and provide an understanding of potential shortcomings of the current safety net for agricultural producers in the Farm Bill. Additionally, findings can assist agricultural lenders in predicting the impact of ad hoc payments on their distressed loan portfolios.

The use of artificial intelligence (AI) in management research is still nascent and has primarily focused on content analyses of text data. Some method scholars have begun to discuss the potential benefits of far broader applications; however, these discussions have not led yet to a wave of corresponding AI applications by management researchers. This chapter explores the feasibility and the potential value of using AI for a very specific methodological task: the reliable and efficient capturing of higher-level psychological constructs in management research. It introduces the capturing of basic emotions and emotional authenticity of entrepreneurs based on their macro- and microfacial expressions during pitch presentations as an illustrative example of related AI opportunities and challenges. Thus, this chapter provides both motivation and guidance to management scholars for future applications of AI to advance management research.

This paper aims to propose a framework for engaging tourists as citizen scientists in marine research. Justification for such a framework is driven by the increasing use of citizen science (CS) as a method for collecting data in various research projects. While the tenets of CS exist, few studies have demonstrated how these could be integrated to address the challenges and benefits of engaging tourists as scientists.

A systematic review focused on CS-based literature within environmental management, tourism and marine tourism revealed 18 models. Key themes, those repeatedly identified, were extracted. These were adapted to inform the proposed theoretical framework for engaging tourist scientists.

The key themes were clarified and incorporated into the framework to enhance the value of marine research by engaging tourists. Important features considered were the singularity and sporadic nature of tourist participation, training and engagement as well as the reliability of data collected by citizen scientists.

The framework informs the unique requirements of research in marine environments and the under-used potential of tourist scientists.

抽象

本文构建了一个吸引游客成为海洋研究的公民科学家的框架。越来越多的人使用公民科学(CS)作为在各种研究项目中收集数据的一种方法, 这证实了这种框架具有合理性。尽管 CS 的原则是存在的, 但很少有研究表明如何将这些原则整合起来, 以便在吸引游客成为科学家的过程中解决问题, 实现效益。

本文对环境管理, 旅游和海洋旅游领域基于 CS 的文献进行了系统的回顾, 发现了18 种模式。接下来, 提取出重复确定的关键主题。为了吸引旅游者成为科学家, 这些都根据所提出的理论框架作了调整。

为了通过吸引游客成为科学家来提高海洋研究的价值，本文明确关键主题并将其纳入框架。同时，本文考虑到了一些重要因素，包括游客参加, 培训和融入的奇异性和零星性，以及公民科学家收集的数据的可靠性

该框架反映出，海洋环境研究具有独特的要求，以及旅游者科学家的潜力尚未得到充分利用。

Resumen

Este artículo propone un marco de trabajo, para involucrar a los turistas como ciudadanos científicos en la investigación marina. La justificación de este marco, está impulsada por el uso creciente de la ciencia ciudadana (CS), como un método para recopilar datos en los proyectos de investigación. Si bien ya existen los principios de la CS, pocos estudios han demostrado cómo estos podrían integrarse para abordar los desafíos y beneficios de involucrar a los turistas como científicos.

Una revisión sistemática, centrada y basada en la literature sobre CS, y al mismo tiempo relacionada con la gestión ambiental, turismo y turismo marino, reveló 18 diferentes modelos. Se extrajeron los temas clave, identificados repetidamente. Estos fueron adaptados para fundamentar el marco teórico propuesto e involucrar a los científicos turísticos.

Se aclararon los temas claves y se incorporaron en el marco de trabajo, para aumentar el valor de la investigación marina, mediante la participación de los turistas. Las características consideradas importantes, fueron la singularidad y la naturaleza esporádica de la participación turística, la capacitación y el compromiso, así como la fiabilidad de los datos recopilados por los científicos ciudadanos.

El marco de análisis, expone los requisitos únicos de investigación en entornos marinos y el potencial infrautilizado de los científicos turísticos.

In this retrospective of Ann Langley's extensive career, she shares how methodological insights emerged in her research career as a qualitative researcher in strategy and management. Her retrospective provides the back story of some of her highly-cited methods papers. Ann acknowledges the role that other researchers and authors have played in her research career.

Natural disasters and other crises present methodological challenges to organizational researchers. While these challenges are well canvassed in the literature, less attention has been paid to understanding how distinct crisis events may present, not only unique challenges, but also important opportunities for research. In this chapter, we draw on our collective experience of conducting post-earthquake research and compare this with the COVID-19 pandemic context in order to identify and discuss the inherent vulnerabilities associated with disaster studies and the subsequent methodological challenges and opportunities that researchers might encounter. Adopting a critical perspective, the chapter grapples with some of the more contentious issues associated with research in a disaster and crisis context including aspects of stakeholder engagement, ethics, reciprocity, inequality, and vulnerability.

In this chapter, we explicate two related techniques that help quantify the sensitivity of a given causal inference to potential omitted variables and/or other sources of unexplained heterogeneity. In particular, we describe the Impact Threshold of a Confounding Variable (ITCV) and the Robustness of Inference to Replacement (RIR). The ITCV describes the minimum correlation necessary between an omitted variable and the focal parameters of a study to have created a spurious or invalid statistical inference. The RIR is a technique that quantifies the percentage of observations with nonzero effects in a sample that would need to be replaced with zero effects in order to overturn a given causal inference at any desired threshold. The RIR also measures the percentage of a given parameter estimate that would need to be biased in order to overturn an inference. Each of these procedures is critical to help establish causal inference, perhaps especially for research urgently studying the COVID-19 pandemic when scholars are not afforded the luxury of extended time periods to determine precise magnitudes of relationships between variables. Over the course of this chapter, we define each technique, illustrate how they are applied in the context of seminal strategic management research, offer guidelines for interpreting corresponding results, and delineate further considerations.

The use of celebrities, and particularly athletes, to influence consumers and sell products is not a new practice, but one that is gaining considerable steam in the sports marketplace. However, many academics and practitioners have long questioned the means by which celebrity endorsement is measured and evaluated. Through the use of validated surveys among US students and the inauguration of the Celebrity-Hero Matrix (CHM), some of their questions are answered. Being labelled a 'heroic' athlete does, it seems, have tremendous power for marketers, and provides endorsement clout for the athlete.