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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.

This chapter pinpoints a chronological and thematic literature review on the concept of Creative Tourism. Even if the concept emerged 21 years ago, its definition keeps being discussed, and different approaches are available. Born in the late 1990s, it developed rapidly due to a very open, flexible and local context design, enabling the development of personal capacity, authentic experiences and involving local culture and communities. This new approach to tourism envisages bringing together local people, their habits and practices in real and everyday contexts to the heart of the tourism experience provided to visitors. Therefore, it looks to be a promising path towards sustainability. Keeping this in mind, one can wonder if Creative Tourism can be a lever for territories' economic, social, cultural and environmental sustainability. In particular, can Creative Tourism's growing importance in Southern Europe be considered a major contribution to the sustainability of those territories? The literature review concludes that most of the studies on Creative Tourism take positive impacts on territories as a kind of ‘belief’. Such an effect is not granted as a beneficial impact on a communities' well-being; it does not result just from the type of resources explored or from the participation of members of the community on the products/services supplied.

This chapter presents a novel method for using PechaKucha presentations to generate and analyze participant-generated video data. As a data source, participatory video (PV) differs from ethnographic or archival video by relying on participants to tell their own stories. As a presentation technique, PechaKucha produces six-minute-and-forty-second, narrated slideshow presentations. The slideshows or recordings from live PechaKucha presentations are a dense form of PV that is easier to code and analyze than traditional sources of PV. This chapter describes the procedures to capture and analyze PechaKucha-based PV and illustrates considerations for researchers who plan to use PV to gather data.

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.

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.

This chapter addresses the issue of the Global South external debt by mobilizing insights from Modern Monetary Theory, Ecological Economics, and Dependency Theory. It argues that the external debt problem of Southern governments is a reflection of their subordinate economic and monetary status. It shows why the argument of foreign currency shortage often used to explain the need for Southern governments to issue foreign currency debts remains superficial. In contrast to the usual focus on creditors, the chapter highlights the role played by foreign direct investment in the genesis of the chronic external indebtedness of most Southern countries. It argues then that the external debt of the South must be understood holistically not only as a manifestation of the unequal ecological exchange between the North and the South but also as an instrument that has contributed to reproducing and amplifying this pattern. Under these conditions, the cancellation or restructuring of the South's external debt stock and a few other unlikely concessions by the Northern countries will not be enough to abolish the “debt system.” This is an important lesson from the antiimperialist critique of the mid-1970s New International Economic Order (NIEO) agenda that current movements for Southern debt cancellation and Climate Justice would do well to remember.

This study examines the relationship between the motives for balanced scorecard (BSC) adoption and the development and use of the BSC. We expect that a stronger focus on economic adoption motives is associated with full development of the BSC and its integration into the performance measurement and control systems of the firm. In contrast, we expect that a higher focus on legitimacy as a motive for adoption leads to loose coupling of the BSC with the control systems of the firm. We expect legitimacy as a catalyst to BSC adoption leads to a lower level of BSC development and use, which enables the organization to keep the environment satisfied, but does not influence processes within the organization.

The data are obtained from a web-based survey with 88 useful responses of firms indicating that they use the BSC. The study investigates the relationship between the motives for BSC adoption (economic and legitimacy) and the development and use of the BSC. The results provide evidence for the hypothesis that economic motives for adoption positively affect the development of the BSC. In addition, the results partially support the hypothesis that legitimacy motives negatively affect the use of the BSC. When legitimacy comes via the mechanism of mimetic isomorphism, it has a negative effect on use of the BSC. Surprisingly, however, legitimacy has a positive effect on use when it comes via the mechanism of normative isomorphism.

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.