The chapter outlines social media and qualitative research. It describes social media for data collection and different qualitative research approaches to data collection. The chapter describes social media as a phenomenon for research and outlines different levels of social media utilization: individual, work-practice and supra-organizational levels. Vignettes for the different levels are provided and the need for qualitative research concluded.

In collaboration with the HR team of a large IT service provider, this chapter relates to a study of fifty individuals who have been identified as high performers by their employer and the search for indicators and patterns of sustainable high performance.

The research design consisted of initial interviews at a virtual day, attendance of 2.5-day off-site coaching workshops and up to 60-minute follow-up interviews. During the workshop days, 24-hour heart rate variability (HRV) measurements were collected – a well-established biomarker of well-being, strain and recovery. As HRV data are difficult to analyze without contextual information, interviews, focus-group sessions, participatory observation and debriefing interviews were combined in order to contextualize the quantitative measurements and involve the participants in the interpretation and sense-making of the findings.

The methodological goal of this chapter is to demonstrate how orchestrating, improvising and performing a mixed-method study has been essential to validate, augment and complement quantitative data. The study results depend on the ability of the researchers to skilfully and empathetically engage with the interviewees and to engage them as participants in the interpretation of their data and thus as co-producers of meaning.

Recognizing the pervasive influence of modern digital technologies, this chapter argues for the supremacy of strategy work in terms of giving shape and effect to the associated agenda for strategic, organizational and technological change. The chapter focuses on the theory and practice of action research as a Mode 2 approach to knowledge production as managers co-inquire into the practice of strategizing. The discussion speaks directly to the practice of action research in government organizations, of enhancing strategy work and its related outcomes, and the broader outcomes of co-inquiry. The chapter affirms the central role of action research in knowledge production and emphasizes how the practice of action research is itself being transformed by enabling digital technologies during the current COVID-19 pandemic. The contention throughout is that good practice informs research and good research informs practice.

This chapter advocates further advancing qualitative research methods by creating tools to investigate digital traces of digital phenomena. It specifically focuses on large-scale textual datasets and shows how interactive visualization can be used to augment qualitative researchers’ capabilities to theorize from trace data. The approach is grounded on prior work in sense-making, visual analytics and interactive visualization, and shows how tasks enabled by visualization systems can be synergistically integrated with the qualitative research process. Finally, these principles are applied with several open-source text mining and interactive visualization systems. The chapter aims to stimulate further interest and provide specific guidelines for developing and expanding the repertoire of open-source systems for qualitative research.

The exponential development of information technologies (IT) which has been described as the digital revolution has led to different IT outcomes at individual, organizational and societal levels. The chapter theorizes these different IT outcomes as digitally led emancipation and digitally led exploitation. The chapter postulates that the attainment of the outcomes depends on different power mechanisms and their associated fault lines. Power mechanisms and IT are theorized to create a framework explicating these dynamics. Power mechanisms are outlined as episodic power and digitally led emancipation (collective action, participation), episodic power and digitally led exploitation (manipulation, information asymmetries), systemic power and digitally led emancipation (empowerment, inclusion) and systemic power and digitally led exploitation (surveillance/monitoring, automation/algorithmification). The chapter concludes with a research agenda to understand these power mechanisms, which may enable digitally led emancipation and digitally led exploitation.

The digitalization of business organizations and of society in general has opened up the possibility of researching behaviours using large volumes of digital traces and electronic texts that capture behaviours and attitudes in a broad range of natural settings. How is the availability of such data changing the nature of qualitative, specifically interpretive, research and are computational approaches becoming the essence of such research? This chapter briefly examines this issue by considering the potential impacts of digital data on key themes associated with research, those of induction, deduction and meaning. It highlights some of the ‘nascent myths’ associated with the digitalization of qualitative research. The chapter concludes that while the changes in the nature of data present exciting opportunities for qualitative, interpretive researchers to engage with computational approaches in the form of mixed-methods studies, it is not believed they will become the sine qua non of qualitative information systems research in the foreseeable future.

This chapter introduces the concept of ‘datafication momentum’, which is the tendency for datafication systems to receive more influence from social systems in their early stages and exert more influence on social systems in their mature stages. Due to datafication momentum, datafication systems are prone to be inscribed with the dark side of social systems in their earlier stages, and then amplify this dark side in their later stages (e.g., leading to outcomes like data-driven discrimination). The chapter calls on qualitative researchers to combat this risk with a ‘qualitative researchers as design thinkers’ mindset. In particular, it proposes ‘design forensics’ as a practice in which qualitative researchers integrate design-thinking principles with design ethnography to identify the risk of datafication and shape it to a more desirable end. The chapter introduces three design principles – empathetic datafication, datafication totality and reflective criticism – and discusses their implications for research and practice.

Qualitative research provides an excellent opportunity to study digitalization. The purpose of this chapter is to explore the digitalization of government services by studying the longitudinal development data-sharing practices across different parts of government in the United Kingdom. This chapter reports on a unique, qualitative, interpretive field study based on the author’s role as a participant observer and his analysis of the discourse and contents of the various documents presented in relation to both the creation and running of data-sharing practices in the United Kingdom. The chapter finds that despite government addressing many of the concerns identified in the literature on data sharing, practical and perceptual issues remain – issues that tell us much about the state of digitalization of government services.

Big data has been proclaimed as revolutionary and transformative, and will alter businesses and society in fundamental ways. Such dramatic claims sound suspiciously like prior technological waves which were also promoted as metamorphic. Most of these past ‘revolutionary’ technologies have proved to be more evolutionary than revolutionary, but is the same likely to be true with big data? This chapter examines a number of the underlying assumptions associated with the supposed big data revolution. It highlights some of the fallacies and misconceptions that lie behind big data, and how these assumptions can lead to unintended and dysfunctional consequences. In particular, it explores how these dysfunctions might manifest themselves when it comes to organizational knowledge and practice. The analysis in the chapter leads to the conclusion that while big data offers promise, the hype surrounding it has obscured the potential dangers in its use. The chapter explains that it is important to take a more nuanced view of this new technology, putting safeguards in place to ensure that big data use (including big data analytics, machine learning, artificial intelligence and the algorithms they develop) does not lead to dysfunction.

Today’s information technology is becoming ever-more complex, distributed and pervasive. Therefore, problematizing what we observe as Information Systems (IS) researchers is becoming ever-more difficult. This chapter offers a new perspective for qualitative empirical research in the IS field. It looks at how we can possibly study dynamically changing, evolving, spatially and temporally distributed phenomena that evade our accustomed concepts and assumptions about the locus of agency. Or asked differently: How can we formally approach phenomena evading our concept of ‘identity’?

Using the mathematical-logical framework of the Laws-of-Form, formulated in 1969 by George Spencer-Brown, the chapter introduces the notion of distinction to capture the manifestation of concepts. It provides a short overview and illustrates how it can be used on sample concepts drawn from IS sociomateriality research.

The chapter advances qualitative methodology by suggesting a formal notation to communication analysis that is reflective of technologies’ complex nature. Applying the framework not only alters the epistemological boundaries for how to experience and study the ‘digital’, but also helps to build a bridge between deep technological insights, our immediate, unbiased and mundane experience of technologies, and how we speak about them.