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.

The chapter re-examines the case study research method and its role and contribution to the IS discipline and focuses on the current status of the case study research and the increased digitalization. The advantages of qualitative interpretive cases studies are identified, recent case studies are described and analyzed, and their contributions highlighted. These examples continue to enhance the discipline and sustain the traditional benefits of the case study research through rich data, analysis and understanding the links between people, organizations and technologies, the advancement and expansion of theory, the identification of hidden aspects, and the emergence of new concepts and theorization. Two of the cases use trace data, a type of data emerging as a product of digitalization. While these cases provide contributions, they also challenge the traditional understanding of what a case study is, and the benefits that accrue. The chapter emphasizes the need for mixed-method and multi-method case studies research in addition to trace data to enhance the benefits of the case study research.

Increasing digitalization means that many of our daily interactions happen within digital environments where they leave digital footprints in the form of trace data. Such digital trace data is often thought to generate insights by virtue of its immense scale. This focus on ‘big data’ tends to overlook the richness and complex characteristics of digital traces that opens new vistas for a multitude of computational analyses that generate new and high-resolution insights to digital environments. As such, paying attention to the characteristics of trace data allows for deep investigations of social-technical interactions in unprecedented detail.

This chapter describes the process of digital trace analysis through four analytical activities aimed at identifying units of analysis, extracting categories, validating patterns and conceptualizing findings from digital trace data. It then shows how analytical activities can be applied to a given digital trace dataset to derive three ‘facets’ that each provide rich conceptualizations of social interaction with technology. It explains each facet and outlines ways to implement digital trace analyses that focus on facets relating to relations (network analysis), processes (sequence analysis) or semantics (text analysis).

The proliferation of digital data has been presented as heralding a revolution in research methods for the study of social phenomena, such as IS. For some authors, this revolution involves the abandonment of the traditional scientific method in favour of purely inductive data-driven research. Others proclaim the emergence of a new, quantitative, computational social science that will displace qualitative methods, while others see digital data as potentially enriching qualitative research. All these claims, however, take the nature of data for granted, assuming that they straightforwardly instrument reality and that understanding of the world can therefore be gained through their analysis alone. This chapter presents a critical analysis of this ‘pre-factual’ view, arguing that data are not natural givens, but are performed, brought into being by situated practices that enact particular representations of the world. The implications of such a conceptualization of data for research methods in Information Systems and organizational research are discussed.

The introduction chapter outlines the need for qualitative research in the age of digitalization. The chapter outlines the opportunities and challenges which digital technologies and digitalization provide and enable. The chapter explicates the need to understand the different aspects of digitalization, the opportunities, issues, challenges and implications at different levels (individual, work, organizational, societal) and outlines the need of qualitative research to understand these phenomena. The chapter discusses the methodological opportunities and challenges for digital qualitative research enabled through digitalization.

How should human values be integrated into the studies of digital qualitative research? This chapter proposes an answer to this question. It discusses the implications for qualitative researchers of human values in a digital-first world. In a digital-first world, digital technologies are simply taken for granted, and people see the world through digitally computed reality. The chapter offers a way to address the question of including human values in qualitative research in a digital society. It provides basic definitions of key concepts and illustrates them using practical examples of how human values are informing digital research from cultural, spiritual and Indigenous perspectives. As the diversity of human values contributes to the richness of meaning and everyday experiences, it is hoped that scholars and students of digital technology will examine, describe and integrate those values in their research. It is suggested that integrating human values in qualitative studies can contribute to interdisciplinarity in research.

An increasingly pervasive digital environment, where technologies mediate social interaction within and outside organizations, creates new rich data sources for IS research. Of primacy to IS scholars, who study phenomena at the intersection of technology, people and organization, is how future research designs can capture such ongoing sociotechnical entanglements occurring in hybrid online and offline spaces. Building on lessons learned from a study of platform workers, this chapter explores three key challenges of a hybrid ethnographic approach to IS research: (1) navigating unbounded fieldsites; (2) managing technological opacity; (3) working with diverse data. The chapter guides researchers by demonstrating how hybrid methods can be used in different configurations across diverse settings. Simultaneously, in the age of web crawlers, data scraping and machine learning, processes that are invaluable in their own rights, this chapter resituates a qualitative ethnographic approach to digital data, introducing participatory digital observation to the rich empirics gathered in face-to-face environments. Rather than reproducing the valuable work done by scholars in digital sociology and ethnography, this chapter brings strands of the conversation together, highlighting the benefits of studying IS phenomena as a hybrid that exists both in physical and virtual spaces.

This chapter discusses the reflexive relationship between qualitative researchers and the process of selecting, forming, processing and interpreting data in algorithmic qualitative research. Drawing on Heidegger’s ideas, it argues that such research is necessarily synthetic – even creative – in that these activities inflect, and are in turn inflected by, the data itself. Thus, methodological transparency is key to understanding how different types of meanings become infused in the process of algorithmic qualitative research. While algorithmic research practices provide multiple opportunities for creating transparent meaning, researchers are urged to consider how such practices can also introduce and reinforce human and algorithmic bias in the form of unacknowledged introduction of perspectives into the data. The chapter demonstrates this reflexive dance of meaning and bias using an illustrative case of topic modelling. It closes by offering some recommendations for engaging actively with the domain, considering a multi-disciplinary approach, and adopting complementary methods that could potentially help researchers in fostering transparency and meaning.

The chapter theorizes power, knowledge and digitalization in the digital era. It theorizes the roles of knowledge and power in the current era and how these are impacted, reinforced, redistributed, challenged and transformed through increased digitalization. The chapter develops a Knowledge-Power-Digitalization framework where the influence of episodic and systemic power on knowledge and the role of Information Systems and digitalization are outlined. The framework outlines the following quadrants: power as possession, power as asymmetries, power as empowerment and power as practice. The role of digitalization is outlined within these quadrants. The Knowledge-Power-Digitalization framework developed outlines avenues for future research in the digital era pertinent to digitalization, knowledge and power dynamics, which are important current and complex phenomena in need of qualitative research understanding and theorization.