Digital technologies such as big data analytics (BDA) are being increasingly used by businesses to create economic and societal value (Ferraris et al., 2019; Constantiou and Kallinikos, 2015; Günther et al., 2017; Rana et al., 2023). As a consequence, academic literature has emphasised their “disruptive potential” for enhancing corporate sustainability performance (Etzion and Aragon-Correa, 2015), creating more equal and inclusive society (Secundo et al., 2017), fostering optimal reallocation of underutilized resources (Etter et al., 2019) and enabling more participatory and democratic forms of governance (Neu et al., 2019; Ojala et al., 2019; Uldam, 2018).
Conversely, the advocates of the critical approach have raised concerns about digital technologies related to privacy and security threats (La Torre et al., 2018), limitations of autonomy and freedom (Andrew and Baker, 2019), labour exploitation (Fuchs, 2010), lack of algorithmic accountability (Martin, 2019), pervasive worker control (Chai and Scully, 2019), and ecological footprint (Corbett, 2018; Lucivero, 2020). Hence, the magnitude and pervasiveness of ethical, social and environmental risks that emerge as a consequence of user data collection, storage and algorithmic processing are imposing additional responsibility upon data processing companies. To this end, the extant literature offers three main reasons for why large technology companies still lack accountability for these consequences. First, the problem resides in the inherent power asymmetries between the companies and individual users that pre-empt the latter from holding the former accountable for their wrongdoings (Rosenblat and Stark, 2016; West, 2019). Such quasi-monopolistic concentration of power in the hands of internet corporations is exerted not only vis-a-vis individual consumers but also other organisations (i.e., suppliers, competitors) whose business survival depends on the services of the large companies (Flyverbom et al., 2019). Second, regulatory efforts in the data economy often take place post hoc (Nunan and Di Domenico, 2017) and do not adequately address the contemporary issues of digitalization (Royakkers et al., 2018). Until recently, a self-regulatory regime prevailed in technology regulation based on “soft” voluntary standards and principles which the large companies developed for themselves. Finally, wrongful practices become pervasive to the extent that the other actors take them for granted and stop questioning them (Ananny and Crawford, 2018).
As a result, companies find themselves in a “dual” position in which they simultaneously need to harness the potential of BDA to generate economic and societal value on the one hand, while at the same time are required establish an effective mechanism for ensuring accountability for the negative consequences of data utilization on the other. Hence, from the accounting perspective, this raises three important questions as to (1) whether accounting scholars can explain the emergent issues with BDA using established accounting theories, (2) whether and, if so, how the processing of BD results in calls for wider organisational accountability and greater regulatory oversight and (3) how the value of BDA can be assessed from a financial accounting standpoint. The present manuscript aims to address these questions.
Chapter 1 “Emerging technologies in accounting” reviews technologies that underlie the use of BDA in accounting, provide definitions, discuss their interdependencies and explain differences between different technologies, illustrating their current and potential applications. In particular, new sources of big data and their characteristics will be discussed; different analytical approaches will be reviewed. The principal goal of this chapter is to establish a clear terminology and introduce key concepts that are fundamental for understanding the role of BDA in accounting.
Chapter 2 “Peculiar and established theories framing studies of BDA in accounting” examines whether and how accounting literature has rooted BDA issues inside theoretical frameworks in order to formulate new concepts and models, to support the adoption of further methods and approaches, to explain and root the solutions used in practice.
Chapter 3 “Data Regulations in the European Union” provides the most recent overview of the legal frameworks and regulatory developments in the European Union with regards to the data collection, use, storage, processing and sharing. Starting with the General Data Protection Regulation (GDPR) implementation in 2018, the European Union is taking a pioneer role in data-related regulations globally, imposes greater obligations, stricter rules and accountability frameworks. The chapter provides business and competitive context to explains the nature of the problem each regulatory initiative seeks to address, provides a general overview of the legal provisions in the context of the theoretical research in law, information systems and accounting and concludes by critical assessment of the effectiveness of the regulation – enforced or proposed – in reaching its goals and formulates a series of recommendations for potential improvement.
Chapter 4 “Assessing the Value of Big Data and Analytics: Issues, Opportunities and Challenges” assesses the value of data that derives, rather than from inherent conditions, from the possibility of generating insights and the actual use of the same (Ferraris et al., 2019; Günther et al., 2017).
“Conclusion” summarizes key research findings useful to provide answers to the above listed three research questions.
