Conceptualizing Workarounds: Meanings and Manifestations in Information Systems Research

We reviewed papers in core IS outlets that defined the term workaround or presented an example of a workaround. In the analysis, we used Ogden and Richard’s triangle of reference as a theoretical framework to analyze the relationship between 1) the term workaround; 2) theories, definitions, and use of the term; and 3) their empirical basis and empirical workaround behavior that the papers describe. First, we summarize the existing theoretical insights regarding workarounds and investigate their validity. Second, we show that studies have defined and used the term workaround differently to the extent that they have not always applied it to the same empirical phenomena, which raises questions about some theoretical insights’ validity. Third, we suggest a definition for workarounds that we inductively derived from empirical accounts of workaround behavior and, therefore, that adequately describes how researchers commonly use the term and makes it possible to distinguish workarounds from other similar phenomena

WORKAROUNDS IN INFORMATION SYSTEMS RESEARCH: A FIVE-YEAR UPDATE

This paper complements an earlier (2019) literature review on workarounds in information systems research by including research that has influenced or been published in core IS outlets during the last five years (2018–2022). Our study captures research that strengthened, widened, and challenged theoretical insights from the previous review. It also provides additional insights and develops seven themes of theoretical insight. The 31 new papers and our updated analysis are most evident in the three themes: Workarounds and power, Temporality of workarounds, and Managing workarounds. We also found additional studies using the term ‘workaround’ differently to the extent that they have not applied the term to the same empirical phenomena, which questions the validity of some theoretical claims. We also found significantly more studies that used quantitative data-collection methods than the previous review

How do unintended consequences emerge from EHR implementation? An affordance perspective

Drawing upon an affordance‐actualisation perspective, we aim to advance our knowledge of the emergence of unintended consequences from the implementation of Electronic Health Record (EHR) systems. Prior research has not yet deeply understood how these unintended consequences unfold. We investigate how the (non‐)actualisation of affordances produces unintended consequences. Our exploratory case study of an EHR system implemented in Italy reveals four types of actions (flexing, bypassing, avoiding, and reorganising) through which different types of unintended consequences occur with the (non‐)actualisation of affordances. We explain and theorise how interactions among technology features and psychosocial and organisational constraints/enablers contribute to users' perception of affordances and technological constraints. This, in turn, influences different types of user actions, leading to unintended consequences. Our findings and insights contribute to the literature on unintended consequences and help organisations better manage implementing new systems.<br/

Between risk mitigation and labour rights enforcement: Assessing the transatlantic race to govern AI-driven decision-making through a comparative lens

In this article, we provide an overview of efforts to regulate the various phases of the artificial intelligence (AI) life cycle. In doing so, we examine whether—and, if so, to what extent—highly fragmented legal frameworks are able to provide safeguards capable of preventing the dangers that stem from AI- and algorithm-driven organisational practices. We critically analyse related developments at the European Union (EU) level, namely the General Data Protection Regulation, the draft AI Regulation, and the proposal for a Directive on improving working conditions in platform work. We also consider bills and regulations proposed or adopted in the United States and Canada via a transatlantic comparative approach, underlining analogies and variations between EU and North American attitudes towards the risk assessment and management of AI systems. We aim to answer the following questions: Is the widely adopted risk-based approach fit for purpose? Is it consistent with the actual enforcement of fundamental rights at work, such as privacy, human dignity, equality and collective rights? To answer these questions, in section 2 we unpack the various, often ambiguous, facets of the notion(s) of ‘risk’—that is, the common denominator with the EU and North American legal instruments. Here, we determine that a scalable, decentralised framework is not appropriate for ensuring the enforcement of constitutional labour-related rights. In addition to presenting the key provisions of existing schemes in the EU and North America, in section 3 we disentangle the consistencies and tensions between the frameworks that regulate AI and constrain how it must be handled in specific contexts, such as work environments and platform-orchestrated arrangements. Paradoxically, the frenzied race to regulate AI-driven decision-making could exacerbate the current legal uncertainty and pave the way for regulatory arbitrage. Such a scenario would slow technological innovation and egregiously undermine labour rights. Thus, in section 4 we advocate for the adoption of a dedicated legal instrument at the supranational level to govern technologies that manage people in workplaces. Given the high stakes involved, we conclude by stressing the salience of a multi-stakeholder AI governance framework

A Holistic Framework for Complex Big Data Governance

Big data assets are large datasets that can be leveraged by organisations if the capabilities exist, but it also brings considerable challenges. Despite the benefits that can be realised, the lack of proper big data governance is a major barrier, making the processing and control of this data exceptionally difficult to execute correctly. More specifically, organisations reportedly struggle to incorporate big data governance into their existing structures and business models to derive value from big data initiatives. Big data governance is an emerging research domain, gaining attention from both Information Systems scholars and the practitioner community. Nonetheless, there appears to have been limited scientific research in the area and most existing data governance approaches were limited, given they do not address end-to-end aspects of how big data could be governed. Furthermore, no suitable framework for handling data governance against big data complexities was found to be available. Thus, the main contribution of the work presented in this thesis is to address this requirement; by advancing research in this field and designing a novel holistic big data governance framework capable of supporting global organisations to effectively manage big data as an asset, thereby obtaining value from their big data initiatives. An extensive systematic literature review was done in order to uncover the published content that reflects the current state of knowledge in big data governance. To facilitate the creation of the proposed framework a grounded theory methodology was used to analyse openly available parliamentary inquiry data sources, with particular focus on identifying the core criteria for big data governance. The resulting novel framework generated provides new knowledge by identifying several big data governance building blocks; which are classified as strategic goals, execution stages, enablers and 22 core big data governance components to ensure an effective big data governance programme. Moreover, thesis findings indicate that big data complexities extend to the ethical side of big data governance and this is taken into consideration in the framework design. An ‘ethics by design’ component is proposed to influence how this can be addressed in a structured way

The Impact of Place-Based Services on Child Maltreatment: Evaluation Through Big Data Linkage and Analytics

There is a clear evidence that place is one factor associated with rates of child maltreatment and that rates of child abuse differ between different neighbourhoods and communities. Although there are few place-based initiatives (PBIs) focused specifically on child maltreatment, there is an increasing policy and research interest on PBIs that address a range of problems for children and families in disadvantaged communities. Evaluating the effectiveness of these initiatives is extremely challenging, both methodologically and ethically, but one potential way forward is to use linked administrative data to track outcomes of children and families. This chapter discusses the opportunities and challenges for the use of administrative data linkage in the evaluation of PBIs. The chapter is informed by interviews with 12 Australian experts on the use of ‘big data’ in public policy