Anna Essén

How do actors overlook uncomfortable information? We add to the understanding of how potential problems can be ignored over long periods, in spite of recurrent warnings. Ignoring then becomes a dynamic process of responding to evolving ignoring ‘threats’ or triggers by combining knowledge-seeking and knowledge avoidance in ways that must be continuously legitimated, both in one’s own eyes and in those of relevant parts of the environment. Drawing on a longitudinal case study, we find that ambiguity-juggling – mutually supporting acts foregrounding and backgrounding ambiguity – constitute a key element of such ignoring work. Our study adds to the literatures on strategic or wilful ignorance and ambiguity management by providing a novel explanation for how actors dynamically mobilize motives for ignoring and thereby navigate uncomfortable information that evolves over time. As we show, sustained forms of ignoring involves interactions between shifting ignoring triggers, adaptive ignoring work, and evolving states of organized ignorance. This conceptualization contributes to the ignoring literature by extending current accounts of the ‘why’, ‘how’, ‘how much’, and ‘who’ of ignoring, as well as its outcomes.

Mission-oriented innovation policies (MOIPs) are promoting the formation of 'mission arenas' (MAs) where actors collectively try to address societal 'wicked problems'. Yet, little is known about how attention — and subsequently time and effort — towards specific problems and solutions, and their geographical dimensions, unfolds within MAs. We conducted a multiple-case study of four MAs mandated and granted public funding to address self-articulated 'missions' in public health. We identify four distinct types of MA organisation with different attention-regulating properties that contribute to significant variation in MAs' flexibility and breadth of attention. We propose a model explicating how all four MA organisations regulate attention in ways that impede future attention to regional problems and solutions — a finding that serves to problematise assumptions about attention in the MOIP literature.

Both research and public discourse have recently drawn attention to the downsides of algorithmic decision-making (ADM), highlighting how it can produce biased and discriminatory outcomes and also pose threats to social justice. We address such threats that emanate from but also go beyond algorithms per se, extending to how public agencies and legal institutions respond or fail to respond to the consequences of ADM. Drawing on a case study of the use of an ADM system in public school administration, we explore the practices through which public institutions avoided engagement with the detrimental consequences of ADM, leading to injustice. We provide a conceptual model outlining how organizational ignoring practices can lead to social and institutional blackboxing of an ADM system, engendering both social and legal injustice. Our work paves the way for interdisciplinary research on the multilayered blackboxing of ADM. We also extend algorithmic injustice research to include a legal dimension and provide practical implications in the form of a legal framework for ADM in the public sector.

Although digital technology (DT) is often introduced with the aim of enhancing organizational knowledge transfer and learning, these aims often fail to materialize. The information systems (IS) literature attributes such unexpected outcomes to inappropriate technology design and implementation, as well as to overuse, misuse, and non-use of technology. However, we know little about how actors misuse or fail to use technology and data, thereby failing to acquire and act upon the knowledge necessary to achieve organizational learning. Leveraging the literature on strategic ignorance, we explore how actors expected to use technology for learning purposes justify their non-engagement with it. Studying an implementation of a DT with the purpose of facilitating organizational learning on basis of provided data in health care, we identify seven ignoring justifications through which the target users of the DT avoided key knowledge acquisition and knowledge-based action activities. These sensemaking behaviors accumulated to a state of collective passivity in relation to the DT. Our conceptualization contributes to and connects theories of organizational learning in the IS literature and strategic ignoring.

Digital platforms have deeply transformed a wide variety of sectors. However, new platform business models often face critical legitimacy challenges. Consequently, new entrants must continuously design and redesign their business model components, particularly when entering non-platformized sectors that are highly regulated. We draw on a longitudinal case study of the emergence of digital healthcare platforms in Sweden between 2013 and 2020. The analysis unravels a dynamic process of redesigning platform business models and their constituent components in response to legitimacy debates, suggesting that permission to operate can be fragile and subject to continuous negotiation. Our findings contribute to current insights into platforms, business models, and digital innovation in complex institutional contexts. Furthermore, our work carries implications for managers and policy makers in the digital health area.
•New entrants are disrupting traditional sectors by introducing platform business models•Legitimacy debates trigger new entrants to design and redesign their platform business models•Legitimacy debates often center on specific platform business model components•Designing platform business models is particularly complex in regulated non-platformized settings•Platform business model transformation can be a vehicle for creating legitimacy

The paper highlights the importance of data sustainability in the data infrastructures aimed at long-term knowledge discoveries. Data sustainability refers to data's capacity to endure across technological and human generations, and it problematizes the data governance literature from a temporal perspective. Existing work has already moved the literature from the organizational setting to more complex interorganizational settings, highlighting discrepancies between normative data governance models and organizational practices. We broaden this literature temporally by examining and outlining research directions for data sustainability from different meta-theoretical perspectives – evolutionary, relational, and durational. Data sustainability across technological and human generations navigates complementary and competing temporal demands: Data need to transition across socio-technical regimes over time, yet be embedded in social and material networks to be meaningful; historical and present data also must remain available and accessible in near and distant futures, for going back in time and seeing new data linkages and combinations. We argue that data sustainability is critical in ensuring progression in social and environmental sustainability. The paper contributes both to data governance and sustainability literatures.
•Data sustainability refers to data's capacity to endure across technological and human generations.•Recent advances in digital technologies have created new opportunities but also competing challenges in terms of data sustainability.•Data may be locked in old socio-technical regimes and unable to transition to next socio-technical regimes; data may fail to translate within and get embedded into social and material networks; without continuing data investments, past data become inaccessible and lose meaning for the future.•Studies can advance data governance by addressing data sustainability challenges.