Information and Organization最新文献

Drawing inspiration from bureaucracy and information security literature, we develop the theory of security bureaucracy—an evolutionary framework that describes how organizations arrive at their information-securing approaches. Within this framework, we describe three general bureaucratic archetypes (i.e., Security Prototype, Security Structure, and Security Superstructure) that emerge from the interplay between control and expertise. We also expound on the phenomenon of security bureaucracy and delineate how organizations can transition from coercive “iron cages” to enabling “iron shields” in information securing. We also use our security establish-enforce-enculturate (3E) evolutionary framework to inform a proposed variance model of security bureaucracy. Our efforts offer significant insights and implications for organizational information security research and practice.

Many blockchain consortia have been established to build blockchain information systems. While the developed blockchain information systems were promising, few have reached market entry. Indeed, blockchain consortia often lost development focus due to high system complexity and a lack of understanding of how to create a system that will serve the needs and bring value to all stakeholders. Thus, stakeholders struggled to leverage blockchain information systems' full value. Prior studies demonstrated that blockchain systems pose not only technical but also organizational challenges. Analysing six blockchain consortia, we identify their value mechanisms, organizational problems, and organizational solutions that successful blockchain consortia experience while organizing themselves for value. As a result, we propose a new organizational form, i.e., a layered organization, for blockchain consortia to achieve better value creation.

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.

Social-mission platforms (SMPs), or platforms that facilitate the interactions between stakeholders across sectors and help them exchange resources to make progress on social and environmental problems, have emerged on a global scale. However, despite their prevalence, little is known about how SMPs organize to orchestrate collective efforts of social innovation. Taking stock of information systems and organizational literature on platforms, we identify four dimensions inherent in platform organizing (i.e., identity, boundary, governance, and technology). We then analyze three case studies to interrogate how these organizing dimensions manifest in SMPs. As a result, we offer a conceptual framework highlighting the trade-offs SMPs face, specifying the design choices they can make, and exposing the interdependences between dimensions. We further illustrate how these interdependences inform a configurational perspective of SMPs and suggest avenues to advance a configurational research agenda to deepen understanding of SMPs as effective vehicles to address Grand Challenges.

This sociomaterial study analyzes the ways that material agency plays a key role in the organizing dynamics of risky work through a study of the carrying and use of handguns by U.S. and U.K. police officers. Qualitative data (interviews and focus groups) were collected over a three-year period with police (N = 61) in New York, where officers routinely carry guns, and in London, where they typically do not. Police unanimously describe the agentic role non-human artefacts like guns play in: a) framing their cognitive processes, b) influencing their behaviour and decision-making processes, and c) impacting individuals around them. Expanding Pickering's theorization of a mangle of practice, we inductively develop a mangle of risk to explain how human and non-human agency become entangled in risky work contexts, where danger is real and time pressure is high. Understanding these dynamics requires analysis of both frontline police narratives and the prescribed organizational policies, procedures, and routines intended to contain risky situations. Findings reveal that the tools provided to police to do their job both frame and constrain operational capabilities, potentially escalating danger for police, suspects, and the community in a mangle of risk.

Technology entrepreneurship, by which we mean entrepreneurship on behalf of a new technology and its organizational and social acquisition, is more than one might think, given present use of the term, which focuses on technology as device. Here, taking the perspective of technology as routine capability, we reframe the concept to incorporate not only distributed agency involved around devices, but also distributed agency concerned with use through associated routines. We argue that technology acquisition in the form of capabilities concerns use and that technology entrepreneurship typically entails substantial institutional work in the promotion of adoption and use. We illustrate this in the case of the long and painful history of the acquisition of electronic health records (EHR). Our reframing leads to new insights. Among these is the identification of what we term path convergence and its importance in the social acquisition of a technology. We argue that technology entrepreneurs must attend to this path convergence, or the technology may not be widely taken up.

Artificial intelligence (AI) promises various new opportunities to create and appropriate business value. However, many organizations – especially those in more traditional industries – struggle to seize these opportunities. To unpack the underlying reasons, we investigate how more traditional industries implement predictive maintenance, a promising application of AI in manufacturing organizations. For our analysis, we employ a multiple-case design and adopt a critical realist perspective to identify generative mechanisms of AI implementation. Overall, we find five interdependent mechanisms: experimentation; knowledge building and integration; data; anxiety; and inspiration. Using causal loop diagramming, we flesh out the socio-technical dynamics of these mechanisms and explore the organizational requirements of implementing AI. The resulting topology of generative mechanisms contributes to the research on AI management by offering rich insights into the cause-effect relationships that shape the implementation process. Moreover, it demonstrates how causal loop diagraming can improve the modeling and analysis of generative mechanisms.

High uncertainty tasks such as making a medical diagnosis, judging a criminal justice case and driving in a big city have a very low margin for error because of the potentially devastating consequences for human lives. In this paper, we focus on how humans learn from uncertainty while performing a high uncertainty task with AI systems. We analyze Tesla's autonomous driving systems (ADS), a type of AI system, drawing on crash investigation reports, published reports on formal simulation tests and YouTube recordings of informal simulation tests by amateur drivers. Our empirical analysis provides insights into how varied levels of uncertainty tolerance have implications for how humans learn from uncertainty in real-time and over time to jointly perform the driving task with Tesla's ADS. Our core contribution is a theoretical model that explains human-AI joint task performance. Specifically, we show that, the interdependencies between different modes of AI use including uncontrolled automation, limited automation, expanded automation, and controlled automation are dynamically shaped through humans' learning from uncertainty. We discuss how humans move between these modes of AI use by increasing, reducing, or reinforcing their uncertainty tolerance. We conclude by discussing implications for the design of AI systems, policy into delegation in joint task performance, as well as the use of data to improve learning from uncertainty.

Digital platforms have grown rapidly by facilitating connections among users to exchange products, services, or information. However, very few platforms have a truly global footprint given that factors such as competition, imitation, innovation, and cultural and political barriers hamper a digital platform's international growth path. The geographical scope of network effects plays a crucial role in this process, impacting users at various levels from the local to the global. We model the dynamics of international platform competition and predict its outcomes in terms of global potential market percentage through a simulation of the international growth of a two-sided platform in competition with two follower platforms in different locations (home/abroad) and different internationalization strategies (gradual/accelerated), and operating under different network effects (local/global). The model contemplates different delays in the launching of the rival platforms and different degrees of innovation (improvement) in comparison with the original platform.

Our findings highlight the crucial role of network effects, with global effects benefiting first movers and local effects favoring followers, especially if they start in a market different from the first mover's. Moreover, domestic followers must innovate, while followers in less competitive markets with local network effects have more options to increase potential market percentage, including launching a clone. These insights offer valuable suggestions for strategy development and regulatory considerations related to market share, market power, and international expansion.

The sociomaterial lens within IS research holds that agency should not be considered as a property solely of humans, or of technology, but instead arises from an emergent interaction between the two. This, emergent, account of agency deepens our understanding of unfolding IS practice, but its largely cognitive orientation remains naïve towards affectively-sensed motivations that also form part of this interaction. By implication, a sociomaterial perspective lacking an affective dimension offers an incomplete conceptualisation of information systems. In response, an affectively-informed negative ontology encourages IS researchers to extend their focus beyond the visible, to encompass how actors' receptiveness towards material objects (discourses, technologies) is shaped by deep, affectively-derived motivations of which they are not focally aware, but which nonetheless acquire agency in contributing to a sociomaterial outcome. A central argument, and illustrative empirical vignette, demonstrate how the concepts of sociomateriality, affect, and negative ontology combine to offer researchers an enhanced understanding of relational agency. A discussion follows, exploring some initial ontological, epistemological and methodological implications of an affectively-informed negative ontology for IS research.