Information and Organization最新文献

This paper investigates data governance empirically by conducting a retrospective study of the ten-year evolution of a national digital service for personal health data in Norway. We show how data governance unfolds over time as data become shared and itinerant across multiple actors. Building on our findings, we introduce the concept of data governance spaces to refer to the authorized relationships among multiple actors, which specify the boundaries of decision-making authority, rights, roles, and responsibilities around data processing. We contribute to the literature on data governance by distinguishing between a) authority multiplication, where data are handed over to other actors to serve diverse purposes triggering horizontal dynamics, and b) actor subordination, where authorities delegate data handling for uniform purposes triggering vertical dynamics. Overall, the paper extends prior research by showing how data governance unfolds beyond intra-, or inter-organizational boundaries and shifts attention to data's pivotal role, and the purposes for which data are collected, shared or used across multiple actors.

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.

Open Social Innovation (OSI) involves the collaboration of multiple stakeholders to generate ideas, and develop and scale solutions to make progress on societal challenges. In an OSI project, stakeholders share data and information, utilize it to better understand a problem, and combine data with digital technologies to create digitally-enabled solutions. Consequently, data governance is essential for orchestrating an OSI project to facilitate the coordination of innovation. Because OSI brings multiple stakeholders together, and each stakeholder participates voluntarily, data governance in OSI has a distributed nature. In this essay we put forward a framework consisting of three dimensions allowing an inquiry into the effectiveness of such distributed data governance: (1) openness (i.e., freely sharing data and information), (2) accountability (i.e., willingness to be held responsible and provide justifications for one's conduct) and (3) power (i.e., resourceful actors' ability to impact other stakeholder's actions). We apply this framework to reflect on the OSI project #WirVsVirus (“We versus virus” in English), to illustrate the challenges in organizing effective distributed data governance, and derive implications for research and practice.

There is widespread agreement in research and practice that data governance is an instrumental element to help organizations leverage and protect data. IS research has observed that our practical and our scientific knowledge of data governance remains limited, and the increasing ability for organizations to generate, acquire, store, transform, process and analyze data calls for us to further identify and address issues on the topic. Striving to contribute to answer this pressing need, we argue that understanding the nature and the implications of governance mechanisms is of high importance as it is these mechanisms that effectively instantiate data governance in an organization. Building on our experience preparing and teaching workshops to 102 executives on the topic, we adopt a position of engaged scholarship and provide a translational account of our pedagogical experience on data governance, highlighting four outstanding themes for IS research. We argue that these four themes—(1) embracing data governance without compromising digital innovation; (2) enacting data governance through repertoires of mechanisms; (3) moving away from data governance toward governing data; and (4) moving away from a view of data at rest to adopt a service-based perspective on data governance—are highly relevant for practice and research. In our view, studying these themes will contribute to inform practitioners who often struggle with the implementation of comprehensive data governance programs and frameworks. At the same time, the ability to leverage theory to study these themes can help research generate novel theoretical contributions on data governance, helping future research on the topic.

This introduction to the special issue on Data Governance, Digital Innovation, and Grand Challenges highlights the importance of data governance when seeking to address grand challenges through the innovative use of digital technologies. The benefits, risks, and consequences of data, ubiquitous in today's data-rich world, can be harnessed for innovation and societal good. However, there are no guarantees that (only) desirable outcomes will develop. The creation and exploitation of vast data stockpiles raise substantial concerns about privacy, data security, equity, and the potential for harm from data misuse. Meaningful approaches to data governance within and across organizations are critically important to facilitate digital innovation and to balance social, economic and technical benefits and risks for individuals, organizations, and societies. In this introductory paper, we reflect on foundations established to date in information systems (IS) research and highlight possible future directions for scholarship on data governance across multiple levels to enhance digital innovations for transformation and societal good.

Information privacy is increasingly important in our digitally connected world, particularly in healthcare, and privacy regulations are ramping up to promote appropriate privacy practices. As a digital platform that enables healthcare providers to exchange protected health information (PHI), a health information exchange (HIE) is governed by health information privacy regulations. The challenge for HIEs is to operate in a way that will maximize information exchange while maintaining compliance with regulations that may constrain the sharing of PHI. Regulations impose a measure of universality through compliance requirements, while being flexible to allow adaptation to the local context. However, our longitudinal case study into the privacy policies of an HIE, demonstrates that the journey of privacy ideas from their original formulation in regulations, to their ultimate enactment in an organizational setting, is accompanied by translations, such that the final implementation may vary extensively from its original form. Such variability often results in interpretations that differ from what the regulators intended. Consequently, translation guardrails are necessary to protect against problematic translations of regulatory ideas which could lead to compliance issues and loss of platform participation. Our findings offer two contributions. First, we contribute to the compliance literature by explaining how guardrails can balance the use of permission and obligation schemas which are necessary to translate regulations into effective organizational policies for the success of HIEs and other information exchange platforms. Second, we contribute to extending translation theory by explaining how pragmatic reasoning schemas function as the mechanism through which translation of regulations occurs.

Prior studies on the diffusion of complex Information Systems (IS) innovations have leaned on the rhetoric of persuasion perspective to formulate rhetorical strategies that can persuade adopters to engage in adoption behaviors. Yet, most of them ignore the shifting priorities and changing identity of the audience. To address this gap, we extend the perspective by examining how innovators need to evolve the adoption message of an innovation by aligning it with the audience's diverse and shifting priorities (and related identities). We trace rhetorical changes that the National Association of State CIOs (NASCIO) introduced to promote the diffusion of enterprise architecture (EA) between 2000 and 2012 among the 50 US states. We conduct a mixed methods analysis: first we qualitatively discern changes in the content of the rhetoric (message change); then we use a latent semantic analysis to measure the frame resonance between NASCIO's shifting rhetoric and the audience's changing priorities (alignment with audience priorities). Our findings highlight the importance of: 1) frame ambiguity that renders a complex IS innovation appealing to varied audiences over its diffusion trajectory; and 2) listening to the community members' priorities and aligning the adoption message with their dominant beliefs. Our analysis posits the rhetoric of identification as a complementary lens to account for the co-evolution of shared priorities and identity alignments between innovators and their audiences.

Nurses use electronic medical record (EMR) systems to accomplish a variety of care-related tasks. Nurses, therefore, encounter a range of stressful situations and events related to using EMR systems, a phenomenon known as technostress. Previous research suggests that individuals appraise technostress differently. However, not much is known about the appraisal process of technostress. By integrating the literature on technostress, affordances, and appraisal theory, this paper introduces the appraisal theory of technostress, which is developed empirically through an interpretive case study involving interviews with hospital nurses. The appraisal theory of technostress explains how individuals process and appraise information about how to potentially act on technology-related stressful events through a system's features. Information about the event, and information about the action potential afforded by the system's features, is evaluated through an appraisal process that includes three appraisal checks: goal relevance, goal conduciveness, and value compatibility. The appraisal checks verify whether the action possibilities of the system's features align or misalign with an individual's goals and values related to action, and shape how the event and the system's features are appraised as challenge or hindrance techno-stressors. Overall, the study offers a novel theoretical perspective and methodological approach to conceptualizing and investigating technostress. More detailed contributions to research and practice are also discussed.

While the self-learning nature of AI systems that use machine learning calls for sustained co-creation between developers and users during development, implementation and use, information systems and management scholars still largely build on a long-established tradition of separating technology development from use. Instead, the self-learning nature of AI calls for letting go of this tradition to separate between development and use, which is starting to happen in practice but has not yet found appropriate theoretical and methodological tools among researchers. In this paper we show some real-life attempts to develop sustained collaboration among developers and users, based on empirical cases of five organizations. In particular, we propose how blurring boundaries makes data production, explainable AI and AI deployment fields of practice where development and use intertwine. We suggest embracing the blurred boundaries of AI implementation in our theorizing, understanding the different parts of AI as fields of practice where development and use come together in the co-creation of AI and work.

The use of Agile practices is typically associated to a wide array of benefits for organizations. This paper extends growing research on the ‘dark’ side of Agile by investigating the depletion of innovation in a large telco company following the large-scale implementation of Agile in R&D units. Our qualitative study reveals a shift in the organizational logics underpinning new product development, from a “navigating through unchartered waters” to a “putting out fires” logic. We tracked the change in key components of logics (goals of teams, source of legitimacy of team members and support and control systems) and explained the multi-level mechanisms through which the shift occurred, i.e., changes in processes of workflow management, work allocation, and performance management. We found that the new organizational logic negatively impacted individual attitudes towards the generation of new ideas by promoting the internalization of short-termism, a perceived drain in competences and confidence, and the lack of accountability for innovation. By focusing on changes in organizational logics, our insights expand current knowledge about the relationship between Agile implementation and individual attitudes. We also explain why unexpected effects of Agile implementation may go undetected in organizations, because they derive from multi-level, diffused, changes in the organization.