Information and Organization最新文献

Information systems development (ISD) requires dynamic and flexible ways of working, particularly when developing requirements in collaboration with customers. Although prior research has acknowledged the importance of objects to support ISD practices, there has been a lack of frameworks to help discern the multiple overlapping roles objects play to support requirements development in a variety of ways throughout an ISD project. This paper explores and theorizes this phenomenon by leveraging multiplexity as a theoretical lens to analyze an extensive qualitative data set from a case study at a Swiss multinational banking software provider. Results show how diagrams and prototypes both play the roles of epistemic, activity, boundary, and infrastructure objects as a reflection of how they are used in requirements development. Our analysis articulates how two classical requirements specifications play multiple overlapping roles to support dynamic and flexible ISD practices. Based on these findings, we advance a framework for discerning the multiplex role of objects in practice

This study examines the impact that the argumentative salience of technology frames of references has on the execution of complex IS implementation projects. It employs Toulmin's argument model to develop argument maps that depict the structure of argumentation that took place during the development and implementation of an interorganizational health information exchange initiative (HIE) that took place in southeast USA. Toulmin's argument model faciliated the portrayal of frame salience in terms of three structural properties (i.e., blindness, indifference, and ownership). The study used these properties to show how the breadth, depth and conspicuity of frame structures changed during the course of the project and demonstrated how these changes impacted both the level of contestation observed in the project as well as project outcomes. In addition to lending insights that are specific to this case, our conceptualization of frame structure lends itself to cross-case comparisons and future theory building as the impact of these argument structures can be evaluated in a multitude of different contexts.

The rise of digital platforms creates knowledge monopolies that threaten innovation. Their power derives from the imposition of data obligations and persistent coupling on platform participation and their usurpation of the rights to data created by other participants to facilitate information asymmetries. Knowledge monopolies can use machine learning to develop competitive insights unavailable to every other platform participant. This information asymmetry stifles innovation, stokes the growth of the monopoly, and reinforces its ascendency. National or regional governance structures, such as laws and regulatory authorities, constrain economic monopolies deemed not in the public interest. We argue the need for legislation and an associated regulatory mechanism to curtail coercive data obligations, control, eliminate data rights exploitation, and prevent mergers and acquisitions that could create or extend knowledge monopolies.

This review focuses on the growing field of distrubuted work, made even more relevant in light of the current pandemic. Many different definitions, labels, and conceptualizations of distributed work exist, resulting in a fragmented field, threatened by a proliferation of concepts. Prior reviews addressed a limited scope of phenomena or review approaches; are narrative, subjective, or not systematic, lacking objectivity, comprehensiveness, and reproducibility; or are not recent. Our study advances the current overview of the field by presenting a compendious review of the development and current state of the field. We implemented three bibliometric approaches (i.e., co-citation, co-word and bibliographic coupling) and interpreted the results using the “invisible colleges” framework. This produced an integrative and holistic framework of the field of distributed work, portraying its historic development and theoretical background, conceptual space, and nomological net, guiding future research on this and connected research fields.

The business analytics and strategic management literatures suggest that organizations should seek to exploit data as a key mechanism for competitive advantage. However, the rules of engagement are evolving, the regulatory landscape is becoming increasingly complex, and examples of poor outcomes are increasingly common. The board – in its role of setting and monitoring risk appetite – needs to be able to govern the risk/reward trade-off of the data asset. Contemporary data governance approaches are inadequate: they are overly rigid and risk oriented, limited in scope to an organization's self-interest rather than considering the broad set of stakeholders, and do not provide a platform for the board to manage this critical risk. This paper uses a unique set of informants – 41 board directors – to demonstrate that differences in board perspectives influence how organizations explore the secondary use of data. Furthermore, this paper identifies a set of relevant individual, organizational and environmental factors and presents empirically based configurations of these factors that lead organizations to consider (or neglect) the secondary use of data as a critical enabler of competitive advantage.

The complex societal problems that we face today require unprecedented collaboration and evidence-based decisions. These collaboration processes are further propelled by the datafication of virtually all spheres of public life. To benefit from this, the data needs to be made available to allow for data analytics. Thus, data sharing becomes a crucial aspect of cross-sector collaborations that aim to create and capture value from information. Compared to collaborations where data sharing is not the main goal, data sharing partnerships face a number of novel challenges, such as mitigating data risks, complying with data protection legislation, and ensuring responsible data use. Navigating these waters and achieving data sharing can be challenging for both governments and businesses, as well as other actors. How do organizations from different sectors manage to achieve data sharing for addressing societal challenges? To address this research question, we apply a framework of three models of cross sector social partnerships developed in the field of organization studies to structure the analysis of six cases. Our analysis suggests that to a certain extent the partnership model determines the types of drivers and challenges to sharing data in a partnership. Leveraging the drivers and anticipating these challenges can help organizations be more aware of key terms of the collaboration and the mechanisms that can be used to succeed in their partnership goals.

When using digital devices and services, individuals provide their personal data to organizations in exchange for gains in various domains of life. Organizations use these data to run technologies such as smart assistants, augmented reality, and robotics. Most often, these organizations seek to make a profit. Individuals can, however, also provide personal data to public databases that enable nonprofit organizations to promote social welfare if sufficient data are contributed. Regulators have therefore called for efficient ways to help the public collectively benefit from its own data. By implementing an online experiment among 1696 US citizens, we find that individuals would donate their data even when at risk of getting leaked. The willingness to provide personal data depends on the perceived risk level of a data leak but not on a realistic impact of the data on social welfare. Individuals are less willing to donate their data to the private industry than to academia or the government. Finally, individuals are not sensitive to whether the data are processed by a human-supervised or a self-learning smart assistant.

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.