Information and Organization最新文献

Information systems (IS) literature has predominantly studied IS project control with a focus on software development projects. However, by virtue of digital transformation, an increasing number of organizations are implementing cross-functional teams, combining software development with software operations tasks. The goal is to react quickly to the ever-changing market requirements.

The DevOps concept aims to effectively orchestrate development and operations activities and smoothly manage tensions within teams, resulting from the heterogeneous composition of skills, responsibilities, and working styles.

In contrast to the predominant project management view of control of prior research, which focuses on software development, this study investigates a different perspective: focusing on exerting control in DevOps teams and simultaneously navigating tensions between software development and operations. Utilizing an inductive theory-building approach, we first identify the four tensions discussed in prior literature—namely, goal conflict, method discomfort, decision rights, and time rhythm—and then empirically derive corresponding resolutions.

Integrating our findings, we present an empirically derived model that can serve as a DevOps control approach for navigating the tensions between development and operations teams. This model extends our theoretical knowledge about control in DevOps teams and serves to inform IT practitioners, helping them successfully implement and manage DevOps teams.

Healthcare systems across the globe are riding a wave of clinical health IT investment, centered on electronic health records (EHR) systems. Supported by governmental incentives, this build-out has positioned the healthcare system for a period of transformation as EHR functionality has become ingrained in the work routines of healthcare providers and other system participants. We report on a field study of healthcare participants in the United States to explore the influence of EHR use on the boundaries and practices of the field. Our grounded theory analysis reveals the interplay between the field practices of individual communities and the boundary spanning practices that unite them. Through the adoption of a practice perspective, we highlight the changes to both boundary spanning and field practices engendered by enterprise EHR use and propose the complementary mechanisms of reticulation and boundary molding by which those changes emerge. We conclude with a consideration of the positive and potentially negative consequences of EHR use for delivery of healthcare services.

This paper addresses the dilemma that organizations face when they introduce information technology (IT) to standardize and guide operations and improve performance, while also supporting staff mindfulness in using IT and questioning it, to safeguard against errors. People are warned to be mindful in using the information provided by IT, yet IT may contribute to their mindlessness. Organizational operations involve routine work, where work is distributed across roles, in space and time. To fully understand mindfulness or mindlessness with IT at work it is necessary to consider the routines in which they are embedded. We sought to investigate what factors might influence mindfulness or mindlessness with IT in the context of organizational routines. We carried out an in-depth study of clinicians using technology during medications routines in a UK hospital. The IT in this context aimed to guide and standardize clinical work to improve medication safety. The study uncovered several factors influencing mindfulness and mindlessness with IT: not only the IT design but also task design, individual experience and history of IT use, distribution of work, and the situation at hand. These are interacting influences on mindfulness and mindlessness with IT, each embodying a tension, as each may influence both mindfulness and mindlessness. The distribution of work and the dynamics of the routine over time mean that individuals in the routine may (mindlessly) entrust mindfulness in using IT to others, or to other moments in time. The study highlights the complexity of achieving mindfulness with IT in organizations, and a nuanced relation between mindfulness and IT in a routine work context.

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.