DEEP STRUCTURE USE OF MHEALTH: A SOCIAL COGNITIVE THEORY PERSPECTIVE

Consumer health information technology, such as mobile health applications (mHealth), enable consumers adopt healthy behaviours and improve health outcomes. We take a closer look at use concepts to understand how mHealth use facilitates behaviour change. We review the mHealth literature in information systems (IS) and health IS journals and find that superficial mHealth use concepts (e.g. binary and duration of use) dominate this literature stream. In line with contemporary IS research, we suggest that rich and theoretically-driven concepts of mHealth use can help to better understand what users do with mHealth and how this affects relevant outcomes. We take a social cognitive theory (SCT) perspective to conceptualize mHealth deep structure use, a rich concept of use centred on the extent to which tasks represented in mHealth facilitate behaviour change. This paper contributes to IS research in three key ways. First, we review mHealth literature and identify use concepts that have been employed to explain effects on outcomes. Second, we provide a theoretically-driven mHealth deep structure use concept from a SCT perspective. Third, we offer a conceptual lens that captures how mHealth deep structure use facilitates behaviour change. Future research will empirically evaluate aspects developed in this mHealth deep structure use concept

Radiologists’ Usage of Diagnostic AI Systems

While diagnostic AI systems are implemented in medical practice, it is still unclear how physicians embed them in diagnostic decision making. This study examines how radiologists come to use diagnostic AI systems in different ways and what role AI assessments play in this process if they confirm or disconfirm radiologists’ own judgment. The study draws on rich qualitative data from a revelatory case study of an AI system for stroke diagnosis at a University Hospital to elaborate how three sensemaking processes revolve around confirming and disconfirming AI assessments. Through context-specific sensedemanding, sensegiving, and sensebreaking, radiologists develop distinct usage patterns of AI systems. The study reveals that diagnostic self-efficacy influences which of the three sensemaking processes radiologists engage in. In deriving six propositions, the account of sensemaking and usage of diagnostic AI systems in medical practice paves the way for future research

Bridging the Vendor-User Gap in Enterprise Cloud Software Development through Data-Driven Requirements Engineering

The shift from on-premise to cloud software has fundamentally changed the interactions between enterprise software vendors and their users. Where user involvement has traditionally been a challenge, increasingly large amounts of user input now allow for data-driven requirements engineering (RE). Research has paid little attention so far to the changes entailed by data-driven RE and addressed neither technical nor empirical perspectives of data-driven RE in enterprise software development. We aim to understand how the increasing availability of large amounts of user input impact RE in enterprise cloud software development. We provide a conceptualization of the newly available user input and how it changes traditional RE. We collect and analyze rich data from multiple product units at a leading enterprise software company and examine the integration of user input into RE; specifically requirements discovery, prioritization, experimentation, and specification. We thereby aim to contribute to non-normative and empirical work on RE

How the Application of Machine Learning Systems Changes Business Processes: A Multiple Case Study

Machine Learning (ML) systems are applied in organizations to substitute or complement human knowledge work. Although organizations invest heavily in ML, the resulting business benefits often remain unclear. To explain the impact of ML systems, it is necessary to understand how their application changes business processes and affects process performance. In our exploratory multiple case study, we analyze the application of multiple productive ML systems in one organization to (1.) describe how activity composition, allocation, and sequence change in ML-supported processes; (2.) distinguish how the applied ML system type and task characteristics influence process changes; and (3.) explain how process efficiency and quality are affected. As a result, we develop three preliminary change patterns: Lift & Shift, Divide & Conquer, and Expand & Intensify. Our research aims to contribute to the future of work and IS value literature by connecting the emerging knowledge on ML systems to their process-level implications

Software Development in Multiteam Systems: A Longitudinal Study on the Effects of Structural Incongruences on Coordination Effectiveness

This study examines structural incongruences between organizational and product domains and their implications for coordination effectiveness in large-scale software development. We use the ongoing shift from on-premise to cloud-based software solutions to examine longitudinal effects of structural incongruences, i.e. the mismatch between organizational structures, including knowledge and task dependencies, and product structures, including technical dependencies, and how they resolve. We integrate extant literature in this field with literature on multiteam systems (MTS) and team composition to guide our longitudinal case study of one particular MTS within a large software organization. First insights from an initial case study of three development teams of different MTSs show how high level structural incongruences emerge on a team-level, providing a foundation for our subsequent study. By exploring the effects of structural incongruences over time, we expect to contribute to existing literature on organizational and product structure alignment as well as on MTS coordination effectiveness research

Assessing the Impact of Empirical Process Control Metrics in Agile Software Development - A Framework based on Improvement Capability

Empirical process control is an integral part of agile software development. A multitude of development metrics has been proposed to implement it. However, the efficacy of control metrics has remained unclear and empirical evidence of their impact is scarce. Methods for assessing whether and how a proposed metric stimulates the improvement of a development process are not yet available. We conduct a design science approach to develop an artifact that assesses the impact of development metrics and we identify their contribution for process improvement at a global software vendor. We draw on the theoretical construct of improvement capability to outline design principles of a measurement framework. Our evaluation of five large-scale agile development projects demonstrates that our framework facilitates to implement development metrics more effectively. The framework has the potential to improve large-scale agile software development and it serves as a useful basis for future empirical research on development metrics

Blockchain in Service Management and Service Research – Developing a Research Agenda and Managerial Implications

As blockchain technology is maturing to be confidently used in practice, its applications are becoming evident and, correspondingly, more blockchain research is being published, also extending to more domains than before. To date, scientific research in the field has predominantly focused on subject areas such as finance, computer science, and engineering, while the area of service management has largely neglected this topic. Therefore, we invited a group of renowned scholars from different academic fields to share their views on emerging topics regarding blockchain in service management and service research. Their individual commentaries and conceptual contributions refer to different theoretical and domain perspectives, including managerial implications for service companies as well as forward-looking suggestions for further research.Information and Communication TechnologyEconomics of Technology and Innovatio

A blockchain research framework : what we (don't) know, where we go from here, and how we will get there

While blockchain technology is commonly considered potentially disruptive in various regards, there is a lack of understanding where and how blockchain technology is effectively applicable and where it has mentionable practical effects. This issue has given rise to critical voices that judge the technology as over-hyped. Against this backdrop, this study adapts an established research framework to structure the insights of the current body of research on blockchain technology, outline the present research scope as well as disregarded topics, and sketch out multidisciplinary research approaches. The framework differentiates three groups of activities (design and features, measurement and value, management and organization) at four levels of analysis (users and society, intermediaries, platforms, firms and industry). The review shows that research has predominantly focused on technological questions of design and features, while neglecting application, value creation, and governance. In order to foster substantial blockchain research that addresses meaningful questions, this study identifies several avenues for future studies. Given the breadth of open questions, it shows where research can benefit from multidisciplinary collaborations and presents data sources as starting points for empirical investigations

Gravitation of SaaS-centric cloud platforms

SaaS-centric Cloud platforms, i.e., platforms that provide marketplaces to trade and execute SaaS solutions, are becoming increasingly important. In creating marketplaces, platform providers open new sales and marketing opportunities for third-party developers of Cloud-based software. Prior research focused on platforms’ development capabilities but left their distribution and transaction facilitation capabilities largely unconsidered. Nonetheless, distribution channels constitute a crucial element in the business model of SaaS-centric Cloud platforms. Addressing this gap, our study examines how and why SaaS-centric Cloud platforms gravitate, i.e., which factors help them attract and retain participants. Our qualitative exploration is based on four case studies of different SaaS-centric Cloud platforms. Our findings suggest the existence of two sets of catalyzing and inhibiting factors for platform gravitation which are contingent upon the platform type. This study contributes to research on Cloud platforms, ecosystem governance and integration issues related to Cloud computing. The examination of the identified factors opens promising avenues for future research