A New Way to Reflect the IS Identity? Uncovering the Intellectual Core of Podcasts

The information systems (IS) discipline has long been critically questioning its identity to determine its central research avenues, its distinction from other disciplines, and the future directions for the field. Although this question is central to all stakeholders of the IS field, so far the debates have been conducted primarily in research papers, editorial commentaries, and opinion pieces published by influential IS scholars. Our study explores how the broader IS community engages in the discourse about IS identity by examining podcasts as an increasingly popular means of communicating IS viewpoints. We apply a podcast ethnography to study the IS podcast universe, consisting of 51 shows with 660 episodes. Our preliminary findings offer insights about the stakeholders, podcast topics, and intellectual core of the audio tracks that shed light on the role of podcasts in constructing and reflecting on IS identity

Trust Recipes for Enhancing the Intention to Adopt Conversational Agents for Disease Diagnosis: An fsQCA Approach

In this study, we examine the configurations of trust-enhancing factors that determine the intention to adopt conversational agents (CAs) for disease diagnosis. After identifying trust factors influencing the behavioral intent to adopt CAs based on the information systems acceptance research field, we assigned 201 participants to use the mobile Ada application and surveyed them about their experience. Ada is a medical diagnostic CA that combines patients’ symptoms with their medical history and provides diagnostic suggestions. The collected data was analyzed using a fuzzy set qualitative comparative analysis to capture the causal complexity of trust. We identified several configurations of trust-enhancing factors affecting the intention to adopt the CA. In particular, our results show that the adoption intentions are strongly determined by trust factors associated with the performance dimension. Furthermore, we derived two propositions for the development of CAs for healthcare purposes and elaborated implications for research and practice

Beyond Digital Data and Information Technology: Conceptualizing Data-Driven Culture

Background: The role of a data-driven culture in improving organizational performance is widely recognized, but its conceptual definition lacks uniformity, leading to the existence of various constructs. This paper proposes a guiding framework for a data-driven culture, aiming to foster a unified understanding that aids both researchers and practitioners in the information systems (IS) field. Method: Adopting a qualitative research approach, this study conducts a systematic literature review to discern the breadth and depth of data-driven culture as portrayed in previous works. Alongside this, ten interviews were carried out with professionals well-versed in the application of data-driven strategies. Results: The study uncovers the multifaceted nature of a data-driven culture, highlighting its influence on decision-making practices within organizations. It identifies a range of characteristics relevant to the construct and consolidates these into an integrative framework, thereby developing a conceptual definition for data-driven culture. Conclusion: The paper contributes to the IS field by providing a framework that illuminates the concept of data-driven culture. This new understanding aids researchers in consistently theorizing the same phenomenon, supports the development of refined metrics for assessing data-driven culture, and paves the way for future research in this area. For practitioners, this framework delineates the characteristics of a data-driven culture and their interplay, enabling a more informed approach to cultural change efforts. Moreover, it highlights the importance of acknowledging the wider cultural context, and provides mechanisms to balance the emphasis on tools and values

Painting A Holistic Picture of Trust in and Adoption of Conversational Agents: A Meta-Analytic Structural Equation Modeling Approach

With their human-like nature, conversational agents (CAs) introduce a social component to human-computer interaction. Numerous studies have previously attempted to integrate this social component by incorporating trust into models such as the technology acceptance model (TAM) to decipher the adoption mechanisms related to CAs. Given the heterogeneity of these previous works, the aim of this paper is to integrate empirical evidence on the role and influence of trust within the nomological network of the TAM. For this purpose, we conduct a meta-analytic structural equation modeling approach based on 45 studies comprising k = 155 correlations, and N = 13,786 observations. Our findings highlight the multifaceted role of trust as a mediator transmitting the effects of the technology-related perceptions that drive the intention to use CAs. Our results present a comprehensive overview in a thriving research field that can guide both future theory building and the designs of more trustworthy CAs

Sozioökonomische Implikationen der Digitalisierung im Kontext von Industrie 4.0: Eine multiperspektivische Analyse aus Sicht der Akteure der Bauindustrie

In der vorliegenden Arbeit werden die sozioökonomischen Auswirkungen der Digitalisierung für die Bauindustrie im Kontext von Industrie 4.0 aus einer mikro-ökonomischen Perspektive adressiert. Vereint in sieben Forschungsbeiträgen und vier zentralen Themenbereichen werden 11 Forschungsfragen mittels Analysen sowie Erklärungs- und Gestaltungsansätzen beantwortet. Ausgehend von der Analyse des Status Quo wird zunächst eine branchenspezifische Begriffsdefinition von Industrie 4.0 abgeleitet, die Nutzen und Herausforderungen benannt und weitere Forschungslücken aufgezeigt. Die bauspezifische Definition impliziert, dass es sich bei Industrie 4.0 um ein Konzept handelt, welches im Kern eine kombinierte Nutzung innovativer Technologien zur Digitalisierung der internen Prozesse, der Wertschöpfungskette sowie der unternehmensübergreifenden Kooperation und Zusammenarbeit darstellt. Bedingt durch die fragmentierte Wertschöpfungskette, die Vielzahl der beteiligten Akteure sowie die strukturellen Besonderheiten der Branche stellt die Nutzung digitaler Technologien insbesondere in der Bauindustrie eine große Herausforderung dar. Dazu zählen die ökonomischen und sozialen Hemmnisse in Form von fehlenden Kosten-Nutzen-Ansätzen bei hohen Investitionskosten sowie die Veränderungen der Kompetenzanforderungen an die Mitarbeiter. Mit einer ausgewogenen Balance zwischen Praxisnähe und theoretischer Fundierung werden diese beiden Aspekte in dieser Arbeit beleuchtet. Insbesondere wird großer Wert auf das Aufzeigen konkreter Handlungsempfehlungen, die Entwicklung von Artefakten zur Anwendung in der Unternehmenspraxis sowie die Vermittlung von Erklärungsansätzen zur Verbesserung des Verständnisses realer Probleme gelegt. Neben der konsequent hohen Praxisorientierung wird auch ein multimethodisches, interdisziplinäres Vorgehen gewählt, bei dem etablierte Methoden, Theorien und Konzepte aus mehreren Disziplinen zur Beantwortung der Forschungsfragen herangezogen werden. An der Schnittstelle zwischen den Disziplinen WI, Accounting, Management Science und dem Bauwesen soll die vorliegende Arbeit eine Lücke schließen, der bisher aufgrund ihrer Interdisziplinarität nicht genügend Aufmerksamkeit gewidmet wurde

The Emperor’s New Clothes or an Enduring IT Fashion? Analyzing the Lifecycle of Industry 4.0 through the Lens of Management Fashion Theory

This paper examines the recent hype around Industry 4.0 through the lens of management fashion theory to answer the question of how Industry 4.0 has emerged as a management fashion and to what extent it has diffused in organizational practice. Therefore, we conducted a comprehensive discourse lifecycle analysis based on 3920 academic and practical publications comprising a rhetoric and content analysis along with a diffusion lifecycle analysis involving selected diffusion indicators. The findings indicate that Industry 4.0 constitutes an enduring management fashion that has recently reached its peak, with the first signs for an upcoming downswing. The discourse around Industry 4.0 illustrates the concept as a panacea for business problems such as a lack of sustainability and intense global competition; however, the diffusion lifecycle analysis indicates hesitation among companies to adopt Industry 4.0 due to the ambiguity in the conceptual interpretation. The findings enable a more holistic understanding of the recent developments around Industry 4.0 and help to identify actions for the involved political, practical and academic actors. To actively shape the Industry 4.0 fashion development path, more institutional work is needed to help Industry 4.0 fashion users with their adoption engagements and hence achieve “professionalization” at an organizational level

Understanding the Operational Value of Big Data Analytics Capabilities for Firm Performance: A Meta-Analytic Structural Equation Modeling Approach

To uncover the key mechanisms of how value is created through big data analytics (BDA), our main research objective is to integrate prior empirical findings on the relationship between BDA capabilities and firm performance. We conducted meta-analytic structural equation modeling based on 271 correlations and 33,281 observations collected from 63 individual studies. The findings confirm that creating business value from BDA is a complex and dynamic process affected by various value creation mechanisms. Aside from direct relationships between BDA capabilities and firm performance, we highlight the mediating role of operational performance in the value transmission to market and financial performance. Our study contributes to the rising debate on the business value of BDA by providing an integrated and novel picture of the value-adding pathways emanating from BDA capabilities. This informs future information systems research on theory building and assists practitioners in effectively formulating their objectives of BDA initiatives

Augmenting the Future: An Exploratory Analysis of the Main Resources, Use Cases and Implications of Augmented Analytics

Recently, augmented analytics has increasingly gained attention as one of the more advanced, novel approaches for handling big data. Based on machine learning and natural language processing, augmented analytics benefits from recent advancements in the artificial intelligence field to automate the analytics cycle. Despite the various benefits that augmented analytics offers for business and society, research on this topic is scarce to date. Based on the IT business value model, we examine the role of technological and social resources as well as the main use cases of augmented analytics. Therefore, we combine quantitative text mining with qualitative content analysis for an exploratory study of 350 academic and practical publications as well as 49 datasets of companies offering augmented analytics software and services. The findings contribute to the body of knowledge by enhancing the understanding of the augmented analytics concept, uncovering prevalent research gaps, and highlighting future research directions

Understanding the Role of Predictive and Prescriptive Analytics in Healthcare: A Multi-Stakeholder Approach

The volume, velocity and variety of data is continuously rising. While many industry sectors are already applying big data analytics for various purposes, the use of big data in healthcare remains limited. A major reason for this development lies in the fragmented structure and conflicts of interests among the various stakeholders in the sector. To date, there is a lack of a comprehensive study that integrates insights from both practical and academic literature with expert knowledge to create a holistic picture of the main use cases, challenges and benefits of predictive and prescriptive analytics (PPA) in healthcare. To fill this gap, we investigated the role of PPA in healthcare from different stakeholder perspectives. We conducted a systematic literature review and applied content analysis to identify the main patterns extracted from the literature. The findings were triangulated with insights gained from 9 interviews with healthcare experts. Overall, we identified 8 use case clusters, 18 key benefits and 10 key challenges for the stakeholders involved. Furthermore, the role of PPA in healthcare is discussed from different stakeholders’ perspectives. Our findings reveal that the stakeholders pursue contrasting interests, which require legal regulation such that PPA can diffuse on a wider scale