Governance of Digital Health Data on Cooperatively Organized Platforms – a Design Thinking Approach

2023 | HICSS | Citations: 0

Authors: Burmann, Anja; Langer, Henrike; Bosompem, Jennifer; Clemens, Timo; Herten, Benjamin; Kerstan, Leah; Lauer, Romy; Naeve, Peter; Geller, Simon; Huhnt, Jönne; Merkel, Sebastian

Abstract: More and more stakeholders are collecting data for improving their services: fro ... Expand

Abstract: More and more stakeholders are collecting data for improving their services: from scientific research over public administration to commercial enterprises. Existing data management services offer few rights of co-determination for their users. Data cooperatives aim to provide a democratic alternative to this. Through such a platform cooperative members are supposed to be enabled to share their data in a self-determined way. In this paper, we present a design thinking-based user research with stakeholders of data cooperatives in a health-data context. We provide an overview of motivations, expectations, and interfaces between a cooperative and individuals as cooperative members, organizations, representatives from research, and policy makers. In an iterative process, 34 interviews were conducted with different stakeholder groups, from which 7 personas were subsequently derived. For these, 4 prototypes were developed and tested with potential users. Our results show that all interviewed groups were very interested in the concept of data cooperatives. At the same time, it proves challenging to reconcile the conflicting internal and external requirements and to implement attractive value propositions for all stakeholders. Collapse

Topics: design thinking healthcare data user centered design value creation organization structure

Methods: qualitative interview design methodology design artifact literature sample literature study

Accelerating sustainability in companies: A taxonomy of information systems for corporate carbon risk management

2023 | European Conference On Information Systems | Citations: 0

Authors: Körner, Marc-Fabian; Michaelis, Anne; Spazierer, Sophie; Strüker, Jens

Abstract: For increasing sustainability and mitigating climate change, corporate carbon r ... Expand

Abstract: For increasing sustainability and mitigating climate change, corporate carbon risk management (CCRM) can be a key enabler. As we outline in this paper, companies currently lack processes and approaches in practice to actively manage complex risks caused by carbon emissions in the form of a comprehensive CCRM. To address this issue and to combine the contributions of currently separate research streams, e.g., corporate carbon accounting and corporate risk management, we develop a taxonomy that illustrates characteristics of IS-based solutions that foster an active CCRM. While our taxonomy builds on a systematic literature review, we evaluate our results with expert interviews. We conclude that CCRM is a complex field in which IS can provide significant support at many stages. Thereby, our taxonomy also contributes to Green IS research and acts as guidance for practitioners. Moreover, we discuss how Blockchains and Artificial Intelligence can pave the way towards targetoriented CCRM in companies. Collapse

Topics: risk management accounting database system artificial intelligence green IS

Methods: qualitative interview literature study structured literature research personal interview design process

Developing a Data Literacy Assessment Tool for the Business Workforce

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Langlais, Jean-François; Mosconi, Elaine; Guillemette, Manon

Abstract: Data is increasingly seen as a source of value for organizations, requiring emp ... Expand

Abstract: Data is increasingly seen as a source of value for organizations, requiring employees' skills to collect, transform, and use to improve decision-making. The sum of these skills is named data literacy. This paper aims to develop a tool to assess the data literacy of individuals in the business workforce. Using a design science research approach, we developed a data literacy assessment tool based on 18 core competencies across three skill levels. We tested this survey tool with 243 respondents in a large North American financial company. Our artifact provided an accurate assessment of intra-and inter-individual competencies and the identification of typical profiles required following the employees' role as data consumers or producers. This study is the first to propose a complete definition of the data literacy construct with a validated data literacy competencies assessment tool adapted to the business workforce. Collapse

Topics: data literacy self efficacy data management task productivity accounting

Methods: survey literature study qualitative interview correlation analysis descriptive statistic

Theories: core competency theory

Decide Now or Later: Making Sense of Incoherence Across Online Reviews

2023 | Information Systems Research | Citations: 0

Authors: Yin, Dezhi; de Vreede, Triparna; Steele, Logan M.; de Vreede, Gert-Jan

Abstract: Mixed or inconsistent opinions are commonplace in online reviews. Prior research ... Expand

Abstract: Mixed or inconsistent opinions are commonplace in online reviews. Prior research shows that review inconsistency has different effects: its product-level manifestation in the form of inconsistent product ratings is associated with poorer sales, but its review-level manifestation in the form of two-sided arguments is associated with greater helpfulness and credibility evaluations of the review. In practice, consumers rarely consult all reviews or just a single review before they make purchase decisions. Instead, they often read a set of featured reviews (i.e., a review set) and some additional reviews if needed. Focusing on inconsistency in a review set, we introduce a new type of inconsistency across reviews that does not exist within a single review or at the level of product ratings: cross-review incoherence, which refers to disagreement among reviewers about specific product features. Based on cognitive dissonance theory, we explore how and when cross-review incoherence influences helpfulness and credibility judgments of the review set, revealing that consumers’ reactions to such incoherence might operate differently beyond an individual review. In addition, we examine how and why consumers’ judgments of a review set influence their purchase deferral—that is, the likelihood of making a buy-or-not-buy decision immediately after consulting the top reviews or deferring it until after obtaining more information. Two laboratory experiments demonstrate that cross-review incoherence increases purchase deferral via more negative evaluations of the review set that, in turn, reduce attitude certainty. In addition, the negative effect of cross-review incoherence on review set evaluations is weaker when the reviewers provide more contextual information behind their opinions. These findings deepen our understanding of inconsistency across multiple pieces of information; reveal the consequences of review evaluations during an understudied stage of consumers’ decision-making process; and provide critical implications for review platforms, companies, and reviewers. History: Paul Pavlou, Senior Editor; David (Jingjun) Xu, Associate Editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1150. Collapse

Topics: decision making online review user-generated content data relevance enterprise information system

Methods: experiment chi squared test laboratory experiment conceptual modelling theory development

Theories: cognitive dissonance theory

Organizations Decentered: Data Objects, Technology and Knowledge

2022 | Organization Science | Citations: 37

Authors: Alaimo, Cristina; Kallinikos, Jannis

Abstract: Data are no longer simply a component of administrative and managerial work but ... Expand

Abstract: Data are no longer simply a component of administrative and managerial work but a pervasive resource and medium through which organizations come to know and act upon the contingencies they confront. We theorize how the ongoing technological developments reinforce the traditional functions of data as instruments of management and control but also reframe and extend their role. By rendering data as technical entities, digital technologies transform the process of knowing and the knowledge functions data fulfil in socioeconomic life. These functions are most of the times mediated by putting together disperse and steadily updatable data in more stable entities we refer to as data objects. Users, customers, products, and physical machines rendered as data objects become the technical and cognitive means through which organizational knowledge, patterns, and practices develop. Such conditions loosen the dependence of data from domain knowledge, reorder the relative significance of internal versus external references in organizations, and contribute to a paradigmatic contemporary development that we identify with the decentering of organizations of which digital platforms are an important specimen. Collapse

Topics: datafication organizational change organizational memory data relevance organizational transformation

Methods: computational algorithm theory development historical review

Combining analytics and simulation methods to assess the impact of shared, autonomous electric vehicles on sustainable urban mobility

2022 | Information & Management | Citations: 5

Authors: Dlugosch, Oliver; Brandt, Tobias; Neumann, Dirk

Abstract: Urban mobility is currently undergoing three fundamental transformations with th ... Expand

Abstract: Urban mobility is currently undergoing three fundamental transformations with the sharing economy, electri­ fication, and autonomous vehicles changing how people and goods move across cities. In this paper, we demonstrate the valuable contribution of decision support systems that combine data-driven analytics and simulation techniques in understanding complex systems such as urban transportation. Using the city of Berlin as a case study, we show that shared, autonomous electric vehicles can substantially reduce resource investments while keeping service levels stable. Our findings inform stakeholders on the trade-off between economic and sustainability-related considerations when fostering the transition to sustainable urban mobility. Collapse

Topics: autonomous driving system ride sharing platform decision support system decision making sharing platform

Methods: simulation sensitivity analysis computational algorithm agent based simulation longitudinal research

Driving Big Data – Integration and Synchronization of Data Sources for Artificial Intelligence Applications with the Example of Truck Driver Work Stress and Strain Analysis

2022 | International Conference on Information Systems | Citations: 0

Authors: Klumpp, Matthias; Severin, Benedikt; Lechte, Henrik; Menck, Jannes; Keil, Maria; Straub, Sarah; Ruiner, Caroline; Milke, Viola; Hagemann, Vera; Hesenius, Marc

Abstract: This paper contributes to the issue of Big Data analysis and data quality with ... Expand

Abstract: This paper contributes to the issue of Big Data analysis and data quality with the specific field of time synchronization. As a highly relevant use case, Big Data analysis of work stress and strain factors for driving professions is outlined. Drivers experience work stress and strain due to trends like traffic congestion, time pressure or worsening work conditions. Although a large professional group with 2.5 million (US) and 3.5 million (EU) truck drivers, scientific analysis of work stress and strain factors is scarce. Driver shortage is growing into a large-scale economic and societal challenge, especially for small businesses. Empirical investigations require Big Data approaches with sources like physiological and truck, traffic, weather, planning or accident data. For such challenges, accurate data is required, especially regarding time synchronization. Awareness among researchers and practitioners is key and first solution approaches are provided, connecting to many further Machine Learning and Big Data applications. Collapse

Topics: data integration artificial intelligence big data smartphone global navigation satellite system

Methods: electroencephalography Cross-Industry Standard Process for Data Mining machine learning theoretical contribution data modeling

Demystifying Industrial Internet of Things start-ups – A multi-layer taxonomy

2022 | International Conference on Business Informatics | Citations: 0

Authors: Jonas, Claudius M; Oberländer, AnnaMaria; Schmitt, Kevin; Wethmar, Simon

Abstract: Described as a fundamental paradigm shift by researchers, the Industrial Interne ... Expand

Abstract: Described as a fundamental paradigm shift by researchers, the Industrial Internet of Things (IIoT) is credited with massive potential. In the context of emerging technologies, such as the IIoT, start-ups occupy a crucial role, as new technologies are often first commercialized by start-ups. Because of the rising importance of IIoT start-ups as drivers of industrial innovation, IIoT solutions demand deepened theoretical insights. As existing classification schemes in the industrial context do not sufficiently account for the ever more critical role of IIoT start-ups, we present a multi-layer taxonomy of IIoT start-up solutions. Building on state-of-the-art literature and a sample of 78 real-world IIoT start-up solutions, the taxonomy comprises ten dimensions and related characteristics structured along the three layers solution, data, and business model. The taxonomy contributes to the descriptive knowledge on the IIoT and enables researchers and practitioners to better understand IIoT start-up solutions. Collapse

Topics: business-to-business price management website business model usability

Methods: survey literature study

Blockchain and Data Protection: An Evaluation of the Challenges and Solutions Mentioned by German Stakeholders

2021 | International Conference on Business Informatics | Citations: 0

Authors: Ebbers, Frank; Karaboga, Murat

Abstract: This paper analyzes data protection challenges and possible solutions associated ... Expand

Abstract: This paper analyzes data protection challenges and possible solutions associated with the usage of the blockchain (BC) technology from the perspective of 94 German companies and organizations. This paper clusters 537 data protection-relevant statements into three subject areas: (1) relevance of data protection in BC, (2) articulated challenges and (3) proposed solutions. Each group is then collated with insights from computer science. The results show that a majority of the respondents do see data protection issues with using BC, which mainly relate to data erasure and identifying the data controller. However, the majority also consider these problems to be solvable utilizing already available technologies, e.g. off-chain storage, encryption, pseudonymization or usage of private BCs. Comparing these proposals with the findings in computer science literature shows that especially off-chain storage, encryption and redactable blockchains can be regarded as adequate solutions. Collapse

Topics: data protection blockchain data encryption data security data relevance

Methods: qualitative content analysis expert panel card sorting qualitative coding

A Taxonomy for Data Ecosystems

2021 | HICSS | Citations: 13

Authors: Gelhaar, Joshua; Groß, Tobias; Otto, Boris

Abstract: In the increasingly interconnected business world, economic value is less and le ... Expand

Abstract: In the increasingly interconnected business world, economic value is less and less created by one company alone but rather through the combination and enrichment of data by various actors in so-called data ecosystems. The research field around data ecosystems is, however, still in its infancy. With this study, we want to address this issue and contribute to a deeper understanding of data ecosystems. Therefore, we develop a taxonomy for data ecosystems which is grounded both theoretically through the linkage to the scientific knowledge base and empirically through the analyses of data ecosystem use cases. The resulting taxonomy consists of key dimensions and characteristics of data ecosystems and contributes to a better scientific understanding of this concept. Practitioners can use the taxonomy as an instrument to further understand, design and manage the data ecosystems their organizations are involved in. Collapse

Topics: digital ecosystem database system system development method knowledge base organization structure

Methods: structured literature research design methodology literature study case study literature sample