Knowledge monopolies and the innovation divide: A governance perspective

2023 | Information and Organization | Citations: 0

Authors: Safadi, Hani; Watson, Richard Thomas

Abstract: The rise of digital platforms creates knowledge monopolies that threaten innovat ... Expand

Abstract: 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 gover­ nance 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 mech­ anism to curtail coercive data obligations, control, eliminate data rights exploitation, and prevent mergers and acquisitions that could create or extend knowledge monopolies. Collapse

Topics: digital platform innovation management legal context digital ecosystem data-driven business model

Methods: machine learning

How to Share Data Online (fast) - A Taxonomy of Data Sharing Business Models

2023 | HICSS | Citations: 0

Authors: Schweihoff, J.; Jussen, I.; Dahms, V.; Möller, F.; Otto, B.

Abstract: Data is an integral part of almost every business. Sharing data enables new opp ... Expand

Abstract: Data is an integral part of almost every business. Sharing data enables new opportunities to generate value or enrich the existing data repository, opening up new potentials for optimization and business models. However, these opportunities are still untapped, as sharing data comes with many challenges. First and foremost, aspects such as trust in partners, transparency, and the desire for security are issues that need to be addressed. Only then can data sharing be used efficiently in business models. The paper addresses this issue and generates guidance for the data-sharing business model (DSBM) design in the form of a taxonomy. The taxonomy is built on the empirical analysis of 80 DSBMs. With this, the primary contributions are structuring the field of an emerging phenomenon and outlining design options for these types of business models. Collapse

Topics: business model database system AngelList value creation data-driven business model

Methods: company material

As Much Art as Science - Examining the Realization of Business Models Driven by Machine Learning Through a Dynamic Capabilities Perspective

2023 | European Conference On Information Systems | Citations: 0

Authors: Vetter, Oliver; Mehler, Maren; Buxmann, Peter

Abstract: Machine learning (ML) technologies open up enormous potential to be unlocked t ... Expand

Abstract: Machine learning (ML) technologies open up enormous potential to be unlocked through entrepreneurial activities in organizations, causing countless novel business models with ML at their core to emerge in the market. As ML technologies differ significantly from other digital technologies both in their characteristics and their effect on organizations, little is currently known about the complexities of the realization process for business models driven by ML and why only some organizations execute it successfully. By building on a qualitative study grounded on cross-industry insights from 20 expert interviews, this paper contributes to a greater understanding of the realization process by identifying ML-specific complications, before aiming to determine the underlying reasons for successful business model realization. We adopt a dynamic capabilities perspective and conceptualize eleven microfoundations that explicate how organizations build, implement, and transform business models driven by ML. Collapse

Topics: business model artificial intelligence digital transformation organization structure electronic business

Methods: qualitative interview cluster analysis theoretical contribution triangulation literature study

Theories: dynamic capabilities theory

Electronic Markets on platform transformation

2022 | Electronic Markets | Citations: 2

Authors: Alt, Rainer

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Topics: business model digital platform innovation management data-driven business model digital transformation

Methods: case study literature study survey design principle qualitative interview

Theories: transaction cost economics

From Personal Data to Service Innovation – Guiding the Design of New Service Opportunities

2022 | HICSS | Citations: 0

Authors: Bloecher, Katharina; Hunke, Fabian; Alt, Rainer; Satzger, Gerhard

Abstract: Stimulated by an ongoing digital transformation, companies obtain a new source f ... Expand

Abstract: Stimulated by an ongoing digital transformation, companies obtain a new source for digital service innovation: The use of personal data has the potential to build deeper customer relationships and to develop individualized services. However, methodological support for the systematic application of personal data in innovation processes is still scarce. This paper suggests a comprehensive approach for service design tools that enable collaborative design activities by participants with different data skills to identify new service opportunities. This approach includes the systematic development of customer understanding as well as a process to match customer needs to existing personal data resources. Following a design science research approach, we develop design principles for service design tools and build and evaluate a service opportunity canvas as a first instantiation. Collapse

Topics: knowledge representation innovation management business process innovation data security privacy

Methods: archival research qualitative interview design artifact ontological modelling design science

Employee perspectives on value realization from data within data-driven business models

2022 | Electronic Markets | Citations: 0

Authors: Förster, Matthias; Bansemir, Bastian; Roth, Angela

Abstract: Firms are innovating data-driven business models (DDBMs) to realize value from d ... Expand

Abstract: Firms are innovating data-driven business models (DDBMs) to realize value from data. Yet, making DDBMs work is challenging, and DDBMs often fall short of expected value realization. One reason for this shortfall is that firms do not know how employees, who decisively influence a DDBM’s value realization, view this complex and multi-facetted topic. We think it is necessary to understand the employees’ perspectives, the dimensions that build these perspectives and the characteristics employees are particularly interested in regarding value realization from data. We address this research gap by applying the Q-methodology to examine the perspectives among 70 employees across twelve DDBMs at a German automotive manufacturer. This yields eight perspectives, e.g., data advocacy, data caution or data practical. By exploring these perspectives, we provide a first groundwork on how employees view and appraise value realization from data which adds to the strive for mastering value realization from data within DDBMs. Collapse

Topics: anonymity organizational value innovation management analytical information system data-driven business model

Methods: qualitative interview factor analysis Q methodology qualitative coding archival research

Theories: stakeholder theory

Linking Data Sovereignty and Data Economy: Arising Areas of Tension

2022 | International Conference on Business Informatics | Citations: 0

Authors: Lauf, Florian; Scheider, Simon; Bartsch, Jan; Herrmann, Philipp; Radic, Marija; Rebbert, Marcel; Nemat, André T; Langdon, Christoph Schlueter; Konrad, Ralf; Sunyaev, Ali; Meister, Sven

Abstract: In the emerging information economy, data evolves as an essential asset and pers ... Expand

Abstract: In the emerging information economy, data evolves as an essential asset and personal data in particular is used for data-driven business models. However, companies frequently leverage personal data without considering individuals’ data sovereignty. Therefore, we strive to strengthen individuals’ position in data ecosystems by combining concepts of data sovereignty and data economy. Our research design comprises an approach to design thinking iteratively generating, validating, and refining such concepts. As a result, we identified ten areas of tension that arise when linking data sovereignty and data economy. Subsequently, we propose initial solutions to resolve these tensions and thus contribute to knowledge about the development of fair data ecosystems benefiting both individuals’ sovereignty and companies’ access to data. Collapse

Topics: digital ecosystem design thinking digital literacy computer supported cooperative work privacy

Methods: design methodology focus group archival research design artifact desk research

Introduction to Smart Mobility Ecosystems and Services Minitrack

2022 | HICSS | Citations: 0

Authors: Lindman, Juho; Rossi, Matti; Tuunainen, Virpi Kristiina

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Topics: information privacy concern data-driven business model privacy

Methods: literature sample qualitative interview literature study

Towards a Taxonomy of API Services in Logistics

2022 | HICSS | Citations: 0

Authors: Möller, Frederik; Stachon, Maleen; Jussen, Ilka; Schweihoff, Julia; Van Der Valk, Hendrik; Schmidt, Michael; Handrup, Stephanie

Abstract: Data are a valuable asset for companies in the logistics sector to optimize inte ... Expand

Abstract: Data are a valuable asset for companies in the logistics sector to optimize internally and develop new business models. They can be like a magnifying glass, making previously opaque logistical processes transparent and finding previously hidden optimization potentials. Typical applications are tracking the transport status, route optimization, monitoring pharmaceutical products, or monitoring shocks for fragile cargo along the trade lanes. One way to use data is to tap into publicly or commercially available Application Programming Interfaces (APIs). As a result, logistics service providers can get or provide data automatically via a machine-tomachine interface. However, the landscape of API service providers is vast, unstructured, and intransparent in terms of potential data that companies can leverage. Given their high potential for logistics, the paper proposes a taxonomy of API services in logistics based on the inductive analysis of three API databases. Collapse

Topics: application programming interface database system logistics management internet technology geographic information system

Methods: computational algorithm

A Reference Model for Data-Driven Business Model Innovation Initiatives in Incumbent Firms

2022 | European Conference On Information Systems | Citations: 0

Authors: Rashed, Faisal; Drews, Paul; Zaki, Mohamed

Abstract: In the past decade, we have witnessed the rise of big data analytics to a well- ... Expand

Abstract: In the past decade, we have witnessed the rise of big data analytics to a well-established phenomenon in business and academic fields. Novel opportunities appear for organizations to maximize the value from data through improved decision making, enhanced value propositions and new business models. The latter two are investigated by scholars as part of an emerging research field of data-driven business model (DDBM) innovation. Aiming to deploy DDBM innovation, companies start initiatives to either renovate their existing BM or develop a new DDBM. Responding to the recent calls for further research on design knowledge for DDBM innovation, we developed a reference model for DDBM innovation initiatives. Building upon a design science research approach and the Work System Theory as a kernel theory and a set of design principles, we propose a reference model comprising a static and a dynamic view. Our results are based on a research study with empirical insights from 18 companies, 19 cases and 16 expert interviews as well as theoretical grounding from a systematic literature research on key concepts of DDBM innovation. The developed reference model fills a gap mentioned in the DDBM innovation literature and provides practical guidance for companies. Collapse

Topics: innovation management enterprise architecture big data business model TOGAF

Methods: reference modelling qualitative interview design principle kernel theory qualitative coding

Theories: work systems theory