Article (Périodiques scientifiques)
Business model archetypes for data marketplaces in the automotive industry: Contrasting business models of data marketplaces with varying ownership and orientation structures
Bergman, Rômy; ABBAS, Antragama Ewa; Jung, Sven et al.
2022In Electronic Markets, 32 (2), p. 747 - 765
Peer reviewed vérifié par ORBi
 

Documents


Texte intégral
Bergman et al., 2022_Business model archetypes for data marketplaces in the automotive industry.pdf
Postprint Éditeur (1.98 MB) Licence Creative Commons - Attribution
Télécharger

Tous les documents dans ORBilu sont protégés par une licence d'utilisation.

Envoyer vers



Détails



Mots-clés :
Data economy; Automotive industry; Business model; Data marketplaces
Résumé :
[en] Policymakers and analysts are heavily promoting data marketplaces to foster data trading between companies. Existing business model literature covers individually owned, multilateral data marketplaces. However, these particular types of data marketplaces hardly reach commercial exploitation. This paper develops business model archetypes for the full array of data marketplace types, ranging from private to independent ownership and from a hierarchical to a market orientation. Through exploratory interviews and case analyses, we create a business model taxonomy. Patterns in our taxonomy reveal four business model archetypes. We find that privately-owned data marketplaces with hierarchical orientation apply the aggregating data marketplace archetype. Consortium-owned data marketplaces apply the archetypes of aggregating data marketplace with additional brokering service and consulting data marketplace. Independently owned data marketplaces with market orientation apply the facilitating data marketplace archetype. Our results provide a basis for configurational theory that explains the performance of data marketplace business models. Our results also provide a basis for specifying boundary conditions for theory on data marketplace business models, as, for instance, the importance of network effects differs strongly between the archetypes.
Centre de recherche :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > FINATRAX - Digital Financial Services and Cross-organizational Digital Transformations
Disciplines :
Gestion des systèmes d’information
Sciences informatiques
Auteur, co-auteur :
Bergman, Rômy;  Faculty of Technology, Policy and Management, Delft University of Technology, Delft, Netherlands
ABBAS, Antragama Ewa  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX ; Faculty of Technology, Policy and Management, Delft University of Technology, Delft, Netherlands
Jung, Sven;  Institute of Technology Management (ITEM), University of St. Gallen, St. Gallen, Switzerland
Werker, Claudia;  Faculty of Technology, Policy and Management, Delft University of Technology, Delft, Netherlands
de Reuver, Mark;  Faculty of Technology, Policy and Management, Delft University of Technology, Delft, Netherlands
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Business model archetypes for data marketplaces in the automotive industry: Contrasting business models of data marketplaces with varying ownership and orientation structures
Date de publication/diffusion :
mai 2022
Titre du périodique :
Electronic Markets
ISSN :
1019-6781
eISSN :
1422-8890
Maison d'édition :
Springer Science and Business Media Deutschland GmbH
Volume/Tome :
32
Fascicule/Saison :
2
Pagination :
747 - 765
Peer reviewed :
Peer reviewed vérifié par ORBi
Focus Area :
Security, Reliability and Trust
Objectif de développement durable (ODD) :
9. Industrie, innovation et infrastructure
Subventionnement (détails) :
The research leading to these results has received funding from the European Union’s Horizon 2020 Research and Innovation Programme, under Grant Agreement no 871481 – Trusted Secure Data Sharing Space (TRUSTS), from the H2020-ICT-2018-20/H2020-ICT-2019-2 Call. This paper is partly based on the thesis work of Bergman ().
Disponible sur ORBilu :
depuis le 04 novembre 2024

Statistiques


Nombre de vues
116 (dont 3 Unilu)
Nombre de téléchargements
26 (dont 0 Unilu)

citations Scopus®
 
31
citations Scopus®
sans auto-citations
26
OpenCitations
 
7
citations OpenAlex
 
27
citations WoS
 
24

Bibliographie


Publications similaires



Contacter ORBilu