Knowledge Leaks in Data-Driven Business Models? Exploring Different Types of Knowledge Risks and Protection Measures

Michael Fruhwirth (), Viktoria Pammer-Schindler () and Stefan Thalmann ()
Additional contact information
Michael Fruhwirth: Know-Center GmbH
Viktoria Pammer-Schindler: Know-Center GmbH
Stefan Thalmann: University of Graz

Schmalenbach Journal of Business Research, 2024, vol. 76, issue 3, 357-396

Abstract: Abstract Data-driven business models imply the inter-organisational exchange of data or similar value objects. Data science methods enable organisations to discover patterns and eventually knowledge from data. Further, by training machine learning models, knowledge is materialised in those models. Thus, organisations might risk the exposure of competitive knowledge by sharing data-related value objects, such as data, models or predictions. Although knowledge risks have been studied in traditional business models, little research has been conducted in the direction of data-driven business models. In this explorative qualitative study, we conducted 28 expert interviews in three rounds (two exploratory and one evaluatory) and identified five types of risks along the three basic types of value objects: data, models and predictions. These risks depend on the context, i.e., when competitive knowledge could be discovered from shared value objects. We found that those risks can be mitigated by technology, contractual regulations, trusted relationships, and adjusting the business model design. In this study, we show that the risk of knowledge leakage is a relevant risk factor in data-driven business models. Overall, knowledge risks should be considered already during business model design, and their management requires an interdisciplinary approach via a balanced assessment. The level of knowledge protection from a technology perspective highly depends on computer science innovations and thus is a moving target. As an outlook, we suggest that knowledge risk will become even more relevant with the extensive usage of machine learning and artificial intelligence in data-driven business models.

Keywords: Business model innovation; Data analytics; Data-driven business models; Knowledge risks; Risk management; Value objects (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s41471-024-00189-z Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sjobre:v:76:y:2024:i:3:d:10.1007_s41471-024-00189-z

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/41471

DOI: 10.1007/s41471-024-00189-z

Access Statistics for this article

More articles in Schmalenbach Journal of Business Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().