Inter-Organizational Collaboration for Machine Learning - Motivating and Discouraging Factors in the Automotive Industry

Aug 5, 2026

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Inter-organizational collaboration for machine learning is positioned as both an opportunity and a risk for organizations seeking to unlock ML value. The paper addresses the lack of knowledge about the determinants behind firms’ decisions to collaborate for ML. Using a ranking-type Delphi study, it identifies 14 motivating and 11 discouraging factors specific to the automotive industry and compares their relative importance. Findings support future ML research that crosses organizational boundaries.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages17

Reading Time43 min

AccessFree to download

Introduction

Inter-organizational collaboration for ML: benefits and risks

Decision factors for engaging in collaboration

Delphi study approach

Motivating factors

Discouraging factors

Findings and implications

Relative importance of factors

Foundations for future boundary-crossing ML research

Association for Information Systems AIS Electronic Library (AISeL) | ECIS 2024 Proceedings | European Conference on Information Systems
(ECIS) | | --- | --- | | June 2024
Inter-Organizational Collaboration for Machine Learning:
Motivating and Discouraging Factors in the Automotive Industry
Sascha Rank
Karlsruhe Institute of Technology, [sascha.rank@kit.edu](sascha.rank@kit.edu)
Florian Leiser
Karlsruhe Institute of Technology, [florian.leiser@kit.edu](florian.leiser@kit.edu)
Scott Thiebes
Karlsruhe Institute of Technology, [scott.thiebes@kit.edu](scott.thiebes@kit.edu)
Ali Sunyaev
Karlsruhe Institute of Technology, [sunyaev@kit.edu](sunyaev@kit.edu)
Follow this and additional works at: [https://aisel.aisnet.org/ecis2024](https://aisel.aisnet.org/ecis2024) | | Recommended Citation Rank, Sascha; Leiser, Florian; Thiebes, Scott; and Sunyaev, Ali, "Inter-Organizational Collaboration for Machine Learning: Motivating and Discouraging Factors in the Automotive Industry" (2024) . ECIS 2024 Proceedings. 14. [https://aisel.aisnet.org/ecis2024/track03_ai/track03_ai/14](https://aisel.aisnet.org/ecis2024/track03_ai/track03_ai/14) This material is brought to you by the European Conference on Information Systems (ECIS) at AIS Electronic Library (AISeL) . It has been accepted for inclusion in ECIS 2024 Proceedings by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please [contact](contact elibrary@aisnet.org)[ elibrary@aisnet.org](contact elibrary@aisnet.org). INTER-ORGANIZATIONAL COLLABORATION FOR MACHINE LEARNING: MOTIVATING AND DISCOURAGING FACTORS IN THE AUTOMOTIVE INDUSTRY Completed Research Paper Sascha Rank, Karlsruhe Institute of Technology, Karlsruhe, Germany, [sascha.rank@kit.edu](sascha.rank@kit.edu)[ ](sascha.rank@kit.edu)Florian Leiser, Karlsruhe Institute of Technology, Karlsruhe, Germany, [florian.leiser@kit.edu](florian.leiser@kit.edu)[ ](florian.leiser@kit.edu)Scott Thiebes, Karlsruhe Institute of Technology, Karlsruhe, Germany, [scott.thiebes@kit.edu](scott.thiebes@kit.edu)[ ](scott.thiebes@kit.edu)Ali Sunyaev, Karlsruhe Institute of Technology, Karlsruhe, Germany, [sunyaev@kit.edu](sunyaev@kit.edu) Abstract Many organizations are still far from harnessing the full potential of machine learning (ML) . An auspicious solution to leverage the potential of ML is inter-organizational collaboration. In the context of ML, interorganizational collaboration can benefit organizations enormously but also introduces some risks. Given these benefits and risks, deciding whether to participate in inter-organizational collaboration can be a delicate decision for organizations. We currently lack knowledge on the factors impacting organizations’decisions to engage in inter-organizational collaboration for ML. Using a ranking-type Delphi study, we identified 14 factors motivating (e.g., acquiring more extensive training data) and 11 factors discouraging (e.g., data protection concerns) inter-organizational collaboration for ML in the automotive industry and shed light on their relative importance. Our results lay the foundation for further research on ML that permeates organizational boundaries. Keywords: Artificial Intelligence, Machine Learning, Inter-Organizational Collaboration, Delphi Study. 1 Introduction Machine learning (ML) is the most prominent contemporary approach to implementing artificial intelligence (AI) . ML applications have shown remarkable progress in recent years. For example, ML has reached or even surpassed human performance in medicine for specific tasks like skin cancer classification (Esteva et al., 2017) and arrhythmia detection (Hannun et al., 2019) . In the automotive industry, ML has enabled progress toward autonomous driving (Grigorescu et al., 2020) and improved vehicle safety using ML-based predictive maintenance (Theissler et al., 2021; Zhao et al., 2017) . Due to advances like these, ML is expected to radica

FAQs

What problem does the paper address about ML collaboration?

It addresses the limited understanding of the factors influencing organizations’ decisions to engage in inter-organizational collaboration for machine learning.

How were motivating and discouraging factors identified?

A ranking-type Delphi study was used to elicit and rank factors for collaboration in the automotive industry.

What are examples of motivating and discouraging factors?

Motivating factors include acquiring more extensive training data, while discouraging factors include data protection concerns.

Aug 5, 2026

Upload by Maeve