Paper Number
1541
Paper Type
Complete Research Paper
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, inter-organizational 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.
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
Inter-Organizational Collaboration for Machine Learning: Motivating and Discouraging Factors in the Automotive Industry
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, inter-organizational 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.
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