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Digital Transformation Learning: The Value of Learning from the Digitalization of Traditional Industries Through Shared Digital Suppliers

Tianyu Hou et al.

MIS Quarterly2026https://doi.org/10.25300/misq/2026/19113article
FT50UTD24AJG 4*ABDC A*
Weight
0.50

What the paper says

While extant studies have investigated how digital industries influence the digital transformation of traditional firms, the impact of learning from the digitalization of traditional industries has been largely overlooked. This study addresses this gap by introducing the concept of digital transformation centrality (DTC) – a novel measure of the informational advantages and associated costs a firm accrues based on its position within a network of traditional firms interconnected through their shared digital suppliers. DTC captures a new type of IT spillover: digital suppliers accumulate digital transformation knowledge by serving traditional firms, and such knowledge then spills over to other traditional firms they serve. Using extensive longitudinal data on global supply chain relationships and firm fundamentals, we find that DTC has an inverted U-shaped relationship with firm value. This shows a non-monotonic impact of digital transformation learning from traditional industries. Moreover, we develop a curvilinear mediation model to uncover the underlying mechanisms, finding that the effect of DTC is partially mediated by both innovation output and the real value of innovations. These results demonstrate the essential roles of digital suppliers in both offering digital technologies and enabling the dissemination of digital transformation knowledge among traditional firms, and provide new insights for understanding digital transformation and IT spillover across industries.

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https://doi.org/https://doi.org/10.25300/misq/2026/19113

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@article{tianyu2026,
  title        = {{Digital Transformation Learning: The Value of Learning from the Digitalization of Traditional Industries Through Shared Digital Suppliers}},
  author       = {Tianyu Hou et al.},
  journal      = {MIS Quarterly},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.25300/misq/2026/19113},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.