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Digital platform capabilities and cross-border innovation in manufacturing firms: a generative learning perspective

Xin Zhao et al.

Journal of Manufacturing Technology Management2026https://doi.org/10.1108/jmtm-05-2025-0384article
AJG 1ABDC B
Weight
0.50

Abstract

Purpose In the digital-platform economy, manufacturers confront both cross-industry disruption and the digital-investment paradox, making it imperative to uncover how platform capability drives cross-border innovation and under what conditions. Design/methodology/approach This study used questionnaires from Chinese manufacturing firms for linear regression and bootstrap tests. Findings The results show that digital platform capabilities (both integration and reconfiguration) facilitate cross-border innovation by stimulating generative learning (both unlearning and exploratory learning). Furthermore, entrepreneurial orientation strengthens the contribution of digital platform capabilities to generative learning, and competitive pressures attenuate the positive impact of generative learning on cross-border innovation. Originality/value By elucidating the mechanistic role of generative learning, this study contributes theoretical insights into how manufacturing firms can strategically leverage digital platforms to enhance the effectiveness of cross-border innovation.

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https://doi.org/https://doi.org/10.1108/jmtm-05-2025-0384

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@article{xin2026,
  title        = {{Digital platform capabilities and cross-border innovation in manufacturing firms: a generative learning perspective}},
  author       = {Xin Zhao et al.},
  journal      = {Journal of Manufacturing Technology Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/jmtm-05-2025-0384},
}

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Digital platform capabilities and cross-border innovation in manufacturing firms: a generative learning perspective

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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.