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Effect of the digital transformation of firms in a developing country on their reverse innovation: the complementary roles of multi-contexts

Xu Chu & Yuntao Bai

International Journal of Technology Management2025https://doi.org/10.1504/ijtm.2025.143582article
AJG 2ABDC B
Weight
0.37

Abstract

Reverse innovation (RI) research on multinational companies (MNCs) in developed countries has flourished, while the driving mechanism of RI in firms in developing countries remains understudied. From a resource dependence theory perspective, this study proposes that digital transformation (DT) can serve as a strategic arrangement with convergence, connectivity, and image-shaping functions to help overcome the liability of foreignness of firms in developing countries, thereby facilitating their RI. Our evidence of 17,397 observations from Chinese listed manufacturing companies supports the relationship between DT and RI. Additionally, we find that several contextual factors (e.g., executives' overseas backgrounds (OSB), firm's internationalisation degree (INT), and regional market intermediaries' development (RMI)) facilitate a positive DT-RI relationship by reducing firms' liability of foreignness. Our research contributes to RI's driving mechanism from firms in developing countries through the DT perspective and the awareness of the complementary roles of multi-contexts.

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https://doi.org/https://doi.org/10.1504/ijtm.2025.143582

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@article{xu2025,
  title        = {{Effect of the digital transformation of firms in a developing country on their reverse innovation: the complementary roles of multi-contexts}},
  author       = {Xu Chu & Yuntao Bai},
  journal      = {International Journal of Technology Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijtm.2025.143582},
}

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Evidence weight

0.37

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

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
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.