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Reassessing the digital transformation of multinational enterprises: The role of cross-country information distance

Sihong Wu & Peter Zámborský

Australian Journal of Management2026https://doi.org/10.1177/03128962261425511article
AJG 2ABDC A
Weight
0.50

Abstract

Digitalization is reshaping global competition, yet cross-country differences in informational globalization create challenges for multinational enterprises (MNEs), especially in information technology (IT)-intensive sectors. This study analyzes the impact of information distance, which reflects disparities in national informational environments, on the digital transformation of MNEs’ subsidiaries. By applying a real options perspective, we highlight intra-firm factors (i.e. financial liquidity and operational flexibility) that play critical roles in mitigating pressures arising from information distance. Using a sample of 136 subsidiaries of MNEs in IT-intensive sectors and employing computer-aided text analysis techniques, this study finds that information distance between home and host countries hinders MNE subsidiaries’ digital transformation and negatively affects their financial performance. However, these adverse effects are mitigated by the subsidiary’s financial liquidity and operational flexibility. This research contributes to the cross-country distance literature and offers new insights into digitalization in international business. JEL Classification : F18, F21, F23

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https://doi.org/https://doi.org/10.1177/03128962261425511

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@article{sihong2026,
  title        = {{Reassessing the digital transformation of multinational enterprises: The role of cross-country information distance}},
  author       = {Sihong Wu & Peter Zámborský},
  journal      = {Australian Journal of Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/03128962261425511},
}

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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.