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AI capabilities and export performance: the moderating role of province market development and cultural distance

Yi-An Chen (Department of Business Administration, National Chengchi University, Taipei City, Taiwan) (Department of Technology Management, National Chiayi University, Chiayi, Taiwan)
Na Dong (Japan Advanced Institute of Science and Technology, Nomi, Japan)

International Journal of Emerging Markets

ISSN: 1746-8809

Article publication date: 3 December 2024

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Abstract

Purpose

Artificial intelligence (AI) is a significant trend in digital technology that is revolutionizing the field of global business and internationalization. Based on institutional theory and resource-based view, this study examines the intricate relationship between AI capabilities and export performance, taking into account the different province market development within a country and cultural distance.

Design/methodology/approach

This study gathered data from the websites of privately owned Chinese exporters and complemented it with a survey in 2023. In conducting the survey, we employed a simple random-sampling approach to select 1,500 exporters in China, with a focus on economic development indicators, particularly GDP contribution. We received 1,000 surveys, but only 749 were valid due to missing data. The study’s comprehensive coverage of regions ensured the inclusion of potential variations and subnational disparities within the country. This study conducted ordinary least squares (OLS) regression, and standardized variables before entering into the regressions.

Findings

The results demonstrate that AI capabilities have a significant positive impact on export performance. In addition, the influence of AI capabilities on export performance varies depending on the home-country and host-country institutional environment. The relationship between AI capabilities and export performance is strengthened by larger cultural distance, while province market development within a country has a negative moderating effect on this relationship. In less developed markets, the AI capabilities can drive significantly export performance. In developed markets with more advanced institutional development, the significance of AI capabilities in reducing transaction costs diminishes due to established institutions and market structures. AI capabilities serve as an intermediary institutional mechanism that connects the institutional context of the home country with the cultural environment of the host country.

Originality/value

While the impact of AI on international business and internationalization performance is a growing area of study, further exploration of the moderating factors that influence this relationship is needed. Organizations operating in diverse global markets are profoundly shaped by institutional contexts in their operational environments. This research addresses the relatively unexplored role of institutional factors within the home country (provincial market development) and host country (cultural distance) in moderating the effects of AI capabilities on export performance. This study illuminates the intricate dynamics underlying the relationship between AI capabilities and export performance, with a specific focus on province-level market development and cultural distance. Employing institutional theory as the overarching framework, this research sheds light on how AI serves as an intermediary institutional mechanism, bridging the gaps related to cultural differences and varying market development levels. In doing so, it contributes to academia by advancing our understanding of how AI is shaping internationalization dynamics and the interaction between AI capabilities and institutional factors. Additionally, it offers insights for business managers and policymakers to make informed strategic decisions regarding AI capabilities.

Keywords

Citation

Chen, Y.-A. and Dong, N. (2024), "AI capabilities and export performance: the moderating role of province market development and cultural distance", International Journal of Emerging Markets, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJOEM-12-2023-2014

Publisher

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Emerald Publishing Limited

Copyright © 2024, Emerald Publishing Limited

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