The Utilisation of Artificial Intelligence in the Export Performance of MNCs: The Role of Cultural Distance

Abstract

Artificial intelligence (AI) is transforming the internationalisation activities of multinational corporations (MNCs) through enhanced operational efficiencies and optimised decision-making; though the moderating factors influencing its impact on export-led internationalisation remain underexplored. This research adopts a Resource-Based View (RBV) approach to examine the complex relationship between AI capabilities and the export performance of Indian MNCs, with cultural distance serving as a moderating factor, analysing how AI adoption influences export intensity, trade expansion, and market penetration strategies. Data from a 2024 survey of 449 Indian exporters across various industries, analysed using Structural Equation Modelling, reveal that AI capabilities positively impact export performance particularly in markets characterised by high institutional uncertainty and complex regulatory environments. Moreover, cultural distance acts as a significant moderator, amplifying the role of AI in navigating consumer preferences, language barriers, and localised business practices. AI-powered analytics help firms better understand foreign markets, adapt to cultural differences, and optimise international operations. This study advances the scholarly understanding and contributes to internationalisation theory by integrating AI-driven trade strategies with institutional and cultural moderating factors and offers a structured framework for corporate managers and policymakers to formulate AI-based strategic decisions that leverage AI to mitigate trade-related uncertainties, improve their compliance with international regulations, and strengthen global trade competitiveness in emerging economies.

1. Introduction

Artificial intelligence (AI) is a transformative digital technology reshaping international business by enhancing operational efficiencies, optimising decision-making, and facilitating cross-border trade (Scuotto et al., 2020). Among digital technologies, AI plays a pivotal role in influencing internationalisation and global trade by improving export processes, logistics, and supply chain efficiency (Burström et al., 2021; Denicolai et al., 2021; Ghauri et al., 2021). In today’s globalised economy, AI-powered analytics enable firms to identify emerging market opportunities, predict demand fluctuations, and streamline global distribution channels (Gölzer & Fritzsche, 2017; Li et al., 2018).

For instance, leading Indian multinational corporations (MNCs), such as Tata Group, Reliance Industries, and Hindustan Unilever, leverage AI-driven solutions in various aspects of their international trade operations. These include predictive analytics in supply chain management, AI-enhanced market intelligence for understanding consumer demand across international markets, and automation in quality control and production processes to maintain global competitiveness. AI applications in logistics optimisation allow MNCs to reduce trade costs, enhance inventory management, and improve the efficiency of export operations, thereby increasing their global market reach. Moreover, AI facilitates real-time regulatory compliance monitoring, which is crucial for firms navigating complex international trade policies and regulations.

Despite the increasing relevance of AI in export-led internationalisation, the moderating factors influencing its impact remain underexplored (Tan & Mathews, 2015). MNCs seeking to expand globally must navigate institutional challenges, such as trade barriers, foreign exchange volatility, and cultural differences, which significantly impact AI-driven internationalisation strategies (Denicolai et al., 2021). The Resource-Based View (RBV) suggests that firms’ internal capabilities, such as AI expertise, interact with external institutional factors to determine export performance (Barney, 1991). While prior research highlights that institutional frameworks shape the relationship between AI adoption and export outcomes, there is still a limited understanding of how AI interacts with export incentives, trade agreements, and country-specific policies to influence MNCs’ global trade expansion (Li et al., 2018).

This study seeks to address this gap by examining the relationship between AI capabilities and the export performance of Indian MNCs, with a specific focus on the moderating roles of cultural distance and provincial market development. Using export data from the Centre for Monitoring the Indian Economy (CMIE), this study analyzes how AI adoption influences export intensity, trade expansion, and market penetration strategies among Indian MNCs. The findings indicate that AI capabilities positively impact export performance, particularly in markets characterised by a high institutional uncertainty and complex regulatory environments. However, in more developed economies where institutional mechanisms for trade facilitation are robust, the influence of AI in reducing transaction costs is less pronounced. Additionally, this study finds that cultural distance acts as a moderating factor, amplifying the role of AI in navigating consumer preferences, language barriers, and localised business practices.

While the existing research predominantly focuses on AI’s role in automation, market expansion, and digital transformation (Burström et al., 2021; Denicolai et al., 2021), it often overlooks how AI interacts with institutional frameworks and export-driven strategies. The lack of insights into AI’s impact on trade policy adaptation, supply chain resilience, and market entry strategies creates a gap in understanding AI-driven internationalisation within emerging economies. This study fills this gap by providing a structured framework for Indian MNCs seeking to leverage AI for export growth, highlighting how AI can mitigate trade-related uncertainties, improve compliance with international regulations, and strengthen India’s global trade competitiveness.

This research extends internationalisation theories by integrating AI-driven trade strategies with institutional and cultural moderations, offering practical implications for Indian policymakers, corporate leaders, and trade strategists. AI’s potential in export enhancement, regulatory compliance, and cross-border business adaptation provides valuable insights for firms looking to scale their international operations efficiently. This study proceeds with a literature review and hypothesis development, followed by the methodology, findings, discussion, and conclusion.

2. Review of Literature and Formulation of Hypotheses

At the company level, the RBV seeks to clarify the sources of long-term competitive advantage (Barney, 1991). According to the RBV (Barney, 1991), resources that make implementing initiatives that increase effectiveness and efficiency easier are considered valuable. Resources not commonly owned by rival or possibly rival companies are rare. For a resource to contribute to a firm’s ability to create and preserve a competitive advantage, it must be non-substitutable. These resources might be intangible (such as brand, proprietary knowledge, and efficient operating procedures) or physical (like infrastructure) (Wernerfelt, 1984; Barney, 1991). According to earlier studies, capacities are the capacity to specify the resources needed to finish an organisational task (Barney, 2001). Capabilities are developed via staff development, learning, and knowledge sharing and arise when resources are integrated (Prieto-Sandoval et al., 2019; Teece & Pisano, 2003). The present position’s capabilities include the client base, intellectual property, technology, and upstream relationships with suppliers. Moreover, these talents comprise the available avenues, including strategic options accessible to the company and current prospects.

2.1. Internationalisation and AI Capabilities

According to the RBV, businesses have unique assets and skills that may provide a competitive edge. One such resource is artificial intelligence (Cahen & Borini, 2020; Mikalef & Gupta, 2021). There is a growing corpus of new research examining AI and its impact on corporate operations and procedures (Furman & Teodoridis, 2020). Research is being conducted to determine how AI affects business functions (Huynh et al., 2020). There is still not enough studies on how AI affects businesses’ global performance, despite more and more research examining how AI affects managerial techniques, technological innovation, and firm innovation processes (Liu et al., 2020; Furman & Teodoridis, 2020).

A company’s ability to understand and adjust to various international markets may be improved by AI skills (Wamba-Taguimdje et al., 2020). ML algorithms may analyse large datasets. This information is crucial for tailoring goods, advertising tactics, and consumer experiences to meet the unique needs of global consumers, which eventually leads to improved export results. Businesses may operate more effectively and economically by using AI capabilities (Mariani & Borghi, 2023). Businesses reduce operating costs by implementing predictive maintenance, optimising inventory management, and streamlining supply chains via automation and data analytics.

Consequently, businesses acquire more resources, which they may use to expand their market reach, engage in foreign growth, or spend money on R&D to stay competitive globally. For example, ChatGPT 4.0.dramatically enhances customer service in the finance industry. Its language-based features enable tailored answers and suggestions, improving the client experience. Furthermore, by analysing enormous volumes of data, offering real-time insights, and assisting financial institutions in making well-informed decisions, ChatGPT helps control risk (Ali & Aysan, 2023). Investment analysis is improved by its ability to decipher intricate financial data, facilitating more effective decision-making procedures (Zaremba & Demir, 2023).

The influence of AI on financial performance is highlighted by studies such as Ahmed et al. (2022), which strongly emphasise gains in risk management, efficiency, and decision accuracy. Similarly, the research by Goodell et al. (2021) lists several particular financial domains where AI and ML are used, such as forecasting, planning, valuation, portfolio creation, and sentiment inference. These developments highlight AI’s revolutionary potential to drive strategic decision-making and optimise financial operations.

International growth may be accelerated by using AI’s decision-making and process automation capabilities. Thanks to AI capabilities, businesses may exchange significant and critical information (Cassetta et al., 2020). Using data analysis, artificial intelligence (AI) makes obtaining insights into foreign markets easier, gathering and evaluating relevant data about foreign markets and consumer preferences and improving marketing initiatives targeting a worldwide clientele (Neubert, 2018). All things considered, AI helps improve decision-making effectiveness when assessing the possibilities of global marketplaces. Advanced artificial intelligence (AI) systems can process and analyse data faster than humans, giving them real-time insights into the competitive landscape and market dynamics. This enables businesses to make better judgments in the context of globalisation, swiftly adjust to changing conditions, and grasp new chances, eventually improving their performance in global marketplaces. These reasons back up AI’s potential as a powerful engine of global expansion. Therefore, a company’s AI skills can significantly improve its export performance.

H1. 

The export performance is positively correlated with AI capabilities.

2.2. Cultural Distance and AI Capabilities

A key component of businesses’ internationalisation strategy is the idea of cultural distance. When businesses start expanding internationally, they often start by focusing on nations comparable in geography and culture. After that, they progressively go to farther-off regions, picking up knowledge at every turn (Johanson & Vahlne, 2017). This poses particular difficulties, which may impede productive cooperation because of the innate feeling of alienation or mental separation. The development of AI has brought about significant developments. Because of these developments, businesses can now more easily learn about other cultures and adjust their tactics to suit the unique requirements of international markets better. Additionally, businesses may now access a greater variety of information and more sophisticated analytical tools thanks to digital technology. According to this research, AI skills may improve export effectiveness, especially with significant cultural variations.

This spectrum also includes sources from different organisations, agencies, and endpoint devices in Internet of Things (IoT) ecosystems. Furthermore, social media postings from both present and future clients are included in the information repository to improve its quality (Wang & Zhang, 2012). This collection is further enhanced by the inclusion of movement data received from smartphones. The variety of sources utilised for data collecting increases with cultural diversity, creating a complete and more complex dataset from which big data may be derived. The second element concerns how managers act in the face of uncertainty. Higher complexity and ambiguity in management decision-making might result from cultural distance (Tihanyi et al., 2005). However, data-driven methods may improve managerial decision-making when businesses face difficult situations (Elmachtoub & Grigas, 2022). Even in tumultuous times, businesses may thrive when they make clear, data-driven decisions. Particularly for businesses functioning in complicated and uncertain situations, artificial intelligence (AI) and big data analytics may help them make decisions more quickly, lower uncertainty levels, and base their conclusions on evidence utilising automated procedures (Giest, 2017). Consequently, the more cultural differences there are, the more uncertainty a business may face and the more likely it will use AI to decide whether to export. Therefore, we submit the following theory:

H2. 

The correlation between export success and AI skills is strengthened by cultural distance.

3. Research Methodology

This research collected information from privately held exporters’ websites and supplemented it with a poll conducted in 2024. A random sampling technique was used to choose 1000 exporters for the survey, concentrating on economic progress metrics, namely GDP contribution. We adhered to earlier research (e.g., Jean et al., 2021) that included areas categorised as developing, moderately developed, and highly developed. To guarantee data accuracy, data were collected via the questionnaire administration. The standard technique bias was avoided by gathering data from different sources. Participants in this research were made fully aware that while their answers would be kept private, anonymity could not be ensured because of the possibility of data combination. Only 449 out of the 800 collected questionnaires were valid, as 351 were excluded due to missing data. To ensure the validity and reliability of the results, this study applied a listwise deletion approach, where cases with substantial missing values were removed from the dataset. This method was chosen to maintain consistency across all variables and prevent biases introduced by partial data (Hair et al., 2019). Before deletion, the missing data pattern was assessed to confirm that the exclusions were random and did not systematically affect the findings. This approach aligns with established practices in quantitative research, ensuring the robustness of the final dataset used for analysis (Lim, 2024). Structural Equation Modelling (SEM) was conducted using LISREL (12.0) to estimate latent constructs and path relationships. The model fit was assessed using standard indices, including χ², RMSEA, CFI, and NNFI, ensuring methodological rigour and reproducibility (Hair et al., 2019).

Potential variances and subnational differences within the nation were guaranteed to be included due to this study’s thorough regional coverage (Zhou & Poppo, 2010). The research includes organisations dealing in home items, metal, chemicals, electronics, and equipment. They were also spread throughout many locations, suggesting a widely dispersed geographic presence in the dataset. SEM was used in this research to assess the measurement and structural models. The demographic characteristics are presented in

Table 1. Demographic details of responding firms.

Demographic Variables	Frequency (n)	Percent (%)
Firm Age
1–5 years	220	48.99
5–10 years	129	28.73
More than 10 years	100	22.27
Total	449	100.0
Experience
1–5 years	178	39.6
5–10 years	184	40.9
More than 10 years	87	19.5
Total	449	100.0
No of employees
1–250	200	44.54
250–500	152	33.85
More than 500	97	21.61
Total	449	100
Industry
Electrical	180	40.08
Telecoms Equipment and Parts Electronics Metal	130	28.95
Chemicals	100	22.23
Rubber	13	2.9
Plastics Machinery	13	2.9
Industrial Parts and Tools	13	2.9
Total	449	100.0

Source: own compilation.

.

Measures

The capacity of a company to choose, coordinate, and use its AI-specific resources is what this research refers to as its AI capabilities (Zhou & Wu, 2010; Khin & Ho, 2019; Mikalef & Gupta, 2021). After modifying survey questions from Mikalef and Gupta (2021) and Chen et al. (2021), AI-related capabilities were considered the independent variable. A five-point Likert scale served as the basis for these items. The export performance was regarded as the dependent variable by other research (e.g., Grant, 1987; Riahi-Belkaoui, 1998; Cloninger, 2004) and recommendations made by Sullivan (1996), who looked at the applicability of several indices; this study uses foreign income to quantify the export performance. An organisation’s level of worldwide market activity is directly reflected in its foreign income. It offers a basic and unambiguous assessment of how well the business makes money from its international activities. Hofstede’s (1980), multiple dimensions might emphasise our emphasis because of the goal of our research; thus, we used Hofstede’s method. Hofstede collected six aspects of national culture that were considered. This research calculated cultural distance using the Squared Euclidean Distance (Gaur & Lu, 2007). Confirmatory Factor Analysis (CFA) was conducted to refine the questionnaire items. The results indicated that Cronbach’s Alpha exceeded 0.80, the Average Variance Extracted (AVE) was above 0.70, the Composite Reliability (CR) reached 0.90, all factor loadings exceeded 0.60, and the t-values were greater than 1.96 for all items, as shown in

Table 2. Measurement model estimation.

Measures	CR	Cronbach Alpha	Min t Values	AVE	Min Loadings
AI	0.94	0.823	4.44	0.712	0.59
CD	0.91	0.812	5.55	0.717	0.53
EP	0.91	0.833	7.56	0.722	0.51

Source: own compilation.

.

To ensure the robustness of the measurement model, Cronbach’s Alpha was assessed for internal consistency, with all values exceeding the 0.70 threshold (Nunnally & Bernstein, 1994). Composite Reliability (CR) values were above 0.70, confirming construct reliability (Hair et al., 2019). The AVE exceeded 0.50 for all constructs, demonstrating that each construct explains more variance than measurement errors, establishing convergent validity (Fornell & Larcker, 1981). Additionally, factor loadings surpassed 0.60, confirming indicator reliability and the significant contribution of each item to its respective construct (Hulland et al., 2018). These results validate the reliability and construct validity of the questionnaire, ensuring the robustness and accuracy of this study’s findings.

4. Results

The structural model is shown in Figure 1. According to Hypothesis 1, export success is favourably correlated with AI capabilities. Figure 1 demonstrates a significant estimate between the export performance and AI capabilities. Hypothesis 1 is therefore validated. According to Hypothesis 2, there is a mediating role of cultural distance between AI and the export performance of MNCs. Thus, Hypothesis 2 is validated. We used a 95% confidence range for the moderator variable to show the moderating impact of cultural distance, using the technique of Meyer et al. (2017).

This study’s main goal was to determine whether and under what conditions AI may improve businesses’ export performance, building on previous research exploring the importance of AI capabilities. Businesses often face infrastructure-related difficulties, restricted access to vital resources, and an unestablished market structure in less developed countries (Marquis & Raynard, 2015). These obstacles may hinder market expansion and operational effectiveness. However, by expanding the market reach, improving supply chains, and filling in institutional support gaps, AI capabilities might be a valuable tool for tackling these issues (Dwivedi et al., 2021; Modgil et al., 2022; Chowdhury et al., 2023). Businesses may improve their operations, address shortcomings in the local institutional environment, and perform better in less developed markets by strategically using AI technology. Cultural distance mediates the relationship between AI and EP in this research. Hypothesis 2 is therefore validated. A considerable cultural distance indicates significant differences in cultural characteristics, which makes it more difficult for businesses to modify their plans to suit regional tastes and habits. AI tools may help businesses better understand local customer preferences, promote intercultural communication, and adapt goods and services to the cultural norms of the host nation. AI helps businesses navigate the complexity of cultural differences, which improves their chances of success abroad.

5. Discussion

A growing corpus of work focused on digitalisation has already examined the idea depicted in the results of Hypothesis 1 (Strange & Zucchella, 2017; Cassetta et al., 2020; Denicolai et al., 2021). However, these studies primarily rely on qualitative evaluations and conceptual reasoning. Additionally, this work represents a significant development in this field of study by enabling us to precisely analyse the influence of AI. AI-related capabilities are now critical for businesses looking to promote exports. However, our results show that investing in AI skills is insufficient.

In the following ways, the findings of this study complement and confirm those of earlier research. Our study and Chung and Yoon’s (2020) research address the impact of internal competencies on a company’s global success. Managers should incorporate AI into company processes, see it as a strategic resource, and take into account its ethical and cultural flexibility, according to Ratten et al. (2024). Ratten et al.’s study (Ratten et al., 2024) and our research are similar in highlighting the important role AI plays in helping businesses expand internationally. Our research addresses the unique impacts of AI on the success of internationalisation. It delves further into how cultural distance affects the function of AI in enhancing firms’ export performance. The application of AI in IB was examined by Menzies et al. (2024), who stress the necessity for businesses to influence export performance to differing degrees in complex and variable institutional settings by the utilisation of AI.

5.1. Research Contributions

First, the Resource-Based View (RBV) holds that businesses have unique assets and skills that may provide a competitive edge. One such resource is artificial intelligence (AI)-related capabilities. AI may be a vital tool for businesses looking to facilitate IB with limited resources (Marquis & Raynard, 2015). AI capabilities can influence export performance significantly in these kinds of settings. Second, according to Institutional Theory, the official and informal institutions in a company’s working environment significantly impact it.

A considerable cultural distance indicates significant differences in cultural characteristics, which makes it more difficult for businesses to modify their plans to accommodate regional tastes and traditions. A higher complexity and ambiguity in management decision-making might result from cultural distance (Tihanyi et al., 2005). However, when businesses face unpredictable and challenging situations, like a pandemic, data-driven methods help them make better management decisions (Stuart Berwick, 2022). The export performance may be improved by using AI skills to help comprehend and navigate these cultural quirks (Giest, 2017). For instance, it examines user information and behaviour to provide tailored experiences, product suggestions, and content. These systems may improve consumer satisfaction by offering pertinent and culturally relevant suggestions and comprehending individual preferences and cultural quirks. Third, cross-cultural management is highlighted in this research. Communication problems, miscommunications, and trouble tailoring goods and services to local tastes might result from increased cultural distance. By promoting intercultural communication, comprehending regional customer preferences, and personalising services, AI technology may help overcome these obstacles (Dwivedi et al., 2021). The benefit of AI skills became increasingly evident in circumstances with greater cultural distances, improving export success.

5.2. Implications

First, managers should acknowledge the increasing significance of AI capabilities in enhancing export success. They should allocate resources to develop and integrate AI technologies across various global operations, including cross-cultural communication, market customization, and supply chain optimisation. Small- and medium-sized enterprises (SMEs) should prioritise AI applications aligned with their core business objectives, focusing on areas where AI can deliver the greatest impact. Additionally, SMEs can leverage cloud-based AI platforms, benefiting from pay-as-you-go pricing models, scalable AI functionalities, and access to advanced AI tools and algorithms. Second, managers must recognise that AI’s effectiveness varies depending on market maturity. In less developed economies, AI can enhance operational efficiency, bridge institutional gaps, and optimise supply chains, compensating for resource limitations. Conversely, in more developed markets, firms must adopt AI strategies that drive competitive differentiation rather than merely addressing institutional deficiencies.

Third, managers should consider the role of cultural distance in global business expansion. AI can facilitate intercultural communication, consumer behaviour analysis, and product localization, thereby mitigating market entry challenges in culturally diverse regions. For example, AI systems can process large-scale data from social media, online reviews, and consumer ratings to extract insights into regional preferences, emerging trends, and consumer behaviours. By leveraging this information, businesses can tailor their products, services, and marketing strategies to align with local cultures and consumer expectations.

The findings offer practical insights for multinational corporations (MNCs) and SMEs seeking to integrate AI-driven solutions for export performance enhancement. AI-powered predictive analytics can enable firms to assess market trends, mitigate risks, and optimise international expansion strategies (Dwivedi et al., 2021). AI-driven language processing tools can reduce cultural barriers and enhance localization efforts, improving customer engagement in foreign markets (Ghauri et al., 2021). Additionally, AI can streamline supply chain management, ensuring greater efficiency in cross-border logistics (Burström et al., 2021). Policymakers can further support AI adoption by implementing regulatory incentives and infrastructure development, which would facilitate SMEs’ internationalisation (Marquis & Raynard, 2015). These insights provide a strategic roadmap for firms seeking to effectively integrate AI into their global expansion strategies.

This study advances internationalisation theory by integrating AI capabilities into discussions on export performance, contributing to the Resource-Based View (RBV) and Institutional Theory (Barney, 1991). While previous research has primarily focused on traditional firm resources, this study underscores AI’s role as a strategic resource, reshaping the competitive advantage in international markets. Furthermore, the findings contribute to cultural distance literature by demonstrating AI’s role in mitigating adaptation challenges, offering a novel perspective on digital transformation in cross-border business. By examining the interplay between AI, institutional environments, and internationalisation, this study presents new avenues for future research, particularly in exploring AI’s longitudinal impact on global strategy and firm performance.

5.3. Conclusions

Even though research on how AI affects international companies and internationalisation success is expanding, more investigation is required to determine the moderating elements affecting this connection. Institutional settings in their operational surroundings have a significant impact on organisations that operate in a variety of international marketplaces. This study examines how institutional characteristics may mitigate the impact of AI capabilities on export success. This research sheds light on the connection between export success and AI capabilities and the understanding of the role of cultural distance in this relationship. Doing this advances our knowledge of how AI influences internationalisation and the interplay between institutional elements and AI capabilities, which benefits academics. It also provides information that helps governments and corporate management make well-informed strategic choices about AI capabilities. In light of this research, these businesses might receive special incentives and assistance from policymakers. The importance of cultural distance in global business may also be known to policymakers.

6. Limitations and Future Research Recommendations

While this study offers insights into AI’s role in export performance, several limitations must be acknowledged. First, this study’s geographic focus limits generalizability, and future research should examine diverse markets (Hair et al., 2019). Second, the reliance on self-reported data may introduce a response bias, which could be mitigated by incorporating objective performance measures. Third, while the listwise deletion ensured data integrity, it may have excluded firms with unique characteristics; future studies could apply multiple imputations to address this (Lim, 2024). Lastly, the cross-sectional design limits causal inferences, and a longitudinal approach would provide deeper insights into AI’s long-term impact on internationalisation. These constraints should be considered when interpreting the findings. Although our study has filled in some knowledge gaps, there are still some gaps and potential directions for further research. Firstly, this study solely looked at export performance in this particular situation. Future research should examine other facets of internationalisation, including its scope and depth, the existence of overseas subsidiaries, internationalisation dummies, and composite metrics. Such studies might provide distinct insights into how AI capabilities impact different aspects of internationalisation success. Second, we looked at companies that sell to clients throughout the world. Companies from other developed or developing nations may be included in future studies to see how various laws and circumstances affect them. Thirdly, businesses selling goods on e-commerce platforms made up most of our samples. Finally, comparing the present viewpoint with another theoretical basis in future research would be intriguing. Fourth, a systematic literature review (SLR) could be conducted to synthesise and critically analyse the existing research on AI-driven internationalisation (Fakhar et al., 2023; Ishrat et al., 2023; Khan et al., 2025a, 2025b; Khan & Azam, 2023; Rashid et al., 2024). An SLR would provide a structured evaluation of theoretical advancements, methodological approaches, and empirical findings, identifying key gaps and setting an agenda for future empirical research. Finally, comparing the present viewpoint with another theoretical basis in future research would be intriguing.