Impact of Artificial Intelligence in Cross-Cultural Knowledge Management for Multinational Enterprises

Abstract

Multinational enterprises (MNEs) are faced more frequently with challenges in managing knowledge across diverse cultural environments, particularly when dealing with tacit knowledge transfer. Tacit knowledge, inherently rooted in personal experiences and social interactions, is significantly challenged from cultural differences, including relational, cognitive, and structural dimensions. Although well known models of Knowledge Management (KM), like Nonaka's SECI model, provide structured knowledge conversion and sharing approaches, they do not inherently address these cross-cultural complexities. As Artificial Intelligence (AI) rapidly evolves, it offers significant potential to bridge these cultural gaps, facilitating more effective and timely tacit knowledge sharing. Thus, this exploratory research addresses the intersection of AI and cross-cultural tacit knowledge sharing within MNEs.

To systematically examine this intersection, a conceptual research model was developed based on a comprehensive review of existing literature on KM, cross-cultural social capital theory (relational, cognitive, and structural), and AI capabilities. The model positions AI tools as key facilitators, bridging specific cultural barriers. Relational dimension addresses interpersonal trust and group dynamics, cognitive dimension emphasizes common understanding and communication clarity, and structural dimension pertains to organizational hierarchies and communication channels. Additionally, AI understanding and trust were hypothesized as critical moderators, affecting user engagement and reliance on AI systems.

This research employed a mixed-method approach centered on structural equation modeling (SEM) to validate the proposed conceptual model. Data were collected from 217 respondents across various industries and organizational departments, emphasizing individuals with recent cross-cultural collaboration and AI tool experience. The analysis proceeded through Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and finally Structural Equation Modeling (SEM), including Common Method Bias (CMB) assessments for transparency and validity.

Results from SEM showed interesting findings. While specific direct paths, such as AI tools' immediate impact on effective tacit knowledge sharing, were not statistically significant, indirect effects were notably evident. Particularly, AI understanding significantly enhanced AI trust, subsequently facilitating AI tools' perceived effectiveness. Moreover, majority of the respondents consistently acknowledged the value of AI in bridging cultural barriers across relational, cognitive, and structural dimensions. Nevertheless, considerable number of respondents expressed uncertainty regarding the precise capabilities of AI, suggesting a threshold of trust and understanding yet to be fully surpassed for substantial impacts to be consistently observable.

In summary, the thesis demonstrates that AI holds promise in addressing cultural barriers in tacit knowledge sharing within MNEs. However, the effectiveness strongly hinges on the users' understanding and trust in AI. These findings provide valuable insights for organizations intending to leverage AI strategically in multicultural contexts. Future research directions include more profound exploration of AI's subtle indirect impacts and longitudinal assessments of trust-building interventions.