Identifying Cross-Cultural Leadership in the Interpretation of Generative Artificial Intelligence (AI) Models

Abstract

The increasing globalization of organizations has intensified the importance of cross-cultural leadership and the ability of leaders to operate effectively in diverse cultural environments. At the same time, generative artificial intelligence (AI) models and large language models (LLMs) have become influential sources of information and interpretation in management and organizational studies. Despite the growing use of AI systems in decision-making and knowledge generation, limited research has examined how generative AI models interpret complex social science concepts such as cross-cultural leadership. This study addresses this gap by exploring whether leading generative AI models synthesize established leadership theories consistently with the academic literature. The purpose of this study is to conduct a qualitative comparative analysis of five leading generative AI models—Google Gemini, OpenAI GPT, Microsoft Copilot, Meta AI, and DeepSeek AI—in relation to their interpretations of cross-cultural leadership. The study employed an exploratory qualitative case study design based on purposive sampling. Data were collected through a structured interview form consisting of five research questions derived from the Hofstede framework and the GLOBE project. The responses generated by the AI models were analyzed using thematic content analysis and comparative analysis to identify common themes, differences, and theoretical consistencies. The findings indicate that all AI models conceptualize leadership as a process combining universal leadership traits with culture-specific behavioral expectations. The models consistently emphasized the importance of cultural intelligence, adaptability, communication, integrity, and contextual leadership effectiveness. The analysis also revealed varying levels of theoretical depth among the models, while Microsoft Copilot and Meta AI produced highly similar responses. This study contributes to the literature by introducing an innovative methodological perspective in which generative AI models are treated as expert participants in qualitative social science research rather than merely analytical tools. The findings provide insights into the capacity of AI systems to synthesize established leadership theories and may support future applications of AI in management education, leadership development, and decision-support processes in multicultural organizations.