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Cultural Intelligence and Generative AI in Cross-Cultural Knowledge Management: A Conceptual Framework for Organizational Learning

IJEBSM | Published: November 5, 2026 | Vol. 2 Issue 4 | ISSN: 3139-0919
Arthur King Ma, Ph.D1✉
1 Assistant Professor of Management, Robbins College of Business and Entrepreneurship, Fort Hays State University
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Abstract

This conceptual article develops the Cultural Intelligence–Generative AI Knowledge Integration (CQ–GKI) framework to explain how managers preserve culturally situated meaning when transferring organizational knowledge using generative artificial intelligence (GenAI). Integrating cultural intelligence, organizational knowledge transfer, absorptive capacity, and organizational learning, it distinguishes information accessibility, source fidelity, and receiving-context applicability. Cultural knowledge recontextualization is proposed as an observable managerial mechanism involving assumption elicitation, cross-context comparison, human verification, and documented adaptation. Eight propositions specify accessibility, managerial recontextualization, complementary knowledge-quality outcomes, moderators, and the institutionalization of validated lessons. The article distinguishes individual interpretation from unit-level reuse and organizational learning, develops falsifiable alternatives, and proposes experimental and multilevel tests. Its contribution is a conditional explanation of when culturally intelligent oversight adds value beyond technical expertise and routine AI verification. The framework treats source and receiving-context verification as complementary practices and differentiates the accessibility of candidate information from validated knowledge reuse. It also outlines research designs capable of testing these distinctions across managerial episodes and organizational settings.
Keywords: Cultural intelligence, knowledge fidelity, recontextualization, tacit knowledge, organizational learning, human oversight, cross-cultural transfer.

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🔓 Open Access | Published by IR Publishing Group. This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

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