Title: Human-machine collaboration and AI-augmented coding across cultures: a process model

Authors: Sven Horak; Ana Alina Tudoran; David Ahlstrom

Addresses: Department of Management, The Peter J. Tobin College of Business, St. John's University, New York, USA ' Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark ' Department of Management and Strategy, Hong Kong Metropolitan University, Homantin, Kowloon, Hong Kong

Abstract: In international management research, cross-cultural qualitative studies are often faced with linguistic diversity, subtle cultural nuances and contextual ambiguity, all of which can complicate data interpretation and interrater reliability. This paper proposes that Artificial Intelligence, and particularly Large Language Models can provide vital support in addressing these challenges by aiding culture-sensitive coding. We develop a step-by-step process model in which Large Language Models are integrated into the interrater reliability workflow as collaborative partners rather than replacements for human coders. By leveraging the scalability, linguistic versatility and pattern-recognition capabilities of Large Language Models, researchers can achieve greater coding consistency, reduce cognitive overload and enhance sensitivity to cultural and contextual variations in qualitative data. Illustrated through a hypothetical example of cross-cultural networking behaviour, the model demonstrates how AI-assisted coding can amplify human judgment in iterative coding cycles, offering a more robust and inclusive approach to interrater reliability. In doing so, this paper advances methodological practice by showing how human-machine collaboration can refine culture-sensitive analysis and strengthen the validity of qualitative research in international management.

Keywords: AI; artificial intelligence; LLMs; large language models; human-machine collaboration; qualitative research methods; qualitative coding; interrater reliability; intercoder reliability; international business; cross-cultural management.

DOI: 10.1504/EJIM.2026.150396

European Journal of International Management, 2026 Vol.28 No.1, pp.1 - 23

Received: 22 Jan 2025
Accepted: 02 Sep 2025
Published online: 12 Dec 2025 *

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