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The establishment of transactive memory system in distributed agile teams engaged in AI-related knowledge work

Xi Zhang (College of Management and Economics, Tianjin University, Tianjin, China)
Tianxue Xu (College of Management and Economics, Tianjin University, Tianjin, China)
Xin Wei (College of Management and Economics, Tianjin University, Tianjin, China)
Jiaxin Tang (Department of Business Administration, Faculty of Economics, The University of Oviedo, Oviedo-Asturias, Spain)
Patricia Ordonez de Pablos (Department of Business Administration, Faculty of Economics, The University of Oviedo, Oviedo-Asturias, Spain)

Journal of Knowledge Management

ISSN: 1367-3270

Article publication date: 15 June 2023

Issue publication date: 4 March 2024

590

Abstract

Purpose

As a kind of knowledge-intensive team coordinated across physical distance, it is necessary to construct a meta-knowledge driven transactive memory system (TMS) for the knowledge management of distributed agile team (DAT). This study aims to explore the comprehensive antecedents of TMS establishment in DATs and considers how TMS establishment is affected by herding behavior under the artificial intelligence (AI)-related knowledge work environment that emerges with technology penetration.

Design/methodology/approach

The data derived from 177 students of 52 DATs in a well-known Chinese business school, which were divided into 26 traditional knowledge work groups and 26 AI-related task groups to conduct a random comparative experiment. The ordinary least squares method was used to analyze the conceptual model and ANOVA was used to examine the differences in herding behavior between the control groups (traditional knowledge work DATs) and treatment groups (DATs engaged in AI-related knowledge work).

Findings

The results showed that knowledge diversity, professional knowledge, self-efficacy and social system use had significantly positive effects on the establishment of TMS. Interestingly, the authors also find that herding behavior may promote the process of establishing TMS of the new team, and this effect will be more significant when AI tasks are involved in team knowledge work.

Originality/value

By exploring the comprehensive antecedents of the establishment of TMS, this study provided a theoretical basis for knowledge management of DATs, especially in AI knowledge work teams. From a practical perspective, when the DAT is involved in AI-related knowledge works, managers should appropriately guide the convergence of employees’ behaviors and use the herding effects to accelerate the establishment of TMS, which will improve team knowledge sharing and innovation.

Keywords

Acknowledgements

No conflict.

Statement of contribution/potential impact: Based on social exchange theory, this paper discusses the antecedents of the establishment of a transactive memory system (TMS) in DAT from a comprehensive perspective, and whether the release of AI tasks will make the establishment of TMS more influenced by herding behavior. Combining the social exchange theory with the ELM model, this paper examines the differential effects of personal range (knowledge diversity and friendship network centrality), personal literacy (professional knowledge and self-efficacy), social system use and herding behavior on the establishment of TMS in DATs involving AI-related knowledge work or traditional knowledge work. It expands the use boundary of social exchange theory and provides a theoretical basis for knowledge management in DATs. In practice, when participating in AI-related knowledge work, DATs should appropriately guide the convergence of employee behavior, use the herd effect to accelerate the establishment of TMS, and better realize knowledge sharing and team innovation.

This paper is supported by the National Natural Science Foundation of China [Grants 72241432], the National Key R&D Program of China [Grant 2020YFA0908600].

Citation

Zhang, X., Xu, T., Wei, X., Tang, J. and Ordonez de Pablos, P. (2024), "The establishment of transactive memory system in distributed agile teams engaged in AI-related knowledge work", Journal of Knowledge Management, Vol. 28 No. 2, pp. 381-408. https://doi.org/10.1108/JKM-10-2022-0791

Publisher

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Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

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