AI-Enhanced Time Management and Employee Performance: Proposing an Integrated Research Framework

DOI

10.2991/978-94-6239-624-1_17How to use a DOI?

Keywords

AI-enabled time management; AMO theory; digital literacy; cognitive offloading; employee performance; workplace AI adoption

Abstract

This study proposes a comprehensive conceptual framework examining the relationships among Ability-Motivation-Opportunity (AMO) factors, AI-enabled time management mechanisms, and employee performance in AI-augmented workplaces. Despite extensive literature on traditional time management and AI adoption, limited research has explored how AI reshapes time management practices and subsequently influences individual performance outcomes. Grounded in AMO theory and integrating insights from the Technology Acceptance Model and distributed cognition theory, the proposed framework identifies three AMO factors, digital literacy for AI tools (Ability), perceived usefulness of AI tools (Motivation), and organizational support for AI adoption (Opportunity), as antecedents influencing three AI-enabled time management mechanisms: time reallocation, cognitive offloading, and temporal flexibility. These mechanisms, in turn, are hypothesized to enhance employee performance. Task type (routine versus non-routine) is proposed as a moderator conditioning the relationships between AMO factors and time management mechanisms. Nine hypotheses are developed to articulate these relationships. This conceptual study contributes to theoretical advancement by extending AMO theory into AI-augmented work contexts and identifying time management as a critical mediating process linking AI adoption to performance outcomes. Practical implications are offered for managers and HR practitioners seeking to optimize AI integration strategies for sustainable performance enhancement.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Tri Minh Nguyen
AU  - Anh Thi Nguyen
AU  - Hang Thuy Nguyen
AU  - Ngan Bao The Vo
AU  - Dung Thi Truong
PY  - 2026
DA  - 2026/04/06
TI  - AI-Enhanced Time Management and Employee Performance: Proposing an Integrated Research Framework
BT  - Proceedings of the International Conference on Sustainable Economics and Finance in the Digital Business Transformation (INCOSEF 2025)
PB  - Atlantis Press
SP  - 226
EP  - 253
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-624-1_17
DO  - 10.2991/978-94-6239-624-1_17
ID  - Nguyen2026
ER  -