The Application of LLMs in Frontline Management and Its Theoretical Implications

DOI

10.2991/978-94-6463-958-2_7How to use a DOI?

Keywords

Bounded Rationality; LLMs; Frontline Management; Decision-Making Theory

Abstract

This study examines the transformative impact of large language models (LLMs) on frontline management decision-making through the theoretical lens of Herbert A. Simon's bounded rationality framework. By analyzing the integration of LLMs across Simon's four-phase decision-making process—intelligence, design, choice, and review—this paper demonstrates how artificial intelligence expands the boundaries of human rationality, enabling a shift from satisficing to augmented rationality. While LLMs enhance information processing, solution generation, and decision optimization, their application introduces critical paradoxes, including algorithmic dependency, accountability ambiguity, and emotional intelligence deficits. Drawing on cross-cultural case studies and organizational behavior theories, this research proposes a human-AI collaboration paradigm that balances technological efficiency with managerial autonomy and ethical responsibility. The findings contribute to the evolving discourse on AI-enhanced management and offer practical insights for implementing LLMs in globally diverse organizational contexts.

Copyright

© 2025 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  - Liulei Shen
PY  - 2025
DA  - 2025/12/26
TI  - The Application of LLMs in Frontline Management and Its Theoretical Implications
BT  - Proceedings of the 5th International Conference on Management Science and Software Engineering (ICMSSE 2025)
PB  - Atlantis Press
SP  - 47
EP  - 56
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-958-2_7
DO  - 10.2991/978-94-6463-958-2_7
ID  - Shen2025
ER  -