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AI-Driven Service Industry Employee Behavior Prediction and Performance Optimization

Jun Wei et al.

Journal of Organizational and End User Computing2026https://doi.org/10.4018/joeuc.406730article
AJG 1ABDC B
Weight
0.50

Abstract

Behavioral, operational, and contextual factors affecting employee performance in the service industry are complex, and hence, predicting and intervening on them are especially difficult. As the banking industry is a crucial component of the service sector, it is particularly important to focus on predicting and intervening in the factors affecting the performance of banking employees. Current AI-based HR analytics solutions do not have a system for preserving fairness and optimizing decisions, as they tend to be based on a single source of signals. To solve these challenges, the present study introduces BEBOP-Net, a composite multi-source behavioral modeling and reinforcement learning agent that is used to elucidate expected employee behaviors and provide managerial control interventions that are comparatively inexpensive to implement. The structured HR features, chronological logs of the operational history, and written interaction records are modeled using a single multi-source behavior encoder with high temporal attention to extract dynamic patterns.

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https://doi.org/https://doi.org/10.4018/joeuc.406730

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@article{jun2026,
  title        = {{AI-Driven Service Industry Employee Behavior Prediction and Performance Optimization}},
  author       = {Jun Wei et al.},
  journal      = {Journal of Organizational and End User Computing},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.4018/joeuc.406730},
}

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AI-Driven Service Industry Employee Behavior Prediction and Performance Optimization

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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