Skip to content

The Application of AI-Based Technology and Psychological Contract in Improving Employee Performance in the Hotel Industry

Sep 2026 · Journal of Business, Social and Technology · 0 citations · 47 references

Abstract

Background: Digital transformation has accelerated the adoption of Artificial Intelligence (AI) in the hospitality industry; however, research integrating AI with Psychological Contract (PC) and Innovative Work Behavior (IWB) to explain Employee Performance (EP) remains limited, particularly in Indonesia. Objective: This study examines the relationships among AI, PC, IWB, and EP and assesses the mediating role of IWB among employees of four-star hotels in Yogyakarta. Methods: A quantitative cross-sectional survey was conducted involving 90 respondents selected through purposive sampling. The data were analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS). Results: All seven hypotheses were supported. AI was positively associated with EP (β = 0.345; p < 0.001) and IWB (β = 0.397; p < 0.001), while PC was positively associated with EP (β = 0.416; p < 0.001) and exhibited the strongest association with IWB (β = 0.526; p < 0.001). IWB was positively associated with EP (β = 0.352; p < 0.001) and partially mediated both the AI–EP (β = 0.140; p < 0.001) and PC–EP (β = 0.185; p < 0.001) relationships. The model explained 68.2% of the variance in EP and 44.7% of the variance in IWB. Conclusion: The findings extend hospitality research by integrating technological and psychological perspectives within a single model and indicate that AI utilization and PC fulfillment are associated with employee performance both directly and through innovative work behavior.

Read PDF

Similar papers

#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17
#artificial intelligence Open access Jul 2024

Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.

Kyra Wilson, Aylin Caliskan · 131 citations · ⚡8
#artificial intelligence Review Nov 2024

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...

Masoud Mohseni, Artur Scherer, K. Johnson et al. · 121 citations · ⚡9
#artificial intelligence Review Oct 2025

Ultralytics YOLO Evolution: An Overview of YOLO26, YOLO11, YOLOv8 and YOLOv5 Object Detectors for Computer Vision and Pattern Recognition

This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...

Ranjan Sapkota, Manoj Karkee · 112 citations · ⚡10

The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Models

This work revisits schema linking when using the latest generation of large language models (LLMs) and finds empirically that newer models are adept at utilizing relevant schema elements during generation even in the presence of large numbers of irrelevant ones.

Karime Maamari, Fadhil Abubaker, Daniel Jaroslawicz et al. · 109 citations · ⚡19

BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models

A novel threat is unveiled in which attackers steer the RAG system's response by injecting malicious passages into its knowledge base, enabling the attacker to steer the response without altering the user input or modifying the RAG weights.

Jiaqi Xue, Meng Zheng, Yebowen Hu et al. · 109 citations · ⚡8

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.