Leveraging Information Systems for Cost Control Optimization in Tourism Hotels

Wenwen CHEN & Aishun Jia

International Journal of Information Systems and Supply Chain Management2025https://doi.org/10.4018/ijisscm.385601article
AJG 1ABDC C
Weight
0.37

What the paper says

This study addresses how hotels can transition from fragmented cost control to systemic value chain optimization. Adopting Michael Porter's value chain framework, the authors analyzed cost drivers across primary services (e.g., room service and guest experience) and support activities (e.g., procurement and human resources) within tourism hotels, emphasizing connections between departments and external partners. An empirical analysis of 36 hotel branches and 15 managerial interviews revealed that coordinated strategies—internal process optimization, vertical collaboration (suppliers/online travel agencies), horizontal benchmarking, and targeted technology investment—reduced total cost-to-revenue ratios by 11.3%~15.7%. Crucially, they identified a digital investment threshold (1.2% of revenue) beyond which cost-saving elasticity significantly increased (0.31–0.58), though excessive automation risked “digital burnout” (7% to 9% labor cost rebound). This paper outlines a strategic to integrated cost control, like a property management system/customer relationship management integration and smart energy monitoring, to help managers implement cost-saving measures without over-automation.

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https://doi.org/https://doi.org/10.4018/ijisscm.385601

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@article{wenwen2025,
  title        = {{Leveraging Information Systems for Cost Control Optimization in Tourism Hotels}},
  author       = {Wenwen CHEN & Aishun Jia},
  journal      = {International Journal of Information Systems and Supply Chain Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.4018/ijisscm.385601},
}

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Evidence weight

0.37

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

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.