Article Per Year (5 Year)
p-Index From 2021 - 2026
0.553
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control)
Rami Mahmoud
The University of Jordan
Computer Science & IT Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Applications of machine intelligence and decision analytics in hospitality: a systematic review Omar Jawabreh; Ehab Abdul Raheem Alshatnawi; Rami Mahmoud
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2024-2040

Abstract

This systematic review synthesizes how machine intelligence (MI) and decision analytics (DA) are deployed across hospitality functions (customer relationship management (CRM)/personalization, revenue management (RM) and pricing, and operations) and clarifies barriers and research frontiers. Following a PRISMA-oriented protocol for the 2008–2025 period, the study searched Scopus, Web of Science, IEEE/Elsevier, and SpringerLink using combined terms for MI/artificial intelligence (AI)/machine learning (ML) and hospitality/tourism with CRM, revenue/pricing, forecasting, and operations. The inclusion criteria focused on peer-reviewed English studies reporting concrete models/applications or empirical evidence. Studies were thematically coded into four streams: CRM and personalization; RM and dynamic pricing; operations and scheduling; and governance (privacy/ethics/skills). The findings consistently show that MI/DA improves customer segmentation, demand forecasting, dynamic pricing (revenue per available room (RevPAR)), and workforce/maintenance efficiency. Yet, adoption is slowed by data governance (privacy/general data protection regulation (GDPR)) and siloed systems. Unlike prior broad IT/e Tourism reviews, this study integrates recent MI advances (spatiotemporal deep learning, real-time decisioning, and human–AI teaming) into a hospitality-specific decision pipeline and outlines a testable research agenda around scalability/drift, privacy-by-design, and controlled field experiments.
Big data in the hospitality industry: a methodical review Omar Jawabreh; Rami Mahmoud; Basel J. A. Ali
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 4: August 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i4.25543

Abstract

This study intends to identify research gaps and future trends and provide a framework for the next generation of research to assess how much big data (BD) is employed in hospitality and tourist research. The study is based on a comprehensive quantitative evaluation of the relevant literature: Scopus and Web of Science (WoS)-listed academic works. The following criteria were used to assess the submissions: those who have the following traits an overview of the study’s subject matter, including its theoretical and conceptual framework, data sources, data kind and quantity, data collection methods, and data analysis methodologies. Research shows that the usage of books on hospitality and tourism management has increased in recent years. Massive volumes of data are analyzed using analytical methods. However, the scope of this investigation is really wide. Furthermore, this research contributes to an in-depth and systematic assessment of the extent to which scholars in hospitality/tourism know and work on business intelligence and BD. This is the first complete survey of the literature on the topic of hospitality and tourism.
Co-Authors Basel J. A. Ali Ehab Abdul Raheem Alshatnawi Omar Jawabreh Omar Jawabreh