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Employing Experience Portfolio Theoryin AI-Driven Hospitality Businesses

Mohammad Shahidul Islam et al.

Tourism: An International Interdisciplinary Journal2025https://doi.org/10.37741/t.73.2.10article
AJG 1ABDC B
Weight
0.41

What the paper says

This paper seeks to identify gaps in the existing literature and provide a unique perspective on experience portfolio theory (EPT) framework development within artificial intelligence (AI)-driven hospitality context. An interpretive conceptual research technique was employed to examine the current literature and propose EPT in the AI-driven hospitality context. Inspired by comparative research focused on guest experiences, this methodological approach recognizes knowledge as socially produced. Findings proved that AI-driven hospitality values benefit significantly from the EPT’s focus on ensuring that guest experiences are consistent with their expectations, which improves accommodation practices that guests find more satisfying and pleasurable. This paper highlights the EPT framework’s potential analytics and benefits in understanding guest motives and enhancing their experiences in AI-driven hospitality settings. By acknowledging heterogeneous characters of guest encounters and welcoming hedonic/utilitarian aspects, EPT provides more all-encompassing and balanced perspectives for hospitality professionals to craft enjoyable and memorable accommodations for their guests.

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https://doi.org/https://doi.org/10.37741/t.73.2.10

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@article{mohammad2025,
  title        = {{Employing Experience Portfolio Theoryin AI-Driven Hospitality Businesses}},
  author       = {Mohammad Shahidul Islam et al.},
  journal      = {Tourism: An International Interdisciplinary Journal},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.37741/t.73.2.10},
}

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Employing Experience Portfolio Theoryin AI-Driven Hospitality Businesses

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Evidence weight

0.41

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

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 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.