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Multilevel Investigation of Higher Tourism Education Service Quality in the Digital Era

Chai Ching Tan et al.

Tourism: An International Interdisciplinary Journal2025https://doi.org/10.37741/t.73.3.5article
AJG 1ABDC B
Weight
0.41

What the paper says

Digital transformation poses significant challenges for higher education institutions in tourism education, where technological integration is crucial. Leveraging the technology affordance theory lens, a multilevel framework posits the concepts of substitution, augmentation, modification, and redefinition (SAMR) as key principles. This model represents a progressive and transformative approach that educators can utilize through digital tools to enhance the quality of educational services by making strategic investments aligned with the needs of digital transformation. Data was collected from 209 faculty members and 2,466 corresponding students across seven Egyptian tourism colleges. Multilevel structural equation modelling results revealed that digital transformation requirements have a positive influence on the enhancement and transformation stages of SAMR, thereby improving educational service quality. Findings demonstrate how institutional requirements shape faculty members' actualization of technological affordances across the SAMR stages, progressing from essential substitution to transformative redefinition.

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https://doi.org/https://doi.org/10.37741/t.73.3.5

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@article{chai2025,
  title        = {{Multilevel Investigation of Higher Tourism Education Service Quality in the Digital Era}},
  author       = {Chai Ching Tan et al.},
  journal      = {Tourism: An International Interdisciplinary Journal},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.37741/t.73.3.5},
}

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Multilevel Investigation of Higher Tourism Education Service Quality in the Digital Era

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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.