Intention to Use Mobile Augmented Reality in the Tourism Sector Article Swipe
Malik Khlaif Gharaibeh
,
Natheer K. Gharaibeh
,
Mohammad Ayoub Khan
,
Waleed Abu-Ain
,
Musab Kasim Alqudah
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.32604/csse.2021.014902
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.32604/csse.2021.014902
This article examines the main variables that influence the intention to use Augmented Reality (AR) applications in the tourism sector in Jordan. The study model has been constructed based on the unified theory of acceptance and the use of technology2 (UTAUT2), by incorporating a new construct (aesthetics) to explore the usage intention of Mobile Augmented Reality in Tourism (MART). A questionnaire was used and distributed to a sample of 450 participants. Data were analyzed using the Smart PLS version 3.0. for testing 12 hypotheses. 29 measurement items were carefully reviewed based on previous studies that were selected to assess the research hypotheses. The findings revealed that the proposed model elucidates 35.7% of the variance in the users’ intention to use MART. The results also showed that both performance expectancy and aesthetics were found to be the most significant factors at level (0.001). Four variables, respectively, were at level (0.01) which consisted of social influence, facilitating conditions, hedonic motivation, and price value. The weakest effect was effort expectancy at level (0.05). As the use of AR has become important for tourists, this study establishes a research base that can be built upon for future researchers. MART developers can benefit from the results of this research to design and deliver this service successfully and to ensure that its adoption by users is achieved.
Related Topics
Concepts
Expectancy theory
Tourism
Unified theory of acceptance and use of technology
Augmented reality
Variance (accounting)
Sample (material)
Construct (python library)
Psychology
Structural equation modeling
Service (business)
Computer science
Marketing
Applied psychology
Social psychology
Business
Human–computer interaction
Geography
Machine learning
Programming language
Chromatography
Accounting
Chemistry
Archaeology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32604/csse.2021.014902
- https://file.techscience.com/ueditor/files/TSP_CSSE-37-2/TSP_CSSE_14902/TSP_CSSE_14902.pdf
- OA Status
- diamond
- Cited By
- 27
- References
- 82
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4322722105
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4322722105Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.32604/csse.2021.014902Digital Object Identifier
- Title
-
Intention to Use Mobile Augmented Reality in the Tourism SectorWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Malik Khlaif Gharaibeh, Natheer K. Gharaibeh, Mohammad Ayoub Khan, Waleed Abu-Ain, Musab Kasim AlqudahList of authors in order
- Landing page
-
https://doi.org/10.32604/csse.2021.014902Publisher landing page
- PDF URL
-
https://file.techscience.com/ueditor/files/TSP_CSSE-37-2/TSP_CSSE_14902/TSP_CSSE_14902.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://file.techscience.com/ueditor/files/TSP_CSSE-37-2/TSP_CSSE_14902/TSP_CSSE_14902.pdfDirect OA link when available
- Concepts
-
Expectancy theory, Tourism, Unified theory of acceptance and use of technology, Augmented reality, Variance (accounting), Sample (material), Construct (python library), Psychology, Structural equation modeling, Service (business), Computer science, Marketing, Applied psychology, Social psychology, Business, Human–computer interaction, Geography, Machine learning, Programming language, Chromatography, Accounting, Chemistry, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
27Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 6, 2023: 6, 2022: 10, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
82Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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