Exploring the impact of virtual reality user engagement on tourist behavioral response integrated an environment concern of touristic travel perspective: A new hybrid machine learning approach
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866914974488592384 |
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| author | Shang, D. W. |
| author_facet | Shang, D. W. |
| contents | Due to the impact of the COVID-19 pandemic, new attractions ways are tended to be adapted by compelling sites to provide tours product and services, such as virtual reality (VR) to visitors. Based on a systematic human-computer interaction (HCI) user engagement and Narrative transportation theory, we develop and test a theoretical framework using a hybrid partial least squares structural equation model (PLS-SEM) and artificial neural network (ANN) machine learning approach that examines key user engagement drivers of visitors' imagery and in-person tour intentions (ITI) during COVID-19. Further, we proposed a novel and hybrid approach called Reflective and Formative PLS-SEM-ANN (FRPSA) with considering both reflective and second-order formative constructs in PLS-SEM giving scope to their different advantages in a complex model. According to a sample of visitors' responses, the results demonstrate that a) user engagement, including felt involvement, aesthetic appeal, perceived usability, focused attention, endurability, and novelty, all directly affect in-person tour intentions; b) environment concern of touristic travel (EC) positively moderates the relationships between user engagement and ITI; c) EC negatively moderates the relationships between imagery and ITI; d) imagery exerts the mediating effect between user engagement and ITI; e) the felt involvement and aesthetic appeal show both the linear significance impact and nonlinear importance. Finally, contributions to theories and practical implications are discussed accordingly. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12223 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring the impact of virtual reality user engagement on tourist behavioral response integrated an environment concern of touristic travel perspective: A new hybrid machine learning approach Shang, D. W. Human-Computer Interaction Machine Learning Due to the impact of the COVID-19 pandemic, new attractions ways are tended to be adapted by compelling sites to provide tours product and services, such as virtual reality (VR) to visitors. Based on a systematic human-computer interaction (HCI) user engagement and Narrative transportation theory, we develop and test a theoretical framework using a hybrid partial least squares structural equation model (PLS-SEM) and artificial neural network (ANN) machine learning approach that examines key user engagement drivers of visitors' imagery and in-person tour intentions (ITI) during COVID-19. Further, we proposed a novel and hybrid approach called Reflective and Formative PLS-SEM-ANN (FRPSA) with considering both reflective and second-order formative constructs in PLS-SEM giving scope to their different advantages in a complex model. According to a sample of visitors' responses, the results demonstrate that a) user engagement, including felt involvement, aesthetic appeal, perceived usability, focused attention, endurability, and novelty, all directly affect in-person tour intentions; b) environment concern of touristic travel (EC) positively moderates the relationships between user engagement and ITI; c) EC negatively moderates the relationships between imagery and ITI; d) imagery exerts the mediating effect between user engagement and ITI; e) the felt involvement and aesthetic appeal show both the linear significance impact and nonlinear importance. Finally, contributions to theories and practical implications are discussed accordingly. |
| title | Exploring the impact of virtual reality user engagement on tourist behavioral response integrated an environment concern of touristic travel perspective: A new hybrid machine learning approach |
| topic | Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2410.12223 |