Travelling with open eyes! A study to measure consumers’ intention towards experiencing immersive technologies at tourism destinations by using an integrated model of TPB, TAM captured through the lens of S-O-R
Bibliographic record
Abstract
Purpose Immersive technologies fully immerse users in augmented environments for interactive experiences. The purpose of this study is to measure consumers’ intention towards experiencing immersive technologies at tourism destinations using an integrated theory of planned behaviour (TPB) and technology acceptance model (TAM) model within the stimulus-organism-response (S-O-R) framework, including motivation (MOT), trust (TR) and perceived risk (PR). Design/methodology/approach The survey data was collected through convenience sampling via an online questionnaire, with a sample size of 487 Indians. Structural equation modelling was conducted using SPSS and AMOS software for data analysis, ensuring a robust examination of the proposed model and its relationships. Findings Virtual interactivity and social interaction influence both attitude and perceived behavioural control. Attitude, perceived behavioural control, perceived usefulness and TR significantly influence intention. However, MOT, PR and perceived ease of use do not exhibit a significant influence on intention. These findings highlight the importance of these variables in shaping consumers’ intention towards experiencing immersive technologies at tourism destinations. Research limitations/implications The findings hold significant implications for various stakeholders, including government agencies, travel firms, content creators and software developers. They can leverage these insights to enhance marketing strategies, develop immersive tourism experiences, innovate in the realm of Web 4.0 and personalize tourism offerings. Originality/value This study offers a distinctive contribution by integrating the S-O-R framework with TPB and TAM, while also incorporating key factors such as MOT, TR and PR. This novel approach provides a fresh perspective on consumer behaviour towards immersive technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".