The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Leveraging human-robot interaction in hospitality services: Incorporating the role of perceived value, empathy, and information sharing into visitors’ intentions to use social robots
Open accessAbstract
Social robots have become pervasive in the tourism and hospitality service environments. The empirical understanding of the drivers of visitors' intentions to use robots in such services has become an urgent necessity for their sustainable deployment. Certainly, using social androids within hospitality services requires organisations' attentive commitment to value creation and fulfilling service quality expectations. In this paper, via structural equation modelling (SEM) and semi-structured interviews with managers, we conceptualise and empirically test visitors' intentions to use social robots in hospitality services. With data collected in Singapore's hospitality settings, we found visitors' intentions to use social robots stem from the effects of technology acceptance variables, service quality dimensions leading to perceived value, and two further dimensions from human robot interaction (HRI): empathy and information sharing. Analysis of these dimensions' importance provides a deeper understanding of novel opportunities managers may take advantage of to position social robot-delivered services in tourism and hospitality strategies.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003) · Journal of Applied Psychology · doi:10.1037/0021-9010.88.5.879 · Cited by 82,194
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035
SERVQUAL: A multiple-Item Scale for measuring consumer perceptions of service quality
Parasuraman, A., Berry, L. L., & Zeithaml, V. A. (1988) · Zenodo · doi:10.5281/zenodo.14579729 · Cited by 21,118
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012) · MIS Quarterly · doi:10.2307/41410412 · Cited by 15,756
The future of employment: How susceptible are jobs to computerisation?
Frey, C. B., & Osborne, M. A. (2017) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2016.08.019 · Cited by 8,727
Servicescapes: The Impact of Physical Surroundings on Customers and Employees
Bitner, M. J. (1992) · Journal of Marketing · doi:10.2307/1252042 · Cited by 7,271
Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003) · Information Systems Research · doi:10.1287/isre.14.2.189.16018 · Cited by 6,479
Effects of externally mediated rewards on intrinsic motivation.
Deci, E. L. (1971) · Journal of Personality and Social Psychology · doi:10.1037/h0030644 · Cited by 4,170
Artificial intelligence in service
Huang, M.-H., & Rust, R. T. (2018) · Journal of Service Research · doi:10.1177/1094670517752459 · Cited by 3,978
Cited by
Pelau, C., Dabija, D.-C., & Ene, I. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106855 · Cited by 889
Budhwar, P., Malik, A., De Silva, M. T. T., & Thevisuthan, P. (2022) · The International Journal of Human Resource Management · doi:10.1080/09585192.2022.2035161 · Cited by 723
To Err Is Human(-oid): How Do Consumers React to Robot Service Failure and Recovery?
Choi, S., Mattila, A. S., & Bolton, L. E. (2021) · Journal of Service Research · doi:10.1177/1094670520978798 · Cited by 502
Tiwari, C. K., Bhat, M. A., Khan, S. T., Subramaniam, R., & Khan, M. A. I. (2024) · Interactive Technology and Smart Education · doi:10.1108/itse-04-2023-0061 · Cited by 315
Cheng, X., Bao, Y., Zarifis, A., Gong, W., & Mou, J. (2022) · Internet Research · doi:10.1108/intr-08-2020-0460 · Cited by 300
Hotel managers’ perceptions towards the use of robots: A mixed-methods approach
Ivanov, S., Seyitoğlu, F., & Markova, M. (2020) · Information Technology & Tourism · doi:10.1007/s40558-020-00187-x · Cited by 243
Xie, C., Yu, J., Huang, S. (., & Zhang, J. (2022) · Tourism Management · doi:10.1016/j.tourman.2022.104513 · Cited by 231
Hewei, T., & Youngsook, L. (2022) · Entertainment Computing · doi:10.1016/j.entcom.2021.100474 · Cited by 169
Tourist acceptance of ChatGPT in travel services: the mediating role of parasocial interaction
Xu, H., Law, R., Lovett, J., Luo, J. M., & Liu, L. (2024) · Journal of Travel & Tourism Marketing · doi:10.1080/10548408.2024.2364336 · Cited by 130
Creepy vs. cool: Switching from human staff to service robots in the hospitality industry
Kang, S.-E., Koo, C., & Chung, N. (2023) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2023.103479 · Cited by 96
Cheng, X., Qiao, L., Yang, B., & Li, Z. (2024) · Internet Research · doi:10.1108/intr-06-2022-0402 · Cited by 76
When Robot (Vs. Human) Employees Say “Sorry” Following Service Failure
Wang, X., Hwang, Y., & Guchait, P. (2023) · International Journal of Hospitality & Tourism Administration · doi:10.1080/15256480.2021.2017812 · Cited by 60
Driving human–robot value co-creation in hospitality: the role of artificial empathy
Zhu, T., Xie, L., & Liu, C. (2025) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2024.104075 · Cited by 27
Examining Humanized Bot Interaction for the Retail Revenue Growth
Kumar, N., Garg, P., Gupta, N., & Upreti, K. (2026) · Journal of Revenue and Pricing Management · doi:10.1057/s41272-025-00549-2 · Cited by 3
Metadata
- Authors
- de Kervenoael, R., Hasan, R., Schwob, A., & Goh, E.
- Venue
- Tourism Management
- Publisher
- Elsevier BV
- Published
- 2020-06
- Volume
- 78
- Pages
- 104042
- ISSN
- 0261-5177 (ISSN-L)
- OpenAlex
- W2991493714
- Cited by
- 610
- References
- 20
- FWCI
- 19.92
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.