Description
Effects of technological readiness and acceptance on guest satisfaction and recommendation intention in hotel industry
Research approach and objective: The tourism and hospitality sectors are digitally transforming their services (Flandrin et al., 2021). Hotel managers must know the right time, how and where to apply technologies and, above all, recognize changes in the consumption and behaviour of the guests (Kansakar et al., 2019; Tavitiyaman, 2022). The attitude towards technology has several factors, such as technological readiness (Parasuraman, 2000), affinity towards technology (Edison & Geissler, 2003), the influence of external factors, such as social relationships (Patel & Patel, 2018), and usefulness perception (Kim, 2016; Davis, 1989). Our research adopts the Technology Acceptance Model (TAM) of Davis (1989) as the theoretical approach, but we propose an extension and adaptation to the hotel sector. Thus, our study has twofold objectives: First, it verifies the effects of guests' technological expectations and readiness and social influence on hotel acceptance of new technology. Second, it confirms how technological acceptance influences guest satisfaction and intention to recommend.
Methodology: From the literature review we proposed a model and validated it based on partial least squares path modeling or partial least squares structural equation modeling (PLS-SEM). Data collection was performed by online questionnaires from June to September 2023. The sample size was 547 Portuguese hotel guests. We propose an update of the TAM (Figure 1) considering three aspects: 1) we updated the dimension measurement scales to reflect the current technological hospitality context; 2) we included new dimensions related to social stimuli, technological readiness and technological expectation since these factors can impact the ability to usefulness perception, ease of use and also produce different effects on the user's attitude; 3) we added a second behavioural response phase that, for the hotel sector, represents the effect of technological acceptance on guest satisfaction and, consequently, on their intention to recommend the hotel.
Figure 1: Extended Technology Acceptance Model to hotels
Main results and findings: The updated proposed scales and adaptation to the hotel sector presented adequate values to guarantee the model's internal reliability and convergent validity. The model also has discriminant validity according to the Fornell Larcker criterion and the Heterotrait-Monotrait ratio of correlations according to Hair et al. (2011) indications. All hypotheses were validated, i.e., the path model was confirmed. Both guests' expectations of finding technologies in hotels and technological guests' readiness significantly affect usefulness perception, technology use, ease of use, and satisfaction with hotel technology. Social influence also affects usefulness perception and ease of use; however, to a lesser extent and with little impact on satisfaction. All variables in the model directly or indirectly affect satisfaction and recommendation intention. The results also indicated that guests expect to find technological facilities at the hotel, even if they are not highly tech prepared.
Implications: The main theoretical implication is validating an updated technological acceptance model for hotels with its measurement scales. This model provides practical implications, as it confirms that new external (social influence) and internal (expectation and preparation) stimuli affect the guest's cognitive and affective response to technology. Therefore, the behavioural response is influenced by more factors than ease of use and has two phases: effective use of technology and loyalty, expressed in the intention to recommend. When guests use the technologies available in hotels, their satisfaction increases. Consequently, they are more likely to share their experiences and recommend the hotel. Findings confirmed the vital interconnection between technology utilization, guest satisfaction, and WoM.
Keywords: Technology acceptance; Technological readiness; Technological expectation; Recommendation intention; Guest satisfaction.
References
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
Edison, S., & Geissler, G. (2003). Measuring attitudes towards general technology: Antecedents, hypotheses and scale development. Journal of Targeting, Measurement and Analysis for Marketing, 12(2), 137-156. https://doi.org/10.1057/palgrave.jt.5740104
Flandrin, P., Hellemans, C., Van Der Linden, J., & Van De Leemput, C. (2021). Smart technologies in hospitality: effects on activity, work design and employment. A case study about chatbot usage. In ACM International Conference Proceeding Series. Association for Computing Machinery. https://doi.org/10.1145/3486812.3486838
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202
Kansakar, P., Munir, A., & Shabani, N. (2019). Technology in the Hospitality Industry: Prospects and Challenges. IEEE Consumer Electronics Magazine, 8(3), 60–65. https://doi.org/10.1109/MCE.2019.2892245
Kim, J. (Sunny). (2016b). An extended technology acceptance model in behavioral intention toward hotel tablet apps with moderating effects of gender and age. International Journal of Contemporary Hospitality Management, 28(8), 1535–1553. https://doi.org/10.1108/IJCHM-06-2015-0289
Tavitiyaman, P., Zhang, X., & Tsang, W. Y. (2022). How Tourists Perceive the Usefulness of Technology Adoption in Hotels: Interaction Effect of Past Experience and Education Level. Journal of China Tourism Research, 18(1), 64–87. https://doi.org/10.1080/19388160.2020.1801546
Patel, K. J., & Patel, H. J. (2018). Adoption of internet banking services in Gujarat. International Journal of Bank Marketing, 36(1), 147–169. https://doi.org/10.1108/ijbm-08-2016-0104
Parasuraman, A. (2000). Technology Readiness Index (Tri). Journal of Service Research, 2(4), 307–320. https://doi.org/10.1177/109467050024001
