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Determinants of Users' Intentions to Use AI-Enabled Technological Innovations in Hotels: A hybrid approach using PLS-SEM and fsQCA

Yıl 2024, Latest Articles

Abraham Terrah Faizan Ali Ghazanfar Ali Abbasi Seden Doğan Cihan Cobanoglu

https://doi.org/10.30519/ahtr.1392494

Öz

This study investigates the factors influencing hotel guests' intentions to adopt next-generation technologies enabled by artificial intelligence (AI). Both affective and cognitive processes, which led to guests' intentions to adopt these new technologies, were considered to have antecedents in the form of intrinsic and extrinsic motives, respectively. The data collected from 331 respondents were analyzed using a combination of methods, including the asymmetrical fuzzy set qualitative comparative analysis (fsQCA) and the symmetrical partial least square-structural equation modeling (PLS-SEM). The results of the symmetrical study indicated that novelty and compatibility have a good impact on both enjoyment and usefulness, which ultimately lead to behavioral intentions. In contrast, asymmetrical studies have shown that all the criteria are necessary conditions to produce users' intention to embrace AI-based technology. By integrating IDT and TAM, this study extends the comprehension of factors driving customers to use AI-enabled technologies during their hotel stays. This study also adds to the existing literature by exploring configurational modeling with fsQCA, as opposed to prior studies that have relied on net impact modeling via SEM.

Anahtar Kelimeler

Novelty Compatibility Usefulness Enjoyment Next-Gen Technologies

Kaynakça

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  • Alalwan, A. A., Baabdullah, A. M., Rana, N. P., Tamilmani, K., & Dwivedi, Y. K. (2018). Examining adoption of mobile internet in Saudi Arabia: Extending TAM with perceived enjoyment, innovativeness, and trust. Technology in Society, 55, 100-11.
  • Ali, F., Kim, W. G., Li, J. J., & Cobanoglu, C. (2018). A comparative study of covariance and partial least squares based structural equation modeling in hospitality and tourism research. International Journal of Contemporary Hospitality Management, 30(1), 416-435.
  • Ali, F., Turktarhan, G., Chen, X., & Ali, M. (2023). Antecedents of destination advocacy using symmetrical and asymmetrical modeling techniques. The Service Industries Journal, 43(7-8), 475-496.
  • Ali, F., Terrah, A., Wu, C., Ali, L., & Wu, H. (2021). Antecedents and consequences of user engagement in smartphone travel apps. Journal of Hospitality and Tourism Technology, 12(2), 335-371.
  • Baccarella, C. V., Wagner, T. F., Scheiner, C. W., Maier, L., & Voigt, K. I. (2021). Investigating consumer acceptance of autonomous technologies: the case of self-driving automobiles. European Journal of Innovation Management, 24(4), 1210-1232.
  • Bilgihan, A., Smith, S., Ricci, P., & Bujisic, M. (2016). Hotel guest preferences of in-room technology amenities. Journal of Hospitality and Tourism Technology, 7(2), 118-134.
  • Cai, R., Cain, L. N., & Jeon, H. (2022). Customers' perceptions of hotel AI-enabled voice assistants: does brand matter? International Journal of Contemporary Hospitality Management, 34(8), 2907-2831.
  • Cha, S. (2020). Customers' intention to use robot-serviced restaurants in Korea: relationship of coolness and MCI factors. International Journal of Contemporary Hospitality Management, 32(9), 2947-2968.
  • Chang, K. C., Chen, M. C., Hsu, C. L., & Kuo, N. T. (2012). Integrating loss aversion into a technology acceptance model to assess the relationship between website quality and website user's Behavioral intentions. Total Quality Management & Business Excellence, 23(7-8), 913-93.
  • Chang, Y. W., & Chen, J. (2021). What motivates customers to shop in smart shops? The impacts of smart technology and technology readiness. Journal of Retailing and Consumer Services, 58, 102325.
  • Chau, P. Y., & Hu, P. J. H. (2001). Information technology acceptance by individual professionals: A model comparison approach. Decision sciences, 32(4), 699-719.
  • Chen, C. H., Wu, M. C., & Wang, C. C. (2019). Cloud-based Dialog Navigation Agent System for Service Robots. Sensors and Materials, 31(6), 1871-1891.
  • Chen, M. Y., Lughofer, E. D. & Hsiao, K. L. (2013). Android smartphone adoption and intention to pay for mobile internet. Library Hi Tech, 31(2), 216-235.
  • Cheng, Y. M. (2015). Towards an understanding of the factors affecting m-learning acceptance: Roles of technological characteristics and compatibility. Asia Pacific Management Review, 20(3), 109-119.
  • Chiu, W., & Cho, H. (2020). The role of technology readiness in individuals' intention to use health and fitness applications: a comparison between users and non-users. Asia Pacific Journal of Marketing and Logistics, 33(3), 807-825
  • Choi, Y., Choi, M., Oh, M., & Kim, S. (2020). Service robots in hotels: understanding the service quality perceptions of human-robot interaction. Journal of Hospitality Marketing & Management, 29(6), 613-635.
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  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace 1. Journal of Applied Social Psychology, 22(14), 1111-1132.
  • El-Manstrly, D., Ali, F., & Steedman, C. (2020). Virtual travel community members' stickiness behaviour: how and when it develops. International Journal of Hospitality Management, 88, 102535.
  • Fichman, R. G., & Kemerer, C. F. (1993). Adoption of software engineering process innovations: The case of object orientation. Sloan management review, 34, 7-7.
  • Flavian, C., Guinaliu, M., & Lu, Y. (2020). Mobile payments adoption–introducing mindfulness to better understand consumer behavior. International Journal of Bank Marketing, 38(7), 1575-1599.
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  • Han, J., & Conti, D. (2020). The use of UTAUT and post acceptance models to investigate the attitude towards a telepresence robot in an educational setting. Robotics, 9(2), 34.
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  • Ifinedo, P. (2017). Students' perceived impact of learning and satisfaction with blogs. The International Journal of Information and Learning Technology, 34(4), 322-337.
  • Im, S., Bhat, S., & Lee, Y. (2015). Consumer perceptions of product creativity, coolness, value, and attitude. Journal of Business Research, 68(1), 166-172.
  • Ivanov, S., & Webster, C. (2024). Automated decision-making: Hoteliers’ perceptions. Technology in Society, 76, 10243.
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  • Kanchanatanee, K., Suwanno, N., & Jarernvongrayab, A. (2014). Effects of attitude toward using, perceived usefulness, perceived ease of use and perceived compatibility on intention to use E-marketing. Journal of Management Research, 6(3), 1-13.
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  • King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740-755.
  • Koenig-Lewis, N., Marquet, M., Palmer, A., & Zhao, A. L. (2015). Enjoyment and social influence: predicting mobile payment adoption. The Service Industries Journal, 35(10), 537-554.
  • Kristi, K. M., & Kusumawati, N. (2021). Technology Acceptance and Customer Perception of Augmented Reality (AR) in Indonesian Beauty Industry. In ICE-BEES 2020: Proceedings of the 3rd International Conference on Economics, Business and Economic Education Science, ICE-BEES 2020, 22-23 July 2020, Semarang, Indonesia (p. 134). European Alliance for Innovation.
  • Kucukusta, D., Law, R., Besbes, A., & Legoherel, P. (2015). Re-examining perceived usefulness and ease of use in online booking: The case of Hong Kong online users. International Journal of Contemporary Hospitality Management, 27(2), 185–198.
  • Kumar, S., Sahoo, S., Ali, F., & Cobanoglu, C. (2023). Rise of fsQCA in tourism and hospitality research: a systematic literature review. International Journal of Contemporary Hospitality Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJCHM-03-2023-0288
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Yıl 2024, Latest Articles

Abraham Terrah Faizan Ali Ghazanfar Ali Abbasi Seden Doğan Cihan Cobanoglu

https://doi.org/10.30519/ahtr.1392494

Öz

Kaynakça

  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211
  • Alalwan, A. A., Baabdullah, A. M., Rana, N. P., Tamilmani, K., & Dwivedi, Y. K. (2018). Examining adoption of mobile internet in Saudi Arabia: Extending TAM with perceived enjoyment, innovativeness, and trust. Technology in Society, 55, 100-11.
  • Ali, F., Kim, W. G., Li, J. J., & Cobanoglu, C. (2018). A comparative study of covariance and partial least squares based structural equation modeling in hospitality and tourism research. International Journal of Contemporary Hospitality Management, 30(1), 416-435.
  • Ali, F., Turktarhan, G., Chen, X., & Ali, M. (2023). Antecedents of destination advocacy using symmetrical and asymmetrical modeling techniques. The Service Industries Journal, 43(7-8), 475-496.
  • Ali, F., Terrah, A., Wu, C., Ali, L., & Wu, H. (2021). Antecedents and consequences of user engagement in smartphone travel apps. Journal of Hospitality and Tourism Technology, 12(2), 335-371.
  • Baccarella, C. V., Wagner, T. F., Scheiner, C. W., Maier, L., & Voigt, K. I. (2021). Investigating consumer acceptance of autonomous technologies: the case of self-driving automobiles. European Journal of Innovation Management, 24(4), 1210-1232.
  • Bilgihan, A., Smith, S., Ricci, P., & Bujisic, M. (2016). Hotel guest preferences of in-room technology amenities. Journal of Hospitality and Tourism Technology, 7(2), 118-134.
  • Cai, R., Cain, L. N., & Jeon, H. (2022). Customers' perceptions of hotel AI-enabled voice assistants: does brand matter? International Journal of Contemporary Hospitality Management, 34(8), 2907-2831.
  • Cha, S. (2020). Customers' intention to use robot-serviced restaurants in Korea: relationship of coolness and MCI factors. International Journal of Contemporary Hospitality Management, 32(9), 2947-2968.
  • Chang, K. C., Chen, M. C., Hsu, C. L., & Kuo, N. T. (2012). Integrating loss aversion into a technology acceptance model to assess the relationship between website quality and website user's Behavioral intentions. Total Quality Management & Business Excellence, 23(7-8), 913-93.
  • Chang, Y. W., & Chen, J. (2021). What motivates customers to shop in smart shops? The impacts of smart technology and technology readiness. Journal of Retailing and Consumer Services, 58, 102325.
  • Chau, P. Y., & Hu, P. J. H. (2001). Information technology acceptance by individual professionals: A model comparison approach. Decision sciences, 32(4), 699-719.
  • Chen, C. H., Wu, M. C., & Wang, C. C. (2019). Cloud-based Dialog Navigation Agent System for Service Robots. Sensors and Materials, 31(6), 1871-1891.
  • Chen, M. Y., Lughofer, E. D. & Hsiao, K. L. (2013). Android smartphone adoption and intention to pay for mobile internet. Library Hi Tech, 31(2), 216-235.
  • Cheng, Y. M. (2015). Towards an understanding of the factors affecting m-learning acceptance: Roles of technological characteristics and compatibility. Asia Pacific Management Review, 20(3), 109-119.
  • Chiu, W., & Cho, H. (2020). The role of technology readiness in individuals' intention to use health and fitness applications: a comparison between users and non-users. Asia Pacific Journal of Marketing and Logistics, 33(3), 807-825
  • Choi, Y., Choi, M., Oh, M., & Kim, S. (2020). Service robots in hotels: understanding the service quality perceptions of human-robot interaction. Journal of Hospitality Marketing & Management, 29(6), 613-635.
  • Cobanoglu, C., Yang, W., Shatskikh, A., & Agarwal, A. (2015). Are consumers ready for mobile payment? An examination of consumer acceptance of mobile payment technology in restaurant industry. Hospitality Review, 31(4), 6.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-34.
  • 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), 982–1002. doi:1.1287/mnsc.35.8.982
  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace 1. Journal of Applied Social Psychology, 22(14), 1111-1132.
  • El-Manstrly, D., Ali, F., & Steedman, C. (2020). Virtual travel community members' stickiness behaviour: how and when it develops. International Journal of Hospitality Management, 88, 102535.
  • Fichman, R. G., & Kemerer, C. F. (1993). Adoption of software engineering process innovations: The case of object orientation. Sloan management review, 34, 7-7.
  • Flavian, C., Guinaliu, M., & Lu, Y. (2020). Mobile payments adoption–introducing mindfulness to better understand consumer behavior. International Journal of Bank Marketing, 38(7), 1575-1599.
  • Goel, P., Kaushik, N., Sivathanu, B., Pillai, R., & Vikas, J. (2022). Consumers' adoption of artificial intelligence and robotics in hospitality and tourism sector: literature review and future research agenda. Tourism Review, 77(4), 1081-1096.
  • Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236.
  • Han, J., & Conti, D. (2020). The use of UTAUT and post acceptance models to investigate the attitude towards a telepresence robot in an educational setting. Robotics, 9(2), 34.
  • Hanafizadeh, P., Keating, B. W., & Khedmatgozar, H. R. (2014). A systematic review of Internet banking adoption. Telematics and Informatics, 31(3), 492-51.
  • Holdack, E., Lurie-Stoyanov, K., & Fromme, H. F. (2020). The role of perceived enjoyment and perceived informativeness in assessing the acceptance of AR wearables. Journal of Retailing and Consumer Services, 65, 1-11.
  • Ifinedo, P. (2017). Students' perceived impact of learning and satisfaction with blogs. The International Journal of Information and Learning Technology, 34(4), 322-337.
  • Im, S., Bhat, S., & Lee, Y. (2015). Consumer perceptions of product creativity, coolness, value, and attitude. Journal of Business Research, 68(1), 166-172.
  • Ivanov, S., & Webster, C. (2024). Automated decision-making: Hoteliers’ perceptions. Technology in Society, 76, 10243.
  • Ivanov, S. H., Webster, C., & Berezina, K. (2017). Adoption of robots and service automation by tourism and hospitality companies. Revista Turismo & Desenvolvimento, 27(28), 1501-1517.
  • Kanchanatanee, K., Suwanno, N., & Jarernvongrayab, A. (2014). Effects of attitude toward using, perceived usefulness, perceived ease of use and perceived compatibility on intention to use E-marketing. Journal of Management Research, 6(3), 1-13.
  • Kim, M., & Qu, H. (2014). Travelers' behavioral intention toward hotel self-service kiosks usage. International Journal of Contemporary Hospitality Management, 26(2), 225-245.
  • King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740-755.
  • Koenig-Lewis, N., Marquet, M., Palmer, A., & Zhao, A. L. (2015). Enjoyment and social influence: predicting mobile payment adoption. The Service Industries Journal, 35(10), 537-554.
  • Kristi, K. M., & Kusumawati, N. (2021). Technology Acceptance and Customer Perception of Augmented Reality (AR) in Indonesian Beauty Industry. In ICE-BEES 2020: Proceedings of the 3rd International Conference on Economics, Business and Economic Education Science, ICE-BEES 2020, 22-23 July 2020, Semarang, Indonesia (p. 134). European Alliance for Innovation.
  • Kucukusta, D., Law, R., Besbes, A., & Legoherel, P. (2015). Re-examining perceived usefulness and ease of use in online booking: The case of Hong Kong online users. International Journal of Contemporary Hospitality Management, 27(2), 185–198.
  • Kumar, S., Sahoo, S., Ali, F., & Cobanoglu, C. (2023). Rise of fsQCA in tourism and hospitality research: a systematic literature review. International Journal of Contemporary Hospitality Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJCHM-03-2023-0288
  • Lai, J. Y., & Ulhas, K. R. (2012). Understanding acceptance of dedicated e‐textbook applications for learning: Involving Taiwanese university students. The Electronic Library, 30(3), 321-338.
  • Lai, P. C. (2016) Design and Security impact on consumers' intention to use single platform E-payment. Interdisciplinary Information Sciences, 22(1), 111-122.
  • Law, R., Lin, K. J., Ye, H., & Fong, D. K. C. (2023). Artificial intelligence research in hospitality: a state-of-the-art review and future directions. International Journal of Contemporary Hospitality Management. https://doi.org/1.1108/IJCHM-02-2023-0189
  • Lee, K. T., & Koo, D. M. (2015). Evaluating right versus just evaluating online consumer reviews. Computers in Human Behavior, 45, 316-327.
  • Lin, S. F., Chang, W. H., & Cheng, Y. J. (2011). The Perceived Usefulness of Teachers’ Guides for Science Teachers. International Journal of Science and Mathematics Education, 9, 1367-1389.
  • Lin, C. H., & Yu, S.-F. (2006). Consumer adoption of the internet as a channel: The influence of driving and inhibiting factors. Journal of American Academy of Business, 9(2), 112–117.
  • Loureiro, S. M. C., Ali, F., & Ali, M. (2024). Symmetric and asymmetric modeling to understand drivers and consequences of hotel chatbot engagement. International Journal of Human–Computer Interaction, 40(3), 782-794.
  • Mercan, S., Cain, L., Akkaya, K., Cebe, M., Uluagac, S., Alonso, M., & Cobanoglu, C. (2020). Improving the service industry with hyper-connectivity: IoT in hospitality. International Journal of Contemporary Hospitality Management, 33(1), 243-262.
  • Merikivi, J., Tuunainen, V., & Nguyen, D. (2017). What makes continued mobile gaming enjoyable? Computers in Human Behavior, 68, 411-421.
  • Mills, M. (2018). There are robots on staff at this San Gabriel hotel. San Gabriel Valley Tribune. Retrieved August 31, 2022, from https://www.sgvtribune.com/2018/08/28/there-are-robots-on-staff-at-this-san-gabriel-hotel/
  • Mitas, O., & Bastiaansen, M. (2018). Novelty: A mechanism of tourists' enjoyment. Annals of Tourism Research, 72, 98-108.
  • Mohammadi, H. (2015). A study of mobile banking loyalty in Iran. Computers in Human Behavior, 44, 35-47.
  • Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information & management, 38(4), 217-23.
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  • Okumus, B., & Bilgihan, A. (2014). Proposing a model to test smartphone users' intention to use smart applications when ordering food in restaurants. Journal of Hospitality and Tourism Technology, 5(1), 31-49.
  • Ozturk, A. B., Bilgihan, A., Nusair, K., & Okumus, F. (2016). What keeps the mobile hotel booking users loyal? Investigating the roles of self-efficacy, compatibility, perceived ease of use, and perceived convenience. International Journal of Information Management, 36(6), 1350-1359.
  • Pappas, I. O., & Woodside, A. G. (2021). Fuzzy-set Qualitative Comparative Analysis (fsQCA): Guidelines for research practice in Information Systems and marketing. International Journal of Information Management, 58, 10231.
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  • Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sciences, 39(2), 273-315.
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  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3) 425-478.
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Toplam 82 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Turizm (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Abraham Terrah 0000-0002-0300-562X

Faizan Ali 0000-0003-4528-3764

Ghazanfar Ali Abbasi 0000-0003-0748-8996

Seden Doğan 0000-0001-8547-7702

Cihan Cobanoglu 0000-0001-9556-6223

Erken Görünüm Tarihi 8 Mayıs 2024
Yayımlanma Tarihi
Gönderilme Tarihi 9 Aralık 2023
Kabul Tarihi 5 Mart 2024
Yayımlandığı Sayı Yıl 2024 Latest Articles

Kaynak Göster

APA Terrah, A., Ali, F., Abbasi, G. A., Doğan, S., vd. (2024). Determinants of Users’ Intentions to Use AI-Enabled Technological Innovations in Hotels: A hybrid approach using PLS-SEM and fsQCA. Advances in Hospitality and Tourism Research (AHTR). https://doi.org/10.30519/ahtr.1392494


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