Driving Innovation: Exploring What Influences Restaurant Customers' Perception and Adoption of Service Robots in Chengdu, China
Keywords:
Service Robot, Perceived Ease of Use, Perceived Usefulness, Social Image, Intention to UseAbstract
Purpose: This paper investigates the crucial factors of service robots’ significant impact on restaurant customers’ perceived usefulness and intention to use them in Chengdu, China. The conceptual framework provided cause-and-effect correlations between perceived ease of use, perceived usefulness, social image, ability, anthropomorphism, autonomy, and intention to use. Research design, data, and methodology: Restaurant customers in Chengdu, China’s national center city, were given the questionnaire by the researcher using a quantitative method (n=500). Non-probability sampling techniques encompassed judgmental sampling, which was used to choose four hot pot and Sichuan cuisine restaurants; quota sampling, which defined the sample size; and convenience sampling, which was used to gather data and send questionnaires online. The investigator carried out the data analysis, including model fit, reliability, and construct validity, using structural equation modeling (SEM) and confirmatory factor analysis. Results: The findings indicated that each exogenous variable had a significant effect on the corresponding endogenous variable, with Perceived usefulness providing the greatest consequence on Intention to use. Perceived ease of use, social image, Ability, Anthropomorphism, and Autonomy used Perceived usefulness as an intermediate variable to influence restaurant customer’s Intention to use. Conclusions: Organizations, managers, and stakeholders in service robots must focus more on perceived ease of use, usefulness, social image, ability, anthropomorphism, and autonomy using automated systems, which enhance the Intention to use robotic restaurants.
References
Abou-Shouk, M., Gad, H. E., & Abdelhakim, A. (2021). Exploring customers’ attitudes to the adoption of robots in tourism and hospitality. Journal of Hospitality and Tourism Technology, 12(4), 762-776. https://doi.org/10.1108/jhtt-09-2020-0215
Bartneck, C., Kulić, D., Croft, E., & Zoghbi, S. (2009). Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. International Journal of Social Robotics, 1(1), 71-81. https://doi.org/10.1007/s12369-008-0001-3
Blut, M., Wang, C., Wünderlich, N. V., & Brock, C. (2021). Understanding anthropomorphism in service provision: A meta-analysis of physical robots, chatbots, and other AI. Journal of the Academy of Marketing Science, 49(4), 632-658. https://doi.org/10.1007/s11747-020-00762-y
Bowen, J., & Morosan, C. (2018). Beware hospitality industry: The robots are coming. Worldwide Hospitality and Tourism Themes, 10(6), 726-733. https://doi.org/10.1108/whatt-07-2018-0045
Breazeal, C. (2003). Toward sociable robots. Robotics & Autonomous Systems, 42(3), 167-175.
https://doi.org/10.1016/s0921-8890(02)00373-1
Carroll, N. (2022, February 4). Behind the scenes at the Winter Olympics: Journalists in a bubble, robots making cocktails. USA TODAY. https://eu.usatoday.com/story/opinion/2022/02/04/winter-olympics-beijing-journalist-bubble-robots/6641663001/
Chuah, H. W., & Yu, J. (2021). The future of service: The power of emotion in human-robot interaction. Journal of Retailing and Consumer Services, 61(3), 102551. https://doi.org/10.1016/j.jretconser.2021.102551
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
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
Epley, N., Waytz, A., & Cacioppo, J. T. (2007). On seeing human: A three-factor theory of anthropomorphism. Psychological Review, 114(4), 864-886. https://doi.org/10.1037/0033-295x.114.4.864
Ezer, N., Fisk, A. D., & Rogers, W. A. (2009). Attitudinal and intentional acceptance of domestic robots by younger and older adults. In Universal Access in Human-Computer Interaction, 2(1), 240-249. https://doi.org/10.1007/978-3-642-02710-9_5
Fazal-E-Hasan, S. M., Amrollahi, A., Mortimer, G., Adapa, S., & Balaji, M. S. (2020). A multi-method approach to examining consumer intentions to use smart retail technology. Computers in Human Behavior, 117, 106622. https://doi.org/10.1016/j.chb.2020.106622
Gao, L., Li, G., Tsai, F., Gao, C., Zhu, M., & Qu, X. (2023). The impact of artificial intelligence stimuli on customer engagement and value co-creation: The moderating role of customer ability readiness. Journal of Research in Interactive Marketing, 17(2), 317-333. https://doi.org/10.1108/jrim-10-2021-0260
Guan, X., Gong, J., Li, M., & Huan, T.-C. (2022). Exploring key factors influencing customer behavioral intention in robot restaurants. International Journal of Contemporary Hospitality Management, 34(9), 3482-3501. https://doi.org/10.1108/ijchm-06-2021-0807
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2006). Multivariate data analysis (6th ed.). Pearson.
Hartog, D. N., & Verburg, R. M. (2010). High-performance work systems, organizational culture, and firm effectiveness. Human Resource Management Journal, 14(1), 55-78.
https://doi.org/10.1111/j.1748-8583.2004.tb00112.x
Heerink, M., Kröse, B., Evers, V., & Wielinga, B. (2009). Measuring acceptance of an assistive social robot: A suggested toolkit (1st ed.). IEEE International Symposium on Robot & Human Interactive Communication.
Holley, P. (2019). At this Chinese hotel, the bellhops have been replaced by talking robots (1st ed.). The Washington Post.
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
Huang, D., Chen, Q., Huang, J., Kong, S., & Li, Z. (2021). Customer-robot interactions: Understanding customer experience with service robots. International Journal of Hospitality Management, 99, 103078. https://doi.org/10.1016/j.ijhm.2021.103078
Huang, D., Chen, Q., Huang, S. (Sam), & Liu, X. (2023). Consumer intention to use service robots: A cognitive-affective-conative framework. International Journal of Contemporary Hospitality Management, 36(6), 1893-1913. https://doi.org/10.1108/ijchm-12-2022-1528
Insider. (2019, January 22). Inside Alibaba’s new hotel in China that looks like a spaceship and is staffed by robot bartenders. https://www.businessinsider.com/r-at-alibabas-futuristic-hotel-robots-deliver-towels-and-mix-cocktails-2019-1
Jia, J. W., Chung, N., & Hwang, J. (2021). Assessing the hotel service robot interaction on tourists’ behavior: The role of anthropomorphism. Industrial Management & Data Systems, 121(6), 1457-1478. https://doi.org/10.1108/imds-11-2020-0664
Li, Y., & Wang, C. (2021). Effect of customer’s perception on service robot acceptance. International Journal of Consumer Studies, 46(4), 1241-1261.https://doi.org/10.1111/ijcs.12755
Li, Y., Wang, C., & Song, B. (2023). Customer acceptance of service robots under different service settings. Journal of Service Theory and Practice, 33(1), 46-71. https://doi.org/10.1108/jstp-06-2022-0127
Lucia-Palacios, L., & Pérez-López, R. (2021). Effects of home voice assistants’ autonomy on intrusiveness and usefulness: Direct, indirect, and moderating effects of interactivity. Journal of Interactive Marketing, 56(1), 41-54. https://doi.org/10.1016/j.intmar.2021.03.005
Marinova, J., Plantenga, J., & Remery, C. (2010). Gender diversity and firm performance: Evidence from Dutch and Danish boardrooms (1st ed.). Taylor & Francis.
Meuter, M. L., Bitner, M. J., & Brown, O. S. W. (2005). Choosing among alternative service delivery modes: An investigation of customer trial of self-service technologies. Journal of Marketing, 69(2), 61-83. https://doi.org/10.1509/jmkg.69.2.61.60759
Park, E., & Del Pobil, A. P. (2013). Users’ attitudes toward service robots in South Korea. Industrial Robot: An International Journal, 40(1), 77-87. https://doi.org/10.1108/01439911311294273
Rejón-Guardia, F., Polo-Peña, A. I., & Maraver-Tarifa, G. (2020). The acceptance of a personal learning environment based on Google apps: The role of subjective norms and social image. Journal of Computing in Higher Education, 32(2), 203-233. https://doi.org/10.1007/s12528-019-09206-1
Said, N., Ben Mansour, K., Bahri-Ammari, N., Yousaf, A., & Mishra, A. (2023). Customer acceptance of humanoid service robots in hotels: Moderating effects of service voluntariness and culture. International Journal of Contemporary Hospitality Management, 36(6), 1844-1867.
https://doi.org/10.1108/ijchm-12-2022-1523
Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. MPR Online, 8(8), 23-74.
Sheeran, P. (2002). Intention-behavior relations: A conceptual and empirical review. European Review of Social Psychology, 12(1), 1-36. https://doi.org/10.1080/14792772143000003
Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power (1st ed.). Leading-Edge Psychological Tests and Testing Research.
Solomon, M. R., Surprenant, C., Czepiel, J. A., & Gutman, E. G. (1985). A role theory perspective on dyadic interactions: The service encounter. Journal of Marketing, 49(1), 99-111. https://doi.org/10.1177/002224298504900110
Steigenberger, N. (2015). Emotions in sense making: A change management perspective. Journal of Organizational Change Management, 28(3), 432-451. https://doi.org/10.1108/jocm-05-2014-0095
Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
https://doi.org/10.1287/mnsc.46.2.186.11926
Warren, C., & Campbell, M. C. (2014). What makes things cool? How autonomy influences perceived coolness. Journal of Consumer Research, 41(2), 543-563. https://doi.org/10.1086/676680
Warshaw, P. R., & Davis, F. D. (1985). Disentangling behavioral intention and behavioral expectation. Journal of Experimental Social Psychology, 21(3), 213-228. https://doi.org/10.1016/0022-1031(85)90017-4
West, M. A. (2002). Sparkling fountains or stagnant ponds: An integrative model of creativity and innovation implementation in work groups. http://www.mendeley.com/catalog/sparkling-fountains-stagnant-pondsnan-integrative-model-creativity-andninnovation-implementation-wor/
Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018). Brave new world: Service robots in the front line. Journal of Service Management, 29(5), 907-931. https://doi.org/10.1108/josm-04-2018-0119
Xiao, L., & Kumar, V. (2019). Robotics for customer service: A useful complement or an ultimate substitute? Journal of Service Research, 24(2), Article 109467051987888. https://doi.org/10.1177/1094670519878881
Yang, H., Yu, J., Zo, H., & Choi, M. (2016). User acceptance of wearable devices: An extended perspective of perceived value. Telematics and Informatics, 33(2), 256-269.
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