The Impact of Consumer Trust in AI and Brand Attitudes on Continued Use of Robotic Menu Customization
Keywords:
AI trust, brand attitude, Service satisfaction, service robots, continuance intention
Abstract
This study investigates the role of consumer trust in artificial intelligence (AI) and brand attitudes on the intention to continue using robotic menu customization services, focusing on the coffee industry as a transformative context for AI-driven innovations. Using the Stimulus-Organism-Response (S-O-R) framework, the study examines how trust and brand attitudes mediate consumer behavior, highlighting the moderating influence of demographics such as age, income, education, and satisfaction. The findings underscore the critical interplay between trust, brand perception, and continued engagement, offering practical implications for businesses to enhance customer loyalty through personalized AI services.
References
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Flavián, C., Pérez-Rueda, A., Belanche, D., & Casaló, L. V. (2022). Intention to use analytical artificial intelligence (AI) in services–the effect of technology readiness and awareness. Journal of Service Management, 33(2), 293-320. https://doi.org/10.1108/JOSM-10-2020-0378
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Kim, J., Giroux, M., & Lee, J. C. (2021). When do you trust AI? The effect of number presentation detail on consumer trust and acceptance of AI recommendations. Psychology & Marketing, 38(7), 1140-1155. https://doi.org/10.1002/mar.21498
Kim, K., & Baker, M. A. (2019). How the employee looks and looks at you: Building customer–employee rapport. Journal of Hospitality & Tourism Research, 43(1), 20-40. https://doi.org/10.1177/1096348017731130
Limna, P., Siripipatthanakul, S., & Phayaphrom, B. (2021). The role of big data analytics in influencing artificial intelligence (AI) adoption for coffee shops in Krabi. Thailand. International Journal of Behavioral Analytics, 1(2), 1-17.
Madu, C. O., & Musa, A. (2024). Lecturers’ level of awareness of artificial intelligence as correlate of their digital competence at Federal University Wukari, Nigeria. Journal of Educational Research in Developing Areas, 5(1), 59-67. https://doi.org/10.47434/JEREDA.5.1.2024.59
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Nguyen, T. M., & Malik, A. (2022). A two‐wave cross‐lagged study on AI service quality: The moderating effects of the job level and job role. British Journal of Management, 33(3), 1221-1237. https://doi.org/10.1111/1467-8551.12540
Nguyen, M. H., Tran, B. T., & Huynh, L. T. (2019). Relation between employees and customers affects to the positive word of mouth through customer satisfaction. Journal of Distribution Science, 17(6), 65-75. http://dx.doi.org/10.15722/jds.17.6.201906.65
Parasuraman, A. B. L. L., Zeithaml, V. A., & Berry, L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12-40.
Pinillos, R., Marcos, S., Feliz, R., Zalama, E., & Gómez-García-Bermejo, J. (2016). Long-term assessment of a service robot in a hotel environment. Robotics and Autonomous Systems, 79, 40-57. https://doi.org/10.1016/j.robot.2016.01.014
Ramirez-Asis, E., Vilchez-Carcamo, J., Thakar, C. M., Phasinam, K., Kassanuk, T., & Naved, M. (2022). A review on role of artificial intelligence in food processing and manufacturing industry. Materials Today: Proceedings, 51, 2462-2465. https://doi.org/10.1016/j.matpr.2021.11.616
Samek, W., Wiegand, T., & Müller, K. R. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv preprint arXiv:1708.08296.
Schroeder, T., Dodds, L., Georgiou, A., Gewald, H., & Siette, J. (2023). Older adults and new technology: mapping review of the factors associated with older adults’ intention to adopt digital technologies. JMIR Aging, 6(1), e44564. https://doi.org/10.2196/44564
Senathirajah, A. R. B. S., bin S Senathirajah, A. R., Alainati, S., Haque, R., Ahmed, S., Khalil, M. I., & Chowdhury, B. (2024). Antecedents and Consequence of Trust-Commitment Towards Artificial Based Customer Experience. UCJC Business and Society Review (formerly known as Universia Business Review), 21(80). https://doi.org/10.3232/UBR.2024.V21.N1.16
Soltani-Fesaghandis, G., & Pooya, A. (2018). Design of an artificial intelligence system for predicting success of new product development and selecting proper market-product strategy in the food industry. International Food and Agribusiness Management Review, 21(7), 847-864. https://doi.org/10.22434/IFAMR2017.0033
Sung, H. J., & Jeon, H. M. (2020). Untact: Customer’s acceptance intention toward robot barista in coffee shop. Sustainability, 12(20), 8598. https://doi.org/10.3390/su12208598
Verhagen, T., and van Dolen, W. (2011), The influence of online store beliefs on consumer online impulse buying: A model and empirical application. Information and Management, 48(8), 320-327. https://doi.org/10.1016/j.im.2011.08.001
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS quarterly, 351-370. https://doi.org/10.2307/3250921
Bindroo, V., Mariadoss, B. J., Echambadi, R., & Sarangee, K. R. (2020). Customer satisfaction with consumption systems. Journal of Business-to-Business Marketing, 27(1), 1-17. https://doi.org/10.1080/1051712X.2020.1713558
Boehm, J., Grennan, L., Singla, A., & Smaje, K. (2022). Why digital trust truly matters. McKinsey & Company. Retrieved March 1, 2024 from https://www.mckinsey.com/capabilities/quantumblack/our-insights/why-digital-trust-truly-matters
Chen, Y., Prentice, C., Weaven, S., & Hisao, A. (2022). The influence of customer trust and artificial intelligence on customer engagement and loyalty–The case of the home-sharing industry. Frontiers in Psychology, 13, 912339. https://doi.org/10.3389/fpsyg.2022.912339
Chi, N. T. K., & Hoang Vu, N. (2023). Investigating the customer trust in artificial intelligence: The role of anthropomorphism, empathy response, and interaction. CAAI Transactions on Intelligence Technology, 8(1), 260-273. https://doi.org/10.1049/cit2.12133
Chi, O. H., Jia, S., Li, Y., & Gursoy, D. (2021). Developing a formative scale to measure consumers’ trust toward interaction with artificially intelligent (AI) social robots in service delivery. Computers in Human Behavior, 118, 106700. https://doi.org/10.1016/j.chb.2021.106700
Dewi, G. M. M., Joshua, L., Ikhsan, R. B., Yuniarty, Y., Sari, R. K., & Susilo, A. (2021, August). Perceived risk and trust in adoption E-wallet: The role of perceived usefulness and ease of use. In 2021 International Conference on Information Management and Technology (ICIMTech) (Vol. 1, pp. 120-124). IEEE. https://doi.org/10.1109/ICIMTech53080.2021.9535033
Duong, L. N., Al-Fadhli, M., Jagtap, S., Bader, F., Martindale, W., Swainson, M., & Paoli, A. (2020). A review of robotics and autonomous systems in the food industry: From the supply chains perspective. Trends in Food Science & Technology, 106, 355-364. https://doi.org/10.1016/j.tifs.2020.10.028
Eiseman, D., and Jonsson, M. (2019), Leveraging the coffee experience as a tool for engagement with climate change. Qualitative Market Research, 22(4), 570-581. https://doi.org/10.1108/QMR-07-2018-0084
Flavián, C., Pérez-Rueda, A., Belanche, D., & Casaló, L. V. (2022). Intention to use analytical artificial intelligence (AI) in services–the effect of technology readiness and awareness. Journal of Service Management, 33(2), 293-320. https://doi.org/10.1108/JOSM-10-2020-0378
Global Network. (2019). Filippo Veglio on Advances in AI, Robotics, Blockchain Enabling Sustainability.
Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627-660. https://doi.org/10.5465/annals.2018.0057
Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157-169. https://doi.org/10.1016/j.ijinfomgt.2019.03.008
Hernández-Orallo, J. (2018). 20 Characterising the trajectories of artificial and natural intelligence. Assessing the Impact of Machine Intelligence on Human Behaviour: An Interdisciplinary Endeavour, 53.
Hochreiter, V., Benedetto, C., & Loesch, M. (2022, September). The Stimulus-Organism-Response (SOR) Paradigm as a Guiding Principle in Environmental Psychology: Comparison of Its Usage in Consumer Behavior and Organizational Culture and Leadership Theory. In Global Business Conference 2022 Proceedings (p. 42).
Jacoby, J. (2002), Stimulus-Organism-Response reconsidered: An evolutionary step in modeling (Consumer) behavior. Journal of Consumer Psychology, 12(1), 51-57. https://doi.org/10.1207/S15327663JCP1201_05
Kasilingam, D. L. (2020). Understanding the attitude and intention to use smartphone chatbots for shopping. Technology in Society, 62, 101280. https://doi.org/10.1016/j.techsoc.2020.101280
Keller, K. and Aaker, D. (1992). The Effects of Sequential Introduction of Brand Extensions. Journal of Marketing Research, 29(1), 35-50. https://doi.org/10.1177/002224379202900104
Kim, J., Giroux, M., & Lee, J. C. (2021). When do you trust AI? The effect of number presentation detail on consumer trust and acceptance of AI recommendations. Psychology & Marketing, 38(7), 1140-1155. https://doi.org/10.1002/mar.21498
Kim, K., & Baker, M. A. (2019). How the employee looks and looks at you: Building customer–employee rapport. Journal of Hospitality & Tourism Research, 43(1), 20-40. https://doi.org/10.1177/1096348017731130
Limna, P., Siripipatthanakul, S., & Phayaphrom, B. (2021). The role of big data analytics in influencing artificial intelligence (AI) adoption for coffee shops in Krabi. Thailand. International Journal of Behavioral Analytics, 1(2), 1-17.
Madu, C. O., & Musa, A. (2024). Lecturers’ level of awareness of artificial intelligence as correlate of their digital competence at Federal University Wukari, Nigeria. Journal of Educational Research in Developing Areas, 5(1), 59-67. https://doi.org/10.47434/JEREDA.5.1.2024.59
Mehrabian, A., and J. A. Russell. (1974), An Approach to Environmemtal Psychology, Cambridge, The MIT Press.
Mende, M., Scott, M. L., van Doorn, J., Grewal, D., & Shanks, I. (2019). Service robots rising: How humanoid robots influence service experiences and elicit compensatory consumer responses. Journal of Marketing Research, 56(4), 535-556. https://doi.org/10.1177/0022243718822827
Mostafa, R. B., & Kasamani, T. (2022). Antecedents and consequences of chatbot initial trust. European journal of marketing, 56(6), 1748-1771. https://doi.org/10.1108/EJM-02-2020-0084
Mukherjee, D., Gupta, K., Chang, L. H., & Najjaran, H. (2022). A survey of robot learning strategies for human-robot collaboration in industrial settings. Robotics and Computer-Integrated Manufacturing, 73, 102231. https://doi.org/10.1016/j.rcim.2021.102231
Nguyen, T. M., & Malik, A. (2022). A two‐wave cross‐lagged study on AI service quality: The moderating effects of the job level and job role. British Journal of Management, 33(3), 1221-1237. https://doi.org/10.1111/1467-8551.12540
Nguyen, M. H., Tran, B. T., & Huynh, L. T. (2019). Relation between employees and customers affects to the positive word of mouth through customer satisfaction. Journal of Distribution Science, 17(6), 65-75. http://dx.doi.org/10.15722/jds.17.6.201906.65
Parasuraman, A. B. L. L., Zeithaml, V. A., & Berry, L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12-40.
Pinillos, R., Marcos, S., Feliz, R., Zalama, E., & Gómez-García-Bermejo, J. (2016). Long-term assessment of a service robot in a hotel environment. Robotics and Autonomous Systems, 79, 40-57. https://doi.org/10.1016/j.robot.2016.01.014
Ramirez-Asis, E., Vilchez-Carcamo, J., Thakar, C. M., Phasinam, K., Kassanuk, T., & Naved, M. (2022). A review on role of artificial intelligence in food processing and manufacturing industry. Materials Today: Proceedings, 51, 2462-2465. https://doi.org/10.1016/j.matpr.2021.11.616
Samek, W., Wiegand, T., & Müller, K. R. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv preprint arXiv:1708.08296.
Schroeder, T., Dodds, L., Georgiou, A., Gewald, H., & Siette, J. (2023). Older adults and new technology: mapping review of the factors associated with older adults’ intention to adopt digital technologies. JMIR Aging, 6(1), e44564. https://doi.org/10.2196/44564
Senathirajah, A. R. B. S., bin S Senathirajah, A. R., Alainati, S., Haque, R., Ahmed, S., Khalil, M. I., & Chowdhury, B. (2024). Antecedents and Consequence of Trust-Commitment Towards Artificial Based Customer Experience. UCJC Business and Society Review (formerly known as Universia Business Review), 21(80). https://doi.org/10.3232/UBR.2024.V21.N1.16
Soltani-Fesaghandis, G., & Pooya, A. (2018). Design of an artificial intelligence system for predicting success of new product development and selecting proper market-product strategy in the food industry. International Food and Agribusiness Management Review, 21(7), 847-864. https://doi.org/10.22434/IFAMR2017.0033
Sung, H. J., & Jeon, H. M. (2020). Untact: Customer’s acceptance intention toward robot barista in coffee shop. Sustainability, 12(20), 8598. https://doi.org/10.3390/su12208598
Verhagen, T., and van Dolen, W. (2011), The influence of online store beliefs on consumer online impulse buying: A model and empirical application. Information and Management, 48(8), 320-327. https://doi.org/10.1016/j.im.2011.08.001
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2024-12-16
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