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AI-Powered Customer Experience: The Impact of AI Stimuli and Chatbots | ||
| International Journal of Digital Content Management | ||
| مقاله 2، دوره 6، شماره 11، مهر 2025، صفحه 32-68 اصل مقاله (1.47 M) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22054/dcm.2025.80014.1246 | ||
| نویسندگان | ||
| Seyyed Reza Jalalzadeh* 1؛ Abbas Ali Haji Karimi Sari2؛ Mahsa Lotfiyan Moghadam3 | ||
| 1Assistant Professor, Department of Management, Faculty of Management and Financial Sciences, Khatam University, Tehran, Iran | ||
| 2Associate Professor, Department of Management, Faculty of Management and Financial Sciences, Khatam University, Tehran, Iran | ||
| 3Master's degree in business management, Faculty of Management and Finance, Khatam University, Tehran, Iran | ||
| چکیده | ||
| Purpose: Unlike human intelligence, which naturally exists, artificial intelligence (AI) is represented by human-like and non-human-like machines that are programmed by humans to serve human and commercial purposes. This technology enables computers to analyze complex data using sophisticated algorithms and mathematical models, allowing them to learn and improve autonomously. The main objective of this research is to examine the impact of AI stimuli and chatbots on smart customer experience and recommendatory marketing, considering the moderating role of technology readiness in platform businesses, based on the Stimulus-Organism-Response framework. Method: This study is classified as descriptive-survey research. Data were collected using a standard questionnaire. The statistical population consisted of users of the Snapp application. Due to the unlimited size of the population, G*Power software was applied for sample size determination. In total, 453 electronic questionnaires were collected, although 406 were required; analyses were conducted on the full set of 453 responses for greater reliability. Given the non-normal distribution of the data, Smart PLS version 4 and SPSS version 27 software were used for analysis. Findings: Of the 12 hypotheses tested, 9 were supported while 3 were rejected. Results confirmed the impact of enthusiasm, usability, and responsiveness on relative advantage and perceived interaction. Moreover, relative advantage and perceived interaction positively influenced recommendatory marketing. The moderating effect of optimism on the relationship between enthusiasm and perceived interaction was also supported. However, the moderating effect of feelings of lack of control on usability–relative advantage and enthusiasm–interaction relationships, as well as the moderating role of optimism on usability–relative advantage, were rejected. These results may be explained by the novelty of chatbot use in Iran and cultural differences. Conclusion: The findings indicate that AI-related stimuli, including chatbots, significantly affect the smart customer experience. In turn, perceived interaction and relative advantage influence recommendatory marketing. Organizations with proper infrastructure and sufficient human resources can therefore effectively leverage AI and chatbot capabilities. | ||
| کلیدواژهها | ||
| Artificial Intelligence؛ Chatbots؛ Smart customer experience؛ Technology Readiness؛ Platform Business؛ Stimulus-Organism-Response Framework | ||
| مراجع | ||
|
Almasi, F., & Hosseinpour, M. (2021). Investigating Factors Affecting Platform Businesses During the Coronavirus Crisis. Karafan Scientific Quarterly, 19(2), 187-202. 10.48301/KSSA.2021.279682.1463 (In Persian).
Chang, C. T., Chu, X. Y. M., & Tsai, I. T. (2020). How Cause Marketing Campaign Factors Affect Attitudes and Purchase Intention: Choosing the Right Mix of Product And Cause Types with Time Duration. Journal of Advertising Research. 10.2501/JAR-2019-046 Published 1 March 2021.
Chen, J. S., Le, T. T. Y., & Florence, D. (2021). Usability and responsiveness of artificial intelligence chatbot on online customer experience in e-retailing. International Journal of Retail & Distribution Management, 49(11), 1512-1531.
Chung, M., Ko, E., Joung, H., & Kim, S.J. (2020). “Chatbot e-service and customer satisfaction regarding luxury brands”, Journal of Business Research, Vol. 117 No. 9, pp. 587-595. https://doi.org/10.1016/j.jbusres.2018.10.004
Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics. SAGE Publications. https://books.google.com/books?id=JIrutAEACAAJ
Flavian, C., Guinalıu, M., & Gurrea, R. (2006). “The role played by perceived usability, satisfaction and consumer trust on website loyalty”, Information and Management, 43( 1), 1-14.
Gao, J., Ren, L., Yang, Y., Zhang, D., & Li, L. (2022). The impact of artificial intelligence technology stimuli on smart customer experience and the moderating effect of technology readiness. International Journal of Emerging Markets.
Gentile C., Spiller, N., & Noci, G. (2007). “How to sustain the customer experience: an overviewof experience components that co‐ create value with the customer”, European Management Journal, 25(5), 395‐ 410, 2007.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Huang, M.H., & Rust, R.T. (2021). “Engaged to a robot? The role of AI in service”, Journal of Service Research,24(1),30-41. https://doi.org/10.1177/1094670520902266
Hu, T., Xu, A., Liu, Z., You, Q., Guo, Y., Sinha, V., Luo, J., & Akkiraju, R. (2018). “Touch your heart: a tone-aware chatbot for customer care on social media”, Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems,1-12. https://doi.org/10.1145/3173574.3173989
Jeon, Y. A. (2022). Let me transfer you to our AI-based manager: Impact of manager-level job titles assigned to AI-based agents on marketing outcomes. Journal of Business Research, 145(C), 892-904.
Kaushal, V., & Yadav, R. (2023). Learning successful implementation of Chatbots in businesses from B2B customer experience perspective. Concurrency and Computation: Practice and Experience, 35(1), e7450. https://doi.org/10.1002/cpe.7450
Khashan, M. A., Elsotouhy, M. M., Ghonim, M. A., & Alasker, T. H. (2023). Smart customer experience, customer gratitude, P-WOM and continuance intentions to adopt smart banking services: the moderating role of technology readiness. The TQM Journal.
Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling, Fourth Edition. Guilford Publications. https://books.google.com/books?id=3VauCgAAQBAJ
Kumar, V., Rajan, B., Venkatesan, R. & Lecinski, J. (2019). “Understanding the role of artificial intelligence in personalized engagement marketing”, California Management Review, 61( 4), 135-155.
Latan, H., & Noonan, R. (2017). Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications. Springer International Publishing. https://books.google.com/books?id=u-M8DwAAQBAJ
Lee, S., Ha, S., & Widdows, R. (2011). Consumer responses to high-technology products; Product attributes Research,64(1),1195-1200.
Lu, L., Cai, R. & Gursoy, D. (2019). “Developing and validating a service robot integration willingness scale”, International Journal of Hospitality Management,80, pp. 36-51. https://doi.org/10.1016/j.ijhm.2019.01.005
Meyer, C., & Schwager, A. (2007). Understanding customer experience. Harvard business review, 85(2), 116.
Moradi, M., & Miralmasi, A. (2021). Data Screening. https://analysisacademy.com/ (In Persian).
Mousavi, S. A., & Zanjani, Sh. (2021). The impact of gamification on digital experience and customer engagement. Explorations of Business Management, 13(25), 395-418 (In Persian).
Nicolescu, L., & Tudorache, M. T. (2022). Human-Computer Interaction in Customer Service: The Experience with AI Chatbots—A Systematic Literature Review. Electronics, 11(10), 1579. https://doi.org/10.3390/electronics11101579
Noort, G.V., Voorveld, H.A.M., & Reijmersdal, E.A.V. (2012). “Interactivity in brand web sites: cognitive, affective, and behavioral responses explained by consumers’ online flow experience”, Journal of Interactive Marketing, 26(4), pp. 223-234.
Parasuraman, A. (2000). “Technology readiness index (TRI): a multiple-item scale to measures readiness to embrace new technologies”, Journal of Service Research,2(4), 307-320. https://doi.org/10.1177/109467050024001
Palmer, A. (2010). “Customer experience management: a critical review of an emerging idea”, Journal of Service Marketing, 24(3), pp. 196-208.
Parasuraman, A., & Colby, C.L. (2015). “An updated and streamlined technology readiness index: TRI 2.0”, Journal of Service Research,18(1), 59-74.
Petre, M., Minocha, S., & Roberts, D. (2006). “Usability beyond the website: an empirically-grounded e-commerce evaluation instrument for the total customer experience”, Behaviour and Information Technology, 25(2), pp. 189-203. https://doi.org/10.1080/01449290500331198
Quintino, A.R.P. (2019). “The impact of chatbot technology attributes on customer experience: an example in telecom”, Doctoral dissertation.
Ramezani, E., Rajabzadeh ghatari, A., Baradaran, V., & Shoar, M. (2022). modeling of Electronic Word of Mouth Marketing with Emphasis on Customer Behavior and Business Improvement. ORMR 11 (4) :25-45 URL: http://ormr.modares.ac.ir/article-28-49537-fa.html Sanzogni, L., Guzman, G., & Busch, P. (2017). Artificial intelligence and knowledge management: questioning the tacit dimension. Prometheus, 35(1), 37-56. https://doi.org/10.1080/08109028.2017.1364547
Shirahada, K., Ho, B.Q., & Wilson, A. (2019). “Online public services usage and the elderly: assessing determinants of technology readiness in Japan and the UK”, Technology in Society, 58 ( 101115), pp. 1-9.
Shankar, V., et al. (2021). How technology is changing retail. J. Retailing 97 (1), 13–27. https://doi.org/10.1016/j.jretai.2020.10.006.
Shirmohammadi, Y., & Bostan Manesh, A. (2022). Designing the purchase model of customers from smart stores in the days of Corona with an emphasis on artificial intelligence. Smart Business Management Studies, 10(40). (In Persian).
Shafiei, N., Ghaffari, M., Farmani, M., & Zandi Nasab, M. (2019). Identifying and prioritizing dimensions affecting customer experience in retail environments; Case study: chain stores of Afogh Korosh. Modern Marketing Research, 9(3(34)), 179-200. SID. https://sid.ir/paper/386795/fa (In Persian)
Song, M., et al. (2022). Will artificial intelligence replace human customer service? The impact of communication quality and privacy risks on adoption intention. J. Retailing Consum. Serv. 66, 102900 https://doi.org/10.1016/j. jretconser.2021.102900, 1-17.
Tegtmeier, T., & Neofotistos, S. (2013). “How Gamification Rewards College-Aged Consumer Loyalty: One Click at a Time”, Social media marketing, .(11), 2013.
Tavallaee, R. (2023). Interaction between humans and artificial intelligence in knowledge management (editor's speech). Organizational Knowledge Management, 6(20), 2-11. SID. https://sid.ir/paper/1050764/fa (In Persian)
Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1(1), 100002.
Wang, D., Xiang, Z., & Fesenmaier, D.R. (2014). “Adapting to the mobile world: a model of smartphone use”, Annals of Tourism Research, Vol. 48, pp. 11-26, doi: 10.1016/j.annals.2014. 04.008
Wang, X., Li, X., Zhen, F., & Zhang, J. (2016). “How smart is your tourist attraction?: measuring tourist preferences of smart tourism attractions via a FCEM-AHP and IPA approach”, Tourism Management, 54, 309-320, doi: 10.1016/j.tourman.2015.12.003
Wang, Y., et al. (2022). The impact of service robots in retail: exploring the effect of novelty priming on consumer behavior. J. Retailing Consum. Serv. 68, 103002 https://doi.org/10.1016/j.jretconser.2022.103002, 1-15
Yang, Z., Zhou, Q., Chiu, D. K., & Wang, Y. (2022). Exploring the factors influencing continuous usage intention of academic social network sites. Online Information Review.
Zangeneh, N., Moeini, A., Haji Heydari, N., & Azar, A. (2021). A framework to develop Platform business mode: Findings based on Meta-synthesis approach. Management Research in Iran, 25(1), 95-115. (In Persian). | ||
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