Issue

Vol. 2 No. 3 (2025): July

Copyright (c) 2025 Azatullah Zaheer, Abdullah Sadiq, Noorulhaq Safi

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

AI in Service Industries: Effects on Customer Satisfaction, Mediated by Service Quality, and Moderated by Customer Trust

https://doi.org/10.62810/jssh.v2i3.82
Azatullah Zaheer
Salam University, Department of Business Administration, Faculty of Economics, Kabul, AF
Abdullah Sadiq
Salam University, Department of Business Administration, Faculty of Economics, Kabul, AF
Noorulhaq Safi
Sayed Jamal Uddin Afghani University, Department of Information Technology, Faculty of Computer Science, Kunar, AF

Corresponding Author(s) : Azatullah Zaheer

azatullahzaheer28@gmail.com

Journal of Social Sciences and Humanities, Vol. 2 No. 3 (2025): July

Abstract

This study examined the application of artificial intelligence in service industries and its impact on customer satisfaction, focusing on the mediating role of service quality perception and the moderating effect of customer trust in AI. AI-driven technologies have transformed customer service by improving efficiency, personalization, and responsiveness. However, the extent to which these enhancements translated into higher customer satisfaction depended on perceived service quality and trust in AI systems. Using a structured survey across various service industries, particularly in empathy-driven sectors like healthcare and education, the research employed statistical analysis to evaluate AI’s direct and indirect effects on customer satisfaction. The findings indicated that AI significantly enhanced customer satisfaction, with a , substantial direct effect (β = 0.642, p < 0.001) and an additional indirect effect through service quality perception (indirect effect = 0.286, p < 0.001). Service quality perception acted as a crucial mediator (β = 0.305, p < 0.001), confirming its importance in shaping satisfaction outcomes. While customer trust positively influenced satisfaction (β = 0.267, p < 0.001), its moderating effect on AI-driven service interactions was not statistically significant (p = 0.199). These results show that AI adoption aligns with customer expectations and ethical considerations. Future research is recommended to explore the long-term impact of AI on customer trust and examine its effectiveness across various industries that require higher levels of emotional intelligence in service delivery.

Keywords

Artificial intelligence (AI) AI-driven Customer Satisfaction Customer Trust in AI Quality Perception TAM
Zaheer, A., Sadiq, A., & Safi, N. (2025). AI in Service Industries: Effects on Customer Satisfaction, Mediated by Service Quality, and Moderated by Customer Trust . Journal of Social Sciences and Humanities, 2(3), 51–73. https://doi.org/10.62810/jssh.v2i3.82
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. Adam, M., Wessel, M., & Benlian, A. (2020). AI-based chatbots in customer service and their effects on user compliance. Electronic Markets, 30(2), 427–445. https://doi.org/10.1007/s12525-020-00414-7 DOI: https://doi.org/10.1007/s12525-020-00414-7
  2. Agarwal, S. (2019). Deep learning-based sentiment analysis: Establishing customer dimension as the lifeblood of business management. Global Business Review, 1–18. https://doi.org/10.1177/0972150919845160 DOI: https://doi.org/10.1177/0972150919845160
  3. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. https://doi.org/10.1109/TAC.1974.1100705 DOI: https://doi.org/10.1109/TAC.1974.1100705
  4. Al-Hyari, H. S. A., Al-Smadi, H. M., & Weshah, S. R. (2023). The impact of artificial intelligence (AI) on guest satisfaction in hotel management: An empirical study of luxury hotels. GeoJournal of Tourism and Geosites, 48, 810–819. https://doi.org/10.30892/gtg.482spl15-1081 DOI: https://doi.org/10.30892/gtg.482spl15-1081
  5. Almarzouqi, A., Bettayeb, A., Rahman, S. A., Salloum, S., & Al-Yateem, N. (2024, July).
  6. Exploring new horizons in dental education: Leveraging AI and the Metaverse for innovative learning strategies. In 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC) (pp. 1881–1886). IEEE. https://doi.org/10.1109/COMPSAC61105.2024.00298 DOI: https://doi.org/10.1109/COMPSAC61105.2024.00298
  7. Angelova, B., & Zekiri, J. (2011). Measuring customer satisfaction with service quality using American Customer Satisfaction Model (ACSI Model). International Journal of Academic Research in Business and Social Sciences, 1(3). D https://doi.org/10.6007/ijarbss.v1i2.35 DOI: https://doi.org/10.6007/ijarbss.v1i2.35
  8. Arora, S., Athavale, V. A., Maggu, H., & Agarwal, A. (2020). Artificial intelligence and virtual assistant—working model. In Mobile Radio Communications and 5G Networks (pp. 163–171). Springer. https://doi.org/10.1007/978-981-15-7130-5_12 DOI: https://doi.org/10.1007/978-981-15-7130-5_12
  9. Ashfaq, M., Yu, S., & Loureiro, S. M. (2020). I, Chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics, 54, 101473. https://doi.org/10.1016/j.tele.2020.101473 DOI: https://doi.org/10.1016/j.tele.2020.101473
  10. Ballestar, T. M., Grau-Carles, P., & Sainz, J. (2019). Predicting customer quality in e-commerce social networks: A machine learning approach. Review of Managerial Science, 13(3), 589–603. https://doi.org/10.1007/s11846-018-0316-x DOI: https://doi.org/10.1007/s11846-018-0316-x
  11. Belanche, D., Casaló, L. V., Flavián, C., & Schepers, J. (2020).
  12. Service robot implementation: A theoretical framework and research agenda. The Service Industries Journal, 40(3–4), 203–225. https://doi.org/10.1080/02642069.2019.1672666 DOI: https://doi.org/10.1080/02642069.2019.1672666
  13. Bennett, T., & Rundel, T. (2021). Personalization through AI: Revolutionizing customer experience. Journal of Digital Marketing Research, 13(1), 23–38. https://doi.org/10.20944/preprints202408.0023.v1 DOI: https://doi.org/10.20944/preprints202408.0023.v1
  14. Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921 DOI: https://doi.org/10.2307/3250921
  15. 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, 632–658. https://doi.org/10.1007/s11747-020-00762-y DOI: https://doi.org/10.1007/s11747-020-00762-y
  16. Boadi, P. O., Guoxin, L., Sai, A. A., & Karikari, A. F. (2018). Customer dissatisfaction and unfavorable word of mouth. Human Systems Management, 37(4), 445–451. https://doi.org/10.3233/HSM-18305 DOI: https://doi.org/10.3233/HSM-18305
  17. Bock, D. E., Wolter, J. S., & Ferrell, O. C. (2020). Artificial intelligence: Disrupting what we know about services. Journal of Services Marketing, 34(3), 317–334. https://doi.org/10.1108/JSM-01-2019-0047
  18. Bock, D. E., Wolter, J. S., & Ferrell, O. C. (2020). Artificial intelligence: Disrupting what we know about services. Journal of Services Marketing, 34(3), 317–334. https://doi.org/10.1108/JSM-01-2019-0047 DOI: https://doi.org/10.1108/JSM-01-2019-0047
  19. Bolton, R. N., Gustafsson, A., McColl-Kennedy, J. R., Sirianni, N. J., & Tse, D. K. (2021). Small details that make big differences: A framework for enhancing service productivity and customer experience. Journal of Service Research, 24(1), 1–14. https://doi.org/10.1108/JOSM-01-2014-0034 DOI: https://doi.org/10.1108/JOSM-01-2014-0034
  20. Brei, V. A., d’Avila, L., Camargo, L. F., & Engels, J. (2011). The influence of adaptation and standardization of the marketing mix on performance: A meta-analysis. Brazilian Administration Review, 8(3), 266–287. DOI:10.1590/S1807-76922011000300004 DOI: https://doi.org/10.1590/S1807-76922011000300004
  21. Brei, V. A., Vieira, V. A., & De Matos, C. A. (2014). Meta-análise em marketing. Revista Brasileira de Marketing, 13(2), 84–97. https://doi.org/10.5585/remark.v13i2.2681 DOI: https://doi.org/10.5585/remark.v13i2.2681
  22. Brill, T. M., Munoz, L., & Miller, R. J. (2019). Siri, Alexa, and other digital assistants: A study of customer satisfaction with artificial intelligence applications. Journal of Marketing Management, 35(15–16), 1401–1436. https://doi.org/10.1080/0267257X.2019.1687571 DOI: https://doi.org/10.1080/0267257X.2019.1687571
  23. Bulchand-Gidumal, J. (2020). Impact of artificial intelligence in travel, tourism, and hospitality. In Z. Xiang, M. Fuchs, U. Gretzel, & W. Höpken (Eds.), Handbook of e-Tourism (pp. 110–1). Springer. https://doi.org/10.1007/978-3-030-05324-6_110-1 DOI: https://doi.org/10.1007/978-3-030-05324-6_110-1
  24. Carter, D. (2018). How real is the impact of artificial intelligence? The business information survey 2018. Business Information Review, 35(3), 99–115. https://doi.org/10.1177/0266382118790150 DOI: https://doi.org/10.1177/0266382118790150
  25. Chen, H., Chiang, R. H. L., & Storey, V. C. (2018).
  26. Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188. https://doi.org/10.2307/41703503 DOI: https://doi.org/10.2307/41703503
  27. Chen, L., & Deng, Y. (2018). A new failure mode and effects analysis model using Dempster–Shafer evidence theory and grey relational projection method. Engineering Applications of Artificial Intelligence, 76, 13–20. https://doi.org/10.1016/j.engappai.2018.08.010 DOI: https://doi.org/10.1016/j.engappai.2018.08.010
  28. Chen, T., Guo, W., Gao, X., & Liang, Z. (2021). AI-based self-service technology in public service delivery: User experience and influencing factors. Government Information Quarterly, 38(4), 101520. https://doi.org/10.1016/j.giq.2020.101520 DOI: https://doi.org/10.1016/j.giq.2020.101520
  29. Chiang, A. H., & Trimi, S. (2020). Impacts of service robots on service quality. Service Business, 14, 439–459. https://doi.org/10.1007/s11628-020-00423-8 DOI: https://doi.org/10.1007/s11628-020-00423-8
  30. Choi, Y., Choi, M., Oh, M. M., & Kim, S. S. (2020). Service robots in hotels: Understanding the service quality perceptions of human–robot interaction. Journal of Hospitality Marketing & Management, 29(6), 613–635. https://doi.org/10.1080/19368623.2020.1703871 DOI: https://doi.org/10.1080/19368623.2020.1703871
  31. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge. https://doi.org/10.4324/9780203771587 DOI: https://doi.org/10.4324/9780203771587
  32. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Lawrence Erlbaum Associates. https://doi.org/10.4324/9780203774441 DOI: https://doi.org/10.4324/9780203774441
  33. Das, A. K., Nayak, J., Naik, B., Maringanti, H. B., Vimal, S., & Pelusi, D. (2024).
  34. Computational Intelligence in Pattern Recognition: Proceedings of CIPR 2024, Volume 2. Proceedings of CIPR, 2. Link
  35. 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 DOI: https://doi.org/10.2307/249008
  36. Dawi, N. (2016). The relationship between service quality, customer satisfaction and behavioral intention with moderating effects of switching barriers. Link
  37. Dobbala, M., & Lingolu, M. S. S. (2024).
  38. Enhancing usability for everyone through web accessibility compliance. Journal of Computer Science and Software Development, 3, 1–13. https://doi.org/10.17303/jcssd.2024.3.105 DOI: https://doi.org/10.17303/jcssd.2024.3.105
  39. Doney, P. M., & Cannon, J. P. (1997). An examination of the nature of trust in buyer–seller relationships. Journal of Marketing, 61(2), 35–51. https://doi.org/10.1177/002224299706100203 DOI: https://doi.org/10.1177/002224299706100203
  40. Donthu, N., Gremler, D. D., Kumar, S., & Pattnaik, D. (2022). Mapping of Journal of Service Research themes: A 22-year review. Journal of Service Research, 25(2), 187–193. https://doi.org/10.1177/1094670520977672 DOI: https://doi.org/10.1177/1094670520977672
  41. Dwivedi, Y. K., Hughes, D. L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002 DOI: https://doi.org/10.1016/j.ijinfomgt.2019.08.002
  42. Frost, J. (2017). How to interpret the F-test of overall significance in regression analysis. Statistics By Jim. Link
  43. Gardner, H. (1999). Intelligence reframed: Multiple intelligences for the 21st century. Basic Books. https://psycnet.apa.org/record/1999-04335-000
  44. Ge, X., Fu, J., Chen, F., An, S., Sebe, N., & Jose, J. M. (2024). Towards end-to-end explainable facial action unit recognition via vision-language joint learning. arXiv Preprint. https://doi.org/10.1145/3664647.3681443 DOI: https://doi.org/10.1145/3664647.3681443
  45. Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519 DOI: https://doi.org/10.2307/30036519
  46. Grewal, D., Roggeveen, A. L., & Nordfält, J. (2017). The future of retailing. Journal of Retailing, 93(1), 1–6. https://doi.org/10.1016/j.jretai.2016.12.008 DOI: https://doi.org/10.1016/j.jretai.2016.12.008
  47. Gupta, S., Modgil, S., Lee, C. K., & Sivarajah, U. (2023). The future is yesterday: Use of AI-driven facial recognition to enhance value in the travel and tourism industry. Information Systems Frontiers, 25(3), 1179–1195. https://doi.org/10.1007/s44163-024-00105-8 DOI: https://doi.org/10.1007/s10796-022-10271-8
  48. Gursoy, D., Chi, C. G., 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 DOI: https://doi.org/10.1016/j.ijinfomgt.2019.03.008
  49. Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004
  50. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th ed.). Pearson Education. Link
  51. Huang, M.-H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
  52. Huang, M.-H., & Rust, R. T. (2018). Artificial intelligence in service: A customer experience perspective. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459 DOI: https://doi.org/10.1177/1094670517752459
  53. Hussain, M., & Manhas, J. (2016). Artificial intelligence for big data: Potential and relevance. International Academy of Engineering and Medical Research, 1(1). Link
  54. Irwin, D. (2019, June 17). The top applications of AI in marketing. SimMachines. Link
  55. Izadi, S., & Forouzanfar, M. (2024).
  56. Error correction and adaptation in conversational AI: A review of techniques and applications in chatbots. AI, 5(2), 803–841. https://doi.org/10.3390/ai5020041 DOI: https://doi.org/10.3390/ai5020041
  57. Jain, P., Vyas, V., & Chalasani, D. P. (2016). Corporate social responsibility and financial performance in SMEs: A structural equation modelling approach. Global Business Review, 17(3), 630–653. https://doi.org/10.1177/0972150916630827 DOI: https://doi.org/10.1177/0972150916630827
  58. Kaplan, A. M., & Haenlein, M. (2019).
  59. Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004 DOI: https://doi.org/10.1016/j.bushor.2018.08.004
  60. Karthikeya, M., & Anand, S. (2024, May).
  61. User feedback module for Women Self Help Group chatbot for increased usability. In 2024 5th International Conference for Emerging Technology (INCET) (pp. 1–7). IEEE. https://doi.org/10.1109/INCET61516.2024.10593339 DOI: https://doi.org/10.1109/INCET61516.2024.10593339
  62. Kutner, M. H., Nachtsheim, C. J., Neter, J., & Li, W. (2005). Applied linear statistical models (5th ed.). McGraw-Hill/Irwin. Link
  63. Kuzior, A., Sira, M., & Brożek, P. (2023). Use of artificial intelligence in terms of open innovation process and management. Sustainability, 15(9), 7205. https://doi.org/10.3390/su15097205
  64. Kuzior, A., Sira, M., & Brożek, P. (2023). Use of artificial intelligence in terms of open innovation process and management. Sustainability, 15(9), 7205. https://doi.org/10.3390/su15097205 DOI: https://doi.org/10.3390/su15097205
  65. Lai, W. C., & Hung, W. H. (2018). A framework of cloud and AI based intelligent hotel. In Proceedings of the 18th International Conference on Electronic Business (ICEB), Guilin, China (pp. 36–43). Link
  66. Lemon, K. N., & Verhoef, P. C. (2016).
  67. Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96. https://doi.org/10.1509/jm.15.0420 DOI: https://doi.org/10.1509/jm.15.0420
  68. Li, Z., Mengyu, P., Yao, F., & Xiyan, L. (2023, September). Can artificial intelligence-enabled service interactions improve the perception of service quality? In Working Conference on Virtual Enterprises (pp. 774–788). Springer. https://doi.org/10.1007/978-3-031-42622-3_55 DOI: https://doi.org/10.1007/978-3-031-42622-3_55
  69. Liu, B. (2022). Sentiment analysis and opinion mining. Springer Nature. Link
  70. McKinsey & Company. (2023). AI customer service for higher customer engagement. Link
  71. McLean, G., & Osei-Frimpong, K. (2019). Artificial intelligence (AI) in customer service: Exploring AI’s impact on perceived service quality. Computers in Human Behavior, 90, 247–254. https://doi.org/10.1016/j.chb.2018.09.020 DOI: https://doi.org/10.1016/j.chb.2018.09.020
  72. McTear, M. F., Callejas, Z., & Griol, D. (2016). The conversational interface (Vol. 6, No. 94, p. 102). Springer. https://doi.org/10.1007/978-3-319-32967-3 DOI: https://doi.org/10.1007/978-3-319-32967-3
  73. Mirrokni, V., & Nazerzadeh, H. (2017, April). Deals or no deals: Contract design for online advertising. In Proceedings of the 26th International Conference on World Wide Web (pp. 7–14). https://doi.org/10.1145/3038912.3052566 DOI: https://doi.org/10.1145/3038912.3052566
  74. Neuhofer, B., Magnus, B., & Celuch, K. (2020). The impact of artificial intelligence on event experiences: A scenario technique approach. Electronic Markets, 31, 601–617. https://doi.org/10.1007/s12525-020-00433-4 DOI: https://doi.org/10.1007/s12525-020-00433-4
  75. Nguyen, T. M., & Malik, A. (2022). Impact of knowledge sharing on employees' service quality: The moderating role of artificial intelligence. International Marketing Review, 39(3), 482–508. https://doi.org/10.1108/IMR-02-2021-0078 DOI: https://doi.org/10.1108/IMR-02-2021-0078
  76. Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469. https://doi.org/10.1177/002224378001700405 DOI: https://doi.org/10.1177/002224378001700405
  77. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12–40. Link
  78. Paschen, J., Wilson, M., & Ferreira, J. J. (2020). Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel. Business Horizons, 63(3), 403–414. https://doi.org/10.1016/j.bushor.2020.01.003 DOI: https://doi.org/10.1016/j.bushor.2020.01.003
  79. Peruchini, M., da Silva, G. M., & Teixeira, J. M. (2024). Between artificial intelligence and customer experience: A literature review on the intersection. Discover Artificial Intelligence, 4(1), 4. https://doi.org/10.1007/s44163-024-00105-8
  80. Peruchini, M., da Silva, G. M., & Teixeira, J. M. (2024). Between artificial intelligence and customer experience: A literature review on the intersection. Discover Artificial Intelligence, 4(1), 4. https://doi.org/10.1007/s44163-024-00105-8
  81. Peruchini, M., Modena da Silva, G., & Teixeira, J. M. (2024). Between artificial intelligence and customer experience: A literature review on the intersection. Discover Artificial Intelligence, 4(4). https://doi.org/10.1007/s44163-024-00105-8 DOI: https://doi.org/10.1007/s44163-024-00105-8
  82. Prentice, C. (2023). Leveraging artificial intelligence for customer satisfaction and loyalty. In Leveraging emotional and artificial intelligence for organisational performance (pp. 71–85). Springer. https://doi.org/10.1007/978-981-99-1865-2_6 DOI: https://doi.org/10.1007/978-981-99-1865-2_6
  83. Prentice, C., Wang, X., & Lin, X. (2020). DOI: https://doi.org/10.1530/EDM-19-0097
  84. Analyzing the influence of customer engagement on customer loyalty in the hotel industry: The mediating role of customer satisfaction. Journal of Retailing and Consumer Services, 57, 102648. https://doi.org/10.1016/j.jretconser.2020.102648
  85. Ritharson, P. I., Raimond, K., Mary, X. A., & Robert, J. E. (2024).
  86. DeepRice: A deep learning and deep feature based classification of rice leaf disease subtypes. Artificial Intelligence in Agriculture, 11, 34–49. https://doi.org/10.1016/j.aiia.2023.11.001 DOI: https://doi.org/10.1016/j.aiia.2023.11.001
  87. Ronanki, R. (2018).
  88. Competing in the age of AI. Harvard Business Review, January–February. https://hbr.org/2018/01/competing-in-the-age-of-ai
  89. Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach (3rd ed.). Pearson. Link
  90. Sanny, L., Susastra, A. C., Roberts, C., & Yusramdaleni, R. (2020). The analysis of customer satisfaction factors which influence chatbot acceptance in Indonesia. Management Science Letters, 10(6), 1225–1232. https://doi.org/10.5267/j.msl.2019.11.030 DOI: https://doi.org/10.5267/j.msl.2019.11.036
  91. Sardesai, S., D'Souza, E., & Govekar, S. (2024). Analysing the impacts of artificial intelligence service quality and human service quality on customer satisfaction and customer loyalty in the hospitality sector. Turizam, 28(1), 37–48. DOI: 10.5937/turizam28-45450 DOI: https://doi.org/10.5937/turizam28-45450
  92. Schlinder, D. H. (2003). The myth of intelligence. The Psychological Record, 53(1), 15–32. Link DOI: https://doi.org/10.1145/640990.640999
  93. Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464. https://doi.org/10.1214/aos/1176344136 DOI: https://doi.org/10.1214/aos/1176344136
  94. Sen, S., & Bhattacharya, C. B. (2001). Does doing good always lead to doing better? Consumer reactions to corporate social responsibility. Journal of Marketing Research, 38(2), 225–243. https://doi.org/10.1509/jmkr.38.2.225.18838 DOI: https://doi.org/10.1509/jmkr.38.2.225.18838
  95. Shankar, V. (2018). How artificial intelligence (AI) is reshaping retailing. Journal of Retailing, 94(4), 6–11. https://doi.org/10.1016/S0022-4359(18)30076-9 DOI: https://doi.org/10.1016/S0022-4359(18)30076-9
  96. Shin, Y., & Thai, V. V. (2015). The impact of corporate social responsibility on customer satisfaction, relationship maintenance and loyalty in the shipping industry. Corporate Social Responsibility and Environmental Management, 22(6), 381–392. https://doi.org/10.1002/csr.1352 DOI: https://doi.org/10.1002/csr.1352
  97. Shukla, S., & Vijay, J. (2013). Applicability of artificial intelligence in different fields of life. International Journal of Scientific Engineering and Research, 1(1), 28–35. Link DOI: https://doi.org/10.70729/1130915
  98. Soni, A., & Dubey, S. (2024). The impact of AI-powered chatbots on customer satisfaction in e-commerce marketing (TAM approach). Journal of Professional Research and Applications, 3(1), 45–60. Link
  99. Sternberg, R. J. (1984). Toward a triarchic theory of human intelligence. The Behavioral and Brain Sciences, 7(2), 269–315. https://doi.org/10.1017/S0140525X00044629 DOI: https://doi.org/10.1017/S0140525X00044629
  100. Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460. https://doi.org/10.1093/mind/LIX.236.433 DOI: https://doi.org/10.1093/mind/LIX.236.433
  101. Ullah, A. (2023). Impact of artificial intelligence on customer experience: A mixed-methods approach to study the impact of artificial intelligence on customer experience with Voice of Customer as the mediator. Link
  102. Van Doorn, J., Mende, M., Noble, S. M., Hulland, J., Ostrom, A. L., Grewal, D., & Petersen, J. A. (2017). Domo arigato Mr. Roboto: Emergence of automated social presence in organizational frontlines and customers’ service experiences. Journal of Service Research, 20(1), 43–58. https://doi.org/10.1177/10946705166792 DOI: https://doi.org/10.1177/1094670516679272
  103. Vu, H. T. M., Lai, V. T. H., Khishigjargal, U., Enkh-Amgalan, S., Tran, H. Q., & Ghozaly, S. (2022). Exploring the impact of AI chatbots on customer satisfaction. International Journal of All Research Writings, 4(12), 62–69. Link
  104. Wirtz, J. (2019). Organizational ambidexterity: Cost-effective service excellence, service robots, and artificial intelligence. Organizational Dynamics, 100719. https://doi.org/10.1016/j.orgdyn.2019.04.005 DOI: https://doi.org/10.1016/j.orgdyn.2019.04.005
Read More
Author biographies is not available.
Received 2025-04-05
Accepted 2025-07-26
Published 2025-07-31
Download
PDF
Statistic
Read Counter : 209 Download : 67