The Impact of AI-powered Services on Customer Satisfaction, Employee Performance, and Employee Work-life Balance Leading to Organizational Performance Growth

Authors

  • Zin Thu Thu Hlaing Master of Business Administration, Graduate School of Business and Advanced Technology Management, Assumption University of Thailand
  • Chompu Nuangjamnong Master of Business Administration, Graduate School of Business and Advanced Technology Management, Assumption University of Thailand

Keywords:

AI-powered services, customer satisfaction, employee performance, employee work-life balance, organizational performance

Abstract

The purpose of this research was to investigate the impact of AI-powered services on customer satisfaction, employee performance, employee work-life balance, leading to organizational performance growth in Bangkok. Four variables--customer satisfaction, employee performance, employee work-life balance and organizational performance--were obtained from secondary data analysis and an archive study method. 450 voluntary respondents provided the data by using the online survey form on four variables under study. The research findings revealed that employee work-life balance has an impact on employee performance; and customer satisfaction has impact on organizational performance. These results can definitely generate practical implications for AI-powered service operators in the sectors of education, retailing and healthcare. This research also acknowledges that the focus on Bangkok residents who interact with AI-powered services, would result in limited generalization to other contexts of AI-powered services.

References

Al-Jedibi, W. (2022). The strategic plan of the information technology deanship--King Abdulaziz University--Saudi Arabia. International Journal for Applied Information Management, 2(4), 84-94. https://doi.org/10.47738/ijaim.v2i4.40

Allen, T. D., Johnson, R. C., Kiburz, K. M., & Shockley, K. M. (2012). Work-family conflict and flexible work arrangements: Deconstructing flexibility. Personnel Psychology, 66(2), 345-376. https://doi.org/10.1111/peps.12012

Al-Shoteri, A. (2022). The role of methods and applications of artificial intelligence tools in the field of medicine to diagnose and discover various diseases. Journal of Applied Data Sciences, 3(1), 1-14. https://doi.org/10.47738/jads.v3i1.48

Anderson, E. W., Fornell, C., & Mazvancheryl, S. K. (2004). Customer satisfaction and shareholder value. Journal of Marketing, 68(4), 172-185. https://doi.org/10.1509/jmkg.68.4.172.42723

Astuti, T., & Pratika, I. (2019). Product review sentiment analysis by artificial neural network algorithm. IJIIS: International Journal of Informatics and Information Systems, 2(2), 61-66. https://doi.org/10.47738/ijiis.v2i2.15

Bakker, A. B., & Bal, M. P. (2010). Weekly work engagement and performance: A study among starting teachers. Journal of Occupational and Organizational Psychology, 83(1), 189-206. https://doi.org/10.1348/096317909x402596

Beauregard, T. A., & Henry, L. C. (2009). Making the link between work-life balance practices and organizational performance. Human Resource Management Review, 19(1), 9-22. https://doi.org/10.1016/j.hrmr.2008.09.001

Becker, B. E., & Huselid, M. A. (2006). Strategic human resources management: Where do we go from here? Journal of Management, 32(6), 898-925. https://doi.org/10.1177/0149206306293668

Behl, A., Chavan, M., Jain, K., Sharma, I., Pereira, V. E., & Zhang, J. Z. (2022). The role of organizational culture and voluntariness in the adoption of artificial intelligence for disaster relief operations. International Journal of Manpower, 43(2), 569-586. https://doi.org/10.1108/IJM-03-2021-0178

Boiarintseva, G., Ezzedeen, S. R., & Wilkin, C. (2022). Definitions of work-life balance in childfree dual-career couples: An inductive typology. Equality, Diversity, and Inclusion, 41(4), 525-548. https://doi.org/10.1108/EDI-12-2020-0368

Brynjolfsson, E., & McAfee, A. (2018). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. https://edisciplinas.usp.br/pluginfile.php/4312922/mod_resource/content/2/Erik%20-%20The%20Second%20Machine%20Age.pdf

Bughin, J., Seong, J., Manyika, J., Chui, M., & Joshi, R. (2018). Notes from the AI frontier: Modeling the impact of AI on the world economy. McKinsey & Company. https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-AI-frontier-modeling-the-impact-of-ai-on-the-world-economy

Chang, K. (2020). Artificial intelligence in personnel management: The development of APM Model. The Bottom Line, 33(4), 377-388. https://doi.org/10.1108/bl-08-2020-0055

Chatterjee, S., Chaudhuri, R., Vrontis, D., & Giovando, G. (2023). Digital workplace and organization performance: Moderating role of digital leadership capability. Journal of Innovation & Knowledge, 8(1), 100334. https://doi.org/10.1016/j.jik.2023.100334

Collins, C. J., & Clark, K. D. (2003). Strategic human resource practices, top management team social networks, and firm performance: The role of human resource practices in creating organizational competitive advantage. Academy of Management Journal, 46(6), 740-751. https://doi.org/10.5465/30040665

Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116. https://blockqai.com/wp-content/uploads/2021/01/analytics-hbr-ai-for-the-real-world.pdf

Fornell, C., Mithas, S., Morgeson, F. V., & Krishnan, M. S. (2006). Customer satisfaction and stock prices: High returns, low risk. Journal of Marketing, 70(1), 3-14. https://doi.org/10.1509/jmkg.70.1.003.qxd

Gelade, G. A., & Young, S. (2005). Test of a service profit chain model in the retail banking sector. Journal of Occupational and Organizational Psychology, 78(1), 1-22. https://doi.org/10.1348/096317904x22926

Greenhaus, J. H., Collins, K. M., & Shaw, J. D. (2003). The relation between work–family balance and quality of life. Journal of Vocational Behavior, 63(3), 510-531. https://doi.org/10.1016/S0001-8791(02)00042-8

Gurbaxani, V., & Dunkle, D. (2019). Gearing up for successful digital transformation. MIS Quarterly Executive, 18(3), 209-220. https://doi.org/10.17705/2msqe.00017

Haar, J. M., Russo, M., Suñe, A., & Ollier-Malaterre, A. (2014). Outcomes of work-life balance on job satisfaction, life satisfaction and mental health: A study across seven cultures. Journal of Vocational Behavior, 85(3), 361-373. https://doi.org/10.1016/j.jvb.2014.08.010

Hair, J., Anderson, R., Black, B., & Babin, B. (2016). Multivariate data analysis. Pearson Higher Education. https://www.drnishikantjha.com/papersCollection/Multivariate%20Data%20Analysis.pdf

Hitoshi, H., Kamei, S., & Ohashi, M. (2021). The effectiveness of the body of knowledge process in the startup analysis of efficiency by applying startup management body of knowledge (SUBOK) guide. International Journal for Applied Information Management, 1(2), 70-80. https://doi.org/10.47738/ijaim.v1i2.11

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

Imsa-ard, P., Wichamuk, P., & Chuanchom, C. (2021). Muffled voices from Thai pre-service teachers: Challenges and difficulties during teaching practicum. Shanlax International Journal of Education, 9(3), 246–260. https://doi.org/10.34293/education.v9i3.3989

Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007

Juwaini, A., Chidir, G., Novitasari, D., Iskandar, J., Hutagalung, D., Pramono, T., Maulana, A., Safitri, K., Fahlevi, M., Sulistyo, A. B., & Purwanto, A. (2022). The role of customer e-trust, customer e-service quality and customer e-satisfaction on customer e-loyalty. International Journal of Data and Network Science, 6(2), 477–486. https://doi.org/10.5267/j.ijdns.2021.12.006

Kineber, A. F., Othman, I., Oke, A. E., Chileshe, N., & Zayed, T. (2021). Value management implementation barriers for sustainable building: A bibliometric analysis and partial least square structural equation modeling. Construction Innovation, 23(1), 38–73. https://doi.org/10.1108/CI-05-2021-0103

Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 13(2), 205–223. https://doi.org/10.1287/isre.13.2.205.83

Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607–610. https://doi.org/10.1177/001316447003000308

Lu, L., Cai, R., & Gursoy, D. (2019). Developing and validating a service robot integration willingness scale. International Journal of Hospitality Management, 80, 36–51. https://doi.org/10.1016/j.ijhm.2019.01.005

MacroTrends. (2024). Bangkok, Thailand metro area population 1950–2024. https://www.macrotrends.net/global-metrics/cities/22617/bangkok/population

Matzler, K., Strobl, A., Thurner, N., & Füller, J. (2015). Switching experience, customer satisfaction, and switching costs in the ICT industry. Journal of Service Management, 26(1), 117–136. https://doi.org/10.1108/JOSM-04-2014-0101

Microsoft Pulse. (2021). AI is driving value across businesses in Europe – so why is only 4% of the public sector seeing real impact? https://pulse.microsoft.com/en/transform-en/government-en/fa2-ai-is-driving-value-across-businesses-in-europe-so-why-is-only-4-of-the-public-sector-seeing-real-impact/

Najmi, A., Kanapathy, K., & Aziz, A. A. (2020). Understanding consumer participation in managing ICT waste: Findings from two-staged structural equation modeling–artificial neural network approach. Environmental Science and Pollution Research, 28(12), 14782–14796. https://doi.org/10.1007/s11356-020-11675-2

Na-Nan, K., Chaiprasit, K., & Pukkeeree, P. (2018). Factor analysis-validated comprehensive employee job performance scale. International Journal of Quality & Reliability Management, 35(10), 2436–2449. https://doi.org/10.1108/ijqrm-06-2017-0117

Otto, A. S., Szymanski, D. M., & Varadarajan, R. (2019). Customer satisfaction and firm performance: Insights from over a quarter century of empirical research. Journal of the Academy of Marketing Science, 48(3), 543–564. https://doi.org/10.1007/s11747-019-00657-7

Paschen, U., Pitt, C., & Kietzmann, J. (2020). Artificial intelligence: Building blocks and an innovation typology. Business Horizons, 63(2), 147–155. https://doi.org/10.1016/j.bushor.2019.10.004

Plester, B., & Hutchison, A. (2016). Fun times: The relationship between fun and workplace engagement. Employee Relations, 38(3), 332–350. https://doi.org/10.1108/ER-03-2014-0027

Richard, P. J., Deviney, T. M., Yip, G. S., & Johnson, G. (2009). Measuring organizational performance: Towards methodological best practice. Journal of Management, 35(3), 718–804. https://doi.org/10.1177/0149206308330560

Rosmi, R., & Syamsir, S. (2021). The effect of integrity and professionalism on employee performance in digital era. Proceedings of the 1st Tidar International Conference on Advancing Local Wisdom towards Global Megatrends. https://doi.org/10.4108/eai.21-10-2020.2311846

Rust, R. T., & Huang, M. H. (2012). Optimizing service productivity. Journal of Marketing, 76(2), 47–66. https://doi.org/10.1509/jm.10.0441

Rust, R. T., Moorman, C., & Dickson, P. R. (2002). Getting return on quality: Revenue expansion, cost reduction, or both? Journal of Marketing, 66(4), 7–24. https://doi.org/10.1509/jmkg.66.4.7.18515

Tetteh, S., Dei Mensah, R., Opata, C. N., & Mensah, C. N. (2022). Service employees’ workplace fun and turnover intention: The influence of psychological capital and work engagement. Management Research Review, 45(3), 363–380. https://doi.org/10.1108/MRR-12-2020-0768

Thu Hlaing, Z. T., & Nuangjamnong, C. (2025). The impact of AI-powered services on organizational performance growth. Unpublished master's thesis, Assumption University of Thailand.

Vijayakumar, M., & Marimuthu, R. (2024). Impact of artificial intelligence on marketing in India. Mahsa International Technology and Engineering Conference. https://www.researchgate.net/publication/378590226_Impact_of_Artificial_Intelligence_on_Marketing_in_India

West, D. M., Korinek, A., & Wirtschafter, D. B. (2018). The future of work: Robots, AI, and automation. Brookings. https://www.brookings.edu/events/the-future-of-work-robots-ai-and-automation/

Wijayati, D. T., Rahman, Z., Fahrullah, A., Rahman, M. F., Arifah, I. D., & Kautsar, A. (2021). A study of artificial intelligence on employee performance and work engagement: The moderating role of change leadership. International Journal of Manpower, 43(2), 486–512. https://doi.org/10.1108/ijm-07-2021-0423

World Population Review. (2024). Bangkok population 2024 (Demographics, maps, graphs). https://worldpopulationreview.com/world-cities/bangkok-population

Zeithaml, V. A., Bitner, M. J., & Gremler, D. D. (2010). Services marketing strategy. In Wiley international encyclopedia of marketing (Vol. 2). Wiley. https://doi.org/10.1002/9781444316568.wiem01055

Downloads

Published

2025-04-27

How to Cite

Hlaing, Z. T. T., & Nuangjamnong, C. (2025). The Impact of AI-powered Services on Customer Satisfaction, Employee Performance, and Employee Work-life Balance Leading to Organizational Performance Growth. RICE Journal of Creative Entrepreneurship and Management www.ricejournal.Net, 6(1), 53–79. Retrieved from https://www.ricejournal.net/index.php/rice/article/view/132

Most read articles by the same author(s)