Employees' Knowledge of ChatGPT and Motivational Factors

Authors

  • Taeyoung Choi Stony brook University, Korea
  • Sira Maliphol Stony Brook University/SUNY Korea

DOI:

https://doi.org/10.47611/jsr.v14i1.2911

Keywords:

Artificial intelligence, Motivation Factors, Human resources management

Abstract

This study investigates the relationship between employees' knowledge of ChatGPT and their motivational factors, such as achievement, recognition, and growth potential. In the context of rapid AI adoption, particularly in South Korea, a survey was conducted to measure employees' technological and empirical knowledge of ChatGPT alongside their motivational factors. Using descriptive statistical analysis, the findings reveal that technological knowledge is more closely related to higher motivational factors than empirical knowledge. Employees more familiar with ChatGPT's function and operation perceive higher achievement, recognition, and growth potential. The study also found that frequent use of ChatGPT positively influences employees' motivation. Ultimately, the study suggests that fostering employees' understanding of Technological Knowledge of AI can enhance their job motivation, contributing to improved job performance and organizational productivity.

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Author Biographies

Taeyoung Choi, Stony brook University, Korea

Senior students of SUNY Korea / Stonybrook University

Dept. of Technology and Society

Sira Maliphol, Stony Brook University/SUNY Korea

Dept. of Technology and Society

Assistant Professor of SUNY KOREA

Ph.D. Seoul National University: Engineering (Technology Management, Economics, and Policy)

 

References or Bibliography

Alshallah, S. (2004). Job satisfaction and motivation: how do we inspire employees? Radiology management, 26(2), 47–51.

Alshmemri, M., Shahwan-Akl, L., & Maude, P. (2017). Herzberg’s two-factor theory. Life Science Journal, 14(5), 12-16.

Aziri, B. (2011) Job Satisfaction: A Literature Review. Management Research and Practice, 3, 77-86.

Bloom, N., Sadun, R., & Reenen, J. V. (2012). Americans do it better: US Multinationals and the productivity miracle. American Economic Review, 102(1), 167–201. https://doi.org/10.1257/aer.102.1.167

Borkman, T. (1976). Experiential Knowledge: A New Concept for the Analysis of Self-Help Groups. Social Service Review, 50(3), 445–456. http://www.jstor.org/stable/30015384

Boshoff, N. (2014). Types of knowledge in science-based practices. Journal of Science Communication, 13(03). https://doi.org/10.22323/2.13030206

Broecke, S. (2023). Artificial Intelligence and the Labour Market: Introduction. OECD Employment Outlook. https://doi.org/10.1787/63bcc69a-en

Brougham, D., & Haar, J. (2020). Technological Disruption and employment: The influence on job insecurity and turnover intentions: A multi-country study. Technological Forecasting and Social Change, 161, 120276. https://doi.org/10.1016/j.techfore.2020.120276

Castellacci, F., & Viñas-Bardolet, C. (2019). Internet use and job satisfaction. Computers in Human Behavior, 90, 141–152. https://doi.org/10.1016/j.chb.2018.09.001

Choi, J., Choi, S., Lee, E.-Y., & Lee, H. (2023, December 31). ICT diffusion and Labor Earnings Gaps. Korea Information Society Development Institute. https://www.kisdi.re.kr/report/view.do?key=m2101113024153&masterId=3934560&arrMasterId=3934560&artId=1671136

Deci, E. L., & Ryan, R. M. (2013). Intrinsic motivation and self-determination in human behavior. Springer Science & Business Media.

Descombe, M. (2017). The Good Research Guide: For Small-Scale Social Research Projects. 6th Edition. London: McGraw Hill.

Draca, M., Sadun, R., & Van Reenen, J. (2006). Productivity and ICT: A review of the evidence. Centre for Economic Performance.

Gilber. R. (1949), The concept of mind, Hutchinson, London, U.K.

Herzberg, F. (1964). The motivation-hygiene concept and problems of manpower. Personnel Administration, 27(1), 3–7.

Hoboubi, N., Choobineh, A., Kamari Ghanavati, F., Keshavarzi, S., & Akbar Hosseini, A. (2017). The impact of job stress and job satisfaction on workforce productivity in an Iranian petrochemical industry. Safety and Health at Work, 8(1), 67–71. https://doi.org/10.1016/j.shaw.2016.07.002

Hoppock, R. (1935). Job Satisfaction, Harper and Brothers, New York, p. 47

Jyung, C.-Y., Lee, Y., Park, S., Cho, E., & Choi, R. (2020). Factors affecting employees’ problem-solving skills in technology-rich environments in Japan and Korea. Sustainability, 12(17), 7079. https://doi.org/10.3390/su12177079

Katebi, A., HajiZadeh, M. H., Bordbar, A., & Salehi, A. M. (2021). The relationship between “job satisfaction” and “Job performance”: A meta-analysis. Global Journal of Flexible Systems Management, 23(1), 21–42. https://doi.org/10.1007/s40171-021-00280-y

Kim, K., Maliphol, S., Shim, D., & Lee, C. (2024). Exploring the interplay between social distancing, innovation adoption, and privacy concerns amid the COVID-19 crisis. Science and Public Policy, scae024.

Koohborfardhaghighi, S., Romero, J. P., Maliphol, S., Liu, Y., & Altmann, J. (2017, August). How bounded rationality of individuals in social interactions impacts evolutionary dynamics of cooperation. In Proceedings of the International Conference on Web Intelligence (pp. 381-388).

Liu, S. S., Pei, J. L., & Zhong, C. Y. (2021). Is the Platform Work Autonomous. The Effect of Online Labor Platform Algorithm Management on Job Autonomy. Foreign Econ. Manag, 43, 51-67.

Locke, E.A. and Latham, G.P. (1990). A theory of goal setting and task performance, Prentice Hall, p.4

Maliphol, S., & Walter, S. (2023, July). A Systematic Review of Digital Skills and Sustainable Development. In 2023 Portland International Conference on Management of Engineering and Technology (PICMET) (pp. 1-9). IEEE.

Maliphol, S. (2019). The Internal and External Effects of Knowledge Sources with Implications for the Middle-Income Trap (Doctoral dissertation, Seoul National University).

MacCormick, E. J., & Tiffin, J. (1974). Industrial psychology. Prentice-Hall.

Myers, M. S. (1964). Who are your motivated workers? Harvard Business Review, 42, 73-88.

Nazareno, L., & Schiff, D. S. (2021). The impact of automation and artificial intelligence on worker well-being. Technology in Society, 67, 101679. https://doi.org/10.1016/j.techsoc.2021.101679

Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of Generative Artificial Intelligence. Science, 381(6654), 187–192. https://doi.org/10.1126/science.adh2586

Obrenovic, B., Jianguo, D., Tsoy, D., Obrenovic, S., Khan, M. A., & Anwar, F. (2020). The enjoyment of knowledge sharing: Impact of altruism on tacit knowledge-sharing behavior. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.01496

Olan, F., Ogiemwonyi Arakpogun, E., Suklan, J., Nakpodia, F., Damij, N., & Jayawickrama, U. (2022). Artificial Intelligence and knowledge sharing: Contributing factors to organizational performance. Journal of Business Research, 145, 605–615. https://doi.org/10.1016/j.jbusres.2022.03.008

Pizzinelli, C. (2023). Labor market exposure to AI: Cross-country differences and distributional implications. IMF Working Papers, 2023(216), 1. https://doi.org/10.5089/9798400254802.001

Raj, R., Singh, A., Kumar, V., & Verma, P. (2023). Analyzing the potential benefits and use cases of chatgpt as a tool for improving the efficiency and effectiveness of business operations. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 3(3), 100140. https://doi.org/10.1016/j.tbench.2023.100140

Smith, H. C., & Wakeley, J. H. (1972). Psychology of industrial behavior by Henry Clay Smith and John H. Wakeley. McGraw-Hill.

Vera, A., & Boateng, P. A. (2015). Impact of reward and recognition on job satisfaction and motivation. The International Institute for Science, Technology and Education (IISTE).

Viete, S., & Erdsiek, D. (2020). Mobile Information Technologies and firm performance: The Role of Employee Autonomy. Information Economics and Policy, 51, 100863. https://doi.org/10.1016/j.infoecopol.2020.100863

Vroom, V.H. (1964). Work and motivation, John Wiley and Sons, New York, p.99

Wijayati, D. T., Rahman, Z., Fahrullah, A., Rahman, M. F., Arifah, I. D., & Kautsar, A. (2022). 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

Xu, G., Xue, M., & Zhao, J. (2023). The relationship of Artificial Intelligence Opportunity Perception and employee workplace well-being: A moderated mediation model. International Journal of Environmental Research and Public Health, 20(3), 1974. https://doi.org/10.3390/ijerph20031974

Yu, P. W., & Golden, J. (2019). Developing tpack in Elementary Mathematics Education. Handbook of Research on TPACK in the Digital Age, 47–68. https://doi.org/10.4018/978-1-5225-7001-1.ch003

Published

02-28-2025

How to Cite

Choi, T., & Maliphol, S. (2025). Employees’ Knowledge of ChatGPT and Motivational Factors. Journal of Student Research, 14(1). https://doi.org/10.47611/jsr.v14i1.2911

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Section

Research Articles