Main Article Content
Abstract
The adoption of Artificial Intelligence (AI) within organizations has introduced several phenomena impacting employee motivation. One significant area of interest is the perception of AI principles among employees Employees' views on the effectiveness of AI principles in their organization can vary greatly depending on their roles and responsibilities. For example, a junior data scientist might perceive the principles differently than a Chief Analytics Officer due to their varying levels of involvement and understanding of AI's strategic implementation. This research aims to examine the influence of AI Adoption in moderating job engagement on employee trust compared to the direct influence of job engagement on employee trust. Quantitative research method using non-probability sampling and purposive sampling technique method of 200 mass media employees in Surabaya. Data analysis techniques using Smart PLS software with hypothesis testing using PLS-based SEM. The research results show that AI Adoption is able to moderate the influence of job engagement on employee trust and there is a direct influence of job engagement on employee trust.
Keywords
Article Details

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
- Creswell. (2021). A Concise Introduction to Mixed Methods Research . USA: SAGE.
- Davis, F. (1989). Perceived Usefulness, Perceived Ease of Use and User Acceptance of Information Technology. MIT Quarterly,13(3),319-340. https://doi.org/10.2307/249008
- Gamito, M. (2023). The Influence of China in AI Governance Through Standardisation.Journal of Telecommunications Policy, 47(10).30-40. https://doi.org/10.1016/j.telpol.2023.102673
- Hammer, & Karmakar. (2021). Automation AI and the Future of Work in India. Journal of Labor and Society, 43(6),1327-1341. https://doi.org/10.1108/ER-12-2019-0452.
- Herzberg, F. (1965). The Motivation to Work Among Finnish Supervisors.Personnel Psychology, 18(4),393-402.
- Kuberkar, S., & Singhai, T. (2020). Factors Influencing Adoption Intention of AI Powered Chatbot for Public Transport Services within a Smart City. International Journal on Emerging Technologies,11(3),948-958.
- Lin, S. (2023). Based on the Two-Factor Theory, The Influence of Artificial Intelligence on Employee Motivation is Analyzed. Frontiers in Business Economics and Management, 11(3),91-94. https://doi.org/10.54097/fbem.v11i3.13194
- Malik, Tripathi, Kar, & Gupta. (2022). Impact of Artificial Intelligence on Employees Working in Industry 4.0 Ied Organizations. International Journal of Manpower,43(2),201-222. https://doi.org/10.1108/ijm-03-2021-0173
- Prentice, C., Lopes, S., & Wang, X. (2019). Emotional Intelligence or Artificial Intelligence-An Employee Perspective. Journal of Hospitality Marketing & Management, 29(1),377-403. https://doi.org/10.1080/19368623.2019.1647124.
- Shaikh, Qureshi, Noordin, Shaikh, Khan, & Shahbaz. (2020). Acceptance of Islamic Financial Technology Banking Services by Malaysian Users : An Extension of Technology Acceptance Model. Foresight, 22(3),367-383. https://doi.org/10.1108/FS-12-2019-0105.
- Worsdorfer, M. (2024). Mitigating the Adverse Effects of AI With European Union's Artificial Intelligence Act : Hype or Hope?.Global Business and Organizational Excellence, 43(3),106-126. https://doi.org/10.1002/joe.22238.
- Yakovenko, Y., Bilyk, M., & Oiiinyk, Y. (2022). The Transformative impact of the Development of Artificial Intelligence on Employment and Work Motivation in Business in the Conditions of the Information Economy. Journal of Management, 2(2),1-6. https://doi.org/10.1109/MEES58014.2022.10005652.
- Zhang, J., Li, X., & Tong, T. (2023). A Tale of Two Types of Standard Setting : Evidence From Artificial Intelligence in China. Journal of Management, 50(4),1393-1423. https://doi.org/10.1177/01492063221145130.
References
Creswell. (2021). A Concise Introduction to Mixed Methods Research . USA: SAGE.
Davis, F. (1989). Perceived Usefulness, Perceived Ease of Use and User Acceptance of Information Technology. MIT Quarterly,13(3),319-340. https://doi.org/10.2307/249008
Gamito, M. (2023). The Influence of China in AI Governance Through Standardisation.Journal of Telecommunications Policy, 47(10).30-40. https://doi.org/10.1016/j.telpol.2023.102673
Hammer, & Karmakar. (2021). Automation AI and the Future of Work in India. Journal of Labor and Society, 43(6),1327-1341. https://doi.org/10.1108/ER-12-2019-0452.
Herzberg, F. (1965). The Motivation to Work Among Finnish Supervisors.Personnel Psychology, 18(4),393-402.
Kuberkar, S., & Singhai, T. (2020). Factors Influencing Adoption Intention of AI Powered Chatbot for Public Transport Services within a Smart City. International Journal on Emerging Technologies,11(3),948-958.
Lin, S. (2023). Based on the Two-Factor Theory, The Influence of Artificial Intelligence on Employee Motivation is Analyzed. Frontiers in Business Economics and Management, 11(3),91-94. https://doi.org/10.54097/fbem.v11i3.13194
Malik, Tripathi, Kar, & Gupta. (2022). Impact of Artificial Intelligence on Employees Working in Industry 4.0 Ied Organizations. International Journal of Manpower,43(2),201-222. https://doi.org/10.1108/ijm-03-2021-0173
Prentice, C., Lopes, S., & Wang, X. (2019). Emotional Intelligence or Artificial Intelligence-An Employee Perspective. Journal of Hospitality Marketing & Management, 29(1),377-403. https://doi.org/10.1080/19368623.2019.1647124.
Shaikh, Qureshi, Noordin, Shaikh, Khan, & Shahbaz. (2020). Acceptance of Islamic Financial Technology Banking Services by Malaysian Users : An Extension of Technology Acceptance Model. Foresight, 22(3),367-383. https://doi.org/10.1108/FS-12-2019-0105.
Worsdorfer, M. (2024). Mitigating the Adverse Effects of AI With European Union's Artificial Intelligence Act : Hype or Hope?.Global Business and Organizational Excellence, 43(3),106-126. https://doi.org/10.1002/joe.22238.
Yakovenko, Y., Bilyk, M., & Oiiinyk, Y. (2022). The Transformative impact of the Development of Artificial Intelligence on Employment and Work Motivation in Business in the Conditions of the Information Economy. Journal of Management, 2(2),1-6. https://doi.org/10.1109/MEES58014.2022.10005652.
Zhang, J., Li, X., & Tong, T. (2023). A Tale of Two Types of Standard Setting : Evidence From Artificial Intelligence in China. Journal of Management, 50(4),1393-1423. https://doi.org/10.1177/01492063221145130.