Artificial intelligence for employee engagement and productivity
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DOIhttp://dx.doi.org/10.21511/ppm.22(3).2024.14
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Article InfoVolume 22 2024, Issue #3, pp. 174-184
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The “new normal” era has made remote work the new standard, making the use of artificial intelligence (AI) increasingly important. Therefore, this study aims to investigate employee perceptions of change leadership in the application of AI that affects employee engagement and productivity according to the resource-based view (RBV). Of the 467 respondents who worked in the banking industry in West Sumatra province, Indonesia, only 359 met the eligibility requirements. The partial least squares (PLS) analysis shows a direct relationship between AI and employee engagement (p < 0.05) and productivity (p < 0.05), as well as employee engagement and employee productivity (p < 0.05). The effect of AI on employee productivity is mediated by employee engagement (p < 0.05), but the moderating effect provided by change leadership is not significant (p > 0.05) in increasing employee productivity. These findings will help managers create a positive work environment through the application of AI, resulting in higher employee engagement and productivity. Specifically, these findings help organizations integrate AI more effectively and provide managers with a comprehensive understanding of the considerations needed to increase productivity through employee engagement for organizational competitiveness.
Acknowledgment
The authors thank Universitas Negeri Padang for helping finish this article. We also appreciate the cooperation and support of each member.
- Keywords
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JEL Classification (Paper profile tab)M15, M54, M50
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References43
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Tables4
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Figures1
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- Figure 1. Research model
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- Table 1. Measurement of constructs
- Table 2. Convergent validity
- Table 3. HTMT ratio (Discriminant validity)
- Table 4. Hypotheses testing
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- Abrokwah-Larbi, K., & Awuku-Larbi, Y. (2024). The impact of artificial intelligence in marketing on the performance of business organizations: evidence from SMEs in an emerging economy. Journal of Entrepreneurship in Emerging Economies, 16(4), 1090-1117.
- Alexander, A., De Smet, A., Langstaff, M., & Ravid, D. (2021). What employees are saying about the future of remote work. McKinsey & Company.
- Arslan, A., Cooper, C., Khan, Z., Golgeci, I., & Ali, I. (2022). Artificial intelligence and human workers interaction at team level: A conceptual assessment of the challenges and potential HRM strategies. International Journal of Manpower, 43(1), 75-88.
- Bagozzi, R. P., & Yi, Y. (2012). Specification, evaluation, and interpretation of structural equation models. Journal of the Academy of Marketing Science, 40, 8-34.
- Barney, J. B., Ketchen Jr, D. J., & Wright, M. (2011). The future of resource-based theory: Revitalization or decline? Journal of Management, 37(5), 1299-1315.
- Bashir, I., Qureshi, I. H., & Ilyas, Z. (2024). How does employee financial well-being influence employee productivity: A moderated mediating examination. International Journal of Social Economics.
- Belhadi, A., Mani, V., Kamble, S. S., Khan, S. A. R., & Verma, S. (2024). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: An empirical investigation. Annals of Operations Research, 333(2), 627-652.
- Chang, S.-J., Van Witteloostuijn, A., & Eden, L. (2020). Common method variance in international business research. In L. Eden, B. B. Nielsen, & A. Verbeke (Eds.), Research Methods in International Business (pp. 385-398). Cham: Palgrave Macmillan.
- Chaudhuri, R., Chatterjee, S., Vrontis, D., & Thrassou, A. (2021). Adoption of robust business analytics for product innovation and organizational performance: The mediating role of organizational data-driven culture. Annals of Operations Research, 23(5), 1-35.
- Chen, D., Esperança, J. P., & Wang, S. (2022). The impact of artificial intelligence on firm performance: An application of the resource-based view to e-commerce firms. Frontiers in Psychology, 13, Article 884830.
- Choi, H., Park, M. J., Rho, J. J., & Zo, H. (2016). Rethinking the assessment of e-government implementation in developing countries from the perspective of the design–reality gap: Applications in the Indonesian e-procurement system. Telecommunications Policy, 40(7), 644-660.
- Dhamija, P., Chiarini, A., & Shapla, S. (2023). Technology and leadership styles: A review of trends between 2003 and 2021. The TQM Journal, 35(1), 210-233.
- Farooq, R., & Sultana, A. (2022). The potential impact of the COVID-19 pandemic on work from home and employee productivity. Measuring Business Excellence, 26(3), 308-325.
- Fu, S., Zheng, X., & Wong, I. A. (2022). The perils of hotel technology: The robot usage resistance model. International Journal of Hospitality Management, 102, Article 103174.
- Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M., Danks, N.P., & Ray, S. (2021). An introduction to structural equation modeling. In Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook (pp. 1-29). Cham: Springer.
- 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.
- Huang, M.-H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155-172.
- Hughes, C., Robert, L., Frady, K., & Arroyos, A. (2019). Artificial intelligence, employee engagement, fairness, and job outcomes. In Managing technology and middle-and low-skilled employees (The Changing Context of Managing People) (pp. 61-68). Leeds: Emerald Publishing Limited.
- Janah, N., Medias, F., & Pratiwi, E. K. (2020). The intention of religious leaders to use Islamic banking services: The case of Indonesia. Journal of Islamic Marketing, 12(9), 1786-1800.
- Jindo, T., Kai, Y., Kitano, N., Tsunoda, K., Nagamatsu, T., & Arao, T. (2020). Relationship of workplace exercise with work engagement and psychological distress in employees: A cross-sectional study from the MYLS study. Preventive Medicine Reports, 17, Article 101030.
- Kashive, N., Powale, L., & Kashive, K. (2021). Understanding user perception toward artificial intelligence (AI) enabled e-learning. The International Journal of Information and Learning Technology, 38(1), 1-19.
- Kassa, A. G., & Tsigu, G. T. (2022). Corporate entrepreneurship, employee engagement and innovation: A resource-basedview and a social exchangetheory perspective. International Journal of Organizational Analysis, 30(6), 1694-1711.
- Kaur, P., Malhotra, K., & Sharma, S. K. (2020). Moderation-mediation framework connecting internal branding, affective commitment, employee engagement and job satisfaction: An empirical study of BPO employees in Indian context. Asia-Pacific Journal of Business Administration, 12(3/4), 327-348.
- Li, M., Yin, D., Qiu, H., & Bai, B. (2021). A systematic review of AI technology-based service encounters: Implications for hospitality and tourism operations. International Journal of Hospitality Management, 95, Article 102930.
- Men, L. R., O’Neil, J., & Ewing, M. (2020). Examining the effects of internal social media usage on employee engagement. Public Relations Review, 46(2), Article 101880.
- Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), Article 103434.
- Omoge, A. P., Gala, P., & Horky, A. (2022). Disruptive technology and AI in the banking industry of an emerging market. International Journal of Bank Marketing, 40(6), 1217-1247.
- Onyeneke, G. B., & Abe, T. (2021). The effect of change leadership on employee attitudinal support for planned organizational change. Journal of Organizational Change Management, 34(2), 403-415.
- Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903.
- Prentice, C., Wong, I. A., & Lin, Z. C. J. (2023). Artificial intelligence as a boundary-crossing object for employee engagement and performance. Journal of Retailing and Consumer Services, 73, Article 103376.
- Saks, A. M. (2006). Antecedents and consequences of employee engagement. Journal of Managerial Psychology, 21(7), 600-619.
- Sandee, H. (2016). Improving connectivity in Indonesia: The challenges of better infrastructure, better regulations, and better coordination. Asian Economic Policy Review, 11(2), 222-238.
- Satrianto, A., Gusti, M. A., Candrianto, & Nurtati. (2023). The role of Islamic work ethics and organizational citizenship behavior in green human resource practices and environmental performance of Indonesian food SMEs. International Journal of Sustainable Development and Planning, 18(8), 2393-2401.
- Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach. John Wiley & Sons.
- Soltani, Z., Zareie, B., Rajabiun, L., & Agha Mohseni Fashami, A. (2020). The effect of knowledge management, e-learning systems and organizational learning on organizational intelligence. Kybernetes, 49(10), 2455-2474.
- Tong, S., Jia, N., Luo, X., & Fang, Z. (2021). The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance. Strategic Management Journal, 42(9), 1600-1631.
- Wheelen, T. L., & Hunger, J. D. (2012). Strategic management and business policy: Towards global sustainability. John Wiley & Sons.
- Wienrich, C., & Latoschik, M. E. (2021). eXtended artificial intelligence: New prospects of human-ai interaction research. Frontiers in Virtual Reality, 2, Article 686783.
- Wijayati, D. T., Rahman, Z., Fahrullah, A., Rahman, M. F. W., Arifah, I. D. C., & 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.
- Xu, Y., Shieh, C.-H., van Esch, P., & Ling, I.-L. (2020). AI customer service: Task complexity, problem-solving ability, and usage intention. Australasian Marketing Journal, 28(4), 189-199.
- Zhang, X., Liao, H., Li, N., & Colbert, A. E. (2020). Playing it safe for my family: Exploring the dual effects of family motivation on employee productivity and creativity. Academy of Management Journal, 63(6), 1923-1950.
- Zhao, C., Cooke, F. L., & Wang, Z. (2021). Human resource management in China: What are the key issues confronting organizations and how can research help? Asia Pacific Journal of Human Resources, 59(3), 357-373.
- Zhou, Y., Liu, G., Chang, X., & Wang, L. (2021). The impact of HRM digitalization on firm performance: Investigating three-way interactions. Asia Pacific Journal of Human Resources, 59(1), 20-43.