With the upcoming of simultaneous digital-intelligent transformation and the aging of the workforce, the reverse intergenerational knowledge transfer has been one of the strategies of the firms in order to increase the digital competencies of the senior staff and to realize the digital-intelligent advancement of the organization. In contrast to the conventional studies of the intergenerational knowledge exchange, which focus on the forward transfer of the knowledge from the older worker to the younger worker, there is a lack of systematic inquiry of the operation difficulty and the practical barrier of the reverse transfer in the digital-intelligent setting. In this study, by using the Job Demands-Resources Model, we review the literatures of the reverse intergenerational knowledge transfer in the digitally intelligent organization. We look at the practical difficulties from four perspectives, the knowledge sender, the knowledge receiver, the organization's institutional environment and the digital-intelligent technological media. The problem is considered in the present paper from point of view of the psychological cognition of the individual, of the mechanism of intergenerational inter-action, of the lack of the organization incentive, and of the lack of the technical adaptability. Therefore, it can be useful for the companies that would like to improve the mechanisms of the reverse intergenerational knowledge transfer of the employees.

Huaqian Huang, Caifeng Qin. Dilemmas and Challenges of Reverse Intergenerational Knowledge Transfer among Enterprise Employees in the Context of Digital-Intelligent Transformation. Academic Journal of Business & Management (2026), Vol. 8, Issue 10: 1-5. https://doi.org/10.25236/AJBM.2026.081001.

[1] He F, Liu H X. The performance improvement effect of digital transformation enterprises from the digital economy perspective [J]. Reform, 2019(4): 137-148.

[2] Zhang M S, Zeng Y T, Feng T. The impact of artificial intelligence on employment demand: Based on the perspective of labor structure [J]. Forum on Science and Technology in China, 2021(12): 125-133.

[3] Wang C Y, Zuo M Y. A study of intergenerational knowledge transfer intention from the perspective of the generativity theory [J]. Science Research Management, 2017, 38(8): 134-142.

[4] Burmeister A, Wang M, Hirschi A. Understanding the motivational benefits of knowledge transfer for older and younger workers in age diverse coworker dyads: An actor–partner interdependence model [J]. Journal of Applied Psychology, 2020, 105(7): 748-759.

[5] Wang C Y, Zuo M Y. Study on the impacts of perceived organizational factors and work meaning on older Employees’ offline intergenerational knowledge transfer [J]. Chinese Journal of Management, 2020, 17(8): 1228-1237.

[6] Li X Y, Gan F F. “Digital Feedback”: Reverse Generation Support under Digital Intelligence Field Operation Logic and Path Support-Take Jiangsu Nanjing “Smart Pension” Platform as an Example [J]. News Research, 2025(3): 13-19+92.

[7] Demerouti E, Nachreiner F, Bakker A B, et al. The job demands resources model of burnout [J]. Journal of Applied Psychology, 2001, 86(3): 499-512.

[8] Bakker A B, Demerouti E. The Job Demands Resources model: state of the art. Journal of Managerial Psychology, 2007, 22(3): 309-328.

[9] Tams S. Helping older workers realize their full organizational potential: a moderated mediation model of age and IT enabled task performance [J]. MIS Quarterly, 2022, 46(1): 1-34.

[10] Wang D Y, Li L, Gao X C. Differences in the motivational paths of bi-directional intergenerational knowledge transfer in Chinese cultural context: From the configuration perspective [J]. Soft Science, 2024, 37(12): 23-30.

[11] Zeng D L, Cai J W, Ouyang T H. A Research on Digital Transformation: Integration Framework and Prospects [J]. Foreign Economics & Management, 2021, 43(5): 63-76. 

[12] Vial G. Understanding digital transformation: A review and a research agenda [J]. The Journal of Strategic Information Systems, 2019, 28(2): 118-144.