Zhang, Fangfang; Preedeesanith, Prich; Parker, Sharon K.

Purpose and theoretical background. Older employees are invaluable to organizations because of their extensive experience, knowledge, and skills. Yet, the widespread adoption of automated and digital technologies presents a significant challenge for these employees in terms of adaptation and learning. To successfully interact and work with new technology, employees need to engage in life-long learning to update their skills as the types of skills needed change rapidly due to technological changes. Scholars have proposed that good work design can enhance employees’ on-job learning through workers executing daily tasks. This study aims to explore how job complexity, a critical job characteristic, affects older employees' motivation and their ability to learn new automation technologies.
Methodology. We conducted a randomized experiment involving a simulated task that represents the type of work in technologically advanced warehouses. Participants, aged 40-60 and recruited from Prolific, assumed the role of warehouse employees. Their task involved using specific programming commands to operate an autonomous robot for package handling. A total of 150 participants completed this simulation, randomly assigned to conditions of high or low job complexity which vary on the complexity level of tasks included.
Findings. Success rates in performing transfer tasks were significantly higher in the high complexity group (94.3%) compared to the low complexity group (67.8%), χ²(1, 124) = 13.1, p < .001. Additionally, the high complexity group completed tasks more efficiently (M = 145.40, SD = 107.77) than the low complexity group (M = 247.75, SD = 204.45), with t(70.6) = 3.08, p = .003, d = .63. These results align with our hypotheses, suggesting that high complexity work environments enhance learning performance.
Research and Practical Implications. This study contributes to our understanding of the impact of work design on employee attitudes, motivation, and learning performance amidst the integration of autonomous technologies. The findings offer valuable insights for organizations looking to optimize the design and management of autonomous systems, thereby improving employee experiences.

At the conclusion of this event, attendees will be able to:
1. understand how work design impacts the motivation and learning performance of older employees within the context of adopting new automation technologies.
2. apply key principles of job design—focusing on job complexity—to create or modify work environments that enhance older employees' engagement and learning with new technologies.
3. design and implement simulation tasks similar to the study's approach, aimed at investigating the effects of work design elements on employee learning and adaptation to new technologies.