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Abstract The increasing adoption of automation will likely replace the tasks performed in many jobs, resulting in new challenges for workers. Yet, little is known regarding how workers perceive automation, including how it may influence their job attitudes and turnover intentions. Automated vehicles (AVs) are one example of new technology poised to alter the job of truck driving, which is overwhelmingly populated by older workers. In this study, we examined truck drivers’, supervisors’, and managers’ attitudes and concerns about AV adoption and its effects on driving jobs to help the transportation industry prepare for automation with minimal workforce disruption. We drew from theorizing on self-interest in economics and lifespan coping theories to contextualize workers’ reactions to automation. We conducted focus groups and interviews with truck drivers (N=18), supervisors of drivers (N=8), and upper-level managers of trucking companies (N=25). Two themes emerged from the thematic analysis: the unknown, and proficiency. AVs may be viewed as threatening by drivers, causing anxiety due to widespread uncertainty and the fear of job loss and loss of control. At the same time, there will be a greater need for drivers to be adaptable for the era of AVs. AVs are also likely to result in other changes to the role of driving, which may have implications for driver recruitment and selection. We interpret our findings together with lifespan theories of control and coping and provide recommendations for organizations to effectively prepare for automation in the trucking industry. 

Automation continues to be a disruptive force in the workforce. In particular, new automated technologies are projected to replace many mid-skill jobs, potentially displacing millions of workers. Career planning agencies and other organizations can help support workers if they are able to effectively identify optimal transition occupations for displaced workers. We drew upon the 24.2 Occupational Information Network (O*NET) Database to conduct two related studies that identify alternate occupations for truck drivers, who are at risk of job loss due to the adoption of autonomous vehicles. In Study 1, we statistically compared the jobs that we identified based on different search methods using O*NET classifications based on their similarity to the knowledge, skills, values, and interests held by truck drivers. In Study 2, we conducted a survey of truck drivers to evaluate their perceptions of the occupations identified as objectively similar to their occupation. Results indicate that optimal transition occupations may be identified by searching for occupations that share skills as well as the same work activities/industry as a given occupation. These findings hold further implications for career planning organizations and policymakers to ease workforce disruption due to automation.