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This exploratory sequential mixed-methods paper explores the relationship between gig and taxi drivers’ perceptions of autonomous vehicles (AVs) and their continuance intentions. Drawing from the Career Construction Model of Adaptation, we examined the relationship between drivers’ expectations about AV-related job changes and their intentions to stay or leave their driving role upon the integration of AVs. In Study 1, we collected qualitative data from gig and taxi drivers (N= 69) in 24 focus groups. In Study 2, we administered a survey to gig and taxi drivers (N= 496). The thematic analysis in Study 1 revealed how drivers expected the onset of AVs to positively and negatively impact their job (changes to work stress, safety, job enjoyment, etc.). These expectations influenced their decisions to remain in or leave their jobs. Multivariate regression in Study 2 showed that multiple factors identified in Study 1 were related to continuance intentions, with some being “retention factors” (related to intentions to stay) and others being “turnover factors” (related to intentions to leave). Our findings contribute to the evolving discourse on the impact of new technologies on continuance intentions by offering theoretical and practical implications in careers and vocational behavior. 

BackgroundThe fast-changing labor market highlights the need for an in-depth understanding of occupational mobility impacted by technological change. However, we lack a multidimensional classification scheme that considers similarities of occupations comprehensively, which prevents us from predicting employment trends and mobility across occupations. This study fills the gap by examining employment trends based on similarities between occupations. MethodWe first demonstrated a new method that clusters 756 occupation titles based on knowledge, skills, abilities, education, experience, training, activities, values, and interests. We used the Principal Component Analysis to categorize occupations in the Standard Occupational Classification, which is grouped into a four-level hierarchy. Then, we paired the occupation clusters with the occupational employment projections provided by the U.S. Bureau of Labor Statistics. We analyzed how employment would change and what factors affect the employment changes within occupation groups. Particularly, we specified factors related to technological changes. ResultsThe results reveal that technological change accounts for significant job losses in some clusters. This poses occupational mobility challenges for workers in these jobs at present. Job losses for nearly 60% of current employment will occur in low-skill, low-wage occupational groups. Meanwhile, many mid-skilled and highly skilled jobs are projected to grow in the next ten years. ConclusionOur results demonstrate the utility of our occupational classification scheme. Furthermore, it suggests a critical need for skills upgrading and workforce development for workers in declining jobs. Special attention should be paid to vulnerable workers, such as older individuals and minorities.