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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. 

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. 

Online surveys are a popular method for collecting data in the social sciences. Despite its cost-effectiveness, concerns regarding the legitimacy of data from online surveys are increasing. One such concern is fraudulent responses or “spam” by malicious agents intentionally deceiving the survey process to gain monetary incentives or sway research results. The research costs of “spam”—their influence on research conclusions and their threat to scientific integrity—are not well understood. Here we show the differences in financial and research costs of spam using data from an online survey of transportation workers that was cleaned using a stringent battery of spam detection techniques that utilized commercially available features and a custom spam detection algorithm. We found that we would have wasted about 73% of our budget on incentivizing spammers if we had stopped data collection upon reaching the intended sample size. We also found significant differences in research conclusions related to the relationships between key organizational constructs, including affective commitment, job satisfaction, and turnover intention, between subsamples with and without spam. Our results demonstrate that researchers who are unaware of spam or do not adequately clean their data may spend substantially more monetary and human resources, as well as derive misleading conclusions. This study highlights the importance of survey researchers being cognizant of spam responses and employing robust spam detection techniques to ensure the scientific integrity of non-probability online survey research. 

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.