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1. Perceptions on the Future of Automation in r/Truckers

   https://doi.org/10.1145//3411763.3451637  

   Orii, Lisa; Tosca, Diana; Kun, Andrew L; Shaer, Orit (May 2021, CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems)

   null (Ed.)

   New developments in automation have led to discussions about the impact that autonomous trucks will have on the trucking industry. However, there is a lack of literature on truck drivers’ perceptions of automation. To gain an understanding of the trucking community’s sentiments towards automation, we analyzed member discussions related to automation in the r/Truckers subreddit. Among the comments we analyzed, concerns about the feasibility of automation were popular and, in general, community members expressed negative perspectives on automation in trucking. This was corroborated by our findings that only 0.98% (9/915) comments had positive views on automation. Speculations on when automation of any degree will take place in the trucking industry varied, but the view that automation would eventually happen but not for a long time was the most common. To conclude, we highlight a need to support and empower truck drivers through the significant changes facing this industry. 

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2. Perceptions of Trucking Automation: Insights from the r/Truckers Community

   https://doi.org/10.1145/3409118.3475154  

   Orii, Lisa; Tosca, Diana; Kun, Andrew L; Shaer, Orit (September 2021, AutomotiveUI 2021)

   null (Ed.)

   Recent technological advancements in automation have sparked interest in how automation will affect truck drivers and the truck- ing industry. However, there is a gap in the literature addressing how truck drivers perceive automation and how they believe it will impact trucking. This study aims to understand truck drivers’ perspectives on automation in the trucking industry. Extending a preliminary study, we conducted a broader analysis of comments discussing automation in the r/Truckers subreddit from February 2017 to March 2021. In general, the community had negative senti- ments towards automation in the trucking industry. Participants speculated when automation would become mainstream in truck- ing and discussed the feasibility of automation in the context of executing non-driving tasks and having accommodating infrastruc- ture. Our findings indicate that truck drivers seek to participate in conversations about the future and to prepare themselves for when automation is more prominent in the trucking industry. 

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3. Concerns, Career Decisions, and Role Changes: A Qualitative Study of Perceptions of Autonomous Vehicles in the Trucking Industry

   https://doi.org/10.1093/workar/waac037  

   Van Fossen, Jenna A; Schuster, Amy M; Sperry, Danielle; Cotten, Shelia R; Chang, Chu-Hsiang (May 2023, Work, Aging and Retirement)

   Wang, Mo (Ed.)

   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. 

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4. Trucking in the Era of COVID-19

   https://doi.org/10.1177/00027642211066039  

   Sperry, Danielle; Schuster, Amy M.; Cotten, Shelia R.; Agrawal, Shubham; Mack, Elizabeth; Britt, Noah; Liberman, Jessica (February 2022, American Behavioral Scientist)

   COVID-19 resulted in health and logistical challenges for many sectors of the American economy, including the trucking industry. This study examined how the pandemic impacted the trucking industry, focused on the pandemic’s impacts on company operations, health, and stress of trucking industry employees. Data were collected from three sources: surveys, focus groups, and social media posts. Individuals at multiple organizational levels of trucking companies (i.e., supervisors, upper-level management, and drivers) completed an online survey and participated in online focus groups. Data from focus groups were coded using a thematic analysis approach. Publicly available social media posts from Twitter were analyzed using a sentiment analysis framework to assess changes in public sentiment about the trucking industry pre- and during-COVID-19. Two themes emerged from the focus groups: (1) trucking company business strategies and adaptations and (2) truck driver experiences and workplace safety. Participants reported supply chain disruptions and new consumer buying trends as having larger industry-wide impacts. Company adaptability emerged due to freight variability, leading organizations to pivot business models and create solutions to reduce operational costs. Companies responded to COVID-19 by accommodating employees’ concerns and implementing safety measures. Truck drivers noted an increase in positive public perception of truck drivers, but job quality factors worsened due to closed amenities and decreased social interaction. Social media sentiment analysis also illustrated an increase in positive public sentiment towards the trucking industry during COVID-19. The pandemic resulted in multi-level economic, health, and social impacts on the trucking industry, which included economic impacts on companies and economic, social and health impacts on employees within the industry levels. Further research can expand on this study to provide an understanding of the long-term impacts of the pandemic on the trucking industry companies within the industry and segments of the trucking industry workforce. 

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5. Identifying Alternative Occupations for Truck Drivers Displaced Due to Autonomous Vehicles by Leveraging the O*NET Database

   https://doi.org/10.1177/00027642221127239  

   Van Fossen, Jenna A.; Chang, Chu-Hsiang; Ford, J. Kevin; Mack, Elizabeth A.; R. Cotten, Shelia (September 2022, American Behavioral Scientist)

   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. 

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