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Continuing the series' tradition of providing scholarly reviews and updates of theory and research, this twenty-seventh volume surveys developments in established areas, such as stress and well-being, consumer behavior, and employee trust, as well as newer topics such as methodological issues in the development and evaluation of multiple regression models, and an examination of the psychological impact of the physical office environment.

This article illustrates how the State of Michigan transformed human resources in state government by developing HR leaders for the future. A transformational plan was developed in March 2000 to enhance the effectiveness of HR programs and services, foster a consultative approach to delivering HR programs and services, develop human resources as a strategic business partner and change agent with agency management, and create the capacity to incorporate HR best practices into state HR systems. This article also provides the HR vision for change, the training design to transform HR into a business partner and change agent, and the methodology and data that support this transformational plan. An innovative multiyear project-based learning design, featuring teams from state agencies, used workshop content to leverage system change across state departments. Finally, this article documents the change projects designed and implemented by agency teams, reports participants' reactions, communicates skill-set and mindset enhancements, and presents ideas for future diffusion.

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.