Rudolph et al. (2021) highlight 10 areas of industrial-organizational (I-O) psychology that are relevant to the influence of the COVID-19 pandemic on work. They also briefly describe the prototypical media headlines that highlight these different topics. Indeed, since the beginning of the pandemic, many popular press articles have been written on how managers and organizations should handle and address a multitude of changes brought on by COVID-19. Although these articles offer a host of practical recommendations, they often lack a theoretical foundation that would provide decision makers with greater understanding of why certain recommendations might be effective. Without an explanation of why recommendations might work, managers might feel uncertain as to which recommendations to try, and if they find that a recommendation does not apply to their context, they might dismiss the suggestions completely. The current work extends et al.'s focal article by focusing on telecommuting and the job demands-resources (JD-R) model (Demerouti et al., 2001), using job demands and resources to provide a framework that underlies the many recommendations provided for managers and organizations during COVID-19. By providing this framework, we hope to empower decision makers to better adapt existing recommendations to their unique work contexts in order to maintain employee wellbeing and performance while telecommuting.

The confluence of the aging population and economic conditions that require working longer necessitate a focus on how to best train and develop older workers. We report a meta‐analysis of the age and training relationship that examines training outcomes and moderators with 60 independent samples (total N = 10,003). Framed within the lifespan development perspective, we expected and found that older trainees perform worse (ρ = −.14, k = 34, N = 5642; δ = 1.08, k = 21, N = 1242) and take more time (ρ = .19, k = 15, N = 2780; δ = 1.25, k = 12, N = 664) in training relative to younger trainees. Further, age was negatively related to post‐training self‐efficacy (ρ = −.08, k = 10, N = 4631), but not related to trainee reactions. Moderator analyses provided mixed support that training alone is related to increased mastery of skills and knowledge. No support was found for the moderating effects of pacing or instructional approach. We call for future research examining the interactive effects of training design on older worker outcomes in ways that capitalize on age‐related growth, compensate for decline, and consider the strategies workers use to mitigate the effect of age‐related losses.

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This chapter describes methods of assessing the learning needs and evaluating the development of individuals within the context of a lifelong learning support system. Because lifelong learning is self-directed and informal in nature, the authors propose a needs assessment and evaluation design that is customized by the learner to their unique needs. Learners are first assessed on organizationally relevant as well as lifelong learning–relevant competencies and then linked, in a matrix format, to lifelong learning opportunities within and outside the organization that suit their competency needs. The chapter then proposes that, as learners engage in lifelong learning activities, they are periodically evaluated in terms of their improvement along these competencies. This information can be used to modify individuals’ lifelong learning program as well as, on the aggregate level, to inform decisions about how to allocate organizational resources and provide evidence to support lifelong learning systems.