A process model linking occupational strength to attitudes and behaviors: The explanatory role of occupational personality heterogeneity.

This study proposes a mediated process model that seeks to explain how occupational strength influences personality heterogeneity, ultimately affecting attitudes and behaviors. Specifically, it proposes that strong occupations restrict personality heterogeneity (defined as the extent to which there is variability in incumbents' personalities), which mediates the effect of occupational strength on work-related outcomes. Using a sample of 178,087 individuals employed in 315 occupations, the results indicate that strong occupations (operationalized as having high task significance) had advantageous effects on occupational satisfaction, tenure, and turnover intentions, and these effects were partially mediated by personality heterogeneity. Task significance had a negative effect on personality heterogeneity, and personality heterogeneity led to less favorable attitudes and behaviors. The occupational autonomy operationalization of situational strength also had advantageous effects on incumbents' occupational satisfaction, tenure, and turnover intentions, but these effects were not mediated by personality heterogeneity. In addition, personality distance (defined as the extent to which incumbents were personally different from others in the occupation) adversely affected within-occupation attitudes and behaviors. This study reexamines situational strength theory, shifting the emphasis away from an interaction (behavior = person × situation) to an explanatory process (behavior is a function of personality heterogeneity, which is a function of situational strength). (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The best laid plans: examining the conditions under which a planning intervention improves learning and reduces attrition.

Planning plays an instrumental role in prominent self-regulation theories (e.g., action regulation, control, goal setting), yet as a scientific community we know little about how people carry out their learning plans. Using an experimental field study, we implemented a repeated-measures intervention requiring trainees to create a plan for when, where, and how much time they intended to devote to training before each of 4 online modules and examined the conditions under which the planning intervention improved learning and reduced attrition. Trainees benefited from the planning intervention when it was paired with another intervention-prompting self-regulation-targeting self-regulatory processes that occur subsequent to planning (e.g., monitoring, concentration, learning strategies). Trainees' learning performance was highest and attrition lowest when they received both interventions. The planning intervention was also advantageous for enhancing learning and reducing attrition when trainees followed through on the amount of time that they planned to devote to training. Finally, the relationship between planned study time, time on task, and learning performance was cyclical. Planned study time had a positive effect on time on task, which, in turn, had a positive effect on learning performance. However, trainees planned to devote less time to training following higher rather than lower learning performance. The current study contributes to our theoretical understanding of self-regulated learning by researching one of the most overlooked components of the process-planning-and examining the conditions under which establishing a learning plan enhances training outcomes.

A meta-analysis of self-regulated learning in work-related training and educational attainment: what we know and where we need to go.

Researchers have been applying their knowledge of goal-oriented behavior to the self-regulated learning domain for more than 30 years. This review examines the current state of research on self-regulated learning and gaps in the field's understanding of how adults regulate their learning of work-related knowledge and skills. Self-regulation theory was used as a conceptual lens for deriving a heuristic framework of 16 fundamental constructs that constitute self-regulated learning. Meta-analytic findings (k=430, N=90,380) support theoretical propositions that self-regulation constructs are interrelated-30% of the corrected correlations among constructs were .50 or greater. Goal level, persistence, effort, and self-efficacy were the self-regulation constructs with the strongest effects on learning. Together these constructs accounted for 17% of the variance in learning, after controlling for cognitive ability and pretraining knowledge. However, 4 self-regulatory processes-planning, monitoring, help seeking, and emotion control-did not exhibit significant relationships with learning. Thus, a parsimonious framework of the self-regulated learning domain is presented that focuses on a subset of self-regulatory processes that have both limited overlap with other core processes and meaningful effects on learning. Research is needed to advance the field's understanding of how adults regulate their learning in an increasingly complex and knowledge-centric work environment. Such investigations should capture the dynamic nature of self-regulated learning, address the role of self-regulation in informal learning, and investigate how trainees regulate their transfer of training.

The effects of technical difficulties on learning and attrition during online training.

Although online instruction has many potential benefits, technical difficulties are one drawback to the increased use of this medium. A repeated measures design was used to examine the effect that technical difficulties have on learning and attrition from voluntary online training. Adult learners (N = 530) were recruited online and volunteered to participate in a 4-hr training program on using computer spreadsheets. Technical difficulties were inserted in some of the training modules in the form of error messages. Using multilevel modeling, the results indicated that the presence of these technical difficulties impaired learning, such that test scores were lower in modules where trainees encountered technical difficulties than in modules where they did not encounter technical difficulties. Furthermore, the effect on learning was greater among trainees who eventually withdrew from the course than among trainees who completed the course. With regards to attrition, pretraining motivation provided a buffer against dropping out, especially when trainees encountered technical difficulties. Learning also predicted attrition from the subsequent module, such that attrition was higher among trainees with low test scores in the previous module. The current study disentangles some of the implications of technical difficulties and suggests that organizations should provide trainees with the technical support required to overcome technical difficulties in training. Furthermore, the findings contribute to our theoretical understanding of the implications of interruptions on performance in online training.

Sometimes you need a reminder: the effects of prompting self-regulation on regulatory processes, learning, and attrition.

Prompting self-regulation involves asking trainees reflective questions to stimulate self-regulatory engagement. Research has found positive effects for prompting self-regulation on learning, but a scarcity of evidence exists regarding whether self-regulatory processes mediate the effect of prompting self-regulation, whether the intervention reduces attrition, and the optimal timing of implementing the intervention. Using a longitudinal design, we found that prompting self-regulation throughout training increased learning and reduced attrition, relative to the control condition. Moreover, the effect on learning was fully mediated by time on task. The intervention also moderated the effect of learning on subsequent self-regulatory activity and attrition. Learning performance had less of a positive effect on subsequent self-regulatory activity and less of a negative effect on subsequent attrition when trainees were prompted to self-regulate. These results highlight the importance of adopting a longitudinal design to examine how self-regulatory interventions affect the cyclical relationships among self-regulatory processes, learning, and attrition.

A review and meta-analysis of the nomological network of trainee reactions.

A review and meta-analysis of studies assessing trainee reactions are presented. Results suggest reactions primarily capture characteristics of the training course, but trainee characteristics (e.g., anxiety and pretraining motivation) and organizational support also have a moderate effect on reactions. Instructional style (rho = .66) followed by human interaction (rho = .56) were the best predictors of reactions. Reactions predicted pre-to-post changes in motivation (beta = .51) and self-efficacy (beta = .24) and were more sensitive than affective and cognitive learning outcomes to trainees' perceptions of characteristics of the training course. Moderator analyses revealed reactions- outcomes correlations tended to be stronger in courses that utilized a high level rather than a low level of technology, and affective and utility reactions did not differ in their relationships with learning outcomes. The current study clarifies the nomological network of reactions and specifies outcomes that are theoretically related to reactions.

Supervisor referrals to work-family programs.

Supervisors play an important role in determining whether employees use work-family programs. Yet little research has examined the factors that relate to supervisor perceptions of and behaviors surrounding work-family programs. This study builds on past research, the theory of reasoned action, and expectancy theory to explore factors that contribute to supervisors' decisions to refer subordinates to work-family programs. Usable surveys assessing perceptions of work-family programs were completed and returned by 1972 managers in a large government agency. Results revealed that program awareness and instrumentality perceptions both contributed uniquely to predicting the frequency of supervisors' referrals to work-family programs. Supportive attitudes also predicted referrals, but only through their shared relationship with instrumentality perceptions.