Sandra L. Fisher

Power to the people: Using learner control to improve trainee reactions and learning in web-based instructional environments.

This experimental study investigated the mechanisms by which learner control influences learning in an e-learning environment. The authors hypothesized that learner control would enhance learning indirectly through its effect on trainee reactions and learner engagement (in particular, off-task attention), such that learners who were more satisfied with the training and devoted more cognitive resources toward the instructional content versus off-task thoughts would possess greater posttraining knowledge. The study also examined the role of individual differences (training motivation and goal orientation) in the prediction of these 2 mediating variables. A sample of 274 undergraduates completed an e-learning program, either with or without interactive, learner control tools. Results suggest that both training satisfaction and off-task attention predicted subsequent learning. Learner control had a positive impact on training satisfaction but was not related to off-task attention. The individual difference variables had a differential impact on the mediating variables. In particular, performance orientation was linked to off-task attention, whereas mastery orientation was found to indirectly influence satisfaction via its direct effect on training motivation. Implications for the design and further study of e-learning are discussed.

E-nabled for E-Learning? The Moderating Role of Personality in Determining the Optimal Degree of Learner Control in an E-Learning Environment

This paper investigates the moderating role of specific Big Five personality traits on the relationship between learner control and training performance in an e-learning environment. Specifically, we examined the role of openness to experience, conscientiousness, and extraversion. Participants completed a video-based e-learning program with either a high or low level of learner control. Results demonstrated that the personality traits of openness to experience and extraversion moderated the relationship between learner control and training performance. Specifically, training performance was higher for trainees higher in openness and extraversion when they were in an environment with high learner control versus low learner control. Conversely, for trainees lower in these traits, performance was higher with low learner control versus high learner control. Future research and practical applications are discussed.

e-HRM Systems in Support of “Smart” Workforce Management: An Exploratory Case Study of System Success

Abstract Purpose As e-HRM systems move into the ‘smart’ technology realm, expectations and capabilities for both the automational and informational features of e-HRM systems are increasing. This chapter uses the well-established DeLone and McLean (D&M) model from the information systems literature to analyze how a smart workforce management system can create value for an organization. Methodology/approach The chapter is based on an exploratory case study conducted with a North American industrial products firm. We review three systems-level predictors of success from the D&M model (system quality, information quality, and service quality) and evaluate the company’s systems on these attributes. Findings The company’s e-HRM systems fall short on the information quality dimension, which limits potential for overall system success related to smart workforce management. Research limitations/implications The e-HRM literature focuses on individual-level factors of system success, while the D&M model uses more macro factors. Blending these may help researchers and practitioners develop a more complete view of e-HRM systems. Conclusions from this chapter are limited due to the use of a single, exploratory case study. Practical implications Companies must pay attention to all three predictors of system quality when developing smart workforce management systems. In particular, implementation of a data governance program could help companies improve information quality of their systems. Originality/value This chapter adds to the literature on smart workforce management by using a model from the information systems literature and a practical example to explore how such a system could add value.