Introduction
According to Thomas Gilbert’s behavior engineering model, three facets of a person’s repertory of behavior (knowledge, capacity, and motivation) intertwine to drive performance. Research examines this dynamic in the context of workplace e-learning and considers how environmental factors such as instructional design also impact e-learning participation.
Article
Garavan, T. N., Carbery, R., O’Malley, G., & O’Donnell, D. (2010). Understanding participation in e-learning in organizations: a large-scale empirical study of employees.  International Journal of Training and Development, 14(3), 155-168. doi: http://dx.doi.org/10.1111/j.1468-2419.2010.00349.x
Background
e-learning is defined as “Learning that is delivered, enabled or mediated by electronic technology for the explicit purpose of training in organisations. It does not include stand-alone, technology-based training such as the use of CD-ROMs in isolation.” (CIPD, 2010).
Organizations spent $20 billion on e-learning in 2009.  Given this investment, it is vital that they understand the factors that impact employee participation in organizational sponsored e-learning programs. Much of the existing literature focuses on external factors beyond learners’ control. In this study, researchers examined not only external factors but also characteristics and behaviors of trainees that are likely to affect participation. Their investigation centered on five sets of variables: 1. general-person characteristics, 2. e-learning instructional design characteristics, 3. motivation to learn, 4. self-efficacy, and 5. perceived barriers and enablers.
By drawing a large sample from 275 organizations, the researchers intended to provide insights that could be generalized across a wide range of organization types.
Research
The researchers formulated five hypotheses on employee e-learning participation based on a review of existing literature:
- H1. General-person characteristics will have a positive influence on participation in e-learning.
- H2. Motivation to learn will mediate the relationships between perceived barriers and enablers, self-efficacy, and participation in e-learning.
- H3. Self-efficacy will mediate the relationship between perceived barriers and enablers and motivation to learn in explaining participation in e-learning.
- H4. The extent to which learners view features as enablers or barriers will be positively related to self-efficacy and instructional design characteristics.
- H5. Instructional design characteristics of e-learning are positively related to participation in e-learning.
A survey instrument was used to collect data. The study was conducted in Ireland. The initial sample consisted of 1,500 employees across 275 organizations that offered voluntary e-learning to staff. The response rate of 37% yielded 557 respondents from multinational corporations, public-sector organizations, and SMEs. Â Of that number, 273 had participated in e-learning within the past year. The male/female ratio was 43: 57. The age range of respondents was 16 to 44. The sample was made up of primarily full-time, highly educated employees. The sample included employees at managerial, administrative, and operative levels.
Employees were asked to complete the survey either online or to mail in a paper-based survey. The survey consisted of items rated by respondents using a Likert-type scale. Measurement of all variables except instructional design characteristics utilized scales developed by Noe et al (1997). A twelve-item scale for instructional design characteristics was created by the authors based on the work of notable researchers in this area. Â Non-scale measures (such as yes/no questions) were used to capture demographic and human capital data.
The researchers used structured equation modeling (SEM) to test their hypotheses. A variety of proven statistical models (such as normed chi squared) were applied as well but did not provide as good fit.
Findings and Implications
A high degree of statistically significant intercorrelations were found across 18 measured variables. Data analysis showed support for all hypotheses (p < .05), with motivation to learn (Hypothesis 2) having the greatest bearing on e-learning participation. General-person characteristics (Hypothesis 1) and instructional design characteristics (Hypothesis 5) also showed significant paths to participation. See https://docs.google.com/open?id=0B3dDDYlZjD2DSVlXUmNiOHBNTUE.
Results suggest that organizations invest in efforts to ensure the motivational state of their workers regarding e-learning in order to increase participation rates. Employees who feel that e-learning will be worthwhile, enjoyable, and lead to desired outcomes are more likely to engage in e-learning programs. Instructional content of e-learning should be crafted with this in mind. Likewise, instructional design characteristics such as learner support, feedback, and recognition can be used to maximum benefit to alleviate self-efficacy issues such as computer anxiety in older or less-educated workers.
Questions for OPWL-N Members
The research indicates that motivation is key to participation in organizational sponsored e-learning. What are some ways organizations can manage employee motivation to learn?
Does your organization offer e-learning? How does it incentivize participation? Are some workers more likely to take advantage of e-learning than others?
Workplace Oriented Research Central (WORC)
Prepared by OPWL Graduate Assistant, Susan Virgilio
Directed by OPWL Professor, Yonnie Chyung
Posted on November 30, 2012