Date on Master's Thesis/Doctoral Dissertation


Document Type

Doctoral Dissertation

Degree Name

Ph. D.

Department (Legacy)

Department of Leadership, Foundations, and Human Resource Education

Committee Chair

Petrosko, Joseph M., 1947-

Committee Co-Chair (if applicable)

Hatcher, Timothy

Author's Keywords

Education; Employees; Barriers; E-learning; Demographics; Self-efficacy


Occupational training--Computer-assisted instruction; Employees--Training of--Computer-assisted instruction


The purpose of this empirical study was to examine the types of e-learning barriers and to establish the nature of relationships among (a) barriers perceived by employee e-learners in the process of starting, continuing, and completing online training; (b) demographic variables; (c) background characteristics; and (d) e-learning self-efficacy. The population was comprised of employees (N = 4807; n = 865) who had participated in Web-based training delivered 100 percent online. Convenient samples of employees were drawn from seven organizations representing (a) IT Manufacturing, (b) Oil Exploration & Manufacturing, (c) Public School District, (d) Health insurance, (e) Wholesale Distribution, (f) IT Consulting, and (g) US Military. The social cognitive learning theory's dimension of self-efficacy examined e-learners' Internet and computer self-efficacy. Schilke's (2001) conceptual framework on e-learning barriers and ideas from various critics of the technological study guided the present study. The E-learning Barriers and Self-Efficacy (ELSE) survey was used to collect data from volunteer employees. This Web-based anonymous survey had 82 questions in three scales: (a) demographics and background characteristics; (b) Barriers in E-leaming (BEL) scale (alpha = .9496) and one open-ended question; and (c) E-learning Self-Efficacy (ELSE) scale (alpha = .9487). The instrument was validated using subject matter experts and a pilot study. Response rates were 52.5% (pilot study) and 18% (main study). Data were analyzed using exploratory factor analysis, multiple regression, MANOVA, and Pearson correlation. Open coding was used for the open-ended responses. Seven categories of barriers (factors) emerged: (1) Dispositional, (2) learning style, (3) instructional, (4) organizational, (5) situational, (6) content-suitability, and (7) technological barriers. The barriers means ranged from 1.29 to 3.00 on a 5-point scale (1 = weak and 5 = strongest barrier). Barrier ratings were weak on all categories. Personal barriers (M = 1.54) were the least common while situational barriers were the most prevalent (M = 2.81). The multidimensional nature of these barriers demands a systemic approach to reduce them. A MANOVA test indicated significant differences in barriers among the seven organizations. The test of relationships using multiple regression revealed four predictors of e-learning barriers: (a) organization type, (b) computer competence, (c) computer training, and (d) e-learning self-efficacy. Results, implications for practice, conclusions, and recommendations for further research are discussed.