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Employees' perceptions of barriers i...

University of Louisville.

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Employees' perceptions of barriers in e-learning: The relationship among barriers, demographics, and e-learning self-efficacy.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Employees' perceptions of barriers in e-learning: The relationship among barriers, demographics, and e-learning self-efficacy./
Author:	Mungania, Penina.
Description:	287 p.
Notes:	Advisers: Joseph Petrosko; Tim Hatcher.
Contained By:	Dissertation Abstracts International65-05A.
Subject:	Education, Adult and Continuing. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3134194
ISBN:	9780496814305

Employees' perceptions of barriers in e-learning: The relationship among barriers, demographics, and e-learning self-efficacy.
Mungania, Penina.

Employees' perceptions of barriers in e-learning: The relationship among barriers, demographics, and e-learning self-efficacy. - 287 p.

Advisers: Joseph Petrosko; Tim Hatcher.

Thesis (Ph.D.)--University of Louisville, 2004.

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.

ISBN: 9780496814305Subjects--Topical Terms:

626632
Education, Adult and Continuing.

Employees' perceptions of barriers in e-learning: The relationship among barriers, demographics, and e-learning self-efficacy.
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020 $a 9780496814305
035 $a (UMI)AAI3134194
035 $a AAI3134194
040 $a UMI $c UMI
100 1 $a Mungania, Penina. $3 1025954
245 1 0 $a Employees' perceptions of barriers in e-learning: The relationship among barriers, demographics, and e-learning self-efficacy.
300 $a 287 p.
500 $a Advisers: Joseph Petrosko; Tim Hatcher.
500 $a Source: Dissertation Abstracts International, Volume: 65-05, Section: A, page: 1627.
502 $a Thesis (Ph.D.)--University of Louisville, 2004.
520 $a 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.
520 $a Results, implications for practice, conclusions, and recommendations for further research are discussed.
520 $a 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.
520 $a 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.
520 $a 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.
520 $a 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.
520 $a 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.
520 $a 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.
590 $a School code: 0110.
650 4 $a Education, Adult and Continuing. $3 626632
650 4 $a Education, Industrial. $3 1017516
650 4 $a Education, Technology of. $3 1018012
690 $a 0516
690 $a 0521
690 $a 0710
710 2 $a University of Louisville. $3 1017614
773 0 $t Dissertation Abstracts International $g 65-05A.
790 $a 0110
790 1 0 $a Hatcher, Tim, $e advisor
790 1 0 $a Petrosko, Joseph, $e advisor
791 $a Ph.D.
792 $a 2004
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3134194