Web Survey Bibliography
Title The Seven E-learning Barriers Facing Employees
Author Mungania, P.
Source The MASIE Center, University of Louisville
Year 2003
Access date 23.02.2006
Full text pdf (487k)
Abstract As investment in human, time, and financial resources for e-learning continues to rise, so should the interest in its effectiveness, quality, barriers faced, and best practices. The purpose of this empirical study was to determine barriers experienced by employee e-learners. Further, this study explored the nature of relationships among (1) e-learning barriers; (2) employee demographics (age, gender, ethnicity, marital status, organization type, and education); and (3) background factors such as experience with e-learning and computers, job position, computer ownership, computer training, and elearning self-efficacy. An anonymous validated Web-based survey was used to collect data, showing the viability of Web-based surveys as a tool for promoting e-learning and organizational research. The Barriers in E-learning and Self-Efficacy (BELSE) survey was used to collect self-reported data, administered to a convenient sample of employee volunteers. The instrument was comprised of (1) demographic questions; (2) the Barriers in Elearning (BEL) scale, developed for the purpose of determining e-learning barriers; and (3) E-learning Self-Efficacy (ELSE) scale, to measure Internet and computer selfefficacy. This exploratory study used quantitative methods for data collection and analysis and some open-ended questions. Data were analyzed through descriptive and inferential statistics. The participants were employees in seven organizations in the United States and abroad. These organizations represent manufacturing, oil exploration & manufacturing, IT consulting, wholesale distribution, public school district, and a military division. A total of 875 employees who had taken e-learning courses delivered 100 percent online by their employers for the past two years participated voluntarily. Their ability to participate in e-learning and in this study from remote locations, from the Pacific Ocean to the Algerian desert, clearly demonstrates the ubiquity of e-learning. The demographic makeup and background characteristics of the respondents were explored to better understand the e-learning population. The figure below highlights the employees’ characteristics.
Access/Direct link Center homepage
Year of publication2003
Bibliographic typeReports, seminars
Web survey bibliography - Reports, seminars (231)
- Standard Definitions: Final Dispositions of Case Codes and Outcome Rates for Surveys 2016; 2016
- Standard Definitions Final Dispositions of Case Codes and Outcome Rates for Surveys; 2016
- FocusVision 2015 Annual MR Technology Report; 2016; Macer, T., Wilson, S.
- Establishing the accuracy of online panels for survey research; 2016; Bruggen, E.; van den Brakel, J.; Krosnick, J. A.
- Mixing modes of data collection in Swiss social surveys: Methodological report of the LIVES-FORS mixed...; 2016; Roberts, C.; Joye, D.; Staehli, M. E.
- Assessment of Innovations in Data Collection Technology for Understanding Society; 2016; Couper, M. P.
- Report of the Inquiry into the 2015 British general election opinion polls; 2016; Sturgis, P., Baker, N., Callegaro, M., Fisher, St., Green, J., Jennings, W., Kuha, J., Lauderdale, B...
- Evaluating a New Proposal for Detecting Data Falsification in Surveys; 2016; Simmons, K.; Mercer, A. W.; Schwarzer, S.; Courtney, K.
- Computer-assisted and online data collection in general population surveys; 2016; Skarupova, K.
- Predictive inference for non-probability samples: a simulation study ; 2016; Buelens, B.; Burger, J.; van den Brakel, J.
- ESOMAR/GRBN Online Research Guideline; 2015
- App vs. Web for Surveys of Smartphone Users: Experimenting with mobile apps for signal-contingent experience...; 2015; McGeeney, K.; Keeter, S.; Igielnik, R.; Smith, A.; Rainie, L.
- On Climbing Stairs Many Steps at a Time: The New Normal in Survey Methodology; 2015; Dillman, D. A.
- Polling Error in the 2015 UK General Election: An Analysis of YouGov’s Pre and Post-Election Polls...; 2015; Wells, A.; Rivers, D.
- GreenBook Research Industry Trends Report; 2015; Murphy, L. (Ed.)
- Standard Definitions: Final Dispositions of Case Codes and Outcome Rates for Surveys 2015; 2015
- Methodology of the RAND Mid-Term 2014 Election Panel; 2015; Carman, K. G; Pollack, S.
- 28 Questions to Help Buyers of Online Samples; 2015; Cape, P. J.; Phillips, A.; Baker, R.; Cooke, M.; Ribeiro, E.; Terhanian, G.
- Understanding Society Innovation Panel Wave 7: Results from Methodological Experiments; 2015; Blom, A. G.; Burton, J.; Booker, C. L.; Cernat, A.; Fairbrother, M.; Jaeckle, A.; Kaminska, O.; Keusch...
- Tips for Creating Web Surveys for Completion on a Mobile Device; 2015; McGeeney, K.
- U.S. Survey Research: Sampling; 2015
- A Comparison of Different Online Sampling Approaches for Generating National Samples; 2014; Heen, M. S. J., Lieberman, J. D., Miethe, T. D.
- FocusVision 2014 Annual MR Technology Report; 2014; Macer, T., Wilson, S.
- The Changing Landscape of Technology and its Effect on Online Survey Data Collection; 2014; Mitchell, N.
- Query on Data Collection for Social Surveys; 2014; Blanke, K., Luiten, A.
- The role of email addresses and email contact in encouraging web response in a mixed mode design ; 2014; Cernat, A., Lynn, P.
- Mixed-mode surveys of the general population - Results from the European Social Survey mixed-mode experiment...; 2014; Park, A., Humphrey, A.
- Mixed-Mode Designs bei Erhebungen mit sensitiven Fragen: Einfluss auf das Teilnahme- und Antwortverhalten...; 2014; Krug, G., Kriwy, P., Carstensen, J.
- Methods and systems for managing an online opinion survey service; 2014; Mcloughlin, M. H., Seton, N., Blesy, K.
- Mobile Technologies for Conducting, Augmenting and Potentially Replacing Surveys: Report of the AAPOR...; 2014; Link, M. W., Murphy, J., Schober, M. F., Buskirk, T. D., Childs, J. H., Tesfaye, C.
- The use of within-subject experiments for estimating measurement effects in mixed-mode surveys ; 2014; Klausch, L. T., Schouten, B., Hox, J.
- Measuring well-being: An analysis of different response scales; 2014; van Beuningen, J., van der Houwen, K., Moonen, L.
- The impact of contact effort and interviewer performance on mode-specific nonresponse and measurement...; 2014; Schouten, B., Cobben, F., van der Laan, J., Arends, J.
- Community Life Survey: Summary of web experiment findings; 2013
- The Short-term Campaign Panel of the German Longitudinal Election Study 2009. Design, Implementation...; 2013; Steinbrecher, M., Rossmann, J.
- Too Fast, Too Straight, Too Weird: Post Hoc Identification of Meaningless Data in Internet ; 2013; Leiner, D. J.
- Postal recruitment into a longitudinal online panel survey. The effects of different number of reminder...; 2013; Martinsson, J.
- The world in 2013. ICT facts and figures; 2013
- Microsoft Security Intelligence Report, Volume 15; 2013
- A Comparison of Results from a Spanish and English Mail Survey: Effects of Instruction Placement on...; 2013; Wang, K., Sha, M.
- Research Note: Reducing the Threat of Sensitive Questions in Online Surveys?; 2013; Couper, M. P.
- Global market research 2013; 2013
- Exploring the Digital Nation: America’s Emerging Online Experience; 2013
- Advantages of a global multimodal print & digital readership survey; 2013; Cour, N., Saint-Joanis, G.
- Australia: building a 21st century readership survey; 2013; Green, A., White, H.
- The new swiss national readership survey: fit for the future ; 2013; Amschler, H., Hoffmann, J.
- ESS Mixed Mode Experiment Results in Estonia (CAWI and CAPI Mode Sequential Design); 2013; Ainsaar, M., Lilleoja, L., Lumiste, K., Roots, A.
- Using smartphones in survey research: a multifunctional tool Implementation of a time use app; a feasability...; 2013; Sonck, N., Fernee, H.
- Adaptive survey designs to minimize survey mode effects. A case study on the Dutch Labour Force Survey...; 2013; Calinescu, M., Schouten, B.
- Optimal Resource Allocation in Adaptive Survey Designs; 2013; Calinescu, M.