Monday , December 17 2018

Volume 24-Issue1-2015-BALOG

Acceptance of e-Learning Systems: a Serial Multiple
Mediation Analysis

Alexandru BALOG
I C I Bucharest
(National Institute for R & D in Informatics)

8-10 Averescu Blvd.
011455 Bucharest 1, Romania
alexb@ici.ro

Abstract: The success of an e-learning system depends strongly on understanding the factors that influence students’ use and acceptance of this kind of system. Technology acceptance models (TAMs) are frequently used in studies investigating the determinants of adoption and usage of new technologies. Drawing on the findings from extant literature on technology acceptance, this study developed and tested a multiple mediator model using TAM3 framework and concepts. The model was tested on a sample of 220 students using Structural Equation Modeling and serial multiple mediation analysis. The study proved that perceived ease of use and perceived usefulness mediate sequentially the relationships between the four external variables – social influence, facilitating conditions, self-efficacy, perceived enjoyment – and behavioral intention to use the e-learning system . The empirical results provide support for the proposed model. The results indicate that the TAM is a theoretically sound model which can be used to predict students’ behavioural intention (BI) to use e-learning systems.

Keywords: E-learning acceptance, technology acceptance model, SEM, multiple mediation.

>>Full text
CITE THIS PAPER AS:
Alexandru BALOG, Acceptance of e-Learning Systems: a Serial Multiple Mediation Analysis, Studies in Informatics and Control, ISSN 1220-1766, vol. 24 (1), pp. 101-110, 2015. https://doi.org/10.24846/v24i1y201511

  1. Introduction

E-learning has become a potential alternative to traditional face-to-face type of learning. It generally refers to the use of computer network technology, primarily over an intranet or through the Internet, to deliver information and instruction to individuals [22]. However, simply providing learners with a web-based learning system does not guarantee a successful e-learning [8]. The success of an e-learning system depends on understanding the factors that influence the students’ acceptance of such learning systems. Technology acceptance models and theories are frequently used in studies investigating the determinants of adoption and usage of new technologies.

A well-known model aiming to explain and predict individual adoption and use of information technologies (IT) is Technology Acceptance Model (TAM), developed and validated by Davis [11], and Davis, Bagozzi and Warshaw [12]. TAM has three key variables: perceived usefulness (PU), perceived ease of use (PEOU), and behavioural intention (BI) to use. The two core beliefs, PU and PEOU, are main determinants of an individual’s BI to use a technology. The PU and PEOU are influenced by a number of external variables such as user characteristics and system features. TAM further theorizes that the effects of external variables on BI will be mediated by PEOU and PU [12].

The original model was revised and extended with new variables (TAM2 [42], TAM3 [41]) in order to provide a broader view and a better explanation of technologies adoption. The TAM and its subsequent extensions has been applied and tested in many studies, including in e-learning contexts.

However, little research has been done to focus on the actual mediating role of core beliefs and few studies have tested whether PEOU and PU mediate external variables (e.g., [1, 5, 7, 31, 40]). Recent research emphasized that there is limited empirical examination of the social factors, organizational factors, and individual factors that may affect the user adoption and acceptance of e-learning systems [36].

The current research attempts to address the inconsistent findings on mediating role of core beliefs in TAM and to apply advanced statistical methods for inferences about mediated effects. This study investigates acceptance of e-learning systems in Romanian context, using the TAM3 framework. Also, advanced statistical methods (e.g., Structural Equation Modeling, bootstrap method) have been applied within this research.

This study is one of the first attempts to test and prove by advanced methods the mediating role of core beliefs in the technology acceptance models.

The paper is organized as follows. Section 2 briefly reviews theoretical framework. Section 3 describes the proposed research model and hypotheses, and Section 4 presents research methods. Section 5 shows results, which will be discussed in Section 6. Finally, Section 7 summarizes the main conclusions.

REFERENCES

  1. AGARWAL, R., J. PRASAD, Are Individual Differences Germane to the Acceptance of New Information Technologies? Decision Science vol. 30, no. 2, 1999, pp. 361-391.
  2. AGUDO-PEREGRINA, A. F., A. HERNANDEZ-GARCIA, F. J. PASCUAL-MIGUEL, Behavioral Intention, Use Behavior and the Acceptance of Electronic Learning Systems: Differences between Higher Education and Lifelong Learning. Computer in Human Behavior vol. 34, 204, pp. 301-314.
  3. ANDERSON, J. C., D. W. GERBING, Structural Equation Modeling in Practice: A Review and Recommended Two-step Approach. Psychological Bulletin., vol. 103, no. 3, 1988, pp. 411-423.
  4. BANCIU, D., A. BALOG, Calitatea sistemelor si serviciilor de e-learning. Editura AGIR, Bucuresti, 2013.
  5. BURTON-JONES, A., G. S. HUBONA, The Mediation of External Variables in the Technology Acceptance Model. Information & Management, 43, no. 6, 2006, pp. 706-717.
  6. BYRNE, B., Structural Equation Modeling with AMOS. Basic Concepts, Applications, and Programming. Lawrence Erlbaum Ass. Publishers 2001.
  7. CHEN, H. R., H. F. TSENG, Factors that Influence Acceptance of Web-based e-Learning Systems for the In-service Education of Junior High School Teachers in Taiwan. Evaluation and Program Planning vol. 35, 2012, pp.398-406.
  8. CHENG, Y. M. Antecedents and Consequences of e-Learning Acceptance. Information System Journal vol. 21, no. 3, 2011, pp. 269-299.
  9. CHIU, C. M., E. T. G. WANG, Understanding Web-based Learning Continuance Intention: The Role of Subjective Task Value. Information and Management, vol. 45(3), 2008, pp. 194-201.
  10. COMPEAU, D. R., C. A. HIGGINS, Computer Self-efficacy: Development of a Measure and Initial Test. MIS Quarterly, vol. 19, no.2, 1995, pp. 189-211.
  11. DAVIS, F. D. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, vol. 13(3), 1989, pp. 319-340.

  1. DAVIS, F. D., R. P. BAGOZZI, P. R. WARSHAW, User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Sci., vol. 35, no. 8, 1989, pp. 982-1003.
  2. DAVIS, F. D., R. P. BAGOZZI, P. R. WARSHAW, Extrinsic and Intrinsic Motivation to Use Computers in the Workplace. Journal of Applied Social Psychology, 22, no. 14, 1992, pp. 1111-1132.
  3. FORNELL, C., D. F. LARCKER, Evaluating Structural Equations Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, vol. 18(1), 1981, pp. 39-50.
  4. HAIR, J. F., W. C. BLACK, B. J. BABIN, R. E. ANDERSON, R. L. TATHAM, Multivariate Data Analysis. 6th, Prentice Hall, 2006.
  5. HAYES, A. Introduction to Mediation, Moderation, and Conditional Process Analysis: a Regression based Approach. Guilford Press, New York, 2013.
  6. HU, L. T., P. M. BENTLER, Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives. Structural Equation Modeling vol. 6, no. 1, 1999, pp. 1-55.
  7. IORDACHE, D. D. Modele de acceptare a tehnologiilor în e-learning. Revista Română de Interacţiune Om Calculator, 3(2), 2010, pp. 125-138.
  8. KARAALI, D., C. A. GUMUSSOY, F. CALISIR, Factors Affecting the Intention to Use a Web-based Learning System among Blue-collar Workers in the Automotive Industry. Computer in Human Behavior, vol. 27(1), 2011, pp. 343-354.
  9. KIM, B. G., S. C. PARK, K. L., LEE, A Structural Equation Modeling of the Internet Acceptance in Korea. Electronic Commerce Research and Applications, vol. 6, 2007, pp. 425-432.
  10. LEE, Y. H. An Empirical Investigation into Factors Influencing the Adoption of an e-Learning System. Online Info. Review, vol. 30, no. 5, 2006, pp. 517-541.
  11. LEE, Y. H., Y. C. HSIEH, Y. H. CHEN, An Investigating of Employees’ Use of e-Learning Systems: Applying the Technology Acceptance Model. Behavioral & Information Technology, vol. 32, no. 2, 2013, pp. 173-189.
  12. LEE, Y. H., Y. C. HSIEH, C. Y. MA, A Model of Organizational Employees’ e-Learning Systems Acceptance. Knowledge-Based Systems 24, 2011, pp. 355-366.
  13. LEGRIS, P., J. INGHAM, P. COLLERETTE, Why Do People Use Information Technology? A Critical Review of the Technology Acceptance Model. Information and Management, vol. 40(3), 2003, pp. 191-204.
  14. MACKINNON, D. P. Introduction to Statistical Mediation Analysis. Erlbaum Psych Press, New York. 2008.
  15. MACKINNON, D. P., C. M. LOCKWOOD, WILLIAMS, Confidence Limits for the Indirect Effect: Distribution of the Product and Resampling Methods. Multivariate Behavioral Research vol. 39(1), 2004, pp. 99-128.
  16. MALDONADO, U. P. T., G. F. KHAN, J. MOON, J. J. RHO, E-Learning Motivation and Educational Portal Acceptance in Developing Countries. Online Info. Review, vol.35, no.1, 2011, pp. 66-85.
  17. NICULESCU, , G. Thorsteinsson, Enabling Idea Generation through Computer-Assisted Collaborative Learning. Studies in Informatics and Control, vol. 20(4), 2011, pp. 403-410.
  18. ONG, C., J. LAI, Gender Differences in Perceptions and Relationships among Dominants of e-Learning Acceptance. Computers in Human Behavior, vol. 22, no. 5, 2006, pp. 816-829.
  19. PARK, S. Y. An Analysis of the Technology Acceptance Model in Understanding University Students’ Behavioral Intention to Use e-Learning. Educational Technology & Society, vol. 12(3), 2009, pp. 150–162.
  20. PITUCH, K. A., Y. K. LEE, The Influence of System Characteristics on e-Learning Use. Computer & Education vol. 47, 2006, pp. 222-244.
  21. PREACHER, K. J., A. F. HAYES, Asymptotic and Resampling Strategies for Assessing and Comparing Indirect Effects in Multiple Mediator Models. Behavior Research Methods vol. 40(3), 2008, pp. 879-891.
  22. PREACHER, K. J., K. KELLY, Effect Size Measures for Mediation Models: Quantitative Strategies for Communicating Indirect Effects. Psychological Methods, vol. 16(2), 2011, pp. 93-115.
  23. RUNGTUSANATHAM, M., J. W. MILLER, K. K. BOYER, Theorizing, Testing, and Concluding for Mediation in SCM Research: Tutorial and Procedural Recommendations. Journal of Operations Management vol. 32, 2014, pp. 99-113.
  24. SHROUT, P. E., N. BOLGER, Mediation in Experimental and Non Experimental Studies: New Procedures and Recommendations. Psychological Methods, vol. 7, no. 4, 2002, pp. 422-445.
  25. TARHINI, A., K. HONE, X. LIU, User Acceptance Towards Web-based Learning Systems: Investigating the Role of Social, Organizational and Individual Factors in European Higher Education. Procedia Computer Science, vol. 17, pp. 189-197.
  26. TAYLOR, A. B., D. P. MACKINNON, J. Y. TEIN, Tests of the Three-path Mediated Effect. Organizational Research Methods vol. 11, no. 2, 2008, pp. 241-269.
  27. TEO, T. Examining the Intention to Use Technology among Pre-Service Teachers: an Integration of the Technology Acceptance Model and Theory of Planned Behavior. Interactive Learning Environments vol. 20(1), 2012, pp. 3-18.
  28. VAN RAAIJ, E. M., J. J. L. SCHEPERS, The Acceptance and Use of a Virtual Learning Environment in China. Computers & Education, vol. 50(3), 2008, pp. 838-852.
  29. VENKATESH, V. Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model. Information Systems Research, vol. 11, no. 4, 2000, pp. 342-365.
  30. VENKATESH, V., H. BALA, Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Science, 39, no. 2, 2008, pp. 273-315.
  31. VENKATESH, V., F. D. DAVIS, A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, vol. 45, no. 2, 2000, pp. 186-204.
  32. VENKATESH, V., M. MORRIS, G. DAVIS, F. DAVIS, User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly vol. 27, no. 3, 2003, pp. 425-478.
  33. YI, M. Y., Y. HWANG, Predicting the Use of Web-based Information Systems: Self-efficacy, Enjoyment, Learning Goal Orientation, and the Technology Acceptance Model, International Journal of Human Computer Studies, vol. 59, no. 4, 2003, pp. 431-449.
  34. YOUSAFZAI, S. Y., G. R. FOXALL, J. G. PALLISTER, Technology Acceptance: a Meta-analysis of the TAM. Journal of Modelling and Management, vol. 2(3), 2007, pp. 251-280.