Measuring the Moderating Effect of Gender and Age on E-Learning Acceptance in England: A Structural Equation Modeling Approach for An Extended Technology Acceptance Model
Restricted accessResearch articleFirst published online September, 2014
Measuring the Moderating Effect of Gender and Age on E-Learning Acceptance in England: A Structural Equation Modeling Approach for An Extended Technology Acceptance Model
The success of an e-learning intervention depends to a considerable extent on student acceptance and use of the technology. Therefore, it has become imperative for practitioners and policymakers to understand the factors affecting the user acceptance of e-learning systems in order to enhance the students' learning experience. Based on an extended Technology Acceptance Model (TAM), the main aims of this study are to investigate the factors affecting students' behavioral intention to adopt e-learning technology and to explore the moderating effect of age and gender on the relationships among the determinants affecting e-learning acceptance. This study is based on a total sample of 604 students who used a Web-based learning system at Brunel University in England. Confirmatory Factor Analysis (CFA) was used to perform reliability and validity checks, and structural equation modeling (SEM) was used to test the research model. The results indicate that perceived ease of use, perceived usefulness, social norm, and self-efficacy were critical factors for students' behavioral intention to use e-learning, with the effect of perceived usefulness found to have the highest magnitude among the main determinants. We also found that age moderates the effect of PEOU, PU, and SE on BI, and that gender moderates the effect of PEOU and SN on BI. However, surprisingly, no significant moderating effect of age on the relationship between SN and BI was found; results also revealed no moderating of gender on PU or SE and BI. Overall, the proposed model achieves acceptable fit and explains 62% of its variance, which is higher than that explained by the original TAM. Based on these findings, implications to both theory and practice are discussed.
Get full access to this article
View all access options for this article.
References
1.
AjzenI. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.
2.
AjzenI.FishbeinM. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice Hall.
3.
ArbaughJ.GodfreyM. R.JohnsonM.PollackB. L.NiendorfB.WreschW. (2009). Research in online and blended learning in the business disciplines: Key findings and possible future directions. The Internet and Higher Education, 12, 71–87.
BagozziR. P. (2007). The legacy of the technology acceptance model and a proposal for a paradigm shift. Journal of the Association for Information Systems, 8(4), 211–218.
6.
BanduraA. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.
7.
BanduraA. (1995). Self-efficacy in changing societies. New York, NY: Cambridge University Press.
8.
BanduraA. (1997). Self-efficacy: The exercise of control. New York, NY: Freeman.
9.
BemS. L. (1981). The BSRI and gender schema theory: A reply to Spence and Helmreich. Psychological Review, 88(4), 369–371.
10.
BenbasatI.BarkiH. (2007). Quo vadis, TAM?Journal of the Association for Information Systems, 8(4), 211–218.
11.
BollenK. A. (1989). Structural equations with latent variables. New York, NY: Wiley.
12.
Burton-JonesA.HubonaG. S. (2006). The mediation of external variables in the technology acceptance model. Information & Management, 43(6), 706–717.
13.
CarminesE. G.McIverJ. P. (1981). Analyzing models with unobserved variables: Analysis of covariance structures. In BohrnstedtG. W.BorgattaE. F. (Eds.), Social measurement: Current issues (pp. 65–115). Beverly Hills, CA: Sage.
14.
ChaffinA. J.HarlowS. D. (2005). Cognitive learning applied to older adult learners and technology. Educational Gerontology, 31(4), 301–329.
15.
ChangS. C.TungF. C. (2008). An empirical investigation of students' behavioural intentions to use the online learning course websites. British Journal of Educational Technology, 39(1), 71–83.
16.
ChatzoglouP. D.SarigiannidisL.VraimakiE.DiamantidisA. (2009). Investigating Greek employees' intention to use Web-based training. Computers & Education, 53(3), 877–889.
17.
ChengT.LamD. Y. C.YeungA. C. L. (2006). Adoption of Internet banking: An empirical study in Hong Kong. Decision Support Systems, 42(3), 1558–1572.
18.
ChristieM. F.FerdosF. (2004). The mutual impact of educational and information technologies: Building a pedagogy of e-learning. Journal of Information Technology Impact, 4(1), 15–26.
19.
ChungJ. E.ParkN.WangH.FulkJ.McLaughlinM. (2010). Age differences in perceptions of online community participation among non-users: An extension of the Technology Acceptance Model. Computers in Human Behavior, 26(6), 1674–1684.
20.
CompeauD. R.HigginsC. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211.
21.
CzajaS. J.CharnessN.FiskA. D.HertzogC.NairS. N.RogersW. A. (2006). Factors predicting the use of technology: Findings from the Center for Research and Education on Aging and Technology Enhancement (CREATE). Psychology and Aging, 21, 333.
22.
DavisF. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
23.
DavisF. D.BagozziR. P.WarshawP. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.
24.
DowneyJ. (2006). Measuring general computer self-efficacy: The surprising comparison of three instruments in predicting performance, attitudes, and usage. Proceedings of the 39th annual Hawaii International Conference on System Sciences (pp. 210–220). IEEE.
25.
GefenD.StraubD. W.BoudreauM. C. (2000, August). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(7), 1–70.
26.
GrandonE. E.AlshareK.KwunO. (2005). Factors influencing student intention to adopt online classes: A cross-cultural study. Journal of Computing Sciences in Colleges, 20(4), 46–56.
27.
GuoY.BarnesS. (2007). Why people buy virtual items in virtual worlds with real money. ACM SIGMIS Database, 38(4), 69–76.
28.
HairJ. F. J.BlackW. C.BabinB. J.AndersonR. E.TathamR. L. (2010). Multivariate data analysis. Englewood Cliffs, NJ: Prentice Hall.
29.
HeJ.FreemanL. A. (2010). Are men more technology-oriented than women? The role of gender on the development of general computer self-efficacy of college students. Journal of Information Systems Education, 21(2), 203–212.
30.
HernandezB.JimenezJ.MartinM. Jose (2009, January). The impact of self-efficacy, ease of use and usefulness on e-purchasing: An analysis of experienced e-shoppers. Interacting with Computers, 21(1/2), 146–156.
31.
HoC.-L.DzengR.-J. (2010). Construction safety training via e-learning: Learning effectiveness and user satisfaction. Computers & Education, 55(2), 858–867.
32.
HoffmannL. W. (1972). Early childhood experiences and women's achievement motives. Journal of Social Issues, 28(2), 129–155.
33.
HofstedeG.HofstedeJ. (2005). Cultures and organizations: Software of the mind. New York, NY: McGraw-Hill.
34.
HuH.Al-GahtaniS. S.HuP. J. H. (2010). Examining gender effects in technology acceptance by Arabian workers: A survey study. Proceedings of PACIS, 2010 (p. 76).
35.
HuL.BentlerP. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(10), 1–55.
36.
HuangW.-H. D.HoodD. W.YooS. J. (2012). Gender divide and acceptance of collaborative Web 2.0 applications for learning in higher education. The Internet and Higher Education, 16, 57–65.
37.
JungY.PengW.MoranM.JinS. A. A.McLaughlinM.CodyM. (2010). Low-income minority seniors' enrollment in a cybercafé: Psychological barriers to crossing the digital divide. Educational Gerontology, 36(3), 193–212.
38.
KingW. R.HeJ. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740–755.
39.
KlineR. B. (2005). Principles and practice of structural equation modeling. New York, NY: Guilford.
40.
KripanontN. (2007). Examining a technology acceptance model of Internet usage by academics within Thai business schools. Melbourne, Australia: Victoria University.
41.
LansengE. J.AndreassenT. W. (2007). Electronic healthcare: A study of people's readiness and attitude toward performing self-diagnosis. International Journal of Service Industry Management, 18(4), 394–417.
42.
LiawS.-S. (2008). Investigating students' perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51(2), 864–873.
LiuI. F.ChenM. C.SunY. S.WibleD.KuoC. H. (2010). Extending the TAM model to explore the factors that affect intention to use an online learning community. Computers & Education, 54(2), 600–610.
45.
LynottP. P.McCandlessN. J. (2000). The impact of age vs. life experience on the gender role attitudes of women in different cohorts. Journal of Women & Aging, 12(1/2), 5–21.
46.
MarakasG. M.MunY. Y.JohnsonR. D. (1998). The multilevel and multifaceted character of computer self-efficacy: Toward clarification of the construct and an integrative framework for research. Information Systems Research, 9(2), 126–163.
47.
MintonH. L.SchneiderF. W.WrightsmanL. S. (1980). Differential psychology. Belmont, CA: Brooks/Cole.
48.
MorrisM. G.VenkateshV. (2000). Age differences in technology adoption decisions: Implications for a changing work force. Personnel Psychology, 53(2), 375–403.
49.
MorrisM. G.VenkateshV.AckermanP. L. (2005). Gender and age differences in employee decisions about new technology: An extension to the theory of planned behavior. IEEE Transactions on Engineering Management, 52(1), 69–84.
50.
NgaiE. W. T.PoonJ. K. L.ChanY. H. C. (2007). Empirical examination of the adoption of WebCT using TAM. Computers & Education, 48(2), 250–267.
51.
ParkS. Y. (2009). An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning. Educational Technology & Society, 12(3), 150–162.
52.
ParkS. Y.NamM. W.ChaS. B. (2012). University students' behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592–605.
53.
PhangC. W.SutantoJ.KankanhalliA.LiY.TanB. C.TeoH.-H. (2006). Senior citizens' acceptance of information systems: A study in the context of e-government services. IEEE Transactions on Engineering Management, 53(4), 555–569.
54.
PituchK. A.LeeY. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47(2), 222–244.
55.
PorterC. E.DonthuN. (2006). Using the technology acceptance model to explain how attitudes determine Internet usage: The role of perceived access barriers and demographics. Journal of Business Research, 59(9), 999–1007.
56.
QingfeiM.YupingL.ShaoboJ. (2009). The effects of individual-level culture on mobile commerce adoption: An empirical study. Proceedings of the 8th International Conference on Mobile Business (pp. 305–312).
57.
RodriguezT.LozanoP. (2011). The acceptance of Moodle technology by business administration students. Computers & Education, 58(4), 1085–1093.
58.
RogersE. M. (2003). Diffusion of innovations. New York, NY: Free Press.
59.
SánchezR. A.HuerosA. D. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in Human Behavior, 26(6), 1632–1640.
60.
SaundersE. J. (2004). Maximizing computer use among the elderly in rural senior centers. Educational Gerontology, 30(7), 573–585.
61.
SchepersJ.WetzelsM. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90–103.
62.
SchumacherP.Morahan-MartinJ. (2001). Gender, Internet and computer attitudes and experiences. Computers in Human Behavior, 17(1), 95–110.
63.
ShihY.-Y.FangK. (2004). The use of a decomposed theory of planned behavior to study Internet banking in Taiwan. Internet Research, 14(3), 213–223.
64.
SriteM.KarahannaE. (2006). The role of espoused national cultural values in technology acceptance. MIS Quarterly, 30(3), 679–704.
65.
StraubD.Burton-JonesA. (2007). Veni, vidi, vici: Breaking the TAM logjam. Journal of the Association for Information Systems, 8(4), 223–229.
66.
ŠumakB.HeričkoM.PušnikM. (2011). A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior, 27(1), 2067–2077.
67.
SunH.ZhangP. (2006). The role of moderating factors in user technology acceptance. International Journal of Human-Computer Studies, 64(2), 53–78.
68.
TarhiniA.HoneK.LiuX. (2013a). Extending the TAM model to empirically investigate the students' behavioural intention to use e-learning in developing countries. Science and Information Conference (pp. 732–737). IEEE.
69.
TarhiniA.HoneK.LiuX. (2013b). Factors affecting students' acceptance of e-learning environments in developing countries: A structural equation modeling approach. International Journal of Information and Education Technology, 3(1), 54–59.
70.
TarhiniA.HoneK.LiuX. (2013c). User acceptance towards Web-based learning systems: Investigating the role of social, organizational and individual factors in European higher education. Procedia Computer Science, 17, 189–197.
71.
TarhiniA.HoneK.LiuX. (2014a). A cross-cultural examination of the impact of social, organisational and individual factors on educational technology acceptance between British and Lebanese university students. British Journal of Educational Technology. Retrieved from http://www.readcube.com/articles/10.1111/bjet.12169
72.
TarhiniA.HoneK.LiuX. (2014b). The effects of individual differences on e-learning users' behaviour in developing countries: A structural equation model. Computers in Human Behaviour, 41, 153–163.
73.
TaylorS.ToddP. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 19(4), 561–570.
74.
TeoT.LuanW. S.SingC. C. (2008). A cross-cultural examination of the intention to use technology between Singaporean and Malaysian pre-service teachers: An application of the Technology Acceptance Model (TAM). Journal of Educational Technology and Society, 11(4), 265–280.
75.
TerzisV.EconomidesA. A. (2011). Computer based assessment: Gender differences in perceptions and acceptance. Computers in Human Behavior, 27(6), 2108–2122.
76.
TurnerP.TurnerS.Van de WalleG. (2007). How older people account for their experiences with interactive technology. Behaviour & Information Technology, 26(4), 287–296.
77.
Van RaaijE. M.SchepersJ. J. L. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50(3), 838–852.
78.
VenkateshV.BalaH. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.
79.
VenkateshV.DavisF. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.
80.
VenkateshV.MorrisM. G. (2000). Why don't men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 24(1), 115–139.
81.
VenkateshV.MorrisM. G.DavisG. B.DavisF. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
82.
WalkerG.JohnsonN. (2008). Faculty intentions to use components for Web-enhanced instruction. International Journal on E-Learning, 7(19), 133–152.
83.
WangY. S.WuM. C.WangH. Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40(1), 92–118.
84.
YiM. Y.HwangY. (2003). 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, 59(4), 431–449.
85.
YuenA. H. K.MaW. W. K. (2008). Exploring teacher acceptance of e-learning technology. Asia Pacific Journal of Teacher Education, 36(3), 229–243.
86.
ZhangS.ZhaoJ.TanW. (2008). Extending TAM for online learning systems: An intrinsic motivation perspective. Tsinghua Science & Technology, 13(3), 312–317.
