Quality & Quantity

, Volume 48, Issue 3, pp 1601–1618 | Cite as

Validating innovating practice and perceptions of course management system solutions using structural equation modeling

  • Shinyi Lin
  • Tse-Hua Shih
  • Shu-Hui Chuang


As adopting information and communications technology to deliver instruction and facilitate learning, course management systems (CMSs) offer an alternative capability to enhance management practices. Based on innovation diffusion theory, this study explores CMS effectiveness (EF) and reliability (RL), and considers both perceived innovative attributes (IA) and demographic characteristics. This study also exams the moderating effect of complexity (CX) and mediating effect of function evaluation (FE) on the causal relationship between IA and outcome variables (i.e., EF, RL). Analysis also includes the differential effects of three types of CMSs and gender differences. Participants were 238 undergraduates, majored in business or management, who volunteered to complete an online survey. Results show that perceived IA affect RL and EF, but not FE. CX moderates the effect of perceived IA on RL, but does not moderate the effects of perceived IA on FE and EF. EF, but not FE, appears to mediate the effects of perceived IA on RL. There is no significant difference in model fit between genders, but there is among the type of CMS solution group. Conclusions and implications are offered regarding the future research for program leaders and practitioners.


Innovative diffusion Course management systems (CMSs) Structural equation modeling (SEM) Management education 



The authors gratefully acknowledge the subsidy of this research grant (99-2511-S-142-012-98WFA0D00038) by the National Science Council of Taiwan.


  1. Abdalla, I.: Evaluating effectiveness of e-blackboard system using tam framework: a structural analysis approach. AACE J. 15(3), 279–287 (2007)Google Scholar
  2. Akman, I., Mishra, A.: Gender, age and income differences in internet usage among employees in organizations. Comput. Hum. Behav. 26(3), 482–490 (2010). doi: 10.1016/j.chb.2009.12.007 CrossRefGoogle Scholar
  3. Aldag, R.J.: Bump it with a trumpet: on the value of our research to management education. Acad. Manag. Learn. Education 11(2), 285–292 (2012)CrossRefGoogle Scholar
  4. Alias, N.A., Zainuddin, A.M.: Innovation for better teaching and learning: adopting the learning management system. Malays. Online J. Instr. Technol. 2(2), 27–40 (2005)Google Scholar
  5. Baldwin, T.T., Pierce, J.R., Joines, R.C., Farouk, S.: The elusiveness of applied management knowledge: a critical challenge for management educators. Acad. Manag. Learn. Education 10(4), 583–605 (2011). doi: 10.5465/amle.2010.0045 CrossRefGoogle Scholar
  6. Baloglu, M., Çevik, V.: Multivariate effects of gender, ownership, and the frequency of use on computer anxiety among high school students. Comput. Hum. Behav. 24(6), 2639–2648 (2008). doi: 10.1016/j.chb.2008.03.003
  7. Bartunek, J.M., Egri, C.P.: Introduction:can academic research be managerially actionable? what are the requirements for determining this? Acad. Manag. Learn. Education 11(2), 244–246 (2012)CrossRefGoogle Scholar
  8. Berking, P., & Gallagher, S. (2011). Choosing a Learning Management System. Accessed 20 Feb 2012
  9. Braak, V.J.: Factors influencing the use of computer mediated communication by teachers in secondary schools. Comput. Education 36(1), 41–57 (2001)CrossRefGoogle Scholar
  10. Braak, V.J., Goeman, K.: Differences between general computer attitudes and perceived computer attributes: development and validation of a scale. Psychol. Rep. 92(2), 655–660 (2003)CrossRefGoogle Scholar
  11. Braak, V.J., Tearle, P.: The computer attributes for learning scale (CALS) among university students: scale development and relationship with actual computer use for learning. Comput. Hum. Behav. 23(6), 2966–2982 (2007)CrossRefGoogle Scholar
  12. Broos, A.: Gender and information and communication technologies (ICT) anxiety: male self-assurance and female hesitation. CyberPsychol. Behav. 8, 21–31 (2005)CrossRefGoogle Scholar
  13. Bulter, R., Ryan, R., Chao, T.: Gender and technology in the liberal arts: aptitudes, attitudes, and skills acquisition. J. Inf. Technol. Education 4, 347–362 (2005)Google Scholar
  14. Casmar, S., & Peterson, N. (2002). Personal factors influencing faculty’s adoption computer technology: a model framework. Accessed 20 Feb 2012
  15. Caspi, A., Chajut, E., Saporta, K.: Participation in class and in online discussions: gender differences. Comput. Education 50(3), 718–724 (2006)CrossRefGoogle Scholar
  16. Chang, S.-C., Tung, F.-C.: An empirical investigation of students’ behavioural intentions to use the online learning course websites. British J. Educational Technol. 39(1), 71–83 (2008)Google Scholar
  17. Cheung, G.W., Rensvold, R.B.: Evaluating goodness-of-fit indexes for testing measurement invariance. Struct. Equ. Model. 9(2), 233–255 (2002)CrossRefGoogle Scholar
  18. Chou, C., Wu, H.-C., Chen, C.-H.: Re-visiting college students’ attitudes toward the internet-based on a 6-T model: gender and grade level difference. Comput. Education 56(4), 939–947 (2011). doi: 10.1016/j.compedu.2010.11.004 Google Scholar
  19. Dambrot, F.H.: The correlates of sex differences in attitudes toward and involvement with computers. J. Vocat. Behav. 27(1), 71–86 (1985)CrossRefGoogle Scholar
  20. Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manag. Sci. 35, 982–1003 (1989)CrossRefGoogle Scholar
  21. Dearing, J.W., Meyer, G.: An exploratory tool for predicting adoption decisions. Sci. Commun. 16(1), 43–57 (1994)CrossRefGoogle Scholar
  22. DeNeui, D.L., Dodge, T.L.: Asynchronous learning networks and student outcomes: the utility of online learning components in hybrid courses. J. Instr. Psychol. 33(4), 256–259 (2006)Google Scholar
  23. Ding, N., Bosker, R.J., Harskamp, E.G.: Exploring gender and gender pairing in the knowledge elaboration processes of students using computer-supported collaborative learning. Comput. Education. doi: 10.1016/j.compedu.2010.06.004 (in press)
  24. Egri, C.P.: Introduction: developing conversations about management education. Acad. Manag. Learn. Education 11(1), 124–124 (2012). doi: 10.5465/amle.2012.0031 CrossRefGoogle Scholar
  25. Engwall, L.: The anatomy of management education. Scand. J. Manag. 23(1), 4–35 (2007). doi: 10.1016/j.scaman.2006.12.003 CrossRefGoogle Scholar
  26. Fulk, J., Gould, J.J.: Features and contexts in technology research: a modest proposal for research and reporting. J. Comput. Mediat. Commun. 14(3), 764–770 (2009). doi: 10.1111/j.1083-6101.2009.01469.x CrossRefGoogle Scholar
  27. Greve, H.R.: Correctly assessing the value of our research to management education. Acad. Manag. Learn. Education 11(2), 272–277 (2012)CrossRefGoogle Scholar
  28. Gutek, B.A., Bikson, T.K.: Differential experiences of men and women in computerized offices. Sex Roles 13(3), 123–136 (1985)CrossRefGoogle Scholar
  29. Ireland, R.D.: Management research and managerial practice: a complex and controversial relationship. Acad. Manag. Learn. Education 11(2), 263–271 (2012)CrossRefGoogle Scholar
  30. Jaccard, J., Choi, K.W.: LISREL Approaches to Interaction Effects in Multiple Regression. Sage Publications, Thousand Oaks, CA (1996)Google Scholar
  31. Jacobsen, D. M.: Examining technology adoption patterns by faculty in higher education. Paper presented at the ACEC2000: learning technologies, teaching and the future of schools, Melbourne, Australia, 6–9 July 2000.Google Scholar
  32. Kay, R.H., Lauricella, S.: Gender differences in the use of laptops in higher education: a formative analysis. J. Educational Comput. Res. 44(3), 361–380 (2011)CrossRefGoogle Scholar
  33. Kelan, E.K., Jones, R.D.: Gender and the MBA. Acad. Manag. Learn. Education 9(1), 26–43 (2010)CrossRefGoogle Scholar
  34. Kenny, D.A., Judd, C.M.: Estimating the nonlinear and interactive effects of latent variables. Psychol. Bull. 96, 201–210 (1984)CrossRefGoogle Scholar
  35. Lane, L.M.: Toolbox or trap? course managment systems and pedagogy. EDUCAUSE Q. 31(2), 4–6 (2008)Google Scholar
  36. Lau, S.-H., Woods, P.C.: Understanding learner acceptance of learning objects: the role of learning object characteristics and individual differences. British J. Educational Technol. 40(6), 1059–1075 (2009)CrossRefGoogle Scholar
  37. Lin, M.-C., Tutwiler, M.S., Chang, C.-Y.: Gender bias in virtual learning environments: an exploratory study. British J. Educational Technol. 43(2), E59–E63 (2012). doi: 10.1111/j.1467-8535.2011.01265.x CrossRefGoogle Scholar
  38. Lin, S., Overbaugh, R.C.: Computer-mediated discussion, self-efficacy, and gender. British J. Educational Technol. 40(6), 999–1013 (2009)CrossRefGoogle Scholar
  39. Malikowski, S.R.: Factors related to breadth of use in course management systems. Internet High. Education 11(2), 81–86 (2008). doi: 10.1016/j.iheduc.2008.03.003
  40. Malikowski, S.R., Thompson, M.E., Theis, J.G.: External factors associated with adopting a CMS in resident college courses. Internet High. Education 9(3), 163–174 (2006). doi: 10.1016/j.iheduc.2006.06.006
  41. Malikowski, S.R., Thompson, M.E., Theis, J.G.: A model for research into course management systems: bridging technology and learning theory. J. Educational Comput. Res. 36(2), 149–173 (2007)CrossRefGoogle Scholar
  42. Martins, L.L., Kellermanns, F.W.: A model of business school students’ acceptance of a web-based course management system. Acad. Manag. Learn. Education 3(1), 7–26 (2004)CrossRefGoogle Scholar
  43. Meredith, W.: Measurement invariance, factor analysis and factorial invariance. Psychometrika 58(4), 525–543 (1993)CrossRefGoogle Scholar
  44. Moosmayer, D.C.: A model of management academics’ intentions to influence values. Acad. Manag. Learn. Education 11(2), 155–173 (2012)CrossRefGoogle Scholar
  45. Morgan, G.: Faculty use of course management systems, vol. 2. EDUCAUSE Center for Applied Research, Boulder, CO (2003)Google Scholar
  46. Naveh, G., Tubin, D., Pliskin, N.: Student LMS use and satisfaction in academic institutions: the organizational perspective. Internet High. Education 13(3), 127–133 (2010). doi: 10.1016/j.iheduc.2010.02.004
  47. Neuman, D.: Naturalistic inquiry and the perseus project. Comput. Humanit. 25(4), 239–246 (1991)CrossRefGoogle Scholar
  48. Oh, S., Ahn, J., Kim, B.: Adoption of broadband Internet in Korea: the role of experience in building attitudes. J. Inf. Technol. 18(4), 267–280 (2003)CrossRefGoogle Scholar
  49. Osborn, D.: Do print, web-based, or blackboard integrated tutorial strategies differentially influence student learning in an introductory psychology class? J. Instr. Psychol. 37(3), 247 (2010)Google Scholar
  50. Park, N., Lee, K.M., Cheong, P.H.: University instructors’ acceptance of electronic courseware: an application of the technology acceptance model. J. Comput. Mediat. Commun. 13(1), (2007). Article 9Google Scholar
  51. Redpath, L.: Confronting the bias against on-line learning in management education. Acad. Manag. Learn. Education 11(1), 125–140 (2012). doi: 10.5465/amle.2010.0044 CrossRefGoogle Scholar
  52. Reinen, I.J., Plomp, T.: Information technology and gender equality: a contradiction in terminis? Comput. Education 28, 65–78 (1997)CrossRefGoogle Scholar
  53. Rogers, E.M.: Diffusion of Innovations, 4th edn. Simon and Schuster, New York (1995)Google Scholar
  54. Rogers, E.M. (ed.): Diffusion of Innovations, 5th edn. Free Press, New York (2003)Google Scholar
  55. Romiszowski, A.J.: How’s the e-learning baby? factors leading to success or failure of an educational technology innovation. Educational Technol. 44(1), 5–27 (2004)Google Scholar
  56. Selim, H.M.: An empirical investigation of student acceptance of course websites. Comput. Education 40(4), 343–360 (2003)CrossRefGoogle Scholar
  57. Shen, D., Laffey, J., Lin, Y., Huang, X.: Social influence for perceived usefulness and ease of use of course delivery systems. J. Interact. Online Learn. 5(3), 270–282 (2006)Google Scholar
  58. Sun, S., Konold, T.R., Fan, X.: Effects of latent variable nonnormality and model misspecification on testing structural equation modeling interactions. J. Exp. Education 79, 231–256 (2011)CrossRefGoogle Scholar
  59. Tella, A., Mutula, S.M.: Gender differences in computer literacy among undergraduate students at the University of Botswana: implications for library use. Malays. J. Libr. Inf. Sci. 13(1), 59–76 (2008)Google Scholar
  60. Trotter, A.: Blackboard vs. Moodle, Education Week’s Digital Directions (2008). 21Google Scholar
  61. Verdegem, P., De Marez, L.: Rethinking determinants of ICT acceptance: towards an integrated and comprehensive overview. Technovation 31(8), 411–423 (2011). doi: 10.1016/j.technovation.2011.02.004 CrossRefGoogle Scholar
  62. von Eye, A., Spiel, C., Wagner, P.: Structural equations modeling in developmental research concepts and applications. Methods Psychol. Res. Online 8(2), 75–112 (2003)Google Scholar
  63. Vovides, Y., Sanchez-Alonso, S., Mitropoulou, V., Nickmans, G.: The use of e-learning course management systems to support learning strategies and to improve self-regulated learning. Educational Res. Rev. 2(1), 64–74 (2007). doi: 10.1016/j.edurev.2007.02.004
  64. Wadsworth, L.M., Husman, J., Duggan, M.A., Pennington, M.N.: Online mathematics achievement: effects of learning strategies and self-efficacy. J. Dev. Education 30(3), 6–14 (2007)Google Scholar
  65. Yen-Chun Jim, W.U., Shihping, H., Lopin, K.U.O., Wen-Hsiung, W.U.: Management education for sustainability: a web-based content analysis. Acad. Manag. Learn. Education 9(3), 520–531 (2010). doi: 10.5465/amle.2010.53791832 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Master Program of Business AdministrationNational Taichung University of EducationTaichungTaiwan
  2. 2.i3 Global, LLC.Hunt ValleyUSA
  3. 3.Department of Business AdministrationAsia UniversityTaichungTaiwan

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