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Innovation attributes and pedagogical quality: a concretization of joint theories on course management systems acceptance

Quality & Quantity volume 47, pages 2309–2317 (2013)Cite this article

Abstract

The utilization of information and communication technologies in support of teaching and learning is still on its way to expand its potentials in higher education. Facing different venues of course delivery, computer-mediated communication technologies seem to be the most efficient and cost-effective. Since the mid-1990s, course management systems (CMSs) have gradually evolved, and taken an irreplaceable important role in higher education. From the perspectives of pedagogical impact and instructional resource consumption, Morgan (2003) considered that CMSs form the academic system equivalent of enterprise resource planning systems. Based on an extensive review on the Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT) at the outset, this study bridges the TAM and IDT, and proposes a conceptual model of the acceptance of CMSs to acknowledge antecedents (i.e., innovation attributes, self-efficacy (SE), pedagogical quality (PQ) and perceived evaluation of function (EF)). Theoretically, the perceived innovation attributes of CMSs impact users’ perceived usefulness (PU) and actual use (AU). The antecedents, such as SE, PQ, and EFs, are also anticipated to have impacts on the AU and use of perception. PU is influenced by perceived ease of use (PEOU), while behavioral intention (BI) is concurrently influenced by PEOU and PU. Moreover, AU is influenced by BI. Finally, discussion and suggestions for future research of the proposed model are also provided.

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References

  • Akers, E.J.: A study of the adoption of digital government technology as public policy in the American states. Ph.D. Dissertation, Auburn University Montgomery, Montgomery, Alabama (2006)

  • Bagozzi R.P., Davis F.D., Warshaw P.R.: Development and test of a theory of technological learning and usage. Hum. Relat. 45(7), 660–686 (1992). doi:10.1177/001872679204500702

    Article  Google Scholar 

  • Barley S.: What can we learn from the history of technology?. J. Eng. Technol. Manag. 15, 237–255 (1998)

    Article  Google Scholar 

  • Braak V.J.: Factors influencing the use of computer mediated communication by teachers in secondary schools. Comput. Educ. 36(1), 41–57 (2001)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Brown S.: From VLEs to learning webs: the implications of Web 2.0 for learning and teaching. Interact. Learn. Environ. 18(1), 1–10 (2010). doi:10.1080/10494820802158983

    Article  Google Scholar 

  • Compeau D., Higgins C.A., Huff S.: Social cognitive theory and individual reactions to computing technology: a longitudinal study. MIS Q. 23(2), 145–158 (1999)

    Article  Google Scholar 

  • Dalsgaard, C.: Pedagogical quality in e-learning—designing e-learning from a learning theoretical approach. e-Learn. Educ. 1(1) (2005). http://eleed.campussource.de/archive/1/78/. Accessed 14 March 2011

  • Davis F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989)

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • Dishaw M.T., Strong D.M.: Extending the technology acceptance model with task-technology fit constructs. Inf. Manag. 36(1), 9–21 (1999)

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Gray V.: Competition, Emulation, and Policy Innovation. CQ Press, Washington, DC (1994)

    Google Scholar 

  • Hamuy E., Galaz M.: Information versus communication in course management system participation. Comput. Educ. 54(1), 169–177 (2010). doi:10.1016/j.compedu.2009.08.001

    Article  Google Scholar 

  • Hong J.-C., Hwang M.-Y., Hsu H.-F., Wong W.-T., Chen M.-Y.: Applying the technology acceptance model in a study of the factors affecting usage of the Taiwan digital archives system. Comput. Educ. 57(3), 2086–2094 (2011). doi:10.1016/j.compedu.2011.04.011

    Article  Google Scholar 

  • Igbaria M., Iivari J.: The effects of self-efficacy on computer usage. Omega 23(6), 587–605 (1995)

    Article  Google Scholar 

  • Jacobsen, D.M.: Examining technology adoption patterns by faculty in higher education. Paper presented at the proceedings of ACEC2000: learning technologies, teaching and the future of schools, Melbourne, Australia, 6–9 July 2000

  • Lau S.-H., Woods P.C.: Understanding learner acceptance of learning objects: the role of learning object characteristics and individual differences. Br. J. Educ. Technol. 40(6), 1059–1075 (2009)

    Article  Google Scholar 

  • Liu I.F., Chen M.C., Sun Y.S., Wible D., Kuo C.-H.: Extending the TAM model to explore the factors that affect intention to use an online learning community. Comput. Educ. 54(2), 600–610 (2010). doi:10.1016/j.compedu.2009.09.009

    Article  Google Scholar 

  • Ma Q., Liu L.: The technology acceptance model: a meta-analysis of empirical findings. J. Organ. End User Comput. 16(1), 59–72 (2004)

    Article  Google Scholar 

  • Malikowski S.R., Thompson M.E., Theis J.G.: External factors associated with adopting a CMS in resident college courses. Internet High. Educ. 9(3), 163–174 (2006). doi:10.1016/j.iheduc.2006.06.006

    Article  Google Scholar 

  • Malikowski S.R., Thompson M.E., Theis J.G.: A model for research into course management systems: bridging technology and learning theory. J. Educ. Comput. Res. 36(2), 149–173 (2007)

    Article  Google Scholar 

  • MartĂ­n-Blas T., Serrano-Fernández A.: The role of new technologies in the learning process: Moodle as a teaching tool in physics. Comput. Educ. 52(1), 35–44 (2009). doi:10.1016/j.compedu.2008.06.005

    Article  Google Scholar 

  • Mintrom M.: Policy entrepreneurs and the diffusion of innovation. Am. J. Political Sci. 41(3), 738–770 (1997)

    Article  Google Scholar 

  • Morgan, G.: Faculty Use of Course Management Systems (vol. 2). EDUCAUSE Center for Applied Research, Boulder (May 20, 2003)

  • Nass C., Mason L.: On the Study of Technology and Task: A Variable-Based Approach. Sage, Newbury Park (1990)

    Google Scholar 

  • Naveh G., Tubin D., Pliskin N.: Student LMS use and satisfaction in academic institutions: the organizational perspective. Internet High. Educ. 13(3), 127–133 (2010). doi:10.1016/j.iheduc.2010.02.004

    Article  Google Scholar 

  • 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)

    Article  Google Scholar 

  • 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), article 9 (2007)

    Google Scholar 

  • Picciano A.: Beyond student perceptions: issues of interaction, presence, and performance in an online course. J. Asynchronous Learn. Netw. 6(1), 21–40 (2002)

    Google Scholar 

  • Pintrich P.R., De Groot E.: Motivational and self-regulated learning components of classroom academic performance. J. Educ. Psychol. 82, 33–40 (1990)

    Article  Google Scholar 

  • Rogers E.M.: Diffusion of Innovations, 4th edn. Free Press, New York (1995)

    Google Scholar 

  • Romero C., Ventura S., GarcĂ­a E.: Data mining in course management systems: Moodle case study and tutorial. Comput. Educ. 51(1), 368–384 (2008). doi:10.1016/j.compedu.2007.05.016

    Article  Google Scholar 

  • Sánchez R.A., Hueros A.D.: Motivational factors that influence the acceptance of Moodle using TAM. Comput. Hum. Behav. 26(6), 1632–1640 (2010). doi:10.1016/j.chb.2010.06.011

    Article  Google Scholar 

  • Sanchez-Franco M.J.: WebCT—the quasimoderating effect of perceived affective quality on an extending technology acceptance model. Comput. Educ. 54(1), 37–46 (2010). doi:10.1016/j.compedu.2009.07.005

    Article  Google Scholar 

  • Selim H.M.: An empirical investigation of student acceptance of course websites. Comput. Educ. 40(4), 343–360 (2003)

    Article  Google Scholar 

  • Shea P., Bidjerano T.: Learning presence: towards a theory of self-efficacy, self-regulation, and the development of a communities of inquiry in online and blended learning environments. Comput. Educ. 55(4), 1721–1731 (2010). doi:10.1016/j.compedu.2010.07.017

    Article  Google Scholar 

  • Snelbecker G.E.: Some thoughts about theories, perfection, and instruction. In: Reigeluth, C.M. (ed.) Instructional-Design Theories and Models. A New Paradigm of Instructional Theory, vol. II, pp. 31–47. Lawrence Erlbaum Associates, Mahwah (1999)

    Google Scholar 

  • Straub E.T.: Understanding technology adoption: theory and future directions for informal learning. Rev. Educ. Res. 79(2), 625–649 (2009). doi:10.3102/0034654308325896

    Article  Google Scholar 

  • Sungur S., Tekkaya C.: Effects of problem-based learning and traditional instruction on self-regulated learning. J. Educ. Res. 99(5), 307–317 (2006)

    Article  Google Scholar 

  • Tabata L.N., Johnsrud L.K.: The impact of faculty attitudes toward technology, distance education, and innovation. Res. High. Educ. 49(7), 625–646 (2008). doi:10.1007/s11162-008-9094-7

    Article  Google Scholar 

  • Teo T.: A path analysis of pre-service teachers’ attitudes to computer use: applying and extending the technology acceptance model in an educational context. Interact. Learn. Environ. 18(1), 65–79 (2010). doi:10.1080/10494820802231327

    Article  Google Scholar 

  • Terzis V., Economides A.A.: The acceptance and use of computer based assessment. Comput. Educ. 56(4), 1032–1044 (2011). doi:10.1016/j.compedu.2010.11.017

    Article  Google Scholar 

  • Trotter, A.: Blackboard vs. Moodle. Education Week’s Digital Directions, 21 (2008)

  • Wadsworth L.M., Husman J., Duggan M.A., Pennington M.N.: Online mathematics achievement: effects of learning strategies and self-efficacy. J. Dev. Educ. 30(3), 6–14 (2007)

    Google Scholar 

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Authors and Affiliations

  1. Graduate Institute of Business Administration, National Taichung University of Education, 227 Min-Shen Road, Taichung, 40306, Taiwan

    Shinyi Lin

  2. Department of Senior Citizen Service Management, Ching Kuo Institute of Management & Health, 336 Fushing Rd., Keelung, 20301, Taiwan

    Shueh-Fen Chen

  3. Department of Health Promotion & Health Education, National Taiwan Normal University, Taipei, Taiwan

    Shueh-Fen Chen

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  1. Shinyi Lin
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  2. Shueh-Fen Chen
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Correspondence to Shinyi Lin.

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Lin, S., Chen, SF. Innovation attributes and pedagogical quality: a concretization of joint theories on course management systems acceptance. Qual Quant 47, 2309–2317 (2013). https://doi.org/10.1007/s11135-011-9657-0

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