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Factors that influence university students’ intention to use Moodle: a study in Macau

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Abstract

Moodle is widely used in higher education institutions in this digital age. With the growing popularity of Moodle use in education, this study aimed to research on the factors that influence student users’ intentions to adopt Moodle for learning purposes in Macau. A total of 564 students from nine departments at the University of Macau responded to a survey in which ten constructs from a framework that integrated the Diffusion of Innovation Theory and Technology Acceptance Model, were measured. The results of this study showed that the research model had a good fit. Two variables—usefulness and ease of use—had significantly influenced Macau students’ attitudes towards Moodle use. Other variables such as usefulness, attitude, and perceived behavioral control were found to be important determinants of students’ behavioral intentions. Furthermore, usefulness was significantly associated with ease of use, output quality, trialability, as well as subjective norm. Students’ perceptions on the ease of use was significantly influenced by technology complexity and trialability. On the whole, the proposed research model had explained 66% of the variance of Macau university students’ behavioral intentions to use Moodle. This study contributed to deepening our understanding of technology acceptance theories by contextualizing the current study within the Macau higher education.

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References

  • Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Computers in Human Behavior, 63, 75–90.

    Article  Google Scholar 

  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

    Article  Google Scholar 

  • Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology, 32(4), 665–683.

    Article  Google Scholar 

  • Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Upper Saddle River: Prentice-Hall.

    Google Scholar 

  • Al-Ani, W. T. (2013). Blended learning approach using Moodle and student’s achievement at Sultan Qaboos University in Oman. Journal of Education and Learning, 2(3), 96–110.

    Article  Google Scholar 

  • Alenezi, A. R., Abdul Karim, A. M., & Veloo, A. (2010). An empirical investigation into the role of enjoyment, computer anxiety, computer self-efficacy and internet experience in influencing the students’ intention to use e-learning: A case study from Saudi Arabian governmental universities. Turkish Online Journal of Educational Technology, 9(4), 22–34.

    Google Scholar 

  • Beatty, B., & Ulasewicz, C. (2006). Faculty perspectives on moving from Blackboard to the Moodle learning management system. TechTrends, 50(4), 36–45.

    Article  Google Scholar 

  • Bray, M. (2015). The growth and diversification of higher education in Macau. International Higher Education, 23, 19–20.

    Google Scholar 

  • Brown, I. T. (2002). Individual and technological factors affecting perceived ease of use of web-based learning technologies in a developing country. The Electronic Journal of Information Systems in Developing Countries, 9, 1–15.

    Article  Google Scholar 

  • Carmines, E. G., & McIver, J. P. (1981). Analyzing models with unobserved variables. In G. W. Bohrnstedt & E. F. Borgatta (Eds.), Social measurement: Current issues. Beverly Hills, CA: Sage.

    Google Scholar 

  • Carvalho, A., Areal, N., & Silva, J. (2011). Students’ perceptions of blackboard and Moodle in a Portuguese university. British Journal of Educational Technology, 42(5), 824–841.

    Article  Google Scholar 

  • Chao, I. T. (2008). Moving to Moodle: Reflections two years later. Educause Quarterly, 31(3), 46–52.

    Google Scholar 

  • Chung, J. E., Park, N., Wang, H., Fulk, J., & McLaughlin, M. (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.

    Article  Google Scholar 

  • Chuo, Y. H., Tsai, C. H., & Lan, Y. L. (2011). The effect of organizational support and self-efficacy on the usage intention of e-learning system in hospital. Key Engineering Materials, 467–469(9), 2137–2142.

    Article  Google Scholar 

  • Churchill, D. (2009). Educational applications of web 2.0: Using blogs to support teaching and learning. British Journal of Educational Technology, 40(1), 179–183.

    Article  Google Scholar 

  • Cigdem, H., & Topcu, A. (2015). Predictors of instructors’ behavioral intention to use learning management system: A Turkish vocational college example. Computers in Human Behavior, 52, 22–28.

    Article  Google Scholar 

  • Costa, C., Alvelos, H., & Teixeira, L. (2016). Acceptance of Moodle by professors: A study in a Portuguese higher education institution. Paper presented at 16ª Conferência da Associação Portuguesa de Sistemas de Informação—CAPSI’16, At Porto.

  • Costello, E. (2013). Opening up to open source: Looking at how Moodle was adopted in higher education. Open Learning: The Journal of Open, Distance and e-Learning, 28(3), 187–200.

    Article  Google Scholar 

  • Damnjanovic, V., Jednak, S., & Mijatovic, I. (2015). Factors affecting the effectiveness and use of Moodle: Students’ perception. Interactive Learning Environments, 23(4), 496–514.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Escobar-Rodriguez, T., & Monge-Lozano, P. (2012). The acceptance of Moodle technology by business administration students. Computers & Education, 58(4), 1085–1093.

    Article  Google Scholar 

  • Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intension and behavior: An introduction to theory and research. Reading, MA: Addison Wesley.

    Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 48, 39–50.

    Article  Google Scholar 

  • Gefen, D., Straub, D. W., & Boudreau, M. C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(1), 7.

    Google Scholar 

  • Hackbarth, G., Grover, V., & Mun, Y. Y. (2003). Computer playfulness and anxiety: Positive and negative mediators of the system experience effect on perceived ease of use. Information & Management, 40(3), 221–232.

    Article  Google Scholar 

  • Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.

    Google Scholar 

  • Hölbl, M., & Welzer, T. (2015). Students’ feedback and communication habits using Moodle. Elektronika ir Elektrotechnika, 102(6), 63–66.

    Google Scholar 

  • Hsu, H. H., & Chang, Y. Y. (2013). Extended TAM model: Impacts of convenience on acceptance and use of Moodle. US-China Education Review, 3(4), 211–218.

    Google Scholar 

  • Huang, H. M. (2002). Toward constructivism for adult learners in online learning environments. British Journal of Educational Technology, 33(1), 27–37.

    Article  Google Scholar 

  • Huang, F., Teo, T., & Zhou, M. (2017). Factors affecting Chinese English as a foreign language teachers’ technology acceptance: A qualitative study. Journal of Educational Computing Research, 52(1), 133–148.

    Google Scholar 

  • Jan, A. U., & Contreras, V. (2016). Success model for knowledge management systems used by doctoral researchers. Computers in Human Behavior, 59, 258–264.

    Article  Google Scholar 

  • Kline, R. B. (2010). Principles and practice of structural equation modelling (3rd ed.). New York: Guilford Press.

    Google Scholar 

  • Lee, Y. H., Hsieh, Y. C., & Hsu, C. N. (2011). Adding innovation diffusion theory to the technology acceptance model: Supporting employees’ intentions to use e-learning systems. Educational Technology and Society, 14(4), 124–137.

    Google Scholar 

  • Lin, T. T., & Bautista, J. R. (2017). Understanding the relationships between mHealth Apps’ characteristics, trialability, and mHealth literacy. Journal of Health Communication, 22(4), 346–354.

    Article  Google Scholar 

  • Lin, P. C., Lu, H. K., & Fan, S. M. (2014). Exploring the impact of perceived teaching style on behavioral intention toward Moodle reading system. International Journal of Emerging Technologies in Learning, 9(3), 64–67.

    Article  Google Scholar 

  • Locke, W. (2009). Reconnecting the research–policy–practice nexus in higher education: “Evidence-based policy” in practice in national and international contexts. Higher Education Policy, 22(2), 119–140.

    Article  Google Scholar 

  • Lonn, S., & Teasley, S. D. (2009). Podcasting in higher education: What are the implications for teaching and learning? Internet and Higher Education, 12(2), 88–92.

    Article  Google Scholar 

  • Luo, X., Li, H., Zhang, J., & Shim, J. P. (2010). Examining multi-dimensional trust and multi-faceted risk in initial acceptance of emerging technologies: An empirical study of mobile banking services. Decision Support Systems, 49(2), 222–234.

    Article  Google Scholar 

  • Ma, W. W. K., Andersson, R., & Streith, K. O. (2005). Examining user acceptance of computer technology: An empirical study of student teachers. Journal of Computer Assisted Learning, 21(6), 387–395.

    Article  Google Scholar 

  • Machado, M., & Tao, E. (2007). Blackboard vs. Moodle: Comparing user experience of learning management systems. Frontiers in Education Conference—Global Engineering: Knowledge Without Borders, Opportunities Without Passports, 2007. Fie ‘07. (pp.S4J-7–S4J-12). IEEE.

  • Marcinkiewicz, H. R., & Regstad, N. G. (1996). Using subjective norms to predict teachers’ computer use. Journal of Computing in Teacher Education, 13(1), 27–33.

    Article  Google Scholar 

  • McCoy, S., Galletta, D. F., & King, W. R. (2005). Integrating national culture into IS research: The need for current individual level measures. Communications of the Association for Information Systems, 15(1), 211–224.

    Google Scholar 

  • Moodle (2015). Moodle.org. Retrieved from https://moodle.org/ in February 2017.

  • Nurakun Kyzy, Z., Ismailova, R., & Dündar, H. (2018). Learning management system implementation: A case study in the Kyrgyz Republic. Interactive Learning Environments. https://doi.org/10.1080/10494820.2018.1427115.

    Article  Google Scholar 

  • Palincsar, A. S. (1998). Social constructivist perspectives on teaching and learning. Annual Review of Psychology, 49(1), 345–375.

    Article  Google Scholar 

  • Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592–605.

    Article  Google Scholar 

  • Pinto-Llorente, A. M., Sánchez-Gómez, M. C., García-Peñalvo, F. J., & Casillas-Martín, S. (2017). Students’ perceptions and attitudes towards asynchronous technological tools in blended-learning training to improve grammatical competence in English as a second language. Computers in Human Behavior, 72, 632–643.

    Article  Google Scholar 

  • Price, L., & Kirkwood, A. (2014). Using technology for teaching and learning in higher education: A critical review of the role of evidence in informing practice. Higher Education Research and Development, 33, 549–564.

    Article  Google Scholar 

  • Raykov, T., & Marcoulides, G. A. (2008). An introduction to applied multivariate analysis. New York: Routledge.

    Google Scholar 

  • Rogers, E. M. (1995). Diffusion of innovations (4th ed.). New York, NY: Free Press.

    Google Scholar 

  • Rubin, B., Fernandes, R., Avgerinou, M. D., & Moore, J. (2010). The effect of learning management systems on student and faculty outcomes. The Internet and Higher Education, 13(1), 82–83.

    Article  Google Scholar 

  • Sánchez, R. A., & Hueros, A. D. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in Human Behavior, 26(6), 1632–1640.

    Article  Google Scholar 

  • Sánchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2016). Informal tools in formal contexts: Development of a model to assess the acceptance of mobile technologies among teachers. Computers in Human Behavior, 55, 519–528.

    Article  Google Scholar 

  • Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information and Management, 44(1), 90–103.

    Article  Google Scholar 

  • Shattock, M. (2003). Managing successful universities. Society for Research into Higher Education. Maidenhead: Open University Press.

    Google Scholar 

  • Siemens, G. (2013). Massive open online courses: Innovation in education. Open Educational Resources: Innovation, Research and Practice, 5, 5–15.

    Google Scholar 

  • Sife, A., Lwoga, E., & Sanga, C. (2007). New technologies for teaching and learning: Challenges for higher learning institutions in developing countries. International Journal of Education & Development Using Information and Communication Technology, 3(2), 57–67.

    Google Scholar 

  • Song, Y., & Kong, S. C. (2017). Investigating students’ acceptance of a statistics learning platform using Technology Acceptance Model. Journal of Educational Computing Research, 55(6), 865–897.

    Article  Google Scholar 

  • Šorgo, A., Bartol, T., Dolničar, D., & Boh Podgornik, B. (2017). Attributes of digital natives as predictors of information literacy in higher education. British Journal of Educational Technology, 48(3), 749–767.

    Article  Google Scholar 

  • Straub, D., Limayem, M., & Karahanna-Evaristo, E. (1995). Measuring system usage: Implications for is theory testing. Management Science, 41(8), 1328–1342.

    Article  Google Scholar 

  • Tang, H. F. J., & Morrison, K. (1998). When marketisation does not improve schooling: The case of Macau. Compare A Journal of Comparative & International Education, 28(3), 245–262.

    Article  Google Scholar 

  • Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(1), 302–312.

    Article  Google Scholar 

  • Teo, T. (2010). Establishing gender structural invariance of technology acceptance model (TAM). The Asia-Pacific Education Researcher, 19(2), 311–320.

    Article  Google Scholar 

  • Teo, T. (2012). 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, 20(1), 3–18.

    Article  Google Scholar 

  • Teo, T. (2015). Comparing pre-service and in-service teachers’ acceptance of technology: Assessment of measurement invariance and latent mean differences. Computers & Education, 83, 22–31.

    Article  Google Scholar 

  • Teo, T., & Fan, X. (2013). Coefficient Alpha and beyond: Issues and alternatives for educational research. Asia-Pacific Education Researcher, 22(2), 209–213.

    Article  Google Scholar 

  • Teo, T., & Huang, F. (2018). Investigating the influence of individually espoused cultural values on teachers’ intentions to use educational technologies in Chinese universities. Interactive Learning Environments. https://doi.org/10.1080/10494820.2018.1489856.

    Article  Google Scholar 

  • Teo, T., Huang, F., & Hoi, C. K. W. (2018). Explicating the influences that explain intention to use technology among English teachers in China. Interactive Learning Environments, 26(4), 460–475.

    Article  Google Scholar 

  • Teo, T., Ursavas, O. F., & Bahcekapili, E. (2012). An assessment of pre-service teachers’ technology acceptance in Turkey: A structural equation modelling approach. The Asia-Pacific Education Researcher, 21(1), 199–210.

    Google Scholar 

  • Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 125–143.

    Article  Google Scholar 

  • Tondeur, J., van Braak, J., Ertmer, P. A., & Ottenbreit-Leftwich, A. (2017). Understanding the relationship between teachers’ pedagogical beliefs and technology use in education: A systematic review of qualitative evidence. Educational Technology Research and Development, 65(3), 555–575.

    Article  Google Scholar 

  • Venkatesh, V. (1999). Creation of favourable user perceptions: Exploring the role of intrinsic motivation. MIS Quarterly, 23(2), 239–260.

    Article  Google Scholar 

  • Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365.

    Article  Google Scholar 

  • Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.

    Article  Google Scholar 

  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.

    Article  Google Scholar 

  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 27(3), 425–478.

    Article  Google Scholar 

  • Yang, M. M. (2007). An exploratory study on consumers’ behavioral intention of usage of third generation mobile value-added services, Unpublished Master’s Thesis, National Cheng Kung University.

  • Yeou, M. (2016). An investigation of students’ acceptance of Moodle in a blended learning setting using technology acceptance model. Journal of Educational Technology Systems, 44(3), 300–318.

    Article  Google Scholar 

  • Zhou, Y. (2008). Voluntary adopters versus forced adopters: Integrating the diffusion of innovation theory and the technology acceptance model to study intra-organizational adoption. New Media & Society, 10(3), 475–496.

    Article  Google Scholar 

  • Zhou, M. (2016). Chinese university students’ acceptance of MOOCs: A self-determination perspective. Computers & Education, 92, 194–203.

    Article  Google Scholar 

  • Zhou, M., Chan, K. K., & Teo, T. (2016). Understanding Mathematics teachers’ use of dynamic geometry software in Macau. Journal of Educational Technology and Society, 19(3), 181–193.

    Google Scholar 

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  1. Timothy Teo and Fang Huang have contributed equally to this work.

Authors and Affiliations

  1. School of Foreign Languages, Qingdao University, Ningxia Road 308, Shinan District, Qingdao, 266071, Shandong Province, China

    Fang Huang

  2. Faculty of Education (E33), University of Macau, Av. da Universidade, Taipa, Macau, China

    Mingming Zhou & Andy Chun Wai Fan

  3. Murdoch University, 90 South Street, Murdoch, WA, 6150, Australia

    Timothy Teo

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Appendix A: constructs and corresponding items

Appendix A: constructs and corresponding items

Perceived usefulness (adapted from Davis 1989; Teo 2009)

PU1: Using Moodle enables me to learn more quickly.

PU2: Using Moodle improves my performance in learning.

PU3: Using Moodle increases my productivity in learning.

PU4: Using Moodle enhances my effectiveness in learning.

PU5: Using Moodle is useful to learning.

Perceived ease of use (adapted from Davis 1989; Teo 2009)

PEU1: It is easy for me to use Moodle in learning to do what I want to do.

PEU2: My interaction with Moodle in learning is simple.

PEU3: It is easy for me to become good at using Moodle in learning.

PEU4: I find Moodle easy to use in learning.

Attitude towards using Moodle (adapted from Davis 1989; Teo 2009)

ATU1: Once I start using Moodle in learning, I find it hard to stop.

ATU2: I look forward to those aspects of learning that require the use of Moodle.

ATU3: I like to use Moodle in learning.

ATU4: I have positive feelings towards the use of Moodle in learning.

Behavioral intention (adapted from Davis 1989; Teo 2009)

BI1: I intend to continue to use Moodle in learning in the future.

BI2: I expect that I would use Moodle in learning in the future.

BI3: I plan to use Moodle in learning in the future.

Technology complexity (adapted from Teo 2009; Thompson et al. 1991)

TC1: Learning with Moodle is so complicated that it is difficult to understand what is going on.

TC2: It takes too long to learn how to use Moodle in learning, such that it is not worth the effort.

TC3: Using Moodle in learning is a complex activity.

Subjective norm (adapted from Fishbein and Ajzen 1975, Teo et al. 2018)

SN1: People who influence my behavior think that I should use Moodle in learning.

SN2: People who are important to me think that I should use Moodle in learning.

SN3: The people whose views I respect support the use of Moodle in learning.

Perceived behavioral control (adapted from Ajzen 1991; Zhou 2016)

PBC1: I have control over Moodle at learning.

PBC2: I have the resources necessary to use Moodle in learning.

PBC3: I have the knowledge necessary to use Moodle in learning.

PBC4: Given the resources, opportunities and knowledge, it is easy for me to use Moodle in learning.

Computer anxiety (adapted from Venkatesh 2000; Abdullah et al. 2016)

ANX1: I feel apprehensive about using Moodle in learning.

ANX2: I hesitate to use Moodle in learning for fear of making mistakes I cannot correct.

ANX3: Using Moodle in learning is intimidating to me.

Output quality (adapted from Venkatesh 2000; Jan and Contreras 2016)

OUT1: Compared to what I had done, using Moodle has improved the quality of learning.

OUT2: Compared to what I had done, using Moodle has made learning easier.

OUT3: Compared to what I had done, using Moodle has enhanced my effectiveness in learning.

OUT4: Compared to what I had done, using Moodle has increased my productivity in learning.

Trialability (adapted from Rogers 1995; Lee et al. 2011).

TRI1: Before using Moodle, I can use it on a trial basis for learning.

TRI2: Before using Moodle, I can test the functions properly for learning.

TRI3: Before using Moodle, I can ensure that it meets my needs in learning.

TRI4: Before using Moodle, I can ensure that it matches my expectations in learning.

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Teo, T., Zhou, M., Fan, A.C.W. et al. Factors that influence university students’ intention to use Moodle: a study in Macau. Education Tech Research Dev 67, 749–766 (2019). https://doi.org/10.1007/s11423-019-09650-x

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