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Higher education instructors’ intention to use educational video games: an fsQCA approach

  • Antonio Sánchez-MenaEmail author
  • José Martí-Parreño
  • María José Miquel-Romero
Research Article
  • 2 Downloads

Abstract

Educational video games (EVGs) offer instructors a myriad of opportunities to motivate and engage students in the learning process. Nevertheless, instructors can be influenced by barriers that prevent them from using EVGs in their courses (e.g. lack of expertise with EVGs). Instructors can also be influenced by different drivers that might increase their intention to use EVGs. This research analyses the effects of four variables (perceived usefulness, perceived ease of use, attention, and relevance) as factors contributing or preventing the use of EVGs by instructors serving in Higher Education institutions. Data of 170 instructors, who were surveyed through an online questionnaire using a snowball sampling, is analysed via fuzzy-set Qualitative Comparative Analysis (fsQCA). Main results suggest that perceived usefulness and perceived ease of use of EVGs are sufficient conditions for Higher Education instructors to show behavioural intention to use EVGs in their courses. Results also suggest that both instructors’ perceived capacity of EVGs to attract students’ attention and perceived relevance of EVGs affect instructors’ behavioural intention. Managerial implications for Instructor Training Programmes (ITP), limitations of the study, and future research lines are also addressed.

Keywords

Educational video games Instructors’ intentions Higher education fsQCA 

Notes

Funding

This work was funded by Laureate International Universities through the David A. Wilson Award for Excellence in Teaching and Learning under Grant LIU-WIL2015.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Abt, C. C. (1970). Serious games: The art and science of games that simulate life. New York: Viking Press.Google Scholar
  2. Andrews, R., Beynon, M. J., & McDermott, A. M. (2016). Organizational capability in the public sector: A configurational approach. Journal of Public Administration Research and Theory, 26(2), 239–258.Google Scholar
  3. Alharbi, S., & Drew, S. (2014). Using the technology acceptance model in understanding academics’ behavioural intention to use learning management systems. International Journal of Advanced Computer Science and Applications, 5(1), 143–155.Google Scholar
  4. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modelling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(May), 411–423.Google Scholar
  5. Bagozzi, R. 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), 244–254.Google Scholar
  6. Bagozzi, R. P., & Baumgartner, H. (1994). The evaluation of structural equation models and hypothesis testing. In Richard P. Bagozzi (Ed.), Principles of marketing research (pp. 386–422). Cambridge: Blackwell Publishers.Google Scholar
  7. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94.Google Scholar
  8. Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37, 122–147.  https://doi.org/10.1037/0003-066X.37.2.122.Google Scholar
  9. Bauer, H. H., Reichardt, T., Barnes, S. J., & Neumann, M. M. (2005). Driving consumer acceptance of mobile marketing: A theoretical framework and empirical study. Journal of Electronic Commerce Research, 6(3), 181–192.Google Scholar
  10. Biernacki, P., & Waldorf, D. (1981). Snowball sampling: Problems and Techniques of Chain Referral Sampling. Sociological Methods and Research, 10(2), 141–163.  https://doi.org/10.1177/004912418101000205.Google Scholar
  11. Bingimlas, K. A. (2009). Barriers to the successful integration of ICT in teaching and learning environments: A review of the literature. Eurasia Journal of Mathematics Science & Technology Education, 5(3), 235–245.Google Scholar
  12. Boyle, E. A., Hainey, T., Connolly, T. M., Gray, G., Earp, J., Ott, M., et al. (2016). An update to the systematic literature review of empirical evidence of the impacts and outcomes of computer games and serious games. Computers & Education, 94, 178–192.Google Scholar
  13. Churchill, G. A. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16, 64–73.Google Scholar
  14. Cohen, J. (1977). Statistical power analysis for the behavioral sciences. New York: Academic Press.Google Scholar
  15. Cox, M., Preston, C. & Cox K. (1999, November). What factors support or prevent teachers from using ICT in their classrooms? Paper presented at the British Educational Research Association Annual Conference, University of Sussex, Brighton.Google Scholar
  16. Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334.Google Scholar
  17. Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. PhD diss., Massachusetts Institute of Technology. Retrieved from http://hdl.handle.net/1721.1/15192
  18. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.  https://doi.org/10.1287/mnsc.35.8.982.Google Scholar
  19. De Grove, F., Bourgonjon, J., & Van Looy, J. (2012). Digital games in the classroom? A contextual approach to teachers’ adoption intention of digital games in formal education. Computers in Human Behavior, 28(6), 2023–2033.Google Scholar
  20. Demirbilek, M., & Tamer, S. L. (2010). Math teachers’ perspectives on using educational computer games in math education. Procedia-Social and Behavioral Sciences, 9, 709–716.Google Scholar
  21. Dempsey, J. V., & Johnson, R. B. (1998). The development of an ARCS gaming scale. Journal of Instructional Psychology, 25, 215–221.Google Scholar
  22. Emin-Martinez, V.& Ney, M. (2013). Supporting teachers in the process of adoption of game based learning pedagogy. Escudeiro, P. & Vaz de Carvalho, C. (Eds.) Proceedings of the 7th European Conference on Games Based LearningECGBL 2013 in Porto, Portugal, Academic Conferences International Limited, pp. 156–162.Google Scholar
  23. Ertmer, P. A. (1999). Addressing first-and second-order barriers to change: Strategies for technology integration. Educational Technology Research and Development, 47(4), 47–61.Google Scholar
  24. Fathema, N., Shannon, D., & Ross, M. (2015). Expanding the technology acceptance model (TAM) to examine faculty use of learning management systems (LMSs) in higher education institutions. Journal of Online Learning & Teaching, 11(2), 210–232.Google Scholar
  25. Fornell, C., & Larcker, D. (1981). Evaluating structural equations models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50.Google Scholar
  26. Friel, T., Britten, J., Compton, B., Peak, A., Schoch, K., & Van Tyle, W. K. (2009). Using pedagogical dialogue as a vehicle to encourage faculty technology use. Computers & Education, 53, 300–307.Google Scholar
  27. Goodwyn, A., Adams, A., & Clarke, S. (1997). The great god of the future: The views of current and future English teachers on the place of IT in literacy. English in Education, 31(2), 54–62.  https://doi.org/10.1111/j.1754-8845.1997.tb00125.x.Google Scholar
  28. Hall, D., & Hall, I. M. (1996). Practical social research: Project work in the community. London: Macmillan.Google Scholar
  29. Hirumi, A., Appelman, B., Rieber, L., & Van Eck, R. (2010). Preparing instructional designers for game-based learning: Part 1. TechTrends, 54(3), 27–37.Google Scholar
  30. Hong, S. J., Thong, J. Y. L., & Tam, K. Y. (2006). Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet. Decision Support Systems, 42(3), 1819–1834.  https://doi.org/10.1016/j.dss.2006.03.009.Google Scholar
  31. Huang, W. H., Huang, W. Y., & Tschopp, J. (2010). Sustaining iterative game playing processes in DGBL: The relationship between motivational processing and outcome processing. Computers & Education, 55(2), 789–797.Google Scholar
  32. Ince, E. Y., & Demirbilek, M. (2013). Secondary and high school teachers’ perceptions regarding computer games with educational features in Turkey. Anthropologist, 16(1–2), 89–96.Google Scholar
  33. Hamari, J.& Nousiainen, T. (2015) Why do teachers use game-based learning technologies? The role of individual and institutional ICT readiness: Proceedings of the 48th Hawaii International Conference on System Sciences (HICSS). IEEE, pp. 682–691.Google Scholar
  34. Juan, Y. K., & Chao, T. W. (2015). Game-based learning for green building education. Sustainability, 7(5), 5592–5608.  https://doi.org/10.3390/su7055592.Google Scholar
  35. Karadag, R. (2015). Pre-Service Teachers' perceptions on game based learning scenarios in primary reading and writing instruction courses. Educational Sciences: Theory and Practice, 15(1), 185–200.Google Scholar
  36. Karoulis, A. & Demetriadis, S. (2005) The motivational factor in educational games. Interaction between learner’s internal and external representations in multimedia environments. Research report, Kaleidoscope NoE JEIRP, D21-02-01-F, 296-312. Retrieved February 12, 2014, from http://athanasis.karoulis.gr/Data/Science/Kaleidoscope/2-MotivFactorEduGames.pdf
  37. Keller, J. M. (1987). Development and use of the ARCS model of motivational design. Journal of Instructional Development, 10(3), 1–10.  https://doi.org/10.1007/BF02905780.Google Scholar
  38. Kenny, R. F., & McDaniel, R. (2011). The role teachers’ expectations and value assessments of video games play in their adopting and integrating them into their classrooms. British Journal of Educational Technology, 42(2), 197–213.Google Scholar
  39. Ketelhut, D. J., & Schifter, C. C. (2011). Teachers and game-based learning: Improving understanding of how to increase efficacy of adoption. Computers & Education, 56, 539–546.Google Scholar
  40. Klein, J. D. (1992). Effect of instructional gaming and reentry status on performance and motivation. Contemporary Educational Psychology, 17, 364–370.Google Scholar
  41. Lai, V., & Li, H. (2005). Technology acceptance model for internet banking: An invariance analysis. Information and Management, 42(2), 373–386.  https://doi.org/10.1016/j.im.2004.01.007.Google Scholar
  42. Leischnig, A., Henneberg, S. C. & Thornton, S. C. (2014). Performing configurational analyses in management research: A fuzzy set approach: Proceedings of the 30th Industrial Marketing and Purchasing Conference, Bordeaux, France.Google Scholar
  43. Lewellyn, K. B., & Muller-Kahle, M. I. (2016). The configurational effects of board monitoring and the institutional environment on CEO compensation: A country-level fuzzy-set analysis. Journal of Management and Governance, 20(4), 729–757.Google Scholar
  44. Lewin, K. (1935). A dynamic theory of personality. New York: McGraw-Hill.Google Scholar
  45. Liu, Y., Li, H., & Carlsson, C. (2010). Factors driving the adoption of m-learning: An empirical study. Computers & Education, 55(3), 1211–1219.Google Scholar
  46. Loftus, G. R., & Loftus, E. F. (1983). Mind at play: The psychology of video games. New York: Basic Books.Google Scholar
  47. Malone, T. W., & Lepper, M. R. (1987). Making learning fun: A taxonomy of intrinsic motivations for learning. In R. E. Snow & M. J. Farr (Eds.), Aptitude, learning, and instruction - Volume 3: Conative and affective process analyses (pp. 223–253). Hillsdale: Lawrence Erlbaum Associates.Google Scholar
  48. Manessis, D. (2011). Early childhood post-educated teachers’ views and intentions about using digital games in the classroom: Proceedings of the 5th European Conference on Games Based LearningECGBL 2011, Reading, England, Academic Conferences International Limited, pp. 753–767.Google Scholar
  49. Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information systems research, 2(3), 173–191.Google Scholar
  50. McFarland, D. J., & Hamilton, D. (2006). Adding contextual specificity to the technology acceptance model. Computers in Human Behavior, 22(3), 427–447.Google Scholar
  51. Mumtaz, S. (2000). Factors affecting teachers’ use of information and communications technology: A review of the literature. Journal of Information Technology for Teacher Education, 9(3), 319–342.  https://doi.org/10.1080/14759390000200096.Google Scholar
  52. Ngai, E. W. T., Poon, J. K. L., & Chan, Y. H. C. (2007). Empirical examination of the adoption of WebCT using TAM. Computers & Education, 48(2), 250–267.Google Scholar
  53. Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York: McGraw-Hill.Google Scholar
  54. Pelgrum, W. J. (2001). Obstacles to the integration of ICT in education: Results from a worldwide educational assessment. Computers & Education, 37(2), 163–178.Google Scholar
  55. Perry, J., & Klopfer, E. (2014). UbiqBio: Adoptions and outcomes of mobile biology games in the ecology of school. Computers in the Schools, 31(1–2), 43–64.Google Scholar
  56. Piaget, J. (1962). Play, dreams and imitation in childhood. New York: W.W. Norton & Co.Google Scholar
  57. Ragin, C. C. (2000). Fuzzy-set social science. Chicago: The University of Chicago Press.Google Scholar
  58. Ragin, C. C. (2006). Set relations in social research: Evaluating their consistency and coverage. Political Analysis, 14, 291–310.  https://doi.org/10.1093/pan/mpj019.Google Scholar
  59. Ragin, C. C. (2008). Qualitative comparative analysis using fuzzy sets (fsQCA). In R. Benoit & C. Ragin (Eds.), Configurational comparative analysis (pp. 87–121). London: Thousand Oaks.Google Scholar
  60. Ragin, C. C., & Sonnet, J. (2005). Between complexity and parsimony: Limited diversity, counterfactual cases, and comparative analysis. In S. Kropp & M. Minkenberg (Eds.), Vergleichen in der Politikwissenschaft¸ (pp. 180–197). Wiesbaden: Springer.Google Scholar
  61. Rieber, L. P. (1996). Seriously considering play: Designing interactive learning environments based on the blending of microworlds, simulations, and games. Educational Technology Research and Development, 44(2), 43–58.Google Scholar
  62. Rihoux, B. (2006). Qualitative comparative analysis (QCA) and related systematic comparative methods. International Sociology, 21(5), 679–706.  https://doi.org/10.1177/0268580906067836.Google Scholar
  63. Robbins, S. (2005). Organizational behavior. Upper Saddle River: Pearson Education.Google Scholar
  64. Rogers, E. M. (2003). Diffusion of innovations. New York: The Free Press a Division of Simon & Schuster Inc.Google Scholar
  65. Sadler, G. R., Lee, H. C., Lim, R. S. H., & Fullerton, J. (2010). Recruitment of hard-to-reach population subgroups via adaptations of the snowball sampling strategy. Nursing & Health Sciences, 12(3), 369–374.  https://doi.org/10.1111/j.1442-2018.2010.00541.x.Google Scholar
  66. Schifter, C. C. (2008). Infusing computers into classrooms: Continuous practice improvement. Hershey: IGI Global.Google Scholar
  67. Schifter, C. & Ketelhut, D. (2009). Teacher acceptance of game-based learning in K-12: the case of River City: Proceedings of the Society for Information Technology & Teacher Education International Conference, pp. 3836–3842Google Scholar
  68. Schneider, M. R., Schulze-Bentrop, C., & Paunescu, M. (2010). Mapping the institutional capital of high-tech firms: A fuzzy-set analysis of capitalist variety and export performance. Journal of International Business Studies, 41(2), 246–266.  https://doi.org/10.1057/jibs.2009.36.Google Scholar
  69. Schneider, C. Q., & Wagemann, C. (2007). Qualitative comparative analysis (QCA) und fuzzy sets: Ein lehrbuch für anwender und alle, die es werden wollen. Verlag: Barbara Budrich.Google Scholar
  70. Sitzmann, T. (2011). A meta-analytic examination of the instructional effectiveness of computer-based simulation games. Personnel Psychology, 64(2), 489–528.Google Scholar
  71. Squire, K. (2005). Changing the game: What happens when video games enter the classroom?. Innovate: Journal of online education 1 (6). Retrieved December 3, 2018, from https://www.learntechlib.org/p/107270/.
  72. Su, C., & Cheng, C. (2015). A mobile gamification learning system for improving the learning motivation and achievements. Journal of Computer Assisted Learning, 31(3), 268–286.  https://doi.org/10.1111/jcal.12088.Google Scholar
  73. Teo, T. (2008). Pre-service teachers´ attitudes towards computer use: A Singapore survey. Australasian Journal of Educational Technology, 24(4), 413–424.Google Scholar
  74. Theodosiou, S., & Karasavvidis, I. (2015). Serious games design: A mapping of the problems novice game designers experience in designing games. Journal of e-Learning and Knowledge Society, 11(3), 133–148.Google Scholar
  75. Tolman, E. C. (1932). Purposive behavior in man and animals. New York: Appleton-Century-Crofts.Google Scholar
  76. Vis, B. (2012). The comparative advantages of fsQCA and regression analysis for moderately large-N analyses. Sociological Methods & Research, 41(1), 168–198.Google Scholar
  77. Wagemann, C., Buche, J., & Siewert, M. B. (2016). QCA and business research: Work in progress or a consolidated agenda? Journal of Business Research, 69(7), 2531–2540.Google Scholar
  78. Wang, A. I. (2015). The wear out effect of a game-based student response system. Computers & Education, 82, 217–227.Google Scholar
  79. Woodside, A. G. (2014). Embrace• perform• model: Complexity theory, contrarian case analysis, and multiple realities. Journal of Business Research, 67(12), 2495–2503.Google Scholar
  80. Woodside, A. G. (2016). The good practices manifesto: Overcoming bad practices pervasive in current research in business. Journal of Business Research, 69(2), 365–381.  https://doi.org/10.1016/j.jbusres.2015.09.008.Google Scholar
  81. Woodside, A. G., Ko, E., & Huan, T. C. (2012). The new logic in building isomorphic theory of management decision realities. Management Decision, 50(5), 765–777.  https://doi.org/10.1108/00251741211227429.Google Scholar
  82. Wu, J. H., & Wang, S. C. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information and Management, 42(5), 719–729.Google Scholar

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© Association for Educational Communications and Technology 2019

Authors and Affiliations

  1. 1.Laureate Education, Inc.BaltimoreUSA
  2. 2.Universidad Europea de ValenciaValenciaSpain
  3. 3.Universitat de ValènciaValenciaSpain

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