Exploring the Factors that Influence the Intention to Play a Color Mixing Game

  • Yong-Ming HuangEmail author
  • Chia-Sui Wang
  • Tien-Chi Huang
  • Chia-Chen Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10108)


Color mixing is viewed as one of the most important stages with regard to learning colors, and digital games have been identified as a useful means for encouraging students in learning. However, little effort has been devoted to using digital games to assist students in learning color mixing. To remedy this deficiency, this study developed a color mixing game and further explored the subjects’ perspectives on the game. More specifically, the technology acceptance model was employed to develop a questionnaire to collect the subjects’ opinions about the game, through which the decisive factors behind the subjects’ intention to play the game can be analyzed. The questionnaire delivered two significant results. First, the subjects’ perceived ease of playing influenced their attitude toward playing through the mediation of perceived usefulness. Second, the subjects’ perceived usefulness influenced their intention to play through the mediation of attitude toward playing. The mediation of perceived usefulness and attitude toward playing implied that both ways of influence were indirect.


Behavioral Intention Learning Motivation Technology Acceptance Model Composite Reliability Primary Color 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We would like to express special thanks to Ms. Ting-Ying Du, Ms. Li-Rong Weng, Mr. Lun Hong, Ms. Yi-Jing Huang, and Mr. Wei-Long Chen who provided effective technical support to implement the color mixing game. The authors also would like to thank the Ministry of Science and Technology of the Republic of China, Taiwan, for financially supporting this research under Contract No. MOST 103-2511-S-041-002-MY3.


  1. 1.
    Perge, E., Zichar, M.: Computer assisted method for cognitive improvement of color aptitude. In: Proceedings of 6th IEEE International Conference on Cognitive Infocommunications, Gyor, Hungary (2015)Google Scholar
  2. 2.
    Holtzschue, L.: Understanding Color. Wiley, Hoboken (2011)Google Scholar
  3. 3.
    Lányi, C.S., Kosztyán, Z., Kránicz, B., Schanda, J., Navvab, M.: Using multimedia interactive e-teaching in color science. LEUKOS 4(1), 71–82 (2007)Google Scholar
  4. 4.
    Chen, N.S., Hwang, G.J.: Transforming the classrooms: innovative digital game- based learning designs and applications. Educ. Technol. Res. Dev. 62(2), 125–128 (2014)CrossRefGoogle Scholar
  5. 5.
    Huang, Y.M., Huang, Y.M.: A scaffolding strategy to develop handheld sensor- based vocabulary games for improving students’ learning motivation and performance. Educ. Technol. Res. Dev. 63(5), 691–708 (2015)CrossRefGoogle Scholar
  6. 6.
    Cagiltay, N.E.: Teaching software engineering by means of computer-game development: challenges and opportunities. Br. J. Educ. Technol. 38(3), 405–415 (2007)CrossRefGoogle Scholar
  7. 7.
    Kinzie, M.B., Joseph, D.R.D.: Gender differences in game activity preferences of middle school children: implications for educational game design. Educ. Technol. Res. Dev. 56(5–6), 643–663 (2008)CrossRefGoogle Scholar
  8. 8.
    Davis, F.D.: Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989)CrossRefGoogle Scholar
  9. 9.
    Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manage. Sci. 35(8), 982–1003 (1989)CrossRefGoogle Scholar
  10. 10.
    Fishbein, M., Azjen, I.: Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Addison-Wesley, Reading (1975)Google Scholar
  11. 11.
    Hong, J.C., Hwang, M.Y., Chen, Y.J., Lin, P.H., Huang, Y.T., Cheng, H.Y., Lee, C.C.: Using the saliency-based model to design a digital archaeological game to motivate players’ intention to visit the digital archives of Taiwan’s natural science museum. Comput. Educ. 66, 74–82 (2013)CrossRefGoogle Scholar
  12. 12.
    Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., Tatham, R.L.: Multivariate Data Analysis, 6th edn. Prentice-Hall, New Jersey (2006)Google Scholar
  13. 13.
    Fornell, C., Larcker, D.F.: Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18(1), 39–50 (1981)CrossRefGoogle Scholar
  14. 14.
    Chin, W.W., Newsted, P.R.: Structural equation modeling analysis with small samples using partial least squares. In: Hoyle, R. (ed.) Statistical Strategies for Small Sample Research, pp. 307–341. Sage Publications, California (1999)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yong-Ming Huang
    • 1
    Email author
  • Chia-Sui Wang
    • 2
  • Tien-Chi Huang
    • 3
  • Chia-Chen Chen
    • 4
  1. 1.Department of Applied Informatics and MultimediaChia Nan University of Pharmacy and ScienceTainanTaiwan, R.O.C.
  2. 2.Department of Information ManagementChia Nan University of Pharmacy and ScienceTainanTaiwan, R.O.C.
  3. 3.Department of Information ManagementNational Taichung University of Science and TechnologyTaichungTaiwan, R.O.C.
  4. 4.Department of Management Information SystemsNational Chung Hsing UniversityTaichungTaiwan, R.O.C.

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