Journal of Computer Sciences and Applications
ISSN (Print): 2328-7268 ISSN (Online): 2328-725X Website: https://www.sciepub.com/journal/jcsa Editor-in-chief: Minhua Ma, Patricia Goncalves
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Journal of Computer Sciences and Applications. 2019, 7(1), 50-55
DOI: 10.12691/jcsa-7-1-8
Open AccessArticle

The Effect of Computer Self-Efficacy and Attitude on Undergraduate Students’ Intention to Use Emerging Technology in Classroom Learning

Bilquis Ferdousi1,

1School of Information Security and Applied Computing, Eastern Michigan University, Ypsilanti, USA

Pub. Date: December 22, 2019

Cite this paper:
Bilquis Ferdousi. The Effect of Computer Self-Efficacy and Attitude on Undergraduate Students’ Intention to Use Emerging Technology in Classroom Learning. Journal of Computer Sciences and Applications. 2019; 7(1):50-55. doi: 10.12691/jcsa-7-1-8

Abstract

Although digital technology is playing an increasingly significant role in education and students are using digital technology in their everyday lives, the use of digital technology in their academic learning is still very limited. The literature indicates that the individual factors, such as computer self-efficacy and attitude, are significant predictors of whether or not individuals intend to use technology. In this context, a research conducted to investigate the effect of undergraduate students’ computer self-efficacy and attitude toward digital technology on their intention to use digital technology in their academic learning. The objective of this research was to examine the effect of individual factors on undergraduate students’ intentions to use innovative digital technology in their academic learning. A survey was conducted on undergraduate students in spring, summer, and fall semesters at a regional campus of a large public university. The research findings support the literature that computer self-efficacy and attitude have significant effects on undergraduate students’ intention to use digital technology in their academic learning. Therefore, both factors should be considered important in the process of implementation of digital technology in undergraduate learning environment. The results from this study will provide educators and administrators in higher educational institutions a better understanding about the undergraduate students’ adoption of digital technology in their learning process.

Keywords:
computer self-efficacy attitude intention to use digital technology undergraduate students academic learning

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References:

[1]  Abuzir, Y., “The development of virtual laboratory using SEMMLABs in Al-Quds Open University,” The International Journal of E-Learning and Educational Technologies in the Digital Media (IJEETDM), 1(2), 133-141. (2015).
 
[2]  Omar, A., Kalulu, D., & Alijani, S. G., “Management of innovative e-learning environments,” Academy of Educational Leadership Journal, 15(3). (2011).
 
[3]  Agbatogun, A. O., “Exploring the Efficacy of Student Response System in a Sub-Saharan African Country: A Sociocultural Perspective,” Journal of Information Technology Education: Research, 11, 249-267. (2012).
 
[4]  Wong, L., & Fong, M., “Student attitudes to traditional and online methods of delivery,” Journal of Information Technology Education: Research, 13, 1-13. (2014).
 
[5]  Abrantes, L. S., & Gouveia, B. L., “Learning Environments,” Proceedings of Informing Science & IT Education Conference (InSITE) 2010.
 
[6]  Dron, J., & Anderson, T., “On the design of social media for learning,” Social Sciences, 3, 378-393. (2014).
 
[7]  Shroff, H. R., Deneen, C. C., & Ng, E. M. W., “Analysis of the technology acceptance model in examining students’ behavioural intention to use an eportfolio system,” Australasian Journal of Educational Technology, 27(4), 600-618. (2011).
 
[8]  Rodriguez, E. T., & Lozano, M. P., “The acceptance of Moodle technology by business administration students,” Computers & Education, 58, 1085-1093. (2012).
 
[9]  Padilla-Meléndez, A., Aguila-Obra, A., R., & Garrido-Moreno, A., “Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario,” Computers & Education, 63, 306-317. (2013).
 
[10]  Chuang, Y-T., “A teaching philosophy that utilizes instructional technologies to improve learning motivation,” The International Journal of E-Learning and Educational Technologies in the Digital Media (IJEETDM), 1(2), 121-132. (2015).
 
[11]  Lai, C., Qiu, W., & Lei, J., “What factors predict undergraduate students’ use of technology for learning? A case from Hong Kong,” Computers & Education 59, 569-579. (2012).
 
[12]  Shih, H., “Using a cognitive-motivation-control view to assess the adoption intention for Web-based learning,” Computer & Education, 50, 327-337. (2008).
 
[13]  Musawi, A. S. A., “Redefining technology role in education,” Creative Education – Scientific Research, 2(2), 130-135. (2011).
 
[14]  Mathieson, K., & Chin, W. W., “Extending the technology acceptance model: The influence of perceived user resources,” The DATA BASE for Advances in Information Systems, 32(3), 86-112. (2001).
 
[15]  Venkatesh, V., Thong, J. Y. L., & Xu, X., “Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology,” MIS Quarterly, 36(1), 157-178. (2012).
 
[16]  Ajzen, I., & Fishbein, M., Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall, 1980.
 
[17]  Ajzen, I. From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action control: From cognition to behavior, New York: Springer, 1985, 11-39.
 
[18]  Bandura, A., Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall, 1986.
 
[19]  Rogers, E. M., Diffusion of innovations (4th ed.). New York: The Free Press, 1995.
 
[20]  Davis, F. D., “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Quarterly, 13(3), 319-340. (1989).
 
[21]  Ferdousi, B., & Levy, Y., “Development and Validation of a Model to Investigate the Impact of Individual Factors on Instructors’ Intention to Use E-learning Systems,” Interdisciplinary Journal of E-Learning and Learning Objects, 6, 1-21. (2010).
 
[22]  Brown, A.S., Venkatesh, V., & Goyal, S., “Expectation confirmation in technology use,” Information Systems Research 23(2), 474-487. (2012).
 
[23]  Compeau, D. R., & Higgins, C. A., “Computer self-efficacy: Development of a measure and initial test,” MIS Quarterly, 19(2), 189-211. (1995).
 
[24]  He, J., & Freeman, A. L., “Are Men More Technology-Oriented Than Women? The Role of Gender on the Development of General Computer Self-Efficacy of College Students,” Journal of Information Systems Education, 21(2). (2010).
 
[25]  Hauser, R., Paul, R., & Bradley, J., “Computer Self-Efficacy, Anxiety, and Learning in Online Versus Face to Face Medium,” Journal of Information Technology Education: Research, 11. (2012).
 
[26]  Downey, J. P., & Kher, H. V., “A longitudinal examination of the effects of computer self-efficacy growth on performance during technology training,” Journal of Information Technology Education: Research, 14, 91-111. (2015).
 
[27]  Ajzen, I., Attitudes, personality, and behavior, McGraw-Hill Education (UK), 2005.
 
[28]  Allport, G. W., Attitudes. In C. M. Murchison (Ed.), Handbook of Social Psychology, Worcester, MA: Clark University Press, 1935, 796-834.
 
[29]  Larbi-Apau, A. J., & Moseley, L. J., “Computer Attitude of Teaching Faculty: Implications for Technology-Based Performance in Higher Education,” Journal of Information Technology Education: Research, 11. (2012).
 
[30]  Davis, F. D., Bagozzi, R. P., & Warshaw, P. R., “User acceptance of computer technology: A comparison of two theoretical models,” Management Science, 35(8), 982-1003. (1989).
 
[31]  Premkumar, G., & Bhattacherjee, A., “Explaining information technology usages: A test of competing models,” Omega – The International Journal of Management Science, 36, 64-75. (2006).
 
[32]  Chan, H. C., & Teo, H., “Evaluating the boundary conditions of the technology acceptance model: An exploratory investigation,” ACM Transactions on Computer-Individual Interaction, 14(2), 1-22. (2007).
 
[33]  Ajzen, I., “Nature and operation of attitudes,” Annual Review of Psychology, 52, 27-58. (2001).
 
[34]  Fishbein, M., & Ajzen, I., Belief, attitude, intention, and behavior: An introduction to theory and research, Reading, MA: Addison-Wesley, 1975.
 
[35]  Male, G., “Enhancing the quality of e-learning through mobile technology. A socio-cultural and technology perspective towards quality e-learning applications,” Campus-Wide Information Systems, 28(5), 331-344. (2011).
 
[36]  Efe, R., “Science Student Teachers and Educational Technology: Experience, Intentions, and Value,” Educational Technology & Society, 14(1), 228-240. (2011).
 
[37]  Ngafeeson, M. N., & Sun, J., “The effects of technology innovativeness and system exposure on student acceptance of e-textbooks,” Journal of Information Technology Education: Research, 14, 55-71. (2015).
 
[38]  Süleyman, N. S. & Özlem, G., “Preservice teachers’ perceptions about using mobile phones and laptops in education as mobile learning tools,” British Journal of Educational Technology, 45(4), 606-618. (2014).
 
[39]  Gonzalez, L., & Young, C., (2015). Can Social media impact learning? Retrieved from www.techlearning.com.
 
[40]  Tarantino, K., McDonough, J., & Hua, M., “Effects of student engagement with social media on student learning: A review of literature,” The Journal of Technology in Student Affairs. (2013).