Abstract
In order to face the global competition, graduates’ competence is nowadays problem faced by many higher learning institutions. This study is aimed to test students’ competence using the Technology Acceptance Model (TAM) framework. It tests the effect of perceived usefulness (PU) and perceived ease of use (PEU) on behavior intention to use (BIU) and student’ competence. The data used is a primary data that was collected by distributing questionnaire to 128 students who use e-learning. The samples were selected using the convenience sampling method. The data obtained was evaluate both by reliability and validity tests, while the hypothesis was tested using multiple regression. The result shows that PU and PEU have significant effect on BIU, and furthermore BIU has significant effect on student’ competence (cognitive, affective, and psychomotor). It provides theoretical contribution that technology utilization can improve student’ competence.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Rahyubi, H.: Teori-teori belajar dan aplikasi pembelajaran motorik; Deskripsi dan tinjauan kritis, Cetakan ke 2, Penerbit Nusa Media, Bandung (2014)
Budiarto, D.S.: Accounting information systems (AIS) alignment and non-financial performance in small firm. Int. J. Comput. Netw. 6(2), 15–25 (2014)
Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–339 (1989)
Anormaliza, R., Sabate, F., Viejo, G.: Evaluating student acceptance level of e-learning system. In: Proceeding of ICERI 2015, Seville, Spain: pp. 2393–2399 (2015)
Constantiou, I.D., Mahnke, V.: Consumer behavior and mobile TV service: do men differ from women in their adoption intentions? J. Electr. Commer. Res. 11(2), 127–139 (2010)
Celik, H.E., Yilmaz, V.: Extending the technology acceptance model for adoption of e-shopping by consumer in turkey. J. Electr. Commer. Res. 12(2), 152–164 (2011)
Talukder, M., Quazi, A., Sathye, M.: Mobile phone banking usage behavior: an Australian perspective. Australas. Acc. Bus. Financ. J. 8(4), 83–100 (2014)
Lane, M., Stagg, A.: University staff adoption of i pads: An empirical study using an extended Technology Acceptance Model. Australas. J. Inf. Syst. 18(3), 53–73 (2014)
Alharbi, S., Drew, S.: Using the technology acceptance model in understanding academics behavioral intention to use learning management systems. Int. J. Adv. Comput Sci. Appl. 5(1), 143–155 (2014)
Veloo, R., Masood, M.: Acceptance and intention to use the i-learn system in an automotive semiconductor company in the northern region of Malaysia. Procedia Soc. Behav. Sci. 116, 1378–1382 (2014)
Lucas Jr., Hendry, C., Spitler, V.K.: Technology use and performance: a field study of broker workstation. Decis. Sci. 30(2), 291–311 (1999)
Buche, M.W., Davis, L.R., Vician, C.: Does technology acceptance affect e-learning in a non-technology intensive course. J. Inf. Syst. Educ. 23(1), 42–50 (2012)
Lee, Y., Hsieh, Y., Hsu, C.N.: Adding innovation diffusion theory to the technology acceptance model: supporting employee intentions to use e-learning systems. Educ. Technol. Soc. 14(4), 124–137 (2011)
Peslak, A., Ceccucci, W., Bhatnagar, N.: Analysis of the variables that affect frequency of use and time spent on text messaging. Issues Inf. Syst. 13(1), 361–370 (2012)
Athanassiou, N., McNett, J.M., Harvey, C.: Critical thinking in the management classroom: Bloom’s Taxonomy as a learning tool. J. Manag. Educ. 27(5), 553–555 (2003)
Kim, H., Chan, H., Gupta, S.: Value-based adoption of mobile internet: an empirical investigation. Decis. Support Syst. 43(1), 111–126 (2007)
Moore, T.: Toward an integrated model of it acceptance in healthcare. Decis. Support Syst. 53, 507–516 (2012)
Al-Adwan, Al-Adwan, A., Smedley, J.: Exploring students acceptance of e-learning using technology acceptance model in Jordanian universities. Int. J. Educ. Dev. Inf. Commun. Technol. 9(2), 4–18 (2013)
Rovai, A.P., Wighthing, M.J., Baker, J.D., Grooms, L.D.: Development of an instrument to measure perceived cognitive, affective, psychomotor learning in traditional and virtual classroom higher education setting. Internet High. Educ. 12(1), 7–13 (2009)
Bloom, B.S.: Taxonomy of educational objectives: the classification of educational goalss. Longmans, Green, New York (1956)
Adams, N.E.: Bloom’s taxonomy of cognitive learning objectives. J. Med. Libr. Assoc. 103(3), 151–153 (2015)
Shepard, K.: Higher Education for Sustainability: Seeking affective learning outcomes. Int. J. Sustain. High. Educ. 9(1), 87–98 (2008)
Miller, C.: Improving and enhancing performance in the affective domain of nursing students: insights from the literature for clinical educators. Contemp. Nurse 35(1), 2–17 (2010)
Cazzell, M., Rodriguez, A.: Qualitative analysis of student beliefs and attitudes after an objective structured clinical evaluation: implications for affective domain learning in undergraduate nursing education. J. Nurs. Educ. 50(12), 711–714 (2011)
Gunther, M., Alligood, M.R.: A discipline-specific determination of high quality nursing care. J. Adv. Nurs. 38(4), 353–359 (2002)
Merritt, R.D.: The psychomotor domain: Research Starter Education. Great Neck Publishing, NY (2008)
DeRouin, R.E., Fritzsche, B.A., Salas, E.: E-learning in organizations. J. Manag. 31(6), 920–940 (2005)
Lau, S.H., Woods, P.: An investigation of user perception and attitudes toward learning object. Br. J. Edu. Technol. 39(4), 685–699 (2008)
Averdung, A., Wagenfuehrer, D.: Consumers acceptance, adoption and behavioral intention regarding environmentally sustainable innovations. E3 J. Bus. Manag. Econ. 2(3), 98–106 (2011)
Ong, C.S., Lai, J.Y., Wang, Y.S.: Factors affecting engineers acceptance of asynchronous e-learning systems in high-tech companies. Inf. Manag. 14, 795–804 (2004)
Goodhue, D., Thompson, R.L.: Task-technology fit and individual performance. MIS Q. 19(2), 213–236 (1995)
Yu, T.K., Yu, T.Y.: Modeling the factors that affect individuals’ utilization of online learning systems: an empirical study combining the task technology fit model with the theory of planned behavior. Br. J. Edu. Technol. 41(6), 1003–1017 (2010)
Zikmund, W.G.: Business research methods. The Dryden Press, Oak Brook (2000)
Saunders, M., Lewis, P., Thornhill, A.: Research methods for business student, 5th edn. Person Education Limited, UK (2009)
Davis, L.R., Johnson, D.L., Vician, C.: Technology-mediated learning and prior academic performance. Int. J. Innov. Learn. 2(4), 386–401 (1995)
Winkel, W.S.: Psikologi pengajaran, Edisi Lima belas, Penerbit Media Abadi, Yogyakarta (2012)
Lee, E., Choi, Y.: A study of the antecedents and the consequences of social network service addition: a focus on organizational behaviors. Glob. Bus. Financ. Rev. 20(2), 83–93 (2015)
Hair, J.R., William, C., Barry, J., Rolph, E.A.: Multivariate data analysis, 7th edn. Prentice Hall, Pearson (2010)
Venkatesh, V., Davis, F.D.: Assessing it usage: the role of prior experience. Manag. Inf. S. Q. 19(4), 561–570 (2000)
Budiarto, D.S., Rahmawati, Prabowo, M.A.: Accounting information systems alignment and SMEs performance: A literature review. Int. J. Manag. Econ. Soc.Sci. 4(2), 58–70 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
Questionnaire for students | |
|---|---|
No | Behavioral intention to use |
1 | I intend to use e-learning in the next semester |
2 | I predict that I would use e-learning in the next semester |
3 | I plan to use e-learning in the next semester |
No | Perceived usefulness |
4 | Using e-learning would enable me to accomplish tasks more quickly |
5 | Using e-learning would make it easier to do my job |
6 | Using e-learning would improve my job performance |
7 | Using e-learning in my job would increase my productivity |
8 | Using e-learning would enhance my effectiveness on the job |
No | Perceived ease of use |
9 | I feel that using e-learning would be easy for me |
10 | I feel that my interaction with e-learning would be clear and understandable |
11 | I feel that it would be easy to become skillful at using e-learning |
12 | I would find e-learning to be flexible to interact with |
13 | It would be easy for me to get e-learning to do what I want to do |
14 | I feel that my ability to determine e-learning ease of use is limited by may lack of experience |
Questionnaire for teachers | |
|---|---|
No | Affective |
15 | Actively provides idea in group |
16 | Defends the idea |
17 | Seriously does all of assignments |
18 | Accepts recommendations and suggestions |
19 | Behaves with discipline |
20 | Accepts the decisions |
No | Psychomotor |
21 | Ability in using tools for serving a presentation |
22 | Ability in arranging material |
23 | Level of speed in doing the assignments |
24 | Behavior in doing a presentation |
25 | Ability in analyzing and answering the questions |
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Budiarto, D.S. et al. (2018). Implementation of Indonesia National Qualification Framework to Improve Higher Education Students: Technology Acceptance Model Approach. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10961. Springer, Cham. https://doi.org/10.1007/978-3-319-95165-2_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-95165-2_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95164-5
Online ISBN: 978-3-319-95165-2
eBook Packages: Computer ScienceComputer Science (R0)