Abstract
Teaching empirical educational research in higher education involves implementing a very useful web tool for bibliography/scientific literature research, a search engine specifically employed for scientific papers, such as “Google Scholar”. The aim of this study is to examine undergraduate students’ acceptance of technology, through their intention to adopt and use a specific search engine for research purposes. To accomplish this goal, a questionnaire was administered to 225 students from two Universities in Greece. The study was based on TAM (Technology Acceptance Model), reinforced by four external determinants (perceived self-efficacy, subjective norms, facilitating conditions and technological complexity), that contributed to the indirect prediction of the behavioral intention to use the particular search engine. The results of the survey confirm that the main factors of TAM, perceived ease of use and perceived usefulness are significant determinants of students’ behavioral intention to use Google Scholar. Moreover, perceived self-efficacy, subjective norms, facilitating conditions and technological complexity have an indirect significant effect on behavioral intention. All these factors explain almost 60% of students’ behavioral intention to use this search engine. The results of this survey could be beneficial to the enrichment of good educational practices for the additional training of teachers, as well as to the improvement of the students’ skills in the implementation of this specific research tool. Besides, more stakeholders, such as librarians, or even human resources of big companies that construct and support similar systems, such as search engines, could also benefit from the present research.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Ajzen, L., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs: Prentice-Hall.
Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational Behavior and Human Decision Processes, 2(50), 248–287. https://doi.org/10.1016/0749-5978(91)90022-L.
Chen, Y., & Chengalur-Smith, I. (2015). Factors influencing students’ use of a library Web portal: Applying course-integrated information literacy instruction as an intervention. The Internet and Higher Education, 26, 42–55. https://doi.org/10.1016/j.iheduc.2015.04.005.
Cohen, J. (1977). Statistical power analysis for the behavioral sciences. (Revised). New York: Academic.
Compeau, D., & Higgins, C. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. https://doi.org/10.2307/249688.
Cordes, S. (2014). Student perceptions of search tool usability. Internet Reference Services Quarterly, 19(1), 3–32. https://doi.org/10.1080/10875301.2014.894955.
Cothran, T. (2011). Google scholar acceptance and use among graduate students: A quantitative study. Library & Information Science Research, 33(4), 293–301. https://doi.org/10.1016/j.lisr.2011.02.001.
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. https://doi.org/10.1007/BF02310555.
Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008.
Davis, F. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475–487. https://doi.org/10.1006/imms.1993.1022.
Davis, F., Bagozzi, R., & Warshaw, P. (1989). User acceptance of computer technology: A comparison od two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982.
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 and Teaching, 11(2), 210–232. Retrieved from https://jolt.merlot.org/Vol11no2/Fathema_0615.pdf.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Thousand Oaks: Sage.
Keshavarz, H., Esmaeili Givi, M., & Vafaeian, A. (2016). Students’ sense of self-efficacy in searching information from the web: A PLS approach. Webology, 13(2), 16–31. Retrieved from http://eprints.rclis.org/32203/.
Liaw, S.-S., & Huang, H.-M. (2003). An investigation of user attitudes toward search engine as an informational tool. Computers in Human Behavior, 19(6), 751–765. https://doi.org/10.1016/S0747-5632(03)00009-8.
Lowry, P. B., & Gaskin, J. (2014). Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it. IEEE Transactions on Professional Communication, 57(2), 123–146. https://doi.org/10.1109/TPC.2014.2312452.
Moorthy, K., Chun T’ing, L., Ming, K. S., Ping, C. C., Ping, L. Y., Joe, L. Q., & Jie, W. Y. (2019). Behavioral intention to adopt digital library by the undergraduates. International Information & Library Review, 51(2), 128–144. https://doi.org/10.1080/10572317.2018.1463049.
Nikolopoulou, K., & Gialamas, V. (2011). Undergraduate students’ information search practices. Themes in Science & Technology Education, 4(1), 21–32. Retrieved from https://files.eric.ed.gov/fulltext/EJ1131504.pdf.
Parissi, M., Κomis, V., Lavidas, K., Dumouchel, G., & Karsenti, T. (2019). A pre-post study to assess the impact of an information-problem solving intervention on university students’ perceptions and self-efficacy towards search engines. International Journal of Technologies in Higher Education, 16(1), 68–87. https://doi.org/10.18162/ritpu-2019-v16n1-05.
Raykov, T. (1997). Estimation of composite reliability for congeneric measures. Applied Psychological Measurement, 21(2), 173–184. https://doi.org/10.1177/01466216970212006.
R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
Salehi, S., Du, J., & Ashman, H. (2018). Use of web search engines and personalisation in information searching for educational purposes. An International Electronic Journal, 23(2). Retrieved from https://files.eric.ed.gov/fulltext/EJ1182241.pdf.
Sanchez, G. (2013). PLS path modeling with R. Berkeley: Trowchez Editions. Retrieved from http://www.gastonsanchez.com/PLS Path Modeling with R.pdf
Sanchez G., Trinchera, L., & Russolillo, G. (2017). plspm: Tools for partial least squares path modeling (PLS-PM). R package version 0.4.9. Retrieved from https://CRAN.R-project.org/package=plspm
Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90–103. https://doi.org/10.1016/j.im.2006.10.007.
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128(0317), 13–35. https://doi.org/10.1016/j.compedu.2018.09.009.
Shen, L. (2012). Graduate students report strong acceptance and loyal usage of Google scholar. Evidence Based Library and Information Practice, 7(4), 96–98. https://doi.org/10.18438/B8RW3P.
Soules, A. (2015). Helping college students make the Most of Google scholar. The Complete Guide to Using Google in Libraries: Instruction, Administration, and Staff Productivity, 1, 41–52.
Tella, A., Oyewole, M., & Tella, A. (2017). An analysis of perceived usefulness of Google scholar by the postgraduate students of the University of Ilorin, Nigeria. South African Journal of Information Management, 19(1), 1–9. Retrieved from https://journals.co.za/content/journal/10520/EJC-8c3027343#abstract_content.
Tella, A., Orim, F., Ibrahim, D. M., & Memudu, S. A. (2018). The use of electronic resources by academic staff at the University of Ilorin, Nigeria. Education and Information Technologies, 23(1), 9–27. https://doi.org/10.1007/s10639-017-9577-2.
Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(2), 302–312. https://doi.org/10.1016/j.compedu.2008.08.006.
Teo, T. (2010). Examining the influence of subjective norm and facilitating conditions on the intention to use technology among pre-service teachers: A structural equation modeling of an extended technology acceptance model. Asia Pacific Education Review, 11(2), 253–262. https://doi.org/10.1007/s12564-009-9066-4.
Teo, T., Doleck, T., Bazelais, P., & Lemay, D. J. (2019). Exploring the drivers of technology acceptance: A study of Nepali school students. Educational Technology Research and Development, 67(2), 495–517. https://doi.org/10.1007/s11423-019-09654-7.
Thompson, R., Higgins, C., & Howell, J. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 125–143. https://doi.org/10.2307/249443.
Venkatesh, V., & Davis, F. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926.
Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540.
Wong, G. K. W. (2016). The behavioral intentions of Hong Kong primary teachers in adopting educational technology. Educational Technology Research and Development, 64, 313–338. https://doi.org/10.1007/s11423-016-9426-9.
Acknowledgements
We thank our students for their participation in the study. Their responses have informed our efforts to improve the quality of our courses.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
Constructs | Items | Adjusted from: |
|---|---|---|
Perceived Usefulness (PU) | 4 | (Davis 1989) |
Perceived Ease of Use (PEOU) | 4 | (Davis 1989) |
Attitude toward usage (ATT) | 4 | (Fathema et al. 2015) |
Behavioral Intention (BI) | 3 | (Fathema et al. 2015) |
Perceived Self-Efficacy (PSE) | 2 | (Compeau and Higgins 1995) |
Subjective Norms (SN) | 2 | (Venkatesh and Davis 2000) |
Facilitating Conditions (FC) | 2 | (Thompson et al. 1991) |
Technological Complexity (TC) | 2 | (Teo 2009) |
Rights and permissions
About this article
Cite this article
Lavidas, K., Achriani, A., Athanassopoulos, S. et al. University students’ intention to use search engines for research purposes: A structural equation modeling approach. Educ Inf Technol 25, 2463–2479 (2020). https://doi.org/10.1007/s10639-019-10071-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10639-019-10071-9