Students’ Behavioral Intentions Regarding the Future Use of Quantitative Research Methods

Abdulah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ perceived ease of use (PEOU) and perceived usefulness (PU) of e-portfolios. Computers in Human Behavior, 63, 75-90. https://doi.org/10.1016/j.chb.2016.05.01410.1016/j.chb.2016.05.014Open DOISearch in Google Scholar

Ali, M., B Raja Yaacob, R.A.I., & Al-Amin B Endut, M.N. (2016). Understanding the academic use of social media: Integration of personality with TAM. Journal of Theoretical & Applied Information Technology, 90(1), 1-11.Search in Google Scholar

Ajzen, I. (1991). The theory of planned behaviour. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T10.1016/0749-5978(91)90020-Open DOISearch in Google Scholar

Antonius, N., Xu, J., & Gao, X. (2015). Factors influencing the adoption of enterprise social software in Australia. Knowledge-Based Systems, 73, 32-43. https://doi.org/10.1016/j.knosys.2014.09.00310.1016/j.knosys.2014.09.003Open DOISearch in Google Scholar

Ameen C., A., Loeffler-Cobia, J., Clawson, E., & Guevara, M. (2010). Evidence-based practices skills assessment for criminal justice organizations. Washington, DC: National Institute of Corrections.Search in Google Scholar

Arthur, D., & Wong, F. (2000). The effects of the ‘learning by proposing to do’ approach on Hong Kong nursing students’ research orientation, attitude toward research, knowledge, and research skill. Nurse Education Today, 20(8), 662-671. https://doi.org/10.1054/nedt.2000.048610.1054/nedt.2000.0486Search in Google Scholar

Bagozzi, R. P. & Yi, Y. (1998). On the evaluation of structural equation model. Journal of the Academy of Marketing Science, 16, 74–94. https://doi.org/10.1007/BF0272332710.1007/BF02723327Open DOISearch in Google Scholar

Biehler, R. (1997). Software for learning and for doing statistics. International Statistical Review, 65(2), 167–189. https://doi.org/10.1111/j.1751-5823.1997.tb00399.x10.1111/j.1751-5823.1997.tb00399.xOpen DOISearch in Google Scholar

Bovas, A. (2007). Implementation of statistics in business and industry. Revista Colombiana de Estadistica, 30(1), 1–11.Search in Google Scholar

Brezavšček, A., Šparl, P., & Žnidaršič, A. (2017). Factors influencing the behavioural intention to use statistical sftware: The perspective of the Slovenian students of social sciences. Eurasia Journal of Mathematics, Scienec and Technology Education, 13(3), 953-986.10.12973/eurasia.2017.00652aSearch in Google Scholar

Chamberlain J., M., Hillier, J., & Signoretta, P. (2015). Counting better? An examination of the impact of quantitative method teaching on statistical anxiety and confidence. Active Learning in Higher Education, 16(1), 51-66. https://doi.org/10.1177/146978741455898310.1177/1469787414558983Open DOISearch in Google Scholar

Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: theory and results. Doctoral dissertation, Sloan School of Management. MIT.Search in Google Scholar

Davis, F. D. (1989). Perceived usefulness, Perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/24900810.2307/249008Open DOISearch in Google Scholar

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.1993.102210.1006/imms.1993.1022Open DOISearch in Google Scholar

Dizon, G. (2016). Measuring Japanese EFL student perceptions of Internet-based tests with the technology acceptance model. The Electronic Journal for English as a Second Language, 2, 1-17.Search in Google Scholar

Emmioğlu, E., & Capa-Aydin, Y. (2012). Attitudes and achievement in statistics: a meta-analysis study. Statistics Education Research Journal, 11(2), 95-102.10.52041/serj.v11i2.332Search in Google Scholar

Field, A. (2009). Discovering statistics using SPSS. London: Sage.Search in Google Scholar

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to the theory and research. Reading, MA: Addison-Wesley.Search in Google Scholar

Gal, I., & Ginsburg, L. (1994). The role of beliefs and attitudes in learning statistics: towards an assessment frame-work. Journal of Statistics Education [online], 2(2), http://www.amstat.org/publications/jse/v2n2/gal.html. https://doi.org/10.1080/10691898.1994.1191047110.1080/10691898.1994.11910471Open DOISearch in Google Scholar

Hsu K., M., Wang S., W., & Chiu K., K. (2009). Computer attitude, statistics anxiety and self-efficacy on statistical software adoption behavior: An empirical study of online MBA learners. Computers in Human Behavior, 25(2), 412-420. https://doi.org/10.1016/j.chb.2008.10.00310.1016/j.chb.2008.10.003Open DOISearch in Google Scholar

Krueger, F., Norris, A., & Carsrud, I. (1993). Entrepreneurial intentions: Applying the theory of planned behavior. Entrepreneurship and Regional Development, 5(4), 315-330. https://doi.org/10.1080/0898562930000002010.1080/08985629300000020Open DOISearch in Google Scholar

Lai, C., Wang, Q., & Lei, J. (2012). What factors predict undergraduate students’ use of technology for learning? A case from Hong Kong. Computers and Education, 59(2), 569-579. https://doi.org/10.1016/j.compedu.2012.03.00610.1016/j.compedu.2012.03.006Open DOISearch in Google Scholar

Letchumanan, M., & Muniandy, B. (2013). Migrating to e-book: A study on perceived usefulness and ease of use. Library Hi Tech News, 30(7), 10-16. https://doi.org/10.1108/LHTN-05-2013-002810.1108/LHTN-05-2013-0028Open DOISearch in Google Scholar

Linan, F., & Alain F. (2015). A systematic literature review on EI: Citation, thematic analyses and research agenda. International Entrepreneurship and Management Journal, 11(4), 907-933. https://doi.org/10.1007/s11365-015-0356-510.1007/s11365-015-0356-5Open DOISearch in Google Scholar

LinkedIn, (2016). Retreived from: https://blog.linkedin.com/2016/10/20/top-skills-2016-week-of-learning-linkedin.Search in Google Scholar

LinkedIn, (2018). Retreived from: https://learning.linkedin.com/blog/top-skills/the-skills-companies-need-most-in-2018--and-the-courses-to-get-t.Search in Google Scholar

Lo S., K., & Stevenson, M. (1991). Attitudes and perceived usefulness of statistics among health sciences students. International Journal of Mathematical Education in Science and Technology, 22(6) 977–983. https://doi.org/10.1080/002073991022061610.1080/0020739910220616Open DOISearch in Google Scholar

Macher, D., Paechter, M., Papousek, I., & Ruggeri, K. (2012). Statistics anxiety, trait anxiety, learning behavior, and academic performance. European Journal of Psychology and Education, 27(4), 483-498. https://doi.org/10.1007/s10212-011-0090-510.1007/s10212-011-0090-5Open DOISearch in Google Scholar

Marjanovič Umek, L., Zupančič, M., Fekonja, U., Kavčič, T., Svetina, M., Tomazo Ravnik, T., & Bratanič, B. (2004). Razvojna psihologija. Ljubljana: Znanstvenoraziskovalni inštitut Filozofske fakultete.Search in Google Scholar

Mondejar-Jimenez, J., & Vargas-Vargas, M. (2010). Determinant factors of attitude towards quantitative subjects: Differences between sexes. Teaching and Teacher Education, 26, 688–693. https://doi.org/10.1016/j.tate.2009.10.00410.1016/j.tate.2009.10.004Open DOISearch in Google Scholar

Murtonen, M., & Lehtinen, E. (2010). Difficulties experienced by education and sociology students in quantitative methods courses. Studies in Higher Education, 28(2), 171-185. https://doi.org/10.1080/030750703200005806410.1080/0307507032000058064Open DOISearch in Google Scholar

Nikou, S., & Economides, A. (2016). The impact of paper-based, computer-based and mobile-based self-assessment on students’ science motivation and achievement. Computers in Human Behavior, 55, 1241-1248. https://doi.org/10.1016/j.chb.2015.09.02510.1016/j.chb.2015.09.025Open DOISearch in Google Scholar

Nunnally, J. C. (1978). Psychometric theory. New York: Mc-Graw-Hill Book Company.Search in Google Scholar

Park S., Y. (2009). An analysis of the technology acceptance model in understanding university students’ behavioral intentions to use e-learning. Educational Technology and Society, 12(3), 150-162.Search in Google Scholar

Pierce, R., Stacey, K., & Barkatsas A., N. (2007). A scale for monitoring students’ attitudes to learning mathematics with technology. Computer and Education, 48(2), 285-300. https://doi.org/10.1016/j.compedu.2005.01.00610.1016/j.compedu.2005.01.006Open DOISearch in Google Scholar

Sabalic, M. & Schoener, J. D. (2017). Virtual reality-based technologies in dental medicine: knowledge, attitudes and practice among students and practitioners, technology, knowledge and learning, In print, DOI: 10.1007/s10758-017-9305-4. https://doi.org/10.1007/s10758-017-9305-410.1007/s10758-017-9305-4.https://doi.org/10.1007/s10758-017-9305-4Open DOISearch in Google Scholar

Šebjan, U., & Tominc, P. (2015). Impact of support of teacher and compatibility with needs of study. Computers in Human Behavior, 53, 354-365. https://doi.org/10.1016/j.chb.2015.07.02210.1016/j.chb.2015.07.022Open DOISearch in Google Scholar

Terzis, V., Moridis, C. N. & Economides, A. A. (2012). How student’s personality traits affect Computer Based Assessment Acceptance: Integrating BFI with CBAAM. Computers in Human Behavior, 28(5), 1985-1996. https://doi.org/10.1016/j.chb.2012.05.01910.1016/j.chb.2012.05.019Open DOISearch in Google Scholar

Venkatesh, V., & Davis F. D. (1996). A model of the antecedents of perceived ease of use: development and test. Decision Sciences, 3, 451-481.10.1111/j.1540-5915.1996.tb01822.xSearch in Google Scholar

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204, doi:10.1287/mnsc.46.2.186.11926. https://doi.org/10.1287/mnsc.46.2.186.1192610.1287/mnsc.46.2.186.11926.https://doi.org/10.1287/mnsc.46.2.186.11926Open DOISearch in Google Scholar

Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365. https://doi.org/10.1287/isre.11.4.342.1187210.1287/isre.11.4.342.11872Open DOISearch in Google Scholar

Venkatesh, V., Morris, M.G., Davis, G. B. & Davis F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27–3. https://doi.org/10.2307/3003654010.2307/30036540Open DOISearch in Google Scholar

Venkatesh, V. & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315. https://doi.org/10.1111/j.1540-5915.2008.00192.x10.1111/j.1540-5915.2008.00192.xOpen DOISearch in Google Scholar

Vos, N., van der Meijden, H., & Denessen, E. (2011). Effects of constructing versus playing an educational game on student motivation and deep learning strategy use. Computers and Education, 56, 127-137. doi:10.1016/j.compedu.2010.08.013 https://doi.org/10.1016/j.compedu.2010.08.01310.1016/j.compedu.2010.08.013https://doi.org/10.1016/j.compedu.2010.08.013Open DOISearch in Google Scholar

World Economic Forum, (2016). The future of jobs and skills, Executive Summary.Search in Google Scholar

World Economic Forum, (2018). Retreived from: https://www.weforum.org/agenda/2016/01/the-10-skills-you-need-to-thrive-in-the-fourth-industrial-revolution/Search in Google Scholar

Yousafzai S., Y., Foxall G., R., & Pallister J., G. (2007). Technology acceptance: a meta-analysis of the TAM: Part 1. Journal of Modelling in Management, 2(3), 251-280. https://doi.org/10.1108/1746566071083445310.1108/17465660710834453Open DOISearch in Google Scholar

Zhang, Y., Shang, L., Wang, R., Zhao, Q., Li, C., Xu, Y., & Su, H. (2012). Attitudes towards statistics in medical postgraduates: Measuring, evaluating and monitoring. BMC Medical Education, 12(117), 1-8. https://doi.org/10.1186/1472-6920-12-11710.1186/1472-6920-12-117Open DOISearch in Google Scholar