Changes regarding the importance of graduates’ competences by employers and changes of competences themselves are to a great extend driven by the technological changes, digitalization, and big data. Among these competences, the ability to perform business and data analytics, based on statistical thinking and data mining, is becoming extremely important. In this paper, we study the relationships among several constructs that are related to attitudes of economics and business students regarding quantitative statistical methods and to students’ intention to use them in the future. Findings of our research provide important insights for practitioners, educators, lecturers, and curricular management teams.
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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.2016.05.014
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 Technology90(1) 1-11.
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-T
Antonius N. Xu J. & Gao X. (2015). Factors influencing the adoption of enterprise social software in Australia. Knowledge-Based Systems73 32-43. https://doi.org/10.1016/j.knosys.2014.09.003
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.
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.2000.0486
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/BF02723327
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.x
Bovas A. (2007). Implementation of statistics in business and industry. Revista Colombiana de Estadistica 30(1) 1–11.
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.
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/10.1177/1469787414558983
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.
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/10.2307/249008
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
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.
Emmioğlu E. & Capa-Aydin Y. (2012). Attitudes and achievement in statistics: a meta-analysis study. Statistics Education Research Journal 11(2) 95-102.
Field A. (2009). Discovering statistics using SPSS. London: Sage.
Fishbein M. & Ajzen I. (1975). Belief attitude intention and behavior: An introduction to the theory and research. Reading MA: Addison-Wesley.
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/10.1080/10691898.1994.11910471
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.2008.10.003
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/10.1080/08985629300000020
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.2012.03.006
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-05-2013-0028
Linan F. & Alain F. (2015). A systematic literature review on EI: Citation thematic analyses and research agenda. International Entrepreneurship and Management Journal11(4) 907-933. https://doi.org/10.1007/s11365-015-0356-5
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/10.1080/0020739910220616
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/s10212-011-0090-5
Marjanovič Umek L. Zupančič M. Fekonja U. Kavčič T. Svetina M. Tomazo Ravnik T. & Bratanič B. (2004). Razvojna psihologija. Ljubljana: Znanstvenoraziskovalni inštitut Filozofske fakultete.
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.2009.10.004
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/10.1080/0307507032000058064
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.2015.09.025
Nunnally J. C. (1978). Psychometric theory. New York: Mc-Graw-Hill Book Company.
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.
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.2005.01.006
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/s10758-017-9305-4
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.2015.07.022
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.2012.05.019
Venkatesh V. & Davis F. D. (1996). A model of the antecedents of perceived ease of use: development and test. Decision Sciences 3 451-481.
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.18.104.22.16826. https://doi.org/10.1287/mnsc.22.214.171.12426
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.11.4.342.11872
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/10.2307/30036540
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.x
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.2010.08.013
World Economic Forum (2016). The future of jobs and skills Executive Summary.
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/
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/10.1108/17465660710834453
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/10.1186/1472-6920-12-117