The Causal Model of Relationship among Computer Attitude, Statistics Anxiety, Self-Efficacy and Statistical Software Acceptance

Document Type : Research Paper

Authors

1 M.A. in Educational Research, Shahid Bahonar University of Kerman, Kerman, Iran

2 Assistant Professor, Department of Education, Faculty of Literature and Humanities, Shahid Bahonar University of Kerman, Kerman, Iran

3 Associate Professor, Department of Education, Faculty of Literature and Humanities, Shahid Bahonar University of Kerman, Kerman, Iran

Abstract

The aim of this research was to develop the causal model of relationship among computer attitude, statistics anxiety, statistical software self-efficacy and statistical software acceptance. Using the convenience sampling and according to the table of Krejcie and Morgan (1970), 260 master students at Shahid Bahonar University of Kerman who knew and worked with at least one statistical software were chosen as example. Using the questionnaire of statistical software acceptance (Hsu, Wang & Chiu, 2009), the scale of computer attitude (Harrison & Rainer, 1992), the scale of statistics anxiety (Cruise, Cash & Bolton, 1985), and the scale of statistical software self-efficacy (Compeau & Higgins, 1995) required data were collected and for their analysis of structural equation modeling was used. The results showed that both perceived usefulness and perceived ease of use positively influence students’ intention to use statistical software, whereas statistics anxiety has negative impact on all of them. In addition, computer attitude has positive effect on perceived usefulness and perceived ease of use, but statistical software self-efficacy has negative impact on perceived ease of statistical software and positive effect on perceived usefulness of use statistical software. Finally, it is concluded that the greatest software usage outcome would occur when a statistical software is perceived both useful and easy to use by the learners.

Keywords

References

احمدی ده‌قطب­الدینی، محمد (1389). روابط ساختاری بین سازه­های مدل پذیرش فناوری دیویس. فصلنامة ­اندیشه­های تازه در علوم تربیتی، 5(2)، 142-129.
احمدی ده­قطب­الدینی، محمد، مشکانی، محمد، و محمدخانی، عظیم (1389). تأثیر خودکارآمدی رایانه و اضطراب رایانه بر سازه­های مدل پذیرش فناوری دیویس: چشم­اندازهای جدید روان­شناسی اجتماعی. فصلنامة پژوهش­های روا­ن­شناختی، 25، 72-51.
اصغری، مریم (1394). بررسی رابطة تجربه رایانه و مهارت­های اجتماعی با خودکارآمدی دانش‌آموزان نابینا. فصلنامة پژوهش­های کاربردی روا­­ن‌شناختی، 6(3)، 18-1.
بهمنی، بهناز، ترابیان، سحرالسادات، رضایی­نژاد، سالار، منظری توکلی، وحید، و معینی­فرد، مژگان (1395). بررسی اثربخشی آموزش خودکارآمدی هیجانی بر سازگاری اجتماعی و اضطراب اجتماعی نوجوانان. فصلنامة پژوهش­های کاربردی روان‌شناختی، 7(2)، 80-69.
فروتن­بقا، پریسا، نظامی، ماندانا؛ سلطانی­نژاد، امیر، اسکندری، حامد، و منظری توکلی، وحید (1394). اثربخشی آموزش مهارت­های زندگی بر عزت نفس و خودکارآمدی تحصیلی دانش­آموزان پسر مقطع اول متوسطه. فصلنامة پژوهش­های کاربردی روان‌شناختی، 6(3)، 72-61.
قلانی، محمدرضا، حربی، آزاده، و بهارلو، مصطفی (1397). طراحی و آزمون الگویی از پیشایندهای عملکرد تحصیلی درس ریاضی. دوفصلنامة مطالعات آموزشی و آموزشگاهی، 5(15)، 83-59.
مطهری­نژاد، حسین، و وزیری شهربابک، بتول (1395). عوامل مدیریتی و سازمانی مؤثر بر پذیرش فناوری اطلاعات در مدارس از دیدگاه معلمان. دوفصلنامة مدیریت بر آموزش سازمان­ها، 5(2)، 202-167.
مهداد، علی، ادیبی، زهرا، و عزتی، منظر (1394). نقش واسطه­ای عزت نفس در رابطة بین حمایت سازمانی ادراک­شده و خودکارآمدی. فصلنامة پژوهش­های کاربردی روان‌شناختی، 6(2)، 195-181.
هومن، حیدرعلی (1391). مدل­یابی معادلات ساختاری با کاربرد نرم‌افزار لیزرل. تهران: انتشارات سمت.
یعقوبی، طاهره، ابویی اردکان، محمد، و گوینده نجف‌آبادی، فاطمه (1393). بررسی عوامل مؤثر بر پذیرش فناوری وب2 از دیدگاه دبیران متوسطه براساس مدل پذیرش فناوری 3. فصلنامة فناوری آموزش، 9(1)، 24-11.
Abdullah, F., & Ward, R. (2016). Developing a general extended technology acceptance model for e-learning (GETAMEL) by analyzing commonly used external factors. Computers in Human Behavior, 56, 238-256.
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice Hall.
Ariff, M.,  Yeow, S. M., Zakuan, N.,  Jusoh, A., &  Bahari, A. Z. (2012). The effects of computer Self-Efficacy and technology acceptance model on behavioral intention in Internet Banking System. Procedia-Social and Behavioral Sciences, 57, 448-452
Bagozzi, R. P., & Richard, P. (2007). The legacy of the technology acceptance model and a proposal for a paradigm shift. Journal of the Association for Information Systems, 8(4), 244-254.
Bandura, A. (2006). Guide for creating self-efficacy scales. In F. Pajares, & T. Urdan (Eds.). Self-efficacy beliefs of adolescents (pp. 307–337). Greenwich: Information Age Publishing.
Bong, M. (2006). Asking the right question. How confident are you that you could successfully perform these tasks? In F. Pajares & T. Urdan, (Eds.), Self-efficacy beliefs in adolescents (pp. 287–305). Greenwich: Information Age Publishing.
Chau, P. Y. K. (2001). Influence of computer attitude and self-efficacy on IT usage behavior. Journal of End User Computing, 13(1), 26-35.
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: development of a measure and initial test. MIS Quarterly, 19(2), 189-211.
Davis, F. D. (1986). A Technology acceptance model for empirically testing new end user informational systems: Theory and result. Doctoral Dissertation, Sloan School of Management, MIT.
DeVellis, R. F. (2016). Scale development: Theory and applications. Thousand Oaks, Calif: SAGE Publications.
Hanna, D., Shevlin, M., & Dempster, M. (2008). The structure of the statistics anxiety rating scale: A confirmatory factor analysis using UK psychology students. Journal of Personality and Individual Differences, 45, 68-74.
Harrison, A. W., & Rainer, R. K. Jr. (1992). The influence of individual differences on skill in end-user computing. Journal of Management Information Systems, 9(1), 93-111.
Hsu, M. K., 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, 412-420.
Huang, S. M., Wei, C. W., Yu, P. T., & Kuo, T. Y. (2004). An empirical investigation on learners’ acceptance of e-learning for public unemployment vocational training. Retrieved from http://www. ec. ccu. edu. tw/teacher/paper/IJIL%203204%20 Huang %20et%20al. pdf.
Karahanna, E., & Limayem, M. (2000). E-mail and V-mail usage: generalizing across technologies. Journal of Organizational Computing and Electronic Commerce, 10(1), 49-66.
Kim, T. G., Lee, J. H., & Law, R., (2008). An empirical examination of the acceptance behavior of hotel front office systems: An extended technology acceptance model. Tourism Management, 29, 500–513.
Kiraz, E., & Ozdemir, D. (2006). The Relationship between educational ideologies and technology acceptance in preservice teachers. Educational Technology & Society, 9(2), 152-165.
Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30, 607-610.
Latikka, R., Turja, T., & Oksanen, A. (2019). Self-efficacy and acceptance of robots. Computers in Human Behavior, 93, 157-163.
Lee, Y. C. (2006). An empirical investigation into factors influencing the adoption of an e-learning system. Online Information Review, 30(5), 517-541.
Muller, R. O. (1996). Basic Principles of Structural Equation Modeling (an Introduction to Lisrel equation. New York: Springer-Verlang.
Onwuegbuzie, A. J., DaRos, D., & Ryan, J. (1997). The components of statistics anxiety: a phenomenological study. Focus on Learning Problems in Mathematics, 19, 11-35.
Pan, W., & Tang, M. (2004). Examining the effectiveness of innovative instructional methods on reducing statistics anxiety for graduate students in the social sciences. Journal of Instructional Psychology, 31(2), 149-159.
Peterson-Graziose, V., Bryer, J., & Nikolaidou, M. (2013). Self-esteem and self-efficacy as predictors of attrition in associate degree nursing students. Journal of Nurses Educational, 52(6), 351–354.
Rainer, R. K. Jr., & Miller, M. D. (1996). An assessment of the psychometric properties of the computer attitude scale. Computers in Human Behavior, 12(1), 93-105.
Rao, S., & Troshani, I. (2007). A conceptual framework and propositions for the acceptance of mobile services. Journal of Theoretical and Applied Electronic Commerce Research, 2(2), 61-73.
Ravid, R., & Leon, M. R. (1995). Students’ perceptions of the research component in master’s level teacher education programs. San Francisco: Annual Meeting of the American Educational Research Association.
Savitskie, K., Royne, M. B., Persinger, E. S., Grunhagen, M., & Witt, C. L. (2007). Norwegian Internet Shopping Sites: An application & extension of the technology acceptance model. Journal of Global Information Technology Management, 10(4), 54-73.
Schoonenboom, J. (2014). Using an adapted, task-level technology acceptance model to explain why instructors in higher education intend to use some learning management system tools more than others. Computers & Education, 71, 247-256.
Stephenson, S., & Bell, J. (1992). What should be taught in the typical business statistics course? A little data and a lot of opinions. In Proceedings of the section on Statistical Education (pp. 448-450), American Statistical Association.
Tabachnick, B. & Fidell, L. S. (1996). Using multivariate statistics. New York: Happer Collins College.
Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52, 302-312.
Teo, T. (2014). Unpacking teachers’ acceptance of technology: Tests of measurement invariance and latent mean differences. Computers & Education, 75, 127-135.
Thompson, R., Higgins, C., & Howell, J. (1994). Influence of experience on personal computer utilization: Testing a conceptual model. Journal of Management Information Systems, 11(1), 167-187.
Venkatesh, V. & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481.
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Science, 39(2), 273-315.
Webster, E. (1992). Evaluation of computer software for teaching statistics. Journal of Mathematics and Science Teaching, 11(3/4), 377–391.
Weston, R. & Gore, Jr. P. (2006). A brief guide to structural equation modeling. The Counseling Psychologist, 34(5), 719-751.
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.
Zeidner, M. (1991). Statistics and mathematics anxiety in social science students-some interesting parallels. British Journal of Educational Psychology, 61, 319-328.

Files

Share

How to cite

Statistics