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Genişletilmiş Teknoloji Kabul Modeli Çerçevesinde SPSS’in Yararlılığı ve İstatistiksel Bilgi Talebine Uyum Algısı

Year 2017, Volume: 2 Issue: 2, 1 - 13, 31.12.2017

Abstract

Eğitim kurumları sürekli olarak öğrencilerin çalışmalarında istatistiksel bilgiye duydukları ihtiyacı ve istatistiksel yazılım desteğinin uyumluluğunu izlemek durumundadır. Bu nedenle, bu çalışmada teknoloji kabul modeli (TKM) kuramsal çerçevesinde Sosyal Bilimler İçin İstatistik Programı (SPSS) yazılımının yararlılığı ve istatistiki bilgi talepleri ile algılanan uyumluluk arasındaki ilişkiler incelenmiştir. Araştırma 300 lisans ve yüksek lisans öğrencisi üzerinde genişletilmiş bir model ile test edilmiştir. Araştırmada dört boyuttan ve (SPSS’in yararları, SPSS’in kullanım kolaylığı, SPSS’i kullanmaya yönelik tutum ve niyet) on sekiz madden oluşan anket kullanılmıştır. Model, iki aşamalı bir yapısal eşitlik modeli kullanılarak analiz edilmiştir. Araştırmamızın önemli sonucu şudur ki; öğrencilerin çalışmaları sırasında karşılaştıkları istatistiksel bilgi talebi ile uyumluluk algıları, SPSS yararlılık algısı ve kullanım kolaylığı algısını etkileyerek onların SPSS yazılımı kullanmaya yönelik tutumları ve niyetlerinin oluşumu üstünde önemli rol oynamaktadır. Eğitim kurumları SPSS kullanımı hakkında bilgilerini ve gelecekte SPSS kullanımını arttırmak için hedef odaklı bir yaklaşım içinde bulunmaları gerekir.

References

  • Agudo-Peregrina, Á. F., Hernández-García, Á., & Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behavior, 34, 301-314.
  • Ajzen, I. (1991). The theory of planned behaviour. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
  • Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.
  • Biehler, R. (1997). Software for learning and for doing statistics. International Statistical Review, 65(2), 167-189.
  • Bovas, A. (2007). Implementation of statistics in business and industry. Revista Colombiana de Estadistica, 30(1), 1-11.
  • Byrne, B. M. (2001). Structural equation modeling with AMOS. Basic concepts, applications and programming. New Jersey: Lawrence Erlbaum Associates.
  • Chung, S. H., Schwager, P. H., & Turner, D. E. (2002). An empirical study of students' computer self-efficacy: Differences among four academic disciplines at a large university. Journal of Computer Information Systems, 42(4), 1-6.
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.)., Hillside, NJ: Lawrence Erlbaum.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
  • Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
  • Fornell, C., & Lacker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39-50.
  • Gentle, J. E. (2004). Courses in Statistical Computing and Computational Statistics. The American Statistician, 58(1), 2-5.
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. New Jersey: Prentice Hall.
  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). London: Sage.
  • Hofstede, G. (1980). Culture’s consequences: International differences in work related values. Beverly Hills, CA: Sage Publications.
  • Hsu, M. K., Wang, S. W., & Chin, 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.
  • Hu, P. J. H., Clark, T. H., & Ma, W. W. (2003). Examining technology acceptance by school teachers: A longitudinal study. Information & Management, 41(2), 227-241.
  • Huang, T. C. K., Liu, C. C., & Chang, D. C. (2012). An empirical investigation of factors influencing the adoption of data mining tools. International Journal of Information Management, 32(3), 257-270.
  • Kirkwood, A., & Price, L. (2005). Learners and learning in the twenty-first century: What do we know about students’ attitudes towards and experiences of information and communication technologies that will help us design courses?. Studies in Higher Education, 30 (3), 257-274.
  • Kock, N. (2013). WarpPLS 4.0 user manual.: Laredo, TX: ScriptWarp Systems.
  • Lee, D. Y., & Lehto, M. R. (2013). User acceptance of YouTube for procedural learning: An extension of the Technology Acceptance Model. Computers & Education, 61, 193-208.
  • Letchumanan, M., & Muniandy, B. (2013). Migrating to e-book: A study on perceived usefulness and ease of use. Library Hi Tech News, 7, 10-15.
  • Lindsay, R., Jackson, T. W., & Cooke, L. (2014). Empirical evaluation of a technology acceptance model for mobile policing. Police Practice and Research: An International Journal, 15(5), 419-436.
  • Monahan, J. (2004). Teaching Statistical Computing at North Carolina State University. The American Statistician, 58(1), 6-8.
  • Mondejar-Jimenez, J., & Vargas-Vargas, M. (2010). Determinant factors of attitude towards quantitative subjects: Differences between sexes. Teaching and Teacher Education, 26, 688-693.
  • Murtonen, M., & Lehtinen, E. (2010). Difficulties experienced by education and sociology students in quantitative methods courses. Studies in Higher Education, 28(2), 171-185.
  • Nah, F. F., Tan, X., & Teh, S. H. (2004). An empirical investigation on end-users’ acceptance of enterprise systems. Information Resources Management Journal, 17(3), 32-53.
  • Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data, 2(1), 1-21.
  • Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York: McGraw-Hill.
  • Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York: The Free press.
  • Saad, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: An extension of the technology acceptance model. Information and Management, 42, 317-327.
  • Schepers, J., Wetzels, M., & Ruyter, R. (2005). Leadership styles in technology acceptance: Do followers practice what leaders preach? Managing Service Quality, 15 (6), 496-508.
  • Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society. Series B, 36(2), 111-133.
  • ŠUmak, B., HeričKo, M., & PušNik, M. (2011). A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior, 27(6), 2067-2077.
  • Tenenhaus, M., Vinzi, V. E., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics and Data Analysis, 4 (1), 159-205.
  • 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.
  • Terzis, V., Moridis, C. N., Economides, A. A., & Rebolledo-Mendez, G. (2013). Computer based assessment acceptance: A cross-cultural study in Greece and Mexico. Educational Technology and Society, 16(3), 411-424.
  • 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.
  • Watson, J. M. (1997). Assessing statistical thinking using the media. In The Assessment Challenge in Statistics Education (pp. 107-121), Gal, I. and Garfield, J. B. (Eds.). Amsterdam: IOS Press and The International Statistical Institute.
  • Wu, J.-H., & Wang, S.-C. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information and Management, 42, 719-729.
  • Yaratan, H. (2017). Sosyal bilimler için temel istatistik, SPSS uygulamalı. Ankara: Anı Yayınevi.
  • Yi, M. Y., Fiedler, K. D., & Park, J. S. (2006). Understanding the role of individual innovativeness in the acceptance of IT-Based innovations: Comparative analyses of models and measures. Decision Sciences, 37(3), 393-426.

Extended Model TAM of Perceived Usefulness of SPSS and Perceived Compatibility with Demands for Statistical Knowledge within Studies

Year 2017, Volume: 2 Issue: 2, 1 - 13, 31.12.2017

Abstract

Educational institutions must constantly monitor the compliance of students with statistical software support and statistical software support. For this reason, the usefulness of statistical software for social sciences (SPSS) software in the theoretical framework of technology acceptance model (TAM) and the relationship between statistical information requirements and perceived compatibility are examined in this study. The study has been tested with an extended model on 300 undergraduate and graduate students. Eighteen questionnaires were used in the study in four dimensions and (the benefits of SPSS, ease of use of SPSS, attitude and intention to use SPSS). The model was analyzed using a two-stage structural equation model. The important consequence of our research is that; students' perceptions of compliance with the statistical information requests and their perceptions of SPSS usefulness and ease-of-use affect their perception of the attitudes and intentions to use SPSS software. Educational institutions should have knowledge of the use of SPSS and a goal-oriented approach to increasing the use of SPSS in the future.

References

  • Agudo-Peregrina, Á. F., Hernández-García, Á., & Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behavior, 34, 301-314.
  • Ajzen, I. (1991). The theory of planned behaviour. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
  • Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.
  • Biehler, R. (1997). Software for learning and for doing statistics. International Statistical Review, 65(2), 167-189.
  • Bovas, A. (2007). Implementation of statistics in business and industry. Revista Colombiana de Estadistica, 30(1), 1-11.
  • Byrne, B. M. (2001). Structural equation modeling with AMOS. Basic concepts, applications and programming. New Jersey: Lawrence Erlbaum Associates.
  • Chung, S. H., Schwager, P. H., & Turner, D. E. (2002). An empirical study of students' computer self-efficacy: Differences among four academic disciplines at a large university. Journal of Computer Information Systems, 42(4), 1-6.
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.)., Hillside, NJ: Lawrence Erlbaum.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
  • Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
  • Fornell, C., & Lacker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39-50.
  • Gentle, J. E. (2004). Courses in Statistical Computing and Computational Statistics. The American Statistician, 58(1), 2-5.
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. New Jersey: Prentice Hall.
  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). London: Sage.
  • Hofstede, G. (1980). Culture’s consequences: International differences in work related values. Beverly Hills, CA: Sage Publications.
  • Hsu, M. K., Wang, S. W., & Chin, 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.
  • Hu, P. J. H., Clark, T. H., & Ma, W. W. (2003). Examining technology acceptance by school teachers: A longitudinal study. Information & Management, 41(2), 227-241.
  • Huang, T. C. K., Liu, C. C., & Chang, D. C. (2012). An empirical investigation of factors influencing the adoption of data mining tools. International Journal of Information Management, 32(3), 257-270.
  • Kirkwood, A., & Price, L. (2005). Learners and learning in the twenty-first century: What do we know about students’ attitudes towards and experiences of information and communication technologies that will help us design courses?. Studies in Higher Education, 30 (3), 257-274.
  • Kock, N. (2013). WarpPLS 4.0 user manual.: Laredo, TX: ScriptWarp Systems.
  • Lee, D. Y., & Lehto, M. R. (2013). User acceptance of YouTube for procedural learning: An extension of the Technology Acceptance Model. Computers & Education, 61, 193-208.
  • Letchumanan, M., & Muniandy, B. (2013). Migrating to e-book: A study on perceived usefulness and ease of use. Library Hi Tech News, 7, 10-15.
  • Lindsay, R., Jackson, T. W., & Cooke, L. (2014). Empirical evaluation of a technology acceptance model for mobile policing. Police Practice and Research: An International Journal, 15(5), 419-436.
  • Monahan, J. (2004). Teaching Statistical Computing at North Carolina State University. The American Statistician, 58(1), 6-8.
  • Mondejar-Jimenez, J., & Vargas-Vargas, M. (2010). Determinant factors of attitude towards quantitative subjects: Differences between sexes. Teaching and Teacher Education, 26, 688-693.
  • Murtonen, M., & Lehtinen, E. (2010). Difficulties experienced by education and sociology students in quantitative methods courses. Studies in Higher Education, 28(2), 171-185.
  • Nah, F. F., Tan, X., & Teh, S. H. (2004). An empirical investigation on end-users’ acceptance of enterprise systems. Information Resources Management Journal, 17(3), 32-53.
  • Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data, 2(1), 1-21.
  • Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York: McGraw-Hill.
  • Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York: The Free press.
  • Saad, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: An extension of the technology acceptance model. Information and Management, 42, 317-327.
  • Schepers, J., Wetzels, M., & Ruyter, R. (2005). Leadership styles in technology acceptance: Do followers practice what leaders preach? Managing Service Quality, 15 (6), 496-508.
  • Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society. Series B, 36(2), 111-133.
  • ŠUmak, B., HeričKo, M., & PušNik, M. (2011). A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior, 27(6), 2067-2077.
  • Tenenhaus, M., Vinzi, V. E., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics and Data Analysis, 4 (1), 159-205.
  • 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.
  • Terzis, V., Moridis, C. N., Economides, A. A., & Rebolledo-Mendez, G. (2013). Computer based assessment acceptance: A cross-cultural study in Greece and Mexico. Educational Technology and Society, 16(3), 411-424.
  • 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.
  • Watson, J. M. (1997). Assessing statistical thinking using the media. In The Assessment Challenge in Statistics Education (pp. 107-121), Gal, I. and Garfield, J. B. (Eds.). Amsterdam: IOS Press and The International Statistical Institute.
  • Wu, J.-H., & Wang, S.-C. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information and Management, 42, 719-729.
  • Yaratan, H. (2017). Sosyal bilimler için temel istatistik, SPSS uygulamalı. Ankara: Anı Yayınevi.
  • Yi, M. Y., Fiedler, K. D., & Park, J. S. (2006). Understanding the role of individual innovativeness in the acceptance of IT-Based innovations: Comparative analyses of models and measures. Decision Sciences, 37(3), 393-426.
There are 42 citations in total.

Details

Primary Language Turkish
Subjects Studies on Education
Journal Section Cilt 2
Authors

Urban šebjan This is me

Polona Tominc This is me

Atila Yıldırım

Publication Date December 31, 2017
Published in Issue Year 2017 Volume: 2 Issue: 2

Cite

APA šebjan, U., Tominc, P., & Yıldırım, A. (2017). Genişletilmiş Teknoloji Kabul Modeli Çerçevesinde SPSS’in Yararlılığı ve İstatistiksel Bilgi Talebine Uyum Algısı. Eğitim Bilim Ve Teknoloji Araştırmaları Dergisi, 2(2), 1-13.
AMA šebjan U, Tominc P, Yıldırım A. Genişletilmiş Teknoloji Kabul Modeli Çerçevesinde SPSS’in Yararlılığı ve İstatistiksel Bilgi Talebine Uyum Algısı. EBTAD (JREST). December 2017;2(2):1-13.
Chicago šebjan, Urban, Polona Tominc, and Atila Yıldırım. “Genişletilmiş Teknoloji Kabul Modeli Çerçevesinde SPSS’in Yararlılığı Ve İstatistiksel Bilgi Talebine Uyum Algısı”. Eğitim Bilim Ve Teknoloji Araştırmaları Dergisi 2, no. 2 (December 2017): 1-13.
EndNote šebjan U, Tominc P, Yıldırım A (December 1, 2017) Genişletilmiş Teknoloji Kabul Modeli Çerçevesinde SPSS’in Yararlılığı ve İstatistiksel Bilgi Talebine Uyum Algısı. Eğitim Bilim ve Teknoloji Araştırmaları Dergisi 2 2 1–13.
IEEE U. šebjan, P. Tominc, and A. Yıldırım, “Genişletilmiş Teknoloji Kabul Modeli Çerçevesinde SPSS’in Yararlılığı ve İstatistiksel Bilgi Talebine Uyum Algısı”, EBTAD (JREST), vol. 2, no. 2, pp. 1–13, 2017.
ISNAD šebjan, Urban et al. “Genişletilmiş Teknoloji Kabul Modeli Çerçevesinde SPSS’in Yararlılığı Ve İstatistiksel Bilgi Talebine Uyum Algısı”. Eğitim Bilim ve Teknoloji Araştırmaları Dergisi 2/2 (December 2017), 1-13.
JAMA šebjan U, Tominc P, Yıldırım A. Genişletilmiş Teknoloji Kabul Modeli Çerçevesinde SPSS’in Yararlılığı ve İstatistiksel Bilgi Talebine Uyum Algısı. EBTAD (JREST). 2017;2:1–13.
MLA šebjan, Urban et al. “Genişletilmiş Teknoloji Kabul Modeli Çerçevesinde SPSS’in Yararlılığı Ve İstatistiksel Bilgi Talebine Uyum Algısı”. Eğitim Bilim Ve Teknoloji Araştırmaları Dergisi, vol. 2, no. 2, 2017, pp. 1-13.
Vancouver šebjan U, Tominc P, Yıldırım A. Genişletilmiş Teknoloji Kabul Modeli Çerçevesinde SPSS’in Yararlılığı ve İstatistiksel Bilgi Talebine Uyum Algısı. EBTAD (JREST). 2017;2(2):1-13.