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USEFULNESS OF SPSS SUPPORT FOR STUDENTS OF ECONOMICS AND BUSINESS

Year 2014, Volume: 1 , 28 - 36, 31.05.2014

Abstract

Faculty of Economics and Business (University of
Maribor) offers complex and useful knowledge in the field of statistics, so
among other things the SPSS (Statistical Package for the Social Sciences) is
part of students’ courses. SPSS is the most widely used statistical package
that is applied by professionals as well as by higher education institutions,
and it represents the important IT support. For that reason in this paper we
present conceptual model of the usability of IT SPSS support, which was tested
on a sample of 300 undergraduate and postgraduate students of economics and
business. The basis for conceptual model developed in this paper represents the
expanded TAM model (Technology Acceptance Model). The conceptual model was
verified using structural equation modeling (SEM). Based on the set of basic
models, we examined connections between formed constructs of the TAM model and
in this way we presented the results of the conceptual models. The study
reveals that there is a positive relationship between perceived usefulness of
statistics and perceived usefulness of SPSS, perceived ease of use of SPSS, and
attitude towards using the SPSS. Research model was analyzed by using the
SmartPLS and WarpPLS approaches. The examination of the usefulness of statistical
information support for educational institutions represents a starting point
for further pedagogical and software development, and it also provides an
opportunity to increase the value of SPSS in planning of IT support

References

  • Afari-Kumah, E. & Achampong, A. K. (2010). Modeling computer usage intentions of tertiary students in a developing country through the Technology Acceptance Model. International Journal of Education and Development using Information and Communication Technology 6 (1), 102–116. Alshare, K. A. & Alkhateeb, F. B. (2008). Predıctıng students usage of ınternet ın two emergıng economıes usıng an extended technology acceptance model (TAM). Academy of Educational Leadership Journal 12 (2), 109–128. Ajzen, I. & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior. Prentice-Hall, Englewood Cliffs, New Jersey. Bagozzi, R.P. & Yi, Y. (1998). On the evaluation of structural equation model. Journal of the Academy of Marketing Science 16, 74–94. Bond, M. E., Perkins, S. N. and Ramirez, C. (2012). Students’ Perceptions of Statistics: An Exploration of Attitudes, Conceptualizations, and Content Knowledge of Statistics. Statistics Education Research Journal 11 (2): 6–25. Basturk, R. (2005). The Effectiveness of Computer-Assisted Instruction in Teaching Introductory Statistics. Educational Technology & Society 8 (2), 170–178. Byrne, B. M. (2001). Structural Equation Modeling with AMOS. Basic Concepts, Applications and Programming. New Jersey: Lawrence Erlbaum Associates. Campbell, D. T. (1960). Recommendations for APA test standards regarding construct, trait, or discriminant validity. American Psychologist 15, 546–553. Cenfetelli, R. T. & Bassellier, G. (2009). Interpretation of formative measurement in information systems research. MIS Quarterly 33 (4), 689–707. Chiesi, F. & C. Primi. (2009). Assessing statistics attitudes among college students: Psychometric properties of the Italian version of the Survey of Attitudes toward Statistics (SATS). Learning and Individual Differences 19 (2), 309–313. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Lawrence Erlbaum, Hillside, New Jersey. Edmunds, R., Thorpe, M. and Conole, G. (2012). Student attitudes towards and use of ICT in course study, work and social activity: a technology acceptance model approach. British Journal of Educational Technology 43(1): 71–84. Davis, F. D. (1989). Perceived Usefulness, Perceived Ease Of Use, And User Accep. MIS Quarterly 13 (3), 319–340. Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies 38 (3), 475–487. Dempste, M. & N.K. McCorry. (2009). The Role of Previous Experience and Attitudes toward Statistics in Statistics Assessment Outcomes among Undergraduate Psychology Students. Journal of Statistics Education 17 (2), 1–7. Fishbein, M. & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Addison-Weley, Reading, MA. Fornell, C. & Lacker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18, 39–50. Garfield, J. (1995). How Students Learn Statistics. International Statistical Review 63 (1), 25–34. Gentle, J. E. (2004). Courses in Statistical Computing and Computational Statistics. The American Statistician 25 (1), 2-5. Gordon, S. (2004). Understandıng students’ experıences of statıstıcs ın a service course. Statistics Education Research Journal 3 (1), 40–59. Hagen, B., O. Awosoga, Kellett, P. and Ofori Dei, S. (2013). Evaluation of undergraduate nursing students' attitudes towards statistics courses, before and after a course in applied statistics. Nurse Education Today 33 (9): 949–955. Hair, J. F., Black, W. C., Babin, B. J. and Anderson, R. E. (2010). Multivariate data analysis. New Jersey: Prentice Hall. Hsu, M. K., Wang, S. W. and 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. Kock, N. (2009). WarpPLS 1.0 User Manual. ScriptWarp Systems, Laredo Texas. Retrieved online June 7, 2010 from http://www.scriptwarp.com/warppls/UserManual.pdf. Kock, N. (2013). WarpPLS 4.0 User Manual. ScriptWarp Systems: Laredo, Texas. Lalayants, M. (2012). Overcoming Graduate Students' Negative Perceptions of Statistics. Journal of Teaching in Social Work 32 (4), 356–375. Latan, H. and I. Ghozali. (2012). Partial least Squares: Concept and application path modeling using program XLSTAT-PLS for empirical research, BP UNDIP. 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. 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. Lu, J., Yu, C.-S., Liu, L. C. and Yao, J. E. (2003). Technology acceptance model for wireless Internet. Internet Research 13 (3), 206–222. Marton, F. & Booth, S. (1997). Learning and Awareness. Mahwah. New Jersey: Lawrence Erlbaum Associates. Park, S. Y. (2009). An Analysis of the Technology Acceptance Model in Understanding University. Students’ Behavioral Intention to Use e-Learning. Educational Technology & Society 12 (3), 150–162. Petocz, P. & Reid, A. (2001). Students’ experience of learning in statistics. Quaestiones Mathematicae, Supplement 1: 37–45. Proctor, J. L. (2002). SPSS vs. Excel: Computing software, criminal justice students, and statistics. Journal of Criminal Justice Education 13 (2), 433–442. Roberts, D. M. & Bilderback, E. W. (1980). Reliability and validity of a statistics attitude survey. Educational and psychological measurement 40, 907–912. Mahat, J., Mohd Ayub, A. F. and Su Luan, W. (2013). Factors influence the acceptance of m-Learning in Malaysia: Perceived Usefulness, Perceived Ease of Use and Attitude. Proceedings of the 21st International Conference on Computers in Education. Indonesia: Asia-Pacific Society for Computers in Education. Nah, F. F.-H., Tan, X. and Teh, S. H. (2004). An empirical investigation on end-users' acceptance of enterprise systems. Information Resources Management Journal 17 (3), 32–53. 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 & Management 42, 317–327. Shroff, R. H., Deneen, C. C. and Ng, E. M. W. (2011). Analysis of the technology acceptance model in examining students’ behavioural intention to use an eportfolio system. Australasian Journal of Educational Technology 27 (4), 600–618. Sriwidharmanely & Syafrudin, V. (2012). V. An Empirical Study of Accounting Software Acceptance among Bengkulu City Students. Asian Journal of Accounting and Governance 3, 99–112. Schepers, J., Wetzels, M. and de 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. Suanpang, P., Petocz, P. and Kalceff, W. (2003). Student Attitudes to Learning Business Statistics Online vs. Tenenhaus, M., Esposito Vinzi, V. Chatelin, Y.-M. and Lauro, C. (2005). PLS path modeling. Computational Statistics and Data Analysis 48 (1), 159–205. Triola, M. F. (2011). Essentials of Statistics. Boston: Person. Turban, E. & Volonino, L. (2010). Information Technology for Management. Transforming Organizations in the Digital Economy. Hoboken: John Wiley & Sons. Venkatesh, V. & Davis, D. F. (1996). A model of the antecedents of perceived ease of use: development and test. Decision Sciences 27 (3), 451–481. Yang, H.-H., Yu, J.-C., Yang, H.-J., Han, W.-H., Li, Y.-J. (2007). Learning Experience is Important for the Attitude of Using Statistical Software. Proceedings of the 6th WSEAS International Conference on Applied Computer Science, Hangzhou, China, April 15-17, 165–169. Yi, M., Jackson, J. Park, J. and Probst, J. (2006). Understanding Information Technology Acceptance by Individual Professionals: Towards an Integrative View. Information and Management 43, 350–363. Zhang, Y., Shang, L. Wang, R., Zhao, Q., Li, C., Xu, Y. and Su, H. (2012). Attitudes toward statistics in medical postgraduates: measuring, evaluating and monitoring. BMC Medical Education 12 (117), 1–8.
Year 2014, Volume: 1 , 28 - 36, 31.05.2014

Abstract

References

  • Afari-Kumah, E. & Achampong, A. K. (2010). Modeling computer usage intentions of tertiary students in a developing country through the Technology Acceptance Model. International Journal of Education and Development using Information and Communication Technology 6 (1), 102–116. Alshare, K. A. & Alkhateeb, F. B. (2008). Predıctıng students usage of ınternet ın two emergıng economıes usıng an extended technology acceptance model (TAM). Academy of Educational Leadership Journal 12 (2), 109–128. Ajzen, I. & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior. Prentice-Hall, Englewood Cliffs, New Jersey. Bagozzi, R.P. & Yi, Y. (1998). On the evaluation of structural equation model. Journal of the Academy of Marketing Science 16, 74–94. Bond, M. E., Perkins, S. N. and Ramirez, C. (2012). Students’ Perceptions of Statistics: An Exploration of Attitudes, Conceptualizations, and Content Knowledge of Statistics. Statistics Education Research Journal 11 (2): 6–25. Basturk, R. (2005). The Effectiveness of Computer-Assisted Instruction in Teaching Introductory Statistics. Educational Technology & Society 8 (2), 170–178. Byrne, B. M. (2001). Structural Equation Modeling with AMOS. Basic Concepts, Applications and Programming. New Jersey: Lawrence Erlbaum Associates. Campbell, D. T. (1960). Recommendations for APA test standards regarding construct, trait, or discriminant validity. American Psychologist 15, 546–553. Cenfetelli, R. T. & Bassellier, G. (2009). Interpretation of formative measurement in information systems research. MIS Quarterly 33 (4), 689–707. Chiesi, F. & C. Primi. (2009). Assessing statistics attitudes among college students: Psychometric properties of the Italian version of the Survey of Attitudes toward Statistics (SATS). Learning and Individual Differences 19 (2), 309–313. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Lawrence Erlbaum, Hillside, New Jersey. Edmunds, R., Thorpe, M. and Conole, G. (2012). Student attitudes towards and use of ICT in course study, work and social activity: a technology acceptance model approach. British Journal of Educational Technology 43(1): 71–84. Davis, F. D. (1989). Perceived Usefulness, Perceived Ease Of Use, And User Accep. MIS Quarterly 13 (3), 319–340. Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies 38 (3), 475–487. Dempste, M. & N.K. McCorry. (2009). The Role of Previous Experience and Attitudes toward Statistics in Statistics Assessment Outcomes among Undergraduate Psychology Students. Journal of Statistics Education 17 (2), 1–7. Fishbein, M. & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Addison-Weley, Reading, MA. Fornell, C. & Lacker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18, 39–50. Garfield, J. (1995). How Students Learn Statistics. International Statistical Review 63 (1), 25–34. Gentle, J. E. (2004). Courses in Statistical Computing and Computational Statistics. The American Statistician 25 (1), 2-5. Gordon, S. (2004). Understandıng students’ experıences of statıstıcs ın a service course. Statistics Education Research Journal 3 (1), 40–59. Hagen, B., O. Awosoga, Kellett, P. and Ofori Dei, S. (2013). Evaluation of undergraduate nursing students' attitudes towards statistics courses, before and after a course in applied statistics. Nurse Education Today 33 (9): 949–955. Hair, J. F., Black, W. C., Babin, B. J. and Anderson, R. E. (2010). Multivariate data analysis. New Jersey: Prentice Hall. Hsu, M. K., Wang, S. W. and 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. Kock, N. (2009). WarpPLS 1.0 User Manual. ScriptWarp Systems, Laredo Texas. Retrieved online June 7, 2010 from http://www.scriptwarp.com/warppls/UserManual.pdf. Kock, N. (2013). WarpPLS 4.0 User Manual. ScriptWarp Systems: Laredo, Texas. Lalayants, M. (2012). Overcoming Graduate Students' Negative Perceptions of Statistics. Journal of Teaching in Social Work 32 (4), 356–375. Latan, H. and I. Ghozali. (2012). Partial least Squares: Concept and application path modeling using program XLSTAT-PLS for empirical research, BP UNDIP. 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. 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. Lu, J., Yu, C.-S., Liu, L. C. and Yao, J. E. (2003). Technology acceptance model for wireless Internet. Internet Research 13 (3), 206–222. Marton, F. & Booth, S. (1997). Learning and Awareness. Mahwah. New Jersey: Lawrence Erlbaum Associates. Park, S. Y. (2009). An Analysis of the Technology Acceptance Model in Understanding University. Students’ Behavioral Intention to Use e-Learning. Educational Technology & Society 12 (3), 150–162. Petocz, P. & Reid, A. (2001). Students’ experience of learning in statistics. Quaestiones Mathematicae, Supplement 1: 37–45. Proctor, J. L. (2002). SPSS vs. Excel: Computing software, criminal justice students, and statistics. Journal of Criminal Justice Education 13 (2), 433–442. Roberts, D. M. & Bilderback, E. W. (1980). Reliability and validity of a statistics attitude survey. Educational and psychological measurement 40, 907–912. Mahat, J., Mohd Ayub, A. F. and Su Luan, W. (2013). Factors influence the acceptance of m-Learning in Malaysia: Perceived Usefulness, Perceived Ease of Use and Attitude. Proceedings of the 21st International Conference on Computers in Education. Indonesia: Asia-Pacific Society for Computers in Education. Nah, F. F.-H., Tan, X. and Teh, S. H. (2004). An empirical investigation on end-users' acceptance of enterprise systems. Information Resources Management Journal 17 (3), 32–53. 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 & Management 42, 317–327. Shroff, R. H., Deneen, C. C. and Ng, E. M. W. (2011). Analysis of the technology acceptance model in examining students’ behavioural intention to use an eportfolio system. Australasian Journal of Educational Technology 27 (4), 600–618. Sriwidharmanely & Syafrudin, V. (2012). V. An Empirical Study of Accounting Software Acceptance among Bengkulu City Students. Asian Journal of Accounting and Governance 3, 99–112. Schepers, J., Wetzels, M. and de 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. Suanpang, P., Petocz, P. and Kalceff, W. (2003). Student Attitudes to Learning Business Statistics Online vs. Tenenhaus, M., Esposito Vinzi, V. Chatelin, Y.-M. and Lauro, C. (2005). PLS path modeling. Computational Statistics and Data Analysis 48 (1), 159–205. Triola, M. F. (2011). Essentials of Statistics. Boston: Person. Turban, E. & Volonino, L. (2010). Information Technology for Management. Transforming Organizations in the Digital Economy. Hoboken: John Wiley & Sons. Venkatesh, V. & Davis, D. F. (1996). A model of the antecedents of perceived ease of use: development and test. Decision Sciences 27 (3), 451–481. Yang, H.-H., Yu, J.-C., Yang, H.-J., Han, W.-H., Li, Y.-J. (2007). Learning Experience is Important for the Attitude of Using Statistical Software. Proceedings of the 6th WSEAS International Conference on Applied Computer Science, Hangzhou, China, April 15-17, 165–169. Yi, M., Jackson, J. Park, J. and Probst, J. (2006). Understanding Information Technology Acceptance by Individual Professionals: Towards an Integrative View. Information and Management 43, 350–363. Zhang, Y., Shang, L. Wang, R., Zhao, Q., Li, C., Xu, Y. and Su, H. (2012). Attitudes toward statistics in medical postgraduates: measuring, evaluating and monitoring. BMC Medical Education 12 (117), 1–8.
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Details

Journal Section Articles
Authors

Urban šebjan This is me

Polona Tomınc This is me

Publication Date May 31, 2014
Published in Issue Year 2014 Volume: 1

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APA šebjan, U., & Tomınc, P. (2014). USEFULNESS OF SPSS SUPPORT FOR STUDENTS OF ECONOMICS AND BUSINESS. The Eurasia Proceedings of Educational and Social Sciences, 1, 28-36.