Araştırma Makalesi
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TEKNOLOJİ KABUL MODELİNİN (TKM) TEKNOLOJİYE HAZIR OLMA TEORİSİYLE GENİŞLETİLMESİ

Yıl 2021, Sayı: 31, 1 - 22, 30.04.2021
https://doi.org/10.18092/ulikidince.700939

Öz

Teknolojik gelişmeler kullanıcılar için sayısız avantaj sağlarken, rekabet koşulları, yatırım maliyetleri ve tüketici beklentileri üreticilerin tutunmasını zorlaştırmaktadır. Bu nedenle, teknoloji kabulü ve yayılması alanında yapılan araştırmalar daha kıymetli hale gelmiş ve sadece akademisyenler değil uygulama camiası tarafından da dikkatle takip edilmektedir. Teknoloji Kabul Modeli (TKM) ve Teknolojiye Hazır Olma İndeksi (THİ) değişkenlerini tek bir bağlamda birleştirme ve birbirlerini karakteristik özellikleri ile açıklamaya yönelik onaylanmış çalışmaya dair bir işaret yoktur. Bu çalışmada, çevrimiçi yoklama sistemlerinin kullanımında, yukarıda bahsedilen koşullar araştırılmıştır. Çalışma kapsamında, Türkiye'deki yedi üniversitede bulunan 389 akademisyene anket gönderilmiştir. Bu çalışmanın temel amacı her iki modeldeki değişkenler arasında Çoklu Doğrusal Regresyon (MLR) testi aracılığı ile değişkenlerin incelenmesidir. Analiz sonuçlarına göre, TKM ve THİ değişkenlerinin kombinasyonu, yüksek tahmin performansını ortaya koymuştur. Araştırma modeli geçerli ve istatistiksel olarak anlamlı bulunmuş ve Davranışsal Niyeti %58,6 açıklama oranına erişmiştir

Kaynakça

  • Ajzen, I. and Fishbein, M. (1977). Attitude-Behavior Relations: A Theoretical Analysis and Review of Empirical Research. Psychological Bulletin, 84(5), 888-918.
  • Badri, M., Al Rashedi, A., Yang, G., Mohaidat, J., & Al Hammadi, A. (2014). Technology Readiness of School Teachers: An Empirical Study of Measurement and Segmentation. Journal of In-formation Technology Education Research, 13, 257-275.
  • Davis, F. (1986). A Technology Acceptance Model for Empirically Testing New End-User Infor-mation Systems. Doctoral Thesis. MIT Sloan School of Management, Cambridge MA.
  • Davis, F. (1989) Perceived Usefulness, Perceived Ease of Use and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340.
  • Davis, F. D. and Venkatesh, V. (1996). A Critical Assessment of Potential Measurement Biases in the Technology Acceptance Model: Three Experiments. International Journal of Human-Computer Studies, 45(1), 19-45.
  • Davis, F. D., Bagozzi, R. P. and Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982-1003.
  • Fabrigar, L.R., Wegener, D.T., MacCallum, R.C. and Strahan, E.J. (1999). Evaluating the Use of Ex-ploratory Factor Analysis in Psychological Research. Psychological Methods. 4(3): 272-299.
  • Field, A. (2009). Discovering Statistics Using SPSS (3rd ed). Thousand Oaks, California: SAGE Pub.
  • Fishbein, M. and Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Reading, Massachusetts: Addison Wesley.
  • Frost, J. (2017). Multicollinearity in Regression Analysis: Problems, Detection, and Solutions. Re-trieved from https://statisticsbyjim.com/regression/multicollinearity-in-regression-analysis/, Accessed date: 09 March 2020.
  • Gupta, V. S. and Garg, R. (2015). Technology Readiness Index of E-Banking Users: Some Measure-ment and Sample Survey Evidence. IUP Journal of Bank Management, 43-58.
  • Hair, J.F., Black, W.C., Babin, B.J. and Anderson, R.E. (2010). Multivariate Data Analysis (7th ed.) Edi-tion. Essex: Prentice Hall.
  • Ke, C. K., Sun, H. M. and Yang, Y.C. (2012). Effects of User and System Characteristics on Perceived Usefulness and Perceived Ease of Use for the Web-Based Classroom Response System. The Turkish Online Journal of Educational Technology, 11(3), 128-143.
  • Kenton, W. (2019). Multiple Linear Regression - MLR Definition. Retrieved from https://www.investopedia.com/terms/m/mlr.asp, Accessed date: 09 March 2020.
  • Koul, S. and Eydgahi, A. (2018). Utilizing Technology Acceptance Model (TAM) for Driverless Car Technology Adoption. Journal of Technology Management & Innovation. 13(4), 37-46.
  • Lai, M. L. (2008). Technology Readiness, Internet Self‐Efficacy and Computing Experience of Pro-fessional Accounting Students. Campus-Wide Information Systems, 25(1), 18-29.
  • Lee, Y., Kozar, K. A. and Larsen, K. R. (2003). The Technology Acceptance Model: Past, Present and Future. Communications of the Association for Information Systems, 12(50), 750-782.
  • Ling, L. M. and Moi, C. M. (2007). Professional Students’ Technology Readiness, Prior Computing Experience and Acceptance of an e-Learning System. Malaysian Accounting Review, 6(1), 85-99.
  • Ling, L. M. and Muhammad, I. (2006). Taxation and Technology: Technology Readiness of Malaysi-an Tax Officers in Petaling Jaya Branch. Journal of Financial Reporting and Accounting, 4(1), 147-163.
  • Massey, A. P., Khatri, V. and Montoya-Weiss, M. (2007). Usability of Online Services: The Role of Technology Readiness and Context. Decision Sciences, 38(2), 277-308.
  • Norusis, M. J. (1993). SPSS for Windows: Professional Statics, Release 6.0, Chicago: SPSS Inc.
  • Nughoro, M.A and Fajar, M.A. (2017). Effects of Technology Readiness Towards Acceptance of Mandatory Web-Based Attendance System. Procedia Computer Science, 124, 319-328.
  • Nunnally, J. C. (1978). Psychometric Theory (2nd ed.). New York: McGraw-Hill.
  • Odlum, M. (2016). Technology Readiness of Early Career Nurse Trainees: Utilization of the Tech-nology Readiness Index (TRI). Studies in Health Technology and Informatics, 314-318.
  • Parasaruman, A. (2000). Technology Readiness Index (TRI): A Multiple-Item Scale to Measure Readiness to Embrace New Technologies. Journal of Service Research, 2(4), 307-320.
  • Parasaruman, A. and Colby, C. L. (2001). Techno-ready Marketing: How and Why Your Customers Adopt Technology. New York: The Free Press.
  • Parasaruman, A. and Colby, C. L. (2015). An Updated and Streamlined Technology Readiness In-dex: TRI 2.0. Journal of Service Research, 18(1), 59-74.
  • Penz, D., Amorim, B. C., Nascimento, S. and Rossetto, C. R. (2017). The Influence of Technology Readiness Index in Entrepreneurial Orientation: A Study with Brazilian Entrepreneurs in the United States of America. International Journal of Innovation, 5(1), 66-76.
  • Pett, M. A., Lackey, N. R. and Sullivan, J. J. (2003). Making Sense of Factor Analysis: The Use of Fac-tor Analysis for Instrument Development in Health Care Research. Thousand Oaks: Sage Publication.
  • Pires, P. J., Filho, B. A. C. and Cunha, J. C. (2011). Technology Readiness Index (TRI) Factors as Dif-ferentiating Elements between Users and Non-Users of Internet Banking, and as Ante-cedents of the Technology Acceptance Model (TAM). In M.M. Cruz-Cunha et al. (Eds.). CENTERIS 2011, Part II, CCIS 220, pp. 215–229. Springer: Berlin.
  • Rose, J. and Fogarty, W. (2010). Technology Readiness and Segmentation Profile of Mature Con-sumers. In: 4th Biennial Conference of the Academy of World Business, Marketing and Management Development, July 12-15 2010, pp. 57-65, Finland.
  • Said, R. F. M., Rahman, S. A., Mutalib, S., Yusoff, M. and Mohamed, A. (2008). User Technology Readiness Measurement in Fingerprint Adoption at Higher Education Institution. In Gervasi O., Murgante B., Lagana A., Taniar D., Mun Y., Gavrilova M. L. (eds). Computational Science and Its Applications – ICCSA 2008. ICCSA 2008. Lecture Notes in Computer Science, 5073, 91-104. Springer: Berlin.
  • Smith, M., Walford, N. G. and Bescos, C. J. (2018). Assessing the User Response to Differences in Functionality When Visualising 3D Models of Cultural Heritage Sites Using the Technology Readiness Index. Digital Applications in Archaeology and Cultural Heritage, 10, 1-10.
  • Şahinoğlu, K. T. and Yakut, S. G. (2019). Yapısal Eşitlik Modeli ile Özgürlüklerin Ekonomik Perfor-mansa Etkisi Üzerine Bir İnceleme. Ekoist: Journal of Econometrics and Statistics, 30, 1-20.
  • Tabachnick, B. G. and Fidell, L. S. (2013). Using Multivariate Statistics. Boston: Pearson.
  • Taherdoost, H., Sahibuddin, S. and Jalaliyoon, N. (2014). Exploratory Factor Analysis; Concepts and Theory. Advances in Applied and Pure Mathematics, 375-382.
  • Yong, A.G. and Pearce, S. (2013). A Beginner’s Guide to Factor Analysis: Focusing on Exploratory Factor Analysis. Tutorials in Quantitative Methods for Psychology, 9(2), 79-94.
  • Wu, M. C., Chen, C. C. and Tseng, C. H. (2013). Constructing a Technology Readiness Scale for Sports Center RFID Door Security System Users. The Journal of Global Business Manage-ment, 9(1), 12-21.
  • Venkatesh, V. and Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Mod-el: Four Longitudinal Field Studies. Institute for Operations Research and the Management Sciences, 46, 186-204.

EXTENDING TECHNOLOGY ACCEPTANCE MODEL (TAM) WITH THE THEORY OF TECHNOLOGY READINESS

Yıl 2021, Sayı: 31, 1 - 22, 30.04.2021
https://doi.org/10.18092/ulikidince.700939

Öz

Technological developments provide numerous advantages for users, while competition conditions, investment costs, and consumer expectations make it difficult for manufacturers to hold on. Thus, research in the field of technology acceptance and dissemination has become more precious and is carefully followed not only by academics but also by the practice community. It has no sign that approved studies on integrating Technology Acceptance Model (TAM) and Technology Readiness Index (TRI) variables in one context and explained each other in the way of characteristics of them. In this study, it was investigated elaborately mentioned circumstances for using online attendance systems. The survey sent to 389 scholars located seven universities in Turkey. The core process for this study is exploring the Multiple Linear Regression (MLR) test. Consequently, significant combination of TAM and TRI variables and reveals the high predictive performance of TAM and TRI constructs together. Research model has been validated and found statistically significant and reached 58,6% explanation rate for Behavioral Intention.

Kaynakça

  • Ajzen, I. and Fishbein, M. (1977). Attitude-Behavior Relations: A Theoretical Analysis and Review of Empirical Research. Psychological Bulletin, 84(5), 888-918.
  • Badri, M., Al Rashedi, A., Yang, G., Mohaidat, J., & Al Hammadi, A. (2014). Technology Readiness of School Teachers: An Empirical Study of Measurement and Segmentation. Journal of In-formation Technology Education Research, 13, 257-275.
  • Davis, F. (1986). A Technology Acceptance Model for Empirically Testing New End-User Infor-mation Systems. Doctoral Thesis. MIT Sloan School of Management, Cambridge MA.
  • Davis, F. (1989) Perceived Usefulness, Perceived Ease of Use and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340.
  • Davis, F. D. and Venkatesh, V. (1996). A Critical Assessment of Potential Measurement Biases in the Technology Acceptance Model: Three Experiments. International Journal of Human-Computer Studies, 45(1), 19-45.
  • Davis, F. D., Bagozzi, R. P. and Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982-1003.
  • Fabrigar, L.R., Wegener, D.T., MacCallum, R.C. and Strahan, E.J. (1999). Evaluating the Use of Ex-ploratory Factor Analysis in Psychological Research. Psychological Methods. 4(3): 272-299.
  • Field, A. (2009). Discovering Statistics Using SPSS (3rd ed). Thousand Oaks, California: SAGE Pub.
  • Fishbein, M. and Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Reading, Massachusetts: Addison Wesley.
  • Frost, J. (2017). Multicollinearity in Regression Analysis: Problems, Detection, and Solutions. Re-trieved from https://statisticsbyjim.com/regression/multicollinearity-in-regression-analysis/, Accessed date: 09 March 2020.
  • Gupta, V. S. and Garg, R. (2015). Technology Readiness Index of E-Banking Users: Some Measure-ment and Sample Survey Evidence. IUP Journal of Bank Management, 43-58.
  • Hair, J.F., Black, W.C., Babin, B.J. and Anderson, R.E. (2010). Multivariate Data Analysis (7th ed.) Edi-tion. Essex: Prentice Hall.
  • Ke, C. K., Sun, H. M. and Yang, Y.C. (2012). Effects of User and System Characteristics on Perceived Usefulness and Perceived Ease of Use for the Web-Based Classroom Response System. The Turkish Online Journal of Educational Technology, 11(3), 128-143.
  • Kenton, W. (2019). Multiple Linear Regression - MLR Definition. Retrieved from https://www.investopedia.com/terms/m/mlr.asp, Accessed date: 09 March 2020.
  • Koul, S. and Eydgahi, A. (2018). Utilizing Technology Acceptance Model (TAM) for Driverless Car Technology Adoption. Journal of Technology Management & Innovation. 13(4), 37-46.
  • Lai, M. L. (2008). Technology Readiness, Internet Self‐Efficacy and Computing Experience of Pro-fessional Accounting Students. Campus-Wide Information Systems, 25(1), 18-29.
  • Lee, Y., Kozar, K. A. and Larsen, K. R. (2003). The Technology Acceptance Model: Past, Present and Future. Communications of the Association for Information Systems, 12(50), 750-782.
  • Ling, L. M. and Moi, C. M. (2007). Professional Students’ Technology Readiness, Prior Computing Experience and Acceptance of an e-Learning System. Malaysian Accounting Review, 6(1), 85-99.
  • Ling, L. M. and Muhammad, I. (2006). Taxation and Technology: Technology Readiness of Malaysi-an Tax Officers in Petaling Jaya Branch. Journal of Financial Reporting and Accounting, 4(1), 147-163.
  • Massey, A. P., Khatri, V. and Montoya-Weiss, M. (2007). Usability of Online Services: The Role of Technology Readiness and Context. Decision Sciences, 38(2), 277-308.
  • Norusis, M. J. (1993). SPSS for Windows: Professional Statics, Release 6.0, Chicago: SPSS Inc.
  • Nughoro, M.A and Fajar, M.A. (2017). Effects of Technology Readiness Towards Acceptance of Mandatory Web-Based Attendance System. Procedia Computer Science, 124, 319-328.
  • Nunnally, J. C. (1978). Psychometric Theory (2nd ed.). New York: McGraw-Hill.
  • Odlum, M. (2016). Technology Readiness of Early Career Nurse Trainees: Utilization of the Tech-nology Readiness Index (TRI). Studies in Health Technology and Informatics, 314-318.
  • Parasaruman, A. (2000). Technology Readiness Index (TRI): A Multiple-Item Scale to Measure Readiness to Embrace New Technologies. Journal of Service Research, 2(4), 307-320.
  • Parasaruman, A. and Colby, C. L. (2001). Techno-ready Marketing: How and Why Your Customers Adopt Technology. New York: The Free Press.
  • Parasaruman, A. and Colby, C. L. (2015). An Updated and Streamlined Technology Readiness In-dex: TRI 2.0. Journal of Service Research, 18(1), 59-74.
  • Penz, D., Amorim, B. C., Nascimento, S. and Rossetto, C. R. (2017). The Influence of Technology Readiness Index in Entrepreneurial Orientation: A Study with Brazilian Entrepreneurs in the United States of America. International Journal of Innovation, 5(1), 66-76.
  • Pett, M. A., Lackey, N. R. and Sullivan, J. J. (2003). Making Sense of Factor Analysis: The Use of Fac-tor Analysis for Instrument Development in Health Care Research. Thousand Oaks: Sage Publication.
  • Pires, P. J., Filho, B. A. C. and Cunha, J. C. (2011). Technology Readiness Index (TRI) Factors as Dif-ferentiating Elements between Users and Non-Users of Internet Banking, and as Ante-cedents of the Technology Acceptance Model (TAM). In M.M. Cruz-Cunha et al. (Eds.). CENTERIS 2011, Part II, CCIS 220, pp. 215–229. Springer: Berlin.
  • Rose, J. and Fogarty, W. (2010). Technology Readiness and Segmentation Profile of Mature Con-sumers. In: 4th Biennial Conference of the Academy of World Business, Marketing and Management Development, July 12-15 2010, pp. 57-65, Finland.
  • Said, R. F. M., Rahman, S. A., Mutalib, S., Yusoff, M. and Mohamed, A. (2008). User Technology Readiness Measurement in Fingerprint Adoption at Higher Education Institution. In Gervasi O., Murgante B., Lagana A., Taniar D., Mun Y., Gavrilova M. L. (eds). Computational Science and Its Applications – ICCSA 2008. ICCSA 2008. Lecture Notes in Computer Science, 5073, 91-104. Springer: Berlin.
  • Smith, M., Walford, N. G. and Bescos, C. J. (2018). Assessing the User Response to Differences in Functionality When Visualising 3D Models of Cultural Heritage Sites Using the Technology Readiness Index. Digital Applications in Archaeology and Cultural Heritage, 10, 1-10.
  • Şahinoğlu, K. T. and Yakut, S. G. (2019). Yapısal Eşitlik Modeli ile Özgürlüklerin Ekonomik Perfor-mansa Etkisi Üzerine Bir İnceleme. Ekoist: Journal of Econometrics and Statistics, 30, 1-20.
  • Tabachnick, B. G. and Fidell, L. S. (2013). Using Multivariate Statistics. Boston: Pearson.
  • Taherdoost, H., Sahibuddin, S. and Jalaliyoon, N. (2014). Exploratory Factor Analysis; Concepts and Theory. Advances in Applied and Pure Mathematics, 375-382.
  • Yong, A.G. and Pearce, S. (2013). A Beginner’s Guide to Factor Analysis: Focusing on Exploratory Factor Analysis. Tutorials in Quantitative Methods for Psychology, 9(2), 79-94.
  • Wu, M. C., Chen, C. C. and Tseng, C. H. (2013). Constructing a Technology Readiness Scale for Sports Center RFID Door Security System Users. The Journal of Global Business Manage-ment, 9(1), 12-21.
  • Venkatesh, V. and Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Mod-el: Four Longitudinal Field Studies. Institute for Operations Research and the Management Sciences, 46, 186-204.
Toplam 39 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm MAKALELER
Yazarlar

Mehmet Oytun Cibaroğlu 0000-0002-5763-0770

Naciye Uğur

Aykut Turan

Yayımlanma Tarihi 30 Nisan 2021
Yayımlandığı Sayı Yıl 2021 Sayı: 31

Kaynak Göster

APA Cibaroğlu, M. O., Uğur, N., & Turan, A. (2021). EXTENDING TECHNOLOGY ACCEPTANCE MODEL (TAM) WITH THE THEORY OF TECHNOLOGY READINESS. Uluslararası İktisadi Ve İdari İncelemeler Dergisi(31), 1-22. https://doi.org/10.18092/ulikidince.700939


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