Journals / Uluslararası İktisadi ve İdari İncelemeler Dergisi / 2021 / Sayı: 31

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

TEKNOLOJİ KABUL MODELİNİN (TKM) TEKNOLOJİYE HAZIR OLMA TEORİSİYLE GENİŞLETİLMESİ

Pages
1–22
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Research model has been validated and found statistically significant and reached 58,6% explanation rate for Behavioral Intention.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Technological developments provide numerous advantages for users, while competition conditions, investment costs, and consumer expectations make it difficult for manufacturers to hold on.
📊
Key Findings & Data:In this study, it was investigated elaborately mentioned circumstances for using online attendance systems.
💡
Conclusion & Impact:Research model has been validated and found statistically significant and reached 58,6% explanation rate for Behavioral Intention.
🔬Design:Survey & Empirical Study
📍Scope:Comprehensive Sample
⚙️Variables:TKM · THİ · Model Önerisi

Abstract

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.

Özet

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

🔖

Cite This Article

CİBAROĞLU, M. O., UĞUR, N. G., TURAN, A. H. (2021). EXTENDING TECHNOLOGY ACCEPTANCE MODEL (TAM) WITH THE THEORY OF TECHNOLOGY READINESS. Uluslararası İktisadi ve İdari İncelemeler Dergisi, Sayı: 31, 1–22. https://ajindex.com/articles/321850

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (13)

  1. Ajzen, I. and Fishbein, M. (1977). Attitude-Behavior Relations: A Theoretical Analysis and Review of Empirical Research. Psychological Bulletin, 84(5), 888-918.
  2. 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.
  3. 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)
  4. Perceived Usefulness, Perceived Ease of Use and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340.
  5. 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.
  6. 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.
  7. 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.
  8. Field, A. (2009). Discovering Statistics Using SPSS (3rd ed). Thousand Oaks, California: SAGE Pub. Fishbein, M. and Ajzen, I. (1975).
  9. 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.
  10. 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.
  11. 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.
  12. Kenton, W. (2019). Multiple Linear Regression - MLR Definition. Retrieved from https://www.investopedia.com/terms/m/mlr.asp, Accessed date: 09 March 2020.
  13. Koul, S. and Eydgahi, A. (2018).