TEKNOLOJİ KABUL MODELİNİN (TKM) TEKNOLOJİYE HAZIR OLMA TEORİSİYLE GENİŞLETİLMESİ

Year 2021, Issue: 31, 1 - 22, 30.04.2021

Mehmet Oytun Cibaroğlu , Naciye Uğur , Aykut Turan

https://doi.org/10.18092/ulikidince.700939

Abstract

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

Keywords

TKM, THİ, Model Önerisi, Regresyon Analizi, Akademisyen

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

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.

Keywords

TAM, TRI, Model Suggestion, Regression Analysis, Academician

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