Understanding Information Technology Usage: A Test of Competing Models

Abstract

The Technology Acceptance Model and two variations of the Theory of Planned Behavior were compared to assess which model best helps to understand usage of information technology. The models were compared using student data collected from 786 potential users of a computer resource center. Behavior data was based on monitoring 3,780 visits to the resource center over a 12-week period. Weighted least squares estimation revealed that all three models performed well in terms of fit and were roughly equivalent in terms of their ability to explain behavior. Decomposing the belief structures in the Theory of Planned Behavior provided a moderate increase in the explanation of behavioral intention. Overall, the results indicate that the decomposed Theory of Planned Behavior provides a fuller understanding of behavioral intention by focusing on the factors that are likely to influence systems use through the application of both design and implementation strategies.

“This is who i am”: Identity expressiveness and the theory of planned behavior

“…The measure of behavioral control was almost identical to the measure applied by Battacherjee (2000) and Taylor and Todd (1995) because two of their original items were used; "I feel free to use MMS as I like" and "Using MMS is entirely within my control".…”

“…The items were similar to those used by, e.g. Taylor and Todd (1995) and included the bipolar adjectives "bad/good", "foolish/wise", "unfavorable/favorable", and "negative/positive". Finally, intention to use was measured by a two-item scale adapted from Battacherjee (2000) and Mathieson (1991).…”

“…The concept of the subjective norm has received much attention in attitude and adoption research. For instance, subjective norms have been identified as an important determinant in explaining adoption of a wide array of technologies such as workplace computer systems (Venkatesh & Davis, 2000), computer resource centers (Taylor & Todd, 1995) and mobile WAP-services (Hung, Ku, & Chang, 2003). Norms are believed to be particularly important to young users of mobile phones.…”

Integrating Diffusion of Innovations and Theory of Reasoned Action models to predict utilization of information technology by end-users

“…(p.458) Furthennore, the direct contributions (effects) of subjective nonn and attitudes to explaining usage in our study (see Figure 2) and to intentions to use in the Hartwick and Barki (1994) study are along the same direction, with attitude having a stronger direct effect on behavior than subjective norm in both studies. Similarly, Taylor and Todd (1995) report that the factors affecting behavioral intent, in order of the strength of their influence, were attitude, perceived behavioral control, and subjective norm. Our study also found the same level of impact on behavior, from highest to lowest, for the direct effects of attitude, voluntariness (corresponding to perceived behavioral control), and subjective norm.…”

Çevrimiçi Sosyal Ağların Öğretim Amaçlı Kabul ve Kullanımı Ölçeğinin Geliştirilmesi

“…Varyanslar arasındaki yaklaşık %3'lük değişim, çalışmaların farklı kültürlerde ve farklı özellikler gösteren örneklemlerle yapılmış olmasından kaynaklandığı düşünülmektedir. Bunun yanında, BT öğretmen adaylarının ÇSA'lar için belirttikleri bu görüşlerden yararlanılarak geliştirilen dört faktörlü bu ölçek yapısının alanyazındaki birçok farklı teknoloji kabul modeli çalışmaları ile de tutarlılık gösterdiği görülmektedir (Davis, 1985;Sert ve Koçak Usluel, 2009;Taylor & Todd, 1995;Venkatesh & Davis, 2000). Bu çalışmalarda, yapıların isimlendirilmesi ve önem sıralaması modelden modele değişmekle birlikte, yeni bir teknolojinin kullanıcılar tarafından kabulü ve benimsenmesinde sözü edilen bu ortak yapıların etkili olduğu öne sürülmüştür (Venkatesh ve ark., 2003).…”