Proposing a Model to Explain Teachers’ Intention to Use Technology: Identifying Constructs and Formulating Hypotheses

Proposing a Model to Explain Teachers’ Intention to Use Technology: Identifying Constructs and Formulating Hypotheses

Timothy Teo (University of Auckland, New Zealand)
DOI: 10.4018/jicte.2012070103
OnDemand PDF Download:
No Current Special Offers


In this paper, the author identifies the constructs that influence teachers’ intention to use technology for instructional purposes. Drawing from widely used and validated theories and models with origins from social psychology, a total of seven constructs were identified to develop a research model. Following, twelve hypotheses were formulated to describe the relationships among the constructs and the teachers’ intention to use. Further studies on the research model for greater validity and expansion are suggested.
Article Preview


Technology has been a mainstay in many educational systems and teachers play a crucial role in the effective implementation of technology for teaching and learning in schools (Zhao, Tan, & Mishra, 2001). Thus it is important to understand the factors that drive teachers’ intention to use technology. However, many studies on educational technology have focused on the practical strategies that motivate teachers to use technology in the curriculum, with limited research on the factors that affect teachers’ intention to use technology. Although most teachers would agree that learning with technology has many benefits, this alone does not ensure effective use of technology in the curriculum. Notwithstanding the level of technological sophistication in a school, the affordances of technology cannot be harnessed if it is not used pervasively in instruction and administration.

From the literature, the factors that influence teachers’ intention to use are typically grouped into personal factors, technical factors, and environmental factors. Some examples include effort expectancy (Venkatesh, Morris, Davis, & Davis, 2003), computer self-efficacy (Teo & Koh, 2010), technological complexity (Teo, 2009; Thong, Hong, & Tam, 2002), computer attitudes (Teo, 2007), and facilitating conditions (Ngai, Poon, & Chan, 2007). To gain insights into teachers’ intentions to use technology, it is essential to identify the significant drivers from the many variables proposed in the literature.

Purpose of the Study

Despite the research that has been conducted to examine the factors that explain teachers’ intention to use technology (Smarkola, 2007), there is currently no model to explain the relationships among the factors and how they impact on teachers’ intention to use technology. Such a model has the advantage of pooling the variables to assess for statistical significance for model expansion and modifications, explaining the interaction among variables, taking into account measurement issues (e.g., errors), and facilitating replication studies to be conducted. The purpose of this study is to propose a model to explain teachers’ behavioural intention to use technology. In the process, the constructs will be identified and this is followed by the formulation of hypotheses to depict the relationships among the constructs.


Research Model And Hypotheses

Arising from the interests in understanding users’ intention to use technology, researchers have adopted several models/theories from social psychology. Among these are the Technology Acceptance Model (Davis, Bagozzi, & Warshaw, 1989), Theory of Planned Behaviour (Ajzen, 1991), and Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2003). For example, the Technology Acceptance Model and Theory of Planned Behaviour have received empirical support for being robust and parsimonious in predicting technology adoption in various contexts and a variety of technologies (Sugar, Crawley, & Fine, 2004; Teo, 2009).

Complete Article List

Search this Journal:
Volume 18: 1 Issue (2022)
Volume 17: 4 Issues (2021)
Volume 16: 4 Issues (2020)
Volume 15: 4 Issues (2019)
Volume 14: 4 Issues (2018)
Volume 13: 4 Issues (2017)
Volume 12: 4 Issues (2016)
Volume 11: 4 Issues (2015)
Volume 10: 4 Issues (2014)
Volume 9: 4 Issues (2013)
Volume 8: 4 Issues (2012)
Volume 7: 4 Issues (2011)
Volume 6: 4 Issues (2010)
Volume 5: 4 Issues (2009)
Volume 4: 4 Issues (2008)
Volume 3: 4 Issues (2007)
Volume 2: 4 Issues (2006)
Volume 1: 4 Issues (2005)
View Complete Journal Contents Listing