2006
DOI: 10.1080/10528008.2006.11488962
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Moving beyond Adoption: Exploring the Determinants of Student Intention to Use Technology

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Cited by 45 publications
(28 citation statements)
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References 32 publications
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“…Kijsanayotin, Pannarunothai, and Speedie (2009) also found social influence to be a contributing factor to the adoption of information technology in community health centers. Similar results relating to social influence were found with student intention to adopt technology within an educational setting (Robinson, 2006). Perceived performance satisfaction, inherent novelty seeking, and need for interaction with a service employee have also been shown to impact a consumer's attitude toward using technology (Dabholkar & Bagozzi, 2002;J.-S. C. Lin & Hsieh, 2006).…”
Section: Sstsupporting
confidence: 54%
“…Kijsanayotin, Pannarunothai, and Speedie (2009) also found social influence to be a contributing factor to the adoption of information technology in community health centers. Similar results relating to social influence were found with student intention to adopt technology within an educational setting (Robinson, 2006). Perceived performance satisfaction, inherent novelty seeking, and need for interaction with a service employee have also been shown to impact a consumer's attitude toward using technology (Dabholkar & Bagozzi, 2002;J.-S. C. Lin & Hsieh, 2006).…”
Section: Sstsupporting
confidence: 54%
“…However, these results gave mixed results. In fact, the impact of social influence on intention mixed results can be attributed to many factors related to study, users age, culture and so on (Venkatesh and Morris, 2000;Robinson, 2006, Abu-Shanab et al, 2007Tarhini et al, 2015c,d). Thus, as our study was in a developing country, examining such factor becomes significant to understand its influence on users' intention to use e-learning systems.…”
Section: Social Influence (Si)mentioning
confidence: 99%
“…As mentioned earlier, we consider a total of six respondent 3 group characteristics (i.e., variables) in our invariance analyses: five variables pertaining to respondents' two technology engagement facets (prior technology knowledge and prior technology usage) and one variable pertaining to their gender. We conduct measurement invariance analyses of the scales for the five key constructs of the UTAUT model: performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), and behavioral intention (IU) [50]. We test the fundamental hypothesis of measurement invariance that the five UTAUT scales being tested in this study exhibit measurement invariance with respect to the six respondent group variables considered in this study.…”
Section: Measurement Invariance Hypothesesmentioning
confidence: 99%
“…The scales for the UTAUT constructs have been assessed in numerous studies for their psychometric properties, including various types of reliabilities and validities, and have been found to exhibit satisfactory measurement properties (e.g., [1,6,9,15,34,[41][42][43]50,60,[62][63][64]). However, to our knowledge, only three studies [37,39,41] have assessed the UTAUT scales for their measurement invariance, but they have done so in a limited way, as discussed in the literature review section below.…”
Section: Introductionmentioning
confidence: 99%