e-ISSN: 1309-517X
Investigation of Emerging Technology Usage Characteristics as Predictors of Innovativeness

Kerem Kilicer 1 * , Salih Bardakci 1, Ibrahim Arpaci 1

CONT ED TECHNOLOGY, Volume 9, Issue 3, pp. 225-245

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For today’s societies trying to cope with the current globally increased competition, existence of individuals who can take risks, solve problems and adopt changes an innovation has gained more importance when compared to the past. This situation brings responsibility to educational institutions for increasing the number of innovative individuals and the qualifications of these individuals. Therefore, in the process of designing and developing any kind of in-class activities which will contribute to innovativeness, it is important to determine the technology usage characteristics that can be used to define individuals who have high levels of innovativeness. The purpose of the present study was to determine the variables related to technology which will be used to discriminate between individuals who have high and low levels of innovativeness. In the study, which was carried out using the causal-comparative design, a logistic regression model was formed by using technology-related variables, and which technology-related variables managed to predict high level of innovativeness was tested. In the logistic model, the technology budget (purchases, internet, and phone bills), technology ownership (smart phones, tablets, laptops, personal computers, internet, websites, blogs), technology renewal/update time (smart phones, computers), the number of utilized internet applications and internet usage habits were analyzed as predictors. The study was conducted with 244 university students from different class grades at a state university in Turkey. The results revealed that among the variables examined, only the variables of Internet usage habit, the number of Internet applications used, blog ownership and the money spent on technology use were significant predictors. In addition, the model in which these variables were used was found to classify high and low levels of innovativeness with accuracy of 71%. Implications are discussed. 



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