AI Summary & Key Takeaways
AI InsightImplications are discussed.
📌 3 Key Takeaways
Abstract
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 innovationhas gained more importance when compared to the past. This situation brings responsibilityto educational institutions for increasing the number of innovative individuals and thequalifications of these individuals. Therefore, in the process of designing and developingany kind of in-class activities which will contribute to innovativeness, it is important todetermine the technology usage characteristics that can be used to define individuals whohave high levels of innovativeness. The purpose of the present study was to determine thevariables related to technology which will be used to discriminate between individuals whohave high and low levels of innovativeness. In the study, which was carried out using thecausal-comparative design, a logistic regression model was formed by using technologyrelated variables, and which technology-related variables managed to predict high level ofinnovativeness was tested. In the logistic model, the technology budget (purchases,internet, and phone bills), technology ownership (smart phones, tablets, laptops, personalcomputers, internet, websites, blogs), technology renewal/update time (smart phones,computers), the number of utilized internet applications and internet usage habits wereanalyzed as predictors. The study was conducted with 244 university students fromdifferent class grades at a state university in Turkey. The results revealed that among thevariables examined, only the variables of Internet usage habit, the number of Internetapplications used, blog ownership and the money spent on technology use were significantpredictors. In addition, the model in which these variables were used was found to classifyhigh and low levels of innovativeness with accuracy of 71%. Implications are discussed.