Differences in Personal Innovativeness in the Domain of Information Technology Among University Students and Teachers

Abstract

Two online surveys among 1105 university students and 656 employees were conducted with the inclusion of the construct Personal Innovativeness in the domain of Information Technologies (PIIT). After calculating descriptive statistics, statistically significant differences between personal innovativeness of university students and teachers were sought by the application of one-way ANOVA. The first and most important finding was that average perceived PIIT of teachers and students falls around the middle of the seven-point scale, which cannot be regarded as a plausible predictor of upgrading the University as an Innovative Ecosystem. The second was that university teachers scored higher than their students, a situation that could produce an expectancy conflict between those who want to work in an innovative way and those who would prefer study by the book. Teaching assistants, who should belong to the generation of digital natives, are only slightly more innovative than university teachers, who can be regarded as digital immigrants. Assuming that innovativeness can be upgraded by learning, means that efforts should be made by University Management to encourage and support Personal Innovativeness (and other creativities, as well) as a preferred teaching practice.

Using Rogers' diffusion of innovation theory to conceptualize the mobile-learning adoption process in teacher education in the COVID-19 era

“…Some of the differences that require attention include their differential training needs (PSTs need better coordination of materials, whereas ISTs welcomed more training and need to be rewarded for their time); their modeling source (PSTs found a model in their peers, whereas the training instructor was the ISTs' model); and their feedback sources (PSTs needed feedback from an authority figure, whereas ISTs relied on their pupil's feedback). Previous studies have documented some of the differences between PSTs' and ISTs' concerning the ML adoption process (Mac Callum et al, 2014), as well as differences between university teachers and students regarding the use of online learning in general (Dolenc et al, 2021;Šorgo et al, 2021). However, the current study further demonstrates Fig.…”

“…The comparison between PSTs' and ISTs' perceptions regarding ML adoption is especially important, given that previous studies have demonstrated that teachers and students are influenced by different factors when adopting technology (Mac Callum et al, 2014). More recently, a few studies have argued that conducting a comparative analysis of the views of teachers vs. students engaged in adopting innovative technology is imperative (Dolenc et al, 2021;Kovacs et al, 2021;Šorgo et al, 2021). Thus, for example, a study that examined educators' and students' views regarding the online education imposed during the COVID-19 pandemic found that while some views were shared by the two groups, in other respects, they differed substantially, thus underscoring the need to explore both perspectives to fully understand the issue.…”