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Examining the influence of pre-service teachers’ digital native traits on their technology acceptance: A Serbian perspective
Many seem to believe that today’s pre-service teachers as born after 1980 are digital natives, or that they are “native speakers” of the digital language. Nevertheless, there is no evidence that their digital native characteristics determine whether or not they would use technology in the classroom....
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8755974/ https://www.ncbi.nlm.nih.gov/pubmed/35039745 http://dx.doi.org/10.1007/s10639-022-10887-y |
Sumario: | Many seem to believe that today’s pre-service teachers as born after 1980 are digital natives, or that they are “native speakers” of the digital language. Nevertheless, there is no evidence that their digital native characteristics determine whether or not they would use technology in the classroom. Although not widely evaluated, the four-factor, 21-item Digital Nativity Assessment Scale (DNAS) was one of the first instruments to assess digital nativeness (DN). This study aim is to explore the influence of pre-service teachers’ DN on their intention to use technology in the future classroom in Serbia, by evaluating the DNAS on Serbian sample and using it for measuring the DN. Six variables were incorporated to examine their mutual relationships based on technology acceptance model: digital nativeness, behavioral intention (BI), perceived usefulness (PU), perceived ease of use (PEU), subjective norm (SN), and technological complexity (TC). Data were collected from 688 pre-service teachers in Serbia. Exploratory factor analysis confirmed a four-factor model for the DNAS, and Serbian pre-service teachers demonstrated a high level of DN. To evaluate the hypothesized model structural equation modeling was utilized. The suggested model had a good fit for describing the BI of Serbian pre-service teachers, accounting for 27.1% of the variance in BI. It was found that direct dominant predictors of BI are digital native traits, perceived usefulness, and perceived ease of use. Significant influence of digital native traits on all other variables in the model was also confirmed. The implications for theory and practice are discussed. |
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