An Investigation of the Predictability of Variables Related to Kindergarten Preservice Teachers’ Technology Intention to Use

The purpose of this study is to investigate the predictability of variables among technology easy to use, perceived usefulness, and technostress that had impacts on kindergarten preservice teachers’ technology intention to use. For this study, the survey data collected by 64 students who were enrolled in the kindergarten preservice teacher education were analysed by using multiple regression analysis. The results of this study showed as follows. First, technology easy to use significantly affected perceived usefulness. Second, technology easy to use negatively affected technostress. Third, perceived usefulness significantly affected technology intention to use while technostress negatively affected it. From this results, it is revealed that various technology training opportunities would be provided for improving preservice teachers’ technology intention to use and lessing preservice teachers’ technostress. Furthermore, effective teaching-learning strategies for utilizing technology as an educational media should be developed in the early childhood educational environment.

[1]  Ji-Eun Lee,et al.  Factors for the Adoption of Smartphone-based Mobile Banking : On User`s Technology Readiness and Expertise , 2011 .

[2]  Douglas E. Loyd,et al.  The Reliability and Validity of an Instrument for the Assessment of Computer Attitudes , 1985 .

[3]  Woo-Suk Yang.  Implementation of Vision System combining Character and Color Recognition , 2016 .

[4]  Prodromos D. Chatzoglou,et al.  Investigating Greek employees' intention to use web-based training , 2009, Comput. Educ..

[6]  오종철,et al.  A Study on Factors of Intention toward Using Mobile Internet Service :Revised TRAM , 2010 .

[7]  Y. Joo,et al.  Identification of the Predictability of SNS Intention to Use and Related Variables in Collaborative Learning , 2015 .

[8]  Robin H. Kay,et al.  Exploring the relationship between emotions and the acquisition of computer knowledge , 2008, Comput. Educ..

[9]  Namho Chung,et al.  The Effect of Tourist’s Technology Readiness on the Acceptance of an Augmented Reality Tour Application , 2014 .

[10]  I. Ajzen,et al.  Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research , 1977 .

[11]  Yu-Na Hong,et al.  An investigation of predictability among variables having an effect on problem solving skill and achievement in collaborative learning by utilizing SNS , 2016 .

[12]  Fred D. Davis.  Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[13]  Susan A. Brown,et al.  Do I really have to? User acceptance of mandated technology , 2002, Eur. J. Inf. Syst..

[14]  이지은Ji-Eun Lee.  Factors for the Adoption of Smartphone-based Mobile Banking : On User’s Technology Readiness and Expertise , 2011 .

[15]  A. Asadi,et al.  Predicting E-Learning Application in Agricultural Higher Education Using Technology Acceptance Model. , 2008 .

[16]  Kwang-Seok Hong,et al.  Gaze Recognition System using Random Forests in Vehicular Environment based on Smart-Phone , 2015 .

[17]  P. J. Sher,et al.  Integrating technology readiness into technology acceptance: The TRAM model , 2007 .

[18]  Peter A. Todd,et al.  Understanding Information Technology Usage: A Test of Competing Models , 1995, Inf. Syst. Res..

[19]  Suh SoonShik.  Exploration of Digital Textbook Adoption and Implementation based on an extended Technology Acceptance Model , 2011 .

[20]  Craig Brod,et al.  Technostress: The Human Cost Of The Computer Revolution , 1984 .

[21]  Jae-Min Kim,et al.  Development of a Performance Reference Model (PRM) for Ubiquitous City Operations , 2010 .

[22]  김민정,et al.  The Comparative Study of Attitude toward E-learning Adoption between Korean and Vietnamese College Students , 2014 .