The advancement of information and communications technology (ICT) has brought about fundamental changes in not only society and culture, but also in values and lifestyles among people. Accordingly, efforts to adopt new media in education sectors have... The advancement of information and communications technology (ICT) has brought about fundamental changes in not only society and culture, but also in values and lifestyles among people. Accordingly, efforts to adopt new media in education sectors have garnered greater attention. As part of such efforts, the Korean government proposed the "Action Plan for Smart Education" in 2011, and since then, the use of technology in the classroom and the effects thereof have been discussed at great length. Nevertheless, while changes in and reforms to education in Korea are initiated by high-level governmental offices, such as the Ministry of Education, a student’s learning experience largely relies on their teachers. Therefore, exploring factors that affect technology use among teachers would be meaningful. Teachers are continuously put under pressure to modify their existing teaching methods and to adopt new teaching methods. Such pressure exacerbates the difficulty faced by teachers learning to utilize new technology in the classroom. The stress that comes from such sources is called technostress, which can be experienced anywhere: at home, work, and school. Combining the terms technology and stress, technostress is a compound word that has come into use in reference to the mental pressure caused by attempts to adopt information technology at work that has already been woven into the daily lives of individuals. Tarafdar, Tu, Ragu-Nathan and Ragu-Nathan (2007) wrote that techno-overload, techno-invasion, techno-complexity, techno-insecurity and techno-uncertainty are creators of technostress. Much research has been conducted on technostress among teachers and factors that affect it, as well as how technostress affects behavioral intentions to use technology. However, there has been relatively less research on how technostress affects actual technology use behaviors. In this context, this seeks to explore relationships among factors influencing behavioral intentions to use technology and technology use behavior by employing variables in the Unified Theory of Acceptance and Use of Technology (UTAUT), developed by Venkatesh, Morris, Davis and Davis (2003), except for effort expectancy: UTAUT is a theory that integrates eight conventional models and theories on technology acceptance, and it has been proven by previous studies to be a robust model, explaining 50% and 70% of behavioral intentions to use technology and technology use behavior, respectively, as well as in explaining technology acceptance. In order to account for performance expectancy and social influences (i.e., situations wherein teaching takes place and realities of said situation), this employs facilitating conditions to indicate any technical infrastructure, as an independent variable, or technostress, the stress teachers encounter during the process of technology integration. Also, as studies have shown that three variables in UTAUT (sex, age and experience) are effective moderate variables, this study also employs these as moderate variables. This research, therefore, aims to outline factors that affect behavioral intentions to use technology and technology use behavior among secondary teachers in Korea using UTAUT variables and to identify structural relationships among these variables. Also, building on its findings, this research further attempts to suggest strategies needed to improve behavioral intentions to use technology and technology use behavior in Korea. The research questions addressed in this study are as follows: 1. What are the structural relationships among performance expectancy, technostress, social influences, facilitating conditions, technology behavioral intention, and technology use behavior? 2. Are there any direct/indirect effects among performance expectancy, technostress, social influences, facilitating conditions, technology behavioral intention, and technology use behavior? 3. Do variables, such as sex, age and experience of using a smart device in class, have any moderating effect on noted relationships among performance expectancy, technostress, social influences, facilitating conditions, technology behavioral intention and technology use behavior? Herein, data were collected from 421 teachers in Seoul, Incheon, and Gyeonggi-do, South Korea. First, correlation analysis was conducted to confirm the normality of the data using SPSS software, version 22. Afterwards, structural equation modeling was performed to test the proposed model and causal relationships among the variables using AMOS. Also, bootstrapping in AMOS was conducted to verify direct and indirect effects among the variables, and multi-group analysis was conducted to verify moderator effects of sex, age and experience with using a smart device in class for teaching thereon. The major findings of this study are as follows: First, the structural equation model revealed significant structural relationships among performance expectancy, technostress, social influences, facilitating conditions, technology behavioral intention, and technology use behavior, with acceptable model fit. Second, the following direct effects between the variables were noted: Performance expectancy, technostress and social influence had a direct effect on behavioral intention. Also, facilitating conditions and behavioral intention had a direct effect on use behavior. Lastly, behavioral intention showed significant indirect effects between performance expectancy and use behavior, technostress, and use behavior, as well as between social influence and use behavior. Third, sex, age and experiences of using a smart device in class did not have a moderator effect on structural relationships among the study variables. However, middle-aged teachers demonstrated lower performance expectancy and behavioral intention to use technology, but higher technostress, than teachers of a younger age. Also, teachers who utilized smart devices in class had higher facilitating conditions and behavioral intention to use technology than teachers who did not, indicating differences among teachers in relation to their experiences with using a smart device in the classroom. Accordingly, the results of the present study suggest that more detailed guidelines or modules on using technology in the classroom that address teaching methods unique to the individual are needed in order to enhance performance expectancy. Meanwhile, technostress can be released by training teachers to acquire Technological, Pedagogical, and Content Knowledge (TPACK), as well as knowledge on technology use. Technostress can also be resolved by improving the classroom environment to facilitate adoption of technology. Also, in order to improve social influences, providing training and creating an online community for teachers, wherein they can share their know-how and difficulties with using technology in class, could be beneficial. To improve facilitating conditions, class environments should be enhanced, and technology experts should be mobilized to provide technical support if needed. Training should also be provided to teachers in a consistent, practically helpful and feasible manner. Lastly, to address the gap created by age and experience with using smart devices during class, support should be tailored to specific age groups, focusing on how to cope with technostress for middle-aged teachers. As well, financial support is needed to provide teachers training on how to utilize technology and software in class and to upgrade equipment and facilities in the classroom. Based on the results of this study, further research is suggested as follows: First, this research was conducted on a total of 421 secondary teachers in Seoul, Incheon and Gyeonggi-do using convenience sampling. For generalization of this research, future studies should seek to include a larger sample of teachers from all regions of Korea. Further studies are also needed to explore the moderator effects of teaching experience, the grade of the students, and subject, in addition to those analyzed in the present study (sex, age and experience with using smart devices in the classroom for teaching purposes). Second, in this study, subjects were asked to identify the technology they used when answering survey questions. However, when measuring behavioral intentions to use technology and technology use behavior, the structural relationship among variables were taken into account without making any distinction as to what technology the teachers used. Therefore, it would be meaningful to conduct follow-up research that focuses on specific technology, such as smartphones and tablet PCs, to investigate intentions to accept technology and technology use behavior and to identify structural relationships between technostress and other variables. Third, this research employed survey tools that were once used in a business context, modifying them to fit a school context. Therefore, development of survey tools that more accurately represent the environment and situations teachers face and that measure their levels of technostress may be warranted. Fourth, this research aimed to obtain qualitative data by adding open-ended questions in the survey to reflect and better understand teachers’ thoughts on using technology and the situations they faced. However, due to the nature of survey, most of the responses were short. Therefore, a follow-up study that conducts focus group interviews to obtain meaningful qualitative data could prove beneficial. Lastly, this research used a self- type survey as a method to measure technostress among teachers. Thus, experimental research that measures technostress through viral reaction analysis, such as analyzing data from galvanic skin reflexes (GSR), could have significance in follow-up research.
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