Abstract
In this era of rapid technology growth, many countries have begun to adopt emerging technologies into their educational systems to improve learning outcomes. The aim of this study is to explore the factors influencing teachers’ experiences of technostress while using new technology in academic classrooms and how it might be mitigated. Prior research has not focused on how technostress develops among individuals over time or how it can be mitigated in an individual context; the intention of this study is to contribute to the technostress literature in these particular areas. To address the research gap, we conducted a qualitative study that collected data through the use of an open-ended question questionnaire. Seventy teachers of different backgrounds and locations responded to the survey. We used thematic analysis to analyze their responses and reveal how lack of school support and their professional identities influence their levels of technostress. Technology characteristics, including the complexity and the benefits of a given technology, and privacy concerns play a crucial role in teachers’ experiences of technostress. Moreover, we found that colleague support in using new technology and open educational resources each contributed to mitigating teachers’ technostress levels. Our study extends technostress research to examine a new learning environment and context. This focus allowed us to highlight the need to develop open educational resources and better social support structures for teachers and to rethink the professional identities of developing teachers to mitigate their levels of technostress. Suggestions for further research that resulted from this work include using a mixed methods research approach in future studies and including more teachers in future work to determine the relationships among the factors identified by this study.
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Appendices
Appendix 1
Systematic Review of Prior Research on Factors Influencing Technostress (2016–2022) Published in Peer-Reviewed Journals Only
Authors | Title | Type of the study | Instruments | Context | Factors |
|---|---|---|---|---|---|
Camarena and Fusi (2022) | Always Connected: Technology Use Increases Technostress Among Public Managers | Quantitative | Existed survey | Business | ICT use, age, gender, organization practices |
Tuan (2022) | Employee mindfulness and proactive coping for technostress in the COVID-19 outbreak: The roles of regulatory foci, technostress, and job insecurity | Quantitative | Existed survey | Service industry, Veitnam | Job insecurity, mindfulness, gender |
Baabdullah et al. (2022) | Usage of augmented reality (AR) and development of e-learning outcomes: An empirical evaluation of students’ e-learning experience | Quantitative study | Scale items of UGT constructs were derived from Nambisan and Baron | Undergraduate students from Saudi Arabia | |
Salo et al. (2022) | Formation and Mitigation of Technostress in the Personal Use of IT | Qualitative study | The researchers developed the tool | IT users, Finland | Invasion, dependency, privacy concerns, time management, and complexity |
Li and Wang (2021) | Technostress inhibitors and creators and their impacts on university teachers’ work performance in higher education | Quantitative study | Developed based on Tarafdar et al. (2007) | University teachers, China | Literacy facilitation, technical support, and involvement facilitation |
Chou and Chou (2021) | A multi-group analysis of factors underlying teachers’ technostress and their continuance intention toward online teaching | Quantitative study | Self-developed questionnaire | Teachers in formal education in Taiwan | Self-efficacy, school support, privacy concerns |
Iskandar (2021) | The Factors Influencing Compulsive Social Apps and Its Impact on Technostress among Students | Quantitative study | Not reported wither adapted or developed tool | High school students, Malaysia | technology feature of using social application does not influence technostress |
Shirish et al. (2021) | Switching to online learning during COVID-19: Theorizing the role of IT mindfulness and techno eustress for facilitating productivity and creativity in student learning | Quantitative study | survey adapted from existed survey | Graduate students, France | IT mindfulness |
Thiyagu (2021) | Techno-Stress Scale of Teacher Educators: Construction of the Tool | Quantitative | Self-developed | Teacher, India | The researcher did not report the dimensions of the survey |
Pflügner et al. (2021) | The direct and indirect influence of mindfulness on techno-stressors and job burnout: A quantitative study of white-collar workers | Quantitative study | Adapted from Ragu-Nathan et al., 2008) | Workers in different fields, Germany | Mindfulness |
Stadin et al. (2021) | Technostress operationalized as information and communication technology (ICT) demands among managers and other occupational groups – Results from the Swedish Longitudinal Occupational Survey of Health (SLOSH) | Quantitative | Adapted existed survey | Health sector, Sweden | ICTs demands, lack of work control, insufficient administrative support, the risk of managerial turnover |
Abd Aziz and Yazid (2021) | Exploratory Factor Analysis of Technostress among University Students | Quantitative study | developed by the researchers | Undergraduate student, Malaysia | Techno-overload, techno-uncertainity, techno-insecurity, and techno-complexity |
Gökbulut (2021) | The Relationship Between Teachers’ Technostress and Their Techno-pedagogical Competence | Quantitative study | Adapted previous tools (TPACK and technostress scale | Teachers, Turkey | TPACK and its sub-factors |
Panisoara et al. (2020) | Motivation and Continuance Intention towards Online Instruction among Teachers during the COVID-19 Pandemic: The Mediating Effect of Burnout and Technostress | Quantitative study | Developed by the researchers | Teachers, Romania | extrinsic, intrinsic motivation, TPK Self-efficacy |
Wang and Li (2019) | Technostress in university students’ technology-enhanced learning: An investigation from multidimensional person-environment misfit | Quantitative study | Adapted from established research | University students, China | Personal factors, environment factors, organization factors, and job factors |
Upadhyaya and Virinda (2020) | Impact of technostress on academic productivity of university students | Quantitative study | Adapted from Tarafdar et al. (2007) | Undergraduate students, India | Gender, level of education, ICT experience, |
Wang et al. (2020) | Measuring university students’ technostress in technology enhanced learning: Scale development and validation | Quantitative study | Self-developed | University students, China | Person-Environment factors which relate to Ability-demands, Needs-supplies |
Dong et al. (2020) | Exploring the Structural Relationship Among Teachers’ Technostress, Technological Pedagogical Content Knowledge (TPACK), Computer Self-efficacy and School Support | Quantitative study | Use different existed instrument and adapted based on the constructs of the study | K-12 in-service teachers in China | Administrative support, collegial support, computer self-efficacy |
Maier et al. (2015) | Personality Profiles that Put Users at Risk of Perceiving Technostress | Quantitative study | Online survey | Workers in different organization in Bavaria | Personal traits |
Özgür (2020) | Relationships between teachers’ technostress, technological pedagogical content knowledge (TPACK), school support and demographic variables: A structural equation modeling | Quantitative study | Developed from other studies and use some scales existed | Teachers in Turkey | Gender, Age, overall school support, technical support, TPACK |
Christian et al. (2020) | Technostress Creators on Teaching Performance of Private Universities in Jakarta During Covid-19 Pandemic | Quantitative study | Not reported | Higher education in Indonesia | Techno-overload, techno-uncertainity, techno-insecurity, and techno-complexity |
Estrada-Muñoz et al. (2020) | Teacher Technostress in the Chilean School System | Quantitative study | RED-TIC questionnaire integrated into the Technical Note of Prevention | Teachers in middle and high schools in ٍ Spain | techno-fatigued, Techno-anxiety, teaching age, and gender |
Oladosu et al. (2020) | Learning with Smart Devices: Influence of Technostress on Undergraduate Students’ Learning at University of Ilorin, Nigeria | Quantitative study | self-developed survey | undergraduate students, Nigeria | Using mobile devices caused technostress for the students because of the tasks and using it for academic performance |
Marchiori et al. (2020) | A RELATIONSHIP BETWEEN TECHNOSTRESS, SATISFACTION AT WORK, ORGANIZATIONAL COMMITMENT AND DEMOGRAPHY: EVIDENCE FROM THE BRAZILIAN PUBLIC SECTOR | Quantitative study | Developed based on literature review | IT users, Brazil | Individual features (age, gender, education, professional experience, occupy). Techno-overload, techno-complexity, techno-insecurity, techno uncertainty |
Christopher B. Califf, Stoney Brooks | An empirical study of techno-stressors, literacy facilitation, burnout, and turnover intention as experienced by K-12 teachers | Quantitative study | Adapted from previous studies | K-12 teachers, USA | techno-complexity, techno-insecurity, techno-invasion, techno-overload, techno-uncertainty, and literacy facilitation |
Califf and Sarker (2020) | The bright and dark sides of technostress: A mixed methods study involving health IT change | mixed methods approach | Developed by the researchers from the qualitative phase based on theories and models | Nurses, USA | Involvement facilitation, technical support, usefulness, unreliability, insecurity, overload, uncertainty, and complexity |
Liu et al. (2019) | Exploring the factors that influence physician technostress from using mobile electronic medical records | Quantitative study | Developed based on theories and previous questionnaires | physicians in hospitals, Taiwan | Personal characteristics, mobile self-efficacy, and technology characteristics composed of perceived usefulness, perceived complexity, and perceived reality |
Mäkiniemi et al. (2022) | How are technology-related workplace resources associated with techno-work engagement among a group of Finnish teachers? | Quantitative study | Developed by the researchers as apart of a large project in Finland | Teachers, Finland | Self-efficacy, technology related competency and autonomy |
Krishnan (2017) | Personality and espoused cultural differences in technostress creators | Quantitative study | The the constructs used in the study were measured using scales adapted from prior studies (see Appendix) to enhance validity | Graduate students in India | Personality traits and espoused culture |
Qi (2019) | A double-edged sword? Exploring the impact of students’ academic usage of mobile devices on technostress and academic performance | Quantitative study | Not reported | University students, China | Individual differences, self-efficacy |
Hauk et al. (2019) | The mediating role of coping behavior on the age-technostress relationship: A longitudinal multilevel mediation model | Quantitative study | The survey adapted from Tarafdar M, Tu Q, Ragu-Nathan BS, Tarafdar et al. (2007) | Employees from Germany, Austria and Switzerland | Age |
Hassan et al. (2019) | THE EFFECTS OF TECHNOSTRESS CREATORS AND ORGANIZATIONAL COMMITMENT AMONG SCHOOL TEACHERS | Quantitative study | The survey adapted from Tarafdar M, Tu Q, Ragu-Nathan BS, Tarafdar et al. (2007) | Teachers, Malaysia | Techno-overload, techno-uncertainty, techno-insecurity, and techno-complexity |
Hauk et al. (2019) | Technostress and the hierarchical levels of personality: a two-wave study with multiple data samples | Quantitative study | Self-developed questionnaire | Germany, Organization (Employees) | All the three personal traits including: neuroticism, personal innovativeness in IT (PIIT), and IT mindfulness influence the perception of technostress |
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Khlaif, Z.N., Sanmugam, M., Joma, A.I. et al. Factors Influencing Teacher’s Technostress Experienced in Using Emerging Technology: A Qualitative Study. Tech Know Learn 28, 865–899 (2023). https://doi.org/10.1007/s10758-022-09607-9
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DOI: https://doi.org/10.1007/s10758-022-09607-9