How are technology-related workplace resources associated with techno-work engagement among a group of Finnish teachers?

©2019 (author name/s). This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license. How are technology-related workplace resources associated with techno-work engagement among a group of Finnish teachers? Jaana-Piia Irene Mäkiniemi Faculty of Management and Business, Tampere University E-mail: [email protected] Salla Ahola Faculty of Management and Business, Tampere University Jhanna Joensuu Faculty of Management and Business, Tampere University Abstract Teachers perceive the digitalisation of teaching not only as demanding but also as an inspiring aspect of their work. Prior studies have mainly focused on teachers’ negative experiences, such as technostress. Therefore, the aim of the current study was to explore how technology-related workplace resources, such as technology-related self-efficacy and autonomy, predict teachers’ positive well-being and techno-work engagement. Based on prior studies, it was hypothesised that three technology-related job resources are associated with higher techno-work engagement, and technology-related self-efficacy is associated with higher techno-work engagement. Data were collected from Finnish teachers and principals (N = 183) via a web-based questionnaire as part of a larger research project. Most of the participants were female teachers. The hypotheses were tested with structural equation modelling. The key findings indicated that technology-related self-efficacy had the strongest impact on techno-work engagement. In addition, technology-related autonomy and technology-related competence support were statistically significant predictors of technowork engagement. The findings suggest that similar workplace resources, which predict How are technology-related workplace resources associated with techno-work (…) Seminar.net - International journal of media, technology and lifelong learning 2 Vol. 15 – Issue 1 – 2019 general work engagement, are also relevant in the context of techno-work engagement. Some practical recommendations are made concerning the enhancement of teachers’ technology-related self-efficacy at schools. Keywords: digitalisation, educational technology, teacher, well-being, techno-work engagement, workplace resources Introduction Digitalisation is a global megatrend in the educational sector. Some teachers perceive the digitalisation of schools and teaching as a demanding aspect of their job (Syvänen, Mäkiniemi, Syrjä, Heikkilä-Tammi, & Viteli, 2016), but in general, they have more positive perspectives. For example, according to a report, 70% of Finnish teachers view the digitalisation of education in a positive light, and 75% would like to use more digital applications (Tanhua-Piiroinen et al., 2016). Although the use of educational technology is often regarded positively, the focus of prior studies has often been on teachers’ negative experiences related to the use of educational technology, such as technostress experiences (e.g. Al-Fudail & Mellar, 2008; Joo, Lim, & Kim, 2016; Syvänen et al., 2016). Therefore, in the current study, we focus on teachers’ well-being experiences, particularly on their techno-work engagement, which can be defined as a positive state of well-being in which one feels fulfilled regarding the use of technology at work. Techno-work engagement is a novel concept based on the notion of work engagement, which is a widely used construct for describing and measuring employees’ positive affective–motivational well-being work (Mäkiniemi, Ahola, & Joensuu, 2019; Mäkiniemi, Ahola, Syvänen, Heikkilä-Tammi, & Viteli, 2017). Work engagement is commonly divided to three main dimensions: vigour (e.g. high levels of energy at work), dedication (e.g. high inspiration to work) and absorption (e.g. full concentration on work), and it has been shown to be associated with positive outcomes, such as commitment to work and good work performance (Albrecht, 2013; Bakker, Albrecht, & Leiter, 2011). The main difference between the two abovementioned two concerns the fact that although techno-work engagement and work engagement both capture the positive state of well-being at work, the former focuses on (digital) technology-intensive or (digital) technology-assisted work or work processes, whereas the latter focuses on work in general. Since the focus of the current study is on teachers’ well-being experiences related to their use of educational technology at work – not their work in general – we suggest that the concept of techno-work engagement is well suited to our framework. Moreover, since some teachers perceive the digitalisation of schools as stressful and demanding, it is important to identify so-called protective factors that can serve as a buffer to stress as well as divergent factors that can enhance well-being (e.g. work autonomy, social support). An understanding of those factors or resources will make it possible to How are technology-related workplace resources associated with techno-work (…) Seminar.net - International journal of media, technology and lifelong learning 3 Vol. 15 – Issue 1 – 2019 influence teachers’ well-being by supporting and developing them. This is an important strategy that takes into account the fact that it is generally not possible to eliminate demanding factors (e.g. time pressures). In the current study, we aim to identify which workplace resources are associated with teachers’ techno-work engagement. According to Nielsen et al. (2017), workplace resources are factors within a workplace that help an employee to achieve goals and complete work tasks. Workplace resources can be divided into four main types: individual (also called personal resources, such as selfefficacy, competence and self-esteem), group-level (e.g. social support, good interpersonal relationships between employees), leader-level (e.g. leadership style) and organisationallevel resources (e.g. autonomy, possibilities to develop capabilities, human resources practices). Based on a large body of empirical findings, the authors found that these kinds of workplace resources (also called personal resources and job resources) enhance work motivation, well-being (e.g. work engagement) and performance (Nielsen et al., 2017). Since the concept of techno-work engagement is based on the concept of work engagement, we assume that workplace resources are also associated with techno-work engagement. Techno-work engagement Recently, a new concept and scale of techno-work engagement was developed to identify the positive well-being aspects of technology use at work (Mäkiniemi et al., 2019). This was considered necessary since prior research has mostly focused on the negative or demanding aspects of technology use (e.g. Ragu-Nathan, Tarafdar, Ragu-Nathan, & Tu, 2008). Further, the fact that an employee reports no or few negative well-being experiences, such as technostress, related to technology use does not necessarily indicate that he or she is having positive experiences. Consequently, it is not possible to measure positive experiences with scales that focus on negative experiences. Relatedly, Tarafdar, Cooper and Stich (2017) recently suggested that there is a need to consider the positive aspect of technostress, which they refer to as techno-eustress (i.e. the perception of technology use as challenging, thrilling and motivating). They argued that mastering such challenges could lead to positive outcomes, such as greater work engagement. Technowork engagement refers to employees’ technology-related experiences of well-being, and it is defined as a fulfilling state of mind associated with the use of technology (Mäkiniemi et at., 2017; Mäkiniemi et al., 2019). Similar to work engagement, it is a positive motivational state characterised by vigour, dedication and absorption. How are technology-related workplace resources associated with techno-work (…) Seminar.net - International journal of media, technology and lifelong learning 4 Vol. 15 – Issue 1 – 2019 Technology-related groupand organisational-level workplace resources So-called supportive workplace factors may enhance techno-work engagement as well as the willingness to use educational technology. In the current study, we call these kinds of factors ‘technology-related workplace resources’. In line with the definition presented above, we suggest that workplace resources are factors that help teachers to integrate and use educational technology at work and complete related work tasks. Prior studies on work engagement suggest that individual-, groupand organisation-level workplace resources, such as social support, autonomy and self-efficacy (Schaufeli & Bakker, 2004; Ventura, Salanova, & Llorens, 2015; Xanthopoulou, Bakker, Demerouti, & Schaufeli, 2007), are associated with higher work engagement. In line with these findings, we assume that high technology-related autonomy (i.e. teachers can freely make decisions regarding the use of educational technology), technology-related social support (i.e. colleagues give advice concerning educational technology) and technology-related competence support (i.e. individuals have enough time to use educational technology) are all associated with higher techno-work engagement. Technology-related self-efficacy as an individual workplace resource Self-efficacy is an important individual resource that is associated with employee wellbeing, such as higher work engagement (Xanthopoulou et al., 2007; Nielsen et al., 2017; Skaalvik & Skaalvik, 2014) and lower burnout in various occupations, including educational occupations (Skaalvik & Skaalvik, 2007; Shoji et al., 2016). According to social cognitive theory, self-efficacy is defined as an individual’s beliefs regarding his or her capability to control situations and challenging demands. People with high levels of selfefficacy tend to set challenging goals, persist in achieving their goals, even under difficult and stressful circumstances, and recover quickly from failure, even in conditions that would appear to be overwhelming to the average person (Bandura, 1997). Self-efficacy can be measured at either a general or situationor domain-specific level. One domain-specific concept, teaching or teacher efficacy, is defined as a teacher’s future-oriented competencybased expectation, which is related to his or her ability to plan, organise and carry out the activities required to attain given educational goals. This expectation is a balanced judgement influenced by the teacher’s perceived capacity to carry out the acts as well as the perceived demands of the working situation (Reeve & Su, 2014). In the current paper, we focus on teachers’ (educational) technology-related self-efficacy as a personal workplace resource. We assume that a teacher has high technology-related self-efficacy, for example, when he or she understands the possibilities of educational technology well enough to How are technology-related workplace resources associated with techno-work (…) Seminar.net - International journal of media, technology and lifelong learning 5 Vol. 15 – Issue 1 – 2019 maximise them in teaching and when he or she feels confident that he or she can help students when they have difficulties (c.f. Wang, Ertmer, & Newby, 2004). In line with previous findings assuming the link between self-efficacy and work engagement among teachers (e.g. Skaalvik & Skaalvik, 2014), we suggest that technology-related self-efficacy is associated with higher techno-work engagement. Figure 1. Hypothetical models. Taken together, the main aims of the current study are to analyse how technology-related individual-, organisationand group-level resources are associated with techno-work engagement among a group of Finnish teachers and determine which are the most influential predictors of techno-work engagement. We pose two hypotheses (Figure 1): technology-related job resources, namely, collegial support (H1a), autonomy (H1b) and competence support (H1c), are associated with higher techno-work engagement, and technology-related self-efficacy is associated with higher techno-work engagement (H2). Based on prior findings and theoretical formulations, it is not possible to hypothesise which predictors are the most influential. In practise, we tested two hypothetical models, as shown in Figure 1. The first model (Model 1) tested how three technology-related job How are technology-related workplace resources associated with techno-work (…) Seminar.net - International journal of media, technology and lifelong learning 6 Vol. 15 – Issue 1 – 2019 resources – technology-related collegial support, technology-related autonomy and technology-related competence support – are associated with techno-work engagement. In the second model (Model 2), technology-related self-efficacy as an individual resource was added to the model to test whether technology-related job resources and an individual resource together are associated with techno-work engagement and which of these are the best predictors of techno-work engagement (Figure 1). Methods Data collection and participants Quantitative data were collected from 15 schools in Finland as a part of a larger research project. Altogether, 183 teachers and principals answered a web-based questionnaire (in Finnish). Three principals had missing values on the Techno-Work Engagement Scale (TechnoWES) and were therefore excluded from the analyses. Of the remaining 180 respondents, 137 (76%) were females, and their mean age was 45 years. The respondents were class teachers (52.2%), subject teachers (43.3%) and principals (4.4%). Measures Techno-work engagement was measured with the TechnoWES (Mäkiniemi et al., 2019), which captures positive well-being aspects of technology use at work. The TechnoWES consists of nine items that represent the three aspects of techno-work engagement (i.e. techno_vigor, techno_dedication, and techno_absorption; measured with three items each). The respondents were asked to evaluate how often they have certain kinds of feelings and thoughts using a 7-point scale (1 = never; 7 = daily). An example of an item describing techno_vigor is ‘When I utilise technology in my work, I feel that I am bursting with energy.’ An exemplary item measuring techno_dedication is ‘I am enthusiastic about utilising technology in my job.” Finally, an example techno_absorption item is ‘I feel happy when I am immersed in using technology in my work.’ The respondents were asked to think about educational technology in particular when answering. Technology-related self-efficacy, as an individual workplace resource, was measured by three items (e.g. ‘I feel confident that I have the necessary skills in educational technology’) on a 5-point scale (1 = strongly disagree; 5 = strongly agree; items adapted from Wang et al., 2004). Technology-related job resources were assessed with three subscales (adapted from Lam, Cheng, & Choy, 2010). Technology-related collegial support (e.g. ‘My colleagues support me if I encounter difficulties in using educational technology’), technology-related How are technology-related workplace resources associated with techno-work (…) Seminar.net - International journal of media, technology and lifelong learning 7 Vol. 15 – Issue 1 – 2019 competence support (e.g. ‘Our school provides sufficient training in using educational technology’) and technology-related autonomy (e.g. ‘I use educational technology voluntarily in my teaching’) were each measured with three items on a 5-point scale (1 = strongly disagree; 5 = strongly agree; for the Finnish versions of the items, see Mäkiniemi et al., 2017). Data analysis First, the mean scores were calculated for each main variable, and differences between gender (calculated by an independent sample t-test) and teacher type (calculated by a oneway analysis of variance, ANOVA) were analysed with IBM SPSS 22. Subsequently, structural equation modelling (SEM) was used to identify the antecedents of techno-work engagement. The hypotheses were tested with SmartPLS 3, which is based on the partial least squares (PLS) SEM theory. A PLS-SEM modelling approach was developed to maximise the explained variance of the dependent variable (Hair, Ringle, & Sarstedt, 2011; Hair, Hult, & Ringle, 2017). This approach was appropriate in this study due to the nonnormality of the data and the small sample size (n = 180). Additionally, PLS-SEM is considered appropriate for exploratory research and the early stages of theory development (Hair, Sarstedt, Ringle, & Mena, 2012). As we were interested in testing and comparing the antecedents of techno-work engagement, which is a recently developed concept, the explorative nature of PLS modelling was advantageous for our study (Henseler, Ringle, Sinkovics, 2009). Results The level of techno-work engagement was quite high (M = 3.93, SD = 1.49). There was no statistically significant difference between females (M = 3.86, SD = 1.46) and males (M = 4.17, SD = 1.57; t(178) = 1.21, p = .229). However, there were differences between different types of teachers (F(2, 177) = 6.78, p = .001). Post-hoc comparisons conducted with the Scheffe test indicated that the mean score for principals (M = 5.71, SD = 1.26) was significantly higher than those for class teachers (M = 3.75, SD = 1.37) and subject teachers (M = 3.97, SD = 1.54) at p <. 01 (Table 1). How are technology-related workplace resources associated with techno-work (…) Seminar.net - International journal of media, technology and lifelong learning 8 Vol. 15 – Issue 1 – 2019 Table 1. Descriptive statistics for the study variables (n = 180). Outer model The assessment of PLS models is twofold; an acceptable judgement of the outer model allows one to proceed with the inner model evaluation. The outer model is assessed by analysing the reliability and validity of the constructs. Reliability and validity are All Female (n = 137) Male (n = 43) Class teacher (n = 94) Subject teacher (n = 78) Principal (n = 8) M SD M SD M SD M SD M SD M SD 1. Techno-work engagement 3.93 1.49 3.86 1.46 4.17 1.57 3.75 1.37 3.97 1.54 5.71 1.26 2. Technologyrelated selfefficacy (individual) 2.98 1.02 2.79 0.98 3.58 0.89 2.94 1.01 2.97 1.04 3.54 0.75 3. Technologyrelated collegial support (job) 3.83 0.86 3.77 0.89 4.01 0.75 3.80 0.76 3.80 0.99 4.38 0.55 4. Technologyrelated competence support (job) 3.26 0.77 3.21 0.77 3.45 0.76 3.23 0.75 3.23 0.78 4.04 0.58 5. Technologyrelated autonomy (job) 4.09 0.63 4.01 0.60 4.35 0.65 4.05 0.56 4.11 0.70 4.42 0.77 How are technology-related workplace resources associated with techno-work (…) Seminar.net - International journal of media, technology and lifelong learning 9 Vol. 15 – Issue 1 – 2019 determined for reflective indicators based on factor loadings, composition reliability (CR), average of variance extracted (AVE) and discriminant validity (Henseler et al., 2009). The estimated loadings of the reflective indicators were all high (0.58–0.92) and statistically significant (see Appendix 1). Statistical significance was achieved by the bootstrap procedure using 5,000 samples. The composite reliability (CR) of constructs can be regarded as more suitable than Cronbach’s alpha when using the PLS method (Hair et al., 2012). CR values indicate the reliability and consistency of constructs (Table 2) (Hair et al., 2011). Convergent validity of constructs is achieved when AVE values are greater than 0.51. Using the Fornell–Larcker criterion to assess the discriminant validity between the constructs, it was determined that the square roots of the AVEs of each construct were larger than the constructs’ correlations with each other (Table 2) (Fornell & Larcker, 1981). Table 2. Construct validity, reliability, discriminant validity, correlations, means and standard deviations for constructs (PLS models, n = 180). CR1 AVE2 (1) (2) (3) (4) (5) Technology-related selfefficacy (1) 0.915 0.783 0.885 Technology-related collegial support (2) 0.914 0.781 0.291 0.884 Technology-related autonomy (3) 0.811 0.592 0.502 0.422 0.770 Technology-related competence support (4) 0.760 0.518 0.387 0.397 0.375 0.720 Techno-work engagement (5) 0.949 0.673 0.529 0.274 0.511 0.425 0.821 Mean 2.98 3.82 4.08 3.28 3.94 SD 1.00 0.87 0.65 0.75 1.50 1 Composite reliability, 2 Average variance extracted. How are technology-related workplace resources associated with techno-work (…) Seminar.net - International journal of media, technology and lifelong learning 16 Vol. 15 – Issue 1 – 2019 Acknowledgements This research project was supported by Finnish Work Environment Fund (Project number: 115412). The authors would like to thank Antti Syvänen, Sannu Syrjä, Jarmo Viteli, and Kirsi Heikkilä-Tammi for collaboration in the project. References Albrecht, S. L. (2013). Work engagement and the positive power of meaningful work. In A. B. Bakker (Ed.), Advances in positive organizational psychology (pp. 237–260). Bingley, UK: Emerald. https://doi.org/10.1108/S2046-410X(2013)0000001013 Al-Fudail, M., & Mellar, H. (2008). Investigating teacher stress when using technology. Computers & Education, 51, 1103–1110. https://doi.org/10.1016/j.compedu.2007.11.004 Bakker, A. B., Albrecht, S. L., & Leiter, M. P. (2011). Key questions regarding work engagement. 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Standardised indicator loadings and respective t-statistics. Standardised loading t-statistic Techno-work engagement Techno-work engagement_enthusiastic 0.800 24.316 Techno-work engagement_inspired 0.860 42.206 Techno-work engagement_proud 0.798 24.871 Techno-work engagement_persevere 0.640 11.646 Techno-work engagement_energy 0.829 32.957 Techno-work engagement_vigorous 0.859 40.572 Techno-work engagement_happy immersed 0.875 43.634 Techno-work engagement_immersed 0.821 27.699 Techno-work engagement_carried away 0.877 48.088 How are technology-related workplace resources associated with techno-work (…) Seminar.net - International journal of media, technology and lifelong learning 20 Vol. 15 – Issue 1 – 2019 Standardised loading t-statistic Technology-related self-efficacy Technology-related self-efficacy_know how to utilize 0.850 37.314 Technology-related self-efficacy_able to help 0.896 42.257 Technology-related self-efficacy_adequate skills 0.907 56.264 Technology-related competence support Technology-related competence support_training 0.743 11.102 Technology-related competence support_time 0.812 15.417 Technology-related competence support_what is expected 0.586 6.580 Technology-related autonomy Technology-related autonomy_opinions respected 0.803 15.491 Technology-related autonomy_voluntariness 0.858 35.412 Technology-related autonomy_freedom to decide 0.629 6.741 How are technology-related workplace resources associated with techno-work (…) Seminar.net - International journal of media, technology and lifelong learning 21 Vol. 15 – Issue 1 – 2019 Standardised loading t-statistic Technology-related collegial support Technology-related collegial support_colleagues support 0.842 17.073 Technology-related collegial support_tips 0.885 18.008 Technology-related collegial support_collaboration 0.922 22.368