Occupational burnout and cyberloafing among teachers: Analysis of personality traits,individual and occupational status variables as predictors

One of the phenomena that causes remarkable losses in terms of productivity and cost in organizations is occupational burnout. Burnout is encountered in different occupational groups. However, in professions that are in contact with a large number of people as part of their responsibilities, such as teaching, burnout is a problem that is far more on the foreground. In addition, emotional burnout, which is seen as the first step before occupational burnout, can cause workers to feel insufficient at the point of carrying out tasks related to their duties, leading to behaviour that is out of their fields such as cyberloafing. Addressing this situation in terms of teaching profession, the feeling of inadequacy in educational activities during the course may prevent the teacher from fulfilling any responsibilities while using a device that has internet access. In this context, the aim of this study is to investigate the factors that predict the cyberloafing and burnout levels of teachers working in different fields. This research is a relational research. This research was carried out with the participations of 194 teachers from different branches working in various provinces in Turkey. In this study, a personal information form and three different data collection tools were used. Analysis of the collected data was performed by hierarchical linear multiple regression analysis. The 9 models, created separately with cyberloafing and burnout, were found to be significant in the study. Demographic variables, ICT usage status variables, occupational variables and personality traits were used in these models. Therefore, all the hypotheses included in the research were accepted. As a result of the research, demographic variables were found to be the most important model to predict teachers’ occupational burnout. The most important model explaining teachers’ cyberloafing situations is their ICT usage.     

Comparisons of tests of distributional assumption in Poisson regression model

Count data consists of discrete non-negative integer values. Poisson regression model is one of the most popular model used to model count data. This model assumes that response variable has Poisson distribution. The purpose of this article is to assess distributional assumption of this model by using some goodness of fit tests. These tests are compared in respect to type I error and power rates of tests with different samples, parameters and sample sizes. Simulation study suggests that the most powerful tests are generally Dean–Lawless and Cameron–Trivedi score tests.     

Risk-taking behavior and academic self-efficacy as variables accounting for problematic internet use in adolescent university students

Objective

This study investigated the relationship between risk-taking behavior and academic self-efficacy and problematic internet use in adolescent university students and whether problematic internet use varies according to the variables of gender, possession of a computer and living with the family.

Methods

The study was performed with 556 students from the Karadeniz Technical University Fatih Faculty of Education in Turkey. The Problematic Internet Use Scale, the Adolescent Risk-Taking Questionnaire, the Academic Self-Efficacy Scale and Personal Information Collection Form were used in the collection of data.

Results

Pearson correlation analysis revealed a significant positive correlation between problematic internet use and risk-taking behavior (r = .37, p < .01) and a significant negative correlation between problematic internet use and academic self-efficacy (r = − .12, p < .05). Multiple regression analysis revealed that risk-taking behavior and academic self-efficacy accounted for 14% of total problematic internet use variance (F_(2,553) = 46.11, p < .05). The individual contribution to the model of risk-taking (β = .37) and academic self-efficacy (β = − .09) is significant. Our results also showed that university adolescents' problematic internet use scores vary by gender (t = − 4.90, p < .05) and possession or otherwise of a computer (t = 3.10, p < .05), but not on the basis of whether they live with their families (t = − .13, p > .05).

Conclusions

Risk-taking behavior and academic self-efficacy emerged as significant predictors of problematic internet use.