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Stress and Resources in Vocational Problem Solving

Abstract

By adapting the job demands-resources model of Demerouti et al. Journal of Applied Psychology, 86(3), 499–512, (2001) to vocational problem-solving situations, we aimed to investigate how, and to what extent, problem-solving demands and personal resources affect stress responses and task interest. Therefore, we used a problem-solving task from the business administration domain in a computer-based office simulation. We assigned 58 participants into two groups. The treatment group worked on the problem scenario, whereas the control group was instructed to inspect the computer-based scenario and to check the software’s usability without solving the problem. Problem-solving demands, perceived stress, task interest, cardiovascular parameters, and cortisol concentration were assessed before, during and after the task at several time points. The vocational problem-solving task was associated with perceived time pressure, uncertainty, mental effort, task difficulty, and perceived stress. In addition, we found higher heart rate and cortisol concentration and lower heart rate variability values in the treatment group (compared to the control group) at the end of the task. Furthermore, we found that content knowledge buffers the impact of problem-solving demands on stress responses and it maintains task interest under high mental effort. Overall, we found evidence that vocational problem-solving activities bear stress-evoking potential and personal resources may provide buffering and maintaining functions.

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Notes

  1. 1.

    The current study precedes another study by Kärner (2017) on stress-inducing potentials of vocational problem-solving activities and personal resources. With regard to the validity of the current findings, the samples of the two studies have been compared to each other (cf. the sample in the current study: Treatment Group, n = 41; Control Group, n = 17; the sample in Kärner (2017), n = 18): using ANOVAs, there were no significant group differences with regard to BMI (p = 0.366), work experience in years (p = 0.312), and general intelligence (p = 0.103); using χ2-tests, there were no significant group differences with regard to sex (p = 0.992) and vocational education and training certificate (% completed) (p = 0.403); with regard to age, participants in the Treatment Group (M = 24.85, ±3.46 SD) from the current study were significantly younger than the participants in the study by Kärner (2017) (M = 27.39, ±3.07 SD) (F(2, 73) = 3.955, η2 = 0.098, p = 0.023, Bonferroni-corrected p-value = 0.035).

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Acknowledgements

The authors would like to thank M.Sc. Nicole Fiedler and M.Sc. Johanna Geiger-Wrzyciel for the assistance with data collection. Furthermore, the authors would like to thank the anonymous reviewers for their critical, but very helpful and constructive comments and suggestions.

The computer-based office simulation—at which we referred to in our study—was developed within a project on domain-specific problem solving competence (DomPL-IK), funded by the German Federal Ministry of Education and Research (IDs 01DB1119 to 01DB1123; for a further description see Rausch et al. 2016 and Seifried et al. 2016).

Abbreviations

ANOVA, analysis of variance; BMI, body mass index; bpm, beats per minute; CG, control group; DV, dependent variable; H, hypotheses; HPA, hypothalamic-pituitary-adrenal (axis); HR, heart rate; HRV, heart rate variability; JD-R model, job demands-resources model; Khz, kilohertz; min., minute; mm, millimeters; ms, milliseconds; nmol/L, nanomoles per liter; NN, normal-to-normal; PS, perceived stress; RMSSD, square root of the mean of the sum of the squares of differences between adjacent NN intervals (HRV indicator); RQ, research questions; TG, treatment group

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Table 6 Moderated regression analyses

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Kärner, T., Minkley, N., Rausch, A. et al. Stress and Resources in Vocational Problem Solving. Vocations and Learning 11, 365–398 (2018). https://doi.org/10.1007/s12186-017-9193-8

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Keywords

  • Vocational problem solving
  • Job demands-resources model
  • Business administration domain
  • Computer-based office simulation
  • Task-related content knowledge
  • Task interest
  • Perceived stress
  • Heart rate (variability)
  • Cortisol