| Literature DB >> 35925955 |
Virginia Navajas-Romero1, Nuria Ceular-Villamandos1, Manuel Adolfo Pérez-Priego1, Lorena Caridad-López Del Río1.
Abstract
The present work aims to analyze the properties of the working conditions recorded in the Sixth European Working Conditions Survey (EWCS); with it, it has being built seven independent indexes about different aspects of work' quality in the health sector, and these constructs are used to evaluate their effects on work engagement (WE). In this sense, the originality of incorporating teamwork as a modulating variable is included. To analyze the effects of the job quality index (JQI) on the WE, a logistic regression model is proposed for a total of 3044 workers within the health sector, differentiating between those who work or not in a team; in a first stage and these estimates are compared with those obtained using an artificial neural network model, and both are used for the consideration of the research hypotheses about several causal factor. An important contributions of the study, it is related to how work commitment is mainly influenced by prospects, social environment, intensity and earnings, all of them related to job performance. Therefore, knowledge of the determinants of work commitment and the ability to modulate its effects in teamwork environments is necessary for the development of truly sustainable Human Resources policies.Entities:
Mesh:
Year: 2022 PMID: 35925955 PMCID: PMC9352011 DOI: 10.1371/journal.pone.0271134
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.752
Variable’s description.
| JQI | ITEMS |
|---|---|
| Physical environment (JQI Physical) | Posture-related (ergonomic) risks |
| Ambient risks | |
| Chemical risks | |
| Biological risk | |
| Work intensity (JQI Intensity) | Quantitative demands |
| Pace determinants and interdependency | |
| Emotional demands | |
| Working time quality (JQI Working time) | Duration |
| Atypical working time | |
| Working time arrangements | |
| Flexibility | |
| Social environment (JQI Social) | Social behavior |
| Social support | |
| Prospects (JQI Prospects) | Employment status |
| Career prospects | |
| Job Security | |
| Downsizing | |
| Skills and discretion (JQI Skills) | Cognitive Dimension |
| Decision latitude | |
| Organizational participation | |
| Training |
Fig 1Description of factor (JQI).
Fig 2Distribution of JQIEarnings in each class defined by teamwork.
Fig 3Phases of the research methodology.
Logit model.
|
| ||||
|---|---|---|---|---|
| Variable | Coefficient | Std. Error | Z-statistic | |
| C | -0.682782 | 0.233021 | -2.930130 | 0.0034 |
| TEAMWORK | 0.296658 | 0.092555 | 3.205192 | 0.0013 |
| JQI PROSPECT | 0.011012 | 0.002358 | 4.670457 | 0.0000 |
| JQI INTENSITY × JQI EARNING | 2.58E-06 | 8.86E-07 | 2.915974 | 0.0035 |
| Akaike info criterion | 1.349057 | Restr. Deviance | 3718.391 | |
| Schwarz criterion | 1.359945 | LR statistic | 69.74887 | |
| Hannan-Quinn criter. | 1.352993 | P(LR statistic) | 0.000000 | |
| Obs with | 1189 | Total obs | 2712 | |
| Obs with | 1523 | |||
Logit model: Predicted-observed classifications.
| Classification | Total | ||
|---|---|---|---|
| 677 | 651 | 1328 | |
| 512 | 872 | 1384 | |
| Total | 1189 | 1523 | 2712 |
| Correct | 677 | 872 | 1549 |
| % Correct | 56.94 | 57.26 | 57.12 |
Fig 4Artificial neural network MLP (5+1; 2; 2).
ARN: Predicted-observed classifications.
| Classification | Total | ||
|---|---|---|---|
| 677 | 565 | 1242 | |
| 558 | 954 | 1512 | |
| Total | 1235 | 1519 | 2754 |
| Correct | 677 | 954 | 1631 |
| % Correct | 54.82 | 62.80 | 59.22 |