Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
The aim of the present study was to show that examining the interactions between personality traits and subjective work experience (SWE) can be an interesting approach to understanding turnover. During the months following their enlistment, 186 resigning and 77 nonresigning military personnel were questioned about six SWE dimensions. During the recruitment process, they had undergone a Big Five personality test. Logistic regression analyses were conducted to study the effects of personality and SWE on turnover. Binomial generalized linear models enabled us to identify interaction effects between personality traits and SWE. These showed that open individuals who feel a high level of specialty satisfaction are less likely to quit. Similarly, individuals with high levels of neuroticism or conscientiousness are more inclined to resign if the environment is perceived to be stressful. The same applies to agreeable individuals who have negative perceptions of their interpersonal relationships. This study highlights the complexity of voluntary turnover and the need to investigate the transactions between personality and contextual characteristics with nonlinear models.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10013319 | PMC |
http://dx.doi.org/10.1080/08995605.2021.1970459 | DOI Listing |
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