Organizational embeddedness, turnover intentions, and voluntary turnover: The moderating effects of employee demographic characteristics and value orientations

2014 ◽  
Vol 36 (2) ◽  
pp. 292-312 ◽  
Author(s):  
Vesa Peltokorpi ◽  
David G. Allen ◽  
Fabian Froese
Keyword(s):  
Turnover Intentions ◽  
Voluntary Turnover ◽  
Demographic Characteristics ◽  
Value Orientations ◽  
Moderating Effects ◽  
Organizational Embeddedness

Merit-based Rewards, Job Satisfaction and Voluntary Turnover: Moderating Effects of Employee Demographic Characteristics

2018 ◽  
Vol 30 (3) ◽  
pp. 610-623 ◽  
Author(s):  
Fabian Jintae Froese ◽  
Vesa Peltokorpi ◽  
Arup Varma ◽  
Azusa Hitotsuyanagi-Hansel
Keyword(s):  
Job Satisfaction ◽  
Voluntary Turnover ◽  
Demographic Characteristics ◽  
Moderating Effects

Moderating effects of turnover intentions on justice and job performance

2010 ◽  
Author(s):  
Brian J. Collins ◽  
Kevin W. Mossholder ◽  
Shannon G. Taylor
Keyword(s):  
Job Performance ◽  
Turnover Intentions ◽  
Moderating Effects

Affective Experiences as a Meditational Variable in Voluntary Turnover Intentions of Nurses

2009 ◽  
Author(s):  
Stephanie E. Granda ◽  
David C. Munz ◽  
Larissa K. Barber ◽  
Matthew J. Grawitch
Keyword(s):  
Turnover Intentions ◽  
Voluntary Turnover ◽  
Affective Experiences

scholarly journals Determinants of voluntary turnover: A data-driven analysis for blue and white collar workers

Work ◽  
2021 ◽  
pp. 1-19
Author(s):  
Evy Rombaut ◽  
Marie-Anne Guerry
Keyword(s):  
Turnover Intentions ◽  
Voluntary Turnover ◽  
White Collar ◽  
Interview Data ◽  
Data Driven ◽  
Employee Behavior ◽  
Number Of Children ◽  
White Collar Workers ◽  
Specific Factors ◽  
Actual Turnover

BACKGROUND: There is a growing interest in HR-analytics because of its ability to analyze employee behavior based on HR data. Predicting voluntary turnover of employees is an important topic of study, both in academia and industry. OBJECTIVE: The current study analyzes determinants for turnover, distinguishing between blue and white collar workers. The turnover analyses are based on a dataset from a payroll company, in contrary to previous turnover studies that used survey and interview data. METHODS: The studied dataset contains demographic and work specific factors for more than 380000 employees in 15692 Belgian corporations. Logistic regression is used to estimate individual turnover probabilities, the goodness of the model is tested with the AUC method. RESULTS: The study confirms turnover determinants and differences between blue and white collar workers that were described in previous work based on survey and interview data. Additionally, the study exposes so far unstudied turnover determinants and differences between blue and white collar workers. Confirmed determinants are among others age, seniority, pay and work distance. New determinants are company car, meal vouchers, night work and sickness. Different relationships to turnover are revealed for blue and white collar workers based on gender, number of children, nationality and pay. CONCLUSIONS: The presented dataset-based approach has its merit in analyzing turnover: it enables to study actual turnover instead of turnover intentions, and reveals new turnover determinants and differences between blue and white collar workers.

scholarly journals Turnover intentions, training and motivations among Australian union staff

2021 ◽  
pp. 0143831X2110172
Author(s):  
Nick Brander-Peetz ◽  
David Peetz ◽  
Paula Brough
Keyword(s):  
Online Survey ◽  
Turnover Intentions ◽  
Voluntary Turnover ◽  
Intention To Leave ◽  
Theory And Practice ◽  
Organisational Support ◽  
Intrinsic Motivations ◽  
Perceived Organisational Support ◽  
The Impact ◽  
Labour Market Mobility

Staff loss and demotivation can be costly for unions. In this article the authors investigate factors influencing expected voluntary turnover, that is Intention to Leave (ITL), of union employees by conducting an online survey of 160 staff in three Australian unions. Moderated multiple regression analyses revealed that perceived organisational support, shortcomings in training and unmet intrinsic needs predicted ITL, after controlling for burnout, labour market mobility and intrinsic motivations. Critically, the results suggest an interaction effect involving training in some circumstances. Training buffered the impact of low support on ITL, however the adverse effect on ITL of organisational failure to meet staff expectations regarding service to members remained, independent of training. Implications for theory and practice are discussed, including the separate significance of resources and purpose.

Moderating effects of social support on job stress and turnover intentions

2018 ◽  
Vol 27 (7) ◽  
pp. 795-810 ◽  
Author(s):  
Lawrence Hoc Nang Fong ◽  
Peter Man Wai Chui ◽  
Iris Sok Cheng Cheong ◽  
Davis Ka Chio Fong
Keyword(s):  
Social Support ◽  
Job Stress ◽  
Turnover Intentions ◽  
Moderating Effects
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