| The purpose of this quantitative, non-experimental study was to evaluate the extent to which layoff survivors' post-layoff perceived organizational support and organizational commitment influence layoff survivors' turnover intentions in the government industry. Although research examining each of the study variables is generous in organization and management literature, research involving layoff survivors in the government industry using this combined model was minimal. Focusing on organizational support, the three-component model of organizational commitment, and process and content turnover theories, this study assessed the relationships and influence of post-layoff perceived organizational support, affective commitment, normative commitment, and continuance commitment on layoff survivors' turnover intentions. This study used multiple linear regression, Pearson's R (product-moment correlation coefficient), and analysis of variance (ANOVA) to analyze data collected from 108 layoff survivors employed in the four sub-sectors of the United States government industry (i.e., city, county, state, and federal) post layoffs. Results of the analysis showed that layoff survivors' perceived organizational support, affective commitment, and normative commitment were moderately and negatively correlated with survivors' turnover intentions. Further, layoff survivors' continuance commitment had a modest, positive correlation with their turnover intentions. Also, all predictor variables (i.e., perceived organizational support, and affective, normative, and continuance commitment) statistically significantly influenced layoff survivors' turnover intentions, and all null hypotheses were rejected. The practical implications tied to this study suggested that government leaders and managers could potentially reduce post-layoff turnover intentions by gaining better understanding of how perceived organizational support and organizational commitment influence turnover intentions. Additionally, recommendations for further research suggested that scholars explore the use of more comprehensive models in assessing the factors that potentially influence turnover intentions of government industry workers after layoffs. |
