Applicability of Civilian Retention Theory in the New Zealand Military

The purpose of this study was to assess the applicability of civilian retention theory in the military by exploring the relationship between established determinants of retention and intentions to remain in the NZ Army. Specific hypotheses were made based on the civilian theory and these formed the basis for a preliminary military retention model. The model was then tested on 97 individuals currently enlisted in the New Zealand Army. Regression and path modeling results indicated that work satisfaction and organisational commitment are the proximal predictors of intentions to remain in the military, and that these predictors mediate the relationship between more distal predictors and retention. The results signified the relevance and usefulness of civilian research in predicting retention in the military, and future research directions are discussed. Key Words: Military Retention, Attitudes, Work Satisfaction, Intentions to Remain. ********** Since the end of the cold war, military forces worldwide have struggled to maintain required staffing levels (Greig, 2001). For instance, between 1999 and 2000 there were 4,947 enlistments compared with 6,467 separations in the Australian Defence Force. Military forces are increasingly forced to compete with civilian employers for talented individuals, and cannot offer the same career opportunities as multi-national organisations. The New Zealand Army is no exception to this trend, especially since staffing levels are maintained entirely by voluntary service. As a consequence, recruiters nationwide are involved in an ongoing campaign to attract new recruits, whilst vigorous advertising campaigns direct interested individuals to the New Zealand Army website. Importance of Personnel Retention All recruits must undergo initial and specialist training prior to their integration in regular force units. This training involves an initial period of 12 weeks, during which recruits are introduced and indoctrinated into the military, before they move on to trade specific training lasting anywhere between 12 weeks and one year. Due to its very nature, the recruitment and training of new personnel is extremely costly. Therefore, retention of personnel should be considered a priority, because the cost of retention initiatives are most probably less than those costs involved with continuously recruiting and training new personnel. Modeling Retention in Military and Civilian Settings Until now, most published studies of military retention have involved data-mining approaches designed to identify demographic (i.e., gender, race, age, marital status) and organisational characteristics (i.e., male/female ratio, length of overseas assignment) related to turnover (Walker, 2003). This is problematic for a number of reasons: 1) many demographic characteristics, such as gender, are inherent and cannot readily be changed; 2) recruiting policy based on demographics would further decrease the already diminishing source of potential recruits; and 3) although data mining can result in relatively high predictive validities, such approaches are ill-suited for building a theory of military retention/turnover. Focusing on demographics, for instance, neglects the actual cause of turnover (e.g., female soldiers may have a significantly lower retention rate than their male counterparts; however, this does not identify the underlying reason for their behaviour). In contrast, civilian research has treated retention/turnover as an instance of motivated personal choice. To model this choice behavior, researchers have introduced and empirically tested a number of direct and indirect links between personal characteristics (i.e., personality, job affect), organisational variables (i.e., organisational support, recruitment techniques), intentions to remain, and actual turnover. As a result, there exists a relatively coherent and integrated theory of civilian retention, which can be easily presented and tested using structural equation modeling methodology (e. …