Factors in the Transition of Career Military Personnel to the Civilian Workforce

Genomary Krigbaum, Bassett Army Community Hospital/Grand Canyon University; Christine C. Good, Colorado Christian University; Ann K. Ogle, Grand Canyon University; Michael Walsh, University of Illinois, Urbana-Champaign; Robert Hess, Grand Canyon University; & Jeff Krigbaum, USA Retired

Abstract: Veterans transitioning to the civilian workforce face many challenges. Yearly, approximately 18,000 veterans reenter civilian life, which includes transitioning into a second career, facing issues of life satisfaction, and encountering differences in military versus civilian work structures. Data on veterans indicates continued struggles in securing civilian employment commensurate with their skills. In this study (N = 146), the predictors of a successful transition from the military to the civilian workforce were analyzed. Demographic data and scores on standardized measurements were computed. Regression analyses yielded results of life satisfaction alone (< .05) and life satisfaction with race-ethnicity (< .01) as strong predictors for securing employment. Age alone and age combined with the number of years of military service, workability, and social capital were found to be strong predictors (< .01) of being employed. Although satisfaction with life increases the likelihood of a successful transition from military service to civilian work, social capital indicates that support is required to bolster one’s personal assessments of self-worth, skills, and ability to work because self-identity does alter self-perceptions (Amiot et al., 2015). Though little can be done to change demographic predictors, such as race-ethnicity, age, and length of service (number of years in the military), initiatives can be designed to strengthen dispositional factors, such as life satisfaction, workability, and social capital. It is recommended that initiatives be explored specifically targeted to such factors. Practical implications of the results are discussed.

Keywords:  Veteran transition, predictors, veterans, employment

Reintegration of post-military personnel, veterans, into the civilian workforce after separation (expiration of term of service [ETS]) or retirement often involves transitioning into a post-military career (Anderson, 2015; Wanberg et al., 2015; Zogas, 2017). Even when officially retiring from the military, many veterans wish to or have the financial need to remain in the workforce in order to supplement their retirement, and even to explore new avenues of personal growth, including education, repurposing of skills, and application of the knowledge gained while in the military.

Veterans go through these transitions regardless of age, race-ethnicity, gender, or physical condition (Anderson, 2015; Wanberg et al., 2015; Zogas, 2017). These transitions result in changes in relationships, roles, routines, and expectations. They vary in quality and success based on factors such as the situation, self, support, and strategies (Ryan et al., 2011; Schlossberg, 1981; Zogas, 2017). Military transitions at ETS involve each factor as well as expectations, such as different definitions of work success and alternative strategies for succeeding at work (Koenig et al., 2014; Ryan et al., 2011; Vigoda-Gadot et al., 2010; Zogas, 2017).

Veterans’ transitions mimic those of civilians moving into second or alternative careers, with the additional stresses generated by the limited understanding of military roles by civilian employers and, in some instances, the veteran’s requirement of not discussing past roles and operational involvement based on level of authorized security clearance (Amiot et al., 2015; Barclay et al., 2011; Cabrera, 2007; Inkson et al., 2012; Kang & Gottfredson, 2015; Linnabery et al., 2014; Nilsson & Ekberg, 2013; Viotti et al., 2017). As in civilian employment, changes and successful occupational placement or reintegration depends on factors related to individual differences and individual motivations (Amiot et al., 2015; Forrier et al., 2015; Grimland et al., 2012; Jaensch et al., 2015; Kordbacheh et al., 2014; Vigoda-Gadot et al., 2010; Zacher, 2014; Zogas, 2017). Reintegration into the civilian workforce has affected each generation of veterans on ETS or retirement (De Groat & Crowley, 2013; Schmidt et al., 2013; Weaver, 2013; Zogas, 2017). Politics, social and economic circumstances, societal perceptions of veterans, resources available for the occupational training of veterans, employment opportunities, life satisfaction, and age (equated to a level of maturity) influence the reintegration process (Daywalt, 2014; Greengard, 2012; U.S. Department of Labor, 2011; Zogas, 2017).

In an effort to assist veterans in the reintegration process, the Serviceman's Readjustment Act of 1944, or GI Bill, was introduced and, as an expansion of the GI Bill, the post 9/11 GI Bill took effect in 2009 (Zhang, 2018); veterans can use either. Whichever GI Bill is selected it assists veterans with going to college or trade school, resulting in nearly 49% entering college within 3 years of their ETS (Greengard, 2012; Zhang, 2018). Additionally, in a “pooled sample from 2005 to 2015… On average, veterans have a higher college enrollment rate (22%) than nonveterans (12%)” (Zhang, 2018, p. 89).

Though the GI Bill assistance is helpful and comes with an allowance (Zhang, 2018), it does not cover other expenses, such as maintaining a family and the quality of life, as well as finances, they may have enjoyed while in the military. This, in addition to other factors such as declines in high paying blue-collar positions requiring no college degree and employers’ potential differential treatment, based on prejudice, of veterans, puts veterans at a disadvantage in securing employment or being employed in comparison to their civilian counterparts, further reducing the veterans’ self-esteem and self-efficacy (Bureau of Labor Statistics, 2017; Daywalt, 2014, De Groat & Crowley, 2013; Greengard, 2012).

Over 18,000 military-trained individuals reenter civilian life annually (Gates et al., 2013; Globalsecurity.org, 2017). Many military personnel have limited civilian work experience, having chosen the military after high school education (or a General Education Diploma), whereas others are not ready for full retirement (BLS, 2017; Cohen et al., 2013; Daywalt, 2014; Gates et al., 2013; Greengard, 2012; U.S. Department of Labor, 2011; Zogas, 2017). As they transition to civilian work, they are challenged with greater unemployment rates than for the general population (BLS, 2017; Daywalt, 2014; De Groat & Crowley, 2013; Griswold & Ellis, 2012; Greengard, 2012; Kuen et al., 2013). In addition, they face losses of social networks, differences in organizational structures (flat versus hierarchical), dissimilar communication styles and cultures (civilian vs. military), changes in geographic location, as well as attempts at self-redefining while finding significance in new roles (De Groat & Crowley, 2013; Kukla et al., 2015; Schmidt et al., 2013; Zogas, 2017). These individuals enter civilian life with varying degrees of preparation and resilience (De Groat & Crowley, 2013; Kukla et al., 2015; Schmidt et al., 2013; Seligman, 2011; Zogas, 2017). Thus, work-related (such as preparation for retirement, resilience, self-esteem, satisfaction with life, emotional intelligence [EQ], cultural intelligence, self-efficacy, work ability, job satisfaction, social capital) and demographic (such as age, gender, race-ethnicity, number of years of military service, number of deployments, highest level of education, financial responsibility for others, how long to secure employment after ETS, how long in current employment) variables and labor-market and economic conditions, in combination, play a role in the transition yet are infrequently identified and studied as challenges of transition to civilian life in the veteran population or by those who seek to assist them as struggles in their adjustment (Hees et al., 2012; Schaefer et al., 2013; Van Til et al., 2013; Vigoda-Gadot et al., 2010; Wilson, 2014; Zogas, 2017).

Challenges of Military-Career Transition to Civilian Life

Cultural/organizational differences between the military, hierarchical, structure and civilian, matrix, organizations leave veterans struggling with person–organization fit. Although common to civilian populations, these differences between the military and the civilian work environment (industry) increase the dissonance in person–organization fit (Zogas, 2017). Matrix structures that are characterized by reporting to more than one superior (e.g., in a managing team) in civilian work environments may become a challenge when compared to the previous hierarchical structure experienced by military personnel, where reporting is through the chain of command and is responsive to mission requirements. Adaptation to these differences requires flexibility, leadership, and emotional buy-in, whereas civilians do not go through this type of adaptation because they are not accustomed to the military organizational culture (De Groat & Crowley, 2013; Schmidt et al., 2013; Zogas, 2017). Being able to adapt including securing employment and then being employed influences veterans’ reintegration process and is challenged by personal as well as cultural/organizational and physical- and mental-health factors often operationally defined through the work related and demographic variables noted previously, which can influence the fit and transition of military to civilian skills (Amiot et al., 2015; Daywalt, 2014; De Groat & Crowley, 2013; Kukla et al., 2015; Weaver, 2013; Zogas, 2017). Each of these variables impacts self-evaluations (self-esteem and self-efficacy), expectation of a financial responsibility for others, and workability (Forrier et al., 2015; Johnston et al., 2010; Nilsson & Ekberg, 2013; Wilson, 2014; Vigoda-Gadot et al., 2010). These challenges are also highlighted in qualitative studies as reintegrating veterans voice their frustration and concern, even when welcomed and supported by the GI Bill and other programs (Greer, 2013; Griswold & Ellis, 2012; Hees et al., 2012; Schaefer et al., 2013). Dunstan and MacEachen (2013) echo these concerns with civilian populations returning to the workforce after an extended absence.

In a 2014 examination of military-personnel reintegration, Wilson found continued qualitative evidence for the need to improve assessments of dispositional factors in veterans (e.g., resilience, self-esteem, satisfaction with life, EQ, cultural intelligence, self-efficacy, work ability, job satisfaction, social capital) in academic institutions focusing on bridging and supporting veterans in premilitary and postmilitary skill sets. These issues are also addressed in civilian literature; for instance, Griswold and Ellis (2012) examined statistical data of unemployed U.S. workers, determining that unemployment and its associated costs mirror the economic strains (personal-dispositional and national) of the U.S. Department of Labor (2011) study focusing on military separation.

Despite the historical and qualitative data, there is a dearth of quantitative information that systematically presents the veteran transition to the civilian workforce. Rather, the information yielded in single qualitative studies has been used to develop programs focusing on resilience and job-search skills, without broader evidence of specific factors affecting the veteran’s successful transition to the civilian workforce (Daywalt, 2014; Weaver, 2013; Wilson, 2014). Allen and colleagues (2012) noted that reintegration programs are challenged in bridging the gap between veteran skills and employer understanding of equivalent skills in civilian positions. The resulting prolonged difficulties of finding work strain the conscientious individual with a steady duty record and varied deployments, generating further loss of self-worth and self-efficacy (Amiot et al., 2015; Anusic & Schimmack, 2016); hence, the need for an integrated framework and to identify predictive factors in this transition.

Broad theoretical frameworks of career development exist, and further targeted quantitative research is needed in the populations they aim to assist (for example, military veterans transitioning to the civilian workforce). In 2002, Feldman developed the life development theory, stating that early experiences in life reinforce and lead to self-selection of future opportunities in education and employment. The result is that individuals spend their lifetimes developing and augmenting each career move, influenced by preceding lifetime experiences inclusive of career, skills, and past training.

Though the life development theory does not have sufficient empirical support, more research, primarily with women, has been conducted on the kaleidoscope career model (KCM; Mainiero & Sullivan, 2006). The KCM suggests that women, and by extension men, evaluate three dimensions when considering a career or a career move. Individuals evaluate dimensions of authenticity, balance, and challenge. It is suggested, with some empirical support, that these dimensions shift throughout life, similar to the changes of a kaleidoscope when turned, altering whether an individual desires a high-challenge or risk-driven position where financial recompense would be high, or would prefer a low financial reward to obtain work–life balance, allowing for family and other interests while remaining actively employed. The final dimension, authenticity, pertains to one’s value alignment with the employing organization (Cabrera, 2007; Mainiero & Sullivan, 2006; Sullivan et al., 2009).  

Another theory, the boundaryless career, developed by Arthur and Rousseau (1996), has received the most empirical focus with its close ties to the protean career model of Hall (2004), though the focus of research for both models has been on the protean personality. Protean careers depend on individual’s personality (dispositional factors) and the focus or drive to make a change while supported by the behaviors to make the change happen (Briscoe et al., 2006). The boundaryless career, like the protean career, focuses on the individual responding independently to organizational change and re-imagining the career as lateral rather than hierarchical (Arthur & Rousseau, 1996). Each model has extensive empirical work focused on the development of measurement scales but limited on its applicability to committed job seekers or second-/later-career individuals (Baruch, 2014; Briscoe et al., 2006; De Bruin & Buchner, 2010; Eby et al., 2003; Enache et al., 2008; Grimland et al., 2012; Volmer & Spurk, 2011).

In 2013 Wang and colleagues took these and additional theoretical models, and attempted to present an integrated perspective. The findings included the challenges present in the combination of models used by practitioners. It was concluded that there is no single, cohesive, broadly accepted model of second careers or mid- to late-career development.

Forrier and colleagues (2015) presents the most recent attempt at an integrated understanding of second careers or mid- to late-career development. This attempt focuses on the transition experience or movement capital, summarized as human capital or self-efficacy, social capital or career networks, adaptability, and self-awareness or self-esteem, and the feedback loop it creates with perceived employability and likelihood that one will be employed. Forrier and colleagues (2015) found that one’s internal, within the current organization, or external, focus on organizations beyond the current, focus was related to employability perceptions.

These theoretical frameworks are relevant in the transition of military veterans to civilian work life, and they address demographic, work, and dispositional factors that influence such a transition. Without sufficient quantitative-research analysis of their predictive transitional value, these factors may seem to remain disjointed. The predictive transitional value is important in shedding light on the needs of civilian individuals to prepare for second or later careers and informing military veterans on their transition. The models and theories have not been specifically referenced for use in the limited program evaluations conducted with regard to military-veteran reintegration (Gates et al. 2013; Weaver, 2013).

The present study brings focus to the cross-model themes within the civilian literature and combines those with the limited information presented in the veteran population. Qualitative reintegration studies emphasize the need for social support to fill gaps that are left by the loss of the military support network and that are increased by family tension (Johnston et al., 2010; Koenig et al., 2014; Kukla et al., 2015; Larson & Norman, 2014; Moorhouse, 2014; Schaefer et al, 2013; Schmidt et al, 2013; Van Til et al., 2013; Weaver, 2013; Wilson, 2014). Weaver (2013) and Zogas (2017) recommend that separating veterans receive training for re-entry to civilian life through a re-acculturation process similar to the immersion in basic-combat-training experiences used to initiate recruits to military culture. This immersion refers to support for the reintegration process that includes how to dress for interviews, preparation for the interviewing process, development of relationships outside of the military hierarchy, and the subjective process of redefining one’s self-concept and personal worth (Koenig et al., 2014; Osilla & Van Busum, 2012). Koenig and colleagues (2014) and Kukla and colleagues (2015) categorize social support needs into three domains: interpersonal support that limits isolation, resources for resilience, and professional support. These studies focus on different elements of social support, with Weaver (2013) and Zogas (2017) focused on general lifestyle and Koenig and colleagues (2014) and Kukla and colleagues (2015) focused on specific skill development and the connection to one’s personal self-esteem and self-efficacy.

Vigoda-Gadot and colleagues (2010) conducted research on military veterans in Israel, finding that social capital, the perception of organizational politics in the new workplace, and work–family conflict were related to success in a second career. In addition, preparation to retire was positively related to social capital, size of personal network, and life satisfaction. Forrier and colleagues (2015) demonstrated that the similar constructs of human capital, social capital, adaptability, and self-awareness (forming movement capital) influenced perceptions of employment opportunities and employability.

Although Forrier and colleagues (2015) combine the factors of self-efficacy, employment-related social networks, adaptability to position demands, and self-esteem or awareness into a single construct of movement capital, Vigoda-Gadot and colleagues (2010) allow for each to individually relate and predict success in a second career. Success, as defined by Vigoda-Gadot and colleagues (2010), is evidenced by career satisfaction, life satisfaction, intention to stay in the job. Each of these success measures requires the individual to hold a position. Forrier and colleagues (2015) examined the individual’s perception of employability both within and outside the current organization. They found that current employment affected perceptions of employability. Because this relationship is in question with exiting veterans, it should also be examined in this population.

Due to the dearth of quantitative research on factors predicting a successful transition of military veterans to the civilian workforce, the present study builds on the Vigoda-Gadot and colleagues’ (2010) and Forrier and colleagues’ (2015) research. In this work, we use selected predictors of Vigado-Gadot and colleagues (2010) duplicated by Forrier and colleagues’ (2015) construct of movement capital, and we add potential predictors congruent with the civilian and qualitative literature in career transition, such as, number of years of military service, number of deployments, age, gender, race-ethnicity, highest level of education, financial responsibility for others, preparation for retirement, resilience, self-esteem, satisfaction with life, EQ, cultural intelligence, self-efficacy, work ability, job satisfaction, and social capital, which have been examined across a veteran population (Bao & Luo, 2015; Greer, 2013; Griswold & Ellis, 2012; Hees et al., 2012; Jaensch et al., 2015; Johnston et al., 2010; Larson & Norman, 2014; MacPhee et al., 2013; Magnano et al., 2016; Nilsson & Ekberg, 2013; Özer et al., 2016; Rasdi et al., 2011; Schneider et al., 2013).

This study is focused on the predictive factors for securing employment and for being employed. The hypotheses are as follows:

Hypothesis 1: Age, gender, race-ethnicity, number of years of military service, number of deployments, highest level of education, financial responsibility for others, preparation for retirement, resilience, self-esteem, satisfaction with life, EQ, cultural intelligence, self-efficacy, work ability, job satisfaction, and social capital are predictive factors for securing employment (as defined by how long to secure employment after ETS).

Hypothesis 2:  Age, gender, race-ethnicity, number of years of military service, number of deployments, highest level of education, financial responsibility for others, preparation for retirement, resilience, self-esteem, satisfaction with life, EQ, cultural intelligence, self-efficacy, work ability, job satisfaction, and social capital are predictive factors for being employed (as defined by how long in current employment).

Method

This study closely follows the Vigado-Gadot and colleagues (2010) methodology. Military veterans were recruited through snowball sampling, including social media (e.g., Facebook, LinkedIn), emails, and fliers sent to military veteran organizations, university list-servs, and government programs serving military veterans. Within the invitation to participate in this study was a link and a QR code for participants to access the informed-consent form and measurements by computer or smartphone. Participants were asked to read the informed-consent form; if they consented to participate, they read the instructions for each instrument and completed them in one sitting in a location of their choice. Anonymity was safeguarded; researchers were blinded to the participants, no names were recorded, and identifying information was codified in the server. No compensation was provided for participation. Guidelines for human research were accounted for and university institutional review board approval was obtained.   

Participants

Of the 150 participants (United States of America military veterans) who volunteered to participate, 146 met the inclusion criteria. The inclusion criteria comprised all military veterans (federal, reserve) who were at ETS and were transitioning to civilian work life. Demographic information on the participants is presented in tables one and two. A power analysis using G*Power 3.1 (Faul et al., 2009) with .80 power, anticipated medium effect size f² = .15 (see Cohen, 1988), and 17 predictors found that a sample size of N = 146 suffices for a multiple regression. The alpha level was set at α = .05.

Measurements

A demographic questionnaire was administered first, followed by 10 standardized measurements, taking approximately 30 minutes to complete. The administration of the demographic questionnaire and measurements was done electronically through a secured survey system server.

Standard demographic variables were collected with regard to age (in years), gender, and race-ethnicity (Larson & Norman, 2014). Unique demographics, consistent with military-related literature, included the number of years of military service and number of deployments (Larson & Norman, 2014; Vigoda-Gadot et al., 2010). Additional demographics, consistent with employment studies, included highest level of education (e.g. high school; bachelors; masters; doctorate), financial responsibility for others (yes/no), how long to secure employment after ETS (in months), and how long in current employment (in months; Larson & Norman, 2014; Vigoda-Gadot et al., 2010).

The measurements consisted of the following: Preparation for Retirement, the Brief Resilience Scale, the Rosenberg Self-Esteem Scale, the Satisfaction with Life Scale, Emotional Intelligence, Cultural Intelligence, the General Self-Efficacy Scale, the Work Ability Score (WAS), the Job Satisfaction Subscale, and Social Capital. The measurements (operationalized variables) and demographics were chosen in line with the literature in career transition and military service (Bao & Luo, 2015: Greer, 2013; Griswold & Ellis, 2012; Johnston et al., 2010; Larson & Norman, 2014; MacPhee et al., 2013; Magnano et al., 2016; Nilsson & Ekberg, 2013; Özer et al., 2016; Rasdi et al., 2011).

Preparation for retirement or separation was operationalized through Baruch and Quick’s measure of efficiency of preparations at an organizational and individual level (Vigoda-Gadot et al., 2010). It included two items with a three-category response scale of 1 = no, 2 = yes, but did not use, and 3 = yes, used it. Cronbach’s alpha, as reported by Vigoda-Gadot and colleagues (2010), was .69.

The Brief Resilience Scale of Smith and colleagues (2008) was used to measure resilience, which was defined as the ability to bounce back from stress. In samples ranging in mean age from 19.8 to 62.8 years, a one-factor solution resulted from principal components factor analysis, explaining 55% to 67% of the variance with a Cronbach’s alpha of .8 to .91. Test–retest reliability at 1 month was .69 and at 3 months .62 (Smith et al., 2008).  Convergent validity with optimism, purpose in life, social support, and behavioral engagement was found (Leontjevas et al., 2014; Smith et al., 2008). In 2014, Leontjevas and colleagues reported a Cronbach’s alpha of .83 and convergent validity of .35 at baseline and .50 at 4 weeks.

Self-esteem was measured by the Rosenberg Self-Esteem Scale containing 10 items of global self-worth (Rosenberg, 1989). Each item is rated on a four-point Likert scale ranging from strongly agree to strongly disagree. High scores reflect feelings that one’s self is respected by self and is someone worth knowing (Rosenberg, 1989). An individual with low scores lacks respect for self and is contemptuous of self (Rosenberg, 1989). Johnson and colleagues (2016) reported a Cronbach’s alpha of .85, and Rasdi and colleagues (2011) reported an internal consistency of .73 while Mullen and colleagues (2013) confirmed the original factor structure of the scale.

The Satisfaction with Life Scale of Diener and colleagues (1985) measures self-reported global life satisfaction. A single factor resulting from five items rated on a 7-point Likert scale accounted for 66% of variance with a 2-month test–retest coefficient of .82 for undergraduate students (Diener et al., 1985).

Emotional intelligence was assessed as a characteristic or trait consisting of emotional assessment of self and others, expression of emotion, regulation of emotion in self and others, and use of emotion to problem solve (Schutte et al., 2009). A 33-item inventory using a 5-point Likert rating scale is composed of four scales: Perception of Emotion, Managing Own Emotions, Managing Others’ Emotions, and Utilization of Emotion (Schutte et al., 2009). A Cronbach’s alpha of .90 was found with a development sample of 346 with additional studies consistently resulting in Cronbach’s alphas of .87 (Schutte & Malouff, 2011; Schutte et al., 1998; Schutte et al., 2009). Age and gender did not moderate results for this scale (Schutte et al., 2007).

Cultural intelligence, the capability of an individual to function and manage in culturally diverse settings, is related to general intelligence but is an individual trait found to be distinct from other stable personality traits (Ang et al., 2007). The original 20-item scale was reduced to 12 items due to overlap with emotional intelligence as measured by Schutte and colleagues (1998), resulting in four factors of four items each (Ang et al., 2007). In initial studies, the scale explained unique variance beyond emotional or general intelligence with factor reliabilities ranging from .70 to .88 (Ang et al., 2007). Additional studies resulted in Cronbach’s alphas of .81 to .89 with 36% of the task performance variance explained by cultural intelligence (Ang et al., 2007).

The sense that one can accomplish a novel task and adjust to adversity from a variety of challenges is general self-efficacy as measured by Schwarzer and Jerusalem’s General Self-Efficacy Scale (Luszczynska, Guitérrez-Doña, et al., 2005). Strong internal consistencies (alpha ranging from .75 to .91) are consistently demonstrated (Scholz et al., 2002). Strong correlations have been found for self-efficacy, psychological quality of life and job and life satisfaction (Luszczynska, Scholz, et al., 2005). The 10-item scale rated on a 4-point Likert scale has repeatedly explained unique variance beyond identified correlations (Luszczynska, Guitérrez-Doña, et al., 2005).

Workability is defined as the worker’s self-reported evaluation of current self-assessment versus the individual’s lifetime best (El Fassi et al., 2013). This assessment forms the WAS as defined by this single question (El Fassi et al., 2013). Convergent validity was found for the WAS and the longer, 7-item, Work Ability Index, allowing for use of the single item to assess the construct (El Fassi et al., 2013).

Bowling and Hammond (2008) demonstrated construct validity of the Job Satisfaction Subscale of the longer Michigan Organizational Assessment Questionnaire. Beyond meta-analytic evidence of construct validity, the Job Satisfaction Subscale possesses face validity to affective job satisfaction, contains only three items, and is a global rather than specific factor assessment of satisfaction (Bowling & Hammond, 2008). Across the meta-analysis the mean internal consistency was .84 and the mean test–retest reliability was .50, both evidence of reliability (Bowling & Hammond, 2008). Joshi and colleagues (2015) reported interitem correlations of .71.  

Social capital derives from social capital theory fostering the relationship of an individual’s social network and career success (Vigoda-Gadot et al., 2010). Important to social capital are access to information, resources, and sponsorship, which are structures creating value and offering aid to individuals in their environment (Vigoda-Gadot et al., 2010). Consistent with previous international employment and military literature, social capital was measured as the number of individuals identified as helping, speaking in favor of, supplying information, and supporting the individual (Vigoda-Gadot et al., 2010). Cronbach’s alpha was .92.

Statistical Analyses

The IBM Statistical Package for the Social Sciences (SPSS) Statistics (IBM Corp, 2016) was used for descriptive and inferential analysis. The descriptive analysis included mean, median, mode, standard deviation, and percentages.  A multiple-regression analysis was conducted for each hypothesis. Multiple-regression assumptions were checked and met, except criterion data was positively skewed; thus, Lg10 transform was applied to normalize the skewed data. Once the data were normalized, a stepwise multiple regression was conducted.

Results

Descriptive Statistics

The results of the descriptive statistics are presented in Tables 1 and 2 as follows:

Table 1

Demographic Characteristics Among Survey Participants

Table 2

Age, Job Search, Employment, Length of Service, and Number of Deployments

Inferential Statistics - Stepwise Multiple Regression Analyses

For the criterion securing employment, the results are as follows:

Model-1 - Predictor satisfaction with life (β = -.201, p .015 < .05), with an R² for the overall model of 4% and an adjusted R² of 3.4%, a medium-sized effect according to Cohen (1988). The F(1, 143) = 6.035, p .015 < .05.

Model-2 - Predictors satisfaction with life (β = -.211, p .010 < .05) and race-ethnicity (β = .182, p .026 < .05), with an R² for the overall model of 7.3% and an adjusted R² of 6%, a medium-sized effect according to Cohen (1988). The F(2, 142) = 5.629, p .004 < .01.

For the criterion, being employed, the results are as follows:

Model-1 - Predictor age (β = .398, p .000 < .01) with an R² for the overall model of 15.8% and an adjusted R² of 15.3%, a large-sized effect according to Cohen (1988). The F(1, 143) = 26.924, p .000 < .01.

Model-2 - Predictors age (β = .617, p .000 < .01) and the number of years of military service (β = -.417, p .000 < .01), with an R² for the overall model of 28.5% and an adjusted R² of 27.5%, a large-sized effect according to Cohen (1988). The F(2, 142) =  28.286, p .000 < .01.

Model-3 - Predictors age (β = .614, p .000 < .01), the number of years of military service (β = -.404, p .000 < .01), and workability (β = .176, p .013 < .05), with an R² for the overall model of 31.6% and an adjusted R² of 30.1%, a large-sized effect according to Cohen (1988). The F(3, 141) = 21.692, p .000 < .01. 

Model-4 - Predictors age (β = .617, p .000 < .01), the number of years of military service (β = -.395, p .000 < .01), workability (β = .190, p .007 < .01), and social capital (β = -.139, p .048 < .05), with an R² for the overall model of 33.5% and an adjusted R² of 31.6%, a large-sized effect according to Cohen (1988). The F(4, 140) = 17.607, p .000 < .01.

Discussion

The challenges of transitioning from the United States military to the civilian workforce are well documented (Zogas, 2017); however, there is a gap in the body of knowledge about what dispositional and work factors can lead to a more successful transition to the civilian workforce. The results of the stepwise multiple regression (all models) present a picture of transition in the veteran population that has some similarities to that of the civilian population when seeking employment at older ages, yet some factors seem to play a key role in the veteran population, such as age, race-ethnicity, the number of years of military service, satisfaction with life, workability, and social capital (Rey & Extremera, 2015; Volmer & Spurk, 2011; Zacher, 2014). Protean or proactive behaviors have been described in the literature and have been stated as vital to career development in a new self-directed career (Briscoe et al., 2012; Grimland et al., 2012; Volmer & Spurk, 2011). Within this perspective is the need for individuals to believe in themselves, be in an environment that fosters growth, and have a sense of satisfaction in life, among other characteristics (Rosenberg, 1989; Schwarzer & Jerusalem, 1995). Together these factors drive individuals to cement their professional development as they mature, acquire longevity in their career settings, assess their current ability to work, and strengthen their social/professional network, which in turn positively influences their life satisfaction (Erdamar & Demirel, 2016; Ferris et al., 2013; Mache et al., 2014). Although satisfaction with life increases the likelihood of a successful transition from military service to civilian work, social capital indicates that support is required to bolster one’s personal assessments of self-worth, skills, and ability to work because self-identity does alter self-perceptions (Amiot et al., 2015).

There were two purposes in conducting this study. The first was to start to close the gap and to begin a dialogue about the factors that may lead to a more successful transition from the military to the civilian workforce. In starting this discussion, the authors hope to increase the number of studies that are conducted to help service members in this critical phase of transition; there are few at present. Such studies should increase the sample size, which was a limitation in the present study.

The second purpose was to determine if there are predictors that can be used to develop initiatives within the military and after service to improve employment outcomes for veterans. Although little can be done to change demographic predictors, such as race-ethnicity, age, and length of service (number of years in the military), initiatives can be designed to strengthen dispositional factors, such as life satisfaction, workability, and social capital. It is recommended that initiatives be explored specifically targeted to such factors. 

Declaration of Conflicting Interests

The authors declare that there is no conflict of interest.

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