Journal of Organizational Culture, Communications and Conflict (Print ISSN: 1544-0508; Online ISSN: 1939-4691 )

Research Article: 2018 Vol: 22 Issue: 1

Understanding Generational Identity, Job Burnout, Job Satisfaction, Job Tenure and Turnover Intention

Jason Abate, Walden University

Thomas Schaefer, Walden University

Theresa Pavone, Capella University


High employee turnover rates are problematic in the retail banking industry because turnover increases the risk of costly regulatory compliance mistakes. The factors that predict turnover in this industry are not well understood, however. The purpose of this correlational study was to examine the relationship between the independent variables of job satisfaction, job burnout, time on the job, generational identity and the dependent variable of turnover intention for retail banking employees in the United States. A random sample of 100 individuals from the banking industry responded to an online survey that combined elements of a job satisfaction survey by Babin & Boles, a turnover intention survey by Boshoff & Allen and the Maslach Burnout Inventory. Results of the multiple linear regression analysis suggested statistically significant (p<0.001) relationships between job burnout and turnover intention (  =0.297) and between job satisfaction and turnover intention (  =0.683). These findings are congruent with research that shows that satisfied employees report less job burnout and are more likely to remain in their job.


Job Burnout, Job Tenure, Turnover Intention.


Changes in the US banking industry drive changes in new consumer banking technologies, competition and regulatory changes (Goyal & Joshi, 2012). Employees remain reluctant to embrace new expectations and regulations, consider retirement or leave their employers because of these changes (Goyal & Joshi, 2012). Banks face high costs as a result of turnover; they not only lose valuable customer relationships formed with these employees, but they also lose the extensive knowledge that these employees possess (Goyal & Joshi, 2012; Sarangi, 2012).

Technological advancements in banking give consumers access to their banking services through mobile and online capabilities. Along with the too-big-to-fail banking crisis and the subsequent overhaul of the bank and financial industry oversight and regulations, many things changed regarding how banks can do business (Suja & Raghavan, 2014). For example, the innovation and implementation of new banking technology, including technology that allows customers to perform banking tasks more efficiently, resulted in a reduction in the number of staff members required by in-person facilities (Suja & Raghavan, 2014). According to the US Bureau of Labor Statistics (BLS, 2013), the number of workers 55 and older rose to 64.5% in 2012 from 61.9% in 2002 and the BLS projects that this trend will continue to grow to 67.2% by the year 2022 (BLS, 2013). Similarly, 40 million millennial will enter the workplace before 2020 (Ferri-Reed, 2012). The combination of these two trends effectively forces employers to consider how work performance by staff members may vary in the context of a multigenerational environment (Lu & Gursoy, 2016).

Few researchers in the field have examined employee turnover in the retail banking industry and how generational identity correlates with it. This gap in the knowledge base offered an opportunity for further research. Because Mannheim’s (1952) theory of generations proposes that members of each generation share a common identity because of their shared historical experiences, the theory of generations provided an excellent theoretical lens through which to study how, if at all, generational affiliation affects turnover and turnover intention in the retail banking industry.

Hellmans & Closon (2013) examined the intention of employees continuing to work until the legal retirement age. Health conditions, professional competence and psychosocial work conditions between two groups included examining employees aged 40 to 49 and employees aged 50 and older (Hellmans & Closon, 2013). However, the researchers did not examine job satisfaction, job tenure or job burnout, which provides the opportunity for further studies into those areas.

Lu & Gursoy (2016) also investigated generational differences in the workplace. Lu and Gursoy examined the moderating effects of how generational differences affected the relationships among job burnout, employee satisfaction and employee turnover intention in the hospitality and tourism industry. Lu & Gursoy (2012) suggested that future researchers should examine how socio-demographic differences affect employees’ work values. As with the hospitality and tourism industry, the retail banking industry is largely customer service-based and therefore, Lu & Gursoy’s study offered a useful model for investigating how generational differences affect employee satisfaction, job burnout and turnover in the retail banking industry.

Fei & Junhui (2013) also studied generational differences among workers in a single industry, focusing their attention on peasant-workers in the urban construction industry. Fei & Junhui found that members of the new generation of peasant-workers were not only less culturally and scientifically sophisticated than their predecessors, but they also have a harder time finding work compared to the older generation of peasant-workers. Fei & Junhui also presented suggestions to combat the new generation of peasant-workers’ employment-related problems.

Matin, Kalali & Anvari (2012) examined job burnout among demographic variables. Their independent variable was job burnout, while the dependent variables were the commitment to the organization, intention to leave and job satisfaction. The researchers used several moderate variables in their study, including age. Matin et al. found that employees experiencing job burnout are less committed to their employers, which in turn leads to a decrease in job satisfaction. Because their study focused on a company in the Iranian public sector, Matin et al. suggested that further research was necessary for other industries. Cekada (2012) explained that in the 2010s, employers must manage four different generations and that understanding each generation’s core values and attitudes are the key to successfully recruiting, retaining and training employees. As a result, Cekada asserted, employers must reevaluate their strategies and focus on learning styles, recruitment, motivating factors, compensation and collaboration. Doing so will foster more productive workplaces with more efficient recruitment practices and higher retention rates (Cekada, 2012). Cekada also suggested that further research could help employers better understand how generational identity relates to turnover intention, employee job satisfaction and job burnout. These researchers all indicated that further study is necessary to understand the relationship between generational affiliation and employee turnover, which suggests that studying how generational affiliation affects turnover in the retail banking industry would not just improve management practices within a single industry, but would also have broader implications for other industries (Kaifi, Nafei, Khanfar & Kaifi, 2012).

The relationship between generational affiliation and turnover within the retail banking industry warrants scholarly attention. As Ferri-Reed (2012) noted, nearly 40 million millennial are already in the workplace, with an additional 40 million projected to enter the workforce shortly. The influx of millennial workers, combined with a projected 49% increase in the population of workers over the age of 65 between 2013 and 2033, presents challenges to bank leaders, who must recruit, retain, train and motivate employees with generation-specific values and attitudes (Paton, 2013). Because employers may see up to five generations working together by 2018, it is imperative to uncover what relationships, if any, exist among generational affiliation, job burnout, job satisfaction and turnover intention (Lub, Bijvank, Bal, Blomme & Shalk, 2012).

Collectively, these researchers have provided a solid foundation for this research study, where the goal was to identify the differences and generalities among employees based on generational affiliation, job satisfaction and employee turnover problems in the retail banking industry. Many researchers have found a correlation relationship between generational affiliation and turnover intention in various industries (Kaifi et al., 2012; Lu & Gursoy, 2016; Lub et al., 2012). The foundational concepts of (a) turnover intention, (b) job burnout, (c) generational affiliation and (d) job satisfaction shaped the basis for the contributing factors leading to employee turnover and its financial effects, which Lu & Gursoy (2016) have documented in prior research.

Generational identity influences workplace attitudes and practices that affect job burnout and turnover rates (Lu & Gursoy, 2016). Millennial, the youngest generation in the workforce, may have less tolerance for high-stress jobs (Shragay & Tziner, 2011). As such, they may quit jobs where they are unhappy, despite fewer job options because of a lack of experience (Matin, Nader & Anvari, 2012). Older employees, such as baby boomers and Gen Xers, may be able to tolerate the stress of being in a job even if they are unhappy because they do not possess the educational or technological background of their younger coworkers. In either case, employees may become less satisfied with their situation, leading to job burnout and possible erosions in the quality of customer service provided (Lu & Gursoy, 2016).

Several factors contribute to employee turnover. As Thompson (2011) found, turnover among older workers can be just as worrisome to employers as retaining younger employees, because older workers bring value to the workplace. Pena (2013) examined job retention issues to determine if generational affiliation influences if employees remain in their current positions. Pena found that Generation X and Y employees both valued their jobs and promotional opportunities, but anticipated looking for a new employer within the next 3 years. Pena also noted that the most important factor for retaining employees seemed to be strong, consistent management practices as well as rewards and recognition of the employees among these two generations. The key finding in Pena’s study was that employers should not assume that employees in Generations X and Y will remain in one position until retirement such as previous generations and those employers should instead find ways to keep employees of each generation happy if they want to retain them.

Dixon, Mercado & Knowles (2013) examined the behavioural and commitment characteristics of different generations in technical and nontechnical occupations and found that across all generations studied, employees in technical occupations had stronger associations of follower behaviours and lower commitment levels, while employees in nontechnical occupations were less likely to display follower behaviours and displayed a stronger sense of engagement. Identifying the differences and perceptions of each generation in the workplace is the first step in addressing the turnover problem. Zopiatis, Krambia-Kapardis & Varnavas (2012) examined each generation’s perceptions of other generations and found that different perceptions exist among workers of different generations. Heyler & Lee (2012); Zopiatis et al. (2012) suggested that to manage effectively; employers must understand how employees of different generations perceive one another.

Costanza, Badger, Fraser, Sever & Gade (2012) found understanding generational differences among employees may not be effective in managing workplace-related issues. The researchers conducted a meta-analysis of 20 published and unpublished studies using three variables related to generational differences: Job satisfaction, intent to turnover and organizational commitment. The studies that Costanza et al. examined included 18 pairwise comparisons using four age generations: Traditionalists, baby boomers, Generation Xers and millennial. After conducting their meta-analysis, Costanza et al. found no significant differences in work-related variables among the generations studied, which suggests that organizational involvement in generational differences may not be productive. The results of Costanza et al. meta-study reinforce the importance of gathering information from participants directly.

Workplace harassment, bullying and employment discrimination are additional potential issues employers face when dealing with a multigenerational workforce. Discrimination suits are financially detrimental, not just regarding judgments awarded, but also because they lead to employee turnover within the organization (Choi & Choi, 2011). Choi & Choi (2011), in a study using 420 participants, found 81% of employees older than 50 had experienced age-related workplace discrimination. Pelczarski (2013) wrote about the number of workers who were older than 55 and reminded employers of the Age Discrimination Act of 1967, which prohibits age discrimination of any kind in the workplace for workers who are 40 years old or older. Valenti & Burke (2012) researched historical cases where employees sued employers. They also surveyed 140 participants on various workplace-related scenarios, including age discrimination, to gauge when, if at all, participants would seek internal or external assistance or even quit their jobs. The survey results revealed that employees considered some workplace problems more discriminatory than others and participants’ based their actions on their perceptions of how discriminatory they deemed each hypothetical scenario (Valenti & Burke, 2012).

Problem and Purpose Statement

Retaining and hiring trained employees to prevent regulatory non-compliance is beneficial for banks to reduce costs (Feldman, Heinecke & Schmidt, 2013). Service workers in many industries show signs of high job burnout rates, but there are additional complications to turnover in the banking sector because inexperienced workers may make mistakes, leading to costly fines (Lu & Gursoy, 2016). The multigenerational workplace also poses problems for managers, as employee turnover intentions may be different for each generation (Lu & Gursoy, 2016). The problem is that management does not understand the relationship between generational identity, job burnout, job satisfaction, time on the job and turnover intention within the retail banking industry.

The purpose of this non-experimental, quantitative, correlational study was to establish whether generational identification, job burnout, job satisfaction and time on the job affect turnover intention within the retail banking industry in the United States. The study predictor variables were (a) generational identity as measured by year of birth and grouped into baby boomer, Generation X and millennial; (b) job burnout as measured by the Maslach Burnout Inventory-General Survey scale; (c) job satisfaction as measured by Babin & Boles’s (1998) six-item scale; and (d) time on the job as measured in years. The dependent variable was turnover intention as measured by Boshoff & Allen’s (2000) three-point scale. The population for this study was financial service employees within the United States.

Research Question and Hypotheses

The research question that guided this study was: What is the relationship between generational identity, job burnout, job satisfaction, time on the job and turnover intention?

H01: There is no statistically significant relationship between generational identity, job burnout, job satisfaction, time on the job and turnover intention.

Ha1: There is a statistically significant relationship between generational identity, job burnout, job satisfaction, time on the job and turnover intention.


To measure the dependent variable of turnover intention, Boshoff & Allen’s (2000) three-item scale was selected. Each statement in the Boshoff & Allen instrument uses a five-point Likert-type scale in which 0=I often think about resigning, 1=strongly agree, 2=agree, 3=neither agree nor disagree, 4=disagree and 5=strongly disagree (Boshoff & Allen, 2000). Not only is the Boshoff and Allen survey easy for participants to complete, but it also takes little time. This instrument includes three statements that will assess and measure retail banking employees’ feelings about their intention to leave.

The Boshoff & Allen scale is a reliable instrument, as evidenced by Park & Gursoy’s (2012) study involving turnover intention and job satisfaction. In their study, the Cronbach’s alpha coefficient for the variable of turnover intention was 0.76. Uludag, Khan & Guden (2011) also found a high level of internal consistency with this scale: A Cronbach’s alpha score of 0.88 for the turnover intention scale. Researchers have also used this to measure turnover intention. Boshoff & Allen (2000) used correlation coefficients when demonstrating how the variable of work engagement is negatively associated with turnover intention; correlations between the two scales were equal to or higher than 0.90. Later work by Park & Gursoy also proved the validity of the Boshoff & Allen scale based on expected results of their studies (2012).

Generational identity explains that a group of individuals who have lived during a time frame of similar years (Tung & Comeau, 2014). To measure the independent variable of generational identity, participants provided their age. Data that were collected was used to assigned participants to one of the following generational identity groupings: Baby boomers (born between 1943 and 1960), Generation X (born between 1961 and 1980) and Generation Y/millennial (born between 1981 and 2000).

For the independent variable of job burnout, permission was obtained to use the MBI-GS instrument for this study to measure the independent variable job burnout. According to the instrument’s creators, the MBI-GS measures three factors that can be present in people who work in customer service: Emotional exhaustion, depersonalization and reduced personal accomplishment (Maslach & Jackson, 1986). Although the MBI-GS instrument includes 22 statements that a person may feel (Maslach & Jackson, 1986), only seven of the statements of feeling to help assess and measure job burnout factors among retail banking employees was used in this study.

The MBI-GS instrument was used to assess the respondents on their work-related feelings toward each statement. Each statement used a seven-point Likert-type scale from 0=never, 1=a few times per year, 2=once a month, 3=a few times per month, 4=once a week, 5=a few times per week and 6=every day (Maslach & Jackson, 1986). The MBI-GS survey is easy to complete and can be completed quickly. The seven-point Likert-type scale also makes for a standardized approach, giving certainty to the meanings assumed by the respondents (Maslach & Jackson, 1986).

The MBI-GS scale is a reliable instrument, as evidenced in Li, Guan, Chang & Zhang (2014). In their work, the Cronbach’s alpha coefficients for the three dimensions of the MBI-GS scale are 0.896, 0.747 and 0.825. Earlier work by Zalaquett & Wood (1997) calculated Cronbach’s coefficient alpha values of 0.90 for emotional exhaustion, 0.79 for depersonalization and 0.71 for personal accomplishment.

The MBI-GS scale is a valid instrument and one of the most accepted scales of measuring job burnout. In fact, Shutte (2000) called the scale the gold standard for measuring job burnout. The scale has proven valid via correlations with behavioural ratings made by an independent person who knew the participant well, such as a family member or close friend. MBI scores are also valid via correlations with typical job characteristics that contribute toward job burnout and other factors that presumably lead to job burnout. Zalaquett & Wood (1997) discovered that when observing people, they aligned with the self-assessments of individuals who rated themselves with high levels of job burnout. Li, Guan, Chang & Zhang (2014) also proved the scale’s validity in their study determining the association between core self-evaluation and the job burnout syndrome among Chinese nurses.

In another research study, Schutte (2000) examined the factorial validity of the MBI-GS job burnout inventory across occupational groups and nations and found that factorial validity across nations and occupational groups existed, using the MBI-GS scale for measuring job burnout. Maslach & Jackson (1986) established the validity of the MBI-GS by correlating an employee’s MBI-GS scores with the behavioural rating of the employee’s colleagues and even from their spouse. Maslach & Jackson were also able to link MBI-GS scores with the measures of outcomes that resulted from poor job satisfaction and the experience of job burnout. These obtained sets of coefficients provided evidence for the MBI-GS scale’s validity as an instrument.

The instrument selected to measure the independent variable of job satisfaction was Babin & Boles’s (1998) six-item scale. This scale is easy and can be completed quickly because there are only nine questions. The instrument aligns well with the framework for assessing retail banking employees’ feelings toward job satisfaction in the retail banking sector. The Babin and Boles scale uses a five-point Likert-type scale of 15 with 1=strongly agree, 2=agree, 3=neither agree nor disagree, 4=does not agree and 5=strongly disagrees. In their study of job satisfaction, Sadozai, Zaman, Marri & Ramay (2012) demonstrated the reliability of this scale with a Cronbach’s alpha score of 0.53. Bute’s (2011) study on the effects of nepotism and favoritism on employees’ behaviours and human resources practices in bank settings also found a Cronbach’s alpha coefficient of 0.53, thus proving the reliability of the scale for measuring turnover intention. All correlations used in Bute’s study were significant at 0.01 levels and the highest correlation was between human resources practices and job satisfaction (r=0.69), thereby proving the validity of Babin & Boles’s scale.

The independent variable of employee tenure uses the question what is the length of time with the current bank and was coded in years. The independent variable of employee tenure is also referred to as time on job. The independent variable of generation affiliation used a single question to determine a respondent’s age.

Data Collection and Analysis Technique

Data was collected in the fall of 2016 using an online survey. The online web-based questionnaires participants completed consisted of four sections. The first section measured job burnout factors as defined by the MBI-GS scale (Maslach & Jackson, 1986). The second section measured the dependent variable of turnover intention as defined by the Boshoff & Allen scale (Boshoff & Allen, 2000). The third section measured the independent variable of job satisfaction as measured by the Babin & Boles scale (Babin & Boles, 1998). The fourth and final section of the survey gathered demographic data about the participants, including information about the length of tenure on the job, the age of respondent and gender of the participant.

Distribution of the survey was completed through Survey Monkey. Survey Monkey selected a random sample of currently employed retail banking employees who were 18 years of age of older to participate in the survey and then collected data based on these employees’ responses. Survey Monkey provided a URL link to participants granting them access to the survey using their preferred Internet browser. Survey Monkey administered the survey, which included both the informed consent form and the survey instrument. Survey Monkey located a random sample of participants and sent them the invitation to participate after meeting the criteria of being a current and active retail banking employee within the United States. Invited participants who chose to participate logged on via a provided web link from Survey Monkey. Participants received a brief overview of the purpose of the research and its potential for positive social change. Before proceeding to the survey questionnaire, respondents received an informed consent form that required them to check the acceptance box if they want to proceed by participating. If they clicked yes, participants proceeded to the survey. Participants who agreed and continued to the survey answered 22 questions. Once the surveys were complete, the data collected from Survey Monkey’s website were downloaded and exported into an Excel spread sheet. Prior to all statistical analyses, the following steps we taken to prepare the data. First, the original dataset had 165 respondents. However, only 100 individuals within the dataset gave their consent to participate and then completed all of the questions in the survey. As such, the dataset was restricted to these individuals. The deletion of the aforementioned cases resulted in a 60.6% attrition of cases from the dataset from the base size of 165 to the final size of 100. The final dataset comprised 71 female and 39 male respondents. Using SPSS, a multiple linear regression was conducted to examine the effects the four independent variables have on the dependent variable of employee turnover intention.

Presentation of Findings

The aim of this hypothesis testing was to examine whether the four independent variables of generational identity, job burnout, job satisfaction, time on the job had a significant relationship with the dependent variable of turnover intention. The research question was as follows: What is the relationship between generational identity, job burnout, job satisfaction, time on the job and turnover intention? Derived from this research question were the following null and alternative hypotheses:

H02: There is no statistically significant relationship between generational identity, job burnout, job satisfaction, time on the job and turnover intention.

Ha2: There is a statistically significant relationship between generational identity, job burnout, job satisfaction, time on the job and turnover intention.

The calculations for means and standard deviation for all variables are presented in Table 1. Ritchey (2008) noted that means and standard deviations are the appropriate descriptive statistics to report for continuous variables. The average years worked among respondents was 13 years and three months. The average age of respondents in the current dataset was approximately 54 years. The midpoint of the Intention to leave scale was 3.0. The mean score was below the midpoint. The mean of 2.21 for the Intention to leave scale suggests that the average respondent gave a response of “disagree” with respect to their intention to leave. The midpoint of the Job Satisfaction scale was 3.0 and the mean score was below the midpoint. The mean of 2.20 for the Job Satisfaction scale suggests that the average respondent gave a response of “disagree” with respect to their job satisfaction. The midpoint of the MBI scale was 4.0. The mean score was also under the midpoint. The mean of 2.96 for the MBI suggests that the average respondent felt that their level of job burnout was slightly below a response of “once a month,” which was coded as 3.0.

Table 1
Descriptive Statistics of the Study Variables
Variable M SD Min. Max.
Years worked at current job 13.24 11.37 0 46
Age of respondent 53.97 11.49 20 78
Intention to Leave scale 2.21 0.91 1 5
Job Satisfaction scale 2.20 0.89 1 5
MBI scale 2.96 1.49 1 7

Internal consistency values were evaluated using the Cronbach’s alpha statistic. The Job Satisfaction scale (α=0.844) and the MBI scale (α=0.928) both have a very good level of reliability. The Intention to Leave scale (α=0.922) has an outstanding level reliability.

Table 2 presents the results of the multiple linear regression of the dependent variable of intention to leave onto the independent variables. The Omnibus F-Test is statistically significant at an alpha level of 0.05 (F=47.540, df=4, 95; p<0.001). As such, decomposition of effects within the regression model can proceed. The coefficient of determination, also known as the R2 value, is 0.667. This means that 66.7% of the variation in the dependent variable of intention to leave is due to the independent variables of years worked at current job, age of respondent and job satisfaction as indicated by the Job Satisfaction scale and job burnout as indicated by the MBI scale. Among the four independent predictor variables, job satisfaction and job burnout emerged as statistically significant predictors of the dependent variable of intention to leave.

Table 2
Multiple Linear Regression of Dependent Variables onto the Independent Variables
Variable B SE(B) Beta p
Constant -0.738 0.428   0.088
Years worked at current job 0.007 0.006 0.066 0.295
Age of respondent 0.010 0.006 0.096 0.131
Job Satisfaction scale 0.683 0.118 0.524 0.000
Maslach Burnout Inventory scale 0.297 0.070 0.379 0.000
N 100      
F 47.540     0.000
R2 0.667      

The positive unstandardized coefficient of the MBI scale (B=0.297, p<0.001) shows that as job burnout increases, intent to leave also increases, even when controlling for age, years at job and job satisfaction at an alpha level of 0.05. The positive unstandardized coefficient of the Job Satisfaction scale (B=0.683, p<0.001) shows that as job dissatisfaction increases, intent to leave also increases, even when controlling for age, years at job and job burnout at an alpha level of 0.05.

Research Question Outcome

In this study, the focus was on the relationship between generational identity, job burnout, job satisfaction, time on job and turnover intention. Results of the study indicated that among the four independent predictor variables, job satisfaction and job burnout emerged as statistically significant predictors of the dependent variable of intention to leave. However, these findings did not support the elements of the alternative hypothesis, which claimed that years worked at current job and age of respondent would also have a significant effect on turnover intention among retail banking employees. Therefore, we could not completely reject the null hypothesis, as empirical support for the alternative hypothesis was somewhat limited.


Based on the results of multiple regression analysis (Table 2), the null hypothesis could not be fully rejected concerning a relationship between job burnout, job satisfaction, generational affiliation, time on job and turnover intention. Thus, generally speaking, the results of the study were consistent with the existing literature to support two of the four factors having a significant positive relationship with turnover intention. As a result of the research findings, it can be concluded that job burnout and job satisfaction were important factors causing or limiting employee turnover, a finding that aligns with previous work (Lu & Gursoy, 2016; Matin et al., 2012; Mujaba, 2011).

Employees are among a company’s most valuable assets, so employers need to understand job burnout and its causes. According to Lu & Gursoy (2016), because employees in customer service-based industries are subjected to customer demands, they are at a high risk for job burnout. Lu & Gursoy noted that job burnout is costly for organizations on two fronts: It leads to higher turnover rates and it decreases worker productivity. Lu & Gursoy also noted that job burnout is one of the best predictors of job satisfaction and turnover intention, which is consistent with the findings of the current study. Matin et al. (2012) reached a similar conclusion, noting that employees who are experiencing job burnout are not only less committed to their employer, but are also more dissatisfied with their job.

Mannheim’s (1923) theory of generations was the theoretical framework guiding this study. Under this theory, a person’s generational affiliation influences their decision to leave or continue employment (Mannheim, 1952). The results of this study did not reveal significant relationships between generational affiliation and intention to leave.

Interestingly, Beutell (2013) questioned whether job satisfaction related to the age of the employees. The results from the Beutell study suggested that work-family conflict synergy had an effect on job satisfaction, irrespective of age.

Recommendations for Further Research

This study was conducted in the retail banking industry within the United States to examine the relationship between turnover intention and the four variables: Job satisfaction, job burnout, generational affiliation and time on job. Results from this study revealed that turnover intention positively relates to two of the four variables. However, prior studies have revealed that time on job was also related to turnover intention (Hellemans et al., 2013; Lub et al., 2012).

We found that results of this study were in conflict with the findings of the other studies in regards to time on job. One study conducted by Zick et al. (2012) concluded that older workers often were not saving enough money toward retirement and actually delayed retirement. This suggests that older workers planned to work as opposed to leaving their jobs. Another study conducted by Luo (2012) revealed that the more positive older employees’ experiences were with their younger colleagues, the more likely they would want to stay in their jobs. Lub et al. (2012) provided an example of a study that found employees may stay based on time on job. The Lub et al. study found Generation X employees valued job security among other factors. The question may be explained by the age of the employees who have more time and are younger to move to different employers, whereas older employees may value the security of having a consistent employer and saving toward retirement.

Because the focus of this study was on the retail banking industry in the United States, the findings of this study may not be applicable to other retail and or customer service industries. We would suggest that future studies be conducted to examine whether turnover intention may be predicted by job satisfaction, job burnout, time on job and generational identity in other customer service front-line industries. Such studies would contribute to the literature by raising awareness of customer service workers and the leadership they report to on the relationship between turnover intention and variables responsible for turnover.


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