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A Quantitative Study on Generational Cohorts, 

Work-Related Quality of Life, and Turnover Intentions in 

the Mid-Atlantic Technology Industry 
 

David Stearrett | Columbia Southern University, Orange Beach, AL, United States 

Contact: dstearrett@outlook.com  

 

Abstract 
 

The labor landscape of the technology industry has been historically competitive, with notably 

high employee turnover. This study examined the relationship between generational cohorts, 

work-related quality of life, and turnover intention to provide a timely review of the current labor 

landscape and the relevance of work-related quality of life to turnover intention across 

generational cohorts. Participants included 87 currently employed IT professionals in the Mid-

Atlantic region of the United States. In a multiple regression analysis, the data revealed that 

generational cohorts value work-related quality of life equally, and no statistically significant 

differences were detected between the groups. The relationship between work-related quality of 

life and turnover intention was also reconfirmed through piecewise regression, which revealed a 

strong inverse relationship. The findings of this study revealed that employers should focus on 

work-related quality of life equally across generations within the workforce, and that investing in 

it yields a significant reduction in turnover intention among employees. 

Keywords: generational cohort, work-related quality of life, turnover intention, employee 

satisfaction, productivity. 

 

 

 

 

 

 

 

 

 

mailto:dstearrett@outlook.com


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Introduction/Background 
 

Voluntary turnover is a challenge for many organizations worldwide and poses a 

substantial financial burden for businesses (Li & Fox, 2023). Replacing employees who have 

voluntarily departed incurs direct costs, including advertising fees for the open position, 

recruiting expenses, and training new hires (Sipple, 2023). These activities cost American 

companies an estimated one trillion dollars annually (McFeely & Wigert, 2019). 

The costs of turnover can be further categorized into decreases in productivity while 

awaiting the replacement employee, losses in technological competencies, and customer 

departures. Employee turnover places additional burdens on staff, increases burnout within 

existing teams, and exacerbates departmental performance. Voluntary turnover has also been 

linked to a contagious effect within the workforce, whereby additional employees consider 

departing their organizations (Chung et al., 2022; Geurts et al., 1998) 

Managing employee turnover is one of the most significant concerns of organizations, as 

evidenced by a Society for Human Resource Management (SHRM)(n.d.) survey, wherein HR 

professionals listed this topic as the top management concern. Of further concern is the increase 

in millennial turnover, which is growing as millennials continue to occupy larger segments of the 

labor pool (Emmanuel, 2021). Numerous research studies have been conducted on management 

styles and techniques that could be leveraged to minimize employee departures (e.g., Barkhuizen 

& Gumede, 2021; Shin & Alam, 2022)  

A significant current problem in business is the declining number of employees in the IT 

industry (Federal Reserve Bank of St. Louis, 2023) and the increased voluntary turnover rate. 

Specifically, managers do not understand how each generation’s turnover intent differs when 

accounting for work-related quality of life (WRQoL). De La Cruz (2022) described how 

different generations hold varying values that influence and shape their actions. Employee 

turnover leads to decreased productivity and increased costs associated with training and 

onboarding new employees (Pirrolas et al., 2023). The threat of turnover can also lead to offering 

higher salaries and benefits packages to retain current employees (Pitstick, 2022; Rubin & 

Cambon, 2023).  

Lewis (2022) reported that the average turnover rate across all industries in 2022 was 

10.6%; the technology industry had the second-highest rate at 12.9%, behind professional 

services. Research has indicated that although external factors are relevant considerations, 

internal factors are a stronger driver of employee turnover (Nguyen & Lee, 2022). Internal and 

controllable conditions are primary contributors to employee turnover and organizational 

success, causing leaders to face greater scrutiny towards their ability to mitigate. Employees who 

feel their employer fails to meet their expectations are more likely to depart due to a breach of 

the psychological contract —an inferred expectation that employees develop (Moquin et al., 

2019). In 2023, the United States saw its largest salary budget increase in over two decades, 

mainly to maintain competition in tight labor markets and meet rising employee expectations 

(Mayer, 2023).  

The purpose of this quantitative correlational study was to examine the relationships 

among work-related quality of life, generational cohorts, and turnover intent among IT 

employees in the Mid-Atlantic region of the United States. The tight labor market for IT 

employers (Kumar, 2022) emphasizes reducing turnover and retaining talent to avoid increased 

costs (Pitstick, 2022; Rubin & Cambon, 2023) and to minimize productivity decreases within the 

workforce (Pirrolas et al., 2023). Work-related quality of life can be used to predict turnover 



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intention (Ishfaq et al., 2022; Poku et al., 2022), and job satisfaction (Rostami et al., 2021). 

Investigating turnover intent amongst generational cohorts aligns with the literature pointing to 

generational differences within the workforce (De La Cruz, 2022) and to notable differences in 

generational cohorts’ responsiveness to variables such as person-environment fit and its 

relationship to turnover intention (Rattanapon et al., 2023). Differences in employee attitudes, 

such as those across generations, influence an individual's decision to depart from their 

organization in the IT industry (Guha & Chakrabarti, 2016). The generational cohorts in the 

labor force include baby boomers, Generation X, millennials, and Generation Z (Dimock, 2019). 

Each generation's turnover intention was examined using the six questions from Bothma and 

Roodt's (2013) Turnover Intention Scale and the 24 questions from Easton and Van Laar's (2018) 

Work-Related Quality of Life Scale. A purposive sample was utilized to obtain the required 

participants. 

 

Literature Review  
 

Turnover has a significant impact on the business community and is considered a major 

issue. Prior research has found a few different outcomes when considering the impact of 

generational cohorts upon turnover intention (Abate et al., 2018; Lee & Kim, 2021; Loignon, 

2021; Lu & Gursoy, 2016), adding to the need to examine it through particularly narrow lenses 

to target specific populations of interest. The literature has also identified numerous variables 

that can affect an employee's desire to leave an organization, furthering interest in understanding 

how a generation may be responsible for this phenomenon. Prior research on work-related 

quality of life has predominantly focused on the healthcare industry, underscoring the need to 

examine other application domains (Barber, 2022; Ishfaq et al., 2022). To this author's 

knowledge, none have combined generational cohorts, turnover intention, and work-related 

quality of life into a single study focused on the IT industry in the US Mid-Atlantic region.  

 

Theoretical Foundations 

 

For this study, many theories were considered as the foundation, yet only one was 

chosen: Strauss and Howe’s generational theory (1991). Strauss and Howe (1991) published a 

book emphasizing the generation cohort structure in America. The resulting theory has become 

known as generational theory and is found across a range of literature on topics of interest to 

generations. The four segments of the theory are: a high, an awakening, an unraveling, and a 

crisis. The first turning is described as a high. This turning is a period of intense acceptance and 

satisfaction. Individuals are eager to conform to society's standards and confident about the 

direction they are heading. This turning is commonly associated with the silent generation. The 

awakening is described as a period in which individuals seek to recapture their personal 

independence and break from the existing social order. While social order is high from the prior 

period, there is a lack of desire among individuals. Most recently, this was the period when the 

baby boomer generation grew into adulthood. The unraveling is described as a period of low 

institutional trust. Social order has become less available, and there is less desire for it. The 

authors reference cynicism as a common theme during this turning. Generation X is the present 

generation linked to this description. Lastly, a crisis is a period in which a low social order exists, 

but a desire for social order reemerges. This is a period of rebuilding in response to a perceived 

necessity to survive. Millennials are associated with this specific turning.  



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The prevailing theme and rationale behind this theory are its cyclical, recurring period 

and traits. Generations that fall into similar turnings are viewed as similar in nature by the 

authors. This similarity is the reason this generational theory was chosen as the founding theory 

for this study. As generational cohorts recur over time, the value of studying their differences is 

critical and reusable for future generations.  

 

Turnover Intention 

 

Turnover is defined as either functional or dysfunctional: functional turnover involves 

removing poor performers from an organization, and dysfunctional turnover involves top 

performers departing (Boswell et al., 2008). When an employee opts to depart on their own, a 

sudden chasm can be created that may require an employer to react to mitigate impacts to 

productivity quickly (Pirrolas et al., 2023), departmental knowledge (Smith, 2023), and 

compensation packages (Pitstick, 2022; Rubin & Cambon, 2023). Employers must continuously 

be apprised of changes in working conditions, employees' personal values, and global economic 

conditions to maintain profitability within their organizations.    

 

Drivers of Turnover Intention 

  

There is an ever-expanding body of literature on turnover intention, especially studies 

examining its motivating factors. Moquin et al. (2020) conducted a quantitative study to examine 

how psychological contract breaches affect employee drivers of turnover intention. These 

psychological contracts are understood as understandings that employees develop, both formally 

and informally, and the outcomes when employees feel their expectations are not met.  

Many employer expectations are set within organizations' human resources departments, 

where a primary focus is on managing employee growth and expectations. Al Balushi Ak et al. 

(2022) constructed a quantitative experiment based upon social exchange theory, where they 

examined the impact of employee growth potential perceptions on turnover. In this experiment, 

the analyzed data confirmed a strong inverse correlation between career growth opportunities 

and turnover intention. Employees who believed these opportunities existed and that employers 

assisted them in their growth felt less inclined to leave their current roles.  

Junianti (2023) conducted a study to examine the impact of wage growth on employee 

turnover intention. The scholars identified a common theme of employees responding to wage 

growth and its potential with a reduced level of turnover intention. However, Alterman et al. 

(2021) examined a salary-related element by investigating whether pay secrecy affected an 

employee's turnover intention. The study's data did not reveal that pay secrecy, on its own, was a 

main influence on turnover intention. Rather, distributive justice was discovered to be the more 

reliable prediction method.  

Elrayah and Mabkhot (2023) examined how organizational commitment and job 

satisfaction impacted employees’ turnover intent. The correlational analysis revealed that both 

organizational commitment and job satisfaction were reliable determinants of employee 

turnover. When employees felt more committed to their organizations and were overall more 

satisfied with their work, they had less desire to leave. Hussain et al. (2023) bolstered the 

strength of the interrelation between job satisfaction and turnover intention in an additional 

study, showing that each independent variable was inversely correlated with turnover intention. 



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Additional support for the role of job satisfaction in turnover intention can also be found in 

Kanchana and Jayathilaka’s (2023) study, which found that job satisfaction was inversely 

correlated with employee turnover.  

 

Impacts of Turnover on Organizations 

 

A review of the literature reveals numerous variables that influence an employee's 

decision to leave their organization. The existence of various reasons for turnover creates a 

critical need for businesses to measure the impact on performance. For firms, financial success is 

a major driver of growth and sustainability that must be taken into account. Mia et al. (2022) 

conducted a study grounded in human and social capital theory to investigate financial success 

and turnover. The authors reported that a negative correlation between firm success and turnover 

levels was confirmed. Liu et al. (2023) focused on a single area of financial risk for organizations 

with high turnover intention: counterproductive work behaviors. The authors revealed a strong 

positive correlation between turnover intention and counterproductive workplace behaviors.  

Chung et al. (2022) conducted a study to examine how employee departures in the 

organization may influence the decision-making of the remaining employees. The study revealed 

that an individual turnover event was positively correlated with other employees’ decisions to 

depart an organization. This influence, dubbed the contagion effect, confirms that turnover is not 

a concern only at the individual level but can also influence other team members. The study also 

confirmed the negative correlation between turnover and firm performance. In a separate study, 

Oh and Chhinzer (2021) confirmed the existence of a contagion effect, as an individual's 

turnover event was positively correlated with other individuals' decisions to depart.  

 

Generational Cohorts 

 

Generational cohorts have long been discussed in a variety of research topics and 

organizational strategic decisions. Considerations surrounding generational cohorts have 

typically arisen from potential differences between groups, including what these differences 

could mean for a wide range of strategic decision-making and forecasting. Generational cohorts 

maintain differences in values and attitudes that could be of interest to organizations seeking to 

capitalize on their workforce composition. 

 Srivastava and Mishra (2019) conducted a study to examine the potential role of age in 

several work-related variables, including turnover intention. The authors found that job 

satisfaction, pay satisfaction, and work-life balance were positively correlated with age. This 

revealed that millennials were more likely to depart their organizations than other generations. 

Hung Kee et al. (2019) conducted a similar study to investigate how generational cohorts may 

influence employees' turnover intentions. This study found that the generational cohorts 

maintained distinct values. One area highlighted was baby boomers' higher overall satisfaction 

and commitment levels.  

To further consider how or why some of these differences exist, it is crucial to understand 

what makes the generations similar or different. Weeks and Schaffert (2019) created a study to 

explore how different generations perceived meaning in the workplace. This mixed-methods 

research study found that each of the four generations shared the belief that it was important to 



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have meaning in the workplace; however, each generation interpreted this differently, which 

shifted and impacted the ability to extrapolate to other areas of interest.  

 

Work-Related Quality of Life 

 

Work-related quality of life is a complex metric that assesses a range of factors in an 

employee’s work life. intention. Engström et al. (2023) designed an experiment to examine 

work-related quality-of-life inputs and their relationships with turnover intention. The results 

indicated that job satisfaction, work-home interface, and stress levels were negatively correlated 

with turnover intention. Supporting the findings of Engström et al. (2023), Els et al. (2021) 

created a research study examining the relationship between work-related quality of life and 

turnover intentions. Each of the nine sub-variables showed a statistically significant negative 

relationship with turnover intention. Poku et al. (2022) conducted a quantitative study examining 

work-related quality of life and turnover intention in Ghana. The author's analysis of the 

calculations revealed that work-related quality of life, along with all its subcomponents, showed 

a strong negative correlation to turnover intention.  

 

Methods 
 

The purpose of this quantitative correlational study was to examine the relationships 

among work-related quality of life, generational cohorts, and turnover intent among IT 

employees in the Mid-Atlantic region of the United States. Within the confines of this study, IT 

employees were defined as individuals working in software development, data processing, or 

system design. Participants were composed solely of currently employed individuals who live in 

the Mid-Atlantic region of the United States.  

Five variables of interest are included in the research study. Turnover intention represents 

the sole dependent variable within the study. The independent variables are Generation X 

membership, Generation Y membership, Generation Z membership, and work-related quality of 

life. Generational membership was captured through demographic questions that solicited an 

individual's date of birth and age at the time of survey completion. The individuals’ year of birth 

was then assigned to the appropriate generational cohort according to the categories defined in 

Table 1. Work-related quality of life was measured using the 24-item Easton and Van Laar 

(2018) Work-Related Quality of Life Scale. Turnover intention was measured using the Six-

Question Turnover Intention Survey from Roodt (2004). The main research question in the scope 

of this study was “What is the relationship between work-related quality of life, generation 

cohorts (Generation X, Y, Z), and turnover intent of employees in the IT sector of the United 

States?”. 

 

Table 1 

Generational Membership 

Year of Birth Generational Membership 



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1965-1980 Generation X 

1981-1996 Generation Y 

1997-2012 Generation Z 

 

Population and Sample Selection 
 

Employees within the IT industry who are 18 years or older and live in the U.S were the 

general population for this research study. There are roughly 12.1 million individuals employed 

within this industry in the United States (Internal Trade Administration n.d.). Within the IT 

industry, the target population was established as active employees from within Generation X, 

Generation Y, and Generation Z cohorts. Additionally, the researcher defined the target 

population as those employed in the Mid-Atlantic region of the United States. For this research 

study, IT industry employees were defined as individuals working in software development, data 

processing, or system design. The designation of Mid-Atlantic region employees was intended to 

capture any unique conditions within this region. This region includes the following states: 

Delaware, Maryland, New Jersey, Pennsylvania, Virginia, and West Virginia. 

Individuals in the sample group completed an online Qualtrics survey that collected data 

relevant to the researcher’s goal. A purposive sample comprised individuals who met the 

research study criteria. An a priori power analysis was completed, and the results indicate a total 

sample size of at least 77 individuals was required for analysis. Prolific facilitated the process of 

recruiting individuals who met the following criteria: individuals who are presently employed, 

working within the definition of the IT industry, belong to Generation X, Y, or Z cohorts, and are 

working within the Mid-Atlantic region of the United States. This survey was completed online, 

so participants needed access to a device with an internet connection.  

The sample size for this research study was calculated using G*Power. The program 

utilized one dependent variable, turnover intention, and three independent variables. While there 

are four total independent variables (Generation X membership, Generation Y membership, 

Generation Z membership, and work-related quality of life) in this study, the decision to utilize a 

slope dummy requires that one dependent variable be excluded at a time, leaving only three 

predictors by which to calculate the sample size. Analysis was run with α = .05 and β = .80. The 

completed analysis indicated that a minimum sample size of 77 was required to use multiple 

linear regression and generalize the results.  

 

Instrumentation 
 

The beginning of the survey presented the participant with the required consent form to 

continue in the research study. Only participants providing an agreement to this consent form 

were allowed to continue. Next, respondents were presented with demographic questions to 

collect data on age, employment status, industry of employment, gender identity, and year of 

birth. Two additional validated instruments were utilized to collect data within the survey. The 

survey authors provided prior permission to use the Turnover Intention Survey and the Work-

Related Quality of Life Survey.  



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Turnover intention was measured using the six-question Turnover Intention Survey 

(Roodt, 2004). The six-question Turnover Intention Survey (TIS-6) uses a Likert scale to 

indicate the degree of agreement with the two extremes. Three of the questions use “Never” to 

“Always”, one question uses “Very Satisfying” to “Totally Dissatisfying”, one question uses 

“Highly Unlikely” to “Highly Likely”, and a final question uses “Always” to “Never”. The 

survey calculates a final score from responses to each of the six questions and indicates the 

individual's turnover intention.  

Work-related quality of life was measured through the Easton and Van Leer (2018) 

Work-Related Quality of Life Survey. The survey is a 23-item psychometric scale with six 

distinct factors of measure. The survey uses a Likert scale ranging from strongly disagree to 

strongly agree. The survey has proven valuable for assessing an employee’s overall level of 

workplace contentment (Easton & Van Leer, 2018). Each of the six factors has its own set of 

scores, which are averaged, and the factor scores are then averaged together to produce a final 

score. 

 

Validity and Reliability 
 

Instruments must be demonstrated to be both valid and reliable. Various types of validity 

exist, each with its own considerations. The TIS-6 demonstrates criterion-predictive validity. 

Criterion-predictive validity refers to the ability of an instrument to accurately predict an 

outcome based on its reported scores (Kimberlin & Winterstein, 2008). To confirm the criterion-

predictive validity of the survey, the authors used a sample of 2,345 individuals within a 

company and compared those who departed within 4 months with a random sample from the full 

sample. When comparing the two groups, the authors detected a statistically significant 

difference in turnover intention scores between those who remained in the organization and those 

who departed (Bothma & Roodt, 2013). The difference in the means of the two compared groups 

was found to have a significant effect, η2 = .14. In terms of reliability, the authors conducted 

further analysis to yield an α = .80, which was in line with other studies that had conducted a 

similar reliability analysis of the scale (Bothma & Roodt, 2013). A Cronbach's alpha of .80 

indicates good reliability.  

The Work-Related-Quality-of-Life Scale also demonstrates acceptable validity and 

reliability. This scale fits a validity profile most fitting with construct validity. Construct validity 

refers to the premise that scales measuring similar outcomes should yield similar results (Easton 

& Van Laar, 2018). The authors have conducted numerous comparative studies to validate the 

construct validity of this scale. Two surveys compared are the GHQ-12 (General Health 

Questionnaire) and the Warr Job Satisfaction Scale. The GHQ-12 showed a negative correlation 

of -.57, in the expected direction, because higher scores on this survey tool reflect poorer mental 

health. The Warr Job Satisfaction Scale has a criterion-adjusted correlation = .873, further 

supporting construct validity. The Work-Related Quality of Life Scale comprises six components 

that contribute to the total score: general well-being, home-work interface, job-career satisfaction, 

control at work, working conditions, and stress at work. The reliability of each component was 

analyzed, as was the overall assessment score. Each component received a good reliability rating, 

as measured by ⍺ (see Table 2).  

 



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Table 2 

Reliability Scores for WRQoL 

Component Cronbach's Alpha 

General Well-Being 0.88 

Home-Work Interface 0.825 

Job-Career Satisfaction 0.863 

Control at Work 0.812 

Working Conditions 0.752 

Stress at Work 0.814 

Overall WRQoL 0.912 

 
 

Data Collection Procedures 
 

The data collection process for this research included using Qualtrics survey creation 

tools to build the required questionnaire and the Prolific service to recruit eligible participants. 

This recruitment process used previously established criteria to select only those within the target 

population. Individuals must have been presently employed, working within the IT industry, 

within either the Generation X, Y, or Z cohorts, and located within the Mid-Atlantic region of 

the United States. These participants were also required to have reliable internet access to 

complete the survey.  

 

Data Analysis Procedures 
 

A total of 88 responses were received from users recruited through Prolific and collected 

via the Qualtrics survey link; however, one response was removed due to data quality issues. 

This resulted in a final sample of 87. Of the respondents, roughly 29% were female and 71% 

were male. The total respondent pool had a corresponding generational cohort distribution of 

about 27% Generation X, 52% Generation Y, and 21% Generation Z.  

 

Table 3 

Sociodemographic Characteristics of Participants  



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Baseline characteristic    

n  %  

Gender      

 Female  25   29  

 Male  61 71 

Generation      

 Gen. X 23  27  

 Gen. Y  45  52  

 Gen. Z  18  21  

Note. N = 87 Participants were on average 36 years old (min = 22, max = 57, SD = 9.64) 

Reflects the number and percentage of participants answering “yes” to each descriptor.  

 

Table 4 provides a review of the descriptive statistics for turnover intention and work-

related quality of life. The mean turnover intention score was 14.663 (SD = 5.654). The values 

ranged from a minimum calculated score of 6 to a maximum score of 29. The skewness score of 

.692 indicates that the data is moderately skewed. The descriptive analysis showed work-related 

quality of life with a mean of 3.821(SD = .664).  

 

Table 4 

Descriptive Statistics 

 

M SD SEM Min Max Skewness Kurtosis 

Turnover 

Intention 

14.663 5.654 0.607 6 29 0.692 -0.289 

Work-Related 

Quality of Life 

3.821 .664 0.072 1.867 4.944 -0.514 -0.087 

 

 

Results 
 

This researcher utilized the results of 87 completed surveys to test the study’s research 

questions. Multiple linear regression was chosen as the statistical method. To run multiple linear 

regression, several assumptions must first be met, including normality, independence of 

observations, homoscedasticity, and absence of multicollinearity. The normality of the residuals 

was tested in JASP using a QQ plot of standardized residuals (Figure 1). The QQ plot indicates 

the residuals are normally distributed, so the data met the first assumption. 

 

Figure 1 



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QQ Plot of Standardized Residuals 

  

An independence-of-fit test was conducted using the Durbin-Watson statistic. The test 

returned a value of 2, indicating there was no correlation between the residuals. To test for 

homoscedasticity, a plot of residuals versus predicted was constructed. As shown in Figure 2, the 

values in the plot are well distributed and meet the requirements for linear regression. 

Multicollinearity was assessed by examining tolerance and variance inflation factors (VIFs) in 

the regression model. The results of the calculation revealed that all tolerance values were above 

.25 and all VIF values were below 4, as shown in Table 5.  

 

Figure 2 

Residuals vs Predicated Plot 



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Table 5 

Tolerance and VIF Values 

 
Tolerance VIF 

Work-Related Quality of Life 0.95 1.053 

Generation X Slope 0.624 1.602 

Generation Y Slope 0.61 1.64 

 

 

 

A multiple regression analysis was conducted to test the hypotheses. Due to the inclusion 

of the slope dummy within the multiple regression model, three calculations were conducted, 

with one slope included for each of the three generations. The results of the multiple regression 

indicated that there was no statistically significant difference in turnover intention related to 

work-related quality of life between the generations. For the Generation X slope, the results of 

the regression produced a Pr(>|t|) value = .98. For the Generation Y slope, the results of the 



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regression provided a Pr(>|t|) value of .799. Finally, for the Generation Z slope, the regression 

results yielded a Pr(>|t|) value of .74.  

The output of the multiple linear regression analysis indicated that generational cohorts 

did not differ significantly in this study, so a stepwise regression was used, and the cohorts were 

removed from the model. This resulted in a simple regression model with turnover intent acting 

as the dependent variable and work-related quality of life acting as the independent variable. 

Results indicate that work-related quality of life explained 66.9% of the change in turnover 

intention. This was calculated from the R-squared value of .669. The r-squared of .669, along 

with an r value of .82, indicated a strong correlation between the variables. The data also 

suggested a significant impact for each degree change in work-related quality of life. The 

regression model estimated a decrease of –6.96 in turnover intention score for each degree 

increase in work-related quality of life. Table 6 summarizes the regression output.  

 

Table 6 

Moderator Analysis: Turnover Intention and Work-Related Quality of Life 

Effect Estimate SE t p 

     

Intercept 41.274 2.073 19.91 < 2e -16 

Work-Related Quality of Life  -6.964 .535 -13.03 < 2e -16 

 

Summary of Findings  
 

The output of this quantitative study revealed that no statistically significant difference 

existed between the generational cohorts in the importance of work-related quality of life for 

turnover intention. Each of the three cohorts’ responses, measured using multiple regression, 

yielded p-values greater than .74, which is significantly higher than the required .05 threshold for 

significance. The results of this analysis indicate that although generational cohorts may have 

noted differences in several valuable attributes, no significant differences exist in the level of 

importance placed on work-related quality of life, after concluding that no differences existing in 

the degree of importance attributed to work-related quality of life amongst the cohorts, a 

secondary analysis using a stepwise regression, was conducted to interpret the degree of impact 

that work-related quality of life had upon turnover intention. This analysis yielded a statistically 

significant impact, with final calculations revealing that each degree of increase in work-related 

quality of life decreased the turnover intention score by 6.96. 

This research study yielded several potential implications for both theoretical and 

practical considerations. The results suggest that work-related quality of life is a valued aspect of 

employment and an important variable for employees across all generations. This study has 

confirmed that work-related quality of life is also a significant predictor of turnover intention for 

employees. The theoretical implications section will discuss how the findings impact the existing 

theoretical literature surrounding generational theory. The professional practice implications will 

discuss how the results of this study can be utilized in the business world.  

The two key results of this study include important implications for professional practice 

across the business landscape. The initial result found that no differences exist amongst the 

generational cohorts in the degree of importance attributed to work-related quality of life. With 



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the growing diversity of the labor pool and within each organization, crafting an optimal 

business strategy to manage generational differences remains highly important. Prior research 

into this area has confirmed that there are differences between the generations (De La Cruz, 

2022), that result in changes in responsiveness to how some variables impact turnover intention 

(Rattanapon et al., 2023). This research concludes that employers can rely on a single strategy 

for work-related quality of life to help manage workforce expectations across generational 

cohorts. Work-related quality of life is equally important for each generation.  

The second result of this study confirmed the strong relationship between work-related 

quality of life and an individual's turnover intention. This relationship is highly valuable to 

understand because it can influence key success factors within an organization and reduce 

turnover. Managing turnover effectively helps the organization increase productivity 

(Madraswale & Velmurugan, 2023) and mitigate the impacts of the highly competitive labor 

market in the IT space (Koong et al., 2002; Kumar, 2022). The study not only confirms the 

strength of the relationship but also provides an impact calculation that organizations can use. As 

the data show, each degree of change in an employee’s work-related quality of life score would 

result in a 6.9-point reduction in their turnover intention survey results. This data point allows 

employers to target the amount of change needed in work-related quality of life to reach their 

desired turnover level in the organization. This can then be translated into a cost equation for an 

organization to leverage in strategic conversations.  

 

Recommendations 

 

While this study achieved its purpose of understanding the relationship among 

generational cohorts, work-related quality of life, and turnover intention in the Mid-Atlantic 

region, there are numerous avenues for future research. These additional areas of consideration 

are intended to expand the academic conversation and provide further guidance to the business 

world. Each of the recommendations for future research maintains the general structure of this 

research study, with some manipulation of a single aspect of the study.  

The first recommendation for future research is to shift the focus from generational 

cohorts to the various regions of the United States. One of the initial assumptions of this research 

study was that the region would shape an individual's perceptions and beliefs through shared 

culture. Future researchers could examine whether differences across geographical regions lead 

to distinct perceptions of the importance of work-related quality of life. This would be a valuable 

input for organizations spanning a larger geographic area that need to maintain strategies to 

manage their dispersed employees properly.  

A second recommendation would be to conduct a similar study but replace turnover 

intention with actual turnover. With this change, the study could be converted into a longitudinal 

study to measure the level of importance individuals claim exists, and then, later, such as 6 

months or a year, check which employees had left their organizations. This would allow the 

determination of how strongly the relationship between work-related quality of life and actual 

turnover intention varied across generations. It is possible that, while intention may not differ 

across generations, actual turnover may show significant differences.  

A final recommendation for future study is to replace turnover intention with 

productivity. One of the impacts of turnover intention discussed was its effect on productivity 

within an organization. The present study could determine the exact degree of change in turnover 

intention when an individual realizes a change in work-related quality of life. Using productivity 



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15  

in place of turnover intention could help an organization to understand the quantitative change in 

productivity they could achieve through encouraging greater levels of work-related quality of 

life.  

 

Conclusion 

 

The labor force in today’s business world continues to become more diverse and 

globalized, and, as such, continued emphasis will be needed on strategies that enhance 

motivation, productivity, and employee commitment. Continued workplace shocks, unionization, 

politics, and global events will be relevant to monitor due to their potential to influence 

employee sentiment. The increase in generational diversity in the workforce and the 

globalization of organizations will add additional challenges for organizations. While numerous 

organizations have remained successful, others have fallen and dissolved due to mismanagement 

and changing conditions. The technology landscape appears poised to remain a competitive labor 

environment, and with the second-highest turnover rate in 2023 (Lewis, 2022), there is a strong 

emphasis on staying apprised of current and forecasted conditions.  

This research has provided further timely context on the perceived differences among the 

generational cohorts comprising today's labor pool, as well as reemphasizing the criticality of 

work-related quality of life to an individual's intention to depart their organization. Work-related 

quality of life among Mid-Atlantic IT employees is a universally influential factor in managing 

turnover intention in the workplace. The result of this research is further justification for 

employers to focus on this critical value to avoid pitfalls such as contagious turnover, decreased 

productivity, loss of trade secrets, and increased costs to support continued recruitment for 

vacated positions. The unique needs of the existing labor pool should remain a point of interest 

for organizations, and inclusion in the overall workforce strategy will enable greater potential to 

navigate this dynamic area.  

 

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