Business Management Research and Applications: A Cross Disciplinary Journal 1 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 November 2025 | Volume 4, Number 3 2 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 Business Management Research and Applications: A Cross Disciplinary Journal 3 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. November 2025 | Volume 4, Number 3 4 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. Business Management Research and Applications: A Cross Disciplinary Journal 5 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 November 2025 | Volume 4, Number 3 6 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 Business Management Research and Applications: A Cross Disciplinary Journal 7 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. November 2025 | Volume 4, Number 3 8 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). Business Management Research and Applications: A Cross Disciplinary Journal 9 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 November 2025 | Volume 4, Number 3 10 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 Business Management Research and Applications: A Cross Disciplinary Journal 11 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 November 2025 | Volume 4, Number 3 12 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 Business Management Research and Applications: A Cross Disciplinary Journal 13 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 November 2025 | Volume 4, Number 3 14 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 Business Management Research and Applications: A Cross Disciplinary Journal 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. 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