Defense

The reasons for someone to leave their community can be categorized as either push or pull factors, where push factors are those that are pushing people away from their rural community, while pull factors are the reasons why people are attracted towards a specific urban setting (Popogbe & Adeosun, 2022; Šlibar et al., 2023). In many cases, the main pull factors are finding better employment, progress in career, higher salary, education, and a better future (Korsi & Vorvornator, 2022; Šlibar et al., 2023). Human capital flight is a vicious cycle for many rural communities, because as educated people leave there are less investments for the community and the next generation is faced still with the same issue. Shortly after another skilled person enters the workforce, they leave.

Table 6

Push factors are not always solely based on the lack of opportunities, poor leadership, decreased job satisfaction, or inadequate compensation. For some people there are four factors besides the traditional push factors that can impact their decision to leave. These four factors are:

· Increase in crime, violence, and illegality

· Mismatch between occupation and skills

· Economic factors

· Lack of social opportunities

(Parkins, 2010; Reissová et al., 2024). These four specific push factors can impact the rural communities and potentially push locals out of their town to much safer towns with better opportunities.

Organizations tend to put job satisfaction before customer satisfaction, due to the fact that a satisfied employee will take better care of the organization’s customer base, produce and deliver high quality products and service, sustain high productivity and stay with the organization (Mardanov, 2021) Job satisfaction is the level of satisfaction that an employee gets from their jobs (Alarabiat & Eyupoglu, 2022; Ibrahim et al., 2024). Each employee might have varied factors that impact their overall satisfaction with their current job. The nature of the work itself is a main motivation in the workplace, and the primary criterion used to evaluate job satisfaction, but it is not the only determinant of job satisfaction, there are more factors that affect job satisfaction (Lien & Hoang, 2022; Young et al., 2023).

Previous work by Herzberg proposed a two-factor theory of work motivation, with the first factor being motivation and the second one being hygiene (Herzberg et al., 1959; Young et al., 2023). Job satisfaction can be examined by the differences in two factors – intrinsic and extrinsic factors – which impact an employees’ sense of job satisfaction.

Table 7

In this, intrinsic factors are motivators – self-esteem and achievement needs – while extrinsic factors are hygiene – basic income, safety, and security needs (Herzberg et al., 1959; Young et al., 2023). Extrinsic factors are those that come as a reward for a job well done, such as remuneration for working effort and other rewards for physical work done, such as recognition, titles, or days off (Butt, 2018; Ismail & El Nakkache, 2014; Mardanov, 2021; Rice et al., 2017). On the other hand, intrinsic rewards are those that come from within, respect for peers and society and a supportive work environment (Rice et al., 2017). Even though that each factor is independently examined, they tend to work together to contribute towards the job satisfaction of an employee. The right work environment is going to be key – one that contributes positively to the employees’ intrinsic and extrinsic motivators.

Two of the top reasons why people tend to leave rural communities are lack of progressive careers, and low wages, both of which are factors that impact job satisfaction among employees (Ismail & El Nakkache, 2014; Rice et al., 2017; Xu, 2024; Young et al., 2023). The concept of job satisfaction normally scrutinizes how levels of job satisfaction impact employee turnover intent, but in this study job satisfaction will be explored in terms of an impact on human capital flight decisions. In business, high job satisfaction is associated with committed employees and increased productivity (Khawrin & Sahibzada, 2023). For rural communities, potential job dissatisfaction can rapidly escalate turnover intent and brain drain if alternative roles are unavailable. So, limited job mobility can affect job satisfaction among people, which can impact their intent to stay or leave their current organization.

Previous studies have found that in poorer and less developed countries, job satisfaction is more closely related to extrinsic job characteristics, such as that of remuneration, job security, and working conditions (Ismail & El Nakkache, 2014). In rural areas compared to urban areas experience a large movement of people to urban areas in search of economic opportunities (Shutters & Applegate, 2022). This phenomenon is known as urban wage premium. Urban wage premium exists when people in larger cities or urban areas earn on average a higher wage compared to rural areas (Shutters & Applegate, 2022).

In Australia, workers in capital cities have earned between 17% to 22% more than workers in other parts of the states (Ho, 2023). This has been a boost for people to leave their rural communities in search of opportunities that not only are different but also that pays significantly more. Yet, Ecuador is not experiencing the same effects as Australia. The urban wage gap tends to be associated with the safety of the urban city. Guayaquil compared to Quito and Cuenca experiences high crime rate, given that the urban wage premium is almost 10% less than the next city (Guevara-Rosero & Del Pozo, 2020). Considering these factors, people tend to move for better opportunities and better remuneration from rural to more urban areas.

Job satisfaction, a key factor in human capital flight, is not only sufficed by remuneration and rewards for a job well done but also factors that come from within (Rice et al., 2017). Even though extrinsic factors alone may impact an employee’s decision to stay or leave their current organization yet having the right mix of intrinsic and extrinsic motivating factors can help an organization be the employer of choice. When an employer can become the employer of choice, they can help reduce the amount of people who may consider leaving the organization. With less people looking for another job due to low job satisfaction, the lower the amount of people potentially considering leaving their rural communities – human capital flight – for a better organization that offers both extrinsic and intrinsic rewards.

The Herzberg theory of motivation considers motivating factors that align with the intrinsic factors that affect job satisfaction (Herzberg et al., 1959). Intrinsic factors often drive people to do something for their own sake, without the need for a reward. A workplace for some can offer job stability and relations with colleagues, which may align with their values. These values are what motivates people in their day-to-day lives, and if it aligns with the organization, people will commit to their job (Parks‐Leduc et al., 2024). This commitment along with workplace relationships tend to be enhanced by small close-knit communities. Intrinsic factors play a significant role not just in job satisfaction, but also job commitment. In certain cases, money cannot outweigh a bad workplace or lack of career progression.

A job that one person gets satisfaction from, under the same circumstances might not give the same satisfaction to another. Job satisfaction is not universal, rather it is by each individual person’ personal preference in the workplace. The differences in job satisfaction can be attributed to different variables such as; nature of the work, work sector, social interactions, coworkers, supervision, pay, cultural values, work values, personality, age, and generational differences (Andrade & Westover, 2018). Age is a factor that has been studied, and one study has indicated that job satisfaction increases with age but in a U shape, so it declines again (Andrade & Westover, 2018). But job satisfaction among generations is an area that requires more study based on the different findings that previous studies found.

The generations are as follows: silent generation (born 1925 – 1945); baby boomers (born 1946 – 1964); generation X (born 1965 – 1976); generation Y/millennials (born 1977 – 1995); and generation Z (1997 – 2012) (Andrade & Westover, 2018; Locker & Teague, 2024). Each generation is influenced by the events that have occurred during that period, with most of the cohort experiencing the same major culture, political and economic experiences (Andrade et al., 2024; Gordon, 2017; Locker & Teague, 2024). A lot of the feelings and emotions that a generation experiences toward the workplace can be significantly different from the next generation because of their previous lived experiences.

The differences among generations are also present in the workplace. With the baby boomer generation being described as hard working and motivated by position, benefits, and prestige while the millennials are motivated by the desire for work-life balance, meaningful work, and are skilled at using technology (Locker & Teague, 2024). Baby boomer generation compared to other generations is characterized for their job commitment and long-term commitment, with them working 7.1 years more with one employer than any other generation (Andrade & Westover, 2018). The number of years spent working for one employer can potentially give an employee the experience in their field of work, which can result in increased self-confidence and skills that can influence their overall job satisfaction (Gordon, 2017).

Table 6. ( Generational Differences in the Workplace [Infographic], n.d.)

Access to education has played a key role in the overall factors that affect job satisfaction among the different generations. The younger generations, millennials, and gen Z, who have higher levels of education – which is a characteristic of the millennial and gen Z generations – are reporting lower levels of job satisfaction (To & Tam, 2014). One of the reasons given for lower job satisfaction among the younger generations is that education has given them higher job expectation where they believe that they deserve better rewards. For the younger generations, a career must be meaningful and must have economic returns for the investment that they made in their education (Gordon, 2017). The investment must come in monetary rewards and significant career progression, which compared to the Baby Boomer generation is not the same. The older generations were valuing long-term careers which gives them the experience and job satisfaction due to the ease of the job.

Job satisfaction considers the emotional response that is experienced when doing their job (Alarabiat & Eyupoglu, 2022; Ibrahim et al., 2024). Attitudes toward the job affects employee behavior both inside and outside of the organization, and it can impact overall job satisfaction of an employee (Urien & Erro-Garcés, 2024). Previous research found that job resources tend to significantly improve employee’s work well-being and job satisfaction (Sun et al., 2023). Work-life balance can enhance job satisfaction when the work resources give a person the opportunity to balance their work-life and their personal-life daily.

One of the latest changes in the workplace that has had significant impact on job satisfaction among different generational cohorts is the work-life balance especially in a post Covid-19 era. Pre Covid-19, people would leave their house every day and commute to work and spend eight to ten hours in the office getting their work done and then commute back home. This has completely changed with Covid-19, as telework became the norm with office being forced to shut down. European employees were one of the first ones to go from in-office to a telework setting, with many employees providing technological media themselves because they appreciated the opportunity to keep their jobs (Urien & Erro-Garcés, 2024). This period was often difficult for workers who also had children, because while they were at home their children also transitioned from in-person schooling to an at home setting.

Previous study on generational job satisfaction has been mixed, with studies reporting both existing differences and no differences at all among generations (Andrade et al., 2024; Cucina et al., 2018). During and after Covid-19, the shift in the workplace dynamics and expectations has made an impact among the different generational cohorts and either their acceptance or rejection of these dynamics (Andrade et al., 2024). One of the most significant changes that occurred during Covid-19 was the change of in-person workplace to a virtual workplace or work-from-home setting. The acceptance of this change varies among the different generations, and it can be attributed to the changes people want in their lives and career (Gordon, 2017). But regardless, there is still differences in the acceptance of the new workplace.

This change for some organizations was a much-needed upgrade to keep their operation running during a massive shutdown, but for some employees this change resulted in emotional and work exhaustion at first (García-Salirrosas et al., 2023). The change of workplace setting has put into perspective the work-life balance that the younger generations – millennials and Gen Z – have been craving from their employers. All of this to avoid some of the burnout that regular and even the newer hybrid workplace models brought (Behera et al., 2024).

Work-life balance, the state where the requirements of an individual’s personal and professional needs are on an equal scale, and where work does not overtake one’s personal live and vice versa (Andrade et al., 2023; Behera et al., 2024; Qadri, 2024). A well-balanced work-life can significantly impact an employee’s overall job satisfaction and job commitment. Andrade et al. (2023), identifies that job satisfaction can spill over to family, friends, and work relationships, and can have a positive impact on physical and emotional wealth. To prevent a spill-over from work into family time, a well-established schedule for work and free time is needed, which is also communicated with colleagues and supervisors to maintain the separation between work and family time (García-Salirrosas et al., 2023).

The concept of work-life balance is one that generations such as millennials and gen Z highly prioritize in their career decisions. Since work-life balance has become important for the employee looking for a job, it is now of strategic importance for the employer seeking to retain top talent and human capital in an everchanging labor market (Qadri, 2024). The concept of work-life balance is changing how the job market works; businesses will have to adapt to become attractive to potential candidates. An attractive workspace and workplace are essential for productivity, efficiency, and overall employee health (Demissie et al., 2024). And, a healthy employee has less absence, is more productive, and satisfied with their job, resulting in a win for the business.

Positive workplace experiences may have a positive outcome on commitment levels of employees towards the workplace. On the contrary, negative workplace experiences can lead to lack of commitment. The lack of commitment can be one of the leading factors that is resulting in turnover among employees of an organization (Luz et al., 2018). Employee turnover is heavily impacted by job satisfaction and job commitment, which in turn impacts brain drain (Yue et al., 2022). The turnover process can negatively impact an organization, as it now needs to search for a suitable candidate whose qualities matches what the organization is looking for. For the employee who left, the search now commences to find another employer that is looking for the qualities that they possess, and this search may not result locally. Lack of opportunities locally can lead qualified candidates to find employment in other cities, which increases brain drain.

Employee turnover is not a new problem, but it is an issue that more companies are facing daily. The position of public school superintendent in Appalachian Kentucky is experiencing significant turnover, which is outpacing the national average (Hub & Hunter, 2025). The rise in employee turnover is resulting in loss of productivity and a rise in recruiting costs. But for the organizations affected, it is important to find the root cause of their employee turnover. Since having the knowledge of the root cause of their turnover could help them focus their efforts into making changes that could retain employees (Oh & Chhinzer, 2021). Once the root cause has been identified, the organization should act quickly to implement changes, as employee turnover has been shown to be contagious, and there will not only be one employee leaving, but it will be the start of more people walking away (Oh & Chhinzer, 2021).

The organization should consider every voluntary turnover as a time to do self-reflection and understand the reason for the employee leaving. Employee turnover has been linked with low employee morale, low productivity, and low efficiency in the workplace (Pollitt, 2008). When the organization sees a decrease in productivity and employee dissatisfaction, they must investigate the employee turnover intention, as these are indicators for potential turnover. Turnover intention is defined as the process where the employee starts to withdrawal thought process. In the withdrawal process, the employee will have thoughts of leaving, the desire to find a new job, and finally the intention to leave the organization (Swe & Lu, 2019).

Preventing employee turnover is not possible, as people tend to be on the lookout for better prospects in their employment all the time (Ellepola, 2013). But switching the focus from preventing people from leaving, to creating an environment where people want to come to, gives an organization a better chance at finding the right talent and retaining them. Attracting the right talent for the organization could impact the amount of turnover or turnover intentions an organization sees (Berisha & Lajçi, 2020). Employee turnover is becoming a problem, and having the right retention and hiring plans in place could prevent talent from going to the competition or leaving the industry as a whole (Salleh et al., 2020). It is not only up to the Human Resource Managers (HRM) of the organization to set the retention plans in motion, but it takes the team to create an environment for the employees, which could include taking care of human and intellectual wealth (Salleh et al., 2020).

While employee turnover has been an ongoing issue that organizations face, the cost of replacing has shifted the focus of the organization from replacing to retaining (C. C. Lee et al., 2022). Instead of just going through employees, a better option is to make changes to the workplace environment to make it one where employees want to come to and where they feel like they belong. This sense of belonging is known as social connectedness, and in terms of the workplace, the environment is one where employees can have interpersonal relationships (Cernas-Ortiz & Wai-Kwan, 2021). Workplace social connectedness is a concept on its own, but it ties back in with extrinsic factors that affect employee morale and overall job satisfaction.

Social connections are essential for human existence, and it enriches life, any lack thereof can lead to poor health, low life satisfaction, and in at times even to suicide and premature death (Cernas-Ortiz & Wai-Kwan, 2021). It is important for an organization to create a well-balanced environment for its employees, as a well-balanced environment can foster a sense of belonging and increase retention (Schaechter et al., 2023). This balance is the mix of work and social connections that can and are established in the workplace – an environment where work is done, and employees can be social with one another. The concept of workplace social connectedness is important in the organization’s goal to retain top talent. Creating an environment that provides employees the space to feel socially connected with their colleagues can affect mental health in a positive manner and contribute towards the effort of employee retention (Nielsen et al., 2021). Businesses that understand this can benefit significantly from keeping their best talent on board with them.

For an employee to feel a sense of belonging in the workplace is crucial in their decision making to either stay with an organization or find an organization that is better suited for them. Research in the health industry has shown that when a whole team supports and gives the newcomer the right attention, that newcomer is more than likely to stay in the field (Chan, 2016; Ching et al., 2022). This feeling for a newcomer in the field is essential for their self-esteem and their future progress in the field. Feeling the sense of belonging does not happen on the first day of the job for everybody, for some it could be a process that they will experience as they become part of the team. Besides, the right introduction into the field can have positive impact on the employee’s perception of the field and their job, which could lead to positive organizational development (Chan, 2016).

The employee feels a sense of social connectedness in their workplace, this feeling satisfies the basic human need of interaction, and it can lead to increased employee motivation and overall well-being (Cernas-Ortiz & Wai-Kwan, 2021). Increased employee motivation has benefitted organizations when it resulted in increased job satisfaction and job performance. Research has shown that increased job satisfaction has led to increased individual performance, and at the same time also had positive effects on overall team performance (Kosec et al., 2022; Politis, 2006). When an employee feels socially connected at work, it not only benefits them, but it has a positive effect and benefit on the organization.

When an organization fosters an environment where its employees can be socially connected with one another, it gives employees a sense of belonging which can lead to increased job performance and satisfaction. Lack of job performance and satisfaction has been linked with employee turnover, which can hurt an organization (Zimmerman & Darnold, 2009). It does not benefit an organization to have an unwelcoming environment for its employees, because an unwelcoming environment has led to lower job performance and satisfaction have which has resulted in loss of organizational competitiveness and success (Otoo, 2022).

Organization success is sometimes defined by their ability to attract and retain key talent, but according to research it is also the area where organizations struggle the most to be successful in (Sturman et al., 2003). The focus of organizations tends to be around winning the talent war and finding the most qualified candidate for their organization. But the best candidate for the position might not always be the right candidate for the organization. The best candidate would be someone whose values, requirements, and interests align with those of the organization, often referred to as person-organization (P-O) fit (Abdalla et al., 2018; Berisha & Lajçi, 2020; Priyadarshi & Premchandran, 2018).

When organization and employee interests, requirements, and interests are aligned both parties will benefit from this relationship. The idea of P-O fit is that when these two parties – employee and organization – comes together, the employee will fulfill the needs of the organization, and in turn, the organization fulfills the needs of the employee (Abdalla et al., 2018). The alignment of goals, vision, and interest can lead to a successful partnership where both parties can benefit from their contributions.

Besides, person-organization fit, there is person-job fit, which is the degree of agreement between employee and job attributes (Du et al., 2023; Kaur & Kaur, 2023; Li et al., 2023; Peng & Mao, 2015). This concept is focused on the feeling an employee has towards their job, which impacts their overall satisfaction towards it. When HRM hires the right candidate that matches the job, the organization benefits. The organization benefits because when the person enjoys their job, their performance increases and they are more committed to their job and organization (Chou et al., 2022).

Recruitment for candidates is an integral part for finding the right people whose vision, values, and interests are a fit for the organization. In certain scenarios, candidates are showing concerns for finding the right organization for them, as it is to be finding the right job (Morley, 2007). Organizations have long focused on finding the right candidate for the job they were trying to fill, but priorities have since changed to finding candidates who fit with the organization (Makraiova et al., 2013; Morley, 2007).

The focus to reduce employee turnover is becoming more prevalent in many organizations who are in an industry that is faced with high employee turnover. Person-organization fit focuses primarily on the fitting relationship that can foster between an organization (Morley, 2007). If the right match is found in the recruitment process, the relationship between organization and employee could result in significant benefits for both parties. In certain cases, organizations and employees have benefitted in pay raises and increased profits due to having engaged employees in the workplace (Makraiova et al., 2013). Hiring is the first step in finding the right candidate for the organization.

Hiring is a powerful tool that organizations possess and use when trying to find candidates. The hiring practices that organizations use helps them create career opportunities for some, while blocking entry for others (Rivera, 2012). In many cases the hiring process is one where the recruiting manager estimates the capabilities of the candidate’s human capital, social capital, and demographic characteristics, when deciding on whether to hire a candidate or not (Rivera, 2012). With estimation being the main contributor in the decision-making process, hiring remains as a decision made by someone who has a feeling about a candidate.

During the recruitment process potential employees are interested in a job with an organization who holds the promise of having a high P-O (Priyadarshi & Premchandran, 2018). Interviewers in their role will be trying to find the candidate whose vision, values, and interests align with those of the organization, resulting in the right fit. Upon finding the right candidate for the job, it is up to the organization and the employee to keep understanding one another, so that their relationship can keep existing moving forward (Berisha & Lajçi, 2020).

The relationship between an organization and the employee can cease to exist when the two parties have either different or conflicting goals (Abdalla et al., 2018). Due to conflict and lack of goal congruence, the organization’s efficiency and performance can suffer. It is imperative that the organization and the employee understands each other’s needs as misunderstanding could potentially lead to conflict. Conflict can cause employees to direct their efforts to benefit their own welfare, and not the welfare of the organization as a whole (Abdalla et al., 2018).

Human capital flight in rural communities is an issue that is worth taking note of, especially if the skilled laborers are the ones leaving their community in search of better education and employment opportunities. Besides better employment and education opportunities, people tend to gravitate towards jobs that are interesting and challenging, requires intelligence, and the jobs that are scarce to find qualified people for (Garbin & Bates, 1961). This is the effect of occupational prestige – the perceived social status that comes with certain jobs, where the value is placed on different occupations by the society (Nwaru et al., 2021). Occupational prestige can serve as a social reward and other crucial rewards like economic power (Yuchtman & Fishelson, 1972). For people in rural communities this can be a reason as to why skilled labor is leaving, in search of an opportunity that gives them a perceived social status and all the benefits that comes with the position.

Intrinsic and extrinsic factors play a significant role in the overall satisfaction an employee has for their job. Occupational prestige is an extrinsic factor that contributes towards the satisfaction that an employee has towards their job, but it also contributes towards their health (Herzberg et al., 1959; Swe & Lu, 2019). Research has found that there is a relationship between the level of occupational prestige and absenteeism in the workplace (Nwaru et al., 2021). Higher occupational prestige has been linked with better self-rated health, while lower occupational prestige and sickness varies by gender in the workplace (Fujishiro et al., 2010; Nwaru et al., 2021).

When society places values on certain positions within the community, people will be lining up for those positions. However, at times people will take a job regardless of whether it matches their education or their potential needs due to the fact the position is with an organization that is considered prestigious in the eyes of the employee (Fischer-Browne et al., 2024). These workers will build a whole identity around their jobs, and their lives will revolve around the prestige that has been associated with their careers. In Romania, the job of a teacher is considered prestigious, as the position did not put them in a new economic class the position is a social honor (Frunzaru & Dumitriu, 2015). Occupational prestige is a social construct set forth by the society in which we live in, and it can impact career moves significantly in certain societies.

Attracting and retaining talent has become one of the primary human resource strategies for obtaining a sustainable completive advantage (Alzaid & Dukhaykh, 2023). One strategy that has emerged as successful in accomplishing this is employer branding. The concept of employer branding is based of relationship marketing, where organizations would provide its customers long-term value (Kalińska-Kula & Stanieć, 2021). Even though the concept of employer branding is based of relationship marketing, it is a focus that organizations are taking to become attractive for potential employees. It is a notion that the desirability of an organization depends on the potential employee’s perception of the attributes associated to it (Jain & Bhatt, 2015).

The popularity around employer branding can be linked to the endless labor shifts and since the bargaining power has shifted from the employer to the employee (Bharadwaj et al., 2022). To overcome the labor shifts and the potential employee having the bargaining power, organizations have begun focusing on creating an image of the organization that paints them better than their competitors. This image can help organizations attract better committed talent at a lower cost and with a lower rate of absenteeism (Dixit, 2024). And this can help an organization achieve competitive advantage with low cost, and a differentiation strategy due to attracting a wide range of talent.

Employer branding can enhance occupational prestige since employer brand establishes the prestige of an organization (Garbin & Bates, 1961; Nwaru et al., 2021). Successful employer branding can result in some competitive advantages, which makes it easier to attract and retain employees (Sivertzen et al., 2013). Yet, in the current employee market, organizations are finding it difficult to recruit decent employees, and the talent pool of competent employees has not experienced an increase (Hardy et al., 2020). With talent becoming scarce organization will have to rely on themselves to place social status on their current job openings, especially if they paint themselves as the employer of choice.

A lot of studies have been done on human capital flight, and it has provided great insights into the issue, but there are gaps in the literature that need to be addressed (Bristol, 2010; Krasulja et al., 2016; Lozano-Ascencio & Gandini, 2012; Popogbe & Adeosun, 2022). Primarily, a lot of the studies have been focused on very underdeveloped countries such as countries in the African continent, but not a lot of them have focused on rural communities. Studies tend to be focused on underdeveloped countries, because in medical research for example, 90% of preventable mortality occurred in underdeveloped countries (Chuan & Schaefer, 2015).

Additionally, economic migration patterns are different than those of a developed nation, and it can highlight how and why people move from their home country. Beyond this, research is focused on underdeveloped countries due to the low financial and regulatory burdens and the researchers’ ability to recruit substantial number of subjects quickly (Chuan & Schaefer, 2015). The other countries where these studies have been focused on has been European countries, which are developed countries and have the resources to conduct the studies themselves but nothing on the Caribbean islands (Kerstin et al., 2020). The lack of research on small developing rural communities, is failing to address how occupational prestige impacts a potential employee to apply for a position with an organization. Rural communities’ unique economic and labor market conditions make it an understudied yet relevant case.

Second, previous literature on job satisfaction has been very directed towards the intrinsic and extrinsic factors that affect satisfaction, mostly focused on career progression and wages and how those impact turnover within an organization (Ismail & El Nakkache, 2014; Rice et al., 2017; Young et al., 2023). But that literature has not focused on how limited job mobility impacts job satisfaction, and this study will bridge that gap by studying how limited job mobility impacts job satisfaction. Job satisfaction is a known predictor of workforce retention, yet limited research examines its role in constrained labor markets like that of rural communities, where mobility options are scarce. Understanding the link between limited job mobility and job satisfaction could provide new insights into why employees choose to leave or remain despite economic constraints. Because limited job mobility can constrain employees’ career progression, investigating its influence on job satisfaction may, in turn, fuel human capital flight, particularly when opportunities elsewhere are perceived as more favorable. This study would be relevant for rural communities, because the size and limited opportunities on the island people are left with little options if their current job is not giving them the satisfaction that they are looking for. If another option is not available the community itself, people may look for opportunities in the city.

Lastly, there has been research done on occupational prestige, but only a few of them have been focused on the perception of prestige among regular workers (Kerstin et al., 2020; Nwaru et al., 2021). Occupational prestige, defined as the social value or status attributed to a particular job role, can influence an individual’s decision to accept, remain in, or leave a position. Due to the size of rural communities compared to urban areas, the experiences and social statuses that comes with a position may differ completely when compared to a larger cities.

Chapter 3 will present the methodology that will be used to collect data, analyze, and contribute to this research. The research has an independent variable – limited job mobility – and a dependent variable – human capital flight intent. Beyond these, the study will also rely on three moderating variables, job satisfaction, occupational prestige, and workplace social connectedness. Together the independent variable and the moderating variables will measure how people make their decisions about staying or leaving rural communities for urban cities, due to employment. Based on these variables, an ANOVA will be utilized to measure human capital flight intent. Aside from that, a regression analysis will be run to measure how human capital intent varies among the different generations.

Data for analysis will be collected utilizing a combination of two different scales. Namely the JSS36 and TIS-6 via an online survey, administered by Survey Monkey. Along with these scales, there will be screening questions that will be utilized to either qualify or disqualify a participant. Once the data is collected and data saturation is met, the analysis will be done utilizing SPSS. Based on the analysis, a conclusion will be made to either accept or fail to reject the null hypotheses.

The research that is presented in this study is of foremost importance as it aims to provide insight into how limited job mobility impacts human capital flight intent in rural southeastern United States. Additionally, the study will also incorporate moderating variables that may have an impact on the relationship between limited job mobility and human capital flight intent. This understanding of these relationships is crucial because human capital flight can negatively impact talent availability for organizations in smaller rural communities. The several factors that are being studied suggest that employees are motivated in the workplace by pay, promotion, social status, and social connections.

Given this consideration, the study will employ a quantitative approach to analyze data on limited job mobility and human capital flight intent utilizing Herzberg’s two-factor theory of motivation. The goal is to identify if limited job mobility and the moderated variables have an impact on human capital flight intent in rural Southeastern United States. This chapter will explore research design, population and sample selection, instruments used for data collection, data analysis methods, and the reliability, validity, and ethical considerations essential to this study.

A quantitative method was chosen for this study because it provides a quantitative description of trends, attitudes, and opinions of a population (Creswell & Creswell, 2023). The survey design is inexpensive and easy to administer. It also can give the researcher the opportunity to reach a large crowd quick, give the respondents the ability to take the survey anonymously – where they are more likely to answer honestly (Leedy & Ormrod, 2021). Besides these reasons, the quantitative method is best suited for this study, because it will minimize personal bias since it is based on numerical data.

In 2024 rural Americans made up between 16% of the total United States population ( World Bank Open Data, n.d.). In comparison to metro or urban areas, the prime working age population decreases significantly in rural areas. Prime working age are people who are between the ages of 25 and 54 and are actively participating in the labor force ( Employment & Education - Rural Employment and Unemployment | Economic Research Service, n.d.). The subjects for this study will come from the Southeastern United States – Florida, Georgia, Alabama, Mississippi, Tennessee, South Carolina, North Carolina, Kentucky and West Virginia. The potential participant will be living in a community that has between 2,500 and 5,000 inhabitants. Based on the map below, the Southeastern States have the largest percentage of rural population.

The population of this study will be limited to rural community residents – those who live in a town with less with a population size between 2,500 and 5,000 – and limited to those in the age range of 25 through 65, and who currently do not hold a managerial position within their organization. This smaller group will focus on some specific criteriums to limit the study to the current working-age population, which will exclude students and retirees. This group is chosen because to participate in the study, the participant must have at least three years of work-experience in the workplace. The minimum age of 25 is chosen because previous research found that college graduates who did not complete an internship may experience first job mismatch (Albert & Davia, 2023). A potential first job mismatch of a recent college graduate can have a negative impact on their first job experience and would negatively impact the data collected. So, the age of 25 is chosen because the potential participant can have some work experience to talk about and could have pass through their first potential job mismatch experience.

The primary focus of this research is to examine the effects that limited job mobility has on human capital flight intent in rural Southeastern United States. Since it would be impossible to survey every single person in rural areas who works a full-time job and who does not hold a managerial role with a company, a sample would be best suited. To get a sample, the researcher will utilize a non-probability sampling method. Probability sampling compared to non-probability sampling is that it involves a random selection, which allows the researcher to make statistical inferences about the whole group (Leedy & Ormrod, 2021). Non-probability sampling on the contrary, relies on a non-random sample selection, which can rely on readily available participants. A non-probability sample is chosen because of time constraints, and because a probability sample would require resources that are not available to the researcher now.

The non-probability sampling method that will be utilized in this study is convenience sampling. Convenience sampling is a non-probability sampling method where the selection of the sample is not random (Jamalludin et al., 2022). The researcher will utilize crowdsourced convenience sampling, by utilizing the Amazon Mechanical Turk (MTurk) to gather the sample (Nikolopoulou, 2022b). Sites like MTurk have access to large demographic populations in the United States and internationally, which would give the researcher access to the targeted demographic in a short amount of time. Additionally, MTurk can give the researcher access to a more diverse pool of respondents in exchange for monetary compensation.

Potential participants will be notified that there is a study being undertaken for research purposes and that their potential answers and data collected in the survey will not be shared with anyone (Chaturvedi et al., 2023). This is where voluntary response sampling is applied – a sampling method where the participants choose to respond to the open invitation to participate in a survey. In certain scenarios, some people are inherently more likely to volunteer than others, which can lead to self-selection bias.

Self-selection bias, where individuals decide for themselves whether they will participate or not in a study (Stone et al., 2024). This form of bias occurs when the non-participants and the participants differ from one another, such as their experience in the workplace or their motive to participate – potential incentives for participation. The difference in participants and non-participants can hinder the research by providing a sample that may not be representative of the entire population (Stone et al., 2024). If the sample is not representative of the entire population, it can threaten the external validity of the findings, which can hinder the ability to make generalization from the sample to the population (Wang et al., 2023). To understand the reason for participating, the researcher can ask the participant at the end why they took the survey, which can give insight into their motive.

The sample size must be large enough in a deductive research approach, such as a quantitative design, to represent the population for the findings to be generalizable. An estimated sample size for the population sample was calculated using G*Power 3.1. The researcher has chosen G*Power 3.1 to calculate the sample size, because the program relies on the effect size and the desired power. With the given sample size, the researcher will not waste people’s time with a large sample, when the power of the study is reached with a smaller sample. The statistical power of a study is the probability of avoiding a Type I or II error (Bhandari, 2021).

For this study, a medium effect size of 0.15 was chosen, an a error probability of 0.05, a power (1-b error probability) of 0.80, and the number of predictors is 5, as seen in Appendix A. A medium effect size of 0.15 was chosen because it aligns with studies of job satisfaction (Kosec et al., 2022) G*Power 3.1 gave a sample size of 92 for the population. However, to allow for the possibility of some responses containing errors and, the goal is to attain 150 responses for the survey, assuming a 20% error rate and to reduce the likelihood of sampling errors.

The calculated sample size of 92 is adequate for this study, since it has considered several factors that the study will rely on. Primarily, G*Power has utilized the number of predictors that are impacting the dependent variable. This study will utilize five distinct factors that measure human capital flight, such as limited job mobility, job satisfaction, occupational prestige, and workplace social connectedness. The a of 0.05 used in this study indicates that there is a 5% chance of rejecting the null hypothesis when it is true. The effect size used in this study is 0.15 which is considered a medium effect and can explain 9% of the total variance (Fields, 2016).

Once the sample size and sampling methods have been decided, the focus shifts to the instrumentation to collect the necessary data. The type of instrumentation that is used to collect the data is important, because the data collected needs to have enough validity for others to trust the information that is collected and analyzed (Patino & Ferreira, 2018). The validity of a study is represented in two parts, internal and external validity. First, the researcher must utilize an instrument that once data is collected represents the truth in the population, and not that the results are due to methodological errors (Patino & Ferreira, 2018). Once internal validity is established, the researcher can proceed to make judgments regarding external validity. The question to be answered at this point is, does the data collected can be applied to the rest of the population? If internal validity is met, external validity will be tested to ensure that the data collected and conclusions reached can in fact be applied to the rest of the population, and others who meet the criteriums to fit in the population (Patino & Ferreira, 2018).

For this research, a survey will be used to collect data from the participants. The survey will consist of six sections as seen in table 7.

Table 7.

In first section, the first three questions will be screening questions, which will ask if the participant lives in a town with less than 5,000 people, in the Southeastern United States (Florida, Georgia, Alabama, South Carolina, North Carolina, Mississippi, Tennessee, Kentucky, or West Virginia), and if they are between the ages of 25 and 65.

https://worldpopulationreview.com/state-rankings/southeast-states

Additionally, the age of 25 is chosen as a beginning age, because if someone went to college or university they would be finishing around the age of 21-23, and upon entering the job market they may have two to three years of experience at age 25. Finally, a full-time job is requirement because it gives a potential participant good insights into what their current employer offers and what is expected of them. If they were part-time employees, they may not get the full experience in the workplace, compared to a full-time employee.

The demographics section is below in Appendix B. Beyond the screening questions, this section will also include questions on gender, job title, and the industry in which they work.

JSS36

The second part of the survey is the job satisfaction survey scale – JSS 36 (Spector, 1985). The job satisfaction scale (JSS36) was developed by Spector and is made up of 36 questions divided up in nine parts of four questions each (Chaturvedi et al., 2023). This scale was chosen over other scales because it focused on four distinct aspects of the workplace that has an impact on their overall job satisfaction. Each section measures one specific job satisfaction category – pay, promotion, supervision, benefits, rewards, operation conditions, coworker, nature of work and communication. This scale has a Cronbach’s alpha of 0.91, which indicates that the response values for a participant is consistent across a set of questions (Creswell & Creswell, 2023; Spector, 1985). In this portion of the survey, the participants will answer 36 questions on a six-point Likert scale from (1) disagree very much to (6) agree very much. Important to note, this survey does not have a neutral answer of either agree or disagree. The survey can be seen in Appendix C.

Social Connectedness Scale (SCS-R)

The third part of the survey is the social connectedness scale – UBC State Social Connection Scale (UBC-SSCS) (Lok & Dunn, 2023). Social Connectedness Scale (SCS-R) which was developed by Lee et al. (2001) was a 20-question survey, and UBC State narrowed it down to ten questions that are focused on capturing a momentary feeling (Lok & Dunn, 2023). The difference between the 20-question survey and 10-question survey is that the 20-question survey is broad and asks about specific social relationships. On the contrary, the 10-question survey is a refined survey of the SCS-R and the General Belonginess Scale (GBS) which was developed by Malone et al. in 2012 (Lok & Dunn, 2023). The shortened version also includes a belonginess component that is not included in the 20-question format, and this fits with the focus of the study. Additionally, this format was chosen because the researcher is trying to keep the survey compact, to not lose the interest of potential participant and increase the non-response bias due to the length of the survey.

The SCS-R has a Cronbach’s alpha of 0.80, which meets one of the key reliability criteria of internal consistency. The UBC-SSCS, is comprised of 10 questions that measure social connectivity by utilizing a Likert scale, by assigning each statement a number. Each statement has a Likert scale of seven points – 1 for strongly disagree, and 7 for strongly agree – as shown in Appendix D.

General Readiness for Between-Occupation Mobility

The fourth part of the survey instrument is the General Readiness for Between-Occupation Mobility by Otto, Glaser, and Dalbert (2004). This specific instrument is made up of ten survey questions and is used to measure job mobility among occupations. This instrument was chosen, because it measures willingness to move around for a job and overall career progression. Additionally, this format was chosen because the researcher is trying to keep the survey compact, to not lose the interest of potential participant and increase the non-response bias due to the length of the survey.

The General Readiness for Between-Occupation Mobility has a Cronbach’s alpha of 0.86, which meets one of the key attributes of reliability, internal consistency (Baluku et al., 2018, 2021). Participants in this research will answer ten statements about their willingness to move between jobs via a Likert scale, by assigning each statement a number. Each statement has a Likert scale of six points – 1 for strongly disagree and 6 for strongly agree – as shown in Appendix E.

General Readiness for Geographic Mobility

The fifth part of the survey instrument is the general readiness for geographic mobility by Otto, Glaser, and Dalbert (2004). Originally a 20-question survey focused on two aspects of mobility – the employees’ between -occupation mobility and their geographic mobility (Otto et al., 2004). Since this study is focused on human capital flight the researcher will use the 10-questions that relate to geographic mobility, which is called the general readiness for geographic mobility. This specific instrument was chosen, because it measures the willingness of someone wanting to leave their current community if the opportunities that are available are not matching their interests or if there are no opportunities available. Additionally, this format was chosen because the researcher is trying to keep the survey compact, to not lose the interest of potential participant and increase the non-response bias due to the length of the survey.

The General Readiness for Geographic Mobility has a Cronbach’s alpha of 0.86, which meets one of the key attributes of reliability, internal consistency (Baluku et al., 2018, 2021). Participants in this research will answer ten statements about their willingness to leave their country for a job via a Likert scale, by assigning each statement a number. Each statement has a Likert scale of six points – 1 for strongly disagree and 6 for strongly agree – as shown in Appendix F.

Occupational Prestige Dropdown

Occupational prestige will be measured with the occupational prestige ratings dataset by Condon and Hughes (2022). The whole data set has different levels of jobs with their prestige rating. To overcome the potential misunderstanding that may arise by giving the participants too many options to choose from, the researcher has opted to give the potential participants 19 sectors to choose from. This part will be asked in the demographics section of the survey, when the participant is asked what sector best fits their job. A dropdown box with the sectors will be given to them as can be seen in Appendix G.

The data collection process will include the use of Amazon’s Mechanical Turk (MTurk). MTurk will be the platform used to recruit potential participants for the study. Data collection for the study will be administered via SurveyMonkey, a global leader in online surveys and forms. This platform is chosen because it has a user-friendly interface for both the researcher and the survey taker, which can help reduce non-response bias ( SurveyMonkey Features, n.d.).

Data for this study will be collected from human subjects, which requires Institutional Review Board (IRB) approval. The researcher will seek the approval for designed data collection protocol from Saint Leo’s IRB to commence data collection. Seeking approval from the IRB board step is essential since this study will rely on human subjects to collect data from The IRB will review the proposed study and proposed tools that will be used to measure human subjects to ensure that the research complies with federal regulations and protects the rights of the participants (Creswell & Creswell, 2023). This process will only start when approval is received by the chair and committee members.

One of the biggest parts of the data collection process is the ethical consideration that will be put into place to protect the participants. The first step in terms of ethical consideration is receiving informed consent from the participant. Informed consent is a legal basis used by a researcher to collect personal data in research. The process involves giving the potential participant to opportunity to decide on their own if they would like to participate in the study or not (Erlen, 2010). The process of informed consent is more than just getting a participant to agree to partake in the study. It is a process where the study is explained in general terms for them to understand, where they can think, ask questions, and finally make the decision to voluntarily either participate or not (Erlen, 2010). At any point throughout the study, the participant can withdraw themselves from the study. For this study, the survey will commence with an explanation of the study, which will identify to the potential participant what will be done with their data and how their answers will remain anonymous and confidential.

Besides informed consent, confidentiality and anonymity are playing a key role in research. It has become a consensus that researchers owe a duty to the participants to protect and care for them in their data collection process (Akuffo, 2023). Anonymity is the process where the data collected cannot be tied back to a specific person. Since the researcher will utilize survey for data collection, the survey will remove direct identifiers such as name, employer, and social categorization. The collected data will remain anonymous, since the researcher will not come in direct contact with the participants during the data collection process – online survey. Anonymity is a crucial point for participants of a study, since it gives them the opportunity to be open and offer counter narratives on different sensitive topics, such as government, employers, and religion (Akuffo, 2023). To maintain anonymity, the survey will not ask for any identifying information, such as names, addresses, and contact details.

In contrast, confidentiality is the obligation to protect the collected data from unauthorized access, use, modification, loss, or theft. To avoid potential data leaks, the data that is collected will be stored on a locked computer, which is encrypted with password protection. Additionally, the computer will have a restricted access folder, which requires biometric screening to get access to the files in the folder. The data collected along with the signed informed consent for the collected data will be retained for at least three years but no more than five, following the United States regulation (Services, 2022).

The data collected will undergo statistical analysis using the Statistical Package for Social Sciences (SPSS). The first step in the statistical analysis process will be running the data for descriptive statistics, which will provide the sample’s mean, frequency, and standard deviation. In addition, tests for normality will be conducted that will analyze the data’s skewness and kurtosis. Moreover, the researcher will run three different analyses, regression, moderated regression, and ANOVA, to study the relationships between the independent variable, moderated variables, and dependent variable. In the table below is a breakdown of how each research question will be analyzed.

Table 8

For research question 1 (RQ1), a simple linear regression model will be used to assess whether limited job mobility significantly predicts human capital flight intent. Simple linear regression includes a dependent variable and an independent variable, which are related to each other (Roustaei, 2024). Conducting simple linear regression analysis for research question 1 (RQ1) allows the researcher to test for underlying pattern between two variables and the relationship between the dependent and independent variables for a specific value. Yet, before conducting regression analyses, key statistical assumptions will be tested to ensure the validity of the models:

· Linearity – the relationship between the independent and dependent variables must be linear.

· Homoscedasticity – the variance of residuals should remain constant across levels of the independent variable.

· Multicollinearity – independent variables should not be highly correlated with each other, as this can distort the estimates.

· Normality of residuals – regression residuals should follow a normal distribution.

For research questions 2, 3, and 4 (RQ2-RQ4): A moderated multiple regression analysis will be conducted to determine whether job satisfaction, occupational prestige, and workplace social connectedness moderate the relationship between limited job mobility and human capital flight intent. Moderated multiple regression analysis or an interaction test is considered a special application of multiple linear regression, where the regression equation contains elements of interaction – the multiplication of two or more independent variables (Ramadhani et al., 2020). This process will measure the strength or direction of the relationship between limited job mobility and human capital flight intent, and the changes this relationship experiences by the introduction of the moderating variables – job satisfaction, occupational prestige, and workplace social connectedness. The analyze this data, the researcher will install a plug-in for SPSS called PROCESS, which will estimate the regression model with moderation effects. This plug-in will allow the researcher to run separate models with each moderator in a separate regression.

For research question 5 (RQ5), analysis of variance (ANOVA) will be used to test the differences in human capital flight intent across generational cohorts. ANOVA is the appropriate test for RQ5, because the test is used to determine the proportion of variance attributed to the diverse groups being studied (Cronk, 2020). In this study the diverse groups will be the different generational cohorts. A one-way ANOVA requires a single dependent variable – human capital flight intent – and a single independent variable – generational cohorts. Additionally, ANOVA assumes that the independent variable is categorical (Cronk, 2020). Categorical variables are those that represents types of data which can be divided up into groups. In this study the independent variable is generational cohort and that is a variable that can be divided up into groups - baby boomers, gen X, millennials, Gen Z.

ANOVA is preferred over other tests, because the ANOVA will conduct one test to establish if there are differences in mean between the diverse groups, instead of doing separate t-tests to find the potential difference. Because running separate t-test could potentially result in the researcher making a Type I error. A Type I error occurs when the null hypothesis is rejected while it was in fact true (Cronk, 2020).

Before conducting the ANOVA, key statistical assumptions will be tested to ensure the validity of the analysis:

· Independence of observation – Each participant belongs to only one generational cohort, ensuring that groups do not overlap. To overcome this, the researcher will ask the participants to identify based on birthyear in which cohort they belong.

· Normality – The dependent variable (human capital flight intent) should be normally distributed within each group. This will be assessed using histograms, Q-Q plots, and the Shapiro-Wilk test.

· Homogeneity of variances (homoscedasticity) – The variances of human capital flight intent across generational cohorts should be equal. Levene’s test will be used to assess this assumption. If violated, a Welch’s ANOVA will be conducted instead. If the collected data is not equal among the different generational cohorts, the researcher will rely on participants of the study to spread the word to people that they may know who belong in the specific generational cohort.

The goal of this study is to investigate how potential limited job mobility in rural Southeastern United States impacts human capital flight intent. Additionally, the study will also investigate if the moderating variables of job satisfaction, workplace social inclusion, and occupational prestige in a limited job mobility setting can have an impact on human capital flight intent in rural Southeastern United States. Data will be collected utilizing an online survey – SurveyMonkey – and analyzed via Statistical Package for Social Sciences (SPSS) to accept or reject the five hypotheses associated with the research questions.

The reliability of research instruments refers to the ability of the instruments to produce consistent results over time. In research, reliability is considered a necessity to ensure that the data collected is consistent, but it without a valid tool, the results can still be invalid (Kioumars & Behnaz, 2022). Reliability and validity need to work together to ensure that the collected data is both reliable and valid. To ensure the reliability of the measurement instruments, there are two attributes that can be used to test reliability:

· Stability (test-retest reliability): The extent to which the same instrument produces consistent results over time. If this same study is done again a year later, and the data is collected under the same circumstances – same instrument, under the same conditions, and in the same period – would yield the same result.

· Internal consistency: the degree to which different items within a survey measure the same construct (e.g., Cronbach’s alpha) (Olmsted, 2024).

To achieve reliability in this study, the researcher has existing measures/surveys that have been tested previously. Choosing to utilize existing measures is efficient and researchers have determined the validity and reliability of the study. The reliability of the survey is quantified by Cronbach’s alpha, which ranges between 0 and 1, with optimal levels between 0.7 and 0.9 (Creswell & Creswell, 2023). The existing measurements used in this study have optimal Cronbach’s alpha’s as can be seen in table 9 below.

Table 9

· Construct Validity: How well a test measures the concept it intends to measure. The validity of this study relies on the three instruments to measure what they intend to measure. Additionally, a construct validity error can occur due to the survey being internet based, anonymous, and voluntary participation, all which can lead to skewed opinions (Burns & Burns, 2008). Internet surveys can lead to self-selection bias, because potential participants may not have access to the internet and may not be able to participate in the study.

· External Validity: External validity is achieved if the current results can be generalized to other samples, time periods and settings (Ihantola & Kihn, 2011). There are three factors that can impact external validity – population, time, and environment. These three factors are independent from one but if the study were to take place a year from today, these factors will impact the outcome of the study. If there is a change in the economy, the results of this test would not be applicable anymore.

This chapter outlined the quantitative methodology that will be used in the study. The instrument used is comprised of three different existing surveys JSS-36, SCS-R, General Readiness for In-Between Job Mobility and General Readiness for Geographical Mobility, to collect data on the several factors that affect human capital flight due to limited job mobility (Lok & Dunn, 2023; Otto et al., 2004; Spector, 1985). A targeted sample of 150 people who live and work in rural communities in the Southeastern United States, who are between the ages of 25 and 65, who hold full-time jobs, and who do not hold managerial roles within their current organization. The sample size was determined through a G*Power priori analysis and supported by previous study (Kosec et al., 2022). Data collection will be conducted using Amazon’s Mechanical Turk (MTurk), using convenience sampling method to recruit the participants. The self-administered questionnaire will be distributed via Survey Monkey which is user friendly. Once the data is collected, it will be analyzed utilizing the Statistical Package for Social Sciences (SPSS) software. Utilizing SPSS, the researcher will conduct regression analysis, multi moderated regression analysis, and ANOVA to help answer the research questions and to either reject or fail to reject the null hypotheses.

CHAPTER FOUR: DATA ANALYSIS AND RESULTS

Introduction

The purpose of this research is to determine whether limited job mobility impacts human capital flight intent in rural communities in Southeastern United States. Additionally the study examines the impact that moderating variables – job satisfaction, workplace social connectedness, and occupational prestige – have on the relationship between limited job mobility and human capital flight intent. This study is specifically focused on rural communities in Southeastern United States – communities with 2,500 inhabitants and less – who experience small opportunities in the job market. A review of the impacts of limited job mobility in these communities is necessary to understand how little job opportunities impact potential human capital flight decisions.

The survey consisted of 67 questions that collected data on limited job mobility, human capital flight intent, job satisfaction, workplace social connectedness, and occupational prestige from people between the ages of 25 and 65 who live in rural communities in Southeastern United States. The skewness and kurtosis of the variables fall between +/- 3 showing reliability and normal distribution of the survey tool.

The research is an attempt to answer the following five questions and hypotheses:

RQ1. How does limited job mobility predict human capital flight intent in rural communities in Southeastern United States?

H01: Limited job mobility does not predict human capital flight intent in rural communities in Southeastern United States

HA1: Limited job mobility does predict human capital flight intent in rural communities in Southeastern United States

RQ2. How does job satisfaction moderate the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States?

H02: Job satisfaction does not moderate the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States

HA2: Job satisfaction moderates the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States

RQ3. How does occupational prestige moderate the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States?

H03: Occupational prestige does not moderate the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States

HA3: Occupational prestige moderates the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States

RQ4. How does workplace social connectedness moderate the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States?

H04: Workplace social connectedness does not moderate the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States

HA4: Workplace social connectedness moderates the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States

RQ5. Is there a difference between different generational cohorts in terms of human capital flight intent in rural communities in Southeastern United States?

H05: Differences in generational cohorts does not moderate the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States

HA5: Generational cohorts moderate the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States

Survey data was analyzed using SPSS for Mac. The data was collected using the SurveyMonkey platform, and the collected data was downloaded as a Microsoft Excel file and then uploaded to SPSS. Regression analysis was done to identify if limited job mobility impacts human capital flight intent. A multi-moderated regression analysis was done to identify is the three moderating variables – job satisfaction, workplace social connectedness, and occupational prestige – impact the relationship between limited job mobility and human capital flight intent. Lastly, an ANOVA analysis was done to measure the difference in impact of limited job mobility on human capital flight intent by the different generations.

Presentation of Findings

As calculated by G-Power, the study requires a sample size estimate of 93 participants. The study acquired information from 110 participants, who completed the survey in full. The sample set of this study was focused on individuals who live in rural areas of Southeastern United States, who are between the ages of 25 and 65, and who do not hold any managerial positions. Additionally, the research integrated additional information to apply links or alternative association to the studied variables. The tables below indicate the frequency and percentile of the demographic variables of all study participants. The frequency represents the number (n), and the percentage represents the percentage of total participants in that category.

Table 10: Demographics Variables: Gender/Degree

Total N=110

Table 10 above summarizes the gender and degree level of the sample. 47.3% (52) of the respondents were female, 35.5% (39) of the respondents were male, and 17.3% of the respondents identified as other. As it pertains to the level of education 14.5% (16) had a high school diploma; 21.8% (24) had an associate degree; 41.8% (46) had a bachelor’s degree; 19.1% (21) had a master’s degree; and 2.7% (3) had a doctorate degree.

Table 11: Demographics Variability: Generations

Total N=110

Table 11 above summarizes the generations of the sample. 6.4% (7) of the respondents were Baby Boomers, 20.0% (22) of the respondents were Generation X, 63.6% (70) were Millennials, and 10% (11) were Generation Z.

Table 12: Demographics Variables: Sector of Employment

Total N=110

Table 12 summarizes the sectors which the sample works in. The top five sectors are education with 17.3% (19), office admin support 12.7% (14), healthcare 10.9% (12), sales related services 10.9% (12), and business and financial operations 10.0% (11).

Descriptive Statistics

Each construct had several questions associated with it, and were calculated by adding the responses for each question. In the survey there were negatively worded questions that had to be coded for the final calculation. For instance a negatively worded question that got a 3 out of 6 in the Likert scale, would now be a 4 for the final calculation. Job satisfaction (JSS-36) was measured using a six-point Likert scale, ranging from (1) disagree very much to (6) agree very much. To get a score per individual, questions that were negatively worded were recoded prior to analysis and the thirty six questions were added up to give a total score. A score of 36 through 108 means that the individual who took the survey is dissatisfied with their job. A score of 108 through 144 means that the individual is ambivalent with their job. And lastly, a score of 144 and greater means that the individual is satisfied with their job. Job satisfaction scores ranged from 52 to 206 (M=142.68, SD=35.37). The mean score of 142.68 on this survey indicates that the respondents were on average ambivalent about their job. Based on the standard deviation (35.37), the skewness (-0.31) and kurtosis (-0.26), the sample population has a close to normal distribution, and fall within accepted thresholds.

Social connectedness was measured using UBC-SCC, which is a 10 question survey using a seven-point Likert scale, ranging from (1) strongly disagree to (7) strongly agree. To get a score per individual, questions that were negatively worded were recoded prior to analysis and the ten question were then added up to get a social connectedness score. Social Connectedness scores ranged from 12 to 70 (M=50.75, SD=15.22). The mean score of 50.75 out of 70 of this survey indicates that the respondents are above average socially connected to their peers in the workplace. The distribution exhibited a moderate negative skew (-0.86) with responses concentrated more toward the higher end, while kurtosis (-0.10) suggests an approximate normal level of peak. The distribution did not substantially violate the assumption of normality.

Human capital flight was measured using the geographical mobility scale, which is a 10 question survey using a six-point Likert scale, ranging from (1) strongly disagree to (6) strongly agree. To get a score per individual, questions that were negatively worded were recoded prior to analysis and the ten question were then added up and then divided by the total amount of questions (10) to get a score for human capital flight intent. Human capital flight scores ranged from 1.1 to 5.3 (M=3.48, SD=0.94). The mean score of 3.48 out of 5 of this survey indicates that the respondents are on average open and willing to move to the city. The distribution exhibited an approximate normal skew (-0.22) and a slightly platykurtic kurtosis (-0.44). The distribution did not substantially violate the assumption of normality.

Job mobility was measured using the occupational mobility scale, which is a 10 question survey using a six-point Likert scale, ranging from (1) strongly disagree to (6) strongly agree. To get a score per individual, questions that were negatively worded were recoded prior to analysis and the ten question were then added up and then divided by the total amount of questions (10) to get a score for job mobility. Job mobility scores ranged from 1.2 to 5.9 (M=3.61, SD=1.03). The mean score of 3.61 out of 5 of this survey indicates that the respondents are on average open change jobs. The distribution exhibited an approximate normal skew with negative skewness (-0.14) and a slightly platykurtic kurtosis (-0.31). The distribution did not substantially violate the assumption of normality.

Table 14

Table 15

Table 16

Table 17

Methods and Reliability

In a quantitative study, reliability is essential for ensuring the accuracy and credibility of the collected data and interpretation of those results. In this case, the study aims to understand human capital flight intent as it relates to limited job mobility as moderated by job satisfaction, occupational prestige and workplace social connectedness. With a sample population of 110 participants, Cronbach’s Alpha is used to assess the extent to which the survey questions reliably and objectively measure participants’ ability to comprehend and respond to the items in relation to the study. The standard for a reliable study as it applies to Cronbach’s Alpha is > 0.70. If the instruments achieve this score, it can be assumed that the questions of the surveys used are reliably answering how people decide to leave their communities, and how job satisfaction, occupational prestige and workplace social connectedness. A score lower than 0.70 suggests that the surveys are measuring other ideas. The table below indicates the reliability of the study with a Cronbach’s Alpha of 0.919, which suggests a high measure of reliability.

Table 18. Study’s Cronbach’s Alpha

Additionally, Cronbach’s Alpha is a measure of internal consistency, as it is one of the test used to measure reliability. Cronbach’s alpha comprises a number of items that make up the scale designed to measure a single construct. Table 19, below shows that Cronbach’s alpha also accounts for omitted items, which indicates how the Cronbach’s alpha is affected when one item is removed from the data. The table of 67 items shows a consistent range of Cronbach’s alpha between 0.914 and 0.930. The higher the Cronbach’s alpha of one construct in comparison to the others item may suggest that it is not accurately measuring the same constructs of the other items. On the other hand, if an item has a low squared multiple correlation value, a low item-adjusted total correlation value, and a substantially lower Cronbach’s alpha, it may have a negative effect on internal consistency and should be excluded.

Table 19. Cronbach’s Alpha for all Omitted Items

Statistical Analysis

The purpose of this research is to understand the impact of limited job mobility on human capital flight intent in rural communities of Southeastern United States. Based on regression, multi moderated regression, and ANOVA analysis, the null hypothesis for RQ1 is rejected.

RQ1: How does limited job mobility predict human capital flight intent in rural Southeastern United States?

H01: Limited job mobility does not predict human capital flight intent in rural communities in Southeastern United States

HA1: Limited job mobility does predict human capital flight intent in rural communities in Southeastern United States

Independent Variable: Job Mobility

Dependent Variable: Geographic Mobility

The results of the regression, model summary, and statistics for each independent variable are as follow:

Table 20. Model Summary

a. Predictors: (Constant), Job Mobility

Anovaa

a. Dependent Variable: Geographic Mobility

b. Predictors: (Constant), Job Mobility

Coefficientsa

a. Dependent Variable: Geographic Mobility

A simple linear regression was calculated to predict participants’ willingness to leave their community (geographical mobility) for another based on their willingness to switch jobs (job mobility). A significant regression equation was found (F(1,108)=15.604, p<.001), with an R2 of .126. Participants predicted willingness to leave their community is equal to 23.103+.324*(JOB MOBILITY). Participant’s willingness to leave increased with .324 when job mobility increased with 1 point.

RQ2: How does job satisfaction moderate the relationship between limited job mobility and human capital flight?

H02: Job satisfaction does not moderate the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States.

HA2: Job satisfaction moderates the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States.

Independent Variable: Job Mobility

Moderating Variable: Job Satisfaction

Dependent Variable: Geographic Mobility

The results of PROCESS is described below:

Table 21

A multiple regression analysis (PROCESS by Andrew P. Hayes) was conducted to examine the moderating role of job satisfaction on the relationship between job mobility and human capital flight intent. The model accounted for 17.28% of variance in human capital flight intent (R2=0.1728)

Job satisfaction did not have a significant impact on human capital flight, (b = 0.0588, p >.05). Furthermore, the interaction effect between job mobility and job satisfaction was found to be insignificant (F(1, 106)= 1.0981, p = .2971), suggesting that the relationship between job mobility and human capital flight intent is not significantly moderated by job satisfaction.

RQ3: How does occupational prestige moderate the relationship between limited job mobility and human capital flight?

H03: Occupational prestige does not moderate the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States

HA3: Occupational prestige moderates the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States

Independent Variable: Job Mobility

Moderating Variable: Social Connectedness

Dependent Variable: Geographical Mobility

The results of PROCESS is described below:

Table 22

A moderated regression analysis (PROCESS by Andrew P. Hayes) was conducted to examine the moderating role of workplace social connectedness on the relationship between job mobility and human capital flight intent. The model accounted for 14.45% of variance in human capital flight intent (R2=0.1445)

Workplace social connectedness did not have a significant impact on human capital flight, ( b = 0.0994 p >.05). Furthermore, the interaction effect between job mobility and social connectedness was found to be insignificant (F(1,106) = 0.0032, p = 0.9548), suggesting that the relationship between job mobility and human capital flight intent is not significantly moderated by workplace social connectedness.

RQ4: How does workplace social connectedness moderate the relationship between limited job mobility and human capital flight?

H04: Workplace social connectedness does not moderate the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States

HA4: Workplace social connectedness moderates the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States

Independent Variable: Job Mobility

Moderating Variable: Social Connectedness

Dependent Variable: Geographical Mobility

The results of PROCESS is described below:

Table 23

A multiple regression analysis (PROCESS by Andrew P. Hayes) was conducted to examine the moderating role of job satisfaction on the relationship between job mobility and human capital flight intent. The model accounted for 13.87% of variance in human capital flight intent (R2=0.1387)

Occupational prestige did not have a significant impact on human capital flight, (B = -.0024, p >.05). Furthermore, the interaction effect between job mobility and occupational prestige was found to be insignificant (F(1, 106)= 1.5286, p = .22), suggesting that the relationship between job mobility and human capital flight intent is not significantly moderated by occupational prestige.

RQ5: Is there a difference between different generational cohorts in terms of human capital flight intent in rural Southeastern United States?

H05: Differences in generational cohorts does not moderate the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States

HA5: Generational cohorts moderate the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States

Independent Variable: Generational Cohorts

Dependent Variable: Geographical Mobility

The results of the ANOVA is as follow:

Table 24 ANOVA

No significant difference was found (F(3,106)= 2.627, p > .05. Participants from 4 different generational cohorts did not differ significantly at making geographical moves.

Research Questions and Hypotheses

RQ1. How does limited job mobility predict human capital flight intent in rural communities in Southeastern United States?

H01: Limited job mobility does not predict human capital flight intent in rural communities in Southeastern United States. Reject

HA1: Limited job mobility does predict human capital flight intent in rural communities in Southeastern United States. Accept

RQ2. How does job satisfaction moderate the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States?

H02: Job satisfaction does not moderate the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States. Accept

HA2: Job satisfaction moderates the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States. Reject

RQ3. How does occupational prestige moderate the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States?

H03: Occupational prestige does not moderate the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States. Accept

HA3: Occupational prestige moderates the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States. Reject

RQ4. How does workplace social connectedness moderate the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States?

H04: Workplace social connectedness does not moderate the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States. Accept

HA4: Workplace social connectedness moderates the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States. Reject

RQ5. Is there a difference between different generational cohorts in terms of human capital flight intent in rural communities in Southeastern United States?

H05: Differences in generational cohorts does not moderate the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States. Accept

HA5: Generational cohorts moderate the relationship between limited job mobility and human capital flight intent in rural communities in Southeastern United States. Reject

CHAPTER FIVE: DISCUSSION, CONCLUSION, AND RECOMMENDATIONS

Discussion

This study looked at how people in smaller isolated communities in Southeastern United States make decisions about their career in their current community. The key focus was on how limited job mobility perceptions influence their decision to stay or leave their small community for larger cities where opportunities are widely available – human capital flight. Additionally, the study examined how different factors such as job satisfaction, occupational prestige and workplace social connectedness impacted the relationship between limited job mobility and human capital flight intent. Limited job mobility as mentioned, is the perception of being stuck at an unsatisfying job yet perceiving that there are limited other opportunities to move and apply for.

To find out how employees make career decisions in rural communities, where they may perceive that they are stuck at an unsatisfying job with limited opportunities, data was collected to study this relationship. The independent variable was limited job opportunities, while the dependent variable was human capital flight intent. Additionally, moderating variables were used to understand how they impact the relationship between limited job mobility and human capital flight intent. The moderating variables were job satisfaction, occupational prestige and workplace social connectedness. The analysis of the data collected showed significance in only one of the hypotheses – where job mobility has a relationship on human capital flight intentions. Data was collected utilizing Survey Monkey and Prolific to solicit participants for the study. The results of the survey were confirmed as reliable using Cronbach’s Alpha, where every research question fall within the range of 0.914 and 0.930, exceeding the acceptable value of 0.70. Lastly, the skewness and kurtosis fall in fall within acceptable thresholds of (+/-) 3, which indicates normal distribution.

This quantitative study utilized, linear regression, multi moderated linear regression (PROCESS), and ANOVA analysis to answer the study’s research questions and hypotheses. A 67-question survey was used to collect data, with 55 of them being six-point Likert scale, 10 of them being seven-point Likert scale, and one multiple choice question. The survey was hosted on Survey Monkey and Prolific was used to recruit participants to fill out the survey. A total of 110 participants took the survey, and filled it out completely, which met the criteria of being an employee who live in a rural community of less than 2,500 inhabitants in Southeastern United States. The survey was constructed using existing questionnaires such as Job Satisfaction Survey, General Readiness for Global Mobility Scale, General Readiness for Between Occupation Mobility, Social Connectedness Scale, and the Occupational Prestige ranking. These tools had a Cronbach’s Alpha of 0.919, which supports the construction of a reliable tool.

To conclude, the analysis of the data resulted in the rejection of the null hypothesis for the first research question, with a p-value < 0.5. This question looked at the relationship between limited job mobility and human capital flight intent. Job mobility does predict human capital flight intent among people living in rural communities in Southeastern United States. The more immobile people are with their careers the less they are willing to leave their communities for larger cities. On the other hand, the more mobile someone is with their careers the higher the likeliness that they will choose to leave their community for larger cities. Yet, job satisfaction, occupational prestige, and workplace social connectedness did not play a significant role in the relationship between limited job mobility and human capital flight intent. Additionally, there was no difference in the impact of limited job mobility on human capital flight intent among different generational cohorts.

Limitations

There were some limitations to the study due to the data collection method used. Community attachment data could not be collected in this study. The study focused heavily on the workplace experience and the possible career decisions that come along with it. But the study excludes a major component, community attachment. Community attachment is the bond that someone has with their community, this includes social networks, local identity, and sense of belonging within the community. This data would give a better insight into the community factors that can impact their decisions to stay or leave their community.

Lastly, this cross-sectional study design limits causal inference. Since the data is collected only at one point in time, there is no opportunity to establish a cause-effect relationship between these two factors. In this study, the exposure and the outcome are measured all at once.

Since the study only focused on one moment in time, it fails to get insight into the lived-experiences that may cause human capital flight intent. These limitations can potentially impact the outcome and recommendations for this study.

Implications

The findings of this study suggests an important implication for theory, practice and future research. The study identified a relationship between job mobility and human capital flight intent, which indicates that the less mobile someone is, the less likely they are to be leaving their current rural community. On the other hand, the more mobile an employee is the more likely they are to leave their community for opportunities in other cities. Job mobility as a concept is the voluntary or involuntary movement of employees between jobs, roles, or organizations.

While, human capital flight intent, is the movement of skilled individuals from their home country to another more developed – economically, social, and politically – for better opportunities.

Human capital flight is macro concept, focused on movement of skilled individuals between different countries. For the purpose of this study, human capital flight focused on employees from rural communities and their potential migration to larger cities that would still give them opportunities in a more developed environment. Since, the study identified to job mobility has an impact on human capital flight intent, this has implications on organizations in the rural communities, such as talent management.

Talent Management

Limited job mobility does not increase human capital flight intent, but high job mobility does. Considering these findings, organizations should be aware of how people move and make decisions about their careers. One key identifier is how many jobs someone has had before the job they are applying for, since this indicates their job mobility. Organizations should use mobility as a screening – especially in areas where applicants are becoming hard to get. Using job mobility as a screening can give organizations an insight into future turnover risks.

When organizations are faced with highly mobile employees they can consider some retention strategies that will keep their current employees local, even when they are in need of employees that are not part of their community currently. To retain employees with an organization in smaller communities, the positions should be tied to geographic stability. A career that is tied to geographic stability is one that where staying in one place normal, rewarded or needed contrary to those that expect frequent moves. These types of careers are those where advancement happens within the same region or organization, local knowledge or community ties are needed, relocation is not a prerequisite for promotion, and long-term residence can strengthen career prospects.

Geographic Stability

Some of the most common career pathways associated with geographic stability in smaller communities are education, healthcare, and government services. Which were the common respondents career pathways. Of the total respondents 17.3% worked in education, 12.7% worked in office admin support, 10.9% worked in healthcare and 10.9% worked in business and financial operations. In the survey, there was not a specific sector that encompassed government services, so depending in which government service someone worked it can be in any different sector. These career pathways offer communities and organizations stability because in terms of education, some certification are either state or region specific and advancement occurs within the same district. Healthcare roles are rewarded with long term presence. And government jobs are tied to jurisdictions and promotions happen internally based on years of service.

Beyond these common sectors that are the base of smaller communities, there are other options that also offer geographic stability. Regionally anchored corporate roles, these types of roles often involve manufacturing plant managers or operations supervisors. In smaller communities these can be opportunities for locals to grow with a national organization that has a footprint in a local community. Skilled trades are also one of the career paths that offer geographic stability as they depend on a local market and relationships that build a client base. For trades, local reputation are place-bound and relationships forged are useful in growing.

Even though these career paths are focused on a couple of sectors, organizations can still adapt to the ever changing landscape utilizing these concepts to remain attractive for the locals. The study did not identify if job satisfaction, occupational prestige or workplace social connectedness plays a role in the relationship between job mobility and human capital flight intent. So, how businesses create a working environment for their employees does not matter at this point, but the job on its own can be a first step to consider. The job has to be stable enough and still provide opportunities within the same region to create geographic stability and be attractive enough to limit job mobility and reduce human capital flight intent in rural communities in Southeastern United States.

Conclusion

The results of this study indicated that human capital flight intent in rural communities in Southeastern United States increased with increased job mobility. On the other hand, a drop in job mobility reduces the willingness to leave the community. Additionally, job satisfaction, occupational prestige, workplace social connectedness did not moderate the relationship between limited job mobility and human capital flight intent. Lastly, the significant difference was found in human capital flight intent among different generational cohorts.

The literature described how small isolated communities tend to suffer the most from human capital flight. Even though most studies have focused on broad regional trends and on larger scales, this study focused on more smaller communities and isolated economies. It has been the intention of this study to highlight how limited job mobility may change in smaller isolated economies, compared to previous studies that have been focused on broader regional trends, and add to the existing literature of human capital flight intent and job mobility. One of the five hypotheses was rejected based on the conclusion that limited job mobility in rural communities in Southeastern United States impact human capital flight intent. What this research could not explain was why the participants of this study responded the way they responded, and if there was any recall bias or other factors that may have influenced their responses. Future qualitative research in rural communities can potentially provide insights into the job commitment of employees in these communities and what might motivate them to stay or leave their communities.

Recommendations for Future Research

This study found that limited job mobility in rural communities in Southeastern United States lead to less human capital flight intent, yet the moderating factors did not provide any extra insight into what may contribute to less human capital flight intent. Since the study was focused only on a snapshot in moment and not over a period of time, a longitudinal study would give a long term insight into career decisions. Also, looking at other moderating variables would give the study an additional insight into non-career specific reasons that may impact job mobility and human capital flight intent.

A longitudinal study will be able to track the same individuals, communities or households over a period of time to understand how circumstances and decisions change. Longitudinal data allows researchers to identify predictors of relocation, examine life-course transitions, analyze push and pull factors, and measure long-term outcomes of human capital flight. The longitudinal study would track or examine people’s decisions from the predictors of relocations which can be income growth, household formation, or job offers, which are all factors that can initiate the intent to leave. And the study will track until the long-term outcomes, which looks at career advancement as a factor that impacts their decisions to stay in their new community or a decision to leave the new community. These factors are important to study, as they can provide context to the relationship between job mobility and human capital flight intention. Since, limited job mobility in rural communities leads to reduced human capital flight intent understanding why people stay within the community can be insightful for future research and can contribute to future literature.

Additionally, to better understand job mobility and human capital flight intent decisions, incorporating other moderating variables which can help explain the relationship between limited job mobility and human capital flight intent. A specific moderating variable is remote and hybrid work option, which is a current topic that is being studies. Since the Covid-19 pandemic, a lot of organizations moved to a remote/hybrid work option that has given organization an upper hand in keeping their organization running during the shutdown. This model is not new, but it has been a recent trend in organizations as a method of recruitment and retention when potential employees are hard to come by. Another moderating variable would be family ties or community ties. This moderating variable would examine personal relationships that would keep a person local to their community or the reason that they might leave the community. Including these extra moderating variables to this study would better highlight reasons outside of the workplace that may impact a person’s job mobility and their decision to stay or leave their rural town for a large city with more opportunities.

Abdalla, A., Elsetouhi, A., Negm, A., & Abdou, H. (2018). Perceived person-organization fit and turnover intention in medical centers: The mediating roles of person-group fit and person-job fit perceptions. Personnel Review, 47(4), 863–881. https://doi.org/10.1108/PR-03-2017-0085

Akuffo, A. G. (2023). When the Researched Refused Confidentiality: Reflections from Fieldwork Experience in Ghana. Journal of Academic Ethics, 21(4), 567–589. https://doi.org/10.1007/s10805-023-09471-x

Alarabiat, Y. A., & Eyupoglu, S. (2022). Is Silence Golden? The Influence of Employee Silence on the Transactional Leadership and Job Satisfaction Relationship. Sustainability, 14(22), 15205. https://doi.org/10.3390/su142215205

Albert, C., & Davia, M. A. (2023). University-supported job search methods and educational mismatch in bachelor’s and master’s graduates. Education & Training, 65(10), 29–45. https://doi.org/10.1108/ET-04-2022-0144

Alzaid, D., & Dukhaykh, S. (2023). Employer Branding and Employee Retention in the Banking Sector in Saudi Arabia: Mediating Effect of Relational Psychological Contracts. Sustainability, 15(7), 6115. https://doi.org/10.3390/su15076115

Andrade, M. S., & Westover, J. H. (2018). Generational differences in work quality characteristics and job satisfaction. Evidence - Based HRM, 6(3), 287–304. https://doi.org/10.1108/EBHRM-03-2018-0020

Andrade, M. S., Westover, J. H., Clark, S., & Schill, A. (2024). Job Satisfaction and Generational Difference: The Shifting Nature of the Workplace. American Journal of Management, 24(3), 1–20.

Andrade, M. S., Westover, J. H., & Cunningham, R. (2023). Work Flexibility and Job Satisfaction: Shifting Workplace Norms. Journal of Management Policy and Practice, 24(2), 15–39.

Ann, S., Hallab, Z. A. A., Choi, H., & Majthoub, U. A. (2023). Motivating Housekeeping Staff in the Lodging Industry in Jordan. Tourism and Hospitality Management, 29(1), 103–118. https://doi.org/10.20867/thm.29.1.9

Baluku, M. M., Groh, J., Dalbert, C., & Otto, K. (2021). Cultural differences in geographic mobility readiness among business management students in Germany and Spain ahead of graduation. Sn Social Sciences, 1(7), 161. https://doi.org/10.1007/s43545-021-00171-0

Baluku, M. M., Löser, D., Otto, K., & Schummer, S. E. (2018). Career mobility in young professionals: How a protean career personality and attitude shapes international mobility and entrepreneurial intentions. Journal of Global Mobility, 6(1), 102–122. https://doi.org/10.1108/JGM-10-2017-0041

Barra, C., & Ruggiero, N. (2023). Governmental stability and emigration in Sub-Saharan Africa: The role of skills and gender. Journal of Economic Studies, 50(7), 1450–1466. https://doi.org/10.1108/JES-04-2022-0219

Behera, N., Sahoo, P., & Swain, J. P. (2024). Job Satisfaction and Work-life Balance of Government Employees: A Study of Gender and Social Support. International Journal of Education and Management Studies, 14(2), 206–209.

Belle, T., Barclay, S., Bruick, T., & Bailey, P. (2022). Understanding Post-Graduation Decision of Caribbean International Students to Remain in the United States. Journal of International Students, 12(4), 955–972. https://doi.org/10.32674/jis.vl2i4.3829

Berisha, G., & Lajçi, R. (2020). Fit to Last? Investigating How Person-Job Fit and Person-Organization Fit Affect Turnover Intention in the Retail Context. Organizations and Markets in Emerging Economies, 11(2), 407–428. https://doi.org/10.15388/omee.2020.11.40

Bhandari, P. (2021, February 16). Statistical Power and Why It Matters | A Simple Introduction. Scribbr. https://www.scribbr.com/statistics/statistical-power/

Bharadwaj, S., Khan, N. A., & Yameen, M. (2022). Unbundling employer branding, job satisfaction, organizational identification and employee retention: A sequential mediation analysis. Asia - Pacific Journal of Business Administration, 14(3), 309–334. https://doi.org/10.1108/APJBA-08-2020-0279

Bothma, F. C., & Roodt, G. (2013). The validation of the turnover intention scale. SA Journal of Human Resource Management, 11(1). https://doi.org/10.4102/sajhrm.v11i1.507

Bristol, M. A. (2010). Brain Drain and Return Migration in Caricom: A Review of the Challenges. Caribbean Studies, 38(1), 129–146. https://doi.org/10.1353/crb.2010.0023

Burns, R. B., & Burns, R. A. (2008). Business research methods and statistics using SPSS. SAGE Publications.

Butt, R. S. (2018). Effect of Motivational Factors on Job Satisfaction of Administrative Staff in Telecom Sector of Pakistan. Journal of Economic Development, Management, I T, Finance, and Marketing, 10(2), 47–57.

Büyükbeşe, T., Dikbaş, T., Çavuş, Ö., & Asiltürk, A. (2023). Herzberg’s Two Factor Theory and Its Impact on Job Satisfaction: A Research on Bank Employees During The Covid-19 Period. Sosyal ve Ekonomik Arastırmalar Dergisi, 25(45), 998–1013.

Cernas-Ortiz, D. A., & Wai-Kwan, L. (2021). Social connectedness and job satisfaction in Mexican teleworkers during the pandemic: The mediating role of affective well-being. Estudios Gerenciales, 37(158), 37–48. https://doi.org/10.18046/j.estger.2021.158.4322

Chan, S. (2016). Belonging to a workplace: First-year apprentices’ perspectives on factors determining engagement and continuation through apprenticeship. International Journal for Educational and Vocational Guidance, 16(1), 9–27. https://doi.org/10.1007/s10775-014-9282-2

Chaturvedi, R. D., Bhandari, M. A., & Jambhulkar, T. N. (2023). Personality Type A and Type B: Influence on Job Satisfaction and Life Satisfaction. Indian Journal of Positive Psychology, 14(4), 431–436.

Chen, C.-W., & Li, L. Y. (2023). Is hiring fast a good sign? The informativeness of job vacancy duration for future firm profitability. Review of Accounting Studies, 28(3), 1316–1353. https://doi.org/10.1007/s11142-023-09797-2

Ching, H. Y., Fang, Y. T., & Yun, W. K. (2022). How New Nurses Experience Workplace Belonging: A Qualitative Study. SAGE Open, 12(3), 21582440221119471. https://doi.org/10.1177/21582440221119471

Chou, N.-W., Hsieh, H.-M., & Hung, T.-K. (2022). The Impact of Person-Job Fit on Job Performance: Job Involvement as Mediator, and Career Plateau as Mediated Moderator. International Journal of Organizational Innovation (Online), 14(3), 115–133.

Chuan, V. T., & Schaefer, G. O. (2015, September 23). Research in Resource-Poor Countries. The Hastings Center. https://www.thehastingscenter.org/briefingbook/multinational-research/

Condon, D., & Hughes, B. (2022). Occupational Prestige Ratings Data [Dataset]. Harvard Dataverse. https://doi.org/10.7910/DVN/G1E4BF

Creswell, J. W., & Creswell, J. D. (2023). Research Design: Qualitative, Quantitative, and Mixed Method Approaches (Sixth). SAGE.

Cronk, B. C. (2020). How to use SPSS - A step-by-step guide to analysis and interpretation (11th ed.). Taylor & Francis.

Cucina, J. M., Byle, K. A., Martin, N. R., Peyton, S. T., & Gast, I. F. (2018). Generational differences in workplace attitudes and job satisfaction: Lack of sizable differences across cohorts. Journal of Managerial Psychology, 33(3), 246–264. https://doi.org/10.1108/JMP-03-2017-0115

Demissie, E. D., Koech, D. K., & Molnár, E. (2024). Work-Life Balance Assessing Post Covid-19 Practice of Work-Life Balance and Employee Job Performance: A Literature Review. Multidiszciplináris Kihívások, Sokszínű Válaszok, 1, 3–26. https://doi.org/10.33565/MKSV.2024.01.01

Dette, D., & Dalbert, C. (2005). Moving for their first job or staying put? – High school students’ attitudes towards geographic mobility. Journal of Applied Social Psychology, 8, 1719–1737.

Dixit, A. S. (2024). Exploring HR practitioner’s perspective on linking of employer branding and porter’s generic strategies: An alignment of business and HR strategy. International Journal of Organizational Analysis, 32(6), 1060–1072. https://doi.org/10.1108/IJOA-02-2023-3635

Docquier, F., Lohest, O., & Marfouk, A. (2005). Brain Drain in Developing Regions (1990-2000). SSRN, 1668. https://doi.org/10.2139/ssrn.761624

Donaldson, S. I., & Grant-Vallone, E. J. (2002). Understanding Self-Report Bias in Organizational Behavior Research. Journal of Business and Psychology, 17(2), 245.

Du, Y., Li, J., & Xu, Q. (2023). Are you satisfied when your job fits? The perspective of career management. Baltic Journal of Management, 18(5), 563–578. https://doi.org/10.1108/BJM-09-2022-0353

Ellepola, M. G. (2013). Reducing Employee Turnover In Apparel Manufacturing Industry: Case Study. I-Manager’s Journal on Management, 8(3), 42–46.

Employment & Education—Rural Employment and Unemployment | Economic Research Service. (n.d.). Retrieved August 31, 2025, from https://www.ers.usda.gov/topics/rural-economy-population/employment-education/rural-employment-and-unemployment

Erlen, J. A. (2010). Informed Consent: Revisiting the Issues. Orthopaedic Nursing, 29(4), 276–280.

Estes, H. K., Estes, S., Johnson, D. M., Edgar, L. D., & Shoulders, C. W. (2016). The Rural Brain Drain and Choice of Major: Evidence from One Land Grant University. NACTA Journal, 60(1), 9–13.

Explore Rural Population in the United States | AHR. (n.d.). Retrieved August 28, 2025, from https://www.americashealthrankings.org/explore/measures/pct_rural_b

Feenstra-Verschure, M. T., Kooij, D., Freese, C., Van der Velde, M., & Lysova, E. I. (2023). “Locked at the job”: A qualitative study on the process of this phenomenon. Career Development International, 28(1), 92–120. https://doi.org/10.1108/CDI-06-2022-0154

Feenstra-Verschure, M. T., Kooij, D., Freese, C., van der Velde, M., & Lysova, E. I. (2024). Building on job immobility concepts: A conceptual model and future research agenda on “locked at the job.” Journal of Organizational Effectiveness, 11(1), 213–233. https://doi.org/10.1108/JOEPP-03-2022-0055

Fields, A. (2016). Discovering statistics using IBM SPSS statistics (4th ed.). SAGE Publications.

Fischer-Browne, M., Ahrens, L., Kleinert, C., & Schels, B. (2024). Compromises in occupational choice and premature termination of vocational education and training: Gender type, prestige, and occupational interests in focus. Empirical Research in Vocational Education and Training, 16(1), 14. https://doi.org/10.1186/s40461-024-00168-y

Flanja, D. P., & Nistor, R.-M. (2017). Brain Drain: Are We Losing Our Minds? A Study on the Romanian Human Capital F(l)ight. On - Line Journal Modelling the New Europe, 24, 58–75. https://doi.org/10.24193/OJMNE.2017.24.05

Frost, J., Mellon, V., Stalmirska, A., & Frost, W. (2024). Employing “Someone to be Your Voice” in an Artisanal Gastronomic Tourism Business: Authenticity, Cultural Capital, and Human Resources. Gastronomy and Tourism, 8(2), 83–98. https://doi.org/10.3727/216929722X16354101932492

Frunzaru, V., & Dumitriu, D.-L. (2015). Self-Perceived Occupational Prestige among Romanian Teaching Staff: Organisational Explicative Factors. Management Dynamics in the Knowledge Economy, 3(4), 629–643.

Fujishiro, K., Xu, J., & Gong, F. (2010). What does “occupation” represent as an indicator of socioeconomic status? Exploring occupational prestige and health. Social Science & Medicine, 71(12), 2100–2107.

Garbin, A. P., & Bates, F. L. (1961). Occupational prestige: An empirical study of its correlates. Social Forces, 40(2), 131–136. https://doi.org/10.2307/2574291

García-Salirrosas, E. E., Rondon-Eusebio, R. F., Geraldo-Campos, L. A., & Acevedo-Duque, Á. (2023). Job Satisfaction in Remote Work: The Role of Positive Spillover from Work to Family and Work–Life Balance. Behavioral Sciences, 13(11), 916. https://doi.org/10.3390/bs13110916

Generational Differences in the Workplace [Infographic]. (n.d.). Purdue Global. Retrieved April 24, 2025, from https://www.purdueglobal.edu/education-partnerships/generational-workforce-differences-infographic/

Githaiga, P. N., & Kilong’i, A. W. (2023). Foreign capital flow, institutional quality and human capital development in sub-Saharan Africa. Cogent Economics & Finance, 11(1). https://doi.org/10.1080/23322039.2022.2162689

Gordon, P. A. (2017). Exploring generational cohort work satisfaction in hospital nurses. Leadership in Health Services, 30(3), 233–248.

Guevara-Rosero, G. C., & Del Pozo, D. (2020). Determination of the urban wage premium in Ecuador. Investigaciones Regionales, 47, 57–77. https://doi.org/10.38191/iirr-jorr.20.G10

Hardy, H., Afrianty, T. W., & Prasetya, A. (2020). The Effect of Employer Branding on Contractual Employees: Engagement and Discretionary Effort. Bisnis & Birokrasi, 27(1), 12–24. https://doi.org/10.20476/jbb.v27i1.11757

Herzberg, F., Mausner, B., & Snyderman, B. B. (1959). The Motivation to Work. John Wiley & Sons.

Ho, P. (2023). SKILLS AND RURAL-URBAN WAGE DIFFERENCES IN AUSTRALIA. Australasian Jounral of Regional Studies, 29(2), 185–206.

Holtom, B., Reeves, C. J., Lei, Z., & Darabi, T. (2020). Reluctant Stayers: Constructing a Profile and Examining the Consequences. Journal of Managerial Issues: JMI, 32(4), 402.

How the brain drain hit Ireland in the 80s. (2009, January 17). Irish Independent. https://www.independent.ie/life/how-the-brain-drain-hit-ireland-in-the-80s/26506538.html

Hub, K., & Hunter, G. (2025). Public School Superintendent Turnover in Appalachian Kentucky. The Rural Educator, 46(1), 22–35. https://doi.org/10.55533/2643-9662.1488

Ibrahim, G., Al-Khatib, N., Ashaal, A., Akkaoui, I. E., & Youssef, S. (2024). Exploring the moderating effects of perceived alternative job opportunities and work experience on the relationship between job satisfaction and turnover intentions: A study among educators in Lebanon. Problems and Perspectives in Management, 22(2), 419–432. https://doi.org/10.21511/ppm.22(2).2024.32

Ihantola, E.-M., & Kihn, L.-A. (2011). Threats to validity and reliability in mixed methods accounting research. Qualitative Research in Accounting and Management, 8(1), 39–58. https://doi.org/10.1108/11766091111124694

Ismail, H., & El Nakkache, L. (2014). Extrinsic and Intrinsic Job Factors: Motivation and Satisfaction in a Developing Arab Country - The Case of Lebanon. Journal of Applied Management and Entrepreneurship, 19(1), 66–82.

Jain, N., & Bhatt, P. (2015). Employment preferences of job applicants: Unfolding employer branding determinants. The Journal of Management Development, 34(6), 634–652. https://doi.org/10.1108/JMD-09-2013-0106

Jamalludin, N. N. M., Idrus, Z., Idrus, Z., Ahmarofi, A. A., Hamid, J. A., & Mahadzir, N. H. (2022). Data Clutter Reduction in Sampling Technique. International Journal of Advanced Computer Science and Applications, 13(12). https://doi.org/10.14569/IJACSA.2022.0131294

Joseph, J., & Jiang, Y. (2023). The Common Factors of International Migration from India & China Focus on Western Countries (SSRN Scholarly Paper No. 4426320). Social Science Research Network. https://doi.org/10.2139/ssrn.4426320

Kalińska-Kula, M., & Stanieć, I. (2021). Employer Branding and Organizational Attractiveness: Current Employees’ Perspective. European Research Studies, 24(1), 583–603.

Kaur, H., & Kaur, R. (2023). Longitudinal effects of high-performance work practices on job performance via person–job fit. The Bottom Line, 36(2), 161–180. https://doi.org/10.1108/BL-02-2022-0030

Kerstin, D., Isabelle, C., & Felder, A. (2020). The challenge of occupational prestige for occupational identities: Comparing bricklaying and automation technology apprentices in Switzerland. Vocations and Learning, 13(3), 369–388. https://doi.org/10.1007/s12186-020-09243-3

Khawrin, M. K., & Sahibzada, A. (2023). Job Satisfaction as an Inverse Predictor of Employees’ Turnover: A Survey of Selected Public Universities in Afghanistan. International Journal of Education and Management Studies, 13(2), 108–113.

Kioumars, R., & Behnaz, R. (2022). Reliability of measuring constructs in applied linguistics research: A comparative study of domestic and international graduate theses. Language Testing in Asia, 12(1). https://doi.org/10.1186/s40468-022-00166-5

Korsi, L., & Vorvornator. (2022). Do we go or we stay? : Drivers of Migration from the Global South to the Global North. African Journal of Development Studies, 12(1), 71–87. https://doi.org/10.31920/2634-3649/2022/v12n1a4

Kosec, Z., Sekulic, S., Wilson-Gahan, S., Rostohar, K., Tusak, M., & Bon, M. (2022). Correlation between Employee Performance, Well-Being, Job Satisfaction, and Life Satisfaction in Sedentary Jobs in Slovenian Enterprises. International Journal of Environmental Research and Public Health, 19(16), 10427. https://doi.org/10.3390/ijerph191610427

Krasulja, N., Blagojevic, M. V., & Radojevic, I. (2016). Brain-Drain -the Positive and Negative Aspects of the Phenomenon. Ekonomika, 62(3), 131–142. https://doi.org/10.5937/ekonomika1603131K

Lee, C. C., Lim, H. S., Seo, D. (Josh), & Kwak, D.-H. A. (2022). Examining employee retention and motivation: The moderating effect of employee generation. Evidence - Based HRM, 10(4), 385–402. https://doi.org/10.1108/EBHRM-05-2021-0101

Lee, H. (2024). Fields of Study and Youth Job Mobility Behaviors in South Korea: Analyzing Voluntary and Involuntary Job Mobility *. Journal of Asian Sociology, 53(3), 195–224. https://doi.org/10.21588/dns.2024.53.3.001

Lee, R. M., Draper, M., & Lee, S. (2001). Social connectedness, dysfunctional interpersonal behaviors, and psychological distress: Testing a mediator model. Journal of Counseling Psychology, 48(3), 310–318. https://doi.org/10.1037/0022-0167.48.3.310

Leedy, P. D., & Ormrod, J. E. (2021). Practical research: Planning and design (12th ed.). Pearson Education.

Li, J., Yang, H., Weng, Q., & Zhu, L. (2023). How different forms of job crafting relate to job satisfaction: The role of person-job fit and age. Current Psychology: Research and Reviews, 42(13), 11155–11169. https://doi.org/10.1007/s12144-021-02390-3

Lien, T. T. H., & Hoang, N. D. (2022). The impact of young employees’ perceptions of current paid jobs on the entrepreneurial intention with the mediator of job satisfaction: The case of Vietnam. Entrepreneurial Business and Economics Review, 10(4), 55–71. https://doi.org/10.15678/EBER.2022.100404

Locker, C., & Teague, J. (2024). Assessing the Role of Generational Conflict in the Performance Appraisal Process. Journal of Behavioral and Applied Management, 24(1), 3–13.

Lok, I., & Dunn, E. (2023). The UBC State Social Connection Scale: Factor Structure, Reliability, and Validity. Social Psychological and Personality Science, 14(7), 835–844. https://doi.org/10.1177/19485506221132090

Lozano-Ascencio, F., & Gandini, L. (2012). Skilled-Worker Mobility and Development in Latin America and the Caribbean: Between Brain Drain and Brain Waste. The Journal of Latino - Latin American Studies, 4(1), 7–26.

Luz, C. M. D. R., de Paula, S. L., & Oliveira, L. M. B. de. (2018). Organizational commitment, job satisfaction and their possible influences on intent to turnover. REGE. Revista de Gestão, 25(1), 84–101. https://doi.org/10.1108/REGE-12-2017-008

Makraiova, J., Woolliscroft, P., Caganova, D., & Cambal, M. (2013). Person-Organisation fit as an Organisational Learning Tool in Employee Selection. International Conference on Intellectual Capital and Knowledge Management and Organisational Learning, 568–XVII. https://www.proquest.com/docview/1468445839/abstract/78935E39D281413EPQ/1

Mandemakers, L., Jaspers, E., & van der Lippe, T. (2024). Not leaving your unsatisfactory job: Analyzing female, migrant, elderly and lower-educated employees. Equality, Diversity and Inclusion: An International Journal, 43(9), 18–38. https://doi.org/10.1108/EDI-07-2023-0223

Mardanov, I. (2021). Intrinsic and extrinsic motivation, organizational context, employee contentment, job satisfaction, performance and intention to stay. Evidence - Based HRM, 9(3), 223–240. https://doi.org/10.1108/EBHRM-02-2020-0018

Mathur, P. (2021). Linking Selective Hiring to Competitive Advantage in Hospitality Industry. OORJA, 19(1). https://www.proquest.com/docview/3161743980/abstract/DC5B29C9CD2E49D4PQ/13

McComb, C. B., & Barnard, A. (2024). Voluntary turnover of high achievers: A systems psychodynamics analysis with CIBART. SA Journal of Industrial Psychology, 50. https://doi.org/10.4102/sajip.v50i0.2212

McPherson, R. (2018). Low-Qualified Labors’ Job Mobility, Boundary Crossing, and Career Success: A Cross-Industry HRM Perspective. Journal of Organizational Psychology, 18(1), 116–129.

Morley, M. J. (2007). Person-organization fit. Journal of Managerial Psychology, 22(2), 109–117. https://doi.org/10.1108/02683940710726375

Moscelli, G., Mello, M., Sayli, M., & Boyle, A. (2024). Nurse and doctor turnover and patient outcomes in NHS acute trusts in England: Retrospective longitudinal study. BMJ : British Medical Journal (Online), 387, e079987. https://doi.org/10.1136/bmj-2024-079987

Nielsen, L., Hinrichsen, C., Madsen, K. R., Nelausen, M. K., Meilstrup, C., Koyanagi, A., Koushede, V., & Santini, Z. I. (2021). Participation in social leisure activities may benefit mental health particularly among individuals that lack social connectedness at work or school. Mental Health and Social Inclusion, 25(4), 341–351. https://doi.org/10.1108/MHSI-06-2021-0026

Nikolopoulou, K. (2022a, June 24). What Is Social Desirability Bias? | Definition & Examples. Scribbr. https://www.scribbr.com/research-bias/social-desirability-bias/

Nikolopoulou, K. (2022b, August 9). What Is Convenience Sampling? | Definition & Examples. Scribbr. https://www.scribbr.com/methodology/convenience-sampling/

Nikolopoulou, K. (2022c, October 24). What Is Recall Bias? | Definition & Examples. Scribbr. https://www.scribbr.com/research-bias/recall-bias/

Nwaru, C. A., Berglund, T., & Hensing, G. (2021). Occupational prestige and sickness absence inequality in employed women and men in Sweden: A registry-based study. BMJ Open, 11(6), e050191. https://doi.org/10.1136/bmjopen-2021-050191

Oberoi, S. S., & Lin, V. (2006). Brain drain of doctors from southern Africa: Brain gain for Australia. Australian Health Review, 30(1), 25–33.

Oh, J., & Chhinzer, N. (2021). Is turnover contagious? The impact of transformational leadership and collective turnover on employee turnover decisions. Leadership & Organization Development Journal, 42(7), 1089–1103. https://doi.org/10.1108/LODJ-12-2020-0548

Olmsted, J. (2024). Research Reliability and Validity: Why do they matter? Journal of Dental Hygiene (Online), 98(6), 53–57.

Otoo, F. N. K. (2022). Human resource development and employee turnover intentions: The mediating role of employee engagement. International Journal of Business Ecosystem & Strategy, 4(4), 1–12. https://doi.org/10.36096/ijbes.v4i4.360

Otto, K., Glaser, D., & Dalbert, C. (2004). Skalendokumentation “Geografische und berufliche Mobilitätsbereitschaft”. Hallesche Berichte Zur Pädagogischen Psychologie, 8.

Parkins, N. C. (2010). Push and pull factors of migration. American Review of Political Economy, 8(2), 6–24.

Parks‐Leduc, L., Dustin, S. L., Wang, G., & Parks, T. W. (2024). Team values and team performance: A two‐study investigation. Applied Psychology, 73(4), 2263–2292. https://doi.org/10.1111/apps.12553

Patino, C. M., & Ferreira, J. C. (2018). Internal and external validity: Can you apply research study results to your patients? Jornal Brasileiro de Pneumologia, 44(3), 183. https://doi.org/10.1590/S1806-37562018000000164

Peng, Y., & Mao, C. (2015). The Impact of Person-Job Fit on Job Satisfaction: The Mediator Role of Self Efficacy. Social Indicators Research, 121(3), 805–813. https://doi.org/10.1007/s11205-014-0659-x

Pinzon, E. A., Miller, L. J., & Miller, A. F. (2023). If and to what extent does organizational learning culture predict turnover intentions of telecommuting call center agents? American Journal of Management, 23(2), 62–87.

Politis, J. D. (2006). Self-leadership behavioural-focused strategies and team performance: The mediating influence of job satisfaction. Leadership & Organization Development Journal, 27(3), 203–216. https://doi.org/10.1108/01437730610657721

Pollitt, D. (2008). LTFS puts the sparkle back into training: Program cuts employee turnover and improves morale. Human Resource Management International Digest, 16(4), 17–19. https://doi.org/10.1108/09670730810878439

Popogbe, O., & Adeosun, O. T. (2022). Empirical analysis of the push factors of human capital flight in Nigeria. Journal of Humanities and Applied Social Sciences, 4(1), 3–20. https://doi.org/10.1108/JHASS-07-2020-0093

Priyadarshi, P., & Premchandran, R. (2018). Job characteristics, job resources and work-related outcomes: Role of person-organisation fit. Evidence - Based HRM, 6(2), 118–136. https://doi.org/10.1108/EBHRM-04-2017-0022

Qadri, N. (2024). Exploring the impact of work-life balance on job satisfaction for Saudi private sector C-level employee. International Journal of Business Analytics, 11(1), 1–19. https://doi.org/10.4018/IJBAN.351217

Ramadhani, M. W., Yusuf, A. U., Rizkiyah, R., Haliah, & Mediaty. (2020). The Effects of Role Stress and Emotional Intelligence on Auditor Performance with Psychological Aspects and Well-Being as a Moderating Variable (the Empirical Study at Public Accounting Firm in Makassar City). International Journal of Information, Business and Management, 12(3), 148–160.

Read, S., Salmela-Aro, K., Kiuru, N., Helenius, J., & Junttila, N. (2024). The cohort trends of social connectedness in secondary school students in Finland between 2017 and 2021. PLoS One, 19(10), e0312579. https://doi.org/10.1371/journal.pone.0312579

Reissová, A., Šimsová, J., Nacházelová, E., & Siviček, T. (2024). Why stay here? Push and pull influencing migration of educated individuals in a disadvantaged region. GeoScape, 18(2), 122–134. https://doi.org/10.2478/geosc-2024-0009

Rice, B., Fieger, P., Rice, J., Martin, N., & Knox, K. (2017). The impact of employees’ values on role engagement: Assessing the moderating effects of distributive justice. Leadership & Organization Development Journal, 38(8), 1095–1109. https://doi.org/10.1108/LODJ-09-2016-0223

Rickmeier, K. (2023). Navigating Regional Barriers to Job Mobility: The Role of Opportunity Structures in Individual Job-to-Job Transitions. Social Sciences, 12(5), 295. https://doi.org/10.3390/socsci12050295

Rivera, L. A. (2012). Hiring as cultural matching: The case of elite professional service firms. American Sociological Review, 77(6), 999–1022. https://doi.org/10.1177/0003122412463213

Roustaei, N. (2024). Application and interpretation of linear-regression analysis. Medical Hypothesis, Discovery & Innovation Ophthalmology Journal, 13(3), 151–159. https://doi.org/10.51329/mehdiophthal1506

Rumawas, W. (2022). Employees’ turnover intention in the construction industry in Indonesia. Journal of Construction in Developing Countries, 27(2), 127–146. https://doi.org/10.21315/jcdc-03-21-0050

Salleh, A. M. M., Omar, K., Aburumman, O. J., Mat, N. H. N., & Almhairat, M. A. (2020). The impact of career planning and career satisfaction on employee’s turnover intention. Entrepreneurship and Sustainability Issues, 8(1), 218–232. https://doi.org/10.9770/jesi.2020.8.1(14)

Sands, S. R., Ingraham, K., & Salami, B. O. (2020). Caribbean nurse migration—A scoping review. Human Resources for Health, 18, 1–10. https://doi.org/10.1186/s12960-020-00466-y

Sanjeev, M. A., & Surya, A. V. (2016). Two Factor Theory of Motivation and Satisfaction: An Empirical Verification. Annals of Data Science, 3(2), 155–173. https://doi.org/10.1007/s40745-016-0077-9

Schaechter, J. D., Goldstein, R., Zafonte, R. D., & Silver, J. K. (2023). Workplace Belonging of Women Healthcare Professionals Relates to Likelihood of Leaving. Journal of Healthcare Leadership, 15, 273–284. https://doi.org/10.2147/JHL.S431157

Services, E. L. (2022, January 4). Research Data Storage and Retention | Elsevier Blog. Elsevier Author Services - Articles. https://scientific-publishing.webshop.elsevier.com/publication-process/research-data-storage-retention/

Shackelford, T. J., Cline, L. L., & Robinson, J. S. (2025). Developing Leadership Capacity in a Rural, Farm-Dependent Community. Journal of Rural Social Sciences, 40(1), 50–63.

Shutters, S. T., & Applegate, J. M. (2022). The urban wage premium is disappearing in U.S. micropolitan areas. PLoS One, 17(4). https://doi.org/10.1371/journal.pone.0267210

Simões, F., Rocca, A., Rocha, R., Mateus, C., Elena, M., & Tosun, J. (2021). Time to Get Emotional: Determinants of University Students’ Intention to Return to Rural Areas. Sustainability, 13(9). https://doi.org/10.3390/su13095135

Singh, R. (2023a). “My contract is breached, and I want to leave, but I am embedded!” how do reluctant stayers respond? Evidence - Based HRM, 11(4), 594–610. https://doi.org/10.1108/EBHRM-03-2022-0073

Singh, R. (2023b). “Reluctant stayers do not get what they want”: The relationship between procedural injustice and workplace incivility. International Journal of Emerging Markets, 18(9), 2663–2679. https://doi.org/10.1108/IJOEM-04-2020-0391

Sivertzen, A.-M., Nilsen, E. R., & Olafsen, A. H. (2013). Employer branding: Employer attractiveness and the use of social media: [1]. The Journal of Product and Brand Management, 22(7), 473–483.

Skelton, A., Nattress, D., & Dwyer, R. (2019). Predicting manufacturing employee turnover intentions. Journal of Economics, Finance and Administrative Science, ahead-of-print. https://doi.org/10.1108/JEFAS-07-2018-0069

Šlibar, B., Oreški, D., & Čalopa, M. K. (2023). Push and pull factors in brain drain among university students. Management : Journal of Contemporary Management Issues, 28(1), 65–80. https://doi.org/10.30924/mjcmi.28.1.5

Spector, P. E. (1985). Measurement of Human Service Staff Satisfaction: Development of the Job Satisfaction Survey. American Journal of Community Psychology, 13(6), 693–713. https://doi.org/10.1007/BF00929796

Stone, A. A., Schneider, S., Smyth, J. M., Junghaenel, D. U., Couper, M. P., Wen, C., Mendez, M., Velasco, S., & Goldstein, S. (2024). A population-based investigation of participation rate and self-selection bias in momentary data capture and survey studies. Current Psychology: Research and Reviews, 43(3), 2074–2090. https://doi.org/10.1007/s12144-023-04426-2

Stratton, S. J. (2023). Population Sampling: Probability and Non-Probability Techniques. Prehospital and Disaster Medicine, 38(2), 147–148. https://doi.org/10.1017/S1049023X23000304

Sturman, M. C., Trevor, C. O., Boudreau, J. W., & Gerhart, B. (2003). Is It Worth It to Win the Talent War? Evaluating the Utility of Performance-Based Pay. Personnel Psychology, 56(4), 997–1035.

Sun, N., Liang, S., Li, H., & Song, H. (2023). Ex post i-deals, work-life balance, and work well-being in the hospitality industry: The moderating role of gender. International Journal of Contemporary Hospitality Management, 35(9), 3077–3094. https://doi.org/10.1108/IJCHM-03-2022-0350

Sürücü, L., & Maslakçi, A. (2020). Validity and Reliability in Quantitative Research. Business & Management Studies: An International Journal, 8(3), 2694–2726. https://doi.org/10.15295/bmij.v8i3.1540

SurveyMonkey Features: Explore Tools & Capabilities. (n.d.). SurveyMonkey. Retrieved May 15, 2025, from https://www.surveymonkey.com/product/features/

Swe, K. T. H., & Lu, L. H. (2019). The enhancement of employee engagement to reduce employee turnover intention and improve employee job satisfaction: An action research on Mirac Company in Yangon, Myanmar. ABAC ODI Journal Vision. Action. Outcome, 6(1), 123–147.

Thomas, C., & Lightman, N. (2022). “Island Girls”: Caribbean Women Care Workers in Canada. Canadian Ethnic Studies, 54(1), 29–58.

To, S. M., & Tam, H. L. (2014). Generational Differences in Work Values, Perceived Job Rewards, and Job Satisfaction of Chinese Female Migrant Workers: Implications for Social Policy and Social Services. Social Indicators Research, 118(3), 1315–1332. https://doi.org/10.1007/s11205-013-0470-0

Urien, B., & Erro-Garcés, A. (2024). Do you prefer logging in? The relevance of the experience of telework for well-being. Employee Relations, 46(3), 641–656. https://doi.org/10.1108/ER-10-2022-0487

Utley, D. R., Westbrook, J., & Turner, S. (1997). The relationship between Herzberg’s two-factor theory and quality improvement implementation. Engineering Management Journal: EMJ, 9(3), 5–13.

van der Hulst, T., & Zwaal, W. (2024). Motivation and retention of outsourced employees. Research in Hospitality Management, 14(2), 141–149. https://doi.org/10.1080/22243534.2024.2395710

Vega-Muñoz, A., González-Gómez-del-Miño, P., & Contreras-Barraza, N. (2025). The Determinants of Brain Drain and the Role of Citizenship in Skilled Migration. Social Sciences, 14(3). https://doi.org/10.3390/socsci14030132

Wang, X., Shaw, F. A., Mokhtarian, P. L., & Watkins, K. E. (2023). Response willingness in consecutive travel surveys: An investigation based on the National Household Travel Survey using a sample selection model. Transportation, 50(6), 2339–2373. https://doi.org/10.1007/s11116-022-10312-w

Warshaw, R. (2017, October 31). Health Disparities Affect Millions in Rural U.S. Communities. AAMC. https://www.aamc.org/news/health-disparities-affect-millions-rural-us-communities

Wintrip, S. (2017). Eliminate Hiring Delays Forever: Why Faster Hiring Results in Higher-Quality Hires. The Journal for Quality and Participation, 40(2), 26–29.

Wong, C. W., Cheng, M. Y., & Lau, T. C. (2016). Impact of external job mobility and occupational job mobility on earnings. Journal of Industrial Engineering and Management, 9(4), 879–898. https://doi.org/10.3926/jiem.1960

Wong, C. Y., Grant, D. B., Allan, B., & Jasiuvian, I. (2014). Logistics and supply chain education and jobs: A study of UK markets. International Journal of Logistics Management, 25(3), 537–552. https://doi.org/10.1108/IJLM-01-2013-0003

World Bank Open Data. (n.d.). Rural Population (% of Total Population) - United States. Retrieved August 31, 2025, from https://data.worldbank.org

Xu, A. (2024). Mind the gap: Exploring urban–rural differences in US inter-county migration decisions. Demographic Research, 50(4), 115–130.

Yang, H., & Hu, P. (2023). Role of job mobility frequency in job satisfaction changes: The mediation mechanism of job-related social capital and person‒job match. Humanities & Social Sciences Communications, 10(1), 156. https://doi.org/10.1057/s41599-023-01657-3

You, K. (2019). Dealing with brain drain: The contributions of Sri Lanka’s peak business interest associations. Journal of Global Responsibility, 10(3), 239–256. https://doi.org/10.1108/JGR-10-2018-0052

Young, D. K., McLeod, A. J., & Carpenter, D. (2023). Examining the influence of occupational characteristics, gender and work-life balance on IT professionals’ occupational satisfaction and occupational commitment. Information Technology & People, 36(3), 1270–1297. https://doi.org/10.1108/ITP-08-2020-0572

Yuchtman, E., & Fishelson, G. (1972). Some problems in the study of occupational prestige with an illistration from Israel. British Journal of Sociology.

Yue, Z., Yang, Q., Li, Y., Wang, J., Nicholas, S., Maitland, E., & Liu, C. (2022). Empathy and burnout in medical staff: Mediating role of job satisfaction and job commitment. BMC Public Health, 22, 1–13. https://doi.org/10.1186/s12889-022-13405-4

Zhang, Z., Xia, Y., & Abula, K. (2023). How Digital Skills Affect Rural Labor Employment Choices? Evidence from Rural China. Sustainability, 15(7). https://doi.org/10.3390/su15076050

Zimmerman, R. D., & Darnold, T. C. (2009). The impact of job performance on employee turnover intentions and the voluntary turnover process: A meta-analysis and path model. Personnel Review, 38(2), 142–158. https://doi.org/10.1108/00483480910931316

APPENDIX A

G*Power 3.1 Sample Size

APPENDIX B

Screening Questionnaire

1. Are you a resident of Southeastern United States (Florida, Georgia, Alabama, Mississippi, Tennessee, South Carolina, North Carolina, Kentucky or West Virginia)?

a. Yes

b. No

2. Do you live in a rural community with less than 5,000 people?

a. Yes

b. No

3. Are you between the ages of 25 and 65?

a. Yes

b. No

4. Do you work full-time?

a. Yes

b. No

5. Do you hold a managerial position now?

a. Yes

b. No

Demographic Questionnaire

1. Choose the year you were born in from the list below:

a. 1946 – 1964

b. 1965 – 1976

c. 1977 – 1996

d. 1997 – 2012

2. Gender

a. Male

b. Female

c. Other

3. What sector do you work in?

a. Drop down

4. What is your highest level of education?

a. High-School

b. Associates Degree

c. Bachelor's degree

d. Master’s Degree

e. PhD

APPENDIX C

Job Satisfaction Survey

(Spector, 1985)

APPENDIX D

UBC-SSCS Survey

UBC State Social Connection Scale (Lok & Dunn, 2023)

APPENDIX E

General Readiness for Between-Occupation Mobility

(Otto et al., 2004)

APPENDIX F

General Readiness for Geographic Mobility

(Otto et al., 2004)

APPENDIX G

Occupational Prestige Scores by Field Title

· Arts, design, entertainment and sports media – 67.75

· Building, grounds, cleaning and maintenance – 24.22

· Business and financial operations – 67.10

· Community and social services – 56.09

· Computer Math – 74.81

· Construction – 42.18

· Education – 58.24

· Engineering and architecture – 79.12

· Farming, fishing and forestry – 39.14

· Food prep and services – 27.42

· Healthcare – 70.51

· Installation, maintenance, and repair – 43.19

· Law and legal services – 76.85

· Life Physical Social Sciences – 78.23

· Management – 63.71

· Office and admin support – 41.63

· Protective services – 55.59

· Sales related services – 36.68

· Transportation and material moving – 35.74

(Condon & Hughes, 2022)

1950's - 1960's

1970's - 1980's

1990's - 2000's

Educated Europeans moving from United Kingdom to United States and Canada

Unskilled, semi-skilled workers and college graduates were moving from Ireland to England

Increased migration from less developed countries to OECD. 64% coming from Africa and Latin America and Caribbean.

2000's - Onward

High migration of young Indian and Chinese professionals to Europe, US, Canada, Australia and New Zealand