







































 Business Management Research and Applications: A Cross Disciplinary Journal 

 

 

 

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Well–being as a Predictor of Turnover Intention: A 
Quantitative Study of Occupational Safety and 
Health Professionals in the U.S. 

 

 

Randall Westmoreland, DBA | Columbia Southern University, Orange Beach, AL, United States  

Jan Tucker, PhD | Columbia Southern University, Orange Beach, AL, United States  

 

Contact: randall.westmoreland@yahoo.com or janice.tucker@umgc.edu 

 

 

 

 

ABSTRACT 

This quantitative correlational regression study examines the relationship between employee well–being 

and turnover intention among occupational safety and health (OSH) professionals in the United States. 

Previous research highlights a decline in well-being and an increase in turnover intention among OSH 

professionals. This study focuses on how well-being predicts turnover intention, utilizing simple linear 

regression data analysis from two surveys. Results show a weak to moderate predictive relationship 

between affective well–being and turnover intention. The sample consists of U.S. OSH professionals, 

predominantly from the Western region. The findings align with self–determination theory (SDT), 

suggesting that satisfying basic psychological needs—autonomy, competence, and relatedness—enhances 

well–being, influencing turnover intention. When individuals experience positive emotional states and 

their emotional needs are met, they exhibit higher well-being and job satisfaction. This study contributes 

to understanding the well–being–turnover intention dynamic in the OSH field, offering insights into 

organizational retention strategies and employee support programs. 

Keywords: Affective well–being, autonomy, burnout, competency, job satisfaction, motivation, self–

determination theory, relatedness, turnover intention, well–being 

 

mailto:PhD–janice.tucker@umgc.edu
mailto:randall.westmoreland@yahoo.com
mailto:janice.tucker@umgc.edu


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Introduction 

Poor mental and physical health is linked to employees' intention to quit and voluntary turnover 

(Akosile & Ekemen, 2022). Since 2019, the turnover rate among occupational safety and health (OSH) 

professionals has increased (NSC, 2023). Burnout, decreased job satisfaction, diminished organizational 

commitment, and sub-optimal performance, all associated with poor well–being, can lead to an intention 

to quit (Davis, 2021). This turnover poses problems for companies, including increased costs, liabilities, 

lost production, and poor morale (NSC, 2023). This study examines whether well–being predicts OSH 

professionals' intention to quit. 

While the negative impacts of OSH employee turnover are known, there is a lack of research on 

antecedent variables linked to the intention to quit among this group (Wang & Wang, 2021). 

Additionally, no studies have examined motivational connections with well-being that might predict 

turnover intention. This study will use a quantitative approach to establish the theoretical connection 

between eudaimonic well–being and turnover intention by interpreting individual experiences that might 

otherwise present confounding variables (Martela & Sheldon, 2019). It will contribute to the body of 

knowledge by measuring variables otherwise inferred logically. 

Employee well–being has been studied under various theoretical frameworks, generally 

categorized by domains of influence (Heinitz et al., 2018). Martela and Sheldon (2019) found that job–

related stressors and unmet human needs are related to poor well–being outcomes such as burnout. A 

logical approach to measuring well–being includes capturing the complete picture of employee well–

being, including subjective aspects related to experiences of autonomy and relatedness. 

Deci and Ryan's (2012) self–determination theory suggests that three innate psychological 

needs—autonomy, relatedness, and competence—contribute to well–being. Martela and Sheldon (2019) 

expanded the concept of a well–being by including both subjective and eudaimonic aspects. Heinitz et al. 

(2018) found that an organizational culture promoting self–efficacy and optimism can significantly 

increase employee well–being. 

Research on the link between OSH employee turnover intention and well–being is sparse, 

creating a gap in the literature (Liu et al., 2018). While some studies have examined the relationship 

between turnover intention and work-related affective well–being (Van Katwyk et al., 2000; Yan et al., 

2021) or intrinsic and extrinsic motivation (Deci, 1971; Deci & Ryan, 2012), there is a lack of research on 

the role well–being plays in OSH employees' intention to leave their jobs. Understanding how well–being 

interventions can help reduce burnout and turnover intention while improving workplace safety and 

health performance is crucial (Schwatka et al., 2022; Smith et al., 2020). 

OSH employees are essential for ensuring a safe and healthy work environment (Stout & Linn, 

2002). Organizations should focus on strategies that support OSH employees' well–being (Board on 

Health Sciences Policy Institute of Medicine, 2000). Studies indicate a relationship between well–being 

and emotional responses such as organizational commitment, job satisfaction, burnout, and turnover 

intention among OSH employees (Liu et al., 2018, 2019; Rifin & Danaee, 2022). Strategies like Total 

Worker Health (Schwatka et al., 2022), the inclusive talent development model (Fang et al., 2020), and 

positive organizational behavior (POB) (Guslina, 2023) influence employee well–being and reduce 

turnover intention. Thus, studying aspects of well–being in a predictive manner might clarify antecedents 

of intention to quit. 

Occupational safety and health (OSH) professionals are experiencing diminished well–being, 

influenced by factors such as autonomy, competency, and relatedness, which in turn leads to increased 

turnover intention (Wang & Wang, 2021). From 2020 to 2022, the turnover rate for OSH employees rose 

from 30% to 41% (Ferguson, 2022), significantly impacting organizational costs related to recruitment, 



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onboarding, and training (Lauby, 2016). Voluntary turnover, a growing challenge for businesses, is 

directly predicted by turnover intention (Yan et al., 2021). 

This turnover introduces administrative disruptions and increases the workload on other 

departments (Ferguson, 2022). Cultural generational shifts have also been linked to higher employee 

turnover (Fang et al., 2020). Poor employee well–being, associated with burnout, emotional exhaustion, 

and social loafing, contributes to the intention to quit (Wang & Wang, 2021). Job burnout negatively 

affects job satisfaction, employee engagement, and performance and is positively related to voluntary 

turnover (Liu et al., 2018). 

The OSH field is inherently stressful, with job demands exacerbating stress levels (Board on 

Health Sciences Policy [BHSP], 2000). Stressors in this field include long working hours, ethical 

conflicts, lack of personal accomplishment, and work-life balance issues, all contributing to increased 

turnover intention (Corso et al., 2019). These stressors vary across different sectors within the OSH field, 

including private and public sectors and specific areas such as general industry, construction, 

enforcement, and consultative roles (BHSP, 2000). 

This quantitative correlational regression study examines the relationship between employee 

well–being and turnover intention among OSH professionals in the United States. Well–being, the 

independent variable, is the degree to which human needs are met (Martela & Sheldon, 2019) and will be 

measured using Paul Spector's General Affective Well-being Survey (Van Katwyk et al., 2000). The 

dependent variable, turnover intention, ranges from the thought of leaving to the initial actions of 

voluntarily exiting a position and will be measured with Paul Spector's (1982) Turnover Intention Survey. 

The study population includes safety specialists and technicians, safety managers, and industrial 

hygienists currently working in the OSH field in the United States. Participants were identified through 

LinkedIn and Facebook, utilizing professional associations such as the American Society of Safety 

Professionals (ASSP) and the American Industrial Hygiene Association (AIHA). The research question 

and corresponding hypotheses that framed this study were:  

R.Q.: To what extent does employee well–being predict the turnover intention of 

occupational safety and health professionals? 

Ho: Employee well–being does not predict the turnover intention of occupational safety 

and health professionals. 

Ha: Employee well–being predicts the turnover intention of occupational safety and 

health professionals. 

The theoretical framework for this study is the self–self-determination theory (SDT), created by 

Edward Deci and Richard Ryan in 1977, which was a derivative product of Deci's 1971 paper in which 

the authors examined motivations in conjunction with intrinsic and extrinsic rewards (O'Hara, 2017). 

Self-determination theory offers some explanation of measurable behavioral outcomes by examining 

antecedents of motivation. This theory provides a framework for understanding the relationships between 

people and their environment, their experiences of well–being, and their motivation, all of which are 

directly associated with positive attributes of organizational commitment (Akosile & Ekemen, 2022). 

 

Literature Review 
 

The various theories and variables related to well–being and turnover intention present 

complexities for research on this topic. Well–being has deep historical roots, dating back to ancient Greek 

philosophers. Crisp (2002) noted that Aristotle viewed well–being and friendship as mutually non–

exclusive, stating that "a friend is a second self" or, in other words, another with reflective values (p. 

133). Furthermore, the text of necomachean ethics explains that values are a core tenant of virtuous 

existence. A mid–20th–century take on Aristotilianism and objectivism posits that virtuous actions are 

linked to an individual's well–being through independent thinking and self–esteem (Rand, 1964). Deci 

and Ryan (2012) contemporarily argued that well–being is enhanced when the human needs of autonomy, 



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competency, and addition of relatedness are met. Aristotle's virtue characteristics historically align with 

these well–being components (Anderson & Fowers, 2019). 

Extensive research has explored workers' well–being and associated factors. Rifin and Danaee's 

(2022) study found burnout among medical researchers significantly associated with diminished well–

being and increased intention to leave. Research by Smith et al. (2020) and Schwatka et al. (2022) 

highlighted the importance of promoting well–being in occupational health and safety. Despite these 

advancements, NIOSH has faced challenges in applying these concepts due to the heterogeneity of factors 

surrounding wellness (Anger et al., 2019).  

Motivational theory, rooted in organizational studies, has expanded our understanding of well–

being. Maslow's hierarchy of needs (1943) proposes a hierarchical fulfillment of physiological, safety, 

belongingness, esteem, and self–actualization needs. Despite its influence, criticisms of its universal 

applicability suggest the need for further empirical research (Yurdakul & Arar, 2023). Discrepancy 

theory, introduced by Hackman (1980), posits that job satisfaction arises from aligning expectations with 

actual outcomes. Discrepancies lead to dissatisfaction, emphasizing the need for further empirical 

examination in different contexts (Jiang et al., 2012). 

Locke's (1978) theory of job satisfaction builds on discrepancy theory, proposing that satisfaction 

depends on meeting or exceeding expectations. Some critics argue that Locke's (1978) theory of job 

satisfaction neglects specific job characteristics and emotional aspects (Tietjen & Myers, 1998). These 

theories converge on well-being, suggesting that fulfilling needs, aligning expectations, and meeting 

outcomes are crucial for well–being. Modern approaches like job demands-resources (JDR) theory, 

conservation of resources (COR) theory, and self–determination theory (SDT) offer precise means of 

quantitatively assessing worker well–being. 

Poor employee well–being significantly impacts organizations, leading to increased turnover 

intentions, reduced job performance, and increased workplace accidents (Liu et al., 2018; Rifin & 

Danaee, 2022). Modern theoretical approaches like JDR, COR, and SDT emphasize providing necessary 

resources, support, and autonomy to foster well–being and job satisfaction (Bakker & Demerouti, 2014; 

Heinitz et al., 2018). 

JDR theory identifies job demands and resources, suggesting that balancing these can reduce 

stress and enhance motivation (Bakker & Demerouti, 2014). COR theory emphasizes preserving and 

acquiring resources to cope with job demands (Hobfoll et al., 1992). SDT highlights the fulfillment of 

autonomy, competence, and relatedness needs for well-being and motivation (Deci & Ryan, 2012; Šakan 

et al., 2020). 

Hobfoll's (1989) COR theory focuses on acquiring and preserving resources, with resource 

availability and loss significantly impacting well–being. Research shows that supportive leadership and 

coworker support mitigate the adverse effects of job demands, whereas the loss of resources leads to 

decreased well–being (Bakker & Demerouti, 2007; Hobfoll et al., 2018). COR theory recognizes the 

spillover effect, where resource loss in one domain affects other life areas. Applying COR theory in 

organizations involves promoting resource gain and minimizing resource loss through effective job 

design, workload management, and supportive organizational culture (Bakker et al., 2023). 

Based on COR theory, the JD–R model explains the relationship between job characteristics, 

well–being, and performance (Bakker & Demerouti, 2007). Job demands require sustained effort and can 

lead to strain or burnout, while job resources help achieve work goals and reduce demands (Bakker et al., 

2023). The JD–R model proposes two processes: the health impairment process, where high demands and 

low resources lead to strain, and the motivation process, where high resources and low demands foster 

positive outcomes. Research consistently shows that high demands increase burnout, while resources 

enhance engagement, well–being, and job satisfaction (Wu et al., 2019). The JD–R model has been 

validated in various contexts, including teaching, healthcare, and telework during the COVID–19 

pandemic, highlighting its relevance in understanding well–being and performance (Dolce et al., 2020; 

Jyoti & Rani, 2019; Meyer et al., 2021).  



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SDT, developed by Deci and Ryan, posits that fulfilling the needs of autonomy, competence, and 

relatedness enhances well–being and motivation (Ryan & Deci, 2012). Research shows that supportive 

work environments increase intrinsic motivation and employee engagement (Gagné & Deci, 2005). 

Burnout is influenced by motivational factors, with studies showing that unmet psychological needs lead 

to higher burnout rates (Wu et al., 2019). Addressing these needs can mitigate burnout and promote well–

being (Heinitz et al., 2018). 

In the context of employee well–being, SDT has been applied to understand the relationship 

between motivation, turnover intention, and burnout (Teixeira et al., 2020). Research by Gagné and Deci 

(2005) found that when employees perceive their work environment as supportive of autonomy, 

competence, and relatedness, they experience higher levels of intrinsic motivation and employee 

engagement.  

Concerning the building blocks of SDT, Akosile and Ekemen (2022) examined the impact of core 

self–evaluations (an individual's fundamental beliefs about themselves) on job satisfaction and turnover 

intention among academic staff in higher education. They found that intrinsic and extrinsic motivation 

mediated the relationship between core self–evaluations and turnover intention, resulting in a connection 

between values and the antecedents of optimal well–being. SDT and basic needs being met have been 

further supported in a meta-study, including 44 quantitative studies, finding that optimizing self–

determined motivation and need satisfaction to enhance well–being further impacts work outcomes 

(Nunes et al., 2023).  

This study uses a quantitative methodology to examine the relationship between turnover 

intention and well–being among OSH professionals. SDT, emphasizing autonomy, competence, and 

relatedness, serves as the framework (Lubbadeh, 2020). Simple linear regression analysis, a statistical 

technique for examining predictive relationships, was conducted on collected data to determine whether 

well–being predicts turnover intention. Surveys were used to collect data. The independent variable, well–

being, was measured using Paul Spector's (2021) General Affective Well–Being Scale (GAWS), while 

the turnover intention was measured using Michaels and Spector's (1982) Three–Item Turnover Intention 

Scale. The GAWS assesses subjective well–being and emotional experiences, aligning with SDT 

principles (Van Katwyk et al., 2000). The Turnover Intention Scale captures employees' thoughts and 

plans about leaving their organization. Both instruments are reliable and valid for measuring the target 

population's well–being and turnover intention (Spector & Jex, 1991). 

Motivation and human capacity theories have advanced our understanding of employee 

performance and behavioral variables. COR, JD–R, and SDT theories emphasize psychological needs and 

well–being, with factors like job satisfaction, emotional exhaustion, and burnout linked to subjective 

well–being. Applying these theories in organizations can enhance employee well–being, reduce turnover 

intentions, and create positive work environments. A linear regression analysis was used to investigate the 

relationship between well–being and turnover intention. The GAWS and Turnover Intention Scale are 

appropriate instruments for measuring these variables in OSH professionals.  

 

Methods 

This correlational study examines the relationship between turnover intentions and the well–being 

of occupational safety and health (OSH) employees. This study aimed to determine relational qualities 

between employee well–being (the independent variable) and turnover intention (the dependent variable). 

For this study, employee well-being is aligned with the self-determination theory (SDT) and is 

characterized by optimizing autonomy, competency, and relatedness (Deci & Ryan, 2012). Paul Spector's 

(2021) General Affective Well–Being Scale (GAWS) was used to measure positive and negative affective 

factors that OSH professionals experience. The Michaels and Spector (1982) Turnover Intention Scale 

measured turnover intention. A regression research design was established to ascertain the predictability 

of the relationship between the independent variable (IV) and dependent variable (DV). The instrument 



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used to measure the dependent variable is Paul Spector's Turnover Intention Survey (Spector, 1982). The 

survey contains three items and measures a person's thoughts and actions, spanning from the 

consideration of a transition to their final steps of job transition. Each item is rated using a 6–6-point 

Likert scale from 1 (Strongly Disagree) to 6 (Strongly Agree). The independent variable is well–being, 

which was measured using the Paul Spector General Affective Well–Being Survey (GAWS). This survey 

measures positive and negative emotional responses (i.e., autonomy, competence, and relatedness) and 

contains 20 items rated on a 5–5-point Likert–type scale from 1 (Never) to 5 (Every day). The surveys are 

presented in Appendix A.  

Respondents in this study were those who worked in the safety and health field, commonly under 

the titles of safety professional or industrial hygienist. The target population was comprised of 

management or technicians within their respective trades. The study's results may be utilized by 

businesses to aid in retaining OSH employees, thereby reducing operational losses. Additional 

demographic data was collected for descriptive statistics and potential inferences that may lead to future 

studies. These data points include age, U.S. region, gender, and experience (years). The survey was 

compiled in Survey Monkey in compliance with the instrumentation requirements. Alternative findings, 

such as the number of discrepancies in data, such as, but not limited to outliers or inconsistencies in 

reporting, are reported. 

 Purposive sampling was used to identify research participants using social media groups like 

LinkedIn and Facebook and through industry professional associations such as the American Society of 

Safety Professionals (ASSP) and the American Industrial Hygiene Association (AIHA). Qualifying 

respondents were provided with the link to a survey on the SurveyMonkey website. Outreach also 

occurred in person through speaking engagements at professional conferences. A quick response Q.R. 

code system was utilized for candidates to access the survey.  

The survey began with informed consent, which elaborated on protecting personal data. The 

information gathered consisted of basic demographic details while precluding identification or place of 

work. Once the data was collected from Survey Monkey, it was exported into R Commander for analysis. 

The required sample size was determined using G*Power Software to avoid type I and II errors.  Type I 

error occurs when the null hypothesis is wrongfully rejected, and a type II error happens when the null 

hypothesis is erroneously not rejected (Senn, 2021). The calculations are based on the inputs of one 

independent variable (well–being) and one dependent variable (turnover intention). Input parameters 

within the software include hypothesis testing, effect size, and power. Since the purpose of the study is to 

reject or fail to reject the null hypothesis, a two-tailed test was selected (Senn, 2021). Effect size refers to 

the magnitude and direction of the relationship between variables in a linear regression model with log-

transformed variables and is represented as Cohen's (f 2). For this study, the sample size was calculated 

with a medium Cohen's effect size (f 2=0.15), which allows a researcher to evaluate the significance of an 

association under moderate conditions. The selection and implementation of effect size mitigates the 

potential for type II errors (Sullivan & Feinn, 2012). A sample size of 60 was needed with a medium 

effect size of Cohen's (f 2=0.15), α = .05, and β =0.8. 

The GAWS is a transformed version of the JAWS and shares its reputation (Van Katwyk et al., 

2000). Questions are identical in intent, and scoring is equivalent. Van Katwyk et al. (2000) publication 

demonstrated that measuring emotional states yields accurate results in repetition. The usefulness of the 

JAWS survey is supported by its utilization in two studies on character strengths (Weziak–Bialowolska et 

al., 2021) and work-related positive effects (Armon et al., 2014). These studies support the validity and 

reliability of the survey in assessing constructs related to health, well–being, and work outcomes. The 

GAWS was selected for this study because of the robust findings and reliability among diverse 

participants. 

The GAWS uses a five-point scale. The scale has specific time anchors that can be adjusted based 

on the assessed timeframe. The options for the response scale are: (1) never, (2) once or twice, (3) once or 

twice per month, (4) once or twice per week, or (5) every day (Van Katwyk et al., 2000). The GAWS 

covers a wide range of negative and positive emotional experiences. These emotions can be categorized 



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into four subtopics or subscales, which fall along two dimensions: pleasurableness (negative vs positive 

emotion) and arousal (low vs high intensity).  

The Michaels and Spector (1982) Turnover Intention Scale is an effective tool in gauging the 

prediction towards voluntary turnover of employees (Spector, 1991). The instrument has a good 

reputation, as demonstrated through a meta-analysis of the relationship between unemployment, job 

satisfaction, and employee turnover (Carsten & Spector, 1987). Obeng et al. (2021) further recognized the 

effectiveness of measuring turnover intention related to employee experience variables. The Turnover 

Intention Scale has shown significant results relating to workplace psychological constructs (Batura et al., 

2016). 

 

Research Question and Hypotheses 

 

RQ1: To what extent does employee well–being predict the turnover intention of occupational 

safety and health professionals? 

 

H1o: Employee well–being does not predict the turnover intention of occupational safety and 

health professionals. 

 

H1a: Employee well–being predicts the turnover intention of occupational safety and health 

professionals. 

 
 

Validity and Reliability 
 

Validity determines how well a measurement tool captures the construct it is intended to measure 

(Creswell, 2014). It encompasses both internal and external components. Internal validity concerns 

establishing a causal relationship within the study's context and ensuring correct inference about causation 

(Duckett, 2021). External validity pertains to the generalizability of the study's findings across various 

settings or populations (Bo & Galiani, 2021). 

 

Ensuring Internal and External Validity 

 

This study reinforced internal validity through precise definitions of well–being and turnover 

intention, uniform data collection processes, including pertinent demographic controls, and timing of the 

data collection to factor in temporal dynamics. External validity was enhanced by selecting a diverse 

sample of OSH professionals and structuring the study to allow precise, relatable participant interactions. 

 

Instrument–Specific Validity 

 

Prior research has validated the General Affective Well-being scale (GAWS). The GAWS 

demonstrates robust construct, convergent, and discriminant validity, making it suitable for assessing the 

positive and negative emotional states proposed by the self–determination theory (Van Katwyk et al., 

2000). Similarly, the Turnover Intention Scale's validity is well established, effectively measuring the 

cognitions associated with voluntary job turnover. It exhibits strong face and content validity, consistently 

capturing essential turnover intention elements across studies (Carsten & Spector, 1987; Michaels & 

Spector, 1982). 

 

Validation Metrics 

 



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Validation of these tools is reflected in their application and supported by empirical evidence, as 

shown in Table 1, detailing the correlation (r2) and Cronbach's alpha values, affirming reliability and 

validity (Van Katwyk et al., 2000). 

 

Table 1 

 

Instrument Validity and Reliability Coefficients 

Variables Affect variable r2 Coefficient α 

GAWS –.6*** .89 

Quit 0.77 

Specific note. ***p<.001. Turnover intention is represented as "quit." 

 

Reliability is the consistency and stability of a measurement instrument (Creswell, 2014). Internal 

consistency reliability can be assessed using Cronbach's alpha. Van Katwyk et al. (2000) reported that the 

survey produced reliable findings (Cronbach's alpha =0.89). Basińska et al. (2014) indicated that the 

GAWS reported a satisfactory Cronbach's alpha value, indicating high internal consistency among both 

scaled versions. Similarly, the Turnover Intention Scale has also demonstrated good reliability, though 

not reported in the seminal research (Michaels & Spector, 1982; Spector et al., 1988). A study by Hu et al. 

(2022) identified a derivative instrument as having acceptable conditions of construct reliability 

(Cronbach's alpha = 0.77), content correlative validity (.68), and a high degree of internal reliability 

(Cronbach's alpha = 0.83). 

Two strategies were implemented to ensure the reliability and validity of the data collected in this 

study. First, the survey instruments were administered consistently and standardized, ensuring all 

participants received the same questions. This helped minimize potential measurement bias and increase 

the reliability of the data. Second, the participants were selected using a purposive sampling strategy to 

include individuals with relevant experience and knowledge related to the research topic. This enhanced 

the credibility and transferability of the findings because the participants shared similar work experiences.  

 

Data Collection and Analysis 

 
Data for this study were gathered using SurveyMonkey, adhering to established survey integrity 

and layout guidelines. The survey was launched upon the approval of the Columbia Southern University 

Institutional Review Board. SurveyMonkey's contingency offerings, such as Market Research Solutions 

for inadequate responses, were prepped but ultimately not used. 

 

Participant Recruitment and Survey Implementation 

 

Target OSH professionals were identified via LinkedIn and professional groups, with permissions 

coordinated through direct communications with organizational leaders. A requisite sample of at least 55 

participants was confirmed before survey distribution, ensuring a representative demographic and 

geographic spread. Participants engaged with the survey via a secure link, initiating an informed consent 

notice that underscored the voluntary nature of participation, confidentiality, and data protection 

measures. 

 

Data Management and Security 

 

SurveyMonkey automatically organized and monitored responses collected until the study's 

conclusion, excluding any incomplete submissions from the analysis. The data were then exported to 

Microsoft Excel for preliminary cleaning and subsequently to R Commander for detailed analysis. 



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Measures to maintain data confidentiality included password–protected and encrypted files and stripping 

datasets of direct identifiers like IP addresses to ensure participant anonymity. 

 

Ethical Adherence and Data Retention 

 

In keeping with the Belmont Report's ethical guidelines, data collection was conducted with 

utmost respect for participant rights, ensuring informed consent and provisions for withdrawal without 

detriment. Data security protocols were rigorously followed, with all information securely stored on 

controlled-access cloud systems. Per Spector's (2022) requirements, data is retained for three years post-

study before secure eradication, except for essential aggregated data maintained for analysis continuity. 

 

Coding 

 

Coding for the delivered instrumentation aligned with specific guidelines, establishing a 

standardized system to capture and categorize participant responses efficiently. SurveyMonkey was 

utilized to assign numerical codes or labels to responses, and participants were given I.D. numbers to 

facilitate data removal requests if needed. According to Van Katwyk et al. (2000), scoring the General 

Affective Well–being Scale (GAWS) involved numerically coding responses from 1 to 5, reversing codes 

for negative emotions for accurate affect measurement, and handling missing items by either averaging or 

substituting central values. Scoring could aggregate all items or differentiate between positive and 

negative responses across subscales of positive and negative arousal states. The three-item Turnover 

Intention Scale by Michaels and Spector (1982) uses a nominal scale to gauge intentions of leaving 

current employment quickly, with no specific coding requirements due to its straightforward design. Both 

instruments, including their scoring guidelines, are available from author Paul Spector. 

 

Reliability Analysis 

 

Following the data summaries, a further analysis of the data's reliability was performed. In this 

study, measuring reliability is crucial to ensure the collected data's consistency and accuracy (García–

Lirios et al., 2022). It indicates the stability and dependability of the measurements and ensures an 

assessment of the degree of measured error present in the data. 

Reliability was measured with Cronbach's alpha, one of the most common methods (Amirrudin et 

al., 2021). Cronbach's alpha calculates the internal consistency reliability of a set of items or variables by 

assessing the degree to which they correlate. The formula for calculating Cronbach's alpha coefficient (α 

= (k / (k – 1)) * [1 – (Σσ²i / σ²X)]) was utilized, yielding coefficient scores of .91 (GAWS) and .89 (T.I.).  

 

Outlier Identification and Examination 

 

Outlier identification and examination in a study can be conducted using various techniques, 

including box plots, quantile–qualile plotting (Q.Q. plots), and residual analysis (Uba et al., 2021). Box 

plots provide a visual representation of the distribution of the data, allowing for the identification of 

extreme values as points located outside the whiskers of the plot. Q.Q. Plots compare data quantiles 

against expected quantiles from a theoretical distribution, enabling the detection of deviations from 

normality and potential outliers. All calculations were performed in the R Commander program. 

 

Test of Normality 

 

Regression analysis allows the researcher to demonstrate the relationship between independent 

and dependent variables by estimating the independent variables' coefficients for their statistical 

significance (Rajalaxmi et al., 2019). Contrary to common belief, normality tests on the broad dataset are 



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unnecessary, as the focus should be on the residuals' distribution rather than the independent variables 

(Knief & Forstmeier, 2021). Residuals representing differences between observed and predicted values 

should ideally follow a normal distribution, confirming the regression model's assumptions (Godfrey, 

2019). Deviations in this distribution, such as issues with homoscedasticity, would require adjustments to 

the model. In linear regression, a "best–fit" line is calculated to minimize the squared differences between 

the observed data and the predicted outcomes (Lee, 2022), where the slope coefficient indicates the unit 

change in the dependent variable to the independent variable. The intercept represents the anticipated 

value of the dependent variable when the independent variables are zero. 

The regression line is determined by calculating the slope coefficient (β₁) and the intercept term 

(β₀) (Godfrey, 2019). The slope coefficient is computed (β₁ = Σ((xᵢ – x̄)(yᵢ – ȳ)) / Σ((xᵢ – x̄)²) and signifies 

the rate or extent of change in the dependent variable per unit change in the independent variable. The 

intercept term is derived by subtracting the product of the slope coefficient and the mean of the 

independent variable from the mean of the dependent variable (β₀ = ȳ – β₁x̄). Computation of these factors 

was done in R Commander. The software further calculated the reportable F–statistic and R2 value 

[(F=mean square regression (MSR) / mean square regression (MSE)); (R2 = regression sums of squares 

(SSR) / total sums of squares (SST))] as a primary relationship indication. Additional statistical measures 

were considered to assess the measured relationship and conduct a hypothesis test on the regression 

coefficient in the regression analysis. This included determining the t–t–score, degrees of freedom, and 

the p-value. The t–t–score is calculated by dividing the estimated coefficient by its standard error. 

Degrees of freedom are obtained by subtracting the number of predictors from the sample size minus one. 

Finally, the p–p–value is obtained by comparing the calculated t–t–score to the critical t–t–value from the 

t–t–distribution table at a specified significance level. If the p-value is less than the chosen significance 

level (p<0.05), the null hypothesis is rejected; otherwise, it will fail to be rejected. The formulas for the t 

are t = (β₁ – β₁̂) / se(β₁̂), df = n – k – 1. Calculations for statistical values were performed in the R 

Commander program. 

Last, the Breusch–Pagan test for heteroscedasticity (homoscedasticity) and variance inflation 

factor (VIF) analysis was conducted in R Commander. According to Đalić and Terzić (2021), 

homoscedasticity refers to the assumption in regression analysis that the variability of the residuals is 

constant across different independent variable levels, indicating equal variance. Multicollinearity is a high 

correlation between independent variables in a regression model, which can lead to issues in the 

estimation and relational interpretation of the coefficients.  

 

Results and Discussion 

This data set reflects the results of 90 individual responses to the employee well–being (IV) and 

turnover intention (DV) surveys. In total, 95 surveys were distributed, of which 90 were valid, yielding a 

response rate of 94.7%. Upon careful examination of each survey, five were excluded because the 

respondents had not answered all the questions or were disqualified. Accordingly, a subset of 90 surveys 

was considered for this analysis. R and R Commander software were used for descriptive statistical 

analysis and outlier analysis. A post–hoc analysis of the sample power was calculated at 0.94. 

Cronbach's alpha (α) assesses the extent to which test items are internally consistent or 

interconnected. The potential range of this measure is from zero to one. All dimensions and sub-

dimensions displayed satisfactory internal reliability (with a Cronbach α value equal to or greater than 

0.70) (Tavakol & Dennick, 2011). A numeric summary breakdown of the target well–being variables, 

including positive and negative emotions and turnover intention, was performed. In this case, turnover 

intention stands as the dependent variable. This included standard deviation (S.D.) measurements, skew, 

and kurtosis with accompanying count. The mean score for the well–being variable was 68.6, with a 

standard deviation of 13.9, a skewness of –0.34, and a kurtosis of –1.35, based on a sample of 90 

participants. Similarly, the mean score for the turnover intention variable was 3.1, with a standard 



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deviation of 1.72, a skewness of 0.29, and a kurtosis of –0.87, based on a sample of 90 participants. These 

results suggest that both categories are normally distributed. 

Table 2 

 

Numeric Summaries of the well–being and Turnover intention (T.I.) variables   

  M SD Skew Kurt n 

Well–being  68.6 13.9 –0.34 –1.35 90 

TI  3.1 1.72 0.29 –0.87 90 

Note. The total for the GAWS is calculated in reverse coding (R.C.).  

 

Exploratory Data Analysis 

The subsequent step involved reviewing each variable for any potential outliers and determining 

the distribution of each variable by subjecting it to standard tests for normal distribution. These tests 

include histograms, boxplots, and Q–Q plotting. Figure 1 illustrates the distribution of data for the well–

being and turnover intention variables. The boxplots showing no outliers are presented in Figure 2, and 

Q–Q plots indicating normal distribution are presented in Figure 3. 

 

Figure 1 

 

Graphical Summaries of Data Distribution for the Well–being and Turnover Intention Variables 

  
Note. Data appears normally distributed. 

 

 

Figure 2 

 

Boxplots of the Well–being and Turnover Intention Variables  



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Note. The Boxplot of the variables shows no outliers. 

Figure 3 

 

Q–Q Plotting of the Well–being and Turnover Intent Variables 

  
Note. Frequency scores for the well–being and turnover intent variable. Data within the shaded area is 

within the 95% confidence interval (p>0.05) and is considered normally distributed. 

 

A simple regression analysis examined whether a person's job satisfaction predicts their 

organizational commitment. The results of the test were significant: F(28.04, 88) = 29.92, p = < 0.001, R2 

= 0.24). This can be interpreted as 24% of the change in turnover intention being attributable to a person's 

affective well–being, indicating weak to moderate predictability. The Breusch–Pagan test is used to 

examine if the variances of the error terms in a regression model are constant or vary across different 

levels of independent variables, otherwise referred to as heteroscedasticity (Astivia & Zumbo, 2019). It 

involves regressing the squared residuals from the original regression onto the independent variables. If 

the coefficients of this auxiliary regression are statistically significant, it suggests the presence of 

heteroscedasticity. Homoscedasticity refers to the assumption in a regression analysis where the variances 

of the error terms are constant across all levels of the independent variables. At the same time, 

heteroscedasticity indicates that error term variances vary across independent variables. The Breusch–

Pagan test helps researchers determine if their regression model violates the assumption of constant 

variances and enables them to make appropriate adjustments. Figure 4 illustrates that the residuals do not 

show problematic patterns, while Q.Q. Plotting indicates acceptable heteroscedasticity. Table 3 shows 

that the data was found to be homoscedastic.  

 

Figure 4 

 

Residual Analysis of Dependent and Independent Variables 



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Note. Residuals vs. fitted indicate no pattern, while Q–Q plotting indicates normal distribution. 

 

Table 3 

 

Breusch–Pegan Test for Homoscedasticity 

Variables p–value B.P. 

Well–being (IV)  

0.83 

 

 

0.04 TI (DV) 

Note. Where applicable, each numerical value is rounded to the second decimal place. 

 

The Breusch–Pegan test results show a p-value greater than 0.05, indicating that the data is 

homoscedastic. 

 

Recommendations and Conclusions 

The analysis of online survey data shows a weak to moderate predictive relationship (r²=0.24) 

between affective well–being and turnover intention, aligning with Deci and Ryan's (2012) self–self-

determination theory (SDT) that emphasizes autonomy, competence, and relatedness. Low pleasure and 

low arousal emotions (e.g., boredom and discouragement) are strongly associated with quitting. 

Emotional exhaustion and cultural factors also influence turnover intentions, with men experiencing 

higher burnout due to less emotional communication. Turnover intention is linked to job satisfaction, 

autonomy, and other factors, suggesting the need for comprehensive evaluation in future studies. 

The findings highlight the importance of emotional well–being in predicting turnover intention 

and suggest that improving affective well–being through targeted interventions can reduce turnover rates. 

To mitigate negative emotions and support employee retention, business leaders should enhance job 

satisfaction, promote autonomy, and foster positive work environments. Future research should explore 

mediating factors, cultural influences, and longitudinal impacts to deepen understanding of these 

dynamics. 

Notably, this study measured well-being in terms of affectation of emotional response. Under 

SDT, well–being is emphasized by eudemonic qualities; however, the physical characteristics of vitality 

are also interconnected (Ryan & Frederick, 1997). This study focused on the affective state of well–being, 



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which refers to an individual's emotional experiences and positive affect, which, Deci and Ryan (2012) 

explain, can serve as a reliable indicator of overall well–being, without the need to measure aspects of 

physical vitality directly. However, it should be noted that physical vitality is associated with emotional 

states; thus, it is crucial to acknowledge this as a potential study limitation. 

According to self-determination theory (SDT), the theoretical implications of these findings are 

significant. SDT posits that psychological well–being is influenced by the satisfaction of three basic 

psychological needs: autonomy, competence, and relatedness (Deci & Ryan, 2012). In this study, 

affective well–being, which encompasses emotional experiences, both positive and negative, was found to 

have a moderately predictive relationship with turnover intention. This finding fits with SDT because it 

suggests that when individuals experience positive emotions and meet their emotional needs, they are 

more likely to have higher levels of well-being, job satisfaction, and autonomy. Furthermore, these 

findings imply that aspects of competency and relatedness are interconnected with arousal states of 

emotion. 

The study found that low–pleasure and low–arousal emotions, such as boredom, discouragement, 

and depression, were more strongly associated with turnover intention. This finding implies that negative 

emotional experiences can substantially impact an individual's intention to leave their job. SDT 

emphasizes the importance of positive affect and emotional well–being in promoting the optimization of 

motivation, engagement, and persistence in goal-directed behavior (Deci & Ryan, 2012). 

The results of this study highlight the importance of considering emotional experiences to 

understand turnover intention. This suggests that businesses need to go beyond traditional measures of job 

satisfaction and consider employees' emotional well–being when designing policies and practices. By 

addressing the emotional needs of employees, organizations can create a more positive and engaging 

work environment that promotes retention and productivity. Subsequently, this study provides insights 

into negative emotions, such as boredom and discouragement, that influence turnover intention. 

Businesses can use this information to identify potential risk factors for turnover intent and develop 

proactive strategies to mitigate negative emotional experiences. Organizations can develop targeted 

interventions and strategies to improve employee satisfaction, engagement, and retention by analyzing 

affective well–being and understanding its relationship with turnover intention.  

Future research should explore the mediating and moderating factors that may influence the 

relationship between affective well–being and turnover intention. This study identified a moderately 

predictive relationship between these variables, but other variables may strengthen or weaken this 

relationship. For example, job satisfaction, perceived organizational support, and work-life balance could 

act as mediators or moderators in this relationship (Wood et al., 2020). Investigating these factors will 

provide a deeper understanding of the complex dynamics between affective well–being and turnover 

intention. Future researchers should consider longitudinal studies to examine the relationship between 

affective well–being and turnover intention. While this study identified a moderately significant 

relationship, it cannot establish temporal associations. Longitudinal research designs would allow for 

examining how changes in affective well–being over time or season predict changes in turnover intention 

(Madigan & Kim, 2021). This would provide stronger evidence for the impact of affective well–being on 

turnover intention and inform the development of interventions aimed at improving affective well–being 

and reducing turnover intention.  

These findings have significant implications for organizations and managers. By understanding 

the impact of affective well–being on turnover intention, businesses can develop targeted strategies and 

interventions to enhance employees' emotional experiences and job satisfaction. This includes promoting 

autonomy, providing opportunities for growth and development, and fostering positive workplace 

relationships. Such efforts can reduce turnover rates, create a more positive and productive work 

environment, and contribute to successful business outcomes. 

In conclusion, this study highlights the critical role of emotional well–being in predicting 

turnover intention among occupational safety and health professionals. The findings suggest that 

organizations can significantly benefit from prioritizing their employees' emotional and psychological 



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needs. By fostering an environment that supports autonomy, competence, and relatedness, businesses can 

enhance job satisfaction while reducing turnover rates and building a more engaged and resilient 

workforce. Future research should continue to explore the intricate relationships between emotional well–

being and turnover intention, considering various mediating and moderating factors and adopting 

longitudinal approaches to capture temporal dynamics. Addressing these aspects will also provide a better 

understanding of how to effectively promote employee well–being and retention in diverse organizational 

contexts, further contributing to the growing body of employee research well–being and work-related 

motivational expressions. 

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APPENDIX A 

Survey Instrument

 


