Charting a Path to Sustainable Workforce: Exploring Influential Factors behind Employee Turnover Intentions in the Energy Industry

Abstract

The challenges of employee retention in the energy industry are more significant than in other industries where absenteeism is also common. The goal of this paper is to understand the variables influencing turnover intention while determining whether absenteeism in the energy sector can be a predictor of turnover intention. The turnover intention model was set up with the following predictor variables: Absenteeism, Affective Organizational Commitment, Organizational Justice, and Alternative Job Opportunities. The structured questionnaire was created by combining previously established scales. A primary survey was conducted on a sample of 156 employees, and a predictor analysis was conducted using regression analysis and SEM. The research results showed that alternative job opportunities have a direct and positive influence on turnover intention (β = 0.186), while organizational justice (β = −0.127) and affective organizational commitment (β = −0.317) have a negative direct influence on turnover intention. Absenteeism (β = 0.098) was found to have no significant influence on turnover intention. Apart from the obtained results indicating that absenteeism in the energy industry cannot be a predictor of turnover intention, the scientific contribution of the paper is also manifested in the analysis and critical review of previous research on turnover and absenteeism in the energy industry. The study’s conclusion is that affective organizational commitment is a key variable for employee retention, i.e., workforce sustainability.

1. Introduction

Sustainability in the energy sector has become a fundamental postulate of development. In sustainable resource management, the economic and business aspects of sustainability must be addressed to achieve economic and social sustainability for people. Sustainable human resource management is primarily aimed at attracting and retaining the best employees, although the latter task is much more difficult. Therefore, managing employee turnover requires that the turnover rate be reduced to a minimum. Employee turnover means that an employee leaves the company permanently. It is the least desirable form of organizational behavior among desirable employees. The cost of employee turnover often exceeds 100% of the annual salary for the vacated position [1]. Studies show that turnover costs for entry-level jobs are 30% of annual salary and can reach 213% for highly skilled positions [2]. Hom et al. [3] suggest to researchers in their overview of One hundred years of employee turnover theory and research to consider the whole industry, not only work groups and companies. There is a need to explore how the specific characteristics of different industries affect employee turnover. In recent years, there has been increased interest in researching employee turnover and turnover intention in the energy industry. Malkowska et al. [4] suggest Employee Financial Wellness Programs (EFWPs) implementation for the energy sector in Poland as an approach that will benefit the company through lower employee turnover, encouraged by, among other financial benefits, job satisfaction and increased motivation to work. Even studies [5] that do not directly address turnover intention point to the problem of high turnover intention and absenteeism in the energy industry as consequences of other factors, such as job satisfaction. For employees to become a truly sustainable resource for the company, they must first be retained.

Absenteeism is one of the most critical organizational behaviors because it slows down work and imposes enormous qualitative and quantitative costs. It seriously harms organizational productivity and strongly impacts organizational structure and culture. The financial costs can be direct and include wages and benefits. However, the hidden indirect costs are far more dangerous, referring to replacing employees, working overtime and losing productivity. Companies lose 19% of their productivity daily due to employee absenteeism [6]. According to the Institute for Absence Management [7], 35% of wages are paid for work not performed, and absenteeism is the second largest labor cost after wage costs. High-quality, systematic absenteeism management can cut these costs by up to 15%, reducing employee turnover by 30%. It also increases employee engagement by 20%, productivity by 22%, and the level of organizational culture by up to 64%, resulting in lower absenteeism abuse. Since the relationship between the above two variables is rarely studied, the main purpose of this paper is to determine if turnover can be predicted in a timely manner by monitoring absenteeism. Confirmation of this finding would be of great importance not only to academics and researchers but, more importantly, to employers who would then be able to predict future departures from the organization.

From an organizational perspective, employees in medium and large organizations, which include most companies in the energy sector, are more prone to absenteeism because employee behavior is more challenging to monitor. They often need to catch up because dealing with individuals seems too expensive and complicated, while recording the workplace is more accessible. In small organizations, there are often only one or two employees at a workstation, making it more difficult for them to allow absences from which work would immediately suffer. The relaxation in medium and large organizations can be attributed to various causes, which is why the problem of absenteeism can only be solved with a systematic approach. For the above reason, small organizations were excluded from the sample of this research.

Shah et al. [8] demonstrate that job embeddedness is key to meeting the needs of organizations to reduce turnover; i.e., the authors believe that to develop sustainable turnover levels, thinking more broadly than the commonly observed variables, such as job satisfaction, is necessary. In addition to identifying the potential predictors of turnover, the study also aims to determine the critical organizational behavior variables, thanks to which employee sustainability, i.e., employee retention, can be achieved. Since negative correlations with turnover intention indicate this, this paper focuses on affective organizational commitment and justice. Alternative job opportunities were an external variable to confirm the model’s validity.

The main research question of this study is, therefore, whether the extent of an employee’s absenteeism can predict their future departure from the organization.

The paper consists of five main parts, of which the introduction (1) indicates the main objective and importance of this work. Work hypotheses are established within the literature review (2), based on which the assumed turnover intention model is created. Following the described methodology (3), the research results (4) and the evaluated model are presented. Finally, the discussion and conclusion (5) are presented, including the limitations and implications of the study.

2. Literature Review

2.1. Sustainable Human Resource Management

Organizational sustainability is based on human resources as one of the most important components of any organization. When talking about sustainable human resource management (SHRM), the basic assumption is that you want to keep good employees, and the most important prerequisite for this is certainly their satisfaction. Sypniewska et al. [9], therefore, associate SHRM with higher employee well-being at work, employee development, employee engagement, employee retention and, of course, employee satisfaction. Unfortunately, it is not enough to monitor and manage employee satisfaction. In order to engage and retain employees, a deeper examination of other factors is required as part of sustainable management [10]. Social capital can be crucial for employee retention in the context of sustainability [11]. It has already been demonstrated that SHRM influences turnover intention [12] and that turnover intention influences SHRM, which slows down the pace of sustainable execution [13]. A sure threat to sustainable SHRM is employee absenteeism [14], which can also be a potential threat of permanent withdrawal as well.

2.2. Turnover Intention

Holtom et al. [15] described the turnover process as follows: distal influences (e.g., individual characteristics, job embeddedness) → indirect influences (e.g., job satisfaction and organizational commitment) → immediate antecedents (e.g., intention to leave the organization and seek a new job) → actual turnover. Turnover intention and actual turnover are not the same concepts. The turnover intention (TI) describes the extent to which an employee considers leaving the current organization, which does not necessarily lead to a behavior—actual turnover.

Turnover intention can predict actual turnover [16,17] and is the last step to be measured before turnover. For this reason, turnover intention is highly correlated with actual turnover [18,19]. It is the best predictor of turnover [20] because it represents employees’ final step when considering leaving [21]. Structural modeling has been used throughout the history of turnover construct research to demonstrate that each part of the differently designed constructs can, directly and indirectly, affect turnover.

Structural modeling has been used throughout the history of turnover construct research to demonstrate that each part of the differently designed constructs can have both a direct and indirect effect on turnover. Like many other authors, Sager, Griffeth, and Hom [22] studied the relationships among the parts of the construct for years based on Mobley’s model. Finally, they demonstrated the direct influence of the thought of leaving the organization on turnover intention. They concluded that employees intend to leave the organization even before they start looking for a new job. Moreover, many employees leave the organization before they have found a new job.

Because turnover is a multidimensional phenomenon, all turnover starts with personal and/or organizational “triggers” (such as personality traits or job characteristics), then attitudes (such as job satisfaction, organizational commitment, organizational justice, and others), and finally, spatial criteria that result in turnover intentions and even specific actions [23]. Harhara et al. [24] proposed a conceptual model specifically for the oil and gas industry in the United Arab Emirates, as an industry so crucial to the country struggles with high employee turnover. Their framework includes environmental factors (working in remote areas and work-life balance), organizational factors (leadership behaviors, growth opportunities, and continuous operations), and individual factors (age, tenure, marital status, and education) as independent variables, organizational commitment as a mediator, and employee turnover as a dependent variable.

A study in an engineering organization [25] showed that transformational leadership reduces turnover intention. A transformational leader can set a personal example to encourage, develop, and inspire employees. The transition to sustainable and green energy will impact the energy industry, which could be especially crucial. However, Li et al. [26] found no significant impact of green transformational leadership on employee turnover intention in the energy industry.

Other studies in the energy industry confirmed the negative influence of organizational commitment on turnover intention [27,28], the negative influence of financial and non-financial compensation (promotion and training) [29], the negative influence of job happiness [30], and the negative influence of performance appraisal and organizational rewards [31]. Molopo et al. [32] concluded that age and occupational level influence turnover intention in their study of engineers at an energy provider. Older employees and senior and executive engineers have lower turnover intention.

Absenteeism, another withdrawal behavior, can be an essential predictor of turnover. Employees who left their jobs were likely to have higher levels of absenteeism immediately prior to leaving the organization than employees who did not leave. Absenteeism may, therefore, be a leading indicator of turnover intention, which is strongest among younger employees [33].

2.3. Absenteeism

As a result of absenteeism, there are real invisible costs associated with the poor work performance of substitute employees, colleagues, and associates, as well as supervisors and managers. Absenteeism is an absence from work that may be planned or unplanned, voluntary or involuntary, justified or unjustified. Voluntary absenteeism occurs when an employee does not show up for work for reasons within the employee’s control, while involuntary absenteeism is more or less beyond the employee’s control, such as illness or injury. It is important to remember that using sick leave without actual illness is under the employee’s control.

Just like the causes, the factors influencing absenteeism can be considered from three aspects: personal, organizational, and attitudinal, of which job satisfaction is the most prominent. Among personal factors, age and gender are most frequently cited as key, such that younger employees are more likely to be absent than older colleagues [34], while women have higher absence rates than men due to their family responsibilities. The latter is also confirmed in the study on the energy industry [35,36], in which pregnancies leading to sick leave, more frequent temporary employment and increased psychological problems are cited as the leading causes of absenteeism among women. In addition, the same study highlights another problem that may be specific to the industry, namely exposure to chemical products, while smoking or alcohol consumption are other common absenteeism factors. Another approach to health-related absenteeism in the energy industry is the model [37], which includes ergonomic factors (e.g., short, repetitive, or monotonous tasks, painful positions, etc.), psychosocial factors (emotionally distressing situations), working conditions (e.g., satisfaction with working conditions, income, noise, etc.), and personal data and physiological characteristics (general health, age, outside education or training, etc.). The authors argue that the industrialized context of the energy sector influences the general health status and, consequently, absenteeism.

Because of the importance of energy management to daily life in today’s society, the COVID-19 pandemic has once again reminded businesses of the need for pandemic planning. In this particular pandemic, Wormuth et al. [38] emphasize absenteeism as the key factor threatening power system operations and control. In many countries, most employees have worked from home. Although they were not essential to daily operations, their absence created long-term problems and stresses. The absence of engineers and managers in the energy industry is further complicated by the fact that they are experts in specific fields, usually with certification, which makes it even more challenging to replace them. The study of the Polish energy sector after the pandemic [39] shows that unplanned employee absenteeism decreased by 43% in medium-sized companies and by 27% in large companies. As mentioned in the introduction, work teams are more prominent in larger organizations, so communication between them is poorer, and cohesion is more challenging. As the number of employees increases, so does the average absenteeism rate [40], which is particularly evident in the group known as “blue-collar” employees. In the study of a multi-utility company providing energy services [30], results indicate that absenteeism is more common among employees with lower levels of education. They tend to perform operational tasks more likely to cause health problems, such as workplace accidents or working outdoors in winter.

Leadership style has a major impact on absenteeism, and it is widely believed that leaders who support their employees have lower absenteeism rates in their teams and organizations. Transformational leadership has been shown to increase job satisfaction, organizational commitment, and workplace productivity. Studies show that absenteeism is lower among transformational leaders than other types of leadership, not in frequency of absenteeism, but in duration of absenteeism [41]. These findings suggest that supervisors who create a vision and focus on rewards and success can reduce absenteeism in the workplace, but the structural model has shown that other variables that exclude leadership are stronger predictors of absenteeism (e.g., organizational climate, which includes collective identity, group performance, organizational support, and empowerment), so leadership indirectly influences absenteeism [41].

Relevant previous research has established a relationship between absenteeism and turnover intention [33,42,43,44,45,46,47], so absenteeism was included as an influencing variable on turnover intention in the proposed model. Hypothesis 1 was derived based on the previously mentioned.

H1: 

The extent of absence has a direct positive correlation with turnover intention.

Landsberger et al. [48] were the first to speak of turnover intention as indicated by an employee’s absence from the workplace, which was later studied by Steers and Rhodes [49] and Mobley et al. [50]. Thus, research confirms that absenteeism can be a valid predictor of turnover intention and, in some cases, actual turnover, as it is the first form of behavior that represents withdrawal from the organization [42,51]. Employees who intend to leave are more likely to be absent from work in the period before certain actions are taken than before, even though the intention itself was not clearly and/or explicitly expressed. This is not only a matter of absenteeism due to the search for a new job but also absenteeism resulting from the awareness that it is only a matter of time and opportunity when the intention will come to fruition. Once the employee becomes aware of his or her intent, he or she is more likely to be unjustifiably absent and to take advantage of every opportunity to be absent (coming to work late, leaving work early, using breaks longer than allowed, unjustifiably taking sick leave, etc.). Therefore, there is a presumption that employees who are absent more often are more likely to intend to leave.

2.4. Affective Organizational Commitment

The three-dimensional model of organizational commitment includes the following three types: affective commitment, normative commitment, and continuance commitment. In the context of employee turnover, this would mean that these dimensions indicate that people stay in their organization “because they want to (affective commitment), because they feel they should (normative commitment), and because they have to (continuance commitment)” [52] (p. 85). Continuance commitment does not actually represent genuine and deep employee commitment, which is why it is usually excluded from the study of turnover intention. Affective organizational commitment (AOC) expresses employees’ emotional attachment to their organization, their desire for the organization to achieve its goals, and their sense of pride in being part of that organization [53]. Therefore, it is not surprising that it is a stronger predictor of turnover than other dimensions [14,54]. Employees with higher levels of affective commitment are unlikely to consider leaving the organization because of their emotional sense of connection and attachment to the organization [55].

As mentioned earlier, studies in the energy sector reported the significant negative impact of organizational commitment on turnover intention [27,28]. In addition, Liu et al. [28] reported a significant mediating effect of organizational commitment between occupational health and turnover intention.

Lambert et al. [55] concluded that researchers usually select one type of commitment to study as a function of other variables related to one form of organizational behavior. All three types of commitment have quite different effects on different behaviors. In the study on the effects of ethical leadership and organizational commitment [56], the researchers only included affective commitment in the model, as in this paper. This can also be supported by study [57], which examined and compared organizational commitment in two Finnish companies in the energy sector. The results between the companies were similar, but only the level of affective commitment was significantly different—in one company, it was significantly higher. Based on the above, Hypothesis 2 was formulated.

H2: 

Affective organizational commitment has a direct negative correlation with turnover intention.

Of all types of commitment, affective organizational commitment correlates most strongly with turnover intention, followed by normative commitment and continuous commitment, which sometimes has very little or no influence. This is the reason why, in research, especially when using the SEM methodology, affective commitment takes precedence over the other types of commitment and can often act as a moderator and mediator in this relationship in addition to its direct relationship with turnover intention [28,58,59,60,61]. The general assumption, therefore, is that employees who have strong affective organizational commitment are less likely to intend to leave the organization.

2.5. Organizational Justice

Organizational justice (OJ) in the context of organizational behavior theory usually includes three forms. Distributive justice refers to promotions or financial rewards by managers, formal justice refers to procedures, and interactional justice refers to the communication of management decisions and organizational processes [62].

Robbins and Judge [63] point out that perception is the key component of justice, fairness or equality. It is a somewhat subjective category that various factors can influence, so errors in judgment are widespread. However, it is a widely held belief that men are more sensitive to distributive and formal injustice, while women are more sensitive to interactional injustice. When evaluating organizational justice, it should be noted that, like any other justice, it is “in the eye of the beholder” [64]. Perceived organizational justice positively affects organizational commitment [65,66,67] and job satisfaction [66,67], while negatively affects absenteeism [68] and turnover [66,69,70].

Employees who perceive the reward system in their organization to be fair have weaker or no expressed intention [69,71]. This proves that when considering each justice dimension, distributive justice has the greatest impact on employee satisfaction, turnover, and organizational commitment [64,72]. A study in the energy industry [65] shows that employees with higher levels of education perceive organizational equity as less equitable. It has also been shown that feelings of distributive injustice, directly and indirectly, increase employee absenteeism and that procedural injustice causes absenteeism through affective commitment [68]. Organizational justice directly affects employee satisfaction, with distributive justice explaining more variance in job satisfaction than formal and interactional justice [73]. Regardless of which dimension of justice is more dominant, perceptions of organizational justice overall can be expected to influence decisions about intentions to stay or leave the organization, as hypothesized in Hypothesis 3.

H3: 

Organizational justice has a direct negative correlation with turnover intention.

As explained earlier, overall organizational justice is divided into distributive, procedural, and interactional justice, and their contribution to turnover intention can be considered together or separately. Typically, all three dimensions are valued equally, i.e., employees who value one type of justice positively think similarly about other types of justice. However, this should be considered a flexible rule. All three types of justice negatively influence turnover intention [66,69,70,71,74,75]. Greater positive perceptions of organizational justice lead to lower intentions to leave, and of course, the reverse is also true.

2.6. Alternative Job Opportunities

When employees perceive more significant opportunities for alternative employment, their intention to leave is more likely to be higher. This is called perceived employment opportunities in another organization, perceived alternative employment opportunities, or alternative job opportunities (AJO). This attitude can indirectly increase job dissatisfaction, further increasing the employee’s desire to leave the organization. Organizational psychology assumes that perceptions and expectations primarily shape the decision to leave the organization, so external factors must also influence the attitude that leads to actual behavior. Situational or contextual factors cannot be ignored when examining turnover, i.e., the intention to leave the organization. Lee et al. [76] cite economic factors and active job search behavior as the most common external “antecedents” of turnover intention, in addition to job satisfaction and organizational commitment. Steel [77] proposes a turnover model that includes three distinct stages of job search: passive scanning, targeted search, and contacting potential employers. In the model, he highlights two types of so-called “escape” that depend on the financial outlook and spontaneous business offers, so he concludes that the entire job search process is related to all other attitudes toward work but is still a separate subsystem. In other words, employees with more information about other jobs in other organizations are likely to be more interested, i.e., their turnover intention is higher, in contrast to those who want to stay in the organization.

Mushtaq et al. [73] argue that job dissatisfaction increases independently of current perceived organizational justice when workers perceive more employment opportunities. They conclude that perceived alternative job opportunities moderate the relationship between organizational justice and job satisfaction. In practice, more employment opportunities can lead to a situation where existing dissatisfaction is greater. Indeed, more available alternatives increase employees’ awareness and expectations of the organization’s values, which is why they do not accept less than what the competition could offer.

Alternative job opportunities affect different mental attitudes or mental states of individuals differently. Employees who leave organizations impulsively leave quickly without securing other job opportunities because they feel an intense negative impact from the organization (they usually have low affective commitment). In contrast, those who plan their departure are often pressured by their family (usually their spouse) to change their current job and then consider other alternatives [23]. In any case, perceived alternative job opportunities may mitigate the strength of the relationship between absenteeism and turnover because, unlike all other withdrawal-related behaviors, turnover is highly dependent on external variables. The alternative job opportunities, therefore, make the proposed model less perfect in the effort to prove the predictive nature of absenteeism on turnover intention, but they still cannot be ignored. Accordingly, the final hypothesis was set.

H4: 

Alternative job opportunities have a direct positive correlation with turnover intention.

Many studies emphasize the need to include alternative job opportunities as a key external variable in the turnover intention model, and the following studies confirm this: [73,77,78,79,80,81,82,83]. The alternative job opportunities as the only external factor of the organization in this research attempt to justify the given model, but also to prove that the state of the labor market largely influences the turnover. Employees who perceive their possible alternatives as more favorable and believe they have more alternatives will have higher turnover intention.

Figure 1 shows the proposed model.

3. Methodology and Data

For the purpose of this study, field primary research was conducted among selected organizations of the energy sector in Croatia. The testing method used is probing with group testing. The physical presence of the investigator allowed for detailed instructions on how to fill out the questionnaire, an explanation of how to fill it out and the meaning of the questions/statements, as well as additional clarification of the necessary questions. Respondents did not communicate before or during the survey to avoid bias errors. The anonymous questionnaire contained statements/questions about the observed variables (Likert scale 1–5) and statements/questions about sociodemographic characteristics. After eliminating the incomplete questionnaires, a total of 156 questionnaires were considered for further analysis.

Primary data analysis included multivariate analysis (factor analysis) and structural equation modeling (SEM), for which the Statistical Package for the Social Science (SPSS) ver. 26.0 for Windows and AMOS for SPSS ver. 26.0. were used.

3.1. Measuring Scales

The turnover intention scale was created based on a combination of the following previously researched and validated questionnaires [83,84,85]. The absenteeism scale was adapted from Nicholson and Payne [86] (there are ordinal variables in the original questionnaire). Affective organizational commitment was examined using a well-known and widely accepted questionnaire designed by Meyer and Allen [87]. The organizational justice scale is from Niehoff and Moorman [88] and alternative job opportunities were examined using the scale from Khatri et al. [89].

3.2. Missing Data and Outliers

Before analyzing the data, the dataset had to be prepared given the specific requirements of structural modeling. This process included cleaning up missing data and removing outliers. Neglecting this aspect can lead to several problems. First, missing data can lead to bias in parameter estimation and reduce the generalizability of results [90]. They can also lead to a loss of information that reduces the statistical power of the prediction and increases standard errors [91]. Moreover, most statistical methods, including SEM, are designed for complete datasets [92]. Before a dataset with missing values can be analyzed, it must be completed in some way to form a comprehensive dataset. If this step is performed inadequately, the data may not be suitable for statistical procedures and may violate the assumptions in the analysis [93]. In general, it is recommended to keep the percentage of missing values low—ideally below 5%—and add them only when necessary [94].

Missing data analysis was performed by frequency analysis and missing value analysis (MVA) using the SPSS program. The results showed that none of the variables exceeded the acceptable threshold of 5% for missing data. In the current dataset, there was a missing data point in only two items, representing 0.6% of the total item data. The Little MCAR test [95] revealed that the data were missing at random, so the EM method could be used to address these missing values. The EM algorithm is an iterative process that aims to estimate missing values through an expectation step (step E—expectation) and a maximization step (step M—maximization). Parameter estimates based on the EM algorithm are considered reliable, as are standard errors after specific adjustments [96].

After dealing with missing data, it was necessary to identify possible outliers. The Mahalanobis distance test was performed to test the multivariate normality of the distribution. MD is a statistical measure that shows which data are outliers or atypical values with a probability of less than 0.001 based on the chi-square distribution. It is essential to analyze outliers in detail because they can significantly impact the arithmetic mean and increase the standard deviation [97]. MD Analysis did not identify any outliers in the dataset.

3.3. Sample Description

The sociodemographic characteristics of the sample are shown in

Table 1. Sociodemographic characteristics of the sample (N = 156).

		%
Gender	Male	64.7
	Female	35.3
Age	24–42	31.4
	43–57	41.6
	>58	25
Education	Primary school	1.3
	High school	58.1
	Faculty	38.7
	Master’s degree and doctorate	1.9
The level of your position in the organization	Operational employee	90.4
	Middle management	9.6
	Top management	0
Number of employees in the organization	<50	21.2
	50–250	78.2
	>250	0.6
Personal income level	<400 €	40.4
	401–800 €	50.6
	801–1200 €	8.3
	1201–1600 €	0.6
Household income level	<400 €	0.6
	401–800 €	11
	801–1200 €	30.5
	1201–1600 €	24.7
	1601–2000 €	20.1
	>2000 €	13

.

4. Results

4.1. Exploratory Factor Analysis

Exploratory factor analysis was conducted using SPSS to identify and confirm factors within each construct. The Kaiser–Meyer–Olkin measure of sampling adequacy yielded a value of 0.860, while Bartlett’s test of sphericity showed significance, indicating that the data were suitable for factor analysis. Because of the high correlations between factors, an oblique rotation was performed. Items with loadings below 0.4 were excluded from further analysis.

Table 2. Exploratory factor analysis results.

Variable	Item Abbr.	Item	Factor Loading
Affective Organizational Commitment	AOC1	I feel emotionally attached to this organization.	0.807
	AOC2	I feel a “strong” sense of belonging to my organization.	0.857
	AOC3	I do not feel like “part of the family” at my organization (reverse-coded).	0.799
	AOC4	I would be very happy to spend the rest of my career with this organization.	0.754
	AOC5	I really feel as if this organization’s problems are my own.	0.761
	AOC6	This organization has a great deal of personal meaning for me.	0.864
	Cronbach’s alpha coefficient: 0.928
Distributive Organizational Justice	DOJ1	My work schedule is fair.	0.511
	DOJ2	I think that my level of pay is fair.	0.552
	DOJ3	I consider my workload to be quite fair.	0.904
	Cronbach’s alpha coefficient: 0.779
Formal Organizational Justice	FOJ1	Job decisions are made by the superior in an unbiased manner.	0.838
	FOJ2	My superior makes sure that all employee concerns are heard before job decisions are made.	0.866
	FOJ3	My superior collects accurate and complete information to make job decisions.	0.873
	Cronbach’s alpha coefficient: 0.921
Interactional Organizational Justice	IOJ1	When decisions are made about my job, my superior discusses the implications of the decisions with me.	0.747
	IOJ2	When decisions are made about my job, my superior offers adequate justification for decisions.	0.892
	IOJ3	When decisions are made about my job, my superior offers explanations that make sense to me.	0.844
	Cronbach’s alpha coefficient: 0.944
Alternative Job Opportunities	AJO1	If I quit my current job, the chances that I would be able to find another job as good as, or better than my present one is high.	0.909
	AJO2	If I had to leave this job, I would have another job as good as this one within a month.	0.902
	AJO3	There is no doubt in my mind that I can find a job that is at least as good as the one I now have.	0.936
	AJO4	Given my age, education and general economic condition, the chance of attaining a suitable position in some other organization is slim (reverse-coded).	0.920
	AJO5	The chance of finding another job that would be acceptable is high.	0.982
	AJO6	It would be easy to find acceptable alternative employment.	0.942
	Cronbach’s alpha coefficient: 0.961
Absenteeism	A1	In the last 12 months, I was absent from my workplace.	0.647
	A2	In the last 12 months, I have been late to my workplace.	0.817
	A3	In the last 12 months, I left my workplace early.	0.649
	A4	In the last 12 months, I was absent from my workplace due to sick leave, even though my actual state of health allowed me to work undisturbed.	0.710
	Cronbach’s alpha coefficient: 0.792
Turnover Intention	TI1	I intend to leave my current organization within the next year.	0.925
	TI2	I intend to leave my current organization within the next two years.	0.941
	TI3	I often think about leaving the organization I work for.	0.666
	TI4	I am currently looking for another job.	0.708
	TI5	I am seriously considering the possibility of resigning.	0.778
	Cronbach’s alpha coefficient: 0.916

shows the items, the factor loadings for each item, and Cronbach’s alpha coefficients for each construct.

4.2. Higher-Order Latent Variables

Higher-order latent variables are formed from two or more latent variables and indicate how a concept can be measured from different aspects or dimensions. In this study, the higher-order latent variable is organizational justice, which is theoretically measured by three dimensions: distributive, formal, and interactional justice. In order to evaluate the adequacy of the latent variable, a confirmatory factor analysis (CFA) was conducted for the variable organizational justice. The results of the CFA showed that all indicators of construct adequacy for this second-order latent variable were within the proposed values: X2/df = 2.033; RMSEA = 0.082; SRMR = 0.059; GFI = 0.934; AGFI = 0.886; NFI = 0.958; RFI = 0.941; IFI = 0.978; TLI = 0.969, and CFI = 0.978.

4.3. Confirmatory Factor Analysis

Data were analyzed using AMOS structural equation modeling software (IBM SPSS AMOS Version 21). Following the recommendation of Bollen [98], several indices of model fit were examined to evaluate both the measurement and structural models since a model may be adequate for one fit index but inadequate for others. The indices were selected based on the suggestions of Hu and Bentler [99].

Table 3. Results of a confirmatory factor analysis—construct validity, composite and convergent reliability of the scales.

Constructs		Items	Standardized Loadings	CR	AVE
Affective Organizational Commitment		AOC1	0.734	0.927	0.680
		AOC2	0.821
		AOC3	0.824
		AOC4	0.862
		AOC5	0.846
		AOC6	0.854
Alternative Job Opportunities		AJO1	0.894	0.964	0.818
		AJO2	0.817
		AJO3	0.918
		AJO4	0.883
		AJO5	0.951
		AJO6	0.957
Absenteeism		A1	0.652	0.802	0.505
		A2	0.801
		A3	0.648
		A4	0.730
Turnover Intention		TI1	0.806	0.916	0.685
		TI2	0.786
		TI3	0.809
		TI4	0.834
		TI5	0.898
Organizational Justice CR = 0.931 AVE = 0.819	Distributive Organizational Justice	DOJ1	0.798	0.865	0.647
		DOJ2	0.715
		DOJ3	0.891
	Formal Organizational Justice	FOJ1	0.882	0.920	0.794
		FOJ2	0.920
		FOJ3	0.870
	Interactional Organizational Justice	IOJ1	0.889	0.941	0.843
		IOJ2	0.944
		IOJ3	0.920
Goodness-of-fit (benchmarked values)			Fit statistics
χ2/DF (1 to 3)			1.775
CFI (0.90)			0.929
IFI (>0.90)			0.929
TLI (>0.90)			0.920
SRMR (<0.08)			0.062
RMSEA (<0.08)			0.071

presents the results of the confirmatory factor analysis.

The results of the confirmatory factor analysis showed that the factor loadings for the latent constructs ranged from 0.648 to 0.957, strongly supporting construct validity. The values of the average variance extracted (AVE) for all constructs exceeded the benchmark of 0.50 recommended by Fornell and Larcker [100], and the composite reliability coefficients for all constructs exceeded 0.70, indicating high internal reliability [100]. All scores from CR were higher than scores from AVE, demonstrating convergent validity. The goodness-of-fit statistics of the measurement model also showed a good fit with the data (χ²/df = 1.775; CFI = 0.929; IFI = 0.929; TLI = 0.920; RMSEA = 0.071; SRMR = 0.071).

Table 4. Correlations between the constructs and a discriminant validity test.

Constructs	Correlation	Correlation Squared $({\mathit{r}}^2$)	AVE1 $(\mathbf{AVE}1 > {\mathit{r}}^2$)	AVE2 $(\mathbf{AVE}1 > {\mathit{r}}^2$)	Discriminant Validity
AOC<->OJ	0.465	0.216	0.680	0.819	Established
OJ<->A	−0.036	0.001	0.819	0.505	Established
OJ<->TI	−0.349	0.122	0.819	0.685	Established
OJ<->AJO	−0.007	0.000	0.819	0.818	Established
AOC<->A	−0.065	0.004	0.680	0.505	Established
AOC<->TI	−0.476	0.227	0.680	0.685	Established
AOC<->AJO	−0.112	0.013	0.680	0.818	Established
A<->TI	0.097	0.009	0.505	0.685	Established
A<->AJO	−0.050	0.003	0.505	0.818	Established
TI<->AJO	0.305	0.093	0.685	0.818	Established

illustrates the correlations between the constructs as well as a test for discriminant validity. Based on the Fornell–Larcker criterion, discriminant validity was successfully demonstrated as none of the squared root correlations between the constructs exceeded the values of AVE for each construct.

4.4. Structural Model Testing

A structural model was proposed to examine the effects of absenteeism, alternative job opportunities, affective organizational commitment, and organizational justice on turnover intention. The hypothetical model is shown in Figure 2.

The results of model testing showed that all indicators of model adequacy were within the recommended values (χ²/df = 1.763; CFI = 0.929; IFI = 0.930; TLI = 0.922; RMSEA = 0.071; SRMR = 0.064). The model explained 32.1% of the turnover intention. The detailed results of the structural model are shown in

Table 5. Structural model testing results.

Relationship			Standardized Total Effects	p Value	Hypothesis
Absenteeism		Turnover Intention	0.098	0.301	Not accepted
Affective Organizational Commitment		Turnover Intention	−0.317	0.000	Accepted
Organizational Justice		Turnover Intention	−0.127	0.042	Accepted
Alternative Job Opportunities		Turnover Intention	0.186	0.000	Accepted

(symbol representing the influence of one variable on another ).

5. Discussion and Conclusions

The purpose of the study was to examine the effects of absenteeism, affective organizational commitment, organizational justice, and alternative employment opportunities on employee turnover intention in the energy sector. The results of the structural model showed that perceived alternative employment opportunities had a direct and positive impact on employees’ intention to leave their current organization (β = 0.186). In contrast, perceived organizational justice (β = −0.127) and affective organizational commitment (β = −0.317) showed a negative direct influence on turnover intention. Consequently, H2, H3, and H4 are accepted. However, absenteeism (β = 0.098) was found to have no significant influence on turnover intention, leading to the rejection of hypothesis H1.

The results of this research refute the basic assumption of the study that employees who intend to leave the organization are more likely to stay away from work. In relation to the research, the problem may also be that the majority of respondents have a weak turnover intention and are mostly “present” at work, so it is quite difficult to detect a significant influence when absenteeism and TI are very low. The results that do not confirm this influence can also be explained by Aziri’s [101] explanation that, as with other relationships studied in the field of organizational behavior and human resource management, there are moderating variables, such as the degree to which people perceive their work as important. This is one possible interpretation of why absenteeism does not correlate with turnover intention. If employees perceive their work as extremely important and, more importantly, their contribution as important or even critical, it is possible that they will not be absent despite an existing intention to leave.

Although Hypothesis 1 is not accepted, in practical terms, this does not change the fact that managers should pay more attention and caution to absenteeism. Employee absenteeism data remain important and valuable, especially in the energy industry, because they are readily available to managers, provide an objective and accurate representation of employee absenteeism, and can be easily quantified to perform predictive analyses of future absenteeism. In the energy sector, large organizations are not uncommon, so monitoring a large number of employees is sometimes extremely difficult. Nevertheless, supervisors know in which organizational units absenteeism is a major problem, so it is possible and necessary to focus attention on specific departments. Since people tend to prefer group behavior, in larger organizations with a larger number of separate work units, it is possible to monitor the deviations of individual work units from the average to determine whether there are problems only in some departments or work units or at the level of the entire organization. This saves time and material that would otherwise have to be spent investigating the entire organization.

In contrast to the first hypothesis, affective commitment proved to be the strongest predictor of turnover intention in this model, which has also been confirmed by other research [1,54]. Affective organizational commitment, unlike other dimensions of commitment (normative and continuance), reflects the employee’s attachment to the organization, so research findings understandably suggest that the relationship between affective commitment and turnover intention is the strongest. Therefore, when examining organizational attitudes in the context of voluntary, intentional turnover, priority should be given to examining organizational commitment, particularly affective commitment, because it has the strongest correlation with both turnover intention and actual turnover. By examining affective commitment, one obtains a sense of the employee’s emotional attachment to the organization, which is not volatile (unlike job satisfaction, which is inherently more variable) and is an important organizational consideration in the decision to leave. This does not mean that the study of job satisfaction should be completely neglected (because it is also useful for predicting some other behaviors), but it is also necessary to include the study of organizational commitment. It is, therefore, not surprising that organizational embeddedness was later developed on the basis of organizational commitment, which represents a whole new level of connection between employees and the organization, other employees in the organization, and the community as a whole. Thus, the recommendation for future research is precisely in the direction of including the aforementioned variable in future models.

Organizational justice was also found to be a valid factor influencing turnover intention, which was confirmed by previous studies [69,74], although the strength of the influence was not expected.

Lower turnover intentions should not have a relaxing effect on organizations because they are not only a product of organizational aspects but also of situational factors. Since alternative job opportunities had a direct positive effect on turnover intentions, this implies that employees who perceive alternative jobs as available and/or better than the current ones are more likely to intend to leave. This is consistent with previous research [78,79].

The limitations of the research are the convenient sample and the survey method (obvious apprehension in filling out the questionnaire on the premises of the organization despite guaranteed anonymity). In addition, the results of the survey do not refer to employees who have been employed in the current organization for less than 12 months (due to the study of deeper attitudes towards the organization), as well as to employees who were not at work at a given time. This suggests that employees who were currently absent for a shorter or longer period of time did not participate in the survey. Given the topic of this study, which also addresses absenteeism, employees who were absent for an extended period of time (due to illness or other reasons) would be an interesting group of respondents.

To reduce undesirable voluntary turnover, organizations in the energy sector should perform the following:

• Shift the focus from primarily material resources to human resources;
• Strengthen affective commitment to build normative and continuance commitment that collectively create loyal employees;
• Pay attention to increased absenteeism rates so that they can be managed both proactively and reactively;
• Due to the sensitivity of the human ego, which constantly compares itself with others (inside and outside the organization—this can also influence the perception of alternatives), ensure justice: eliminate differences in material compensation between women and men; perceive, praise and reward results; treat employees kindly and continuously inform them about decisions that affect their work;
• Try to identify the reasons why employees leave after they have left.

Despite the three hypotheses being confirmed, the strength of the influence is not extremely strong, suggesting that much research is still needed in the energy industry to identify the key predictors of turnover. Given the emerging trends promoting individualism, organizational commitment should be supplemented by occupational commitment, which in some cases may be a stronger predictor of turnover than organizational commitment as traditionally measured.

For a more permanent and deeper sustainability of employees, it is necessary to keep in mind that loyalty and affective commitment are not the results of random circumstances but of invested organizational efforts to fairly empower and support employees so that they feel comfortable and useful in it in the long run, i.e., “at home”. Only positive emotions can lead to positive results, and only a healthy work environment can produce a healthy employee who neither misses work nor wants to leave the workplace and the organization permanently.