# The Outcomes of Organizational Fairness among Precarious Workers: The Critical Role of Anomie at the Work

Authors: Asghar Afshar Jahanshahi; Milagros Isabel Mendoza Rivas; Alberto Yony Quiroz Castillo
Journal: Human Behavior and Emerging Technologies
 (2022)

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## Abstract

Precarious workers usually have some of the most unstable working conditions in any country. In this research, we firstly investigated the influence of organizational fairness on the emotional exhaustion and leave intentions of Peruvian precarious workers. Then, we tested the mediating role of anomie at work in the relationship between organizational fairness and the emotional exhaustion and leave intentions of precarious workers. To identify the impact of organizational fairness on these workers, we conducted a cross-sectional survey among 206 precarious workers in Peru. Our results showed that treating precarious workers fairly reduced their emotional exhaustion and leave intentions. Furthermore, we found that anomie at work mediate the relationship between organizational fairness and the emotional exhaustion and the relationship between organizational fairness and leave intentions of precarious workers. We provide empirical evidence of the critical role of organizational fairness in the reduction of emotional exhaustion and leave intentions among precarious workers. Examining the consequences of precarious workers’ perceptions of organizational fairness is needed for owners and managers of companies to have a clear understanding of how precarious workers’ working environments may shape their attitudes and work behaviors.


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## Body

## 1. Introduction

Precarious work is characterized by (1) temporary or casual employment; (2) the reception of limited or unstable social benefits and statutory entitlements; and (3) lower wages. Precarious workers can be contrasted with the permanently employed, even though the former also fills permanent job needs [1, 2]. According to Anderson [3], the instability of working conditions, a lack of sufficient protection in the workplace, and economic vulnerability are the main features of precariousness. Tsurugano et al. [4] believe that precarious workers suffer from fewer regulations and enforcements of worker protections, contingent temporary employment, low wages, and poor working conditions. Because of the high instability of the working conditions, precarious employees are more likely than permanent employees to be vulnerable to physical and mental health issues such as fatigue, lack of energy, poor sleep, backache, and muscular pains [5]. This group of workers within an organization is also more likely to feel emotionally worn out and express more intentions to leave their current jobs[6, 7]. Therefore, we believe that precarious employees are generally in need of more fair treatment on the part of managers and supervisors.While our understanding of how the perception of organizational fairness by permanent employees influences their work-related behaviors and attitudes has improved in recent years [8–11], there remains a dearth of research on the consequences of the perception of organizational fairness among precarious workers. It is therefore important to learn more about this group of employees because they constitute a large part of the workforce in developing and developed countries. For instance, Frade and Darmon [2] found that more than 40% of the workforce in five European countries, including France, Germany, Italy, Spain, and the United Kingdom, was comprised of precarious workers. To fill this gap in the research, we investigated the influence of organizational fairness on the emotional exhaustion and leave intentions of Peruvian precarious workers. Furthermore, we tested the mediating role of anomie at work in the relationship between organizational fairness and the emotional exhaustion and leave intentions of precarious workers.It has been suggested that the growth rate of precarious employment within the society may finally lead to the racialization of poverty [1]. Despite some recent progress in clarifying this concept [2, 12, 13], precarious employment is still an underresearched issue [2]. In this respect, Inoue et al. [13] have argued that a lack of fairness in the workplace may cause psychological distress among precarious workers. Contributing to this line of research, we provide empirical evidence of the critical role of organizational fairness in the reduction of emotional exhaustion and leave intentions among precarious workers. Examining the consequences of precarious workers’ perceptions of organizational fairness is needed for owners and managers of companies to have a clear understanding of how precarious workers’ working environments may shape their attitudes and work behaviors.


## 2. Literature Review and Hypothesis Development

### 2.1. Organizational Fairness

Organizational fairness refers to the extent to which employees feel that they are treated fairly in their workplace [14]. The impartiality of organizational norms and procedures is one of main pathways toward improving the perception of organizational fairness among employees in the workplace. The perception of fairness is an important factor for each business because it enhances workers’ participation in the workplace; therefore, those organizations that work to ensure such fairness will be in a better position to reach their objectives [15]. Empirical evidence from the health sciences has shown that a perceived lack of organizational fairness among employees may result in the risk of psychological distress [16] and a high level of psychological distress may hurt employees’ job performance in the long term [17–19].In the context of Latin America, organizational culture has been found to be an important predictor of the perception of organizational fairness (by using survey data from 80 employees in the public sector) [20]. In Mexico, survey data from 307 teachers working in higher education institutions also demonstrated that organizational climate is another predictor of the perception of organizational fairness [21]. In Peru, a survey conducted with 54 bank workers revealed that employee job satisfaction was the main outcome of organizational fairness [22].


### 2.2. Anomie at Work

Anomie as a concept comes from the Greek “nomos,” meaning degradation or the absence of the law or norms [23]. Anomie will increase when an organization pays less attention to moral and ethical standards at work and focuses instead (and almost exclusively) on goal achievement [24]. Top-level managers usually establish different kinds of policies, procedures, and regulations to keep their business running smoothly. Anomie at work happens once the existing normative system (policies, procedures, and regulations) loses its value in the eyes of employees. It is important to mitigate perceptions of anomie among workers since it could generate feelings of despair and helplessness [25]. Merton Merton [26] believes that in a workplace with a high level of anomie, most of the employees prefer to pursue their organizational or job-related goals through illegitimate and illegal means. Usually, organizations are interested in minimizing those behaviors that violate well-established organizational norms, policies, and internal regulations. Previous studies have shown that employees’ engagement in such deviant behaviors is less frequent in a workplace with low levels of anomie [27]. Increasing the quality of work life in the workplace is an important way of deterring the anomic behavior of employees [28]. According to the study in the context of public sector organizations, fair wages, rewards, security, reduction of the gender pay gap, improvements to work happiness, and promotion and learning opportunities for all employees are some solutions that can reduce the level of normlessness or anomie at work.


### 2.3. Intentions to Leave

Hiring new employees is a complicated and time-consuming process, and business owners spend a significant amount of time, energy, and money in order to recruit experienced talent. Therefore, a successful business constantly looks for different ways to keep the current employees motivated and reduce their intentions to leave. The intention to leave an organization refers to the willingness of an employee to leave his or her current job in the near future for any reason [29]. The reduction of employees’ leave intentions is an important factor for organizations because the loss of a skilled employee generates many monetary and nonmonetary costs for a business [30].On this point, Nowrouzi-Kia and Fox [31] and Lambert et al. [32] have argued that organizational and work environment factors are important for retaining employees and a satisfied employee is less likely to express an intent to leave than unsatisfied employees. Disappointment in unmet expectations has been identified as another reason for employees to leave their jobs [33].In the context of Latin America, Dorigan et al. [34] analyzed survey data from 262 Brazilian health professionals and found that providing a climate that is supportive of teamwork can help organizations to diminish the employees’ intentions to leave. In Mexico, a study conducted by Máynez Guaderrama and Cavazos Arroyo [35] revealed that effective commitment and job satisfaction were the main requirements for retaining skilled employees. Contributing to this line of research, Torres Portilla [36] conducted semistructured interviews and surveys with 182 Peruvian workers in the hotel industry. According to the results, the lack of (1) organizational support, (2) an effective remuneration system, and (3) promotion opportunities were among the main reasons explaining employees’ leave intentions.


### 2.4. Emotional Exhaustion

Emotional exhaustion has received a great deal of attention from business and organizational behavior researchers in recent years, and scholars have used this concept to explain different types of employee behaviors and attitudes in the workplace [37]. Emotional exhaustion refers to a syndrome of emotional or physical exhaustion caused by job stressors or excessive job demands [38]. It is important to recognize this type of exhaustion among employees to prevent the advent of mental distress and to reduce behaviors that are harmful to the organization [39]. Emotional exhaustion can be the result of many internal and external work-related factors. For instance, interactions with difficult clients or a high workload [39] may lead to exaggerated levels of emotional exhaustion in the long term [40].


### 2.5. Organizational Fairness and Intentions to Leave

In the first hypothesis of the present study, we predicted that the perception of organizational fairness would be found to have reduced precarious workers’ intentions to leave. Organizational fairness is a critical factor in the enhancement of employees’ job satisfaction in the workplace [41, 42]. In this respect, Janssen [43] found that the satisfaction of employees was higher when they perceived effort-reward fairness in their organizations. Further, it has been found that treating employees fairly in the workplace makes them happier and enhances their commitment to the organizational goals [41]. There is also some empirical evidence that highlights the importance of job satisfaction as a deterrent of the turnover intentions of employees [44]. Treating fairly the workers within the workplace may enhance their commitment towards their organization and motivates them to make the organization part of their social identity [45]. Receiving fair treatment within the company may convey a sense of belonging [46] and highlighted that a worker is an important and critical member of the organization and may eventually diminish their intention to leave. Therefore, we hypothesized that there is a negative association between organizational fairness and intentions to leave (H1).


### 2.6. Organizational Fairness and Emotional Exhaustion

The unstable and highly uncertain employment conditions for precarious workers have made the study of emotional exhaustion increasingly important. According to a literature review, continuous stress is one of the major drivers of emotional exhaustion [47, 48]. Precarious employees may feel more stress in their jobs and lives than permanent or lifetime employees because of two reasons. First, due to not having many alternative job offers, they may not be in a good bargaining position; therefore, they may receive a lower compensation package [49]. Having financial problems makes it difficult to face the various challenges that arise in life, which eventually may lead to extreme stress. Second, the majority of precarious employees have very short work contracts. The high possibility of losing one’s job or thoughts about not receiving a contract extension may lead to feelings of stress and tension in the long term [50]. It has been shown that treating employees fairly is one way to reduce employees’ stress and improve their mental health in the public sectors [51]. In the healthcare sector, Sutinen et al. Sutinen et al. [16] have found that exhibiting fair behaviors, decisions, and actions toward employees is crucial for lowering the risk of psychological distress. Building on this literature, in the second hypothesis of the study, we predicted that the perception of organizational fairness would be found to have reduced precarious workers’ emotional exhaustion. There is a negative association between organizational fairness and emotional exhaustion (H2).


### 2.7. The Mediating Role of Anomie at Work

Organizational fairness plays a very important role in shaping employees’ behavior and attitudes in the workplace [52, 53]. Perceived organizational fairness is higher in those organizations that have fair and transparent reward and payment systems as well as managers and supervisors who treat all employees fairly ([54]; A. [8, 55]). In the third and fourth hypotheses of the study, we predicted that the absence of the perception of fairness by precarious workers would be found to have increased anomie at work, which in turn may have led to higher levels of emotional exhaustion and intentions to leave among them.Individuals who feel greater injustice in their lives and workplaces tend to exhibit more deviations in their behaviors and actions [56–58]. Furthermore, Inoue et al. Inoue et al. [13] have reported that the lack of organizational fairness can be a source of psychological distress for precarious workers. A close relationship has also been found between psychological distress and individual perceptions of anomie in social systems [59]. Precarious employees, especially those working the night shift, have been shown in another study to be enduring a higher level of work-related stress [60]. Further, high levels of occupational stress may press employees to consider leaving their jobs [61].Higher levels of anomie in an organization have been shown to foster a sense of futility, distrust, normlessness, alienation, and powerlessness among employees [28], which eventually may lead to stronger leave intentions. In contrast, lower levels of anomie at the workplace may assist the organization in creating a special work climate that eliminates employees’ unethical behaviors and provides them with mechanisms to resolve their ethical and personal conflicts in a more constructive way [57]. It has been demonstrated that perceptions of a positive ethical climate can reduce employees’ leave intentions [62] and their emotional exhaustion [63]. As shown in Figure 1, we predicted that anomie at work mediates the association between organizational fairness and intentions to leave (H3). Anomie at work mediates the association between organizational fairness and emotional exhaustion (H4).Figure 1
The framework of the study.


## 2.1. Organizational Fairness

Organizational fairness refers to the extent to which employees feel that they are treated fairly in their workplace [14]. The impartiality of organizational norms and procedures is one of main pathways toward improving the perception of organizational fairness among employees in the workplace. The perception of fairness is an important factor for each business because it enhances workers’ participation in the workplace; therefore, those organizations that work to ensure such fairness will be in a better position to reach their objectives [15]. Empirical evidence from the health sciences has shown that a perceived lack of organizational fairness among employees may result in the risk of psychological distress [16] and a high level of psychological distress may hurt employees’ job performance in the long term [17–19].In the context of Latin America, organizational culture has been found to be an important predictor of the perception of organizational fairness (by using survey data from 80 employees in the public sector) [20]. In Mexico, survey data from 307 teachers working in higher education institutions also demonstrated that organizational climate is another predictor of the perception of organizational fairness [21]. In Peru, a survey conducted with 54 bank workers revealed that employee job satisfaction was the main outcome of organizational fairness [22].


## 2.2. Anomie at Work

Anomie as a concept comes from the Greek “nomos,” meaning degradation or the absence of the law or norms [23]. Anomie will increase when an organization pays less attention to moral and ethical standards at work and focuses instead (and almost exclusively) on goal achievement [24]. Top-level managers usually establish different kinds of policies, procedures, and regulations to keep their business running smoothly. Anomie at work happens once the existing normative system (policies, procedures, and regulations) loses its value in the eyes of employees. It is important to mitigate perceptions of anomie among workers since it could generate feelings of despair and helplessness [25]. Merton Merton [26] believes that in a workplace with a high level of anomie, most of the employees prefer to pursue their organizational or job-related goals through illegitimate and illegal means. Usually, organizations are interested in minimizing those behaviors that violate well-established organizational norms, policies, and internal regulations. Previous studies have shown that employees’ engagement in such deviant behaviors is less frequent in a workplace with low levels of anomie [27]. Increasing the quality of work life in the workplace is an important way of deterring the anomic behavior of employees [28]. According to the study in the context of public sector organizations, fair wages, rewards, security, reduction of the gender pay gap, improvements to work happiness, and promotion and learning opportunities for all employees are some solutions that can reduce the level of normlessness or anomie at work.


## 2.3. Intentions to Leave

Hiring new employees is a complicated and time-consuming process, and business owners spend a significant amount of time, energy, and money in order to recruit experienced talent. Therefore, a successful business constantly looks for different ways to keep the current employees motivated and reduce their intentions to leave. The intention to leave an organization refers to the willingness of an employee to leave his or her current job in the near future for any reason [29]. The reduction of employees’ leave intentions is an important factor for organizations because the loss of a skilled employee generates many monetary and nonmonetary costs for a business [30].On this point, Nowrouzi-Kia and Fox [31] and Lambert et al. [32] have argued that organizational and work environment factors are important for retaining employees and a satisfied employee is less likely to express an intent to leave than unsatisfied employees. Disappointment in unmet expectations has been identified as another reason for employees to leave their jobs [33].In the context of Latin America, Dorigan et al. [34] analyzed survey data from 262 Brazilian health professionals and found that providing a climate that is supportive of teamwork can help organizations to diminish the employees’ intentions to leave. In Mexico, a study conducted by Máynez Guaderrama and Cavazos Arroyo [35] revealed that effective commitment and job satisfaction were the main requirements for retaining skilled employees. Contributing to this line of research, Torres Portilla [36] conducted semistructured interviews and surveys with 182 Peruvian workers in the hotel industry. According to the results, the lack of (1) organizational support, (2) an effective remuneration system, and (3) promotion opportunities were among the main reasons explaining employees’ leave intentions.


## 2.4. Emotional Exhaustion

Emotional exhaustion has received a great deal of attention from business and organizational behavior researchers in recent years, and scholars have used this concept to explain different types of employee behaviors and attitudes in the workplace [37]. Emotional exhaustion refers to a syndrome of emotional or physical exhaustion caused by job stressors or excessive job demands [38]. It is important to recognize this type of exhaustion among employees to prevent the advent of mental distress and to reduce behaviors that are harmful to the organization [39]. Emotional exhaustion can be the result of many internal and external work-related factors. For instance, interactions with difficult clients or a high workload [39] may lead to exaggerated levels of emotional exhaustion in the long term [40].


## 2.5. Organizational Fairness and Intentions to Leave

In the first hypothesis of the present study, we predicted that the perception of organizational fairness would be found to have reduced precarious workers’ intentions to leave. Organizational fairness is a critical factor in the enhancement of employees’ job satisfaction in the workplace [41, 42]. In this respect, Janssen [43] found that the satisfaction of employees was higher when they perceived effort-reward fairness in their organizations. Further, it has been found that treating employees fairly in the workplace makes them happier and enhances their commitment to the organizational goals [41]. There is also some empirical evidence that highlights the importance of job satisfaction as a deterrent of the turnover intentions of employees [44]. Treating fairly the workers within the workplace may enhance their commitment towards their organization and motivates them to make the organization part of their social identity [45]. Receiving fair treatment within the company may convey a sense of belonging [46] and highlighted that a worker is an important and critical member of the organization and may eventually diminish their intention to leave. Therefore, we hypothesized that there is a negative association between organizational fairness and intentions to leave (H1).


## 2.6. Organizational Fairness and Emotional Exhaustion

The unstable and highly uncertain employment conditions for precarious workers have made the study of emotional exhaustion increasingly important. According to a literature review, continuous stress is one of the major drivers of emotional exhaustion [47, 48]. Precarious employees may feel more stress in their jobs and lives than permanent or lifetime employees because of two reasons. First, due to not having many alternative job offers, they may not be in a good bargaining position; therefore, they may receive a lower compensation package [49]. Having financial problems makes it difficult to face the various challenges that arise in life, which eventually may lead to extreme stress. Second, the majority of precarious employees have very short work contracts. The high possibility of losing one’s job or thoughts about not receiving a contract extension may lead to feelings of stress and tension in the long term [50]. It has been shown that treating employees fairly is one way to reduce employees’ stress and improve their mental health in the public sectors [51]. In the healthcare sector, Sutinen et al. Sutinen et al. [16] have found that exhibiting fair behaviors, decisions, and actions toward employees is crucial for lowering the risk of psychological distress. Building on this literature, in the second hypothesis of the study, we predicted that the perception of organizational fairness would be found to have reduced precarious workers’ emotional exhaustion. There is a negative association between organizational fairness and emotional exhaustion (H2).


## 2.7. The Mediating Role of Anomie at Work

Organizational fairness plays a very important role in shaping employees’ behavior and attitudes in the workplace [52, 53]. Perceived organizational fairness is higher in those organizations that have fair and transparent reward and payment systems as well as managers and supervisors who treat all employees fairly ([54]; A. [8, 55]). In the third and fourth hypotheses of the study, we predicted that the absence of the perception of fairness by precarious workers would be found to have increased anomie at work, which in turn may have led to higher levels of emotional exhaustion and intentions to leave among them.Individuals who feel greater injustice in their lives and workplaces tend to exhibit more deviations in their behaviors and actions [56–58]. Furthermore, Inoue et al. Inoue et al. [13] have reported that the lack of organizational fairness can be a source of psychological distress for precarious workers. A close relationship has also been found between psychological distress and individual perceptions of anomie in social systems [59]. Precarious employees, especially those working the night shift, have been shown in another study to be enduring a higher level of work-related stress [60]. Further, high levels of occupational stress may press employees to consider leaving their jobs [61].Higher levels of anomie in an organization have been shown to foster a sense of futility, distrust, normlessness, alienation, and powerlessness among employees [28], which eventually may lead to stronger leave intentions. In contrast, lower levels of anomie at the workplace may assist the organization in creating a special work climate that eliminates employees’ unethical behaviors and provides them with mechanisms to resolve their ethical and personal conflicts in a more constructive way [57]. It has been demonstrated that perceptions of a positive ethical climate can reduce employees’ leave intentions [62] and their emotional exhaustion [63]. As shown in Figure 1, we predicted that anomie at work mediates the association between organizational fairness and intentions to leave (H3). Anomie at work mediates the association between organizational fairness and emotional exhaustion (H4).Figure 1
The framework of the study.


## 3. Research Method

In this research, we firstly investigated the influence of organizational fairness on the emotional exhaustion and leave intentions of Peruvian precarious workers. Then, we tested the mediating role of anomie at work in the relationship between organizational fairness and the emotional exhaustion and leave intentions of precarious workers. The nature of our study is quantitative research. We have used this method mostly because of having well-established scales in the literature for measuring the core variables of the study.


### 3.1. Data Collection Process

Peru, as a member of the Latin America region, is a country with a dynamic economy; as of 2018, the country’s gross domestic product (GDP) reached US $220 billion. In comparison with other countries in Latin America, a less volatile economy has been experienced in the last decade. However, as a developing country, there is still inequality of income and opportunities in the country [64]. Specifically, bribery is a big challenge in the business context [65, 66], temporary employees receive a lower compensation package, and relatively limited permanent employment opportunities are available for foreigners [67, 68].In this study, we have targeted night-shift employees who worked for small private companies (having less than 50 employees) in the districts of Chimbote and Nuevo Chimbote, the northern part of Peru. As a first step, we identified all the companies that provided their services 24 hours a day and were registered in the municipalities of Chimbote and Nuevo Chimbote. These small private companies were mainly active as hotels and hostels, full-service filling stations, casinos, surveillance services companies, and private clinics (we targeted only security guards and cleaning staff in this sector). The companies were young and had started their operations between 2009 and 2019. In the first step of selecting the sample of the study, we randomly picked 130 out of 196 companies registered in the municipalities [69]. In the next step, after explaining the purpose of our study to the CEOs or founders of these companies, 53 agreed to participate in the study, of which 23 signed an acceptance letter and 30 verbally agreed to participate in the research.A group of local assistants who were trained in the SurveyMonkey (online survey) program helped us throughout the data collection process. To encourage participants and gain their trust, an informed consent letter from a local university (Los Angeles Catholic University of Chimbote) was provided to each respondent. We translated the measurement items of the study from English into Spanish (Peru) using the standard translation method of back translation [70]. Before launching the survey, we conducted in-depth interviews with 10 employees to confirm the suitability of our measurement items. With some minor changes, we revised all the ambiguous words and items.According to Ballafkih et al. [12], a precarious worker is a person who has an (atypical) employment contract that falls below the standard and well-accepted norms for employment. To identify the final sample of the study, we asked the managers of each company to provide us with the names and job-related information of the precarious employees. The local data collectors met with the employees during break times or shift changes to explain the purpose of the study. In this stage, 351 precarious employees from the 53 companies voluntarily agreed to fill out our survey and the online questionnaires were personally taken to them. Local assistants asked the respondents to fill out the online survey on their tablets. Each survey lasted approximately 7–9 minutes.For detecting the common method bias, we first conducted the Harman one-factor test [71] which is very common in business, entrepreneurship, and management studies [72–74]. The finding of this test confirmed that the first factor could explain 14.2% of the total variance. In this analysis, the Kaiser–Meyer–Olkin (KMO) measure of the sampling adequacy value was 0.859, while the Bartlett test of sphericity’s value was 2020.458 and statistically significant as 0.000. Accordingly, not a single factor emerged which could explain the majority of the total variance [75]. Second, before launching the final survey, we randomized the order of the questions; in this way, we reduce the threat of the common method bias. Lastly, in order to minimize social desirability bias as much as possible, we guaranteed the participants’ anonymity and promised to protect the confidentially of their responses.The first part of the survey was designed to address various personal and job-related factors (control variables). The second part contained items measuring the independent (organizational fairness), mediator (anomie at work), and dependent variables (intentions to leave and emotional exhaustion). Finally, 206 employees completed the survey, representing a response rate of 58.69%. The 206 respondents had an average age of 34.02 years (standard deviationSD=9.726). Almost 21% of the respondents were immigrants. We have used Macro PROCESS (SPSS) developed by [76, 77] for testing the hypotheses of the study. Table 1 presents the demographic characteristics of study’s participants.Table 1
Demographic characteristics of study’s participants.
GenderMen 141 (68%)Women 65 (32%)EducationPrimary school, 1.5%Secondary level diploma, 36.8%Associate degree, 21%Bachelor’s degree, 37.3%, and master’s degree, 3.4%Marital statusSingle, 51.5%Married, 47%Divorced, 1.5%No. of childrenOne or more children, 37.7%Did not have children, 62.3%Receiving bonusesReceived a bonus, 21.1%Did not receive bonus, 78.9%Having insuranceHad some insurance, 71.4%Did not have, 28.6%Work experience (with current company)One year, 41.7%Two years, 16.7%Three years or more, 41.6%SalaryLess than the average salary in Peru (1700 soles), 90.2%Little above the average Peruvian salary, 9.8%Contract typeNo formal contract, 51.5%Three-month contract, 13.9%Six-month contract, 25.4%One-year contract, 9.2%


### 3.2. Measurements

#### 3.2.1. Organizational Fairness

Organizational fairness was measured by four items adapted from the work of Guest and Conway [9]. Two example items are the following: (1) “Do you feel you are rewarded fairly for the amount of effort you put into your job?” and (2) “Do you feel that organizational changes are implemented fairly in your organization?” Similar to the original measurement scale, five-point Likert scales with responses ranging from (1) “strongly disagree” to (5) “strongly agree” were used. Cronbach’s alpha for these items was acceptable (α=0.83). A higher score represented a higher perceived level of organizational fairness by the participant.


#### 3.2.2. Anomie at Work

Eight items that were originally developed by Tsahuridu [25] were used for measuring anomie at work. Two example items from this construct are the following: (1) “These days in business, I do not really know whom one can depend on” and (2) “In this organization, no one cares what happens when you get right down to it.” All eight items were rated on a five-point Likert scale that ranged from (1) “strongly disagree” to (5) “strongly agree.” Cronbach’s alpha for the scale was 0.785.


### 3.3. Intentions to Leave

The employees’ intentions to leave were measured by four items adapted from the study conducted by Rosin and Korabik [78]. The two example items are the following: (1) “I often think about quitting” and (2) “It is likely that I will actively look for a new job next year.” These four items had excellent reliability (α=0.91).


#### 3.3.1. Emotional Exhaustion

A well-established scale of five items originally developed by Maslach and Jackson [79] was used for measuring emotional exhaustion. This scale has been used frequently by many businesses and psychology scholars. Two example items of the scale are the following: “I feel emotionally drained from my work” and “I feel fatigued when I get up in the morning and have to face another day on the job.” Cronbach’s alpha for the scale was 0.80. A higher score represented a higher level of emotional exhaustion for a precarious worker. The full list of measurement items is available in the Appendix.


#### 3.3.2. Control Variables

We used several personal and work-related factors as control variables in the final analyses. Following similar studies, we used some typical personal or demographic variables as control variables, such as the respondents’ age, gender, educational level, marital status (single, married, or divorced), and total number of children under 18 years of age. We also controlled for the respondents’ work-related factors, such as their work experience (total number of years working for the current company), their job position in the company, the duration of their contract, their number of work hours per day, and whether they had received a bonus or health insurance.


### 3.4. Findings

Table2 presents the means and standard deviations of all the variables of the study. To resolve the collinearity issue, variance inflation factors (VIF) were used [8]. The highest VIF was 1.805, which was significantly below the problematic level of five. Therefore, multicollinearity did not influence the results of the study. In Table 3, you can find the full VIF scores.Table 2
Means and standard deviation of all variables.
MeanStd. deviation(1) Age34.01969.72679(2) Gender1.31370.46515(3) Education4.04410.96898(4) Marital status1.50500.54861(5) Children1.12251.11844(6) Experience4.41675.41462(7) Job position3.92651.29016(8) Contract3.59201.61640(9) Work hour10.64222.71320(10) Bonus1.78920.40887(11) Insurance1.28640.45318(12) Fairness3.49870.80357(13) Anomie2.44060.67962(14) Exhaustion2.91911.50640(15) Leave2.31801.06135Table 3
VIF scores.
VariableAnomie at the workplaceLeave intentionEmotional exhaustionVIFVIFVIF(1) Age1.0141. 0151.014(2) Gender1.0371.0281.038(3) Education1.1061.1051.105(4) Marital status1.0231.0211.022(5) Children1.0911.1041.104(6) Experience1.1081.1111.111(7) Job position1.1451.1541.152(8) Contract1.0771.0781.079(9) Work hour1.0981.0961.098(10) Bonus1.3261.3221.324(11) Insurance1.0581.0611.060(12) Fairness1.8011.8041.805(13) Anomie1.3581.358We used the Pearson correlation coefficient to determine how the variables of the study were correlated. Table4 presents the Pearson correlations for all the variables that were included in the final analysis.Table 4
Pearson correlations for all the measures used in the model.
Correlations1234567891011121314(1) Age1(2) Gender−0.0671(3) Education−0.226∗∗0.319∗∗1(4) Marital status0.446∗∗−0.064−0.141∗1(5) Children0.468∗∗−0.036−0.178∗0.487∗∗1(6) Experience0.482∗∗0.022−0.0680.400∗∗0.229∗∗1(7) Job position−0.208∗∗0.0060.034−0.136−0.082−0.1201(8) Contract0.170∗0.0980.0130.0910.0440.311∗∗0.0311(9) Work hour0.189∗∗−0.352∗∗−0.145∗0.182∗∗0.145∗0.033−0.032−0.0191(10) Bonus−0.1080.168∗−0.101−0.051−0.083−0.0760.1290.048−0.1221(11) Insurance−0.240∗∗0.168∗0.017−0.197∗∗−0.147∗−0.155∗0.248∗∗0.002−0.295∗∗0.295∗∗1(12) Fairness0.130−0.198∗∗−0.220∗∗0.0350.1280.008−0.220∗∗−0.058−0.026−0.197∗∗−0.0341(13) Anomie−0.174∗0.1200.155∗−0.071−0.047−0.0970.233∗∗−0.0960.0250.0910.053−0.416∗∗1(14) Exhaustion−0.169∗0.153∗0.184∗∗−0.029−0.0380.0170.308∗∗−0.0370.1000.190∗∗0.146∗−0.429∗∗0.358∗∗1(15) Leave−0.425∗∗0.144∗0.273∗∗−0.291∗∗−0.324∗∗−0.263∗∗0.310∗∗−0.021−0.147∗0.267∗∗0.245∗∗−0.449∗∗0.478∗∗0.486∗∗∗∗Correlation is significant at the 0.01 level (2 tailed). ∗Correlation is significant at the 0.05 level (2 tailed).The procedure recommended by Hayes [76] and Preacher et al. [80] was used for testing the hypotheses. We ran two mediation tests using the fourth model in the Hayes PROCESS. In the first test, organizational fairness, anomie at work, and intentions to leave were represented by the variables of X, M, and Y, respectively. In the second test, organizational fairness, anomie at work, and emotional exhaustion were represented. In addition to the regression coefficients, PROCESS provided tests of significance and confidence intervals for the conditional direct and indirect effects, with the latter based on a bootstrap confidence interval [76]. In the first hypothesis of the study, we predicted a negative association between organizational fairness and intentions to leave. As shown in Table 5, we found a negative and significant association between organizational fairness and intentions to leave (coeff.=−0.2953, P=0.0005, and {LLCI=−0.4604 and ULCI=−0.1302}). Therefore, our first hypothesis was confirmed. In the second hypothesis, we predicted a negative association between organizational fairness and emotional exhaustion. As shown in Table 5, we found a negative and significant association between organizational fairness and emotional exhaustion (coeff.=−0.4429, P=0.0015, and {LLCI=−0.7132 and ULCI=−0.1726}). Therefore, we also confirmed our second hypothesis.Table 5
Testing the hypothesis of the study (intentions to leave as a dependent variable).
VariableAnomieAnomieLeaveLeaveCoeff. (P){LLCI, ULCI}Coeff. (P){LLCI, ULCI}(1) Age−0.0063 (0.3258){−0.0189, 0.0063}−0.0175 (0.0307){−0.0333, −0.0016}(2) Gender0.0803 (0.4817){−0.1445, 0.3052}−0.1790 (0.2112){−0.4606, 0.1025}(3) Education0.0404 (0.4449){−0.0637, 0.1445}0.1534 (0.0214){0.0230, 0.2839}(4) Marital status−0.0227 (0.8280){−0.2283, 0.1830}−0.0816 (0.5322){−0.3388, 0.1756}(5) Children0.0480 (0.3409){−0.0511, 0.1470}0.0788 (0.2124){−0.2030, 0.0455}(6) Experience−0.0018 (0.8587){−0.0221, 0.0184}−0.0155 (0.2277){−0.0409, 0.0098}(7) Job position0.0672 (0.0734){−0.0064, 0.1407}0.0768 (0.1045){−0.0161, 0.1697}(8) Contract−0.0457 (0.1321){−0.1052, 0.0139}0.0296 (0.4375){−0.0454, 0.1046}(9) Work hour0.0137 (0.4980){−0.0262, 0.0536}−0.0380 (0.1352){−0.0879, 0.0120}(10) Bonus−0.0253 (0.8401){−0.2729, 0.2222}0.4296 (0.0068){0.1200, 0.7392}(11) Insurance0.0037 (0.9741){−0.2191, 0.2264}0.1766 (0.2125){−0.1019, 0.4551}(12) Fairness−0.3098 (0.0000){−0.4335, −0.1861}−0.2953 (0.0005){−0.4604, −0.1302}(13) Anomie0.4507 (0.0000){0.2641, 0.6372}As you can see in Table5, among the control variables, the respondent age is negatively related to their intention to leave (β=−0.0307; P<0.05). This means that younger respondents have higher level of intention to leave their current job. Respondents’ education is positively related to their intention to leave (β=0.0214; P<0.05). This means that educated respondents have higher level of intention to leave their current job.As you can see in Table6, among control variables, we found a positive and significant association between job position and respondents’ emotional exhaustion (β=0.0125; P<0.05) and a positive and significant association between work hour and respondents’ emotional exhaustion (β=0.0214; P<0.05).Table 6
Testing the hypothesis of the study (emotional exhaustion as a dependent variable).
VariableAnomieAnomieEmotional exhaustionEmotional exhaustionCoeff. (P){LLCI, ULCI}Coeff. (P){LLCI, ULCI}(1) Age−0.0064 (0.3199){−0.0190, 0.0063}−0.0155 (0.2374){−0.0414, 0.0103}(2) Gender0.0812 (0.4782){−0.1443, 0.3067}0.1823 (0.4360){−0.2786, 0.6432}(3) Education0.0392 (0.4609){−0.0654, 0.1438}0.1960 (0.0721){−0.0178, 0.4098}(4) Marital status−0.0226 (0.8289){−0.2288, 0.1836}0.0635 (0.7663){−0.3574, 0.4843}(5) Children0.0511 (0.3179){−0.0495, 0.1516}0.0526 (0.6150){−0.1533, 0.2585}(6) Experience−0.0020 (0.8473){−0.0223, 0.0183}0.0401 (0.0581){−0.0014, 0.0815}(7) Job position0.0685 (0.0698){−0.0056, 0.1426}0.1951 (0.0125){0.0425, 0.3478}(8) Contract−0.0463 (0.1284){−0.1061, 0.0135}−0.0667 (0.2858){−0.1896, 0.0562}(9) Work hour0.0137 (0.4994){−0.0263, 0.0537}0.0993 (0.0175){0.0176, 0.1810}(10) Bonus−0.0317 (0.8029){−0.2820, 0.2186}0.4882 (0.0610){−0.0227, 0.9992}(11) Insurance0.0028 (0.9804){−0.2206, 0.2261}0.3601 (0.1207){−0.0957, 0.8160}(12) Fairness−0.3087 (0.0000){−0.4328, −0.1846}−0.4429 (0.0015){−0.7132, −0.1726}(13) Anomie0.3198 (0.0402){0.0144, 0.6252}In hypothesis three, we expected that anomie at work would mediate the association between organizational fairness and intentions to leave. As shown in Table7, from the conditional indirect effect model, bootstrapping analyses demonstrated that the 95% confidence intervals for the indirect effect (−0.1396) excluded zero (95% BootLLCI: −0.2393, BootULCI: −0.0699). This means that anomie at work negatively mediated the association between organizational fairness and intentions to leave. Thus, the third hypothesis was fully supported.Table 7
Indirect effect of organizational fairness on intentions to leave through anomie at work.
Indirect effect ofX (organizational fairness) on Y (intention to leave)EffectBoot SEBootLLCIBootULCIAnomie−0.13960.0425−0.2393−0.0699In the last hypothesis, we predicted that anomie at work would mediate the association between organizational fairness and emotional exhaustion. As shown in Table8, from the conditional indirect effect model, bootstrapping analyses demonstrated that 95% confidence intervals for the indirect effect (−0.0987) excluded zero (95% BootLLCI: −0.2236, BootULCI: −0.0076). This means that anomie at work negatively mediated the association between organizational fairness and emotional exhaustion. Therefore, our last hypothesis was fully supported.Table 8
Indirect effect of organizational fairness on emotional exhaustion through anomie at work.
Indirect effect ofX (organizational fairness) on Y (intention to leave)EffectBoot SEBootLLCIBootULCIAnomie−0.09870.0541−0.2236−0.0076


## 3.1. Data Collection Process

Peru, as a member of the Latin America region, is a country with a dynamic economy; as of 2018, the country’s gross domestic product (GDP) reached US $220 billion. In comparison with other countries in Latin America, a less volatile economy has been experienced in the last decade. However, as a developing country, there is still inequality of income and opportunities in the country [64]. Specifically, bribery is a big challenge in the business context [65, 66], temporary employees receive a lower compensation package, and relatively limited permanent employment opportunities are available for foreigners [67, 68].In this study, we have targeted night-shift employees who worked for small private companies (having less than 50 employees) in the districts of Chimbote and Nuevo Chimbote, the northern part of Peru. As a first step, we identified all the companies that provided their services 24 hours a day and were registered in the municipalities of Chimbote and Nuevo Chimbote. These small private companies were mainly active as hotels and hostels, full-service filling stations, casinos, surveillance services companies, and private clinics (we targeted only security guards and cleaning staff in this sector). The companies were young and had started their operations between 2009 and 2019. In the first step of selecting the sample of the study, we randomly picked 130 out of 196 companies registered in the municipalities [69]. In the next step, after explaining the purpose of our study to the CEOs or founders of these companies, 53 agreed to participate in the study, of which 23 signed an acceptance letter and 30 verbally agreed to participate in the research.A group of local assistants who were trained in the SurveyMonkey (online survey) program helped us throughout the data collection process. To encourage participants and gain their trust, an informed consent letter from a local university (Los Angeles Catholic University of Chimbote) was provided to each respondent. We translated the measurement items of the study from English into Spanish (Peru) using the standard translation method of back translation [70]. Before launching the survey, we conducted in-depth interviews with 10 employees to confirm the suitability of our measurement items. With some minor changes, we revised all the ambiguous words and items.According to Ballafkih et al. [12], a precarious worker is a person who has an (atypical) employment contract that falls below the standard and well-accepted norms for employment. To identify the final sample of the study, we asked the managers of each company to provide us with the names and job-related information of the precarious employees. The local data collectors met with the employees during break times or shift changes to explain the purpose of the study. In this stage, 351 precarious employees from the 53 companies voluntarily agreed to fill out our survey and the online questionnaires were personally taken to them. Local assistants asked the respondents to fill out the online survey on their tablets. Each survey lasted approximately 7–9 minutes.For detecting the common method bias, we first conducted the Harman one-factor test [71] which is very common in business, entrepreneurship, and management studies [72–74]. The finding of this test confirmed that the first factor could explain 14.2% of the total variance. In this analysis, the Kaiser–Meyer–Olkin (KMO) measure of the sampling adequacy value was 0.859, while the Bartlett test of sphericity’s value was 2020.458 and statistically significant as 0.000. Accordingly, not a single factor emerged which could explain the majority of the total variance [75]. Second, before launching the final survey, we randomized the order of the questions; in this way, we reduce the threat of the common method bias. Lastly, in order to minimize social desirability bias as much as possible, we guaranteed the participants’ anonymity and promised to protect the confidentially of their responses.The first part of the survey was designed to address various personal and job-related factors (control variables). The second part contained items measuring the independent (organizational fairness), mediator (anomie at work), and dependent variables (intentions to leave and emotional exhaustion). Finally, 206 employees completed the survey, representing a response rate of 58.69%. The 206 respondents had an average age of 34.02 years (standard deviationSD=9.726). Almost 21% of the respondents were immigrants. We have used Macro PROCESS (SPSS) developed by [76, 77] for testing the hypotheses of the study. Table 1 presents the demographic characteristics of study’s participants.Table 1
Demographic characteristics of study’s participants.
GenderMen 141 (68%)Women 65 (32%)EducationPrimary school, 1.5%Secondary level diploma, 36.8%Associate degree, 21%Bachelor’s degree, 37.3%, and master’s degree, 3.4%Marital statusSingle, 51.5%Married, 47%Divorced, 1.5%No. of childrenOne or more children, 37.7%Did not have children, 62.3%Receiving bonusesReceived a bonus, 21.1%Did not receive bonus, 78.9%Having insuranceHad some insurance, 71.4%Did not have, 28.6%Work experience (with current company)One year, 41.7%Two years, 16.7%Three years or more, 41.6%SalaryLess than the average salary in Peru (1700 soles), 90.2%Little above the average Peruvian salary, 9.8%Contract typeNo formal contract, 51.5%Three-month contract, 13.9%Six-month contract, 25.4%One-year contract, 9.2%


## 3.2. Measurements

### 3.2.1. Organizational Fairness

Organizational fairness was measured by four items adapted from the work of Guest and Conway [9]. Two example items are the following: (1) “Do you feel you are rewarded fairly for the amount of effort you put into your job?” and (2) “Do you feel that organizational changes are implemented fairly in your organization?” Similar to the original measurement scale, five-point Likert scales with responses ranging from (1) “strongly disagree” to (5) “strongly agree” were used. Cronbach’s alpha for these items was acceptable (α=0.83). A higher score represented a higher perceived level of organizational fairness by the participant.


### 3.2.2. Anomie at Work

Eight items that were originally developed by Tsahuridu [25] were used for measuring anomie at work. Two example items from this construct are the following: (1) “These days in business, I do not really know whom one can depend on” and (2) “In this organization, no one cares what happens when you get right down to it.” All eight items were rated on a five-point Likert scale that ranged from (1) “strongly disagree” to (5) “strongly agree.” Cronbach’s alpha for the scale was 0.785.


## 3.2.1. Organizational Fairness

Organizational fairness was measured by four items adapted from the work of Guest and Conway [9]. Two example items are the following: (1) “Do you feel you are rewarded fairly for the amount of effort you put into your job?” and (2) “Do you feel that organizational changes are implemented fairly in your organization?” Similar to the original measurement scale, five-point Likert scales with responses ranging from (1) “strongly disagree” to (5) “strongly agree” were used. Cronbach’s alpha for these items was acceptable (α=0.83). A higher score represented a higher perceived level of organizational fairness by the participant.


## 3.2.2. Anomie at Work

Eight items that were originally developed by Tsahuridu [25] were used for measuring anomie at work. Two example items from this construct are the following: (1) “These days in business, I do not really know whom one can depend on” and (2) “In this organization, no one cares what happens when you get right down to it.” All eight items were rated on a five-point Likert scale that ranged from (1) “strongly disagree” to (5) “strongly agree.” Cronbach’s alpha for the scale was 0.785.


## 3.3. Intentions to Leave

The employees’ intentions to leave were measured by four items adapted from the study conducted by Rosin and Korabik [78]. The two example items are the following: (1) “I often think about quitting” and (2) “It is likely that I will actively look for a new job next year.” These four items had excellent reliability (α=0.91).


### 3.3.1. Emotional Exhaustion

A well-established scale of five items originally developed by Maslach and Jackson [79] was used for measuring emotional exhaustion. This scale has been used frequently by many businesses and psychology scholars. Two example items of the scale are the following: “I feel emotionally drained from my work” and “I feel fatigued when I get up in the morning and have to face another day on the job.” Cronbach’s alpha for the scale was 0.80. A higher score represented a higher level of emotional exhaustion for a precarious worker. The full list of measurement items is available in the Appendix.


### 3.3.2. Control Variables

We used several personal and work-related factors as control variables in the final analyses. Following similar studies, we used some typical personal or demographic variables as control variables, such as the respondents’ age, gender, educational level, marital status (single, married, or divorced), and total number of children under 18 years of age. We also controlled for the respondents’ work-related factors, such as their work experience (total number of years working for the current company), their job position in the company, the duration of their contract, their number of work hours per day, and whether they had received a bonus or health insurance.


## 3.3.1. Emotional Exhaustion

A well-established scale of five items originally developed by Maslach and Jackson [79] was used for measuring emotional exhaustion. This scale has been used frequently by many businesses and psychology scholars. Two example items of the scale are the following: “I feel emotionally drained from my work” and “I feel fatigued when I get up in the morning and have to face another day on the job.” Cronbach’s alpha for the scale was 0.80. A higher score represented a higher level of emotional exhaustion for a precarious worker. The full list of measurement items is available in the Appendix.


## 3.3.2. Control Variables

We used several personal and work-related factors as control variables in the final analyses. Following similar studies, we used some typical personal or demographic variables as control variables, such as the respondents’ age, gender, educational level, marital status (single, married, or divorced), and total number of children under 18 years of age. We also controlled for the respondents’ work-related factors, such as their work experience (total number of years working for the current company), their job position in the company, the duration of their contract, their number of work hours per day, and whether they had received a bonus or health insurance.


## 3.4. Findings

Table2 presents the means and standard deviations of all the variables of the study. To resolve the collinearity issue, variance inflation factors (VIF) were used [8]. The highest VIF was 1.805, which was significantly below the problematic level of five. Therefore, multicollinearity did not influence the results of the study. In Table 3, you can find the full VIF scores.Table 2
Means and standard deviation of all variables.
MeanStd. deviation(1) Age34.01969.72679(2) Gender1.31370.46515(3) Education4.04410.96898(4) Marital status1.50500.54861(5) Children1.12251.11844(6) Experience4.41675.41462(7) Job position3.92651.29016(8) Contract3.59201.61640(9) Work hour10.64222.71320(10) Bonus1.78920.40887(11) Insurance1.28640.45318(12) Fairness3.49870.80357(13) Anomie2.44060.67962(14) Exhaustion2.91911.50640(15) Leave2.31801.06135Table 3
VIF scores.
VariableAnomie at the workplaceLeave intentionEmotional exhaustionVIFVIFVIF(1) Age1.0141. 0151.014(2) Gender1.0371.0281.038(3) Education1.1061.1051.105(4) Marital status1.0231.0211.022(5) Children1.0911.1041.104(6) Experience1.1081.1111.111(7) Job position1.1451.1541.152(8) Contract1.0771.0781.079(9) Work hour1.0981.0961.098(10) Bonus1.3261.3221.324(11) Insurance1.0581.0611.060(12) Fairness1.8011.8041.805(13) Anomie1.3581.358We used the Pearson correlation coefficient to determine how the variables of the study were correlated. Table4 presents the Pearson correlations for all the variables that were included in the final analysis.Table 4
Pearson correlations for all the measures used in the model.
Correlations1234567891011121314(1) Age1(2) Gender−0.0671(3) Education−0.226∗∗0.319∗∗1(4) Marital status0.446∗∗−0.064−0.141∗1(5) Children0.468∗∗−0.036−0.178∗0.487∗∗1(6) Experience0.482∗∗0.022−0.0680.400∗∗0.229∗∗1(7) Job position−0.208∗∗0.0060.034−0.136−0.082−0.1201(8) Contract0.170∗0.0980.0130.0910.0440.311∗∗0.0311(9) Work hour0.189∗∗−0.352∗∗−0.145∗0.182∗∗0.145∗0.033−0.032−0.0191(10) Bonus−0.1080.168∗−0.101−0.051−0.083−0.0760.1290.048−0.1221(11) Insurance−0.240∗∗0.168∗0.017−0.197∗∗−0.147∗−0.155∗0.248∗∗0.002−0.295∗∗0.295∗∗1(12) Fairness0.130−0.198∗∗−0.220∗∗0.0350.1280.008−0.220∗∗−0.058−0.026−0.197∗∗−0.0341(13) Anomie−0.174∗0.1200.155∗−0.071−0.047−0.0970.233∗∗−0.0960.0250.0910.053−0.416∗∗1(14) Exhaustion−0.169∗0.153∗0.184∗∗−0.029−0.0380.0170.308∗∗−0.0370.1000.190∗∗0.146∗−0.429∗∗0.358∗∗1(15) Leave−0.425∗∗0.144∗0.273∗∗−0.291∗∗−0.324∗∗−0.263∗∗0.310∗∗−0.021−0.147∗0.267∗∗0.245∗∗−0.449∗∗0.478∗∗0.486∗∗∗∗Correlation is significant at the 0.01 level (2 tailed). ∗Correlation is significant at the 0.05 level (2 tailed).The procedure recommended by Hayes [76] and Preacher et al. [80] was used for testing the hypotheses. We ran two mediation tests using the fourth model in the Hayes PROCESS. In the first test, organizational fairness, anomie at work, and intentions to leave were represented by the variables of X, M, and Y, respectively. In the second test, organizational fairness, anomie at work, and emotional exhaustion were represented. In addition to the regression coefficients, PROCESS provided tests of significance and confidence intervals for the conditional direct and indirect effects, with the latter based on a bootstrap confidence interval [76]. In the first hypothesis of the study, we predicted a negative association between organizational fairness and intentions to leave. As shown in Table 5, we found a negative and significant association between organizational fairness and intentions to leave (coeff.=−0.2953, P=0.0005, and {LLCI=−0.4604 and ULCI=−0.1302}). Therefore, our first hypothesis was confirmed. In the second hypothesis, we predicted a negative association between organizational fairness and emotional exhaustion. As shown in Table 5, we found a negative and significant association between organizational fairness and emotional exhaustion (coeff.=−0.4429, P=0.0015, and {LLCI=−0.7132 and ULCI=−0.1726}). Therefore, we also confirmed our second hypothesis.Table 5
Testing the hypothesis of the study (intentions to leave as a dependent variable).
VariableAnomieAnomieLeaveLeaveCoeff. (P){LLCI, ULCI}Coeff. (P){LLCI, ULCI}(1) Age−0.0063 (0.3258){−0.0189, 0.0063}−0.0175 (0.0307){−0.0333, −0.0016}(2) Gender0.0803 (0.4817){−0.1445, 0.3052}−0.1790 (0.2112){−0.4606, 0.1025}(3) Education0.0404 (0.4449){−0.0637, 0.1445}0.1534 (0.0214){0.0230, 0.2839}(4) Marital status−0.0227 (0.8280){−0.2283, 0.1830}−0.0816 (0.5322){−0.3388, 0.1756}(5) Children0.0480 (0.3409){−0.0511, 0.1470}0.0788 (0.2124){−0.2030, 0.0455}(6) Experience−0.0018 (0.8587){−0.0221, 0.0184}−0.0155 (0.2277){−0.0409, 0.0098}(7) Job position0.0672 (0.0734){−0.0064, 0.1407}0.0768 (0.1045){−0.0161, 0.1697}(8) Contract−0.0457 (0.1321){−0.1052, 0.0139}0.0296 (0.4375){−0.0454, 0.1046}(9) Work hour0.0137 (0.4980){−0.0262, 0.0536}−0.0380 (0.1352){−0.0879, 0.0120}(10) Bonus−0.0253 (0.8401){−0.2729, 0.2222}0.4296 (0.0068){0.1200, 0.7392}(11) Insurance0.0037 (0.9741){−0.2191, 0.2264}0.1766 (0.2125){−0.1019, 0.4551}(12) Fairness−0.3098 (0.0000){−0.4335, −0.1861}−0.2953 (0.0005){−0.4604, −0.1302}(13) Anomie0.4507 (0.0000){0.2641, 0.6372}As you can see in Table5, among the control variables, the respondent age is negatively related to their intention to leave (β=−0.0307; P<0.05). This means that younger respondents have higher level of intention to leave their current job. Respondents’ education is positively related to their intention to leave (β=0.0214; P<0.05). This means that educated respondents have higher level of intention to leave their current job.As you can see in Table6, among control variables, we found a positive and significant association between job position and respondents’ emotional exhaustion (β=0.0125; P<0.05) and a positive and significant association between work hour and respondents’ emotional exhaustion (β=0.0214; P<0.05).Table 6
Testing the hypothesis of the study (emotional exhaustion as a dependent variable).
VariableAnomieAnomieEmotional exhaustionEmotional exhaustionCoeff. (P){LLCI, ULCI}Coeff. (P){LLCI, ULCI}(1) Age−0.0064 (0.3199){−0.0190, 0.0063}−0.0155 (0.2374){−0.0414, 0.0103}(2) Gender0.0812 (0.4782){−0.1443, 0.3067}0.1823 (0.4360){−0.2786, 0.6432}(3) Education0.0392 (0.4609){−0.0654, 0.1438}0.1960 (0.0721){−0.0178, 0.4098}(4) Marital status−0.0226 (0.8289){−0.2288, 0.1836}0.0635 (0.7663){−0.3574, 0.4843}(5) Children0.0511 (0.3179){−0.0495, 0.1516}0.0526 (0.6150){−0.1533, 0.2585}(6) Experience−0.0020 (0.8473){−0.0223, 0.0183}0.0401 (0.0581){−0.0014, 0.0815}(7) Job position0.0685 (0.0698){−0.0056, 0.1426}0.1951 (0.0125){0.0425, 0.3478}(8) Contract−0.0463 (0.1284){−0.1061, 0.0135}−0.0667 (0.2858){−0.1896, 0.0562}(9) Work hour0.0137 (0.4994){−0.0263, 0.0537}0.0993 (0.0175){0.0176, 0.1810}(10) Bonus−0.0317 (0.8029){−0.2820, 0.2186}0.4882 (0.0610){−0.0227, 0.9992}(11) Insurance0.0028 (0.9804){−0.2206, 0.2261}0.3601 (0.1207){−0.0957, 0.8160}(12) Fairness−0.3087 (0.0000){−0.4328, −0.1846}−0.4429 (0.0015){−0.7132, −0.1726}(13) Anomie0.3198 (0.0402){0.0144, 0.6252}In hypothesis three, we expected that anomie at work would mediate the association between organizational fairness and intentions to leave. As shown in Table7, from the conditional indirect effect model, bootstrapping analyses demonstrated that the 95% confidence intervals for the indirect effect (−0.1396) excluded zero (95% BootLLCI: −0.2393, BootULCI: −0.0699). This means that anomie at work negatively mediated the association between organizational fairness and intentions to leave. Thus, the third hypothesis was fully supported.Table 7
Indirect effect of organizational fairness on intentions to leave through anomie at work.
Indirect effect ofX (organizational fairness) on Y (intention to leave)EffectBoot SEBootLLCIBootULCIAnomie−0.13960.0425−0.2393−0.0699In the last hypothesis, we predicted that anomie at work would mediate the association between organizational fairness and emotional exhaustion. As shown in Table8, from the conditional indirect effect model, bootstrapping analyses demonstrated that 95% confidence intervals for the indirect effect (−0.0987) excluded zero (95% BootLLCI: −0.2236, BootULCI: −0.0076). This means that anomie at work negatively mediated the association between organizational fairness and emotional exhaustion. Therefore, our last hypothesis was fully supported.Table 8
Indirect effect of organizational fairness on emotional exhaustion through anomie at work.
Indirect effect ofX (organizational fairness) on Y (intention to leave)EffectBoot SEBootLLCIBootULCIAnomie−0.09870.0541−0.2236−0.0076


## 4. Discussion and Conclusion

A precarious employee is a person who is working in a temporary, part-time, or contingent job receiving low wage compare with lifetime workers. This group of employees in any business has a lower socioeconomic position and, most importantly, has also been shown to have a worse health status [81]. The objective of the present study was to determine the antecedents of emotional exhaustion and leave intentions among precarious workers, who have some of the most difficult working conditions in any country. In doing so, by using survey-based data from 206 precarious workers in Peru, we developed and tested four hypotheses. In the first hypothesis, we expected that organizational fairness would reduce the intentions to leave among this group of workers. As supported by the rich literature on organizational justice [7, 52], we found that a higher perceived level of organizational fairness diminished the precarious employees’ intentions to leave their current jobs. Thus, if companies treat employees fairly, they are likely to stay longer in their current jobs. In this line of research, Hassan [15] also found that perceiving internal and external equity (or fairness) is an important factor for reducing the quit intentions of middle- and lower-level managers.Previous studies have shown that a positive perception of employees in relation to organizational fairness can result in many favorable results for both the employees and their organizations [54, 82, 83]. Contributing to this line of research, we found that organizational fairness can reduce precarious workers’ emotional exhaustion. It is important to understand which organizational factors predict workers’ emotional exhaustion because a higher level of emotional exhaustion can increase organizational turnover and reduce workers’ job performance [84]. Thus, our findings support the idea that workers’ emotional exhaustion is a critical factor that influences their organizational commitment and job satisfaction [85].We also found empirical evidence for the mediating role of anomie at work on the association between organizational fairness and intentions to leave and the association between organizational fairness and emotional exhaustion. Accordingly, anomie at work emerged as one of the underlying mechanisms explaining the relationship between organizational fairness and both the emotional exhaustion and leave intentions of precarious workers. Along similar lines, using survey-based data from 250 public sector workers in Iran, Jahanshahi and Dehghani [28] found that enhancing the quality of work life can reduce anomic behaviors at work. As a consequence, according to Choi et al. [27], anomie at the workplace can increase deviant workplace behavior. In this regard, Manrique et al. Manrique et al. [86] have indicated that increased anomie at the workplace can negatively impact the motivation of workers to engage in positive behaviors such as organizational citizenship behavior. Contributing to this body of research, our study has shown that organizations’ fairness can reduce anomie in the workplace, which, in turn, may reduce precarious workers’ intentions to leave and their emotional exhaustion.It is worth to mention that we found some interesting association among some of our control variables with outcome variables. For instance, in contrast with other types of workers [87], in our research, we found that younger precarious workers had higher level of intention to leave their current jobs and organizations. Surprisingly, the higher level of education resulted to a higher level of leave intention among precarious workers. It seems that both younger and educated precarious workers are looking for new opportunities in the job market and especially more stable jobs. Furthermore, we found that a higher level of working hours and holding a higher job position resulted to a higher level of emotional exhaustion among precarious workers. We recommend the future researchers to pay special attention to these demographical variables on their likely impacts on worker’s intention and emotion at the workplace.


### 4.1. Practical Implication of the Results

The findings of the investigation into the perceptions of precarious workers in small-sized companies in Peru have some implications for managers and strategic decision makers of private businesses. The results of our study confirm the importance of organizational fairness and offer new insights into how treating precarious workers fairly may affect employee work outcomes. To reduce employees’ emotional exhaustion and leave intentions, we recommend that those organizations who hire many precarious workers should carefully design and implement fair wage, reward, and employment systems. Many of the precarious workers are highly skilled and talented, and losing them may eventually hurt the business financial performance. Therefore, offering them a longer contract and fair payment package may reduce their leave intention.


### 4.2. Limitation of the Study

Like the survey-based research, our study suffers from some limitations. Using cross-sectional data is one of the major limitations of the study. We encourage future researchers in the context of Peru and other Latin American countries to use a longitudinal approach for further testing of our hypotheses. In addition, a single informant was used for measuring all the variables of the study (including the dependent, independent, control, and mediators); this reduces the generalizability of our results. To test these types of hypotheses, future researchers may use multiple respondents. For instance, human resource managers of the companies can be asked for demographic data and information on the dependent variables can be obtained from the direct supervisor of each employee. Other limitations of the study are using quite a sample size and participants coming from a limited number of geographical areas (just from the northern part of Peru). We have started our data collection a few weeks before the COVID-19 pandemic in Chimbote-Peru. Due to strict lockdown policies in Peru, we could not enlarge our sample size. In this study, we did not consider the impact of cultural factors. According to previous studies, cultural factors are one of the main determinants of employees’ perception and subsequently behaviors at the workplace. Lastly, in this research, we only considered anomie at the workplace as the sole mediator variables. However, there are other work-related variables such “occupational stress” which can play as the mediator for explaining the relationship among organizational fairness, emotional exhaustion, and leave intentions of precarious workers. Treating fairly workers in the workplace indeed has some positive impact on the ongoing stress that precarious workers may experience in the workplace.


## 4.1. Practical Implication of the Results

The findings of the investigation into the perceptions of precarious workers in small-sized companies in Peru have some implications for managers and strategic decision makers of private businesses. The results of our study confirm the importance of organizational fairness and offer new insights into how treating precarious workers fairly may affect employee work outcomes. To reduce employees’ emotional exhaustion and leave intentions, we recommend that those organizations who hire many precarious workers should carefully design and implement fair wage, reward, and employment systems. Many of the precarious workers are highly skilled and talented, and losing them may eventually hurt the business financial performance. Therefore, offering them a longer contract and fair payment package may reduce their leave intention.


## 4.2. Limitation of the Study

Like the survey-based research, our study suffers from some limitations. Using cross-sectional data is one of the major limitations of the study. We encourage future researchers in the context of Peru and other Latin American countries to use a longitudinal approach for further testing of our hypotheses. In addition, a single informant was used for measuring all the variables of the study (including the dependent, independent, control, and mediators); this reduces the generalizability of our results. To test these types of hypotheses, future researchers may use multiple respondents. For instance, human resource managers of the companies can be asked for demographic data and information on the dependent variables can be obtained from the direct supervisor of each employee. Other limitations of the study are using quite a sample size and participants coming from a limited number of geographical areas (just from the northern part of Peru). We have started our data collection a few weeks before the COVID-19 pandemic in Chimbote-Peru. Due to strict lockdown policies in Peru, we could not enlarge our sample size. In this study, we did not consider the impact of cultural factors. According to previous studies, cultural factors are one of the main determinants of employees’ perception and subsequently behaviors at the workplace. Lastly, in this research, we only considered anomie at the workplace as the sole mediator variables. However, there are other work-related variables such “occupational stress” which can play as the mediator for explaining the relationship among organizational fairness, emotional exhaustion, and leave intentions of precarious workers. Treating fairly workers in the workplace indeed has some positive impact on the ongoing stress that precarious workers may experience in the workplace.



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*Source: 1288273-2022-07-08.xml*