Abstract
Purpose
Several organizations decided to work remotely after the Indian Government proclaimed a state of emergency on March 24, 2020, to prevent the spread of COVID-19. Employees across all industries, particularly the Generation-Y, were stressed as a result of the COVID-19 outbreak. The purpose of this study is to fills that gap by looking at the effects of occupational stress factors (such as coworker support, work–life balance [WLB] and role expectation conflict) on Generation-Y employees in the educational sector during COVID-19.
Design/methodology/approach
The causes and consequences of the three occupational stresses listed above were investigated. In total, 231 surveys from workers at private and public educational institutions in India were analyzed using partial least square-structural equation modeling analysis.
Findings
The results revealed that support from coworkers greatly reduced job stress, the WLB had a positive impact on the work and personal lives, and the lack of role specification in Generation-Y employees had a negative impact on their job performance.
Originality/value
This study has considered the occupational stress variables among Generation-Y in the era of COVID-19, which need attention to improve the performance of the academic sector.
Keywords
Citation
Said, D.S., Agarwal, S., Alzoubi, Y.I., Momin, M.M. and Naqvi, A. (2023), "Occupational stress among Generation-Y employees in the era of COVID-19: cases from universities in India", Industrial and Commercial Training, Vol. 55 No. 2, pp. 234-252. https://doi.org/10.1108/ICT-03-2022-0017
Publisher
:Emerald Publishing Limited
Copyright © 2022, Emerald Publishing Limited
Introduction
The COVID-19 pandemic has had a significant worldwide impact. The mental effects of the pandemic, which will surely survive the epidemic, are less well recognized; consequently, knowing at-risk generation groups is critical (Holmes et al., 2020; Vigo et al., 2020). Younger generations, such as “Millennials” and “Generation-Y,” have greater rates of stress and concern than older generations, such as “Baby Boomers” and “Greatest/Silent Generations” (Basevitz et al., 2008; Gould and Edelstein, 2010). In general, as people get older, they are more engaged in preventative health measures and behavioral reactions, therefore, younger generations are less engaged in well-being-enhancing activities (Rudolph and Zacher, 2017).
Generation-Y or Millennials, born between 1981 and 1994/1996, is one of the three newest generations often characterized in demographic literature studies. The term “Generation” is used in two ways: to group age cohorts, which are groups of people born at the same time, and to track and analyze particular age range behaviors and traits (Mahmoud et al., 2020). Generations have comparable life experiences and familiarities during their formative years because of their chronological age, which are likely to have an impact on their shared attitudes, values and actions in response to certain stimuli (Rudolph and Zacher, 2017). As per Census Report (2011), between the ages of 15 and 59, the employed population in India makes up 56.9% of the overall population, with nearly half of that number being young people between the ages of 15 and 24 (Arora and Dhole, 2019). UNESCO estimates that even while there are already approximately 1.2 billion young people in the globe (18% of the total population), that number will increase by almost 72 million by the year 2025 (Arora and Dhole, 2019).
Young people will be the most important workforce group in the next decade, and our ability to operate will be determined by how the COVID-19 pandemics are handled. Understanding their health and overall well-being is crucial because it sets the tone for future output and outcomes (Yunita and Saputra, 2019). Moreover, the role of occupational stress, which is defined as a strain placed on employees as a result of work-related variables, is another aspect that should be investigated among Generation-Y employees. When the number of resources available to employees is insufficient in proportion to the obligations and demands placed on them, they become stressed. Globalization, technical innovation, severe rivalry, job responsibilities, coworker support (CS), work–life balance (WLB), role expectation conflict (REC), etc., are all factors that contribute to this sort of occupational stress (Prasad and Vaidya, 2020). Very few studies have been found in previous literature which examined the impact of occupational stress on Generation-Y personnel in Indian educational institutions in previous literature (Kploanyi et al., 2020; Prasad and Vaidya, 2020). The list of acronyms used throughout the study is mentioned in Table 1.
Therefore, this study gives a glimpse into the influence of COVID-related occupational stress on India’s Generation-Y in the educational sector. Occupational stress among Generation-Y is a relatively recent concept in Indian education (Prasad and Vaidya, 2020). Accordingly, this result investigates Generation-Y personnel in the private and public education sectors in India. This study’s goal is to find out the answers to the following research questions:
What is the relationship between CS and job stress of Generation-Y?
What is the relationship between WLB and job stress of Generation-Y?
What is the relationship between REC and job stress of Generation-Y?
We used quantitative analysis to answer the aforementioned research questions and investigate the correlations between CS, WLB and REC with Generation-Y. The information was gathered from 231 people in India who work in private and public organizations. Then, using partial least square-structural equation modeling (PLS-SEM), the gathered data was evaluated. CS significantly reduced workplace stress, the WLB had a positive impact on both work and personal lives and Generation-Y employees’ lack of role definition had a negative impact on their job performance, according to the research.
The remainder of this study is arranged in the following manner: In the next section, the background of the research is explained, and then the research method, findings, discussion and conclusion are given, along with implications, limitations and the study’s future scope.
Research background and related literature
People have been hunting for academic professions for a long time. Faculty were to be granted autonomy, employment and funds for research and service as remuneration for their duties as teachers. Security responsibilities, low governmental intervention and freedom of opinion (Shin et al., 2020). The benefits of higher education can outweigh the pressures, which can compromise its efficacy. It can lead to poor job performance as a result of an excessive workload, failure to achieve a WLB, insufficient compensation and a lack of recognition. A lack of concern for one’s family or a health deterioration, academic life circumstances and perceived stress are linked (Nabawanuka and Ekmekcioglu, 2021).
Generation-Y
Generation-Y, born in the era of the Internet, which propelled the notion of speed and materialism and it is integral to assess their mechanism of stress infliction, and to measures to mitigate it. In a recent study, it was noted that self-esteem and peer communication are the significant nodes that influence their self-concept, thus leading them to devise measures to mitigate stress (Inseng Duh et al., 2021). Furthermore, it has been studied that distance from coworker interactions and virtual communication has hinted at counterproductive work behavior in millennials during the COVID-19 pandemic (Yiwen and Hahn, 2021), thus hinting at the need to study their unique needs and interactions for theory development. Integrating the social identity theory, it could be argued that millennials’ (Generation-Y) group prestige and identification influence their organizational identification over a period of time, mediated by the perception of organizational identification. This embeddedness could help them mitigate stress, as their institutional socialization is evinced to help them exhibit prosocial and organizational behaviors (Abdi et al., 2019; Gorgenyi-Hegyes et al., 2021).
Employees (Generation-Y) in the education industry are experiencing a lack of engagement as a result of COVID-19’s high workload (Yiwen and Hahn, 2021). This epidemic has given employees the ability to complete online education from their homes or offices, but it has also given organizations the impression that their employees are unrestricted (Nabawanuka and Ekmekcioglu, 2021). Job stress (JS) is an unavoidable part of the workplace, and it arises from a person’s contact with his or her surroundings, as well as an inability to cope with job obligations. Employers put so much pressure on Generation-Y that it produces a stressful scenario for them, which is the polar opposite of their behavior (Abdi et al., 2019; Gorgenyi-Hegyes et al., 2021). Generation-Y has been discussed by nonprofits, companies and academics. They are the most recent employees to enter the workforce, yet they are also the most likely to change occupations because of stress (Nabawanuka and Ekmekcioglu, 2021).
Occupational stress
Occupational stress has been classified by the World Health Organization as a worldwide epidemic (Bellizzi et al., 2021; Khasawneh et al., 2021; Said et al., 2021). The COVID-19 epidemic, and everything that came with it, was a major source of stress. Occupational stress is defined by the National Institute of Occupational Safety and Health as the negative physical and emotional reactions that occur when a job’s requirements do not match the employee’s abilities or needs (Mustafa et al., 2015). Occupational stress can entail a range of experiences in the job, depending on the employees’ backgrounds and situations. Employee traits and the work environment both contribute to the development of occupational stress, which causes strains through nervous system disturbances (Holm, 2021). Recognizing the origins of workplace stress is the first step toward assisting employees and minimizing workplace strain.
Many causes contribute to stress, which has many different manifestations. Competition, favorness, innovation, harsh rivalry, job obligations, CS, WLB, REC, etc. contribute to professional occupational stress. Individuals suffering from stress affects how they deal with a variety of academic and professional obstacles (Towbes and Cohen, 1996). Stress has been defined in a number of different ways over the years. Stress is defined as any situation in which internal and/or external demands surpass an individual’s or a social system’s adaptive resources, while stressors are defined as the internal or external environmental stimuli that place demands on the equilibrium, altering an individual’s physical and psychological well-being (Hamaideh, 2011).
Relationship between coworker support and Generation-Y
Employees in today’s companies are expected to operate in collaborative professional environments. The quality of work connections, among other things, determines the performance of teams and individuals. There have been many studies aimed at objectively measuring the impact of coworker relationships on job-related stress in employees (Beehr et al., 2000; Cecil and Forman, 1990; Loi et al., 2014). However, there is still a need to study such a relationship with segregated age groups (Campione, 2015; Badri et al., 2021). However, further research on such a link with segregated age groups is still required. Because employees spend so much time at work, their teammates’ and superiors’ support and collaboration are extremely important. Employees’ organizational commitment grows and their well-being improves when they think they have the support of their teammates and supervisors (Sloan et al., 2013). It has been noted that CS is critical to organizational settings, which further determines congenial (or deterrent) organizational behaviors and job attitudes. In one of the studies, wherein perceived demographic similarity was triangulated with organizational outcomes like job satisfaction and organizational commitment, it was identified that CS largely mitigates their relationship; especially in the case of same-gender coworkers. The studies have exhibited empirical evidence in establishing that coworkers can aid organizational outcomes like enhanced work motivation (Ng, 2017), cooperation (Jehn and Shah, 1997; Gersick et al., 2000), triggered turnover contagion (Felps et al., 2009), mitigate relational dynamics, especially abusive supervision (Peng et al., 2014; Baer et al., 2018); and fostered diversity (Cox et al., 1991; Podsakoff et al., 2000; Hopkins and Hopkins, 2002), productivity (Manning et al., 2008) and trust (Crary, 1987).
On an individual front, supportive peer relationships are evinced to aid career success (Ibarra, 1997), improve self-esteem (Thomas, 1993), improve individual work performance (Schaubroeck and Fink, 1998) and reduce occupational stress (Fried and Tiegs, 1993; Chenji and Sode, 2019). Though it has been recently established that, CS has been identified as integral, especially in the case of capitalization, i.e. the process of sharing a personal positive event with others; it has also been identified that this could have opposing effects, in cases wherein there is intrateam competition (Watkins, 2021). This indicates that, while CS is fundamental, the shades of which are contextual; and could thus be a reason for stress (Peng et al., 2014). Furthermore, with strong evidence for the notion that stress could be mitigated by peer support, it becomes imperative for us to analyze their association (Fried and Tiegs, 1993; Schaubroeck and Fink, 1998), especially in younger groups.
Aside from the impact of CS on employee stress levels, research has shown, as discussed above, that overall work engagement has a major impact on the link between job demands and job performance. When coworker emotional support was relatively high, the favorable association between job design and coworker engagement was more significant (Shin et al., 2020). Coworker emotional support, according to the same study, acts as a facilitative moderator, amplifying the beneficial association between job demands and work engagement. Another related study found that team and leader support can moderate the effect of stress on job satisfaction for Generation-Y, emphasizing the importance of CS. According to the same study, firms may lessen the impact of stress on employee job satisfaction by improving CS, resulting in higher motivation, dedication and productivity. This in turn may also lower variety of human resources (HR)-related costs such as recruiting, health-care costs and so on. Consequently, we propose:
CS negatively affects the JS of Generation-Y in the educational sector in India.
Relationship between work–life balance dissatisfaction and Generation-Y
When Generation-Y first entered the workforce, it set a new benchmark for companies. Generation-Y values WLB, friendships and social and environmental activities. They are more motivated to work and achieve when they have more time for their personal lives (Agarwal and Lenka, 2014). Differences in occupational stress levels have been linked to parameters such as age, job experience, remuneration, marital status, qualification and gender (Agarwal and Lenka, 2014; Mohamed, 2015). A lack of WLB is seen as a source of occupational stress. WLB is a notion derived from an employee’s many life roles outside of work, such as nonwork family roles or individual needs that may be crammed into the working day, severely impacting individual health and performance at work as a result of stressful conditions. This particular type of stress limit may be exceeded during a pandemic, and this is likely to have a negative impact on Generation-Y employees (Parent-Lamarche and Boulet, 2021).
The Person-Organization (P–O) fit is described as the degree to which individuals and groups get along when at least one of them meets the demands of the other, or when both exhibit comparable underlying traits (Kristof, 1996). The basic idea of P–O fit is that the extent of a relationship between a person and their work environment influences their attitude, behavior and other person-level outcomes (Westerman and Vanka, 2005). This could result in a mismatch between Generation-Y employees’ values and the organization’s values which creates occupational stress (Miller and Yu, 2003).
Furthermore, stress is regarded as a multifaceted emotion that affects an individual’s social, physical, spiritual, intellectual and environmental well-being. Because we spend so much of our lives at work, occupational stress has been proven to affect employees in every industry (Tamunomiebi and Mezeh, 2021). The susceptibility to stress varies from one employee to another. Employees’ stress-coping mechanisms differ depending on their qualities. One of the elements that are impacted by stress is age (Kploanyi et al., 2020). Among young adults, depression and anxiety are the common mental health symptom (Chen et al., 2019; Liu et al., 2019). The aim of the study is to keep an eye on Generation-Y employees, and intervene when needed to address the work stress as soon as it is noticed. Based on the high rates of workplace psychological deaths revealed that European Agency for Safety and Health at Work (EU-OSHA) focused on the significance of providing the guidance and support in companies for regulating and coping with workplace stress (Ohadomere and Ogamba, 2020). Consequently, we propose:
WLB negatively affects the JS of Generation-Y in the educational sector in India.
Relationship between role expectation and Generation-Y
Conflicting role expectations indicate a lack of clarity regarding individual and team responsibilities. Such a lack of role specialization is likely to result in subpar work performance, as well as intergroup or intragroup disputes. When designing HR practices, companies should consider the expectations and demands of Generation-Y employees (Chopra and Bhilare, 2020). Regardless of generational differences in behaviors and attitudes, Generation-Y employees have shown to be more self-reliant and different than previous generations. According to the findings, Generation-Y employees want their employers to offer them role clarity that will help them advance in their careers. Understanding and addressing the expectations and role clarity demands of this generational group will help organizations harness their energy (Chopra and Bhilare, 2020). Moreover, conflicting task perceptions were shown to have a detrimental influence on organizational commitment, although relationship-related conflicts were not found to have as much of an impact. The impacts of task-role and management team conflict on Generation-Y employees’ stress levels and job performance were also discovered in this study (Orlowski et al., 2017). Critical theories suggest that traditionally emotions should not be subsumed under coworker affection and peer support (Bolton, 2005); though social exchange theory indicates that employee–peer relationships can lead to employee commitment (Tsui et al., 1997). Furthermore, it has been empirically noted that employees consider care and nurture as roles embedded in the very nature of supervisor leadership, whereas emotional support is perceived to be discretionary from the perspective of managers themselves (Toegel et al., 2013), which hints at the necessity to understand the linkage between CS and role-expectation facets in various avenues, and more so in the case of Generation-Y. Incongruent expectations are argued to be deterrent to organizational and individual positive outcomes; and thus, its antecedents have to be investigated. Furthermore, with noted instances that incongruent role expectations have been influenced by CS and have the potential to mitigate stress (Mayo et al., 2012), it is integral to decipher their association and the magnitude of it in the current genre of employees. While CS is found to be integral to role performance, it is established to be an influential performance and job satisfaction (Babin and Boles, 1996). Hitherto, it has been identified that for Generation-Y congenial peer support, appreciation and higher role clarity help them exhibit job performance and consequentially, job satisfaction (Muskat and Reitsamer, 2019). Thus, the earlier two prepositions could be linked to understanding that CS and clarity on role expectations could bring the best among the Generation-Y employees, within an organization. Consequently, we propose:
REC positively affects the JS of Generation-Y employees in the educational sector in India.
This paper aims to study the impact of occupational stress on Generation-Y by investigating if CS affects Generation-Y (RQ1), if the WLB affects Generation-Y (RQ2) and if the REC affects Generation-Y (RQ3). The research model and related hypotheses are shown in Figure 1.
Research method
Research process
To assess the connections and hypothesis shown in Figure 1, we created a survey questionnaire. For examining and validating interactions between a variety of structures or factors over a big population, Gable (1994) suggests using the survey technique. In addition, unlike other methods such as observation, the survey technique provides more detail, which may aid researchers in generalizing their findings to the whole sample group (Agrawal et al., 2020a). A closed-ended questionnaire was used to poll faculty representatives from India’s private academic institutes. There are two key elements to this questionnaire: demographics and variables considered in this study. In the demographic area, we requested the respondent to indicate their age, income, gender, educational background and work experience at the institution. In the JS phase, the responder was asked to answer various questions concerning their CS, WLB, REC and JS. A five-point Likert scale was used in this study (with 1 indicating strongly disagree, 2 indicating disagree, 3 indicating neutral, 4 indicating agree and 5 indicating strongly agree). We added the “neutral” option because it has no drawbacks on the scale and may lessen bias in respondents’ replies by not forcing them to choose between positive and negative reactions (Chyung et al., 2017).
The questionnaire was presented to four academics who teach management and human resource disciplines to ensure content validity (three assistant professors, one associate professor and one full professor). This was necessary to reach a final agreement on the items that would be investigated in this questionnaire, as well as their accuracy (Agrawal et al., 2020b). Three PhD students were given the questionnaire to test its face validity. This was done to assess how long the survey would take to complete and receive feedback. This is also required by other aspects of the questionnaire, such as readability, organization and language (Singh et al., 2022). The survey questionnaire was delivered to seven people based on the author’s relationships. The respondents were chosen using a predetermined unit of analysis (i.e. an academic teacher who works in a private university in India). We invited respondents to fill out the survey and provide feedback on how to improve it or if they had any problems doing so. We rewrote the questionnaire and made the necessary modifications based on their feedback.
Participants and data collection
We concentrated on doctoral, postgraduate, graduate and diploma degrees at the university. The study was performed in February and March of 2021. At India’s private institutions, the survey questionnaire was provided to potential responders. More than 200 Generation-Y polls were used to create our target sample (Hair, 2009). As a result, we distributed the survey form through email to 1,000 possible responders. A link to the survey was sent to potential respondents by email and LinkedIn message. The questionnaire did not include any identifying information, such as the respondent’s or institution’s names.
A total of 669 questionnaires were received, yielding a 66.9% survey response rate. To prepare the data for analysis, cleaning, missing data and a multiscreening procedure are used (Hair, 2009). The preparation procedure resulted in the exemption of 53 surveys owing to incomplete replies in more than 10% of the cases, as advised by Hair (2009). As a consequence, there were a total of 616 valid responses. However, because this work focuses primarily on Generation-Y, only 231 of the 616 replies were included in this investigation for final analysis, which is larger than the minimum sample size (200 responses) recommended by PLS-SEM (Hair, 2009).
Questionnaire items and analysis technique
The questionnaire has been taken from Shukla and Srivastava (2016). In this questionnaire, five questions were used to assess the REC as these questions measure how difficult it is for employees to recognize their roles in the company; four questions were modified and were used to assess CS as these questions examine how successfully employees in the workplace receive appropriate support from their peers; four questions were modified and were used to assess the WLB as these questions measure a worker’s capacity to achieve a WLB. Nine questions derived from Jamal and Baba(1992) were used to assess the JS inventory. These questions determine whether or not employees are anxious at work. The full list of all items is illustrated in Appendix.
We used PLS-SEM to analyze the data. PLS-SEM is a multivariate research approach that combines measurement and structural model (Hair et al., 2017). The link between survey findings and the latent variable is investigated using the measurement model. The structural model assesses the strength of latent variable relationships as well as the correlation between exogenous and endogenous variables all at once (Hair et al., 2017). To assess a large research model with several constructs, without enforcing distributional assumptions on the data, several researchers find the PLS-SEM technique to be particularly intriguing. The PLS-SEM was used in several recently published works in a similar context to examine the correlations between native and exogenous variables, such as Nwachukwu et al. (2022), Boon et al. (2020) and Ettis (2021).
Findings
Demographic profile of respondents
Table 2 shows the demographic characteristics of the respondents. Gender, qualification, work experience and income are all factors to be considered. The age is not shown in Table 2 because we are only interested in Generation-Y. The majority of respondents (43%) have a doctoral degree and the majority (51%) was female. The majority (51%) of respondents earns $250–$500 per month and the majority (45%) has 5–10 years of academic experience.
The high proportion of nonresponse may make data analysis challenging. To ensure that nonresponse was not a problem, we divided the sample into two groups: early group and late group (Sivo et al., 2006). The demographic data was then used to compare the responses of the two groups. The test revealed no significant differences between the two groups, suggesting that nonresponse bias is unlikely to be a major issue. Another difficulty with data is the problem of process bias. We used Harman’s one-factor test statistical technique, which demonstrated that the maximum covariance represented by one component was only 31%; that is, no single factor explained a substantial amount of variation (MacKenzie and Podsakoff, 2012). As a result, this issue does not represent a difficulty when it comes to calculating validity.
Measurement validation
WLB, CS, REC and JS indicators were all modeled as reflecting indicators induced by their latent components (Hair et al., 2017). To validate the measurement model, we used the Smart PLS 3.0 software (Hair et al., 2017). All items were subjected to construct validity and reliability testing. As a consequence of the satisfactory level of findings, all items were found to be valid and dependable. Several measurements were recommended to verify the measurement model including composite reliability (CR), Cronbach’s alpha (α), indicator reliability, internal consistency and discriminant validity (Hair et al., 2017).
To check the construct validity and reliability, several tests have to be conducted to ensure that the data are ready for analysis:
the individual indicator reliability, which refers to how free of random mistakes a measurement item is and how steady and consistent its results are over time (Straub et al., 2004);
the measure’s internal consistency, which was assessed using CR and α;
the degree to which one measure of a concept correlates with other measures of the same construct is known as convergent validity (Straub et al., 2004); and
discriminant validity relates to a construct’s uniqueness in comparison to other constructs (Straub et al., 2004).
The indicator reliability of reflected items was determined using the outer loadings of each item on its corresponding latent construct, which should be more than 0.7 and have a t-statistical value greater than 1.96 (Hair et al., 2017). Table 3 shows how each item is loaded, as well as how it interacts with other things. All exterior loadings were above the required values, indicating the indicator’s dependability. However, several items with loadings of less than 0.5 were left out of the study (Hair et al., 2017). As a consequence, only three items for CS, four items for JS, three items for REC and four items for WLB. Excluding these items has little effect on the overall model fit.
As seen in Table 3, all constructions outperformed the intended CR value of 0.70. Moreover, we used the average variance extracted (AVE) value to see if the data was convergent. The AVEs were all greater than the 0.70 suggested threshold (Hair et al., 2017). Finally, we assessed discriminant validity by looking at the cross-loadings of the indicators as well as the Fornell and Larcker criterion (Fornell and Larcker, 1981). First, the cross-loadings of the indicators suggest that no indicator has a greater burden on the competing endogenous constructs, as shown in Table 3.
Second, the square root of each construct’s AVE value should be greater than its maximum similarity with any other construct, according to the Fornell and Larcker criterion. The values in bold type along the diagonal define the square root of the AVE score, while the off-diagonal elements reflect correlations among latent constructs. As shown in Table 4, the square root of AVE is larger than the variance shared by each construct and its opposing constructions in all cases. As a consequence, all discriminant validity requirements for all constructs were met.
Test of the structural model
Model fit, path coefficient and coefficient of determination were used to quantify the relevance of correlations among all components using the PLS method and bootstrapping techniques (Hair et al., 2017). The standardized root mean square residual (SRMR) (R2) value was used to determine the model fit. The model fit is a metric that reflects how well empirical data fits the conceptual model under consideration (i.e. how closely the model’s structures match the real-world phenomena) (Straub et al., 2004). The difference between the observed and predicted correlation is known as the SRMR (Henseler et al., 2015). As a result, SRMR allows the average size of discrepancies between observed and projected correlations to be evaluated as an absolute criterion of (model) fit (Henseler et al., 2015). An SRMR score of less than 0.10 or 0.08 (in a more cautious sense) is considered a good match (Henseler et al., 2015). The SRMR value is 0.072, which is less than 0.08, according to the PLS analysis, suggesting that the test model is well-fit.
Second, the path coefficients that describe the strength of the associations (B-value) between variables should be strong and consistent with specified directions to guarantee that a relationship between two constructs happens (Hair et al., 2017). Figure 2 shows the route coefficients and the importance of each path. The effect of CS on JS (H1) was found to be significant (B = 0.162, p < 0.05, T = 2.334), the effect of WLB on JS (H2) was found to be significant (B = 0.146, p < 0.05, T = 2.064) and the effect of role conflict on JS (H3) was found to be significant (B = 0.133, p < 0.05, T = 1.968). Moreover, we tested if there is any significant relationship between CS and REC and the relationship was positive but not significant (B = 0.112, p > 0.1, T = 1.379). Also, we tested the relationship between CS and WLB and found an insignificant positive relationship (B = 0.003, p > 0.1, T = 0.029). Finally, we tested the relationship between the REC and WLB and found an insignificant negative relationship (B = −0.015, p > 0.1, T = 0.202).
Third, R2, the primary measure for assessing the inner model, indicates the cumulative impacts of the independent (exogenous) latent variable on the dependent (endogenous) latent variable and is applied to the model’s predictive precision (Hair et al., 2017). An appropriate R2 of 0.75, 0.50 or 0.25 indicates that the prediction accuracy is significant, moderate or modest, respectively (Henseler et al., 2015). CS accounts for 1.3% of the variance in REC and 0.00% of the variance in WLB, both of which are considered to have low predictive accuracy. CE, REC and WLB all account for 6.1% of the variance in JS, indicating modest predictive accuracy.
Finally, we investigate the impact of CS in mediating the link between role conflict and WLB, as well as JS. The indirect effects (i.e. REC * CS and WLB * CS) should be considered if CS mediates the effect of role conflict and WLB on JS, according to Zhao et al. (2010). We look for the direct effect if the indirect effect is significant. We have complementary (partial) mediation if the direct effect is considerable; however, we only have indirect (full) mediation if the direct effect is minor. The direct effects of REC and CS; and WLB and CS were not significant, the direct effect of REC and WLB on JS was the relationships between REC and WLB on JS are not mediated by CS.
Discussion and conclusion
This research paper aimed to study the impact of occupational stress on Generation-Y by investigating the effect of CS on Generation-Y, WLB on Generation-Y and the impact of REC on Generation-Y. The researcher used PLS-SEM to analyze the data collected from the close-ended survey questionnaires administered to the volunteer sample group of 231. To test the hypotheses and achieve the research objectives, the researcher used a closed-ended survey questionnaire methodology to poll faculty representatives from India’s private academic institutes. This method allowed the study to yield greater detail, which benefitted the researcher in generalizing their findings to the whole sample group. Any negative or positive impacts on the stress levels of employees in the proposed category would be viewed as either a confirmation or disproval of the suggested hypothesis. The accepted hypothesis developed from these research objectives stated that CS negatively affects the JS of Generation-Y employees in the educational sector in India, that WLB negatively affects the JS of Generation-Y employees in the educational sector in India and that REC positively affects the JS of Generation-Y employees in the educational sector in India.
CS can influence an employee’s perception of the working environment and impact their performance levels, as well as their job satisfaction levels. Particularly, CS minimizes the negative impact that JS has on Generation-Y employees including their mental and physical distress (Shin et al., 2020). About the second research aim, the study established that a properly maintained WLB had a positive impact on the work and personal lives of Generation-Y employees. This implies that WLB negatively impacted the JS experienced during COVID-19 parameters (Yiwen and Hahn, 2021). Most Generation-Y employees already had different coping mechanisms to ensure a well-balanced outlook on their personal life and work life. However, most of these coping mechanisms were severely affected by the acute onset of the global pandemic which saw new restrictive public health measures enforced at work and public places (Nabawanuka and Ekmekcioglu, 2021). Concerning the final research aim and hypothesis, it was discovered that the lack of role specification in Generation-Y employees had a negative impact on their job performance. This was due to Generation-Y employees having a disconnect between what they think their role is, what their actual role is and what was expected of them. The constant pressure on top of the global pandemic served to increase their JS levels. Thus, REC positively affected the JS levels of Generation-Y employees in the academic sector. Other research studies have also established that employees suffer more JS if they do not understand what their roles are clearly (Abdi et al., 2019; Gorgenyi-Hegyes et al., 2021). Not only does this reduce efficiency and cause performance issues but it also demoralizes employees and leads to job dissatisfaction. However, further research in the field and topic is still required to gain a more in-depth understanding of the core topic, as well as use a more diversified sample of qualified participants.
Implications
The results of this research study imply that the occupational stress variables explored in this paper have a significant impact on the JS levels of Generation-Y employees in the private and public education sectors. This is paying particular focus on the COVID-19 period (Chhatwani, 2022). Higher stress levels ultimately lead to poor performance, poor health lifestyles and diminished capacities of both employees and learners (Foy et al., 2019).
Furthermore, it is noted from previous studies that lower stress levels enhance employee engagement enabling them to better engage with training and development (Saxena and Rai, 2015; Ahmad et al., 2019). The findings of this study can be used by various stakeholders in the industry to improve the work experiences of employees in the education sector which ultimately improves their engagement levels, commitment to training and development and overall performance. This study gives the direction to the management of academic institutions that how the stress can be reduced during training and development. The organization should provide the training to the faculties in between of the career by identification of level of their CS, WLB and role conflict. These are the vital variables that effect their job satisfaction, absenteeism, performance, etc. This can be also done through the application of employee well-being initiatives that concentrate on ensuring that employees maintain healthy behaviors and actions during on and off-working hours. This will help their bodies and minds to rejuvenate properly and minimize their stress levels.
Future scope and limitations of the study
The biggest limitation in this research was getting participants for the sample group survey. Even though the study did get 231 qualified participants, which is well above the PLS-SEM recommended minimum sample size of 200; this number is far too low to gain a conclusive understanding of the subject matter, especially given the large population of India. To curb this limitation for future researchers, it is imperative to introduce an incentive to coax qualified participants into joining the sample group (Park et al., 2022). It is also advisable to have a wider and more effective system of communication in regard to alerting potential participants of the study. The research implications may be stronger if this study is broadened to reach additional participants. Thus, it is recommended that similar research should be conducted on other sectors to reveal the effect of occupational stress on the workforce and how it can be managed to have positive employee-related outcomes. This study will also be validated by collecting the case studies from the large sample to explore various variables such as working environment, perception, attitude, way of communication and hierarchy. Furthermore, this research can be conducted by using multicriteria decision-making methods to find the cause-and-effect relationship among the variables (Bandil and Agrawal, 2021).
Figures
Figure 1
Research model
Figure 2
Partial least square-SEM results
List of acronyms used throughout the study
| Acronym | Definition | Acronym | Definition |
|---|---|---|---|
| AVE | Average variance extracted | PLS-SEM | Partial least square-structural equation modeling |
| CR | Composite reliability | REC | Role expectation conflict |
| CS | Coworker support | SRMR | Standardized root mean square residual |
| JS | Job stress | WLB | Work–life balance |
Respondents demographics
| Characteristics | Frequency | (%) |
|---|---|---|
| Gender | ||
| Female | 119 | 51 |
| Male | 112 | 49 |
| Qualification | ||
| Diploma | 15 | 6 |
| Graduate | 27 | 12 |
| Post graduate | 89 | 39 |
| Doctoral | 100 | 43 |
| Work experience | ||
| Less than 5 years | 79 | 34 |
| 5–10 years | 105 | 45 |
| 10–15 years | 41 | 18 |
| 15–20 years | 6 | 3 |
| Income | ||
| Less than $250 per month | 10 | 4 |
| $250–$500 per month | 117 | 51 |
| $500–$750 per month | 93 | 40 |
| More than $750 | 11 | 5 |
Construct indicator: loading and cross-loading
| CS | JS | REC | WLB | |
|---|---|---|---|---|
| CS2 | 0.879*** | −0.116 | 0.144 | −0.042 |
| CS3 | 0.867*** | −0.113 | 0.078 | 0.078 |
| CS4 | 0.849*** | −0.152 | 0.059 | −0.027 |
| JS4 | −0.165 | 0.870*** | 0.100 | −0.136 |
| JS6 | −0.099 | 0.884*** | 0.095 | −0.127 |
| JS7 | −0.149 | 0.870*** | 0.100 | −0.133 |
| JS9 | −0.084 | 0.866*** | 0.114 | −0.118 |
| REC2 | 0.124 | 0.092 | 0.965 *** | −0.014 |
| REC3 | 0.128 | 0.094 | 0.955 *** | −0.021 |
| REC5 | 0.047 | 0.139 | 0.809 *** | −0.007 |
| WLB1 | −0.033 | −0.144 | 0.001 | 0.902 *** |
| WLB2 | 0.010 | −0.134 | −0.043 | 0.886 *** |
| WLB3 | −0.004 | −0.145 | 0.010 | 0.889 *** |
| WLB4 | 0.030 | −0.088 | −0.032 | 0.870 *** |
*p < 0.10, **p < 0.05, ***p < 0.01
Composite reliability (CR) and average variance extracted (AVE)
| Latent construct | α | CR | AVE | CS | JS | REC | WLB |
|---|---|---|---|---|---|---|---|
| CS | 0.830*** | 0.900*** | 0.748*** | 0.865 | |||
| JS | 0.900*** | 0.930*** | 0.761*** | −0.15 | 0.873 | ||
| REC | 0.900*** | 0.940*** | 0.833*** | 0.11 | 0.12 | 0.913 | |
| WLB | 0.910*** | 0.940*** | 0.786*** | 0.00 | −0.15 | −0.02 | 0.887 |
Questionnaire Shukla and Srivastava (2016) (1 – totally disagree, 2 – disagree, 3 – neutral, 4 – agree, 5 – total agree)
| I | Job stress | |||||
| 1. | I have a lot of work and fear that very little time to do it | 1 | 2 | 3 | 4 | 5 |
| 2 | I feel so burdened that even a day without work seems bad | 1 | 2 | 3 | 4 | 5 |
| 3 | I feel that I never take a leave | 1 | 2 | 3 | 4 | 5 |
| 4 | Many people at my office are tired of the company demand | 1 | 2 | 3 | 4 | 5 |
| 5 | My job makes me nervous | 1 | 2 | 3 | 4 | 5 |
| 6 | The effect of my job on me is too high | 1 | 2 | 3 | 4 | 5 |
| 7 | Many a times, my job becomes a big burden | 1 | 2 | 3 | 4 | 5 |
| 8 | Sometimes when I think about my job, I get a tight feeling in my chest | 1 | 2 | 3 | 4 | 5 |
| 9 | I feel bad when I take a leave | 1 | 2 | 3 | 4 | 5 |
| II | Role expectation conflict | 1 | 2 | 3 | 4 | 5 |
| 1. | I’m able to satisfy the different demands of various peoples above me | 1 | 2 | 3 | 4 | 5 |
| 2 | I’m able to satisfy the conflicting demands of my colleagues and juniors | 1 | 2 | 3 | 4 | 5 |
| 3 | I’m able to satisfy the demands of clients and others, because they are opposite to each other | 1 | 2 | 3 | 4 | 5 |
| 4 | The expectations of my seniors different from my juniors | 1 | 2 | 3 | 4 | 5 |
| 5 | I am concerned about the different expectations of different peoples | 1 | 2 | 3 | 4 | 5 |
| III | Coworker support | 1 | 2 | 3 | 4 | 5 |
| 1 | My colleagues have given me information or advice I need | 1 | 2 | 3 | 4 | 5 |
| 2 | My colleagues understand me and given advice | 1 | 2 | 3 | 4 | 5 |
| 3 | My colleagues have given me a clear and helpful feedback about my work | 1 | 2 | 3 | 4 | 5 |
| 4 | My colleagues have given me assistance in my work | 1 | 2 | 3 | 4 | 5 |
| IV | Work–life balance | 1 | 2 | 3 | 4 | 5 |
| 1 | I am able to balance between time at work and time at other activities | 1 | 2 | 3 | 4 | 5 |
| 2 | I have difficulty balancing my work and other activities | 1 | 2 | 3 | 4 | 5 |
| 3 | I feel that the job and other activities are currently balanced | 1 | 2 | 3 | 4 | 5 |
| 4 | Overall, I believe that my work and other activities are balanced | 1 | 2 | 3 | 4 | 5 |
Appendix
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Further reading
Spiro, C. (2006), “Generation Y in the workplace”, Defense AT&l, Vol. 35 No. 6, pp. 16-19.
Corresponding author
About the authors
Dina Sabry Said is an Assistant Professor at the College of Business Administration, at the American University of the Middle East, Kuwait, where she currently teaches Business Organization and Management, Organizational Behavior, Leadership and Career Planning. She holds a PhD and MA degree in Business Administration from Ain Shams University, Cairo, Egypt. She received her BA (Hons) in Commerce from Ain Shams University, Cairo, Egypt. During her PhD studies she worked as a Staff Development and Learning Team Assistant at the World Health Organization, Regional Office for the Eastern Mediterranean Region, Egypt. During her Master’s studies, she worked as an HR Director Assistant in an Arbitration Law Firm. Her research includes talent management, organizational learning, service quality and employee creativity.
Sucheta Agarwal is an Associate Professor at the Institute of Business Management, GLA University, Mathura, India. She pursued PhD in Entrepreneurship from the Department of Management Studies, Indian Institute of Technology, Roorkee, India. Her areas of interest include education, entrepreneurship, human resource management, industrial relations and organizational behavior. She has authored/coauthored several publications and has contributed several research papers to SCOPUS abstracted and ABDC-ranked journals of international and national repute. Her recent publications were in the International Journal of Social Economics (Emerald, UK), Journal of Asia Business Studies (Emerald, UK) and International Journal of Entrepreneurship and Small Business (Inderscience), to name a few. She has also presented many papers in national and international conferences in India and abroad. She is the recipient of the “Young Research Scholarship Award” at AIT Bangkok by GRDS and the “Young Woman Management Researcher Award” by AIMS International. She also received the “Outstanding Reviewer Award” from Elsevier. She is a Supervisor for doctoral scholars as well as many masters’ students.
Yehia Ibrahim Alzoubi has a PhD in Information Systems from the University of Technology, Sydney. His research focuses on agile development, cybersecurity and big data analysis. He received his MSc in IT from Central Queensland University in Sydney. He has a number of published works in scholarly peer-reviewed international scientific journals and conferences such as Information and Management, Journal of Strategic Security, Journal of Network and Computer Applications and Computer Communications. Currently, he is an Assistant Professor at AUM.
Mujtaba M. Momin is Assistant Professor at the College of Business Administration at the American University of Middle-East, Kuwait. He previously worked with Prince Salman Bin Abdul Aziz University, Kingdom of Saudi Arabia. He is a passionate and inquisitive educator with over 12 years of experience in higher education and corporate training.
Ariz Naqvi is a CIPD Level 7 certified HRD professional with over 15 years of professional experience in the Academics and L&D functions with AACSB-accredited institutions located in India and Kuwait. Currently, he is associated with American University of the Middle East, Kuwait as an Associate Professor in HRM department. Prior to AUM, Kuwait, he had a career span of over eight years with Lancaster University, UK (India Centre) and NIS Sparta Ltd. (a reliance ADA group company) in India handling senior responsibilities. Dr Ariz Naqvi obtained PhD in Commerce from the Department of Business Administration, University of Allahabad in 2010. He did his Master of Psychology with specialization in Human Resource Development and Management from Department of Psychology, Centre for Advanced Study, University of Allahabad in 2006 and earned a postgraduate certificate in academic practices from Lancaster University, UK in 2013.