The work environment has witnessed great disruption due to COVID-19 in recent times. As the world entered 2021, a sudden change in employee attitude became apparent, and cases of mass resignations started increasing exponentially, leaving businesses to struggle to acquire and retain talent. This mass resignation was termed ‘The Great Resignation’ by Prof. Anthony Klotz in May 2021. Therefore, this situation and the new term given to it is the motivation behind this research. The research aims to understand the underlying factors and their interrelationships, as well as how they affect people’s decision to quit their jobs. This research based on survey data and multivariate statistical treatment of these data is a serious attempt to explore the factors responsible for mass resignation during the unprecedented times. In addition, a sentiment analysis is performed on tweets pertaining to the great resignation to understand the sentiments and emotions of people towards the new phenomenon of the great resignation. The results of the study suggest that some new dimensions majorly trust and self-reliance, responsible for turnover intention. This research attempts to add substantial literature to the existing body of knowledge and also aims to provide some meaningful insights that can be of use to human resource managers in helping them strategize their HR policies so as to maintain the right pool of talents in their organizations.
Research Article
The Great Resignation: An Empirical Study on Employee Mass Resignation and its Associated Factors
https://doi.org/10.21203/rs.3.rs-1690874/v1
This work is licensed under a CC BY 4.0 License
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posted 03 Jun, 2022
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The Great Resignation
Emotion
Work-life Balance
Uncertainty
Behavioural Decision Theory
Structural Equation Modelling
The global pandemic due to the novel coronavirus has changed the way we think about work and life. The terms and conditions of the jobs and the labour market have changed drastically, leading to an increase in employee turnover both voluntarily and mandatorily. During the work-from-home situation or job loss due to mandatory resignation, people explored some of the skills that they already had but could not find time to explore while working in a standard 9-5 job environment. They have also taken several online courses during this period. This led to greater confidence and self-reliance among working people, be they white-collar or blue-collar workers. Empowered with new skills and self-reliance, working people turned to mass resignation from their jobs, which was termed as ‘The Great Resignation’ by Klotz (2021). He called it a pandemic epiphany. The great resignation can be considered as a worker’s revolution due to the legacy of the pandemic.
The scenario of the great resignation is very alarming and imminent. According to the U.S. Department of Labour, from April to June 2021, a total of 11.5 million workers quit their jobs. This figure stands at 4.3 million for August 2021. This represents 2.9% of the total workforce nationwide, which is the highest number to date (Cohen & Roeske-Zummer, 2021). A survey of more than 30,000 workers conducted by Microsoft found that 41% are considering quitting and that number is expected to jump to 54% among Gen Z workers alone. This is expected not to end and continue in the future (Kane, 2021). According to data collected and summarized by the OECD, 20 million less people are back to work in 38 countries (Taylor, 2021), of which 14 million claim to leave the workforce and can be classified as ‘not working’ or ‘currently not on the search for jobs.’ The trends in the resignation are much higher in low-wage workers when compared to higher ones, and sectors which are most affected include hospitality, transport, retail, education, etc. (Patel, 2022). In the Indian IT sector, the hiring of skilled professionals is increasing by 52%, indicating high job changes and an imminent requirement of skilled workforce (Kothari, 2022). Furthermore, more than a million employees are expected to resign, further leading to 23% attrition rate in the tech sector, which is at an all-time high (Kajarekar, 2021). In terms of cohort, Gen Z and millennium workers are switching the most and leading the wave followed by Gen X and then Baby Boomers (Smith, 2021).
There is an obvious question as to where the employees are going and what they are doing after quitting their jobs. A report by Mitchell & Dill (2021) shows that some of the employees have quit full-time jobs and began freelancing, allowing more time with family. The increase in this self-employment and entrepreneurial zeal is evident after the pandemic phase. This has led to a gig economy in which there is a transitory legal compulsion and agreement in terms of employment conditions (Nadler, 2017). It provides both the employer and the employee flexibility since it is an on-demand fulfilment where the commitment is not a standard one.
In a different choice, many employees have decided to go for upskilling where they plan to improve their skills so they possess better skills and be industry ready to get better jobs. There is a section of employees who consider themselves to be in the wrong place and in the wrong career. These individuals explored their pleasant hobbies that led to thinking about turning them into careers (Lim, 2021). Another interesting option which many have opted for is to go for their own start-ups. A start-up boom has been seen during and after the pandemic in which 32% of people in the US have left jobs to enter this field (Bienasz, 2021). In India, there was a start-up boom, as the year 2020 witnessed as many as 1600 start-ups and 12 unicorns, which is highest in a calendar year with a future of 8-10% year-on-year growth (NASSCOM-ZINNOV REPORT, 2021).
The concept of mass resignation, hereby termed as the great resignation, is attracting growing attention both by the academia and the corporates across the world. However, the concept is still in nascent stage and very little is known about it through the academic literature. Therefore, this study is conducted to find the factors and their interrelationships that fuel this mass exodus. An attempt has also been made to understand the underlying sentiments and emotions of the people that propel them to resign from their jobs. Although many studies have been done in the past to find out the reasons for employee burnout, this work offers one of the first investigations into the mass resignation phenomenon in light of the global pandemic and work-from-home situations. This work makes a significant contribution to the field of human resource and organizational behaviour in terms of theoretical and practical implications. The study in the form of this paper is organized as follows.
The first section outlines some of the key literature and builds a conceptual framework for further investigation. On the basis of the conceptual framework, eight hypotheses have been developed and represented in the form of a conceptual model. This section is followed by the Materials and methods section detailing about the research framework, sampling, and methodology used for the research. The materials and methods section is further divided into two parts, one explaining material and methods for the sentiment analysis and another one for the survey method. The third section details the statistical treatment of the data followed by the results. The next section is about the discussion based on these results. Finally, the article closes with the conclusion and implications of the study followed by limitations and directions for future research.
The terms turnover intention and intention to resign are used interchangeably throughout the paper and are synonymous to each other.
Conceptual Framework and Hypotheses Development
Work-Life Balance and Turnover Intention
Work-Life balance is a crucial issue for organizations to manage as it is a serious issue for the employees. There has been a gradual rise in concerns regarding work-life balance due to its importance not only for family members, but also for the organization (Bourhis & Mekkaoui, 2010). After offering and providing the right balance between work and life, human resource management can expect to retain talented employees (Vidal-Salazar, Cordón-Pozo & José, 2016) for having a competitive edge. The importance of work-life balance is evident in the sense that properly managing it leads to less turnover and better retention (Hill et al., 2008; Society for Human Resource Management, 2010), while poor work-life balance leads to job dissatisfaction and job switching behaviour (Allen et al., 2013), costing the organization in various ways. Furthermore, the quality of work-life balance and job embeddedness leads to job enrichment, providing reasons to stay in an organization (Mitchell., Holtom, Lee, Sablynski & Erez, 2001; Wheeler, Harris & Harvey, 2010). Various studies suggest that job embeddedness mediates the effect of family-work conflict on turnover intention (Afsar & Rehman, 2017; Thakur & Bhatnagar, 2017), while workplace flexibility moderates this relationship (Jiang, Liu, McKay, Lee & Mitchell, 2012). These articles highlight the importance of working-life balance in the intention of turnover. Based on these findings, it is hypothesized that:
H1: The lack of work-life balance has positive and significant effect on the intention to resign.
Uncertainty at Work and Turnover Intention
Uncertainty at work leads to a feeling of tension, anxiety, worry, and stress along with a feeling of insecurity about the job of the employees (Huang, Niu, Lee & Ashford, 2012). The pandemic has brought to the minds of the employees, uncertainty related to their continuation of jobs, their pay protection, uncertainty about job location, etc. These uncertainties leading to fear of job loss are detrimental to employee job attitudes and, more importantly, their behavioural relationship with the organization (Sverke, Hellgren & Näswall, 2002). Kurniawaty, Ramly & Ramlawati (2019) opines that stress and insecurity at work have a positive influence on turnover intention. However, during the unprecedented global pandemic situation, co-worker and family support reduces the negative impact of uncertainty and emotional exhaustion at work (Usman, Cheng, Ghani, Gul & Shah, 2021), thus reducing the intention to turnover (Akgunduz & Eryilmaz, 2018). The role of supervisors is crucial, as it influences the degree of uncertainty and role stress experienced by their subordinates, which affects their satisfaction level and contributes to turnover intention (O'Driscoll & Beehr, 1994). Based on these findings from the previous research, it is postulated that:
H2: Uncertainty at work has a positive and significant effect on the intention to resign.
Mental Well-Being and Turnover Intention
The mental well-being of employees on the job is of utmost importance. Its absence can lead to depression, and many times it is underdiagnosed and not treated in a proper manner (Goetzel & Ozminkowski, 2000; Goetzel, Ozminkowski, Meneades, Stewart & Schutt, 2000). The organization should ensure the well-being of the mental health of the employees, as employee productivity and efficiency are connected to it (Goetzel, Ozminkowski, Sederer & Mark, 2002; Goetzel, Hawkins, Ozminkowski & Wang, 2003; Goetzel et al., 2004). Work-based cultural activities play an important role in the mental and emotional well-being of employees, thus contributing to a better employee-employer relationship (Theorell et al., 2013). Siu, Cooper & Donald (1997) adopted an occupational stress indicator for the investigation of the source of stress, coping with the strategies for better mental and physical well-being, and concluded that people in the managerial role are more prone to both mental and physical ill-health. The ill effects of lack of mental well-being range from employee depression to increased absenteeism, which in turn leads to loss in productivity and increased cost for employers (Goetzel et al., 2018). Nadinloyi, Sadeghi & Hajloo (2013) concluded that job satisfaction and mental health are positively correlated, in addition to those employees who have a longer history in their jobs, having much job satisfaction. All these studies indicate that mental and physical well-being is of primary importance and should be focused on both by the employees and employers. Mental and physical well-being is directly related to job satisfaction, which in turn translates to engagement with the organization. Thus, the hypothesis
H3: Lack of mental well-being has a positive and significant effect on intention to resign.
Monetary Compensation and Turnover Intention
Monetary compensation is perceived as one of the most important motivators in a job. It enables an individual to fulfil physiological as well as psychological needs (Kasser & Ryan, 1993). Money determines the standard of living and socioeconomic status, which in turn leads to happiness (Chitchai, Senasu & Sakworawich, 2020). Extrinsic rewards lead to intrinsic motivation (Deci, Koestner & Ryan, 1999). To retain talent, employers try to offer attractive salaries and timely incentives. However, it is the natural tendency of employers to eventually lay off highly paid workers and replace them with cheap labour available (Sachau, 2007). During the current pandemic, both macro and micro factors led to cost reduction by organizations, mainly through layoffs or cutting down the payments to employees. Many organizations have not been able to properly compensate their employees, creating a fear of job continuity and searching for alternative jobs or other options. Based on these studies, it is deducted that monetary compensation plays an important role in determining the turnover intentions of an employee, and thus the hypothesis is:
H4: Lack of proper monetary compensation has a positive and significant effect on intention to resign.
Career Growth and Turnover Intention
Career progression is one of the strongest determinants of employee motivation and commitment to the job (Karavardar, 2014). In normal or unprecedented times, employees compare their career progress with others and also with possible alternative careers. Weng & Hu (2009) argued that career growth can be measured by three factors, namely career progression, capacity development, and promotion opportunity. All these factors negatively affect employee turnover intention while career progression has a significantly positive impact on the organizational commitment, thus reducing turnover intention (Weng & McElroy, 2010; Weng, McElroy, Morrow & Liu, 2010; Biswakarma, 2016). Formal and informal mentoring positively affects organizational citizenship behaviour (OCB), which in turn has a negative impact on the intention to turnover intention (Okurame & Balogun, 2005; Okurame, 2012). The career growth policy in an organization should have a mutual feeling of psychological contract where there is a scope for progress (Bedeian, Pizzolatto, Long & Griffeth, 1991) leading to better organizational commitment (Coyle-Shapiro & Morrow, 2006). Individual career concerns have a direct relationship with their intention of resigning, along with the buffer influence of organizational commitment on this relationship (Hess, Jepsen & Dries, 2012). From these studies, it is noted that the career growth of the employees has a negative impact on their intention of leaving. Accordingly, it is postulated that:
H5: Slow career growth has a positive and significant effect on intention to resign.
Influence of Self-reliance and Turnover Intention on Actual Turnover
Individuals always tend to weigh the jobs they are into versus anything else that they can do on their own for a better living. If at any point of time in their career they feel that they can be self- reliant, then there is a cognitive dissonance in the mind to leave the job and be a master of their own work. Regular salaried jobs are always at a risk of job loss due to supply outweighing demand leading to cheap labour options (Ghose, 2016) and this risk increases manifold during unprecedented times like the current global pandemic situation and subsequent lockdowns globally (Ghose, 2020). To mitigate such risks and uncertainties, people tend to look for other sustainable options in which self-reliance is the key. Mitchell & Dill (2021) report how freelancing can be the new career for employees as they get to spend more time with their family and have better autonomy. Some have moved on to entrepreneurship (Szostak & Sułkowski, 2021), which not only protects their identity, but also makes them self-sufficient and independent. Most qualified professionals shifted from their traditional office jobs to become gig workers, and the gig economy is on a rise (Vega, Cecchinato & Rooksby, 2020; Umar, Xu & Mirza, 2021). During the pandemic, most of the work shifted to online mode, and this fuelled the labour supply on the Gig economy platforms, which increased by 25% during the pandemic (Cao, Zhang & Huang, 2020). Inferring these findings in the wake of the global pandemic and the current employment scenario, it is noted that self-reliance is the key factor leading people to quit their regular jobs and taking up freelancing or entrepreneurship as an alternative career option. Thus, it is hypothesized that
H6a: The intention to resign has a positive and significant effect on actual resignation of employees.
H6b: Employee self-reliance mediates the relationship between intention to resign and actual resignation.
The Moderating Effects of Work Experience on Turnover Intention
Work experience or the length of employment of an employee is an influencing factor when it comes to the intention to change. Employees who serve organizations for a considerable period of time are less likely to quit than those who have a relatively lesser stay at the organization, since they adapt to the working environment (Jiang, Hu & Wang, 2018). But employees with less work experience expect better growth and more self-regulation resources at their workplace (Yang, Guan, Lai, She & Lockwood, 2015) and have a stronger urge for positive change (Huang, X., Shi, Zhang & Cheung, 2006). Wright & Bonett (2002) in their study concluded that while controlling for age and another demographic variable, tenure had a very strong moderating effect on the turnover intention of the employees. As the tenure increases, the turnover intention decreases exponentially. From these studies, it is observed that the tenure and work experience of the employees have a significant effect on their intention to resign. Therefore, it is postulated that:
H7a: The employee’s work experience moderates the association between work-life balance and intention to resign.
H7b: Employee work experience moderates the association between uncertainty at work and intention to resign.
H7c: The work experience moderates the association between mental well-being and intention to resign.
H7d: Employee work experience moderates the association between monetary compensation and intention to resign.
H7e: The work experience moderates the association between career growth and the intention to resign.
The Moderating Effects of Employee-Employer Trust on Turnover Intention
Trust between the employee and the employer is a very important attribute for the success of the organization (Brockner, Siegel, Daly, Tyler & Martin, 1997). Building trust in the workplace is very crucial for the employer because employees at low-trust workplaces feel very insecure, disengaged, and uncommunicative leading to a stressful work environment (Jiang & Probst, 2015; 2019). The talent and creativity of the employees cannot get the right space, and thus a drop in productivity. Collective trust in management leads to better teamwork and collaboration among different levels of employees, ultimately reducing job stress, anxiety, and insecurity (Wang, Mather & Seifert, 2018). Trust as an intermediary variable moderates the relationship between employer fairness and employee engagement (Abbas & Wu, 2018). Direct leaders (eg, supervisors) in an organization are the first reference point of trust between the employee and the organization (Tan & Tan, 2000; Dirks & Ferrin, 2002). Although another study by Aryee, Budhwar & Chen (2002) found that only interactional justice is related to trust in the supervisor. The same study also concludes that trust in the organization fully mediates the relationship between interactional justice and turnover intentions. From these studies, it is noted that the employee-employer trust is crucial to reducing the turnover intentions of the employees, and hence, the hypothesis
H8a: Employee-employer trust moderates the association between work-life balance and intention to resign.
H8b: Employee-employer trust moderates the association between uncertainty at work and intention to resign.
H8c: Employee-employer trust moderates the association between mental well-being and intention to resign.
H8d: The employee-employer trust moderates the association between monetary compensation and intention to resign.
H8e: Employee-employer trust moderates the association between career growth and intention to resign.
From the extant literature cited above, the interrelations between the various constructs were visualized and the conceptual framework was drawn. The same is present in Fig.1 below:
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We summarize our hypotheses in the following conceptual model (see Fig. 2) where we show that how the constructs of resignation and intention to resign are related to each other and are moderated by the work experience and employee-employer trust. We also show in the conceptual model that self-reliance mediates the relationship between the intention to resign and actual resignation.
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The study is divided into two parts: The first part consists of a sentimental and emotional analysis of people about the great resignation. When the motive of is to understand the natural opinion of people which is free from bias, then sentiment analysis is the best technique to deploy (Mohammed, Shukla & Pandey, 2020). Sentiment analysis is also a powerful technique for classifying people based on their opinions and emotions (Shukla, Prashar & Pandiya, 2021).
The second part of the study is based on survey data collected through the structured questionnaire. These data are also used to test the hypotheses and the conceptual model derived from them.
Materials and Methods for Sentiment Analysis
For the sentiment analysis, tweets posted by netizens around the world between the period May 2021 to March 2022 were scraped from Twitter using the hashtag ‘#TheGreatResignation’. May 2021 was chosen as the starting point because this was the period when the term ‘The Great Resignation’ was coined by Prof. Anthony C. Klotz. Twitter’s standard API restricts users to only scrape recent tweets from the past seven days, so to scrape historical data, an application was submitted to Twitter Inc. for the Twitter Academic Research API, which supports scraping of historical tweets. The application was approved by Twitter Inc. and then we started scraping the tweets using R programming. We wanted only original tweets, so the parameter for retweets and replies was set to ‘FALSE’. Also, the geographical location parameter was not specified to receive tweets from Twitter users throughout the world.
Materials and Methods for the Survey
An intensive literature review has been done to identify the determinants of resignation. To date, very few studies have been conducted to identify the determinants of Great Resignation. Although the Great Resignation situation is constant during the COVID-19 situation, there is a dearth of literature in this novel area, and hence a thorough review of various published news articles and reports was done. Exploring the various antecedents of intention to resign and actual resignation was done. The theoretical model has been proposed by incorporating various determinants from previous research on turnover intention, job satisfaction, employee-employer trust, etc. which are somehow related to great resignation. The proposed model comprises six constructs, viz. Work-life balance, uncertainty at work, mental well-being, self-reliance, monetary compensation, and career growth. These constructs were measured using items adopted from previous studies, as indicated in the Appendix.
Questionnaire Design and Validation
Data were collected with the help of a well-structured questionnaire. The questionnaire was developed in English. The questionnaire was divided into two parts. The first part presents questions related to demographics such as gender, age, employment status, work experience, etc. The second part of the questionnaire consists of the constructs of the Great Resignation. The construct consists of work-life balance, uncertainty at work, mental well-being, self-reliance, monetary compensation, and career growth. All the items of constructs were measured using the 5-point Likert scale range 1 stand for strongly disagree to 5 strongly agree (Premkumar & Ramamurthy 1995). To make the questionnaire more appropriate and error-free, we performed the pre-test. The pre-test helps ensure a clear understanding of concepts, and the items help to prevent respondents from developing their idiosyncratic meanings for the terms (Talukder, Sorwar, Bao, Ahmed & Palash, 2020). The pre-test was conducted through review by the four experts from the field of psychology and human resource management and a professor of English, to check the language and content.
Sample and Data Collection
For Sentiment Analysis
For the purpose of sentiment analysis using R programming, we used the key search term #TheGreatResignation and set the parameter to extract 100K tweets from May 2021 to March 2022. However, after running the codes, only 19220 tweets could be extracted using the search term, since only this many tweets containing the search term were posted on Twitter during the specified period of study. Furthermore, the tweets were cleaned to convert all text to lowercases, removing punctuation, URLs, numbers, and stop words. The cleaned tweets were then saved for further analysis.
For Survey Data Analysis
For survey-based data collection, the targeted respondents were employees of IT/ITES organizations located in Bangalore, India. Bangalore was chosen as the study area because it is the home of many national and international IT & ITES companies and is also known as the ‘Silicon Valley’ of India. Since no informed sample frame was available, a sample frame was developed by gathering the information of the employees from these firms. The primary source of information were the team leaders at these firms. After that, employees were approached and asked some informal questions about their work, satisfaction, and intention to stay or turnover. They were assured of complete anonymity and confidentiality. From them we also got the details of their colleagues who have quit their jobs in the past year and are either working as freelancers or as entrepreneurs. Through this method, we got a final sample frame of 1,743 people, some of them being expats from different countries. After that, we estimated the sample size using the Yamane (1967) formula, which is:
where n = sample size.
N= Population size
e = acceptable sampling error (here 5% or 0.05 in our case)
As noted by Israel (1992), the difficulty in obtaining a good estimate of population variance has increased the popularity of sample size. The Yamane (1967) formula, which is a simplified formula for proportion, has become popular with researchers for these reasons. The Yamane formula assumes a normal distribution. In our case, it was assumed that the respondents were normally distributed in terms of their attitude toward the intention of visiting the destination. Therefore, Yamane’s formula could be considered suitable for determining the sample size for a confidence level with ±5% precision.
Putting all the values into this formula, we get a sample size of 326. The samples were then drawn from the sample frame using a simple random sampling method.
The survey was conducted online between November 2021 and January 2022. A total of 326 questionnaires were sent to the selected respondents using the single random sampling method. Of 326, only 308 questionnaires were received after more than two rounds of follow-up and reminders. After reviewing the responses received, 297 were found to be fully filled and suitable for further analysis. The remaining 11 responses were discarded due to incomplete and biased responses. Therefore, the final response rate achieved was 91%, which is a good estimate.
Statistical Treatment and Data Analysis
Statistical treatment and analysis of the data for this study was carried out in five steps, of which two steps were for Twitter data and three steps for survey data. In the first step of Twitter data analysis, cleaned tweets were scored and sentiment analysis was performed using the NRC Emotion Lexicon, which is a bag of English words representing two types of sentiment (positive and negative) and eight types of emotion. The scores obtained were then visualized using a bar graph where each bar represented the frequency or weight of the feelings and the emotions (see Figure 3). In the second step, a comparative word cloud was made to represent the most common words that represent a particular emotion (see Figure 4).
In the first step of survey data analysis, descriptive statistics were performed on the study constructs to examine the view of the respondents on the various indicators. Then, preliminary tests of statistical assumptions such as normality, linearity, and common method bias (CMB) were conducted to ensure that the data are suitable for multivariate statistical analysis. In the second step, the fitness of the measurement model was assessed using confirmatory factor analysis along with internal reliability, convergent validity, and discriminant validity. In the third step, the data were subjected to statistical treatment for hypothesis testing using the structural equation modelling (SEM) path analysis technique. SEM is widely used by researchers when testing a theory (Bollen, Dean, Siegert, Howe & Goodwin, 2014; Hair, Ortinau & Harrison, 2010). In this step, the mediator effect of self-reliance on the relationship between the intention to resign and the actual resignation has been tested. Finally, multigroup analysis was applied to investigate the moderating effects of work experience and employee-employer trust on the relationship between the factors responsible for turnover and intention to resign.
Sentiment Analysis Results
Sentiment analysis was performed using R programming. This was done in two steps. In the first step, sentiment analysis on 19220 tweets containing the search term #TheGreatResignation using the NRC emotion lexicon. This produced two sentiments and eight emotions, as shown in Fig. 3 below.
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The bar graph shows that most people expressed a positive sentiment about the great resignation. This could be a result of their positivity towards their future endeavours either as an entrepreneur or as a freelancer due to the boom in the gig economy (Vega et al., 2020; Umar et al., 2020; Cao et al., 2020). If we see emotions, then the highest emotion reflected in the tweets is trust. This is in line with several studies that state that trust between the employee and the employer is the deciding factor in retention of an employee and has a negative impact on the turnover intention (Brockner et al., 1997; Abbas & Wu, 2018). Based on the existing literature, we have also hypothesized that trust between the employee and the employer moderates the association between factors of resignation and turnover intention. Therefore, from the sentiment analysis, we conclude that trust is an important emotion that is vital for the intention of employee turnover, and during the unprecedented times of the COVID-19 pandemic, people are leaving their employers due to positive sentiment towards self-reliance (mediating variable in the study) fuelled by the gig economy.
Furthermore, we also made a comparison plot showing the words from the tweets that represent a particular emotion. This is depicted in the Fig. 4 below:
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Survey Data Analysis Results
Data collected through the survey questionnaire were subjected to statistical treatment in three steps. The results are explained in the following subsections:
Step 1. Assumptions of Linearity, Multicollinearity and CMB
This study adopted multivariate analysis techniques. Therefore, certain assumptions of multivariate analysis were tested. Firstly, a linearity and multicollinearity test have been applied. Linearity was assessed through curve-estimated regression methods and the F-value was found to be significant. Furthermore, multicollinearity was estimated using the collinearity statistics test (Mishra & Gupta, 2020). The results of the collinearity statistics test found that all explanatory variables had VIF between 1.52 and 2.89. The results also found a low correlation among the variables. Furthermore, the tolerance values were between 2.0 and 5.0; therefore, there was no multicollinearity among the variables. Furthermore, Harman single-factor test (Podsakoff, MacKenzie, Lee & Podsakoff, 2003) was administered to ensure that the data collected from the respondents are not suffering from common-method bias (Craighead, Ketchen, Dunn, K. S., & Hult, 2011). The test value obtained was 28.29%, which was less than 50% as suggested by Podsakoff et al. (2003). Therefore, this study is free of common method bias.
Step 2: Testing of the Measurement Model
The confirmation factor analysis (CFA) test was applied to test the measurement model. IBM-SPSS AMOS 22.0 software was used with a maximum likelihood estimation method to analyse the measurement model (Byrne & Campbell, 1999). The CFA results show that all the indices are in an acceptable fit range of fit for the measurement model, such as χ2/df = 2.55, p-value < 0.001, Goodness-of-Fit Index (GFI) = 0.92, Comparative fit index (CFI) = 0.92, Non-Normed Fit Index (NNFI) = 0.91, Root Mean Square Residual (RMSR) = 0.05, Root Mean Square Error of Approximation (RMSEA) = 0.06. All these indices and their corresponding values reflect the fitness of the measurement model.
Furthermore, the standardized factor loading of all 40 items was found to be greater than 0.60 and composite reliability values exceed 0.60 (Bagozzi & Yi, 1988). This confirmed the convergent validity of the constructs in the model. Table 1 below delineates these results.
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Next, discriminant validity of the measurement model was assessed by comparing the square root of the average variance extracted (AVE) value with the correlation estimates between the same pair. The findings reported in Table 2 below indicated that the square root of the AVE for each construct exceeded its correlation values with other constructs (Fornell & Larcker, 1981). This finding confirmed the discriminant validity and concluded that five constructs are distinct.
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Step 3: Hypothesis Testing
The findings of the path analysis for the combined analysis are represented in Fig. 5 and Table 3 which indicates that the six hypotheses are supported. The first hypothesis, i.e., work-life balance, has a positive and significant effect on the intention to resign. The path for the first hypothesis was significant (β = 0.16, p = .013) supporting H1. The second hypothesis proposed the positive and significant effect of uncertainty at work on the intention to resign. The results of the path analysis revealed that the second hypothesis was significant (β = 0.39, p = .000) supporting H2. The third hypothesis proposed the positive and significant effect of lack of mental health on the intention to resign. The path result shows that the third path was significant (β = 0.19, p = .007) supporting H3. The fourth hypothesis states that monetary compensation has a positive and significant effect on the the intention to resign. This hypothesis was fully supported, as the path was significant (β = 0.14, p = .010). The fifth hypothesis was that career growth has a positive and significant effect on intention to resign. The path for the fifth hypothesis was significant (β = 0.12, p = .002) supporting H5. The last hypothesis was related to the prediction of the effect of intention to resign on actual resignation. The path result shows that the hypothesized path was significant (β = 0.44, p = .042). Therefore, H6a was supported.
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Mediating Effect of Self-Reliance on Turnover Intention
This section describes the test results of the mediation effect on whether self-reliance serves as a mediator between the intention to resign and actual resignation. Model 4 of the PROCESS macro with a bootstrapped sample of 5000 was used to test the mediation effect (Hayes, 2013). The result is presented in Table 4 below:
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Three linear regression models were run by PROCESS macro along with the covariates. The results of the bootstrapping revealed that there is an indirect effect of self-reliance on the relationship between the intention to resign and the actual resignation (β = 0.20) with the confidence interval [0.01, 0.05] at the 95% confidence interval. The confidence interval did not contain a zero value; therefore, it was concluded that the indirect effect was significant and positive, but the direct effect was more (β = 0.44) than the indirect effect. Therefore, it is concluded that self-reliance partially mediates the relationship between the intention to resign and the actual resignation.
Moderating effects of work experience and Employee-Employer Trust on turnover intention
The study has used SEM multigroup analysis to investigate the moderating effect of work experience and employee-employer trust on the relationship between the intention to resign and the proposed determinants of great resignation. In this technique, data are divided into two subgroups, and then the SEM model was run on both samples simultaneously (Tarhini, Hone & Liu, 2014). The moderating effects of work experience in the hypothesized relationships were studied with the help of a pairwise comparison of two predefined categories of work experience i.e., experience <5 years and >5 years. The results show that the moderating effect of work experience was significant between WLB→ ITR (Z = 2.18*, p < .10), MWB→ ITR (Z = 1.70*, p < .10), MC→ ITR (Z = -1.48*, p < .10) and CG→ ITR (Z = 1.49*, p < .10). However, the relationship of UAW→ ITR was not moderated by the work experience (Z = 1.86, p > .10).
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Similarly, the moderating effects of employee-employer trust in the hypothesized relationships were studied with the help of pairwise comparison of two predefined categories of employee-employer trust, that is, low and high. The results show that the moderating effect of employee-employer trust was significant among UAW→ ITR (Z = 6.46*, p < .10), MWB→ ITR (Z= 6.01*, p < .10), and CG→ ITR (Z= 3.04* p < .10). However, the relationship between WLB→ ITR was not significant (Z= 0.45, p > .10) and the relationship between MC→ ITR was also found to be non-significant (Z = 0.97, p > .10). Therefore, these two relationships were not moderated by employee-employer trust.
In this paper, we have attempted to describe the factors that lead to mass resignations by employees. At first, we tried to understand the sentiments and the emotions of the people towards the mass resignations, which has been termed the great resignation. The sentiment analysis done on the tweets indicates that most of a people have the positive sentiment towards the great resignation due to an obvious reason of boom in the gig economy. The sentiment analysis also reflected trust as one of the key factors responsible for the turnover intention. In addition, based on the existing literature, we arrive at eight hypotheses and presented them in the form of a conceptual model. Hypotheses were tested using confirmatory factor analysis and structural equation modelling (SEM). The results of the path analysis support the first five hypotheses (H1-H5) indicating that work-life balance, uncertainty at work, mental well-being, monetary compensation, and career growth have a significant impact on the turnover intention of the employees. Lack of proper work-life balance, uncertainty at work, lack of mental well-being, improper monetary compensation, and stagnation in the career growth enhance the turnover intention of the employees. In addition, it is suggested from the findings that the intention to resign has a positive influence on the actual resignation, and this relationship is partially mediated by self-reliance, as indicated by the mediation analysis. Sentiment analysis indicated trust as one of the important emotions that cause turnover intention. This provides further evidence to examine the role of trust in the turnover intention. From the multigroup analysis, it was found that the employee-employer trust has a moderating effect on the relationship between the turnover intention and three constructs, i.e., uncertainty at work, mental well-being, and career growth. However, no significant moderating effect of it was found on the relationship between the turnover intention and the remaining two constructs, i.e., work-life balance and monetary compensation. These results support the finding of Abbas & Wu (2018) that trust as an intermediary variable moderates the relationship between employer fairness and employee engagement. The multigroup analysis also indicated that the employee’s work experience has a moderating effect on the relationship between their intention and the resignation except for the uncertainty at work. Employees with less work experience (< 5 years) are more likely to change their jobs as compared to the employees with more work experience (> 5 years). These results support the findings of Wright & Bonett (2002) that tenure has a very strong moderating effect on the turnover intention of the employees, and Jiang et al. (2018) that employees who serve organizations for a considerable period of time are less likely to quit than those who have a relatively lesser stay at the organization, since they adapt to the working environment. Interestingly, we also found that the majority of employees who have switched jobs during the great resignation were millennials (Chavadi, Sirothiya, & Vishwanatha, 2022; Thakur & Arora, 2022). This finding is in line with a Gallup (2016) report stating that 21% of millennials have switched jobs in the between the year 2015-16, which is three times as compared to the non-millennials. An obvious reason could be the availability of many avenues and opportunities available to them to up-skill themselves and a mindset to explore some alternate career opportunities in the gig economy and start-up ecosystem rather than being a conventional full-time employee to an organization.
This article attempts to find the reasons responsible for affecting the decision of people to turn to go for mass resignation hereby termed the great resignation. We have presented through the sentiment analysis that most of the people across the globe are having a positive sentiment towards the great resignation owing to the independence they seek in form of being a freelancer or an entrepreneur in the booming gig economy. Trust has emerged as the main emotion of people and is one of the moderating factors on the peoples’ turnover intention. The results from the path analysis and the structural equation modelling have established a positive relationship between the five factors responsible for people leaving the job and their turnover intention. The findings also establish self-reliance as a mediator between the intention of turnover and the actual turnover of the trust of the employee-employer trust and the work experience act as a moderator on the relationship between the factors responsible for turnover and the intention of turnover.
Overall, these findings suggest that the employees are taking a decision to quit their present jobs and onboard on a new journey toward their entrepreneurial venture or freelancing due to the poor work-life balance, uncertainty at the workplace, lack of mutual trust between the employee and the employer, poor monetary compensation, and sluggishness in the career growth. All these factors have, however, been existing in the past too, but the global pandemic has fuelled them to a higher extent leading to the mass resignation. The results of this study validate the finding of previous studies in the new context of the great resignation.
Theoretical Implications
Our study has five theoretical implications. First, it adds substantially to the understanding of the factors that are responsible to foster the turnover intentions among the employees. Building on previous studies (Deci et al., 1999; Mitchell et al., 2001; Goetzel et al., 2002; 2003; 2004; Bourhis & Mekkaoui, 2010; Weng and McElroy, 2010; Huang et al., 2012), we offer a comprehensive theoretical framework as to how the factors like work-life balance, uncertainty at work, mental well-being, monetary compensation, and career growth affect employees’ turnover intentions. Although most of these factors were already present and discussed in various organizational studies since the era of industrial revolution, we have presented them in the contemporary context when every business is a service business (Kotler, Kartajaya & Setiawan, 2019) and employees are the determinants of success of an organization. Secondly, we have obtained satisfactory results that demonstrate that mental well-being is equally important when it comes to employee turnover. We have highlighted in our study that if the mental well-being of the employee is hampered, their chances of leaving the organization increase as they look towards a new job or venture where they can find mental peace. In this context, we conclude that in the contemporary business world, mental well-being has taken the front stage when compared to the monetary compensation. Mutual trust between the employee and the employer is the key factor in determining employee retention (Brockner et al., 1997), but the problem is that it cannot be measured directly. Most of the previous studies have tried to measure it through interviews or survey questionnaires, but there are probably chances that the responses might be biased. In our study, we have proposed an innovative method to measure trust as well as other emotions of the employees through sentiment and emotion analysis by gathering data from their social media accounts, as the posts on the online social networks tend to be quite natural and free from bias. Most of the previous studies deal with determining the factors responsible for turnover intention, but what is the interrelationship between these factors have less been addressed. The novelty of our work lies in finding these interrelationships and proposing a theoretical model that represents these interrelationships. The model has been further validated with supporting data. Our research has also highlighted the importance of self-reliance as a mediating factor between the intention to resign and actual resignation, which has not been addressed in the previous studies as they were only limited to finding the factors affecting turnover intentions and not the actual turnover. This is one of the important value contributions of this study to the existing body of knowledge related to the organizational studies. Overall, the findings from our study add substantially to the existing body of knowledge in context of the new concept of mass resignation termed as the great resignation that arises as an after-effect of the global pandemic and work-from-home situations.
Practical Implications
The findings of our study have the potential to provide practical implications to the people dealing with the human resources in the organization, policy makers, and the governments. The findings of this study can be utilized by the HR managers to frame their policies that are in the favour of the employees. Our proposed model has the potential to help policy makers understand the factors of turnover intention and their interrelationship as and how they affect the turnover intention of the employees. These factors and their interrelationships can be taken into account by HR managers and the business owners while framing their HR policies. The study findings revealed that work-life balance is one of key determinants of the turnover intention, as during the work from home situation people have realized the importance of being with their families. HR managers can learn a lesson from the global pandemic and consequent work-from-home situation that people can be equally productive if they spend more time with their families, thus there should be a policy in place that can give more flexibility to the people so as to help them have a better balance between their work and personal space. Uncertainty at work is another major issue that leads to turnover intention. This uncertainty has increased manifold during the pandemic, leading to mass employee turnover. This issue can be addressed by businesses with proper communication with employees and assuring them a safe and stable working environment. There should be a fair compensation policy and performance-related incentive, as our study indicates that monetary compensation and career growth play an important role in turnover intention. At this point, government intervention is also required to make regulations pertaining to the safe working environments, fair compensation, and job security. At the onset of the pandemic many governments assured people of these things but with time they got diluted and not followed in true spirit. Employees, particularly in private organizations were left at the decision of their employers, ultimately leading to the loss of trust of the employees on their employers. This proved to be detrimental and a major factor contributing to the great resignation. Trust has been indicated as a moderating factor on turnover intention in our study and needs to be taken care of through proper policy implementation and monitoring. According to our findings, self-reliance is a mediating factor between the intention to resign and actual resignation. This has often been ignored by the business owners. It is a common conception among business owners that if people become more skilled, they will be more demanding or may leave the job. This misconception needs to be addressed, and business owners must create an ecosystem where employees upskill themselves and contribute more to their organization through the acquired skills. This can only be possible if the trust factor between the employee and the employer is promoted. Work experience is another moderating factor in turnover intention as found in our study. People with less work experience or short time engagement with their employers tend to switch jobs easily. Here, the business owners or the HR managers can create a conducive environment for the holistic development of the people, fostered by proper communication so that the people, particularly the new joiners and interns feel connected to the organization. Finally, the sentiment analysis approach used in our study could be applied to business organizations to understand the feelings and emotions of their people and to take corrective actions to create a positive feeling and emotion. This method and our proposed model, if utilized properly, have the potential to decrease employee turnover from an organization and can prevent situations like the great resignation.
Limitations and Directions for Future Research
Like any empirical study, our study is subjected to many limitations, which in turn can offer opportunities for future research. The sentiment analysis applied in this study is based on tweets gathered from Twitter. There are chances that many people are not so active on Twitter as they are on some other online social networks such as Facebook, Instagram, etc. Hence, only tweets could not be an only appropriate source of information for analysing the sentiments and the emotions. Future research could be conducted by performing similar analyses on data gathered from other social media posts.
The survey for the study was conducted on the present and past employees of IT/ITES companies located in Bangalore, India, while almost all types of industries across the world are facing the problem of the great resignation. So, the study is limited to the IT/ITES sector only, and future study could also be conducted on other sectors. In the survey, some of the respondents were the expats from other countries working in India, but their size was too less to represent the global population. Therefore, future research can take into account representative samples from the global population and conduct similar studies in a cross-cultural context.
Abbas, W., & Wu, W. 2018. Employee engagement, fairness and trust: An empirical assessment. Human Systems Management, 37(3): 287–297.
Afsar, B., & Rehman, Z. U. 2017. Relationship between work-family conflict, job embeddedness, workplace flexibility, and turnover intentions. Makara Human Behavior Studies in Asia, 21(2): 92–104.
Aggarwal, S., Singh, M., Chauhan, S., Sharma, M., & Jain, D. 2022. Employee Attrition Prediction Using Machine Learning Comparative Study. In Intelligent Manufacturing and Energy Sustainability, 265: 453-466.
Aina, M. A., Oyerinde, D. O., Onajite, O. G., & Falade, C. A. 2020. Evaluating perceived entrepreneurial skills for self-reliance among business education undergraduates in South-Western States’ Universities in Nigeria. Mediterranean Journal of Social Sciences, 11(2): 25–25.
Akgunduz, Y., & Eryilmaz, G. 2018. Does turnover intention mediate the effects of job insecurity and co-worker support on social loafing? International Journal of Hospitality Management, 68: 41-49.
Allen, R., Judkins-Cohn, T., deVelasco, R., Forges, E., Lee, R., Clark, L., & Procunier, M. 2013. Moral distress among healthcare professionals at a health system. Jona’s Healthcare Law, Ethics, and Regulation, 15(3): 111-118.
Altinay, L., Dai, Y.-D., Chang, J., Lee, C.-H., Zhuang, W.-L., & Liu, Y.-C. 2019. How to facilitate hotel employees’ work engagement: The roles of leader-member exchange, role overload and job security. International Journal of Contemporary Hospitality Management, 31(3): 1525-1542.
Anderson, S. 2021. CEOS WIN WHILE LOW-WAGE WORKERS SUFFER IN THE PANDEMIC. AMASS, 25(4): 34–35.
Aryee, S., Budhwar, P. S., & Chen, Z. X. 2002. Trust as a mediator of the relationship between organizational justice and work outcomes: Test of a social exchange model. Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior, 23(3): 267-285.
Atouba, Y. 2021. How does participation impact IT workers’ organizational commitment? Examining the mediating roles of internal communication adequacy, burnout and job satisfaction. Leadership & Organization Development Journal, 42(4): 580-592.
Azis, E., Prasetio, A. P., & Utomo, K. H. 2019. Overcoming Turnover Intention Problems: Direct–Indirect Model to Identify the Effect of Perceived Organizational Support and Job Satisfaction in Service-Based Organizations. Jurnal Aplikasi Manajemen, 17(3): 555–566.
Bagozzi, R. P., & Yi, Y. 1988. On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1): 74–94.
Barhate, B., & Hirudayaraj, M. 2021. Emerging Career Realities during the Pandemic: What Does it mean for Women’s Career Development? Advances in Developing Human Resources, 23(3): 253–266.
Basak, S. 2021. Factors affecting work-life balance of women in Bangladesh: A study during COVID-19 pandemic. Canadian Journal of Business and Information Studies, 3(3): 38–48.
Bedeian, A. G., Pizzolatto, A. B., Long, R. G., & Griffeth, R. W. 1991. The measurement and conceptualization of career stages. Journal of Career Development, 17(3): 153–166.
Bienasz, G. 2021. One-Third of People Who Quit Their Jobs Plan to Start Businesses. Available at: www.inc.com/gabrielle-bienasz/quitting-entrepreneurship-survey-great-resignation.html (accessed 02 December 2021)
Biswakarma, G. 2016. Organizational career growth and employees’ turnover intentions: An empirical evidence from Nepalese private commercial banks. International Academic Journal of Organizational Behavior and Human Resource Management, 3(2): 10-26.
Bollen, J. C., Dean, S. G., Siegert, R. J., Howe, T. E., & Goodwin, V. A. 2014. A systematic review of measures of self-reported adherence to unsupervised home-based rehabilitation exercise programmes, and their psychometric properties. BMJ Open, 4(6): e005044.
Bourhis, A., & Mekkaoui, R. 2010. Beyond work-family balance: Are family-friendly organizations more attractive? Relations Industrielles/Industrial Relations, 65(1): 98–117.
Brockner, J., Siegel, P. A., Daly, J. P., Tyler, T., & Martin, C. 1997. When trust matters: The moderating effect of outcome favorability. Administrative science quarterly, 42(3): 558-583.
Brough P., Timms C., Chan X.W., Hawkes A., Rasmussen L. 2020. Work–Life Balance: Definitions, Causes, and Consequences. In: Theorell T. (eds) Handbook of Socioeconomic Determinants of Occupational Health. Handbook Series in Occupational Health Sciences. Springer, Cham.
Byrne, B. M., & Campbell, T. L. 1999. Cross-cultural comparisons and the presumption of equivalent measurement and theoretical structure: A look beneath the surface. Journal of Cross-Cultural Psychology, 30(5): 555–574.
Cao, X., D. Zhang, & L. Huang 2020. ‘The Impact of COVID-19 Pandemic on Gig Economy Labor Supply.’ SSRN.
Chavadi, C. A., Sirothiya, M., & Vishwanatha, MR 2022. Mediating role of job satisfaction on turnover intentions and job mismatch among millennial employees in Bengaluru. Business Perspectives and Research, 10(1): 79–100.
Chitchai, N., Senasu, K., & Sakworawich, A. 2020. The moderating effect of love of money on relationship between socioeconomic status and happiness. Kasetsart Journal of Social Sciences, 41(2): 336–344.
Cohen, D. & Roeske-Zummer, K 2021. With So Many People Quitting, Don’t Overlook Those Who Stay. Available at: https://hbr.org/2021/10/with-so-many-people-quitting-dont-overlook-those-who-stay (accessed 01 December 2021)
Coyle-Shapiro, J. A., & Morrow, P. C. 2006. Organizational and client commitment among contracted employees. Journal of Vocational Behavior, 68(3): 416–431.
Craighead, C. W., Ketchen, D. J., Dunn, K. S., & Hult, G. T. M. 2011. Addressing common method variance: Guidelines for survey research on information technology, operations, and supply chain management. IEEE Transactions on Engineering Management, 58(3): 578–588.
Crowley, M. 2022. Its-not-just-money-this-is-whats-still-driving-the-great-resignation. [Weblog]. Available at: www.fastcompany.com/90727646/its-not-just-money-this-is-whats-still-driving-the-great-resignation (accessed 08 March 2022)
Deci, E. L., Koestner, R., & Ryan, R. M. 1999. A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychological bulletin, 125(6): 627.
Dhanpat, N., Manakana, T., Mbacaza, J., Mokone, D., & Mtongana, B. 2019. Exploring retention factors and job security of nurses in Gauteng public hospitals in South Africa. African Journal of Economic and Management Studies, 10(1): 57-71.
Dirks, K. T., & Ferrin, D. L. 2002. Trust in leadership: meta-analytic findings and implications for research and practice. Journal of applied psychology, 87(4): 611.
Evanoff, B. A., Strickland, J. R., Dale, A. M., Hayibor, L., Page, E., Duncan, J. G., Kannampallil, T., & Gray, D. L. 2020. Work-related and personal factors associated with mental well-being during the COVID-19 response: Survey of health care and other workers. Journal of Medical Internet Research, 22(8): e21366.
Fornell, C., & Larcker, D. F. 1981. Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1): 39-50.
Gallup Report 2016. “How Millennials Want to Work and Live”. Gallup Global Great Job Reports.
Ghose, A. K. 2016. India employment report 2016: Challenges and the imperative of manufacturing-led growth. OUP Catalogue.
Ghose, A. K. 2020. The pandemic, lockdown and employment. The Indian Journal of Labour Economics, 63(1): 67–71.
Goetzel, R. Z., & Ozminkowski, R. J. 2000. Disease management as a part of total health and productivity management. Disease Management and Health Outcomes, 8(3): 121-128.
Goetzel, R. Z., Hawkins, K., Ozminkowski, R. J., & Wang, S. 2003. The health and productivity cost burden of the" top 10" physical and mental health conditions affecting six large US employers in 1999. Journal of occupational and environmental medicine, 45(1): 5-14.
Goetzel, R. Z., Long, S. R., Ozminkowski, R. J., Hawkins, K., Wang, S., & Lynch, W. 2004. Health, absence, disability, and presenteeism cost estimates of certain physical and mental health conditions affecting US employers. Journal of occupational and environmental medicine, 46(4): 398-412.
Goetzel, R. Z., Ozminkowski, R. J., Meneades, L., Stewart, M., & Schutt, D. C. 2000. Pharmaceuticals—Cost or investment? An employer’s perspective. Journal of Occupational and Environmental Medicine, 42(4): 338–351.
Goetzel, R. Z., Ozminkowski, R. J., Sederer, L. I., & Mark, T. L. 2002. The business case for quality mental health services: Why employers should care about the mental health and well-being of their employees. Journal of Occupational and Environmental Medicine, 44(4): 320–330.
Goetzel, R. Z., Roemer, E. C., Holingue, C., Fallin, M. D., McCleary, K., Eaton, W., & Mattingly, C. R. 2018. Mental health in the workplace: A call to action proceedings from the mental health in the workplace: Public health summit. Journal of occupational and environmental medicine, 60(4): 322-330.
Klotz, Anthony. “How to Quit Your Job in the Great Post-Pandemic Resignation Boom” Interview by Arianne Cohen, Bloomberg Businessweek Workplace, May 10, 2021, https://www.bloomberg.com/news/articles/2021-05-10/quit-your-job-how-to-resign-after-covid-pandemic
Hair, J. F., Ortinau, D. J., & Harrison, D. E. 2010. Essentials of marketing research (Vol. 2). McGraw-Hill/Irwin New York, NY.
Hayes, J. R. 2013. Mediation, moderation, and conditional process analysis. Introduction to mediation, moderation, and conditional process analysis: A regression-based approach edn. New York: Guilford Publications, 120.
Hess, N., Jepsen, D. M., & Dries, N. 2012. Career and employer change in the age of the ‘boundaryless’ career. Journal of Vocational Behavior, 81(2): 280–288.
Jeffrey Hill, E., Grzywacz, J. G., Allen, S., Blanchard, V. L., Matz-Costa, C., Shulkin, S., & Pitt-Catsouphes, M. 2008. Defining and conceptualizing workplace flexibility. Community, Work and Family, 11(2): 149-163.
Huang, G., Niu, X., Lee, C., & Ashford, S. J. 2012. Differentiating cognitive and affective job insecurity: Antecedents and outcomes. Journal of Organizational Behavior, 33(6): 752–769.
Huang, X., Shi, K., Zhang, Z., & Cheung, Y. L. 2006. The impact of participative leadership behavior on psychological empowerment and organizational commitment in Chinese state-owned enterprises: The moderating role of organizational tenure. Asia Pacific Journal of Management, 23(3): 345–367.
Israel, G. D. 1992. Sampling the evidence of extension program impact. Gainesville, FL: University of Florida Cooperative Extension Service, Institute of Food and Agriculture Sciences, EDIS.
Jiang, K., Liu, D., McKay, P. F., Lee, T. W., & Mitchell, T. R. 2012. When and how is job embeddedness predictive of turnover? A meta-analytic investigation. Journal of Applied psychology, 97(5): 1077.
Jiang, L., & Probst, T. M. 2015. Do your employees (collectively) trust you? The importance of trust climate beyond individual trust. Scandinavian Journal of Management, 31(4): 526-535.
Jiang, L., & Probst, T. M. 2019. The moderating effect of trust in management on consequences of job insecurity. Economic and Industrial Democracy, 40(2): 409–433.
Jiang, Z., Hu, X., & Wang, Z. 2018. Career adaptability and plateaus: The moderating effects of tenure and job self-efficacy. Journal of Vocational Behavior, 104: 59–71.
Kajarekar, R. 2021. 10 Lakh Indian IT Employees Will Resign in 2021; Record Attrition Rate at 23% Expected. Available at: https://trak.in/tags/business/2021/10/11/10-lakh-indian-it-employees-will-resign-in-2021-record-attrition-rate-at-23-expected (accessed 30 October 2021).
Kalliath, T., & Brough, P. 2008. Work–life balance: A review of the meaning of the balance construct. Journal of Management & Organization, 14(3): 323–327.
Kane, P. 2021. The Great Resignation Is Here, and It's Real. Available at: www.inc.com/phillip-kane/the-great-resignation-is-here-its-real.html (accessed 25 October, 2021)
Karavardar, G. 2014. Organizational career growth and turnover intention: an application in audit firms in Turkey. International business research, 7(9): 67.
Kasser, T., & Ryan, R. M. 1993. A dark side of the American dream: correlates of financial
success as a central life aspiration. Journal of personality and social psychology, 65(2), 410.
Kothari, S. 2022 How the Great Resignation wave is impacting hiring, job
shifting. Available at: www.livemint.com/industry/human-resource/great-resignation-wave-
impacting-new- hiring-trends-job-shifting/amp-11642405623659.html (accessed 22 January
2022)
Kotler, P., Kartajaya, H., & Setiawan, I. 2019. Marketing 3.0: From products to customers to the human spirit. In Marketing wisdom (pp. 139–156). Springer.
Kurniawaty, K., Ramly, M., & Ramlawati, R. 2019. The effect of work environment, stress, and job satisfaction on employee turnover intention. Management science letters, 9(6): 877-886.
Li, Y.-F., Chao, M., & Shih, C.-T. 2018. Nurses’ intention to resign and avoidance of emergency department violence: A moderated mediation model. International Emergency Nursing, 39: 55–61.
Lim, R. 2021. Turning hobby into a side job. Available at: www.thestar.com.my/news/nation/2021/11/20/turning-hobby-into-a-side-job (accessed 02 December 2021)
Mishra, O. N., & Gupta, S. 2020. Antecedents and impact of e-commerce adoption among new venture firms: Evidence from tourism and hospitality industry. Vision, 24(4): 431–440.
Mitchell, J. & Dill, K. 2021. Workers Quit Jobs in Droves to Become Their Own Bosses. Available at: www.wsj.com/articles/workers-quit-jobs-in-droves-to-become-their-own-bosses (accessed 01 November 2021)
Mitchell, T. R., Holtom, B. C., Lee, T. W., Sablynski, C. J., & Erez, M. 2001. Why people stay: Using job embeddedness to predict voluntary turnover. Academy of Management Journal, 44(6), 1102–1121.
Mohammed, A. S., Shukla, V., & Pandey, A. C. 2020. Enhancing sentiment analysis using enhanced whale optimisation algorithm. International Journal of Intelligent Information and Database Systems, 13(2-4), 208–230.
Nadinloyi, K. B., Sadeghi, H., & Hajloo, N. 2013. Relationship between job satisfaction and employees mental health. Procedia-Social and Behavioral Sciences, 84: 293-297.
Nadler, M. L. 2017. Independent employees: A new category of workers for the gig economy. NCJL & Tech., 19: 443.
NASSCOM-ZINNOV REPORT 2021. Digital acceleration, Deep-tech adoption, Driving steady growth for India’s Tech start-up ecosystem in 2021
Nnaji, F. O. 2019. Assessment of the contribution of office technology and management programme on skill development for self-reliance in Bali local government Taraba State. Nigerian Journal of Business Education (NIGJBED), 6(1): 283–290.
O’Driscoll, M. P., & Beehr, T. A. 1994. Supervisor behaviors, role stressors and uncertainty as predictors of personal outcomes for subordinates. Journal of Organizational Behavior, 15: 141–155.
Okurame, D. 2012. Impact of career growth prospects and formal mentoring on organisational citizenship behaviour. Leadership & Organization Development Journal, 33: 66-85.
Okurame, D. E., & Balogun, S. K. 2005. Role of informal mentoring in the career success of first‐line bank managers: A Nigerian case study. Career Development International, 10(6-7): 512–521.
Pariyanti, E., Sofiyanti, N., & Rosid, A. 2020. Layoffs and the Mental Health of Remaining Workers in Pandemic Covid 19. Sustainable Competitive Advantage (SCA), 10(1)
Patel, M. (2022) Explained: What is the Great Resignation and what can be done to arrest it. Available at: https://indianexpress.com/article/explained/what-is-great-resignation-7728419 (accessed 22 January 2022)
Petrișor, C., Breazu, C., Doroftei, M., Mărieș, I., & Popescu, C. 2021. Association of Moral Distress with Anxiety, Depression, and an Intention to Leave among Nurses Working in Intensive Care Units during the COVID-19 Pandemic. 9(10): 1377.
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. 2003. Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5): 879.
Premkumar, G., & Ramamurthy, K. 1995. The role of interorganizational and organizational factors on the decision mode for adoption of interorganizational systems. Decision Sciences, 26(3): 303–336.
Purba, A., & Demou, E. 2019. The relationship between organisational stressors and mental wellbeing within police officers: A systematic review. BMC Public Health, 19(1): 1–21.
Robinson, S. L. 1996. Trust and breach of the psychological contract. Administrative science quarterly, 41(4): 574-599.
Sachau, D. A. 2007. Resurrecting the motivation-hygiene theory: Herzberg and the positive psychology movement. Human Resource Development Review, 6(4): 377–393.
Shin, Y., Hur, W.-M., Moon, T. W., & Lee, S. 2019. A motivational perspective on job insecurity: Relationships between job insecurity, intrinsic motivation, and performance and behavioral outcomes. International Journal of Environmental Research and Public Health, 16(10): 1812.
Shukla, V., Prashar, S., & Pandiya, B. 2021. Is price a significant predictor of the churn behavior during the global pandemic? A predictive modeling on the telecom industry. Journal of Revenue and Pricing Management, 1–14.
Siu, O., Cooper, C. L., & Donald, I. 1997. Occupational stress, job satisfaction and mental health among employees of an acquired TV company in Hong Kong. Stress Medicine, 13(2): 99–107.
Smith, M. (2021) Gen Z and millennial workers are leading the latest quitting spree—here’s why. Available at: www.cnbc.com/2021/09/03/gen-z-and-millennial-workers-are-leading-the-latest-quitting-spree-.html (accessed 30 October 2021)
Society for Human Resource Management. 2010. Employee Benefits: Examining employee benefits in the midst of a recovering economy. Alexandria, VA: SHRM
Son, S., & Kim, D.-Y. 2021. Organizational career growth and career commitment: Moderated mediation model of work engagement and role modeling. The International Journal of Human Resource Management, 32 (20): 4287–4310.
Szostak, M., & Sułkowski, Ł. 2021. Identity crisis of artists during the Covid-19 pandemic and shift towards entrepreneurship. Entrepreneurial Business and Economics Review (EBER) 9(3).
Sverke, M., Hellgren, J., & Näswall, K. 2002. No security: a meta-analysis and review of job insecurity and its consequences. Journal of occupational health psychology, 7(3): 242.
Talukder, M. S., Sorwar, G., Bao, Y., Ahmed, J. U., & Palash, M. A. S. 2020. Predicting antecedents of wearable healthcare technology acceptance by elderly: A combined SEM-Neural Network approach. Technological Forecasting and Social Change, 150: 119793.
Tan, H. H., & Tan, C. S. 2000. Toward the differentiation of trust in supervisor and trust in organization. Genetic, social, and general psychology monographs, 126(2), 241.
Taylor, P. 2021. The good news about labor shortages. Available at: www.politico.eu/article/good-news-labor-shortages-coronavirus-economic-recovery/ (accessed 01 December 2021)
Tarhini, A., Hone, K., & Liu, X. 2014. The effects of individual differences on e-learning users’ behaviour in developing countries: A structural equation model. Computers in human behavior, 41, 153-163.
Thakur, P., & Arora, R. 2022. Exploring the Relationship Between Satisfaction and Intention to Stay Among Millennial Employees: The Moderating Role of Managerial Support. International Journal of Human Capital and Information Technology Professionals (IJHCITP) 13(1): 1–17.
Thakur, S. J., & Bhatnagar, J. 2017. Mediator analysis of job embeddedness: Relationship between work-life balance practices and turnover intentions. Employee Relations, 39(5): 718-731.
Thanuja, K. A. 2021. A Study on Pandemic Related Problems among IT Company Employees with Reference to Bengaluru. In Virtual National Conference on The Changing Economic Models and trends In The Post Covid-19 World (130).
Theorell, T., Osika, W., Leineweber, C., Magnusson Hanson, L. L., Bojner Horwitz, E., & Westerlund, H. 2013. Is cultural activity at work related to mental health in employees? International Archives of Occupational and Environmental Health, 86(3): 281–288.
Umar, M., Xu, Y., & Mirza, S. S. 2021. The impact of Covid-19 on Gig economy. Economic Research-Ekonomska Istraživanja, 34(1): 2284-2296.
Usman, M., Cheng, J., Ghani, U., Gul, H., & Shah, W. U. 2021. Social support and perceived uncertainties during COVID-19: Consequences for employees’ wellbeing. Current Psychology, 1–12
Vega, J. C. A. de la, Cecchinato, M. E., & Rooksby, J. 2020. The Gig Economy in Times of COVID-19: Designing for Gig Workers’ Needs. In Microsoft Research Symposium on The New Future of Work, August 05–07, 2020, Virtual Symposium
Vidal-Salazar, M. D., Cordón-Pozo, E., & José, M. 2016. Flexibility of benefit systems and firms’ attraction and retention capacities. Employee Relations, 38(4): 487-504.
Wang, W., Mather, K., & Seifert, R. 2018. Job insecurity, employee anxiety, and commitment: The moderating role of collective trust in management. Journal of Trust Research, 8(2): 220–237.
Weng, Q., McElroy, J. C., Morrow, P. C., & Liu, R. 2010. The relationship between career growth and organizational commitment. Journal of vocational behavior, 77(3): 391-400.
Weng, Q., & Hu, B. 2009. The structure of career growth and its impact on employees’ turnover intention. Industrial Engineering and Management, 14(1): 14–21.
Weng, Q., & McElroy, J. C. 2010. Vocational self-concept crystallization as a mediator of the relationship between career self-management and job decision effectiveness. Journal of Vocational Behavior, 76(2): 234–243.
Wheeler, A. R., Harris, K. J., & Harvey, P. 2010. Moderating and mediating the HRM effectiveness—Intent to turnover relationship: The roles of supervisors and job embeddedness. Journal of Managerial Issues, 22(2): 182–196.
Wright, T. A., & Bonett, D. G. 2002. The moderating effects of employee tenure on the relation between organizational commitment and job performance: a meta-analysis. Journal of applied psychology, 87(6): 1183.
Yamane, T. 1967. Problems to Accompany Statistics: an introductory analysis. Harper and
Row.
Yang, W., Guan, Y., Lai, X., She, Z., & Lockwood, A. J. 2015. Career adaptability and perceived overqualification: Testing a dual-path model among Chinese human resource management professionals. Journal of Vocational Behavior, 90: 154–162.
Zaman, K., Rana, M. S., & Iftikhar, U. 2019. A Multilevel Analysis of Job Demands and Intention to Resign Through Perceived Service Recovery Performance. Business & Economic Review, 11(2): 67–82.
Table 1 Confirmatory Factor Analysis and Convergent Validity
|
Constructs |
Items |
Std Factor Loading |
C.R. |
AVE |
|
Work life balance (WLB) |
WLB1 |
0.83 |
0.87 |
0.57 |
|
WLB2 |
0.81 |
|||
|
WLB3 |
0.76 |
|||
|
WLB4 |
0.69 |
|||
|
WLB5 |
0.67 |
|||
|
Uncertainty at Work (UAW) |
UAW1 |
0.84 |
0.85 |
0.54 |
|
UAW2 |
0.77 |
|||
|
UAW3 |
0.75 |
|||
|
UAW4 |
0.67 |
|||
|
UAW5 |
0.63 |
|||
|
Mental wellbeing (MWB) |
MWB1 |
0.84 |
0.86 |
0.55 |
|
MWB2 |
0.79 |
|||
|
MWB3 |
0.76 |
|||
|
MWB4 |
0.66 |
|||
|
MWB5 |
0.64 |
|||
|
Monetary compensation (MC) |
MC1 |
0.84 |
0.89 |
0.59 |
|
MC2 |
0.81 |
|||
|
MC3 |
0.77 |
|||
|
MC4 |
0.77 |
|||
|
MC5 |
0.74 |
|||
|
MC6 |
0.68 |
|||
|
Career Growth (CG) |
CG1 |
0.84 |
0.89 |
0.58 |
|
CG2 |
0.79 |
|||
|
CG3 |
0.78 |
|||
|
CG4 |
0.74 |
|||
|
CG5 |
0.71 |
|||
|
CG6 |
0.69 |
|||
|
Self-Reliance (SR) |
SR1 |
0.82 |
0.85 |
0.58 |
|
SR2 |
0.77 |
|||
|
SR3 |
0.74 |
|||
|
SR4 |
0.71 |
|||
|
Employee-employer trust (EET) |
EET1 |
0.80 |
0.79 |
0.57 |
|
EET2 |
0.75 |
|||
|
EET3 |
0.69 |
|||
|
Intention to resign (ITR) |
ITR1 |
0.80 |
0.82 |
0.53 |
|
ITR2 |
0.78 |
|||
|
ITR3 |
0.67 |
|||
|
ITR4 |
0.65 |
|||
|
Actual Resignation (AR) |
AR1 |
0.79 |
0.69 |
0.53 |
|
AR2 |
0.66 |
Table 2 Discriminant Validity
|
AVE |
WLB |
UAW |
MWB |
MC |
CG |
SR |
EET |
ITR |
AR |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
WLB |
0.57 |
0.75† |
|
|
|
|
|
|
|
|
|
UAW |
0.54 |
0.44 |
0.74† |
|
|
|
|
|
|
|
|
MWB |
0.55 |
0.31** |
0.33** |
0.74† |
|
|
|
|
|
|
|
MC |
0.59 |
0.27** |
0.25** |
0.17** |
0.77† |
|
|
|
|
|
|
CG |
0.58 |
0.31** |
0.29** |
0.27** |
0.26** |
0.76† |
|
|
|
|
|
SR |
0.58 |
0.25** |
0.22** |
0.41** |
0.34** |
0.37** |
0.76† |
|
|
|
|
EET |
0.57 |
0.29** |
0.27** |
0.25** |
0.39** |
0.33** |
0.36** |
0.75† |
|
|
|
ITR |
0.53 |
0.19** |
0.22** |
0.33** |
0.25** |
0.28** |
0.20** |
0.29** |
0.73† |
|
|
AR |
0.53 |
0.23** |
0.22** |
0.34** |
0.27** |
0.28** |
0.27** |
0.27** |
0.25** |
0.73† |
†Square root of the AVE for each construct
** p < 0.001
Table 3 Structural Equation Modelling Results
|
Hypotheses |
Path |
Estimate |
P-Value |
Decision |
|
H1 |
WLB → ITR |
0.16 |
.013 |
Supported |
|
H2 |
UAW → ITR |
0.39 |
.000 |
Supported |
|
H3 |
MWB → ITR |
0.19 |
.007 |
Supported |
|
H4 |
MC → ITR |
0.14 |
.010 |
Supported |
|
H5 |
CG → ITR |
0.12 |
.002 |
Supported |
|
H6a |
ITR → AR |
0.44 |
.042 |
Supported |
Table 4 Results of the mediation analysis
|
Bootstrapping |
Direct effect |
Indirect effect |
BootSE |
95% confidence interval |
Mediation Result |
|
|
LLCI |
ULCI |
|||||
|
ITR<-SR->AR |
.44 |
0.20 |
0.02 |
0.01 |
0.05 |
Partial Mediation |
Table 5 Moderation effect of Work Experience and Employee-Employer Trust
|
Moderation effect Work Experience |
|||||
|
|
Less than 5 years (180) |
More than 5 years (155) |
|
||
|
Path |
Estimate |
P-value |
Estimate |
P-Value |
Z score |
|
WLB → ITR |
0.39 |
.000 |
0.59 |
.000 |
2.18* |
|
UAW → ITR |
0.04 |
.584 |
0.09 |
.313 |
1.86 |
|
MWB→ ITR |
0.20 |
.009 |
0.19 |
.038 |
1.70* |
|
MC → ITR |
0.29 |
.000 |
0.15 |
.011 |
-1.48* |
|
CG → ITR |
0.02 |
.685 |
0.16 |
.005 |
1.49* |
|
Moderation effect of Employee-Employer trust |
|||||
|
|
Low trust (217) |
High Trust (118) |
|
||
|
Path |
Estimate |
P-value |
Estimate |
P-Value |
Z score |
|
WLB → ITR |
0.46 |
.390 |
0.36 |
.109 |
0.45 |
|
UAW → ITR |
0.25 |
.006 |
0.11 |
.009 |
6.46* |
|
MWB→ ITR |
0.23 |
.001 |
0.57 |
.000 |
6.01* |
|
MC → ITR |
0.31 |
.174 |
0.08 |
.281 |
0.97 |
|
CG → ITR |
0.32 |
.000 |
0.49 |
.000 |
3.04* |
A statement on participant consent- The participants were approached through their team leads and prior consent was taken from them to participate in the survey.
Competing interests: The authors declare no competing interests.