Economic Stress, Employee Commitment, and Subjective Well-Being

Introduction

Because of the number of infected people and deaths in a short period of time, the rapid spread of the contagion, the absence of a vaccine, the number of countries struggling to cope with the infection, the lockdowns imposed by governments, and the shutdown of whole sectors to limit social contact to control the spread, the COVID-19 pandemic and the subsequent health, economic, and employment crises have no parallel in the history of humankind. Economists say that the economy is now in the equivalent of a medically induced coma (Krugman, 2020).

Business shutdown is causing severe hardships for workers and organizations, with millions of jobs lost in a few weeks and millions of businesses closed across the world. Some workers can work remotely, but many others are confined at home, often in extremely difficult conditions (e.g., reduced space, lack of income, home overcrowding, lack of medical care, lack of water, among others). Working routines have been altered, with both short and long-term effects. Clearly, the COVID-19 pandemic has changed many aspects of our working and nonworking lives. As a contribution of I/O psychology to the understanding of how COVID-19 has affected employees and organizations, the aim of this study is to examine the impact of COVID-19 income-related and employment-related stressors on psychological well-being (PWB) and employee commitment (EC).

PWB has also been referred to as subjective well-being and happiness (see, for instance, Gómez-Borges et al., 2022; Martínez-Tur et al., 2022; Moscoso & Salgado, 2021; Salgado & Moscoso, 2022). Concerning employees’ PWB, Probst et al. (2018, see also, Probst et al., 2017) examined the impact of the economic crisis of 2007-2008 on PWB. Probst et al. (2018) distinguished between two types of economic-related stressors. On the one hand, they identified employment-related stressors, both objective and subjective, e.g., job insecurity (subjective stressor), underemployment, and unemployment (objective stressors). On the other hand, there are income-related stressors, such as perceived final inadequacy (subjective stressor) and economic deprivation (objective stressor). Probst et al. (2018), using two large nationally representative samples obtained during the last economic crisis, found that PWB correlated negatively with income-related stress (-.46 and -.51) and with employment-related stress (-.18 and -.14). Since the COVID-19 pandemic resulted in a combination of a health crisis and an economic crisis, their effects could have even more dramatic consequences for the PWB of employed and unemployed individuals. Based on these findings, the first two hypotheses are the following:

Hypothesis 1: Income-related stress correlates negatively with PWB as measured by the balance between positive and negative emotions.

Hypothesis 2: Employment-related stress correlates negatively with PWB as measured by the balance between positive and negative emotions.

EC refers to an employee’s psychological attachment to the organization (Gurbuz et al., 2022; Kuok, 2022). At present, Meyer et al.'s (1993) three-component model of EC has achieved the largest consensus among researchers. According to Meyer and Allen 1991; Meyer et al., 1993; and Meyer et al., 2000, EC consists of three different-but-related aspects: affective (AFC), normative (NOC), and continuity (COC). They predict intention to leave and turnover, based-on-job behavior (e.g., job performance, leadership, and attendance), and employee health and well-being (e.g., work stress, job insecurity). Meta-analytic research by Mathieu and Zajac (1990) found that EC correlated positively with age, salary, job performance, job satisfaction, and job involvement, and negatively with stress, intention to leave, turnover, among others. The meta-analysis of Meyer et al. (2002) found that AFC and NOC correlated positively with age, transformational leadership, job involvement, job satisfaction, performance, and citizenship behaviors, and negatively with turnover. Furthermore, AFC correlated negatively with stress, while COC correlated positively with age and stress and negatively with transformational leadership and turnover. Moreover, Meyer et al. (2013) suggested that employees’ level of commitment may make them more eligible to receive both extrinsic rewards (e.g., wages and benefits) and psychological rewards (e.g., intrinsic job satisfaction and increased job security).

These findings, per se, support the importance of examining EC in the context of an economic crisis. Nonetheless, additional research is even more necessary because, although a great deal of research has been devoted to investigating the antecedents, consequences, and correlates of EC, less is known about the effects of economic crises on EC. To the best of our knowledge, only two studies have examined the effects of economic crises on EC. The first one was conducted by Markovits et al. (2014), who assessed EC in samples collected before and during the Greek economic crisis of 2011-2012. Markovits et al. (2014) found lower levels of both AFC and NOC during the crisis than before, but CC levels did not differ across the two time periods. The second study has been recently carried out by Meyer et al. (2018), who used data collected before and during the Turkish economic crisis of 2001. Contrary to the findings of Markovits et al. (2014), Meyer et al. (2018) found that AFC and NOC levels were not lower following the crisis than before. Moreover, Meyer et al. (2018) found that the level of CC was higher after the crisis, while Markovits et al. (2014) found no differences in the level of CC before and after the crisis. Thus, the two studies arrived at different conclusions. Moreover, neither of them examined the effects of income-related and employment-related stress on EC and its components. Consequently, it is an open question as to whether economic crises have effects on EC. In this regard, the COVID-19 pandemic offers a unique opportunity to examine whether the most severe health and economic crisis we have known has immediate consequences on EC. Based on previous findings and rationale, the next three hypotheses of the study are the following:

Hypothesis 3: Income-related stress correlates negatively with overall EC and its three components.

Hypothesis 4: Employment-related stress correlates negatively with overall EC and its three components.

Hypothesis 5: Affective commitment correlates positively with psychological well-being as defined by the balance between positive and negative emotions.

Method

Participants

There were 941 participants (381 men and 560 women), collected through the contacts of the authors (e.g., colleagues) using a snowball procedure across all the Spanish regions and provinces. By occupation, 46.0% were employees in the private sector, 23.0% were civil servants, 12.5% were self-employed, 13.9 furloughed, and 4.6% unemployed. The monthly net salary/income was distributed as follows: no income (4.2%), less than €1,000 (13.6%), €1,000 to €1,500 (28.9%), €1,501 to €2,000 (19.2%), €2,001 to €2,500 (13.2%), €2,501 to €3,500 (14.9%), €3,501 to €4,500 (4.4%), €4,501 to €6,000 (1.4%), more than €6,000 (0.3%).

Measures

Cognitive Well-being (CWB)

The Satisfaction with Life Scale (SWLS; Diener et al., 1985) assesses the cognitive component of subjective well-being. The Satisfaction with Life Scale scale consists of five items that evaluate the perception of one’s life satisfaction. Examples of items are “In most ways, my life is close to my ideal” and “The conditions of my life are excellent”. The SWLS uses a seven-point Likert format. The SWLS is probably one of the most frequently used scales for measuring happiness, and a large number of studies have examined its psychometric properties. Pavot and Diener (1993) revised the evidence on its internal consistency and temporal stability and reported alpha coefficients ranging from .79 to .89, and test-retest coefficients ranging from .84 to .54 for intervals from 1 month to 4 years. This scale has been validated with Spanish samples and normative data are available. The mean and SD of the normative Spanish sample of currently employed adults (n = 1,807) were 24.33 and 5.63, respectively. The reliability (Cronbach’s alpha) of the SWLS in the current sample was .89.

Affective Well-being

To measure the emotional component of the SWB, we used the seven positive adjective and seven negative adjectives of the Scale of Positive and Negative Experience (SPANE; Diener & Biswas-Diener, 2008; Diener et al., 2009; Li et al., 2013; Silva & Caetano, 2013). Examples of the positive and negative adjectives were delighted, happy, joyful, negative, stressed, sad, and depressed. Respondents indicated how often they had experienced these feelings during the previous four weeks using a 5-point Likert scale (1 = very rarely or never, 5 = very often or always). The sum of the ratings to the seven positive adjectives served to create a positive affect (PA) compound, and the sum of the seven negative adjectives served to create a negative affect (NA) compound. A score of affective well-being (AWB) (emotional balance) was obtained subtracting the NA score from the PA score. The potential range of the PWB score is -28 to +28. The alpha coefficient was .91 for AWB, .90 for PA, and .83 for NA. For instance, Diener et al. (2009) reported a correlation of -.62 between the two scales, and test-retest reliabilities of .62 and .63 for positive and negative emotions, respectively. The corrected correlation is .62/(.62 + .63)1/2 = .97. Similar correlations between positive and negative emotions were reported in literature (e.g., Li et al., 2013; Rahm et al., 2017; Silva & Caetano, 2013; Telef, 2015) and identical or larger test-retest reliabilities (Rahm et al., 2017; Telef, 2015) depending on the interval width. Additionally, large correlations between SWLS and SPANE have been found (e.g., Li et al., 2013; Rahm et al., 2017; Silva & Caetano, 2013; Telef, 2015). The respondents indicated how much they had experienced these feelings during the four weeks previous to the testing session. Items in the SPANE are responded by using a 5-point Likert scale (1 = very rarely or never, 5 = very often or always). The alpha coefficient was .91 in this sample.

Objective Indicators of Employment-related and Income-related Stress

Objective employment-related stress (OES) was estimated using two items: participants’ employment type (i.e., full-time employed, self-employed, part-time, furloughed, unemployed), and participants’ contract type (i.e., temporary vs. permanent). The combination of these two items produces five categories of OES (from lower to higher): 0 = full-time permanent employee, 1 = self-employed, 2 = part-time and temporary employees, 3 = furloughed employees, and 4 = unemployed.

Two indicators of objective income-related stress were used. The first one was monthly net income, which was coded as follows: 1 = no income; 2 = less than €1,000; 3 = €1,000 to €1,500; 4 = €1,501 to €2,000; 5 = €2,001 to €2,500; 6 = €2,501 to €3,500; 7 = €3,501 to €4,500; 8 = €4,501 to €6,000; 9 = more than €6,000). The second objective indicator was economic deprivation (ECD). Following Probst et al. (2018), ECD was estimated as the per capita household income by taking the self-reported participant income range divided by the total of one (i.e., the respondent) plus the number of dependents. To be consistent with the hypotheses, the sign of the ECD correlations was reversed, so that the higher the ECD the more the negative effects.

Subjective Indicator of Income-related and Employment-related Stress

Five items assessed participants’ perceptions of the effects of the COVID-19 crisis on their income-and-employment situations. Three items served as an indicator of subjective income-related stress (SIS): “What effect has COVID-19 crisis had on your employment contract?”, “What effect has COVID-19 crisis had on your salary/income in the last four weeks?”, “What effect has COVID-19 crisis had on your workday in the last four weeks?”. Participants answered using the following 5-point scales: 1 = positively affected, 5 = negatively affected (item 1), 1 = very much increased, 5 = very much reduced (items 2 and 3). Thus, a lower score means more SIS. The alpha coefficient was .79. Two items served to create an indicator of the subjective employment-related stress (SES): “Do you consider that the current economic situation at your home is better or worse than six months ago?”, “Do you consider that the situation of your home within six months will be better or worse than at present?”. Participants could answer each item on a 5-point scale were 1 = much worse, and 5 = much better. Therefore, a lower score means more SES. The alpha coefficient was .73.

Organizational Commitment

The study used a Spanish version of the well-known scale by Meyer and Allen (1993) for assessing affective, normative, and continuity commitment. The AFC scale consists of 6 items, the NOC scale was assessed with eight items, and the COC scale was assessed with six items, all of which were responded to on a 5-point scale. The alpha coefficients were .90, .83, and .91, for AFC, NOC, and COC respectively. The alpha coefficient was .91 for the whole scale in this sample.

Job Performance (JP)

This was assessed by using the instrument developed by Black and Porter (1991). This scale asks the participants to think back to the last performance appraisal in their current job. Next, the participants specify where the score would place them relative to their colleagues on a percentage basis. The scale contains five dimensions: (1) overall performance, (2) ability to get along with others, (3) completing tasks on time, (4) quality (as opposed to quantity) of performance, and (5) achievement of work goals. The coefficient Alpha was .86 in this study.

Procedure

A survey containing the scales was distributed through the author’s contacts. The response to the survey was anonymous, as no identification clues were recorded. The survey was answered on-line, and no IP addresses were recorded. The sample is not a random one, but it is representative of the Spanish working population in terms of age, income, and occupations. The survey contained four sections that requested the following information: (1) background information (e.g., sex, age, occupation type, contract type, income), (2) income-related and economic-related stress, (3) psychological well-being scale, and (4) employment commitment scales. The responses were collected between April 13^th and April 18^th, 2020, two weeks after the peak of the deaths reported by the COVID-19 pandemic in Spain.

Results

Zero-order Correlation Analysis

Table 1 reports the correlations among the variables. Concerning Hypothesis 1 about the negative relationship between the estimates of income-related stress and PWB, income correlated -.19 (p < .001) with PWB, .23 (p < .001) with NA, and -.11 (p < .01) with PA. Economic deprivation correlated positively with NA (.11,p < .01) and marginally-and-negatively with PWB (-.07, p < .07). SIS correlated -.21 (p < .001) with PWB, .24 (p < .001) with NA, and -.14 (p < .001) with PA. Therefore, Hypothesis 1 was generally supported. Regarding Hypothesis 2, OES correlated -.10 (p < .01) with PWB and .14 (p < .001) with NA. The subjective estimate of employment-related stress (SES) correlated significantly with PWB (-.09, p < .02) and NA (.12, p < .001). Thus, the results also supported Hypothesis 2. Regarding Hypothesis 3, income correlated with the three components of commitment and with overall employment commitment. The correlations were -.31, -.20, -.14, and -.28 for AFC, NOC, COC, and OEC, respectively (all significant at p < .001). Economic deprivation correlated significantly with AFC (-.11, p < .01), NOC (-.08, p < .01), and OEC (-.10, p < .01), but it did not correlate significantly with COC. The subjective measure of income-related stress (SIS) correlated significantly with AFC (-.16, p < .001), NOC (-.09, p < .02), and OEC (-.14, p < .001). The correlation with COC was marginally significant (-.07, p < .07). So, the results also supported Hypothesis 3. Regarding Hypothesis 4, the four estimates of commitment correlated significantly with OES. The correlations were -.23, -.16, -.12, and -.21 with AFC, NOC, COC, and OES, respectively (all significant at p < .001). The four estimates of commitment also correlated with SES, with correlations of -.20, -.12, -.11, and -.19, for AFC, NOC, COC, and OEC, respectively (all significant at p < .001). Therefore, the results also supported Hypothesis 4. Finally, AFC correlated .24 with PA, -.18 with NA, and .23 with PWB. Consequently, these results supported Hypothesis 5.

Although no hypotheses have been advanced, it must be noted that sex (male = 0, female = 1) correlated -.12 (p < .001) with AFC, .09 (p < .02) with OEC, .23 (p < .001) with NA, and -.17 (p < .001) with PWB. Age correlated with AFC, OEC, NA, and PWB, with correlations of .17, .14, -,19, and .10, respectively (all significant p < .01). Work experience correlated significantly with AFC, OES, NA, and PWB (.17, .15, -.17, and .09, respectively). Finally, the number of dependents correlated significantly with all the commitment estimates and psychological well-being estimates.

Finally, an indicator of economic-related stress (IES) was created using the five indicators of income-related and employment-related stress. The correlations between IES and PA, NA, and PWB, corrected for measurement error X and Y, were -.11, .27, and -.21, respectively, and the corrected correlations between IES and AFC, NOC, COC, and OEC were -.31, -.24, -.15, and -.29, respectively (p < .001).

In conclusion, the zero-order correlation analysis provides support for the five hypotheses. Besides, it is important to remark that the hypotheses were generally supported for both the objective and subjective estimates of income-related and employment-related stress. It is also interesting to mention that objective income-related estimates and employment-related stress showed higher correlations with commitment than subjective estimates. This was not the case with PWB as both objective and subjective income-related and employment-related estimates showed very similar correlations. Finally, the economic-related stress indicator correlated significantly with the three PWB estimates and with the four EC estimates.

Multiple Regression Analyses

Table 2 reports the results of the multiple regression analyses carried out using the four estimates of employee commitment as dependent variables and the background measures, and the income-related and employment-related stress estimates as independent variables. Table 3 presents the results of the multiple regression analyses for the psychological well-being estimates.

As can be seen in Table 2, when all the independent variables were entered into the analyses, the multiple R was .304, .193, .169, and .280 for AFC, NOC, COC, and OEC, respectively. In the case of AFC, only the number of dependents, economic deprivation, and OES were significant predictors. The same three variables predicted NOC significantly. The number of dependents and economic deprivation predicted COC, and the number of dependents, economic deprivation, and OES predicted OEC. As a summary, then, employment commitment is solely predicted by employment-related stress. Neither background variables nor income-related variables were predictors of employee commitment. Therefore, the employment-related stress induced by the COVID-19 crisis is the only factor which explains the variations on commitment scores.

Table 3 shows that multiple Rs were .204, .327, and .26 for PA, NA, and PWB, respectively. These results mean that NA was better predicted than PA and PWB. Concerning predictors of the psychological well-being measures, the subjective income-related stress consistently predicted PA, NA, and PWB. Moreover, the number of dependents predicted PA and PWB significantly. Sex predicted NA and PWB and age predicted NA. Therefore, income-related stress but not employment-related stress explained the variations in the psychological well-being measures.

In summary, the multiple regression analyses showed that the COVID-19 induced stress has effects on both psychological well-being and employee commitment, but the sources of the stress influence are different. Employment-related stress affects employee commitment, while income-related stress affects psychological well-being. Moreover, employee commitment is not affected by sex, age, work experience, and the number of dependents. Sex, age, and number of dependents have a role in the effects of psychological well-being.

Discussion

This study has some unique features. First, no previous study had investigated the effects of simultaneously occurring health and economic crises on psychological well-being and employee commitment. Second, the dataset has been collected during the worst moments of the COVID-19 pandemic. Third, compared to previous research, the current study included both objective and subjective indicators of income-related and employment-related stress and both an overall estimate of PWB and measures of positive affect and negative affect. For instance, Probst et al.’s (2018) study did not include objective and subjective indicators of employment-related stress and did not distinguish between positive and negative components of affect.

These study findings support previous findings by Probst et al. (2018) on the role of economic-related stress (income-related and employment-related stress), as both objective and subjective estimators of economic-related stress correlated with PWB in the hypothesized direction. Furthermore, this study showed that the relationship of economic-related stress with NA is remarkably higher than it is with PA (.27 vs. .11) and that the correlations of objective and subjective economic-related stress indicators with PWB, PA, and NA showed similar sizes. These relationships have not been researched before.

Regarding EC, the findings showed a negative relationship between economic-related stress indicators and EC and its components. Nineteen out of 20 correlations were statistically significant. Economic-related stress, considered as a whole, showed significant correlations with the three components of EC and with the overall EC, suggesting that the current economic crisis plays a critical role in lowering the level of all EC components. In this sense, the current findings concur with the findings of Markovits et al. (2014) and contradict the results of Meyer et al. (2018).

This study has made several unique contributions. First, the study did not only examine whether economic crises affect EC and its components but also revealed the specific mechanisms (antecedents) by which an economic crisis affects EC and its components. In this regard, EC is more affected by employment-related stress than by income-related stress. The second unique contribution is that the study showed the specific relationship between objective and subjective estimates of income-related and employment-related stress and psychological well-being, positive affect, and negative affect. The third unique contribution has been to show that, among the crisis-related variables, only a few were significant predictors of employee commitment and well-being and that they are not the same for psychological well-being and employee commitment.

The findings of this study suggest some avenues for maintaining or even increasing employee commitment and psychological well-being. In the case of EC, the findings suggest that organizations should concentrate their efforts on employment-related stress by reducing the uncertainty and insecurity of employees about their jobs. In connection with EC, employees are more stressed about the possibility of losing their jobs than they are about their incomes. Regarding psychological well-being, companies should concentrate their efforts on reducing the subjective income-related stress, helping their employees to develop realistic expectations about future income, and managing their financial resources. Some companies are already doing excellent work on this issue. For example, the Spanish multinational clothing company Inditex, the biggest fashion group in the world, early in March informed their employees that the company would try to maintain their current level of employment for the next few months. Undoubtedly, this news has been very welcome to their over 162,000 employees worldwide. Of course, not all companies can do the same.

A limitation of this study should be mentioned. This cross-sectional study does not permit the establishment of causal links between economic related-stress and PWB and EC. Nevertheless, because the data was collected in the precise moment of the crisis and that the sample contains individuals who are employed, unemployed, and furloughed, the causal effects of EIC can be assumed.