Quantifying Employee Emotions in Longitudinal Work Environment Questionnaires

Quantifying Employee Emotions in Longitudinal Work Environment Questionnaires | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Quantifying Employee Emotions in Longitudinal Work Environment Questionnaires Lucas H. Craven, Petri J. Kajonius This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5237797/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract An individual differences' perspective posits that relatively stable emotions dictate an employee’s perception of the work environment. The present study, based on the Danish Psychosocial Questionnaire, set out to quantify to what extent individuals’ negative and positive affect account for the longitudinal relationships between baseline (Wave T1; N = 3,970) work environment (i.e., job demands or job relationships) and employee well-being (i.e., job satisfaction or perceived job stress) six months later (Wave T2; N = 2,375). Partial correlation analyses showed that very brief measures of employee emotions accounted for 75% of the variance between the initial work environment and later employee well-being. We argue and discuss the importance of considering individual differences in emotional traits in work environments, a dynamic often overlooked in research and practice in workplace interventions and improvements. Biological sciences/Psychology Biological sciences/Psychology/Human behaviour work environment individual differences well-being negative affect positive affect Introduction Work environment conditions (e.g., the job demands) are being related to later employee well-being (e.g., satisfaction, stress, or health). We still have limited knowledge of what extent such relationships reflect the actual work environment or mostly individual characteristics (Kasl, 1998; Rugulies, 2019; Theorell & Hasselhorn, 2005). This line of research often relies on employee surveys, and self-reported work environment data is at least partly explained by the employee's own emotions (Lazarus, 2003). Overlooking and not controlling for this fact in psychosocial questionnaires may confound analyses and interpretations of how the work environment impacts employees. The present study had access to the stratified Danish Psychosocial Questionnaire (DPQ) and attempted to quantify the extent to which individual differences in emotions explain the longitudinal relationship between the work environment and employee well-being. Work Environment and Employee Well-being W ork environment is here defined in broad terms referring to what the workplace is like at the current time, including job demands, job role clarity, and work relationships. Employee well-being refers to how workers feel over time based on their workplace, such as job satisfaction, perceived stress, and perceived health ( See Lesener et al., 2019 for a comprehensive review). The work environment predicts how satisfied or stressed an employee is with their job (Fried & Ferris, 1987; Kavosi et al., 2018). Positive work environments are often the precursor for employees to feel positively engaged at work (Clausen et al., 2017). Job demands encompass a job's physical, psychological, social, and organisational work environment. Quantitative job demands are about the extent to which a job requires employees to work long hours, meet tight deadlines, or handle a large workload (Clausen et al., 2017). Meanwhile, emotional demands relate to aspects of the job that require sustained emotional effort from the employee, such as interacting with challenging colleagues or customers (Jonge et al., 2008). Both quantitative and emotional demands are related to job dissatisfaction and reduced well-being (Bowling et al., 2015; Edvik et al., 2020; Idris et al., 2011; Li et al., 2022; Scanlan & Still, 2019). Job role clarity is another aspect of the work environment, which refers to how employees understand their current job duties, objectives, and expectations. Roles are further limited by the level of influence an employee can exert to make decisions, set priorities, and solve problems at work (Clausen et al., 2017). Both job role clarity and employee influence have shown negative associations with job stress (Kavosi et al., 2018). Work relationships emphasise the interpersonal environment with colleagues and leadership. Teamwork reflects the willingness and ability of colleagues to work together supportively (Clausen et al., 2017; 2019), whereas leadership relationships refer to the leader's ability to communicate, motivate, and prioritise employee well-being (Clausen et al., 2017). Leadership is predictive of employee well-being (Clausen & Borg, 2010, 2011; Kelloway & Barling, 2010; Kuoppala et al., 2008; Mullen et al., 2008). This line of research has suggested that leadership quality affects employee well-being more than most other factors (Gilbreath & Benson, 2004). Employees in collaborative relationships report higher job satisfaction (Clausen & Borg, 2011; Edmondson & Lei, 2014; Nielsen & Daniels, 2012; Stansfeld & Candy, 2006). Negative Affect and Positive Affect at Work Not every employee reacts in the same way to the same work environment. For instance, some people are more content, while others are more stressed (Steel et al., 2008). One of the most commonly used instruments to capture individual differences in emotions is PANAS (Positive and Negative Affect Schedule; Watson & Clark, 1994). PANAS is most often used for measuring ongoing emotional experiences (Merz & Roesch, 2011) and has reported relatively consistent test-retest stability over a year (Watson & Clark, 1994). Moreover, most of this stability seems to originate in the individual’s genetic make-up, and accounts for about half of the variance in most individual differences (Polderman et al., 2015). The impact of environmental experiences (for instance having a wage rise) on most individual differences tends to be small over time (e.g., Bühler et al., 2023). A study using PANAS longitudinally reported that at most up to 18% of within-person variance in positive and negative affect was due to effects from the environment (Cloos et al., 2023). Thus, work environments are accordingly not expected to greatly change employees, and is the reason why PANAS in the present study is treated as an individual control variable. The two fundamental temperament states, negative affect and positive affect, have been shown to play a significant role in all aspects of well-being (Anglim et al., 2020; Steel et al., 2008). Negative affect is characterised by a heightened susceptibility to negative experiences and stressful environments, including stress in the workplace (Bowling & Eschleman, 2010; Carlson, 1999; Ebstrup et al., 2011; Heinisch & Jex, 1997; Luo et al., 2023). This type of emotional experience also extends to physical and mental health (Jeronimus et al., 2014; Kotov et al., 2010; Lahey, 2009). Individuals high in negative affect tend to be less happy in any kind of relationship and are more inclined to negatively attribute faults to others (Karney & Bradbury, 2000). In contrast, positive affect is a core aspect of outgoing and energetic behaviours and is associated with job satisfaction, general happiness, and overall life satisfaction (Wilmot et al., 2019). Thus, such individuals would be more likely to experience working environments more positively because of their predispositions. Research Question The present study aimed to quantify the extent to which negative and positive affect explain the longitudinal relationships between the work environment and employee well-being. The research question was as follows: How much of the relationship between the work environment (i.e., quantitative and emotional demands, job role clarity, employee influence, teamwork, and leadership) and employee well-being (i.e., stress, health, and job satisfaction) can be explained by individual differences in emotional experiences (i.e., negative affect and positive affect)? Method Participants and Procedure The DPQ was distributed to a stratified sample of 8,958 employed individuals in Denmark, representing the Danish labour market. Data from this survey have been used for other studies (Clausen et al., 2022a ; 2022b ; 2023 ; Lunen et al., 2023 ). More details about the sampling are described in Clausen et al. ( 2019 ; 2023 ). At the first baseline (Wave T1) survey measurement, 48.4% response rate, N = 3,970 remained after cleaning incomplete questionnaires and unemployed workers. The sample was equally distributed across 14 different job groups, including office work, healthcare and social services, education, manufacturing, law enforcement, and business management (range = 5–9%). Women constituted 47% of the sample, and the age distribution was as follows: 6% between 18–34, 19% between 35–44, 32% between 45–54, and 43% over 54. Additionally, 26% had a high school diploma, 15% had a short-cycle tertiary education, 27% had a medium-cycle tertiary education, and 19% had a long-cycle tertiary education. After 6 months, a follow-up (Wave T2) questionnaire was conducted of which N = 2,375 met the inclusion criteria. Instruments 1 Quantitative Demands/Work Environment. The quantitative demands scale in the DPQ was used to estimate workload. The scale consists of four items with questions like "How often is it the case that you do not have time to complete all your work tasks?" and "How often do you have deadlines that are hard to meet?" using a 5-point Likert scale ranging from "Never/almost never" (1) to "Always" (5). The reliability (Cronbach's alpha) is reportedly .84 with a test-retest reliability of .78 (Clausen et al., 2019 ). Emotional Demands/Work Environment. The emotional demands scale in the DPQ consists of four items with questions like "Are you placed in emotionally demanding situations at work?" and "Do you have to deal with relationships at work that are emotionally challenging?" using a 5-point Likert scale ranging from "To a very small extent" (1) to "To a very large extent" (5). The Cronbach's alpha was .83 with a test-retest reliability of .74 (Clausen et al., 2019 ). Job Role Clarity/Work Environment. The role clarity scale in the DPQ consists of four items. Questions such as "Are there any conflicting demands in your work?" and "Does your job involve tasks that conflict with your personal values?" were answered using a 5-point Likert scale ranging from "To a very small extent" (1) to "To a very large extent" (5). The Cronbach's alpha documented for this scale is .81 with a test-retest reliability of .73 (Clausen et al., 2019 ). Employee Influence/Work Environment. The influence at work scale in the DPQ consists of four items such as "Do you have any influence on how you carry out your work tasks?" and "Do you have sufficient authority to deal with the responsibilities you have in your work?" using a 5-point Likert scale ranging from "To a very small extent" (1) to "To a very large extent" (5). The Cronbach's alpha documented for this scale is .87, with a test-retest reliability of .78 (Clausen et al., 2019 ). Teamwork/Work Environment. This scale in the DPQ was used to capture the sense of cooperation measured by four items, such as "Do you and your colleagues work well together when problems emerge which require cooperation among you?" and "Is there a sense of community and cohesion between you and your colleagues?" using a 5-point Likert scale ranging from "To a very small extent" (1) to "To a very large extent" (5). The Cronbach's alpha documented for this scale is .82 with a test-retest reliability of .75 (Clausen et al., 2019 ). Leadership/Work Environment. The quality of leadership scale in the DPQ consists of four items with questions such as "Is your immediate supervisor good at motivating the employees?" and "Is your immediate supervisor good at communicating clear goals for the work of you and your colleagues?" using a 5-point Likert scale ranging from "To a very small extent" (1) to "To a very large extent" (5). The Cronbach's alpha documented for this scale is .91 with a test-retest reliability of .87 (Clausen et al., 2019 ). Job Stress/Employee Well-being. Perceived job stress was measured with a single item: "How often have you felt stressed in the last two weeks?". This question was answered using a Likert scale ranging from (1) Never – (5) Always. This item has a reported test-retest reliability of .75 (Clausen et al., 2019 ). Health/Employee Well-being. Self-rated health was measured with a single item: "Overall, how do you perceive your health?". This question was answered using a Likert scale ranging from (1) Very low – (5) Very high. Job Satisfaction/Employee Well-being. One item was used to operationalise how much the employee likes the job: "Overall, how satisfied are you with your job?" 0 denotes the lowest possible level of job satisfaction, and 10 denotes the highest possible level. The test-retest reliability for this item is .77 (Clausen et al., 2019 ). Negative Affect/Control Variable. A brief measure of negative affect (e.g., the state of emotionally negative experiences) was applied with four items on a 6-point Likert scale from "At no time" (1) to "All the time" (6): Item 1) "How much of the time in the last two weeks have you felt calm and relaxed?", Item 2) "How much of the time in the last two weeks have you been sad or upset?", Item 3) "How much of the time in the last two weeks have you had lower self-confidence?", and Item 4) "How much of the time in the last two weeks have you had a bad conscience or feelings of guilt?" (see the clinical use origins by Bech, 1999 ; Bech et al., 2015 ). At the six-month follow-up in the present study, this scale showed test-retest reliability of .63. See Appendix Table S3 for item correlations. These four items bear resemblance to the well-cited Positive and Negative Affect Schedule (see Table 1 , PANAS; Watson & Clark, 1994 ). This scale had its origins in clinical use, but has since been much used in workplace environments (e.g., Ilies, & Judge, 2003 ). The 12-month test-retest reliability of PANAS has been reported to be over .60 (Watson et al., 1988 ). In the present study, we asked for the emotions based on the last two weeks. Positive Affect/Control Variable. A brief measure of positive affect (e.g., the state of emotionally positive experiences) was captured with three items using a 6-point Likert scale from "At no time" (1) to "All the time" (6): Item 1) "How much of the time in the last two weeks have you felt quiet or reserved?", Item 2) "How much of the time in the last two weeks have you been active and energetic?", Item 3) "How much of the time in the last two weeks have you been happy and in good mood?" (Bech, 1999 ; Bech et al., 2015 ). See Table 1 . At the six-month follow-up in the present study, this scale showed test-retest reliability of .61. See Appendix Table S3 for item correlations. The 12-month test-retest reliability of PANAS positive affects has been reported to be .63 (Watson et al., 1988 ). Table 1 Positive and Negative Affect in the Present Study DPQ PANAS Positive Affect Quiet or reserved (-) Enthusiastic Active and energetic Active and alert Good mood Excite and inspired Negative Affect Calm and relaxed (-) Nervous Sad and upset Upset Low self-confidence Strong Feelings of guilt Guilty Note . See Appendix Table S4 for the complete study variable items. Statistical Analysis Assumption checks, including missing values and outliers, assessing the normality of the sampling distribution, evaluating linearity, and verifying multivariate normality were performed on the dataset (Tabachnick et al., 2013 ). Follow-up data were used to calculate test-retest reliabilities for negative affect and positive affect scales. Partial correlational analyses were conducted to address the first research question. We calculated correlations for all study variables while controlling for the overlapping variance of positive and negative affect. We also calculated correlations between baseline (Wave T1) work environment and employee well-being six months later (Wave T2), and quantified the difference in explained variance between the zero-order and partial correlations by calculating the percentage reduction (see Burke et al., 1993 ; Williams et al., 1996 ). The explained variance reduction rate indicates the percentage of the work environment conditions-outcomes relationship that is accounted for by the two individual characteristics. We used Chen and Spector’s ( 1991 ) formula to calculate the explained variance reduction rate: $$\:Percentage\:reduction\:rate\:in\:{R}^{2}=\frac{\left(\:{R}_{zero-order\:-\:}^{2}{R}_{partial}^{2}\right)}{{R}_{zero-order\:\:}^{2}}\:$$ We also calculated the reduction rate of BIC values between the zero-order and partial correlations to address model complexity; larger reduction rates indicate that negative and positive affect improve model fit substantially relative to the increase in complexity (Schwarz, 1978 ). To interpret correlations, we adhered to the effect size guidelines for individual differences research (Gignac & Szodorai, 2016 ). Ethical Consideration The Danish National Research Centre for the Working Environment (NRCWE) collected and handled the data in line with the EU's General Data Protection Regulation (GDPR), ensuring respondents' anonymity and confidentiality. This study was waived from ethical approval as it did not require interventions or contact with participants. The study was approved by NRCWE and the local university, following national research ethics guidelines. Each participant gave informed consent prior to filling out the questionnaire. The data were fully anonymised, and discretion consideration was applied to prevent the possibility of identifying individual persons in the data material. Results In preparatory analyses, Little's MCAR test indicated that the observed pattern of missing values was random (χ²(df = 7741) = 4671, p = 1) 2 , and was handled by multiple imputation technique (Little et al., 2014 ). Outliers defined as Mahalanobis distance leverage score five times greater than the sample average leverage value were screened using leverage indices. Univariate normality was tested with the Shapiro-Wilk normality test, while multivariate normality was tested with Mardia's multivariate kurtosis test (Mardia, 1970 ). Due to the sensitivity of statistical tests to a large sample, normality was further confirmed with skew and kurtosis values and graphics. All descriptive statistics at the first survey measurement T1 are reported in Table 2 . All variable reliabilities were satisfactory, α > .77. The results from the cross-sectional baseline measurements at T1 are presented in Table 2 . Overall, correlations between work environment and employee well-being were moderate ( r ≈ .20) to high ( r ≈ .40). Some of the higher correlations were between quantitative demands and job stress at r = .44 and job satisfaction and leadership at r = .48. Overall, both negative affect and positive affect correlated moderately with work environment, 1–6 ( r mean = .28), and stronger with employee well-being, 7–9 ( r mean = .45). All longitudinal zero-order correlations between T1 work environment and T2 employee well-being are found in Appendix Tables, with similar correlational sizes. Table 2 Descriptive Statistics and Study Correlations between Work Environment (1–6) T1 and Employee Well-being (7–9) T1 Variables M SD α 1 2 3 4 5 6 7 8 9 11 1. Quantitative Demands 8.91 3.16 .84 - 2. Emotional Demands 7.18 3.68 .84 .34 - 3. Job Role Clarity 12.33 2.68 .82 − .24 − .12 - 4. Employee Influence 11.28 3.44 .87 .00 − .07 .35 - 5. Teamwork 11.54 2.85 .82 − .20 − .09 .42 .33 - 6. Leadership 10.05 3.73 .91 − .20 − .14 .46 .33 .50 - 7. Job Stress 2.52 1.04 .44 .27 − .22 − .13 − .23 − .23 - 8. Health 3.52 0.91 − .08 − .09 .15 .20 .19 .18 − .27 - 9. Job Satisfaction 7.30 2.05 − .21 − .20 .46 .49 .44 .48 − .30 .26 - 10. Negative Affect 5.55 3.39 .80 .29 .24 − .34 − .23 − .31 − .27 .55 − .34 − .44 - 11. Positive Affect 12.00 2.44 .77 − .21 − .17 .35 .28 .34 .32 − .47 .43 .48 − .73 Note . N = 3,970. M = Mean. SD = Standard deviation. α = Chronbach's alpha. Variables 1–6 are work environment conditions; Quantitative Demands, Emotional Demands, Job Role Clarity, Employee Influence, Teamwork, Leadership (scaled 1–17); In grey are employee well-being variables (7, 8, 9); Job stress and Health (scaled 1–5); Job Satisfaction (scaled 0–10); Negative affect (scaled 1–21); Positive affect (scaled 1–16). r > .03 is significant ( p < .01). See Appendix for correlational table between work conditions T1 and outcomes T2. Work Environment and Longitudinal Employee Well-being We conducted partial correlations, controlling for negative affect and positive affect, cross-sectionally for baseline T1 (first four columns in Table 3 ) and longitudinally with follow-up T2 well-being measures 6 months later (last four columns in Table 3 ). Overall, individual differences in negative and positive affect could explain large portions of the work environment/employee well-being: On average, 79% of the variance between work environment measurements and job stress, 93% with health, and 49% with job satisfaction. The size of these explanations was largely similar whether employee well-being were measured in baseline Wave T1 or six months later in Wave T2. In many cases, individual differences were able to explain all or most of the work environment relationships. For instance, between employee influence and job stress (99%) or job role clarity and health (100%). On average, individual differences measured by negative affect and positive affect explained 75% of all work environment conditions relating to all employee outcomes. Overall, the improvement of model fit BIC ranged from 5.8–12.7% cross-sectionally, and 3.8–5.8% longitudinally. Table 3 Relationships between Work Environment Measures Wave T1 and Three Employee Well-being Measures in Waves T1 and T2 T1 -> T1 T1 -> T2 Zero- order ( r ) Partial ( r ) % PANAS ΔBIC rate Zero-order (r) Partial (r) % PANAS ΔBIC rate Job Stress Quant. Demands sDemands .44 .36 34% 11.3% .36 .28 42% 4.4% Emo. Demands .27 .17 60% 11.9% .25 .17 56% 5.2% Job Role Clarity − .22 − .02 99% 11.6% − .19 − .04 96% 5.2% Employee Influence − .13 .01 99% 12.7% .13 − .03 94% 5.8% Teamwork − .23 − .05 95% 11.6% − .18 − .04 96% 5.3% Leadership Quality − .23 − .09 85% 11.8% − .18 − .06 88% 5.5% Mean Average .25 .12 79% 11.8% .22 .10 79% 5.2% Health Quant. Demands − .08 .02 95% 6.7% − .11 − .01 99% 4.6% Emo. Demands − .09 − .02 96% 6.6% − .09 − .02 94% 4.8% Job Role Clarity .15 .00 100% 6.1% .19 .05 94% 3.9% Employee Influence .20 .09 80% 5.8% .21 .11 71% 4.0% Teamwork .19 .05 93% 5.7% .20 .07 87% 3.8% Leadership Quality .18 .05 92% 5.9% .17 .05 92% 4.2% Mean Average .15 .04 93% 6.1% .16 .05 90% 4.2% Job Satisfaction Quant. Demands − .21 − .10 78% 8.6% − .19 − .08 82% 5.3% Emo. Demands − .20 − .11 68% 8.9% − .17 − .09 72% 5.6% Job Role Clarity .46 .34 46% 6.3% .36 .23 59% 3.5% Employee Influence .49 .43 26% 7.7% .43 .35 32% 4.2% Teamwork .44 .33 44% 6.5% .36 .24 55% 3.7% Leadership Quality .48 .39 34% 7.1% .39 .29 44% 3.9% Mean Average .38 .28 49% 7.5% .32 .21 57% 4.4% Note. Underscores signify employee well-being measures. Zero-order correlation = bivariate Pearson's correlations at T1; Partial correlation = Zero-order correlation controlled for negative affect and positive affect at T1; T1 = baseline N = 3,970, T2 = 6 months follow-up N = 2,375; T2 % Ind. Differences are the explained variance between T1 work conditions and T2 employee outcomes. All correlations r > .03, p < .001; % Individual Differences = R 2 reduction rate (%) between zero-order and partial correlations; ΔBIC rate = reduction rate in BIC value between zero-order and partial correlations. Discussion In the present stratified, mostly representative Danish sample ( N = 3,970), two brief individual state characteristics, negative affect and positive affect, accounted for a majority of the longitudinal variance between work environment and employee well-being. This is fairly consistent with previous literature (e.g., Brief et al., 1988 ; Payne, 1988 ). Approximately 50% of job performance predicted by job engagement has been accounted for by individual trait characteristics (Bowling, 2007 ; Kajonius et al., 2023 ). Our present study, however, is arguably the first to show that individual differences in two emotional experiences explain the majority of variance between work environment variables in a Nordic sample. Argumentatively, as an alternative view to the present study, emotional characteristics could be seen as dependent of the work environment rather than predictors. There might not be sufficient causal evidence for this. Genetic analyses report that workplace variables are heritable and much mediated by employee characteristics (Ilies, & Judge, 2003 ). When using monozygotic twins, sharing 100% of the same genes, experiencing different environments has not reported notable environmentally driven effects, most often ranging below the r E = .10s (e.g., Kandler et al., 2012 ). Even so, when it comes to even more ability-based individual characteristics such as intelligence, the longitudinal mediation by environment often is close to zero (Willoughby et al., 2021 ). One workplace study reports that positive and negative affect at the workplace genetically and stably mediated job satisfaction ratings between three waves (Li et al., 2016 ). Limitations Despite using longitudinal data, there are still limits to the ability of the present study to present causality or lasting effects from how one experiences the work environment. Also, we were unable to operationalise individual employee emotions fully as we were confined to the pre-made variables in the Danish Psychosocial Questionnaire. Furthermore, all variables were operationalised using only self-reported data, which is normally a weakness and tends to introduce noise. The present study shares all challenges encountered in psychological research, including common method variance which may lead to inflated relationships and reduced ecological validity. Despite the large sample size, participants may also be prone to self-selection bias due to voluntary involvement and could overrepresent certain types of individuals. The brevity of the negative affect and positive affect measures in the present study is arguably also an important limitation. These few items may not capture the depth and breadth of individual differences in emotions, representing, at best, a preview and a conservative estimate. In addition, these affect measures overlapped strongly ( r = − .74; See Appendix Table S2), notably more than in other surveys ( r ≈ − .48; Vedel et al., 2019 ). This could diminish their unique explanatory contributions and using longer and more extensive individual measurements could arguably deliver more clear results. Recommendations and Concluding Thoughts Based on the rather strong explanation from emotional experiences, we are inclined to recommend including individual measures as controls in future studies, especially for subjective self-report questionnaires. This is to avoid misinterpreting correlations between work environment and well-being in surveys as having similar effects on all employees. This would allow organisations to apply interventions and resources more effectively. Taking overall individual differences into account in work environment surveys may help mitigate the share of explanations that “lies in the eye of the beholder.” Declarations Disclosure Statement The authors’ report no potential conflict of interest. Funding This research was not supported by any funding. Author Contribution PJK encouraged LHC to investigate the presented idea and supervised the findings of this work. LHC performed statistical analyses and prepared the first draft. LHC and PJK discussed the results and contributed equally to the final manuscript. Data Availability This study’s design and its analysis were not preregistered and materials are not available because they are not owned by the authors. The dataset is owned by Danish National Research Centre for the Working Environment (NRCWE) and is not publicly accessible. Contact NRCWE ( [email protected] ) to explore possibilities for accessing the data. The first author had access to the dataset through an employment affiliation. The code is available upon request. References Anglim, J., Horwood, S., Smillie, L. D., Marrero, R. J., & Wood, J. K. (2020). Predicting psychological and subjective well-being from personality: A meta-analysis. Psychological Bulletin , 146 (4), 279-323. https://doi.org/10.1037/bul0000226 Bech, P. (1999). Health‐related quality of life measurements in the assessment of pain clinic results. 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The PANAS-X: Manual for the positive and negative affect schedule-expanded form . Psychology Publications, New York (1999). Watson, D., & Clark, L. A. (1997). Extraversion and its positive emotional core. In R. Hogan, J. Johnson, & S. Briggs (Eds.), Handbook of personality psychology (pp. 767-793). Academic Press. https://doi.org/10.1016/B978-012134645-4/50030-5 Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: the PANAS scales. Journal of personality and social psychology, 54 (6), 1063-1070. Williams, L. J., Gavin, M. B., & Williams, M. L. (1996). Measurement and nonmeasurement processes with negative affectivity and employee attitudes. Journal of Applied Psychology , 81 (1), 88-101. https://doi.org/10.1037/0021-9010.81.1.88 Wilmot, M. P., Wanberg, C. R., Kammeyer-Mueller, J. D., & Ones, D. S. (2019). Extraversion advantages at work: A quantitative review and synthesis of the meta-analytic evidence. Journal of Applied Psychology , 104 (12), 1447-1470. https://doi.org/10.1037/apl0000415 Willoughby, E. A., McGue, M., Iacono, W. G., & Lee, J. J. (2021). Genetic and environmental contributions to IQ in adoptive and biological families with 30-year-old offspring. Intelligence, 88 , 101579. Young, H. R., Glerum, D. R., Wang, W., & Joseph, D. L. (2018). Who are the most engaged at work? A meta-analysis of personality and employee engagement. Journal of Organizational Behavior , 39 (10), 1330-1346. https://doi.org/10.1002/job.2303 Footnotes See appendix table S4 for complete overview of the questionnaire and constructs used In cases of a random missing data pattern with only a small percentage of values missing (< 5%) from a large dataset, any method for handling missing data yields comparable results (Tabachnick et al., 2013 ). Additional Declarations No competing interests reported. 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Kajonius","email":"","orcid":"","institution":"Lund University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Petri","middleName":"J.","lastName":"Kajonius","suffix":""}],"badges":[],"createdAt":"2024-10-10 08:23:41","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-5237797/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5237797/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105407384,"identity":"6e4b3b05-15ad-442c-8d8b-9dc65119f102","added_by":"auto","created_at":"2026-03-25 16:40:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1108947,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5237797/v1/aa1762e6-7244-49dc-95dd-256bca636f19.pdf"},{"id":67463058,"identity":"45826b44-d035-4f5b-9431-b3c220a7e321","added_by":"auto","created_at":"2024-10-25 10:05:16","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":28076,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-5237797/v1/158a1eb15449e724d9888354.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantifying Employee Emotions in Longitudinal Work Environment Questionnaires","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWork environment conditions (e.g., the job demands) are being related to later employee well-being (e.g., satisfaction, stress, or health). We still have limited knowledge of what extent such relationships reflect the actual work environment or mostly individual characteristics (Kasl, 1998; Rugulies, 2019; Theorell \u0026amp; Hasselhorn, 2005). This line of research often relies on employee surveys, and self-reported work environment data is at least partly explained by the employee's own emotions (Lazarus, 2003). Overlooking and not controlling for this fact in psychosocial questionnaires may confound analyses and interpretations of how the work environment impacts employees. The present study had access to the stratified Danish Psychosocial Questionnaire (DPQ) and attempted to quantify the extent to which individual differences in emotions explain the longitudinal relationship between the work environment and employee well-being.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWork Environment and Employee Well-being\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e W\u003c/strong\u003eork environment is here defined in broad terms referring to what the workplace is like at the current time, including job demands, job role clarity, and work relationships. Employee well-being refers to how workers feel over time based on their workplace, such as job satisfaction, perceived stress, and perceived health (\u003cstrong\u003eSee Lesener et al., 2019 for a comprehensive review).\u003c/strong\u003e The work environment predicts how satisfied or stressed an employee is with their job (Fried \u0026amp; Ferris, 1987; Kavosi et al., 2018). Positive work environments are often the precursor for employees to feel positively engaged at work (Clausen et al., 2017). \u003c/p\u003e\n\u003cp\u003eJob demands encompass a job's physical, psychological, social, and organisational work environment. Quantitative job demands are about the extent to which a job requires employees to work long hours, meet tight deadlines, or handle a large workload (Clausen et al., 2017). Meanwhile, emotional demands relate to aspects of the job that require sustained emotional effort from the employee, such as interacting with challenging colleagues or customers (Jonge et al., 2008). Both quantitative and emotional demands are related to job dissatisfaction and reduced well-being (Bowling et al., 2015; Edvik et al., 2020; Idris et al., 2011; Li et al., 2022; Scanlan \u0026amp; Still, 2019). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJob role clarity is another aspect of the work environment, which refers to how employees understand their current job duties, objectives, and expectations. Roles are further limited by the level of influence an employee can exert to make decisions, set priorities, and solve problems at work (Clausen et al., 2017). Both job role clarity and employee influence have shown negative associations with job stress (Kavosi et al., 2018). \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWork relationships emphasise the interpersonal environment with colleagues and leadership. Teamwork reflects the willingness and ability of colleagues to work together supportively (Clausen et al., 2017; 2019), whereas leadership relationships refer to the leader's ability to communicate, motivate, and prioritise employee well-being (Clausen et al., 2017). Leadership is predictive of employee well-being (Clausen \u0026amp; Borg, 2010, 2011; Kelloway \u0026amp; Barling, 2010; Kuoppala et al., 2008; Mullen et al., 2008). This line of research has suggested that leadership quality affects employee well-being more than most other factors (Gilbreath \u0026amp; Benson, 2004). Employees in collaborative relationships report higher job satisfaction (Clausen \u0026amp; Borg, 2011; Edmondson \u0026amp; Lei, 2014; Nielsen \u0026amp; Daniels, 2012; Stansfeld \u0026amp; Candy, 2006).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNegative Affect and Positive Affect at Work\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot every employee reacts in the same way to the same work environment. For instance, some people are more content, while others are more stressed (Steel et al., 2008). One of the most commonly used instruments to capture individual differences in emotions is PANAS (Positive and Negative Affect Schedule; Watson \u0026amp; Clark, 1994). PANAS is most often used for measuring ongoing emotional experiences (Merz \u0026amp; Roesch, 2011) and has reported relatively consistent test-retest stability over a year (Watson \u0026amp; Clark, 1994). Moreover, most of this stability seems to originate in the individual’s genetic make-up, and accounts for about half of the variance in most individual differences (Polderman et al., 2015). The impact of environmental experiences (for instance having a wage rise) on most individual differences tends to be small over time (e.g., Bühler et al., 2023). A study using PANAS longitudinally reported that at most up to 18% of within-person variance in positive and negative affect was due to effects from the environment (Cloos et al., 2023). Thus, work environments are accordingly not expected to greatly change employees, and is the reason why PANAS in the present study is treated as an individual control variable. \u003c/p\u003e\n\u003cp\u003eThe two fundamental temperament states, negative affect and positive affect, have been shown to play a significant role in all aspects of well-being (Anglim et al., 2020; Steel et al., 2008). Negative affect is characterised by a heightened susceptibility to negative experiences and stressful environments, including stress in the workplace (Bowling \u0026amp; Eschleman, 2010; Carlson, 1999; Ebstrup et al., 2011; Heinisch \u0026amp; Jex, 1997; Luo et al., 2023). This type of emotional experience also extends to physical and mental health (Jeronimus et al., 2014; Kotov et al., 2010; Lahey, 2009). Individuals high in negative affect tend to be less happy in any kind of relationship and are more inclined to negatively attribute faults to others (Karney \u0026amp; Bradbury, 2000). In contrast, positive affect is a core aspect of outgoing and energetic behaviours and is associated with job satisfaction, general happiness, and overall life satisfaction (Wilmot et al., 2019). Thus, such individuals would be more likely to experience working environments more positively because of their predispositions. \u003c/p\u003e\n\u003cp\u003eResearch Question \u003c/p\u003e\n\u003cp\u003eThe present study aimed to quantify the extent to which negative and positive affect explain the longitudinal relationships between the work environment and employee well-being. The research question was as follows: How much of the relationship between the work environment (i.e., quantitative and emotional demands, job role clarity, employee influence, teamwork, and leadership) and employee well-being (i.e., stress, health, and job satisfaction) can be explained by individual differences in emotional experiences (i.e., negative affect and positive affect)? \u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and Procedure\u003c/h2\u003e \u003cp\u003eThe DPQ was distributed to a stratified sample of 8,958 employed individuals in Denmark, representing the Danish labour market. Data from this survey have been used for other studies (Clausen et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e; \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lunen et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). More details about the sampling are described in Clausen et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). At the first baseline (Wave T1) survey measurement, 48.4% response rate, \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3,970 remained after cleaning incomplete questionnaires and unemployed workers. The sample was equally distributed across 14 different job groups, including office work, healthcare and social services, education, manufacturing, law enforcement, and business management (range\u0026thinsp;=\u0026thinsp;5\u0026ndash;9%). Women constituted 47% of the sample, and the age distribution was as follows: 6% between 18\u0026ndash;34, 19% between 35\u0026ndash;44, 32% between 45\u0026ndash;54, and 43% over 54. Additionally, 26% had a high school diploma, 15% had a short-cycle tertiary education, 27% had a medium-cycle tertiary education, and 19% had a long-cycle tertiary education. After 6 months, a follow-up (Wave T2) questionnaire was conducted of which \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2,375 met the inclusion criteria.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInstruments\u003csup\u003e1\u003c/sup\u003e\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eQuantitative Demands/Work Environment.\u003c/b\u003e The quantitative demands scale in the DPQ was used to estimate workload. The scale consists of four items with questions like \"How often is it the case that you do not have time to complete all your work tasks?\" and \"How often do you have deadlines that are hard to meet?\" using a 5-point Likert scale ranging from \"Never/almost never\" (1) to \"Always\" (5). The reliability (Cronbach's alpha) is reportedly .84 with a test-retest reliability of .78 (Clausen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eEmotional Demands/Work Environment.\u003c/b\u003e The emotional demands scale in the DPQ consists of four items with questions like \"Are you placed in emotionally demanding situations at work?\" and \"Do you have to deal with relationships at work that are emotionally challenging?\" using a 5-point Likert scale ranging from \"To a very small extent\" (1) to \"To a very large extent\" (5). The Cronbach's alpha was .83 with a test-retest reliability of .74 (Clausen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eJob Role Clarity/Work Environment.\u003c/b\u003e The role clarity scale in the DPQ consists of four items. Questions such as \"Are there any conflicting demands in your work?\" and \"Does your job involve tasks that conflict with your personal values?\" were answered using a 5-point Likert scale ranging from \"To a very small extent\" (1) to \"To a very large extent\" (5). The Cronbach's alpha documented for this scale is .81 with a test-retest reliability of .73 (Clausen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eEmployee Influence/Work Environment.\u003c/b\u003e The influence at work scale in the DPQ consists of four items such as \"Do you have any influence on how you carry out your work tasks?\" and \"Do you have sufficient authority to deal with the responsibilities you have in your work?\" using a 5-point Likert scale ranging from \"To a very small extent\" (1) to \"To a very large extent\" (5). The Cronbach's alpha documented for this scale is .87, with a test-retest reliability of .78 (Clausen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTeamwork/Work Environment.\u003c/b\u003e This scale in the DPQ was used to capture the sense of cooperation measured by four items, such as \"Do you and your colleagues work well together when problems emerge which require cooperation among you?\" and \"Is there a sense of community and cohesion between you and your colleagues?\" using a 5-point Likert scale ranging from \"To a very small extent\" (1) to \"To a very large extent\" (5). The Cronbach's alpha documented for this scale is .82 with a test-retest reliability of .75 (Clausen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eLeadership/Work Environment.\u003c/b\u003e The quality of leadership scale in the DPQ consists of four items with questions such as \"Is your immediate supervisor good at motivating the employees?\" and \"Is your immediate supervisor good at communicating clear goals for the work of you and your colleagues?\" using a 5-point Likert scale ranging from \"To a very small extent\" (1) to \"To a very large extent\" (5). The Cronbach's alpha documented for this scale is .91 with a test-retest reliability of .87 (Clausen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eJob Stress/Employee Well-being.\u003c/b\u003e Perceived job stress was measured with a single item: \"How often have you felt stressed in the last two weeks?\". This question was answered using a Likert scale ranging from (1) Never \u0026ndash; (5) Always. This item has a reported test-retest reliability of .75 (Clausen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eHealth/Employee Well-being.\u003c/b\u003e Self-rated health was measured with a single item: \"Overall, how do you perceive your health?\". This question was answered using a Likert scale ranging from (1) Very low \u0026ndash; (5) Very high.\u003c/p\u003e \u003cp\u003e \u003cb\u003eJob Satisfaction/Employee Well-being.\u003c/b\u003e One item was used to operationalise how much the employee likes the job: \"Overall, how satisfied are you with your job?\" 0 denotes the lowest possible level of job satisfaction, and 10 denotes the highest possible level. The test-retest reliability for this item is .77 (Clausen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eNegative Affect/Control Variable.\u003c/b\u003e A brief measure of negative affect (e.g., the state of emotionally negative experiences) was applied with four items on a 6-point Likert scale from \"At no time\" (1) to \"All the time\" (6): Item 1) \"How much of the time in the last two weeks have you felt calm and relaxed?\", Item 2) \"How much of the time in the last two weeks have you been sad or upset?\", Item 3) \"How much of the time in the last two weeks have you had lower self-confidence?\", and Item 4) \"How much of the time in the last two weeks have you had a bad conscience or feelings of guilt?\" (see the clinical use origins by Bech, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Bech et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). At the six-month follow-up in the present study, this scale showed test-retest reliability of .63. See \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e Table S3 for item correlations. These four items bear resemblance to the well-cited Positive and Negative Affect Schedule (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, PANAS; Watson \u0026amp; Clark, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). This scale had its origins in clinical use, but has since been much used in workplace environments (e.g., Ilies, \u0026amp; Judge, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The 12-month test-retest reliability of PANAS has been reported to be over .60 (Watson et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). In the present study, we asked for the emotions based on the last two weeks.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePositive Affect/Control Variable.\u003c/b\u003e A brief measure of positive affect (e.g., the state of emotionally positive experiences) was captured with three items using a 6-point Likert scale from \"At no time\" (1) to \"All the time\" (6): Item 1) \"How much of the time in the last two weeks have you felt quiet or reserved?\", Item 2) \"How much of the time in the last two weeks have you been active and energetic?\", Item 3) \"How much of the time in the last two weeks have you been happy and in good mood?\" (Bech, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Bech et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. At the six-month follow-up in the present study, this scale showed test-retest reliability of .61. See \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e Table S3 for item correlations. The 12-month test-retest reliability of PANAS positive affects has been reported to be .63 (Watson et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1988\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePositive and Negative Affect in the Present Study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDPQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePANAS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive Affect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuiet or reserved (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnthusiastic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive and energetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActive and alert\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood mood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcite and inspired\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative Affect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalm and relaxed (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNervous\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSad and upset\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUpset\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow self-confidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeelings of guilt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGuilty\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cem\u003eNote\u003c/em\u003e. See \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e Table S4 for the complete study variable items.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAssumption checks, including missing values and outliers, assessing the normality of the sampling distribution, evaluating linearity, and verifying multivariate normality were performed on the dataset (Tabachnick et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Follow-up data were used to calculate test-retest reliabilities for negative affect and positive affect scales.\u003c/p\u003e \u003cp\u003ePartial correlational analyses were conducted to address the first research question. We calculated correlations for all study variables while controlling for the overlapping variance of positive and negative affect. We also calculated correlations between baseline (Wave T1) work environment and employee well-being six months later (Wave T2), and quantified the difference in explained variance between the zero-order and partial correlations by calculating the percentage reduction (see Burke et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Williams et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The explained variance reduction rate indicates the percentage of the work environment conditions-outcomes relationship that is accounted for by the two individual characteristics. We used Chen and Spector\u0026rsquo;s (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) formula to calculate the explained variance reduction rate:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Percentage\\:reduction\\:rate\\:in\\:{R}^{2}=\\frac{\\left(\\:{R}_{zero-order\\:-\\:}^{2}{R}_{partial}^{2}\\right)}{{R}_{zero-order\\:\\:}^{2}}\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWe also calculated the reduction rate of BIC values between the zero-order and partial correlations to address model complexity; larger reduction rates indicate that negative and positive affect improve model fit substantially relative to the increase in complexity (Schwarz, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1978\u003c/span\u003e). To interpret correlations, we adhered to the effect size guidelines for individual differences research (Gignac \u0026amp; Szodorai, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical Consideration\u003c/h3\u003e\n\u003cp\u003eThe Danish National Research Centre for the Working Environment (NRCWE) collected and handled the data in line with the EU's General Data Protection Regulation (GDPR), ensuring respondents' anonymity and confidentiality. This study was waived from ethical approval as it did not require interventions or contact with participants. The study was approved by NRCWE and the local university, following national research ethics guidelines. Each participant gave informed consent prior to filling out the questionnaire. The data were fully anonymised, and discretion consideration was applied to prevent the possibility of identifying individual persons in the data material.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn preparatory analyses, Little's MCAR test indicated that the observed pattern of missing values was random (χ\u0026sup2;(df\u0026thinsp;=\u0026thinsp;7741)\u0026thinsp;=\u0026thinsp;4671, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1)\u003csup\u003e2\u003c/sup\u003e, and was handled by multiple imputation technique (Little et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Outliers defined as Mahalanobis distance leverage score five times greater than the sample average leverage value were screened using leverage indices. Univariate normality was tested with the Shapiro-Wilk normality test, while multivariate normality was tested with Mardia's multivariate kurtosis test (Mardia, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1970\u003c/span\u003e). Due to the sensitivity of statistical tests to a large sample, normality was further confirmed with skew and kurtosis values and graphics. All descriptive statistics at the first survey measurement T1 are reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. All variable reliabilities were satisfactory, α\u0026thinsp;\u0026gt;\u0026thinsp;.77.\u003c/p\u003e \u003cp\u003eThe results from the cross-sectional baseline measurements at T1 are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Overall, correlations between work environment and employee well-being were moderate (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026asymp;\u0026thinsp;.20) to high (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026asymp;\u0026thinsp;.40). Some of the higher correlations were between quantitative demands and job stress at \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.44 and job satisfaction and leadership at \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.48. Overall, both negative affect and positive affect correlated moderately with work environment, 1\u0026ndash;6 (\u003cem\u003er\u003c/em\u003e \u003csub\u003emean\u003c/sub\u003e = .28), and stronger with employee well-being, 7\u0026ndash;9 (\u003cem\u003er\u003c/em\u003e \u003csub\u003emean\u003c/sub\u003e = .45). All longitudinal zero-order correlations between T1 work environment and T2 employee well-being are found in \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e Tables, with similar correlational sizes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eDescriptive Statistics and Study Correlations between Work Environment (1\u0026ndash;6) T1 and Employee Well-being (7\u0026ndash;9) T1\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eα\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Quantitative Demands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Emotional Demands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Job Role Clarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Employee Influence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Teamwork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. Leadership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. Job Stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8. Health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9. Job Satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10. Negative Affect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11. Positive Affect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003cem\u003eNote\u003c/em\u003e. \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3,970. \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Mean. \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Standard deviation. α\u0026thinsp;=\u0026thinsp;Chronbach's alpha. Variables 1\u0026ndash;6 are work environment conditions; Quantitative Demands, Emotional Demands, Job Role Clarity, Employee Influence, Teamwork, Leadership (scaled 1\u0026ndash;17); In grey are employee well-being variables (7, 8, 9); Job stress and Health (scaled 1\u0026ndash;5); Job Satisfaction (scaled 0\u0026ndash;10); Negative affect (scaled 1\u0026ndash;21); Positive affect (scaled 1\u0026ndash;16). \u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.03 is significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01). See \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e for correlational table between work conditions T1 and outcomes T2.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eWork Environment and Longitudinal Employee Well-being\u003c/h2\u003e \u003cp\u003eWe conducted partial correlations, controlling for negative affect and positive affect, cross-sectionally for baseline T1 (first four columns in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and longitudinally with follow-up T2 well-being measures 6 months later (last four columns in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Overall, individual differences in negative and positive affect could explain large portions of the work environment/employee well-being: On average, 79% of the variance between work environment measurements and job stress, 93% with health, and 49% with job satisfaction. The size of these explanations was largely similar whether employee well-being were measured in baseline Wave T1 or six months later in Wave T2. In many cases, individual differences were able to explain all or most of the work environment relationships. For instance, between employee influence and job stress (99%) or job role clarity and health (100%). On average, individual differences measured by negative affect and positive affect explained 75% of all work environment conditions relating to all employee outcomes. Overall, the improvement of model fit BIC ranged from 5.8\u0026ndash;12.7% cross-sectionally, and 3.8\u0026ndash;5.8% longitudinally.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eRelationships between Work Environment Measures Wave T1 and Three Employee Well-being Measures in Waves T1 and T2\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eT1 -\u0026gt; T1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eT1 -\u0026gt; T2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZero-\u003c/p\u003e \u003cp\u003eorder (\u003cem\u003er\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePartial (\u003cem\u003er\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% PANAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eΔBIC\u003c/p\u003e \u003cp\u003erate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eZero-order (r)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePartial (r)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e% PANAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eΔBIC\u003c/p\u003e \u003cp\u003erate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eJob Stress\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuant. Demands sDemands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmo. Demands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob Role Clarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployee Influence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e94%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTeamwork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeadership Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e79%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eHealth\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuant. Demands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmo. Demands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e94%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob Role Clarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e94%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployee Influence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e71%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTeamwork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeadership Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eJob Satisfaction\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuant. Demands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmo. Demands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob Role Clarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployee Influence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTeamwork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeadership Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Underscores signify employee well-being measures. Zero-order correlation = bivariate Pearson\u0026apos;s correlations at T1; Partial correlation = Zero-order correlation controlled for negative affect and positive affect at T1; T1 = baseline N = 3,970, T2 = 6 months follow-up N\u003csub\u003e\u0026nbsp;\u003c/sub\u003e= 2,375; T2 % Ind. Differences are the explained variance between T1 work conditions and T2 employee outcomes. All correlations \u003cem\u003er\u003c/em\u003e \u0026gt; .03, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001; % Individual Differences = \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e reduction rate (%) between zero-order and partial correlations; \u0026Delta;BIC rate = reduction rate in BIC value between zero-order and partial correlations.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present stratified, mostly representative Danish sample (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3,970), two brief individual state characteristics, negative affect and positive affect, accounted for a majority of the longitudinal variance between work environment and employee well-being. This is fairly consistent with previous literature (e.g., Brief et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Payne, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Approximately 50% of job performance predicted by job engagement has been accounted for by individual trait characteristics (Bowling, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Kajonius et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Our present study, however, is arguably the first to show that individual differences in two emotional experiences explain the majority of variance between work environment variables in a Nordic sample.\u003c/p\u003e \u003cp\u003eArgumentatively, as an alternative view to the present study, emotional characteristics could be seen as dependent of the work environment rather than predictors. There might not be sufficient causal evidence for this. Genetic analyses report that workplace variables are heritable and much mediated by employee characteristics (Ilies, \u0026amp; Judge, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). When using monozygotic twins, sharing 100% of the same genes, experiencing different environments has not reported notable environmentally driven effects, most often ranging below the r\u003csub\u003eE\u003c/sub\u003e = .10s (e.g., Kandler et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Even so, when it comes to even more ability-based individual characteristics such as intelligence, the longitudinal mediation by environment often is close to zero (Willoughby et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). One workplace study reports that positive and negative affect at the workplace genetically and stably mediated job satisfaction ratings between three waves (Li et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eDespite using longitudinal data, there are still limits to the ability of the present study to present causality or lasting effects from how one experiences the work environment. Also, we were unable to operationalise individual employee emotions fully as we were confined to the pre-made variables in the Danish Psychosocial Questionnaire.\u003c/p\u003e \u003cp\u003eFurthermore, all variables were operationalised using only self-reported data, which is normally a weakness and tends to introduce noise. The present study shares all challenges encountered in psychological research, including common method variance which may lead to inflated relationships and reduced ecological validity. Despite the large sample size, participants may also be prone to self-selection bias due to voluntary involvement and could overrepresent certain types of individuals.\u003c/p\u003e \u003cp\u003eThe brevity of the negative affect and positive affect measures in the present study is arguably also an important limitation. These few items may not capture the depth and breadth of individual differences in emotions, representing, at best, a preview and a conservative estimate. In addition, these affect measures overlapped strongly (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.74; See \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e Table S2), notably more than in other surveys (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026asymp;\u0026thinsp;\u0026minus;\u0026thinsp;.48; Vedel et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This could diminish their unique explanatory contributions and using longer and more extensive individual measurements could arguably deliver more clear results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRecommendations and Concluding Thoughts\u003c/h2\u003e \u003cp\u003eBased on the rather strong explanation from emotional experiences, we are inclined to recommend including individual measures as controls in future studies, especially for subjective self-report questionnaires. This is to avoid misinterpreting correlations between work environment and well-being in surveys as having similar effects on all employees. This would allow organisations to apply interventions and resources more effectively. Taking overall individual differences into account in work environment surveys may help mitigate the share of explanations that \u0026ldquo;lies in the eye of the beholder.\u0026rdquo;\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003eDisclosure Statement\u003c/p\u003e\n\u003cp\u003eThe authors\u0026rsquo; report no potential conflict of interest.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was not supported by any funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePJK encouraged LHC to investigate the presented idea and supervised the findings of this work. LHC performed statistical analyses and prepared the first draft. LHC and PJK discussed the results and contributed equally to the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThis study\u0026rsquo;s design and its analysis were not preregistered and materials are not available because they are not owned by the authors. The dataset is owned by Danish National Research Centre for the Working Environment (NRCWE) and is not publicly accessible. Contact NRCWE ([email protected]) to explore possibilities for accessing the data. The first author had access to the dataset through an employment affiliation. The code is available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnglim, J., Horwood, S., Smillie, L. D., Marrero, R. J., \u0026amp; Wood, J. K. (2020). 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Who are the most engaged at work? A meta-analysis of personality and employee engagement. \u003cem\u003eJournal of Organizational Behavior\u003c/em\u003e,\u003cem\u003e 39\u003c/em\u003e(10), 1330-1346. https://doi.org/10.1002/job.2303 \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e See \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003eappendix\u003c/span\u003e table S4 for complete overview of the questionnaire and constructs used\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e In cases of a random missing data pattern with only a small percentage of values missing (\u0026lt;\u0026thinsp;5%) from a large dataset, any method for handling missing data yields comparable results (Tabachnick et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"work environment, individual differences, well-being, negative affect, positive affect","lastPublishedDoi":"10.21203/rs.3.rs-5237797/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5237797/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAn individual differences' perspective posits that relatively stable emotions dictate an employee\u0026rsquo;s perception of the work environment. The present study, based on the Danish Psychosocial Questionnaire, set out to quantify to what extent individuals\u0026rsquo; negative and positive affect account for the longitudinal relationships between baseline (Wave T1; N\u0026thinsp;=\u0026thinsp;3,970) work environment (i.e., job demands or job relationships) and employee well-being (i.e., job satisfaction or perceived job stress) six months later (Wave T2; \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2,375). Partial correlation analyses showed that very brief measures of employee emotions accounted for 75% of the variance between the initial work environment and later employee well-being. We argue and discuss the importance of considering individual differences in emotional traits in work environments, a dynamic often overlooked in research and practice in workplace interventions and improvements.\u003c/p\u003e","manuscriptTitle":"Quantifying Employee Emotions in Longitudinal Work Environment Questionnaires","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-25 10:05:12","doi":"10.21203/rs.3.rs-5237797/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e55ff019-08fa-4b6b-9ff1-b02855b680fa","owner":[],"postedDate":"October 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":39324138,"name":"Biological sciences/Psychology"},{"id":39324139,"name":"Biological sciences/Psychology/Human behaviour"}],"tags":[],"updatedAt":"2026-03-25T16:40:23+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-25 10:05:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5237797","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5237797","identity":"rs-5237797","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}