Antecedents of sustainable working among Finnish employees in light of the ability–motivation–opportunity framework

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This paper examines the individual and organizational determinants of employee green behaviour (EGB) using the classical ability–motivation–opportunity (AMO) behavioural model as a theoretical framework. The study draws on a large and statistically representative survey sample of 5,742 Finnish employees. The results of the ordinal logistic regression models show that, of the factors included in the models, employee motivation, and especially the opportunity to influence one’s work, had a strong positive association with the activity of employees to change their key working practices and methods to be more ecologically sustainable. Adding organizational factors – the extent to which ecological sustainability is integrated into workplace strategy or as part of internal workplace interaction and supervisor support – to the model increased the explanatory power in a statistically significant way, although only by one percentage point. The findings suggest that increasing self-direction and autonomy at work could be an important means of promoting EGB and the ecological sustainability of organizations in many industries – with the partial exception of fossil-intensive industries, where the focus obviously should be placed on system-level changes to a greater degree. AMO framework ecological sustainability employee green behaviour sustainability agency 1 Introduction Over the past decades, economic growth in developed industrial countries has been based on the overconsumption of natural resources. This has served to accelerate global warming and led to loss of biodiversity. The lion’s share of greenhouse gas (GHG) emissions in Finland, as in many other developed industrial countries, comes from a few industries, such as energy production, capital-intensive process industries, transport and storage, and agriculture [ 1 ]. However, industries are interconnected in various value chains and networks and other inter-organizational dependencies. Thus, the challenge of ecological sustainability broadly applies to all industries and their workplaces. There is a long tradition in management, organizational and work life studies regarding the role of employees in innovation and development in the workplace. The studies have been conducted with the help of a variety of theoretical frameworks and concepts [ 2 – 8 ]. The research has justified the participation of employees in innovation and development from three perspectives, in particular. The first of these departs from the premise that employee participation allows for a more diverse use of knowledge and expertise in innovation and development, leading to more effective solutions. Participation has also been considered to strengthen employees’ motivation and their commitment to new solutions. Finally, participation and the collaboration based on it can be held to promote learning and knowledge sharing within organizations. Concerns about global warming and biodiversity loss have increased academic interest in the role of employees also in contributing to the ecological sustainability of their work and workplaces in recent years. The role of employees has been examined through concepts such as ‘green behaviour’ [ 9 – 14 ], ‘green workplace behaviour’ [ 15 – 16 ], ‘pro-environmental behaviour’ [ 17 – 21 ] and ‘sustainability agency’ [ 22 ]. The diversity of concepts and the lack of consistency in their content indicate that the role of employees in promoting ecological sustainability in the workplace is a relatively new research topic and that the issue has been examined through the use of highly divergent theoretical frameworks. We study Finnish employees’ green behaviour, as well as the various individual and organizational factors related to it, using a large and statistically representative survey dataset covering all industries. We are particularly interested in the importance of factors that are related to employees’ activity to change their working practices and methods to be more ecologically sustainable. This paper contributes to the literature on employee green behaviour (EGB) in three important ways. First, we link our analysis to the micro-level AMO framework, which seeks to explain EGB as a combination of employees’ self-perceived abilities (possessed skills and knowledge), motivations and opportunities in terms of their job autonomy and organizational support. The AMO framework, which is based on a classic behaviour and work performance model in organizational research [ 23 ], has been widely applied in human resource management (HRM) research as a theoretical approach for examining the HRM–performance relationship [ 24 – 25 ]. Another stream of studies to which we contribute has utilized the framework to predict individual-level behaviour like EGB [ 10 , 13 , 26 ]. Second, we aim to increase understanding of the relative importance of individual and organizational factors in terms of EGB. Knowledge of the importance of factors at different levels helps us to better understand the relationship between employee agency and the contextual factors that enable or constrain it in increasing the ecological sustainability of workplace operations [ 22 , 27 – 28 ]. Third, we use data from Statistics Finland’s 2023 Quality of Work Life Survey, which are statistically representative of the entire Finnish wage-earner population. The target group consists of employees aged 18–67 who regularly work at least 10 hours per week. The survey’s broad coverage and large sample size (N = 5,742) provide good opportunities for diverse statistical analyses. A large part of previous empirical studies on EGB have focused on smaller and narrower target groups, such as specific industries or organizations. As Carbone et al. (2024) point out, there is a significant need in EGB research to include samples that are representative of the entire working population. 1.1 Employee green behaviour and its determinants In this article, we refer to EGB as actions that employees engage in that are linked with and contribute to environmental sustainability [ 11 ]. The actions can be included as part of the employee’s job tasks, or be voluntary and based on the employee’s own initiative. The actions can have direct effects, such as changing how one’s work is done or actively participating in the development of new, more ecologically sustainable products and services, or be more indirect, such as influencing others or taking initiative. The intensity and risk level of the actions can also vary, ranging from low-intensity actions related to one’s own work habits (such as turning off the lights) to more radical development initiatives to senior management to increase the ecological sustainability of workplace operations [ 11 , 13 , 16 ]. All in all, EGB can take many forms in the workplace and be an essential component of organizational ecological sustainability [ 19 ]. Despite the efforts of many researchers, no single, universally applicable approach has been taken to classify and operationalize EGB and its various forms. The concept of EGB also includes the idea that the content and outcome of behaviour are within employees’ control, and do not just mechanically reflect a way of acting required by the work organization. Green behaviour requires agency from the employee. In this way, EGB is linked to the idea of ​​sustainability agency. Sustainability agency has been conceptualized in the literature as the capacity of an individual or community to act reflectively in a way that promotes ecological sustainability [ 22 ]. Green behaviour should also not be confused or identified with the concept of a green job. Green jobs refer to jobs that are done in connection with ecologically sustainable products and services, or as part of ecologically more sustainable production processes [ 29 ]. The concept as such does not include the idea of ​​employee agency. EGB is not limited to certain industries, jobs or tasks. However, in addition to the employee’s characteristics and factors related to their job, EGB is influenced by various contextual factors related to the employee’s social relationships, team and organizational levels, and institutional factors [ 10 , 13 ]. Examples of these include social norms of work communities, management systems, organizational strategies, professional and industry-specific standards, and legislation. On the other hand, structures that enable or constrain employee agency in the form of rules and resources are not immutable. Social practices, such as green behaviour, can also lead to changes in structures when interacting with them. Giddens [ 30 ] calls such an interactive process between agents and structures structuration. Researchers’ interest in the impact of EGB on the rules and resources perceived as essential for ecological sustainability in workplaces has increased in recent years, especially as part of studies on sustainability transitions [ 27 – 28 , 31 ]. Research on EGB has largely focused on its individual and organizational antecedents. Studies that have examined individual factors have focused on employee demographics, personality traits, job attitudes and work-related perceptions. A meta-analysis of studies found that EGB is positively associated with employee age, tenure and level of education [ 9 ]. Personality traits that have been shown to relate to EGB include curiosity, openness to experience, conscientiousness, moral reflexiveness and self-efficacy [ 9 , 13 , 15 ]. Employees’ pro-environmental attitudes and norms, job satisfaction, organizational commitment and identification, and opportunities to influence their own work are also found to be positively associated with EGB [ 9 – 10 , 13 , 21 ]. Among the organizational factors of relevance, EGB has been found to be positively associated with perceived support received from the supervisor and the management system, as well as pro-environmental psychological climates in workplaces [ 9 – 10 , 13 , 15 , 21 ]. Researchers conducting meta-analyses have developed various models that combine individual, organizational, or other contextual factors to describe the factors associated with EGB [ 9 – 10 , 13 – 15 , 21 ]. However, the theoretical frameworks and concepts underlying the models differ from each other. The models contain assumptions about the importance of certain factors, but do not help to understand in more detail the relative importance of different levels of different factors for EGB, which is the focus of this article. 1.2 Research questions and hypotheses Our first research question is to what extent EGB can be explained by individual factors alone using the AMO framework. Second, we ask to what extent the relationship between individual factors and EGB is moderated by organizational factors. By answering the research questions, we will be able to better understand the structural dependencies of EGB and employee sustainability agency in workplaces, and to direct research and measures promoting ecological sustainability of workplaces in this direction. Among the different forms of EGB, we focus on the activity of employees to change their working practices and methods to be more ecologically sustainable. This is a direct form of EGB, which Ones and Dilchert [ 11 ] place in the EGB category ‘working sustainably’ and Francoeur et al. [ 16 ] in ‘transforming’. We chose this form of EGB as the subject of analysis, since it most clearly includes the idea of ​​employee sustainability agency that has led to concrete changes in their own work. Based on the AMO framework, we propose the following three hypotheses: H1: An employee’s skills and knowledge related to the ecological sustainability of their job and workplace operations is positively associated with green behaviour. H2: An employee’s view of the importance of ecological sustainability as part of their job and workplace operations is positively associated with green behaviour. H3: An employee’s opportunities to influence the ecological sustainability of their job and workplace operations is positively associated with green behaviour. According to the AMO framework, the most favourable situation for employees to perform well is when they possess the skills and knowledge to do so, when they have the motivation to do so, and when their job provides the opportunity to do so [ 26 ]. Based on this, we propose the following hypothesis: H4: An employee’s high skills and knowledge, strong motivation, and good opportunities to influence their work, in combination, explain a greater proportion of variance in green behavior than any other combination of these factors, or any of these factors alone. EGB is not independent of the organizational context in which the employee acts. The context can have an enabling or constraining impact on EGB in alternative ways. These can include, in particular, to what extent the pursuit of ecological sustainability is integrated as part of workplace strategy, internal workplace interaction and supervisor support. All these aspects can be broadly defined as elements of an organization’s green HRM [ 19 ]. Based on this thinking, we present the following hypothesis: H5: Organizational factors moderate the relationship between employee skills and knowledge, motivation and job-related influence opportunities and green behaviour. Such factors include workplace strategy (H5a), internal workplace interaction (H5b) and supervisor support (H5c). 2 Materials and methods 2.1 Data Our data are based on Statistics Finland’s Quality of Work Life Survey of 2023. The target group of the survey consists of employees aged 18–67 years who regularly work at least 10 hours a week. Statistics Finland has conducted similar surveys every few years since 1977, but the 2023 survey is the first to include questions on the ecological sustainability of work. Data collection was carried out as an online survey using Finnish, Swedish or English between September 2023 and January 2024. The sample was drawn in two ways: from those who participated in Statistics Finland’s Labour Force Survey in 2023 and from those who responded to Statistics Finland’s online survey on the impact of the COVID-19 crisis on work life carried out two years earlier. Of the more than 8,000 employees who met the sample criteria, 5,742 responded to the survey (response rate 71.5%). Any skew in the data has been corrected by weighting factors to correspond to the target group of employees working at least 10 hours a week. Weighting factors were used to correct the distribution of the sample by respondent gender, age, province, level of education and socioeconomic group [ 32 ]. 2.2 Measures Among the categories of EGB, in this paper we focus on the category working sustainably . This was assessed with the question ‘Have you yourself changed your key working practices or methods to be more ecologically sustainable?’. The response options were ‘yes, significantly’, ‘yes, to some extent’ and ‘not at all’. An employee’s skills and knowledge related to the ecological sustainability of their job and workplace operations were assessed with two questions. First, employees were asked about their involvement in the production of six different environmental products or services (renewable energy, energy-efficient products and services, reduction and elimination of pollutants and GHGs, waste recycling and reuse) at work during the past three months. Next, they were asked how much of their working time during this period was spent producing these products or services. The five-point response scale ranged from ‘almost all the time’ to ‘less than one quarter of the time’. In the analysis, we used the working time spent on the production of any of the six environmental products or services as an indicator of the employee’s skills and knowledge related to ecological sustainability. An employee’s view of the importance of ecological sustainability as part of their job and workplace operations was assessed with the question ‘How important do you think it is that ecological sustainability is taken into account in your workplace?’. The three-point response scale ranged from ‘very important’ to ‘not particularly important’. An employee’s opportunities to influence the ecological sustainability of their job and workplace operations were assessed with the question ‘To what extent can you influence the ecological sustainability of your working methods or work?’. The four-point response scale ranged from ‘a lot’ to ‘not at all’, in addition to which the employee was given the option ‘I have not thought about it’. The strategic positioning of the workplace towards ecological sustainability was assessed with the question ‘Is ecological sustainability taken into account in the objectives or strategy of your workplace?’. The response options were ‘yes, my workplace has a climate programme or strategy’, ‘yes, objectives have been written into the organization’s strategy’, ‘yes, objectives are otherwise taken into account’, ‘no’, ‘does not apply to my workplace’ and ‘I don’t know’. The role of ecological sustainability as part of internal workplace interaction was assessed with the statement ‘In our workplace, we have been thinking together about how we can act in an ecologically sustainable manner’. The five-point response scale ranged from ‘strongly agree’ to ‘strongly disagree’, in addition to which the employee could choose the option ‘does not apply to my workplace’. The support an employee receives from her/his supervisor to promote ecological sustainability was assessed with the statement ‘My supervisor instructs her/his employees to act in an environmentally friendly manner at work’. The five-point response scale ranged from ‘strongly agree’ to ‘strongly disagree’. In the regression analysis we adjusted for the respondent’s age (18–24, 25–34, 35–44, 45–54, 55–67 years) and level of education (three levels), both of which have been found to be positively associated with EGB in previous studies [ 9 ]. No statistically significant association was found between gender and EGB in the meta-analysis by Katz et al. [ 9 ]. However, we also adjusted for gender, as studies have shown that at least in Finland, female employees have a higher degree of climate concern than male employees, which may also be reflected in behaviour at work [ 33 ]. We further adjusted for the nature of the employment relationship. The fixed-term nature of the employment relationship can weaken organizational identification [ 34 ]. Studies have shown that weakened organizational identification is negatively associated with EGB [ 9 – 10 , 21 ]. In addition, we adjusted for industry. Although the issue of ecological sustainability applies to all industries and their workplaces [ 1 ], differences in EGB between industries may stem from differences in organizational cultures or the strategic importance of the industries, for example in reducing GHG emissions [ 15 , 26 ]. We divided industries into four categories based on their level of GHG emissions and economic sector. The first category is fossil-intensive industries, in which annual GHG emissions in Finland exceeded 10 tons per employee in 2022 (level 1 codes ABCDEFH in NACE Rev.2.1). As other categories, we distinguished between private services, public services and universities. However, among private service industries, transportation and storage (level 1 code H in NACE Rev.2.1) was included in the category of fossil-intensive industry, due to its high level of GHG emissions. Finally, we adjusted for whether the employee does or has previously done remote work. This allowed us to better distinguish from the data employees’ activities that promote ecological sustainability at work, in a way which would not solely reflect increased remote work following the COVID-19 pandemic. The response options to the question ‘Do you work remotely’ were ‘yes’, ‘no, but I have done it before’ and ‘no’. 2.3 Statistical models To analyse ordinal survey responses, we used ordinal logistic regression models tailored to specific needs. The Proportional Odds Model (POM) [ 35 ] assumes a consistent relationship between predictors and outcomes across thresholds, simplifying interpretation. When this assumption does not hold, the Partial Proportional Odds Model (PPOM) provides flexibility by allowing some predictors to vary across thresholds [ 36 – 39 ]. In our analysis, both models were assessed using statistical criteria, including the Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC), to determine the most appropriate model. The Brant test [ 40 ] in STATA was performed to assess the proportional odds assumption. For all hypotheses, an insignificant test statistic indicated that the final model did not violate the assumption. However, partial violations were observed, as several independent variables failed to meet the criteria. In terms of goodness of fit, the AIC and BIC values were smaller for PPOM compared to POM, indicating a better model fit (Table 1 ). For all hypotheses, PPOM also had a higher log likelihood, also indicating a better fit to the data, and a higher Wald Chi-Square value, suggesting stronger overall significance of the predictors in the model. Additionally, PPOM demonstrated better explanatory power, as evidenced by higher Pseudo R2-values (Table 2 ). Altogether, PPOM was found to be a better fit for our data and research objectives. 2.4 Data Analysis A user-written STATA command, gologit2, with the autofit option [ 41 ], was utilized to assess factors associated with EGB. This option simplifies the process of identifying Partial Proportional Odds Models that fit the data. The autofit option identifies which variables do not meet the proportional odds (parallel lines) assumption, and relaxes this constraint for those variables. Essentially, it allows the effects of these variables to vary across the different categories of the dependent variable. Gologit2 provides an odds ratio (OR) and a B-coefficient for both thresholds of the dependent variable, based on responses to the survey question ‘Have you yourself changed your key working practices or methods to be more ecologically sustainable?’. The first threshold for the dependent variable is ‘not at all’ versus ‘yes, to some extent’ or ‘yes, significantly’, and the second threshold ‘not at all’ or ‘yes, to some extent’ versus ‘yes, significantly’. The values are the same for the variables that meet the proportional odds assumption, and differ between the two thresholds for variables that violate the assumption. Table 1 Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values for POM and PPOM in the research hypotheses Proportional Odds Model (POM) Partial Proportional Odds Model (PPOM) AIC BIC AIC BIC H1 9617.2 9750.1 9568.3 9741.1 H2 8850.1 8963.0 8738.0 8884.3 H3 7616.2 7742.4 7533.2 7679.3 H4 6871.9 7044.6 6735.8 6948.3 H5 6633.9 6898.4 6504.2 6815.1 Table 2 Log likelihood, LR Chi-Square and Pseudo R2-values for POM and PPOM in the research hypotheses Proportional Odds Model (POM) Partial Proportional Odds Model (PPOM) n Log likelihood Wald Chi-Square Pseudo R2 n Log likelihood Wald Chi-Square Pseudo R2 H1 5,681 -4788.6 544.6(18) 0.0759 5,681 − 4758.1 616.2(24) 0.0831 H2 5,666 -4408.1 941.1(15) 0.1473 5,666 -4347.0 1068.8(20) 0.1604 H3 5,669 -3789.1 1519.7(17) 0.2675 5,669 -3744.6 1877.7(20) 0.2771 H4 5,654 -3410.0 1563.1(24) 0.3392 5,654 -3335.9 1875.6(30) 0.3545 H5 5,505 -3276.9 1624.4(38) 0.3465 5,505 -3205.1 1927.1(45) 0.3626 3 Results The regression analyses results for H1, H2 and H3 are presented in Tables 3 , 4 and 5 , respectively. Employees who spent any amount of time in the past three months working on one or more of the six assessed environmental products or services were more likely to engage in higher levels of our measure of EGB than others, as suggested in H1. The regression analysis further showed that those dedicating approximately three-quarters of their time to producing environmental products or services were 8.18 times more likely to exhibit higher levels of EGB, compared to those who did not engage in such activities. The model explained 8.3% of the variation in the dependent variable. Employees who considered it ‘very important’ for ecological sustainability to be prioritized in their workplace were 21.50 times more likely (OR = 21.50) to exhibit higher levels of EGB than the reference group (those who did not consider it particularly important). For those who indicated that ecological sustainability is ‘quite important’, the ORs differed across the two thresholds: 7.20 for the first threshold and 2.14 for the second threshold. In other words, respondents were 7.20 times more likely to respond ‘yes, to some extent’ or ‘yes, significantly’ than ‘not at all’ to the question about changing their key working practices or methods to be more ecologically sustainable, and 2.14 times more likely to respond ‘yes, significantly’ than ‘not at all’ or ‘yes, to some extent’ compared to the reference group. The model explained 16% of the variation in the dependent variable. Finally, employees who replied ‘a lot’ to the question ‘To what extent can you influence the ecological sustainability of your working methods or work?’ were 57.10 times more likely to engage in changing their key working practices or methods ‘significantly’ or ‘to some extent’ towards more ecologically sustainable ones than the reference group, i.e. those having not at all influence at work (first threshold). For the second threshold, the OR was as high as 133.60. The model explained almost 28% of the variation in the dependent variable. Furthermore, the regression results for H1, H2 and H3 revealed that women were more likely to engage in changing their key working practices or methods to be more ecologically sustainable, in comparison to men. Employees aged 35 years or more also had higher ORs in comparison to younger employees across all three hypotheses. Current remote work was linked to the dependent variable only in the model for H1. Employment outside of fossil-intensive industries was positively associated with the dependent variable in two of the models (H1 and H2). Interestingly, high educational attainment was found to decrease the likelihood of engaging in working sustainably in two models (H2 and H3). Table 3 Hypothesis 1 (ability): odds ratios of Partial Proportional Odds Model Threshold 1 OR (CI) Threshold 2 OR (CI) Variables Categories SE p-value SE p-value Time spent None Reference Less than ¼ of the time 3.17*** (2.78–3.62) 0.21 0.00 2.29*** (1.78–2.93) 0.29 0.00 About ¼ of the time 6.79*** (5.25–8.77) 0.89 0.00 6.79*** (5.87–8.77) 0.89 0.00 About ½ of the time 5.87*** (4.01–8.59) 1.14 0.00 5.87*** (4.01–8.59) 1.14 0.00 About ¾ of the time 8.18*** (4.81–13.90) 2.21 0.00 8.18*** (4.81–13.90) 2.21 0.00 Almost all of the time 3.54*** (2.51–4.98) 0.62 0.00 8.00*** (5.15–12.43) 1.80 0.00 Gender Male Reference Female 1.79*** (1.57–2.05) 0.12 0.00 1.39** (1.11–1.75) 0.16 0.00 Age (years) 18–24 Reference 25–34 1.25 (0.91–1.71) 0.20 0.17 1.25 (0.91–1.71) 0.20 0.17 35–44 1.53** (1.12–2.09) 0.24 0.01 1.53** (1.12–2.09) 0.24 0.01 45–54 1.73** (1.26–2.37) 0.28 0.00 1.73** (1.26–2.37) 0.28 0.00 55–67 1.81*** (1.32–2.49) 0.29 0.00 1.13 (0.76–1.67) 0.23 0.55 Level of education Secondary Reference Primary or missing information 0.86 (0.64–1.17) 0.13 0.34 1.51 (0.95–2.38) 0.35 0.08 Higher degree 0.91 (0.80–1.04) 0.06 0.16 0.91 (0.80–1.03) 0.06 0.16 Employment contract Permanent Reference Fixed term 1.05 (0.86–1.27) 0.11 0.65 1.05 (0.86–1.27) 0.11 0.65 Remote work No Reference Not now, but before 0.97 (0.80–1.19) 0.10 0.78 0.97 (0.80–1.19) 0.10 0.78 Yes 1.16* (1.01–1.33) 0.08 0.03 1.16* (1.01–1.33) 0.08 0.03 Industry Fossil intensive Reference University 1.75** (1.16–2.65) 0.37 0.01 1.75** (1.16–2.65) 0.37 0.01 Public service 1.36*** (1.15–1.62) 0.12 0.00 1.36*** (1.15–1.62) 0.12 0.00 Private service 1.17* (1.00–1.38) 0.10 0.05 1.69*** (1.33–2.16) 0.21 0.00 * p < 0.05, ** p < 0.01, *** p < 0.001, Threshold 1 = not at all vs. yes, to some extent + yes, significantly, Threshold 2 = not at all + yes, to some extent vs. yes, significantly Table 4 Hypothesis 2 (motivation): odds ratios of Partial Proportional Odds Model Threshold 1 OR (CI) Threshold 2 OR (CI) Variables Categories SE p-value SE p-value Importance Not particularly important Reference Quite important 7.20*** (5.90–8.80) 0.73 0.00 2.14*** (1.52–3.02) 0.37 0.00 Very important 21.50*** (17.23–26.82) 2.43 0.00 21.50*** (17.23–26.82) 2.43 0.00 Gender Male Reference Female 1.34*** (1.16–1.54) 0.10 0.00 0.96 (0.75–1.19) 0.11 0.64 Age (years) 18–24 Reference 25–34 1.31 (0.95–1.82) 0.22 0.10 1.31 (0.95–1.82) 0.22 0.10 35–44 1.63** (1.18–2.26) 0.27 0.00 1.63** (1.18–2.26) 0.27 0.00 45–54 1.85*** (1.33–2.56) 0.31 0.00 1.85*** (1.33–2.56) 0.31 0.00 55–67 1.79*** (1.29–2.48) 0.30 0.00 1.07 (0.72–1.59) 0.22 0.73 Level of education Secondary Reference Primary or missing information 1.01 (0.73–1.39) 0.17 0.97 2.43*** (1.52–3.88) 0.58 0.00 Higher degree 0.68*** (0.59–0.78) 0.05 0.00 0.68*** (0.59–0.78) 0.05 0.00 Employment contract Permanent Reference Fixed term 1.00 (0.82–1.24) 0.11 0.97 1.00 (0.82–1.24) 0.11 0.97 Remote work No Reference Not now, but before 1.03 (0.83–1–27) 0.11 0.78 1.03 (0.83–1–27) 0.11 0.78 Yes 0.90 (0.78–1.03) 0.07 0.13 0.90 (0.78–1.03) 0.07 0.13 Industry Fossil intensive Reference University 1.49 (0.99–2.25) 0.31 0.06 1.49 (0.99–2.25) 0.31 0.06 Public service 1.31** (1.09–1.57) 0.12 0.00 1.31** (1.09–1.57) 0.12 0.00 Private service 1.13 (0.95–1.33) 0.10 0.16 1.64*** (1.28–2.10) 0.21 0.00 * p < 0.05, ** p < 0.01, *** p < 0.001, Threshold 1 = not at all vs. yes, to some extent + yes, significantly, Threshold 2 = not at all + yes, to some extent vs. yes, significantly Table 5 Hypothesis 3 (opportunity): odds ratios of Partial Proportional Odds Model Threshold 1 OR (CI) Threshold 2 OR (CI) Variables Categories SE p-value SE p-value Influence No Reference Have not thought about it 1.06 (0.84–1.33) 0.12 0.62 1.06 (0.84–1.33) 0.12 0.62 Some 15.85*** (13.24–18.97) 1.45 0.00 4.40*** (3.25–6.14) 0.75 0.00 Quite a lot 31.99*** (23.57–43.42) 4.99 0.00 31.99*** (23.57–43.42) 4.99 0.00 A lot 57.10*** (29.56–110.32) 19.19 0.00 133.60*** (85.76–208.12) 30.22 0.00 Gender Male Reference Female 1.85*** (1.60–2.13) 0.14 0.00 1.85*** (1.60–2.13) 0.14 0.00 Age (years) 18–24 Reference 25–34 1.30 (0.92–1.83) 0.22 0.13 1.30 (0.92–1.83) 0.22 0.13 35–44 1.50* (1.07–2.11) 0.26 0.02 1.50* (1.07–2.11) 0.26 0.02 45–54 1.58* (1.13–2.20) 0.27 0.01 1.58* (1.13–2.20) 0.27 0.01 55–67 1.51* (1.08–2.11) 0.26 0.02 1.03 (0.69–1.54) 0.21 0.88 Level of education Secondary Reference Primary or missing information 0.99 (0.71–1.37) 0.16 0.94 0.99 (0.71–1.37) 0.16 0.94 Higher degree 0.78** (0.67–0.91) 0.06 0.00 0.78** (0.67–0.91) 0.06 0.00 Employment contract Permanent Reference Fixed term 1.10 (0.87–1.36) 0.12 0.44 1.10 (0.87–1.36) 0.12 0.44 Remote work No Reference Not now, but before 0.85 (0.67–1.07) 0.10 0.17 0.85 (0.67–1.07) 0.10 0.17 Yes 0.93 (0.79–1.08) 0.07 0.33 0.93 (0.79–1.08) 0.07 0.33 Industry Fossil intensive Reference University 1.11 (0.69–1.79) 0.27 0.67 1.11 (0.69–1.79) 0.27 0.67 Public service 1.17 (0.97–1.42) 0.11 0.11 1.17 (0.97–1.42) 0.11 0.11 Private service 1.15 (0.97–1.36) 0.10 0.12 1.15 (0.97–1.36) 0.10 0.12 * p < 0.05, ** p < 0.01, *** p < 0.001, Threshold 1 = not at all vs. yes, to some extent + yes, significantly, Threshold 2 = not at all + yes, to some extent vs. yes, significantly Table 6 reports the Pseudo R2-values for the regression incorporating all three explanatory variables both alone and in various combinations. The combination of all three had the highest variation explained as expected. Table 6 Pseudo R2-values for different combinations of AMO factors used in the regression Time spent H1 Importance H2 Influence H3 Time spent and Importance H1 and H2 Time spent and InfluenceH1 and H3 Importance and Influence, H2 and H3 All three H4 Pseudo R2 0.0831 0.1604 0.2771 0.1988 0.2897 0.3464 0.3545 The regression analysis results for H4, which covers all three individual-level elements of the AMO framework, are presented in Table 7 . The analysis shows statistically significant OR values ranged from 1.70 to 2.51 for time spent (ability), from 1.94 to 12.70 for importance (motivation) and from 1.52 to 47.03 for influence (opportunity). Notably, these OR values were smaller compared to those observed in regressions in Tables 3 , 4 and 5 that included only one explanatory variable. Table 7 Hypothesis 4 (ability, motivation and opportunity): odds ratios of Partial Proportional Odds Model Threshold 1 OR (CI) Threshold 2 OR (CI) Variables Categories SE p-value SE p-value Time spent None Reference Less than ¼ of the time 1.73*** (1.46–2.04) 0.15 0.00 1.25 (0.95–1.65) 0.18 0.11 About ¼ of the time 2.51*** (1.88–3.35) 0.37 0.00 2.51*** (1.88–3.35) 0.37 0.00 About ½ of the time 1.81** (1.18–2.79) 0.40 0.01 1.81** (1.18–2.79) 0.40 0.01 About ¾ of the time 2.34** (1.30–4.23) 0.71 0.01 2.34** (1.30–4.23) 0.71 0.01 Almost all of the time 1.70** (1.17–2.47) 0.33 0.01 1.70** (1.17–2.47) 0.33 0.01 Importance Not particularly important Reference Quite important 5.48*** (4.31–6.96) 0.67 0.00 1.94** (1.31–2.87) 0.39 0.00 Very important 12.70*** (9.73–16.59) 1.73 0.00 12.70*** (9.73–16.59) 1.73 0.00 Influence No Reference Have not thought about it 1.52** (1.18–1.95) 0.19 0.00 1.52** (1.18–1.95) 0.19 0.00 Some 13.73*** (11.23–16.78) 1.41 0.00 3.47*** (2.45–4.91) 0.62 0.00 Quite a lot 20.29*** (15.06–27.33) 3.08 0.00 20.29*** (15.06–27.33) 3.08 0.00 A lot 47.03*** (29.91–73.95) 10.86 0.00 47.03*** (29.91–73.95) 10.86 0.00 Gender Male Reference Female 1.50*** (1.27–1.77) 0.13 0.00 1.10 (0.84–1.44) 0.15 0.47 Age (years) 18–24 Reference 25–34 1.31 (0.93–1.84) 0.23 0.12 1.31 (0.93–1.84) 0.23 0.12 35–44 1.52* (1.08–2.14) 0.26 0.02 1.52* (1.08–2.14) 0.26 0.02 45–54 1.56* (1.11–2.19) 0.27 0.01 1.56* (1.11–2.19) 0.27 0.01 55–67 1.33 (0.94–1.87) 0.23 0.11 0.85 (0.56–1.29) 0.18 0.45 Level of education Secondary Reference Primary or missing information 0.98 (0.69–1.40) 0.18 0.91 1.93* (1.08–3.45) 0.57 0.03 Higher degree 0.65*** (0.55–0.76) 0.05 0.00 0.65*** (0.55–0.76) 0.05 0.00 Employment contract Permanent Reference Fixed term 0.98 (0.79–1.23) 0.11 0.89 0.98 (0.79–1.23) 0.11 0.89 Remote work No Reference Not now, but before 0.83 (0.65–1.07) 0.11 0.15 0.83 (0.65–1.07) 0.11 0.15 Yes 0.86 (0.74–1.02) 0.07 0.08 0.86 (0.74–1.02) 0.07 0.08 Industry Fossil intensive Reference University 1.33 (0.81–2.17) 0.33 0.26 1.33 (0.81–2.17) 0.33 0.26 Public service 1.41** (1.15–1.73) 0.15 0.00 1.41** (1.15–1.73) 0.15 0.00 Private service 1.28** (1.07–1.53) 0.12 0.01 1.28** (1.07–1.53) 0.12 0.01 * p < 0.05, ** p < 0.01, *** p < 0.001 , Threshold 1 = not at all vs. yes, to some extent + yes, significantly, Threshold 2 = not at all + yes, to some extent vs. yes, significantly To assess H5, we performed an ordered logit regression that included all AMO factors along with variables representing organizational context (ecological sustainability in workplace strategy, ecological sustainability in internal workplace interaction and supervisor support to promote ecological sustainability). Incorporating the three organizational variables into the regression model increased the Pseudo R2-value from 0.35 (Table 7 ) only to 0.36 (Table 8 ). A likelihood-ratio test was conducted and the increase in the Pseudo R2-value was found to be statistically significant. However, none of the individual organizational factors were statistically significant, except for the second threshold of ‘somewhat disagree’ and the first threshold of ‘somewhat agree’ option of the internal workplace interaction variable. The ORs for the AMO variables differed from those in the previous regression model for H4. The statistically significant ORs ranged from 1.60 to 2.22 for time spent (ability). For importance (motivation), ORs ranged from 1.87 to 11.90, while for influence (opportunity), they ranged from 1.48 to 42.11. In this model, also age groups from 35 to 54 years, female gender, and employment in public and private services were in a statistically significant way associated with higher levels of our measure of EGB. Additionally, a statistically significant OR (0.67) was identified for employees with higher education, indicating that this group was less likely to engage in EGB. Table 8 Hypothesis 5 (AMO and organizational factors): odds ratios of Partial Proportional Odds Model Threshold 1 OR (CI) Threshold 2 OR (CI) Variables Categories SE p-value SE p-value Time spent None Reference Less than ¼ of the time 1.63*** (1.37–1.93) 0.14 0.00 1.12 (0.84–1.49) 0.17 0.43 About ¼ of the time 2.22*** (1.65–2.99) 0.34 0.00 2.22*** (1.65–2.99) 0.34 0.00 About ½ of the time 1.64* (1.06–2.56) 0.37 0.03 1.64* (1.06–2.56) 0.37 0.03 About ¾ of the time 2.01* (1.08–3.73) 0.63 0.03 2.01* (1.08–3.73) 0.63 0.03 Almost all of the time 1.60* (1.10–2.32) 0.30 0.01 1.60* (1.10–2.32) 0.30 0.01 Importance Not particularly important Reference Quite important 5.33*** (4.16–6.83) 0.67 0.00 1.87** (1.25–2.80) 0.39 0.00 Very important 11.90*** (9.01–15.72) 1.69 0.00 11.90*** (9.01–15.72) 1.69 0.00 Influence No Reference Have not thought about it 1.48** (1.14–1.91) 0.19 0.00 1.48** (1.14–1.91) 0.19 0.00 Some 12.65*** (10.28–15.57) 1.34 0.00 3.18*** (2.23–4.53) 0.58 0.00 Quite a lot 17.08*** (12.45–23.43) 2.76 0.00 17.08*** (12.45–23.43) 2.76 0.00 A lot 42.11*** (26.86–66.01) 9.66 0.00 42.11*** (26.86–66.01) 9.66 0.00 Workplace strategy No Reference Does not apply to workplace 0.97 (0.63–1.49) 0.21 0.89 0.97 (0.63–1.49) 0.21 0.89 Don’t know 1.02 (0.77–1.34) 0.44 0.91 1.02 (0.77–1.34) 0.44 0.91 Yes, but no climate objectives or strategy 1.16 (0.85–1.58) 0.18 0.34 1.16 (0.85–1.58) 0.18 0.34 Yes, climate objectives written into strategy 0.96 (0.72–1.30) 0.15 0.81 0.96 (0.72–1.30) 0.15 0.81 Yes, a separate climate strategy 0.95 (0.69–1.30) 0.15 0.75 0.95 (0.69–1.30) 0.15 0.75 Workplace interaction Strongly disagree Reference Does not apply to my workplace 0.84 (0.59–1.18) 0.15 0.32 0.84 (0.59–1.18) 0.15 0.32 Somewhat disagree 0.92 (0.87–1.23) 0.13 0.57 0.43** (0.26–0.71) 0.11 0.00 Does not agree nor disagree 1.13 (0.87–1.47) 0.15 0.36 1.13 (0.87–1.47) 0.15 0.36 Somewhat agree 1.51** (1.12–2.03) 0.23 0.01 0.99 (0.69–1.43) 0.19 0.97 Strongly agree 1.29 (0.90–1.85) 0.24 0.17 1.29 (0.90–1.85) 0.24 0.17 Supervisor support Strongly disagree Reference Somewhat disagree 0.89 (0.61–1.29) 0.17 0.53 0.89 (0.61–1.29) 0.17 0.53 Does not agree nor disagree 0.73 (0.52–1.02) 0.12 0.06 0.73 (0.52–1.02) 0.12 0.06 Somewhat agree 0.96 (0.67–1.36) 0.17 0.81 0.96 (0.67–1.36) 0.17 0.81 Strongly agree 1.00 (0.69–1.45) 0.19 0.99 1.00 (0.69–1.45) 0.19 0.99 Gender Male Reference Female 1.51*** (1.28–1.79) 0.13 0.00 1.10 (0.84–1.45) 0.15 0.49 Age (years) 18–24 Reference 25–34 1.30 (0.92–1.83) 0.23 0.14 1.30 (0.92–1.83) 0.23 0.14 35–44 1.50* (1.06–2.13) 0.27 0.02 1.50* (1.06–2.13) 0.27 0.02 45–54 1.53* (1.08–2.15) 0.27 0.02 1.53* (1.08–2.15) 0.27 0.02 55–67 1.28 (0.90–1.82) 0.23 0.18 0.82 (0.54–1.26) 0.18 0.37 Education Secondary Reference Primary or missing information 1.18 (0.83–1.68) 0.21 0.36 1.18 (0.83–1.68) 0.21 0.36 Higher degree 0.67*** (0.57–0.80) 0.06 0.00 0.67*** (0.57–0.80) 0.06 0.00 Employment contract Permanent Reference Fixed term 0.97 (0.77–1.22) 0.11 0.80 0.97 (0.77–1.22) 0.11 0.80 Remote work No Reference Not now, but before 0.82 (0.64–1.06) 0.11 0.13 0.82 (0.64–1.06) 0.11 0.13 Yes 0.87 (0.73–1.03) 0.07 0.10 0.87 (0.73–1.03) 0.07 0.10 Industry Fossil intensive Reference University 1.34 (0.81–2.24) 0.35 0.26 1.34 (0.81–2.24) 0.35 0.26 Public service 1.46** (1.18–1.80) 0.16 0.00 1.46** (1.18–1.80) 0.16 0.00 Private service 1.31** (1.09–1.57) 0.12 0.00 1.31** (1.09–1.57) 0.12 0.00 * p < 0.05, ** p < 0.01, *** p < 0.001, Threshold 1 = not at all vs. yes, to some extent + yes, significantly, Threshold 2 = not at all + yes, to some extent vs. yes, significantly Workplace strategy: ‘Is ecological sustainability taken into account in the objectives or strategy of your workplace?’, Internal workplace interaction: “In our workplace, we have been thinking together about how we can act in an ecologically sustainable manner”, Supervisor support: “My supervisor instructs her/his employees to act in an environmentally friendly manner at work”. 4 Discussion Using a statistically representative Finnish employee survey, we investigated individual-level and organizational factors associated with the activity of employees to change their key working practices and methods to be more ecologically sustainable. The analysis gave support for all of our five hypotheses. Adding organizational factors to the regression model significantly increased its explanatory power, although the increase was modest. However, the individual organizational variables were not statistically significant, and the observed changes in OR suggest shared variance or possible mediation and moderation effects between the individual-level AMO factors and the organizational context. Following the design of the study of Rayner and Morgan [ 26 ], we used the AMO framework as a high-level generalization and conceptual model for explaining our measure of EGB. Scholars have different views on whether AMO should be considered a multiplicative model, a summative model or a combinative model, in which one of the three factors (ability, motivation or opportunity) is believed to be more important than the others [ 25 ]. We did not make such an assumption either. However, our empirical analysis showed that the importance of the different elements of the model was clearly different. Of the three elements of the AMO framework, our measure of ability was less strongly linked to our measure of EGB than that of motivation and opportunity. One reason for this may be methodological. There was no question in the survey that directly addressed green skills and knowledge [ 42 ]. The variable used described time spent producing environmental products or services, and did not directly measure the respondent’s green skills and knowledge, thus not being ideal for analysis. Our assumption was that time spent producing such products or services as part of an employee’s job is positively associated with her/his green skills and knowledge. However, green skills and knowledge can also be acquired in the workplace through training or in various ways outside one’s job and workplace. The result can also be due to the fact that the dependent variable used in the analysis describes a rather low-intensity form of EGB, and does not necessarily require high-level green skills and knowledge to the same extent as some other forms of EGB, such as influencing others or taking initiative [ 6 ]. The importance of motivation and especially opportunity to influence one’s own work was emphasized in the models, also compared to factors describing the organizational context. This might suggest that people engage in EGB largely endogenously, largely regardless of how strongly organizations support it. However, such an interpretation is too far-reaching. One possible explanation for the results is that good opportunities to influence the ecological sustainability of one’s own work and the motivation to do so in themselves reflect the organization’s positive attitude towards these issues and active green HRM [ 19 ]. In other words, the contextual, more indirect factors used in the analysis no longer have a significant direct and additional explanatory value, besides that of individual factors, for EGB [ 26 ]. It is also possible, as in the case of ability, that motivation in particular arises from outside the workplace and not so much through the workplace context. Yuriev et al. [ 21 ] refer to spillover effects between home and work in pro-environmental behaviours; a phenomenon, however, about which there is not much empirical research so far. In most statistical analyses, working outside of fossil-intensive industries increased the likelihood that an employee would have changed their key working practices or methods to be more ecologically sustainable. This suggests that in fossil-intensive industries with high GHG emissions, there are more profound system-level factors beyond the control of individual employees that constrain their sustainability agency compared to agency typically experienced in other industries. Such sectoral path dependencies can be structural (e.g. attachment to certain raw materials, tools, technologies or operational processes) or cognitive, cultural or institutional in nature [ 43 ]. The results of our statistical analysis suggest that the factors associated with EGB may be weighted quite differently across industries, a factor that should be paid attention to in future studies. Age had positive relationships with our measure of EGB in all models, although the connection was not entirely linear. The result is consistent with those of previous research [ 9 , 44 ]. Women were also more active than men in changing their key working practices and methods to be more ecologically sustainable. The result is consistent with our own previous research results (Moilanen et al. 2024), but contradicts the findings of the meta-analysis of Katz et al. [ 9 ]. A surprising result, which was contrary to our expectations and the results of many previous studies [ 9 ], was the negative association found between level of education and our measure of EGB. We cannot provide an explanation for the deviating finding based on our data, but it is possible that the measure we used does not capture the more typical forms of green behaviour of the more highly educated. The type of employment relationship was not significant in any model. We also adjusted for the employees’ current or previous remote work because some employees may think that remote work is in itself a significant change in work practices or methods, which has positive effects on ecological sustainability, for example through reduced commuting or use of office space. However, the respondent’s current remote work was positively connected to our measure of EGB only in one of the models. In any case, the increasing prevalence of remote and hybrid work is an important change in working practices that should also be taken into account in future studies on EGB. The main strength of our analysis was that it was based on a large, statistically representative dataset of Finnish employees, with a response rate of over 70%. Its key limitation was the cross-sectional nature of the study, which did not allow causal or bidirectional inference. This was the first time that the Quality of Work Life Survey included questions on ecological sustainability, so implementing a longitudinal study design was not feasible. Another limitation related to the novelty of the theme was that the functionality of the questions on ecological sustainability had not been tested for the survey to the same extent as many of the more traditional questions on working conditions and practices. We already mentioned above that the survey did not include a question that directly addressed the respondent’s green skills and knowledge (ability). A methodological strength of this study was the application of the Partial Proportional Odds Model (PPOM), a type of ordinal logistic regression. While PPOM offers more flexibility than POM, it may not fully capture non-linear or interaction effects. Nevertheless, exploring alternative ordinal logistic regression models in future research could be pursued to enhance robustness and deepen insights. Regarding studies on EGB, our analysis was limited by the fact that we focused on only one EGB category (working sustainably), and only one element (changing how work is done) within it. Results from a single category cannot be mechanically generalized to other categories, as demonstrated, for example, by the meta-analysis of Wiernik et al. [ 44 ] on the association of age with EGB. We were unable to cover all EGB categories in our analysis due to the lack of suitable questions in the survey. At the same time, we consciously wanted to focus on a form of EGB which explicitly addresses concrete changes in one’s own work. In future studies, it is important to shed more light on the connections and dependencies between different EGB categories. 5 Conclusions Our study demonstrates the importance of employee motivation, and in particular, job-related influence, in fostering more ecologically sustainable ways of working. In contrast, the direct connection of organizational factors to the promotion of more ecologically sustainable working practices and methods remained limited by our findings. However, organizational context, such as the extent to which ecological sustainability is integrated into the workplace strategy or as part of internal workplace interaction and supervisor support, may have an important indirect effect on sustainable ways of working and EGB as a whole through individual-level factors included in the AMO framework. The results highlight the importance of gaining a deeper understanding of the specific industry characteristics that shape the mechanisms driving EGB. More detailed research is also needed on the connections and dependencies between different categories of EGB. For example, of the components of the AMO framework, the importance of ability may be more emphasized in some other forms of EGB than in changing one’s own work in a more ecologically sustainable direction. Furthermore, to better understand the significance of organizational contextual factors on EGB, more empirical research is also needed on possible spillover effects between home and work. A key practical implication of the study concerns the importance of employees’ influence over their own work in bringing about more ecologically sustainable working practices and methods. Work autonomy and self-direction have traditionally been considered important dimensions of quality of work life [45]. Our results suggest that increasing self-direction and autonomy at work could also be an important means of promoting EGB and, thereby, the ecological sustainability of organizations in many industries – with the partial exception of fossil-intensive industries, where the focus obviously should be placed on system-level changes to a greater degree. Declarations Funding declaration : Funding provided by the Finnish Institute of Occupational Health. Clinical trial number: Not applicable. 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Asia Pacific Journal of Human Resources. 2018;56(1):56–78. https://doi.org/10.1111/1744-7941.12151. Moilanen, F, Alasoini, T. Workers as actors at the micro-level of sustainability transitions: a systematic literature review. Environmental Innovation and Societal Transitions. 2023;46:100685. https://doi.org/10.1016/j.eist.2022.100685. Süßbauer, E, Maas-Deipenbrock, RM, Friedrich, S, Kreß-Ludwig, M, Langen, N, Muster, V. Employee roles in sustainability transformation processes: a move away from expertise and towards experience-driven sustainability management. GAIA. 2019;28(1):210–217. https://doi.org/10.14512/gaia.28.S1.7. Urban, P, Rizos, V, Ounnas, A, Kassab, A, Kalantaryan, H. Jobs for the green transition: definitions, classifications and emerging trends. CEPS In-Depth Analysis 2023-12. Brussels: CEPS; 2023. Giddens, A. The constitution of society: outline of the theory of structuration. Cambridge: Polity Press; 1984. Moilanen, F, Toikka, A. Measuring employees’ perceptions of sustainability transitions at work: a novel survey with findings from Finland. Discover Sustainability. 2023;4(45). https://doi.org/10.1007/s43621-023-00163-5. Sutela, H, Viinikka, J, Pärnänen, A. Työolot murrosten keskellä – työolotutkimuksen tuloksia 1977–2023. Helsinki: Statics Finland; 2024. Moilanen, F, Ala-Laurinaho, A, Alasoini, T. Climate change and everyday work: a survey of the views of Finnish employees. Helsinki: Finnish Institute of Occupational Health; 2024. Koene, B, van Riemsdijk, M. Managing temporary workers: work identity, diversity and operational HR choices. Human Resource Management Journal. 2005;15(1):76–92. https://doi.org/10.1111/j.1748-8583.2005.tb00141.x. McCullagh, P. Regression models for ordinal data. Journal of the Royal Statistical Society: Series B. 1980;42(2):109–127. Bender, R, Grouven, U. Using binary logistic regression models for ordinal data with non-proportional odds. Journal of Clinical Epidemiology. 1998;51(10):809–816. https://doi.org/10.1016/s0895-4356(98)00066-3. Fullerton, AS, Xu, J. The proportional odds with partial proportionality constraints model for ordinal response variables. Social Science Research. 2012;41(1):182–198. https://doi.org/10.1016/j.ssresearch.2011.09.003. Lelisho, ME, Wogi, AA, Tareke, SA. Ordinal logistic regression analysis in determining factors associated with socioeconomic status of household in Tepi Town, Southwest Ethiopia. The Scientific World Journal. 2022;Article ID:2415692. https://doi.org/10.1155/2022/2415692. Long, JS, Freese, J. Regression models for categorical dependent variables using Stata. College Station, TX: Stata Press; 2006. Brant, R. Assessing proportionality in the proportional odds model for ordinal logistic regression. Biometrics. 1990;46(4):1171–1178. Williams, R. Generalized ordered logit/partial proportional odds models for ordinal dependent variables. The Stata Journal. 2006;6(1):58–-82. http://dx.doi.org/10.1177/1536867X0600600104. European Commission. Green skills and knowledge concepts: labelling the ESCO classification. Brussels: European Commission, Employment, Social Affairs and Inclusion; 2025. Schienstock. G. From path dependency to path creation: Finland on its way to the knowledge-based economy. Current Sociology. 2007;55(1):92–109. https://doi.org/10.1177/0011392107070136. Wiernik, BM, Ones, DS, Dilchert, S. Age and employee green behaviors: a meta-analysis. Frontiers in Psychology. 2016;7:194. https://doi.org/10.3389/fpsyg.2016.00194. Guest, D, Knox, A, Warhurst, C. Humanizing work in the digital age: lessons from socio-technical systems and quality of working life initiatives. Human Relations. 2022;75(8):1461–1482. https://doi.org/10.1177/00187267221092674. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7533093","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":522110523,"identity":"097c0c86-4077-4126-9379-437a98b62139","order_by":0,"name":"Tuomo Alasoini","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYBADGRBxgMHAhoGBnYHhQwIepTzINFBLmgQPMwPjDKK1AMFhiBZ8DrKXSD664QODHQ9//+GDhwsKztfZA7U0PMBni0Ra2s0ZDMk8EjfSEg7PMLgNtqUBr8Mkcsxu8zAw8zDc4DE4zAPRwv6ACC31PPLnz4C0nCPaFqDiAzkgLQeI0HLmGdAvBsd5DEF+4TFIluw5zNiIVwt7e/KxGx8qquXkzh8+/Jnnjx0/e3vzwcYfeLRAgAEKj7GBoIZRMApGwSgYBfgBAJyFRJNCdaibAAAAAElFTkSuQmCC","orcid":"","institution":"Finnish Institute of Occupational Health","correspondingAuthor":true,"prefix":"","firstName":"Tuomo","middleName":"","lastName":"Alasoini","suffix":""},{"id":522110524,"identity":"afaf133c-627e-4008-ab45-7257d11b09d6","order_by":1,"name":"Vaula Siltala","email":"","orcid":"","institution":"Finnish Institute of Occupational 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17:27:19","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":225252,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7533093/v1/c333113446e596f7f0fa17da.html"},{"id":98245546,"identity":"5cd2925b-b5d2-4d72-8c22-4bc4a4e5b08f","added_by":"auto","created_at":"2025-12-15 16:18:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1721646,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7533093/v1/b4a14dc6-cb15-4acd-97f6-d4618c44de9c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Antecedents of sustainable working among Finnish employees in light of the ability–motivation–opportunity framework","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eOver the past decades, economic growth in developed industrial countries has been based on the overconsumption of natural resources. This has served to accelerate global warming and led to loss of biodiversity. The lion\u0026rsquo;s share of greenhouse gas (GHG) emissions in Finland, as in many other developed industrial countries, comes from a few industries, such as energy production, capital-intensive process industries, transport and storage, and agriculture [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, industries are interconnected in various value chains and networks and other inter-organizational dependencies. Thus, the challenge of ecological sustainability broadly applies to \u003cem\u003eall\u003c/em\u003e industries and their workplaces.\u003c/p\u003e\u003cp\u003eThere is a long tradition in management, organizational and work life studies regarding the role of employees in innovation and development in the workplace. The studies have been conducted with the help of a variety of theoretical frameworks and concepts [\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The research has justified the participation of employees in innovation and development from three perspectives, in particular. The first of these departs from the premise that employee participation allows for a more diverse use of knowledge and expertise in innovation and development, leading to more effective solutions. Participation has also been considered to strengthen employees\u0026rsquo; motivation and their commitment to new solutions. Finally, participation and the collaboration based on it can be held to promote learning and knowledge sharing within organizations.\u003c/p\u003e\u003cp\u003eConcerns about global warming and biodiversity loss have increased academic interest in the role of employees also in contributing to the ecological sustainability of their work and workplaces in recent years. The role of employees has been examined through concepts such as \u0026lsquo;green behaviour\u0026rsquo; [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], \u0026lsquo;green workplace behaviour\u0026rsquo; [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], \u0026lsquo;pro-environmental behaviour\u0026rsquo; [\u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and \u0026lsquo;sustainability agency\u0026rsquo; [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The diversity of concepts and the lack of consistency in their content indicate that the role of employees in promoting ecological sustainability in the workplace is a relatively new research topic and that the issue has been examined through the use of highly divergent theoretical frameworks.\u003c/p\u003e\u003cp\u003eWe study Finnish employees\u0026rsquo; green behaviour, as well as the various individual and organizational factors related to it, using a large and statistically representative survey dataset covering all industries. We are particularly interested in the importance of factors that are related to employees\u0026rsquo; activity to change their working practices and methods to be more ecologically sustainable. This paper contributes to the literature on employee green behaviour (EGB) in three important ways.\u003c/p\u003e\u003cp\u003eFirst, we link our analysis to the micro-level AMO framework, which seeks to explain EGB as a combination of employees\u0026rsquo; self-perceived abilities (possessed skills and knowledge), motivations and opportunities in terms of their job autonomy and organizational support. The AMO framework, which is based on a classic behaviour and work performance model in organizational research [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], has been widely applied in human resource management (HRM) research as a theoretical approach for examining the HRM\u0026ndash;performance relationship [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Another stream of studies to which we contribute has utilized the framework to predict individual-level behaviour like EGB [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSecond, we aim to increase understanding of the \u003cem\u003erelative\u003c/em\u003e importance of individual and organizational factors in terms of EGB. Knowledge of the importance of factors at different levels helps us to better understand the relationship between employee agency and the contextual factors that enable or constrain it in increasing the ecological sustainability of workplace operations [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThird, we use data from Statistics Finland\u0026rsquo;s 2023 Quality of Work Life Survey, which are statistically representative of the entire Finnish wage-earner population. The target group consists of employees aged 18\u0026ndash;67 who regularly work at least 10 hours per week. The survey\u0026rsquo;s broad coverage and large sample size (N\u0026thinsp;=\u0026thinsp;5,742) provide good opportunities for diverse statistical analyses. A large part of previous empirical studies on EGB have focused on smaller and narrower target groups, such as specific industries or organizations. As Carbone et al. (2024) point out, there is a significant need in EGB research to include samples that are representative of the entire working population.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Employee green behaviour and its determinants\u003c/h2\u003e\u003cp\u003eIn this article, we refer to EGB as actions that employees engage in that are linked with and contribute to environmental sustainability [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The actions can be included as part of the employee\u0026rsquo;s job tasks, or be voluntary and based on the employee\u0026rsquo;s own initiative. The actions can have direct effects, such as changing how one\u0026rsquo;s work is done or actively participating in the development of new, more ecologically sustainable products and services, or be more indirect, such as influencing others or taking initiative. The intensity and risk level of the actions can also vary, ranging from low-intensity actions related to one\u0026rsquo;s own work habits (such as turning off the lights) to more radical development initiatives to senior management to increase the ecological sustainability of workplace operations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. All in all, EGB can take many forms in the workplace and be an essential component of organizational ecological sustainability [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Despite the efforts of many researchers, no single, universally applicable approach has been taken to classify and operationalize EGB and its various forms.\u003c/p\u003e\u003cp\u003eThe concept of EGB also includes the idea that the content and outcome of behaviour are within employees\u0026rsquo; control, and do not just mechanically reflect a way of acting required by the work organization. Green behaviour requires agency from the employee. In this way, EGB is linked to the idea of ​​sustainability agency. Sustainability agency has been conceptualized in the literature as the capacity of an individual or community to act reflectively in a way that promotes ecological sustainability [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Green behaviour should also not be confused or identified with the concept of a green job. Green jobs refer to jobs that are done in connection with ecologically sustainable products and services, or as part of ecologically more sustainable production processes [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The concept as such does not include the idea of ​​employee agency.\u003c/p\u003e\u003cp\u003eEGB is not limited to certain industries, jobs or tasks. However, in addition to the employee\u0026rsquo;s characteristics and factors related to their job, EGB is influenced by various contextual factors related to the employee\u0026rsquo;s social relationships, team and organizational levels, and institutional factors [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Examples of these include social norms of work communities, management systems, organizational strategies, professional and industry-specific standards, and legislation. On the other hand, structures that enable or constrain employee agency in the form of rules and resources are not immutable. Social practices, such as green behaviour, can also lead to changes in structures when interacting with them. Giddens [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] calls such an interactive process between agents and structures structuration. Researchers\u0026rsquo; interest in the impact of EGB on the rules and resources perceived as essential for ecological sustainability in workplaces has increased in recent years, especially as part of studies on sustainability transitions [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eResearch on EGB has largely focused on its individual and organizational antecedents. Studies that have examined individual factors have focused on employee demographics, personality traits, job attitudes and work-related perceptions. A meta-analysis of studies found that EGB is positively associated with employee age, tenure and level of education [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Personality traits that have been shown to relate to EGB include curiosity, openness to experience, conscientiousness, moral reflexiveness and self-efficacy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Employees\u0026rsquo; pro-environmental attitudes and norms, job satisfaction, organizational commitment and identification, and opportunities to influence their own work are also found to be positively associated with EGB [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Among the organizational factors of relevance, EGB has been found to be positively associated with perceived support received from the supervisor and the management system, as well as pro-environmental psychological climates in workplaces [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eResearchers conducting meta-analyses have developed various models that combine individual, organizational, or other contextual factors to describe the factors associated with EGB [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, the theoretical frameworks and concepts underlying the models differ from each other. The models contain assumptions about the importance of certain factors, but do not help to understand in more detail the \u003cem\u003erelative importance\u003c/em\u003e of different levels of different factors for EGB, which is the focus of this article.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Research questions and hypotheses\u003c/h2\u003e\u003cp\u003eOur first research question is to what extent EGB can be explained by individual factors alone using the AMO framework. Second, we ask to what extent the relationship between individual factors and EGB is moderated by organizational factors. By answering the research questions, we will be able to better understand the structural dependencies of EGB and employee sustainability agency in workplaces, and to direct research and measures promoting ecological sustainability of workplaces in this direction.\u003c/p\u003e\u003cp\u003eAmong the different forms of EGB, we focus on the activity of employees to change their working practices and methods to be more ecologically sustainable. This is a direct form of EGB, which Ones and Dilchert [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] place in the EGB category \u0026lsquo;working sustainably\u0026rsquo; and Francoeur et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] in \u0026lsquo;transforming\u0026rsquo;. We chose this form of EGB as the subject of analysis, since it most clearly includes the idea of ​​employee sustainability agency that has led to concrete changes in their own work.\u003c/p\u003e\u003cp\u003eBased on the AMO framework, we propose the following three hypotheses:\u003c/p\u003e\u003cp\u003eH1: An employee\u0026rsquo;s skills and knowledge related to the ecological sustainability of their job and workplace operations is positively associated with green behaviour.\u003c/p\u003e\u003cp\u003eH2: An employee\u0026rsquo;s view of the importance of ecological sustainability as part of their job and workplace operations is positively associated with green behaviour.\u003c/p\u003e\u003cp\u003eH3: An employee\u0026rsquo;s opportunities to influence the ecological sustainability of their job and workplace operations is positively associated with green behaviour.\u003c/p\u003e\u003cp\u003eAccording to the AMO framework, the most favourable situation for employees to perform well is when they possess the skills and knowledge to do so, when they have the motivation to do so, and when their job provides the opportunity to do so [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Based on this, we propose the following hypothesis:\u003c/p\u003e\u003cp\u003eH4: An employee\u0026rsquo;s high skills and knowledge, strong motivation, and good opportunities to influence their work, in combination, explain a greater proportion of variance in green behavior than any other combination of these factors, or any of these factors alone.\u003c/p\u003e\u003cp\u003eEGB is not independent of the organizational context in which the employee acts. The context can have an enabling or constraining impact on EGB in alternative ways. These can include, in particular, to what extent the pursuit of ecological sustainability is integrated as part of workplace strategy, internal workplace interaction and supervisor support. All these aspects can be broadly defined as elements of an organization\u0026rsquo;s green HRM [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Based on this thinking, we present the following hypothesis:\u003c/p\u003e\u003cp\u003eH5: Organizational factors moderate the relationship between employee skills and knowledge, motivation and job-related influence opportunities and green behaviour. Such factors include workplace strategy (H5a), internal workplace interaction (H5b) and supervisor support (H5c).\u003c/p\u003e\u003c/div\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data\u003c/h2\u003e\u003cp\u003eOur data are based on Statistics Finland\u0026rsquo;s Quality of Work Life Survey of 2023. The target group of the survey consists of employees aged 18\u0026ndash;67 years who regularly work at least 10 hours a week. Statistics Finland has conducted similar surveys every few years since 1977, but the 2023 survey is the first to include questions on the ecological sustainability of work. Data collection was carried out as an online survey using Finnish, Swedish or English between September 2023 and January 2024. The sample was drawn in two ways: from those who participated in Statistics Finland\u0026rsquo;s Labour Force Survey in 2023 and from those who responded to Statistics Finland\u0026rsquo;s online survey on the impact of the COVID-19 crisis on work life carried out two years earlier. Of the more than 8,000 employees who met the sample criteria, 5,742 responded to the survey (response rate 71.5%). Any skew in the data has been corrected by weighting factors to correspond to the target group of employees working at least 10 hours a week. Weighting factors were used to correct the distribution of the sample by respondent gender, age, province, level of education and socioeconomic group [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Measures\u003c/h2\u003e\u003cp\u003eAmong the categories of EGB, in this paper we focus on the category \u003cem\u003eworking sustainably\u003c/em\u003e. This was assessed with the question \u0026lsquo;Have you yourself changed your key working practices or methods to be more ecologically sustainable?\u0026rsquo;. The response options were \u0026lsquo;yes, significantly\u0026rsquo;, \u0026lsquo;yes, to some extent\u0026rsquo; and \u0026lsquo;not at all\u0026rsquo;.\u003c/p\u003e\u003cp\u003eAn employee\u0026rsquo;s \u003cem\u003eskills and knowledge related to the ecological sustainability\u003c/em\u003e of their job and workplace operations were assessed with two questions. First, employees were asked about their involvement in the production of six different environmental products or services (renewable energy, energy-efficient products and services, reduction and elimination of pollutants and GHGs, waste recycling and reuse) at work during the past three months. Next, they were asked how much of their working time during this period was spent producing these products or services. The five-point response scale ranged from \u0026lsquo;almost all the time\u0026rsquo; to \u0026lsquo;less than one quarter of the time\u0026rsquo;. In the analysis, we used the working time spent on the production of any of the six environmental products or services as an indicator of the employee\u0026rsquo;s skills and knowledge related to ecological sustainability.\u003c/p\u003e\u003cp\u003eAn employee\u0026rsquo;s \u003cem\u003eview of the importance of ecological sustainability\u003c/em\u003e as part of their job and workplace operations was assessed with the question \u0026lsquo;How important do you think it is that ecological sustainability is taken into account in your workplace?\u0026rsquo;. The three-point response scale ranged from \u0026lsquo;very important\u0026rsquo; to \u0026lsquo;not particularly important\u0026rsquo;.\u003c/p\u003e\u003cp\u003eAn employee\u0026rsquo;s \u003cem\u003eopportunities to influence the ecological sustainability\u003c/em\u003e of their job and workplace operations were assessed with the question \u0026lsquo;To what extent can you influence the ecological sustainability of your working methods or work?\u0026rsquo;. The four-point response scale ranged from \u0026lsquo;a lot\u0026rsquo; to \u0026lsquo;not at all\u0026rsquo;, in addition to which the employee was given the option \u0026lsquo;I have not thought about it\u0026rsquo;.\u003c/p\u003e\u003cp\u003e\u003cem\u003eThe strategic positioning of the workplace towards ecological sustainability\u003c/em\u003e was assessed with the question \u0026lsquo;Is ecological sustainability taken into account in the objectives or strategy of your workplace?\u0026rsquo;. The response options were \u0026lsquo;yes, my workplace has a climate programme or strategy\u0026rsquo;, \u0026lsquo;yes, objectives have been written into the organization\u0026rsquo;s strategy\u0026rsquo;, \u0026lsquo;yes, objectives are otherwise taken into account\u0026rsquo;, \u0026lsquo;no\u0026rsquo;, \u0026lsquo;does not apply to my workplace\u0026rsquo; and \u0026lsquo;I don\u0026rsquo;t know\u0026rsquo;.\u003c/p\u003e\u003cp\u003e\u003cem\u003eThe role of ecological sustainability as part of internal workplace interaction\u003c/em\u003e was assessed with the statement \u0026lsquo;In our workplace, we have been thinking together about how we can act in an ecologically sustainable manner\u0026rsquo;. The five-point response scale ranged from \u0026lsquo;strongly agree\u0026rsquo; to \u0026lsquo;strongly disagree\u0026rsquo;, in addition to which the employee could choose the option \u0026lsquo;does not apply to my workplace\u0026rsquo;.\u003c/p\u003e\u003cp\u003e\u003cem\u003eThe support an employee receives from her/his supervisor to promote ecological sustainability\u003c/em\u003e was assessed with the statement \u0026lsquo;My supervisor instructs her/his employees to act in an environmentally friendly manner at work\u0026rsquo;. The five-point response scale ranged from \u0026lsquo;strongly agree\u0026rsquo; to \u0026lsquo;strongly disagree\u0026rsquo;.\u003c/p\u003e\u003cp\u003eIn the regression analysis we adjusted for the respondent\u0026rsquo;s age (18\u0026ndash;24, 25\u0026ndash;34, 35\u0026ndash;44, 45\u0026ndash;54, 55\u0026ndash;67 years) and level of education (three levels), both of which have been found to be positively associated with EGB in previous studies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. No statistically significant association was found between gender and EGB in the meta-analysis by Katz et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, we also adjusted for gender, as studies have shown that at least in Finland, female employees have a higher degree of climate concern than male employees, which may also be reflected in behaviour at work [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. We further adjusted for the nature of the employment relationship. The fixed-term nature of the employment relationship can weaken organizational identification [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Studies have shown that weakened organizational identification is negatively associated with EGB [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn addition, we adjusted for industry. Although the issue of ecological sustainability applies to all industries and their workplaces [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], differences in EGB between industries may stem from differences in organizational cultures or the strategic importance of the industries, for example in reducing GHG emissions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. We divided industries into four categories based on their level of GHG emissions and economic sector. The first category is fossil-intensive industries, in which annual GHG emissions in Finland exceeded 10 tons per employee in 2022 (level 1 codes ABCDEFH in NACE Rev.2.1). As other categories, we distinguished between private services, public services and universities. However, among private service industries, transportation and storage (level 1 code H in NACE Rev.2.1) was included in the category of fossil-intensive industry, due to its high level of GHG emissions.\u003c/p\u003e\u003cp\u003eFinally, we adjusted for whether the employee does or has previously done remote work. This allowed us to better distinguish from the data employees\u0026rsquo; activities that promote ecological sustainability at work, in a way which would not solely reflect increased remote work following the COVID-19 pandemic. The response options to the question \u0026lsquo;Do you work remotely\u0026rsquo; were \u0026lsquo;yes\u0026rsquo;, \u0026lsquo;no, but I have done it before\u0026rsquo; and \u0026lsquo;no\u0026rsquo;.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Statistical models\u003c/h2\u003e\u003cp\u003eTo analyse ordinal survey responses, we used ordinal logistic regression models tailored to specific needs. The Proportional Odds Model (POM) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] assumes a consistent relationship between predictors and outcomes across thresholds, simplifying interpretation. When this assumption does not hold, the Partial Proportional Odds Model (PPOM) provides flexibility by allowing some predictors to vary across thresholds [\u003cspan additionalcitationids=\"CR37 CR38\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In our analysis, both models were assessed using statistical criteria, including the Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC), to determine the most appropriate model.\u003c/p\u003e\u003cp\u003eThe Brant test [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] in STATA was performed to assess the proportional odds assumption. For all hypotheses, an insignificant test statistic indicated that the final model did not violate the assumption. However, partial violations were observed, as several independent variables failed to meet the criteria. In terms of goodness of fit, the AIC and BIC values were smaller for PPOM compared to POM, indicating a better model fit (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For all hypotheses, PPOM also had a higher log likelihood, also indicating a better fit to the data, and a higher Wald Chi-Square value, suggesting stronger overall significance of the predictors in the model. Additionally, PPOM demonstrated better explanatory power, as evidenced by higher Pseudo R2-values (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Altogether, PPOM was found to be a better fit for our data and research objectives.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Data Analysis\u003c/h2\u003e\u003cp\u003eA user-written STATA command, gologit2, with the autofit option [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], was utilized to assess factors associated with EGB. This option simplifies the process of identifying Partial Proportional Odds Models that fit the data. The autofit option identifies which variables do not meet the proportional odds (parallel lines) assumption, and relaxes this constraint for those variables. Essentially, it allows the effects of these variables to vary across the different categories of the dependent variable.\u003c/p\u003e\u003cp\u003eGologit2 provides an odds ratio (OR) and a B-coefficient for both thresholds of the dependent variable, based on responses to the survey question \u0026lsquo;Have you yourself changed your key working practices or methods to be more ecologically sustainable?\u0026rsquo;. The first threshold for the dependent variable is \u0026lsquo;not at all\u0026rsquo; \u003cem\u003eversus\u003c/em\u003e \u0026lsquo;yes, to some extent\u0026rsquo; or \u0026lsquo;yes, significantly\u0026rsquo;, and the second threshold \u0026lsquo;not at all\u0026rsquo; or \u0026lsquo;yes, to some extent\u0026rsquo; \u003cem\u003eversus\u003c/em\u003e \u0026lsquo;yes, significantly\u0026rsquo;. The values are the same for the variables that meet the proportional odds assumption, and differ between the two thresholds for variables that violate the assumption.\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\u003eAkaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values for POM and PPOM in the research hypotheses\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eProportional Odds Model (POM)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003ePartial Proportional Odds Model (PPOM)\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\u003eAIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9617.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9750.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9568.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9741.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8850.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8963.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8738.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8884.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7616.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7742.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7533.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7679.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6871.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7044.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6735.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6948.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6633.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6898.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6504.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6815.1\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\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\u003eLog likelihood, LR Chi-Square and Pseudo R2-values for POM and PPOM in the research hypotheses\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\u003eProportional Odds Model (POM)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003ePartial Proportional Odds Model (PPOM)\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\u003en\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLog likelihood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWald Chi-Square\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePseudo R2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLog likelihood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eWald Chi-Square\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ePseudo R2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-4788.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e544.6(18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5,681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;4758.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e616.2(24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.0831\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-4408.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e941.1(15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5,666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-4347.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1068.8(20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.1604\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-3789.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1519.7(17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5,669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-3744.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1877.7(20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.2771\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-3410.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1563.1(24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5,654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-3335.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1875.6(30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.3545\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-3276.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1624.4(38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3465\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5,505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-3205.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1927.1(45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.3626\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eThe regression analyses results for H1, H2 and H3 are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, respectively.\u003c/p\u003e\u003cp\u003eEmployees who spent any amount of time in the past three months working on one or more of the six assessed environmental products or services were more likely to engage in higher levels of our measure of EGB than others, as suggested in H1. The regression analysis further showed that those dedicating approximately three-quarters of their time to producing environmental products or services were 8.18 times more likely to exhibit higher levels of EGB, compared to those who did not engage in such activities. The model explained 8.3% of the variation in the dependent variable.\u003c/p\u003e\u003cp\u003eEmployees who considered it \u0026lsquo;very important\u0026rsquo; for ecological sustainability to be prioritized in their workplace were 21.50 times more likely (OR\u0026thinsp;=\u0026thinsp;21.50) to exhibit higher levels of EGB than the reference group (those who did not consider it particularly important). For those who indicated that ecological sustainability is \u0026lsquo;quite important\u0026rsquo;, the ORs differed across the two thresholds: 7.20 for the first threshold and 2.14 for the second threshold. In other words, respondents were 7.20 times more likely to respond \u0026lsquo;yes, to some extent\u0026rsquo; or \u0026lsquo;yes, significantly\u0026rsquo; than \u0026lsquo;not at all\u0026rsquo; to the question about changing their key working practices or methods to be more ecologically sustainable, and 2.14 times more likely to respond \u0026lsquo;yes, significantly\u0026rsquo; than \u0026lsquo;not at all\u0026rsquo; or \u0026lsquo;yes, to some extent\u0026rsquo; compared to the reference group. The model explained 16% of the variation in the dependent variable.\u003c/p\u003e\u003cp\u003eFinally, employees who replied \u0026lsquo;a lot\u0026rsquo; to the question \u0026lsquo;To what extent can you influence the ecological sustainability of your working methods or work?\u0026rsquo; were 57.10 times more likely to engage in changing their key working practices or methods \u0026lsquo;significantly\u0026rsquo; or \u0026lsquo;to some extent\u0026rsquo; towards more ecologically sustainable ones than the reference group, i.e. those having not at all influence at work (first threshold). For the second threshold, the OR was as high as 133.60. The model explained almost 28% of the variation in the dependent variable.\u003c/p\u003e\u003cp\u003eFurthermore, the regression results for H1, H2 and H3 revealed that women were more likely to engage in changing their key working practices or methods to be more ecologically sustainable, in comparison to men. Employees aged 35 years or more also had higher ORs in comparison to younger employees across all three hypotheses. Current remote work was linked to the dependent variable only in the model for H1. Employment outside of fossil-intensive industries was positively associated with the dependent variable in two of the models (H1 and H2). Interestingly, high educational attainment was found to decrease the likelihood of engaging in working sustainably in two models (H2 and H3).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHypothesis 1 (ability): odds ratios of Partial Proportional Odds Model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThreshold 1\u003c/p\u003e\u003cp\u003eOR (CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eThreshold 2 OR (CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime spent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLess than \u0026frac14; of the time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.17***\u003c/p\u003e\u003cp\u003e(2.78\u0026ndash;3.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.29***\u003c/p\u003e\u003cp\u003e(1.78\u0026ndash;2.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbout \u0026frac14; of the time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.79***\u003c/p\u003e\u003cp\u003e(5.25\u0026ndash;8.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.79***\u003c/p\u003e\u003cp\u003e(5.87\u0026ndash;8.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbout \u0026frac12; of the time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.87***\u003c/p\u003e\u003cp\u003e(4.01\u0026ndash;8.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.87***\u003c/p\u003e\u003cp\u003e(4.01\u0026ndash;8.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbout \u0026frac34; of the time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.18***\u003c/p\u003e\u003cp\u003e(4.81\u0026ndash;13.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.18***\u003c/p\u003e\u003cp\u003e(4.81\u0026ndash;13.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAlmost all of the time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.54***\u003c/p\u003e\u003cp\u003e(2.51\u0026ndash;4.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.00***\u003c/p\u003e\u003cp\u003e(5.15\u0026ndash;12.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.79***\u003c/p\u003e\u003cp\u003e(1.57\u0026ndash;2.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.39**\u003c/p\u003e\u003cp\u003e(1.11\u0026ndash;1.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u0026ndash;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003cp\u003e(0.91\u0026ndash;1.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003cp\u003e(0.91\u0026ndash;1.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.53**\u003c/p\u003e\u003cp\u003e(1.12\u0026ndash;2.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.53**\u003c/p\u003e\u003cp\u003e(1.12\u0026ndash;2.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45\u0026ndash;54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.73**\u003c/p\u003e\u003cp\u003e(1.26\u0026ndash;2.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.73**\u003c/p\u003e\u003cp\u003e(1.26\u0026ndash;2.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55\u0026ndash;67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.81***\u003c/p\u003e\u003cp\u003e(1.32\u0026ndash;2.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003cp\u003e(0.76\u0026ndash;1.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLevel of education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary or missing information\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003cp\u003e(0.64\u0026ndash;1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.51\u003c/p\u003e\u003cp\u003e(0.95\u0026ndash;2.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigher degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003cp\u003e(0.80\u0026ndash;1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003cp\u003e(0.80\u0026ndash;1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment contract\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePermanent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFixed term\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003cp\u003e(0.86\u0026ndash;1.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003cp\u003e(0.86\u0026ndash;1.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRemote work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot now, but before\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003cp\u003e(0.80\u0026ndash;1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003cp\u003e(0.80\u0026ndash;1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.16*\u003c/p\u003e\u003cp\u003e(1.01\u0026ndash;1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.16*\u003c/p\u003e\u003cp\u003e(1.01\u0026ndash;1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFossil intensive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUniversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.75**\u003c/p\u003e\u003cp\u003e(1.16\u0026ndash;2.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.75**\u003c/p\u003e\u003cp\u003e(1.16\u0026ndash;2.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePublic service\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.36***\u003c/p\u003e\u003cp\u003e(1.15\u0026ndash;1.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.36***\u003c/p\u003e\u003cp\u003e(1.15\u0026ndash;1.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrivate service\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.17*\u003c/p\u003e\u003cp\u003e(1.00\u0026ndash;1.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.69***\u003c/p\u003e\u003cp\u003e(1.33\u0026ndash;2.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Threshold 1\u0026thinsp;=\u0026thinsp;not at all vs. yes, to some extent\u0026thinsp;+\u0026thinsp;yes, significantly, Threshold 2\u0026thinsp;=\u0026thinsp;not at all +\u0026thinsp;yes, to some extent vs. yes, significantly\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHypothesis 2 (motivation): odds ratios of Partial Proportional Odds Model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThreshold 1 OR (CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eThreshold 2 OR (CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImportance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot particularly important\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuite important\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.20***\u003c/p\u003e\u003cp\u003e(5.90\u0026ndash;8.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.14***\u003c/p\u003e\u003cp\u003e(1.52\u0026ndash;3.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery important\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.50*** (17.23\u0026ndash;26.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21.50***\u003c/p\u003e\u003cp\u003e(17.23\u0026ndash;26.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.34***\u003c/p\u003e\u003cp\u003e(1.16\u0026ndash;1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003cp\u003e(0.75\u0026ndash;1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u0026ndash;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003cp\u003e(0.95\u0026ndash;1.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003cp\u003e(0.95\u0026ndash;1.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.63**\u003c/p\u003e\u003cp\u003e(1.18\u0026ndash;2.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.63**\u003c/p\u003e\u003cp\u003e(1.18\u0026ndash;2.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45\u0026ndash;54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.85***\u003c/p\u003e\u003cp\u003e(1.33\u0026ndash;2.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.85***\u003c/p\u003e\u003cp\u003e(1.33\u0026ndash;2.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55\u0026ndash;67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.79***\u003c/p\u003e\u003cp\u003e(1.29\u0026ndash;2.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003cp\u003e(0.72\u0026ndash;1.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLevel of education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary or missing information\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003cp\u003e(0.73\u0026ndash;1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.43***\u003c/p\u003e\u003cp\u003e(1.52\u0026ndash;3.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigher degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.68***\u003c/p\u003e\u003cp\u003e(0.59\u0026ndash;0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.68***\u003c/p\u003e\u003cp\u003e(0.59\u0026ndash;0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment contract\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePermanent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFixed term\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003cp\u003e(0.82\u0026ndash;1.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003cp\u003e(0.82\u0026ndash;1.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRemote work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot now, but before\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003cp\u003e(0.83\u0026ndash;1\u0026ndash;27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003cp\u003e(0.83\u0026ndash;1\u0026ndash;27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003cp\u003e(0.78\u0026ndash;1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003cp\u003e(0.78\u0026ndash;1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFossil intensive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUniversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003cp\u003e(0.99\u0026ndash;2.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003cp\u003e(0.99\u0026ndash;2.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePublic service\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.31**\u003c/p\u003e\u003cp\u003e(1.09\u0026ndash;1.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.31**\u003c/p\u003e\u003cp\u003e(1.09\u0026ndash;1.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrivate service\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003cp\u003e(0.95\u0026ndash;1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.64***\u003c/p\u003e\u003cp\u003e(1.28\u0026ndash;2.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Threshold 1\u0026thinsp;=\u0026thinsp;not at all vs. yes, to some extent\u0026thinsp;+\u0026thinsp;yes, significantly, Threshold 2\u0026thinsp;=\u0026thinsp;not at all +\u0026thinsp;yes, to some extent vs. yes, significantly\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\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 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHypothesis 3 (opportunity): odds ratios of Partial Proportional Odds Model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThreshold 1 OR (CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eThreshold 2 OR (CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfluence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHave not thought about it\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003cp\u003e(0.84\u0026ndash;1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003cp\u003e(0.84\u0026ndash;1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.85***\u003c/p\u003e\u003cp\u003e(13.24\u0026ndash;18.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.40***\u003c/p\u003e\u003cp\u003e(3.25\u0026ndash;6.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuite a lot\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.99***\u003c/p\u003e\u003cp\u003e(23.57\u0026ndash;43.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e31.99***\u003c/p\u003e\u003cp\u003e(23.57\u0026ndash;43.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA lot\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57.10***\u003c/p\u003e\u003cp\u003e(29.56\u0026ndash;110.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e133.60***\u003c/p\u003e\u003cp\u003e(85.76\u0026ndash;208.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e30.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.85***\u003c/p\u003e\u003cp\u003e(1.60\u0026ndash;2.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.85***\u003c/p\u003e\u003cp\u003e(1.60\u0026ndash;2.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u0026ndash;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003cp\u003e(0.92\u0026ndash;1.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003cp\u003e(0.92\u0026ndash;1.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.50*\u003c/p\u003e\u003cp\u003e(1.07\u0026ndash;2.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.50*\u003c/p\u003e\u003cp\u003e(1.07\u0026ndash;2.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45\u0026ndash;54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.58*\u003c/p\u003e\u003cp\u003e(1.13\u0026ndash;2.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.58*\u003c/p\u003e\u003cp\u003e(1.13\u0026ndash;2.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55\u0026ndash;67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.51*\u003c/p\u003e\u003cp\u003e(1.08\u0026ndash;2.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003cp\u003e(0.69\u0026ndash;1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLevel of education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary or missing information\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003cp\u003e(0.71\u0026ndash;1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003cp\u003e(0.71\u0026ndash;1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigher degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.78**\u003c/p\u003e\u003cp\u003e(0.67\u0026ndash;0.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.78**\u003c/p\u003e\u003cp\u003e(0.67\u0026ndash;0.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment contract\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePermanent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFixed term\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003cp\u003e(0.87\u0026ndash;1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003cp\u003e(0.87\u0026ndash;1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRemote work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot now, but before\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003cp\u003e(0.67\u0026ndash;1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003cp\u003e(0.67\u0026ndash;1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003cp\u003e(0.79\u0026ndash;1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003cp\u003e(0.79\u0026ndash;1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFossil intensive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUniversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003cp\u003e(0.69\u0026ndash;1.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003cp\u003e(0.69\u0026ndash;1.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePublic service\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003cp\u003e(0.97\u0026ndash;1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003cp\u003e(0.97\u0026ndash;1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrivate service\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003cp\u003e(0.97\u0026ndash;1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003cp\u003e(0.97\u0026ndash;1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Threshold 1\u0026thinsp;=\u0026thinsp;not at all vs. yes, to some extent\u0026thinsp;+\u0026thinsp;yes, significantly, Threshold 2\u0026thinsp;=\u0026thinsp;not at all +\u0026thinsp;yes, to some extent vs. yes, significantly\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e reports the Pseudo R2-values for the regression incorporating all three explanatory variables both alone and in various combinations. The combination of all three had the highest variation explained as expected.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePseudo R2-values for different combinations of AMO factors used in the regression\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTime spent H1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eImportance\u003c/p\u003e\u003cp\u003eH2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInfluence H3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTime spent and Importance H1 and H2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTime spent and InfluenceH1 and H3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eImportance and Influence, H2 and H3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAll three H4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePseudo R2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0831\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.3464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.3545\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\u003eThe regression analysis results for H4, which covers all three individual-level elements of the AMO framework, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The analysis shows statistically significant OR values ranged from 1.70 to 2.51 for \u003cem\u003etime spent\u003c/em\u003e (ability), from 1.94 to 12.70 for \u003cem\u003eimportance\u003c/em\u003e (motivation) and from 1.52 to 47.03 for \u003cem\u003einfluence\u003c/em\u003e (opportunity). Notably, these OR values were smaller compared to those observed in regressions in Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e that included only one explanatory variable.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHypothesis 4 (ability, motivation and opportunity): odds ratios of Partial Proportional Odds Model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThreshold 1 OR (CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eThreshold 2 OR (CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime spent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLess than \u0026frac14; of the time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.73***\u003c/p\u003e\u003cp\u003e(1.46\u0026ndash;2.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003cp\u003e(0.95\u0026ndash;1.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbout \u0026frac14; of the time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.51***\u003c/p\u003e\u003cp\u003e(1.88\u0026ndash;3.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.51***\u003c/p\u003e\u003cp\u003e(1.88\u0026ndash;3.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbout \u0026frac12; of the time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.81**\u003c/p\u003e\u003cp\u003e(1.18\u0026ndash;2.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.81**\u003c/p\u003e\u003cp\u003e(1.18\u0026ndash;2.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbout \u0026frac34; of the time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.34**\u003c/p\u003e\u003cp\u003e(1.30\u0026ndash;4.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.34**\u003c/p\u003e\u003cp\u003e(1.30\u0026ndash;4.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAlmost all of the time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.70**\u003c/p\u003e\u003cp\u003e(1.17\u0026ndash;2.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.70**\u003c/p\u003e\u003cp\u003e(1.17\u0026ndash;2.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImportance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot particularly important\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuite important\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.48***\u003c/p\u003e\u003cp\u003e(4.31\u0026ndash;6.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.94**\u003c/p\u003e\u003cp\u003e(1.31\u0026ndash;2.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery important\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.70***\u003c/p\u003e\u003cp\u003e(9.73\u0026ndash;16.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.70***\u003c/p\u003e\u003cp\u003e(9.73\u0026ndash;16.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfluence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHave not thought about it\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.52**\u003c/p\u003e\u003cp\u003e(1.18\u0026ndash;1.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.52**\u003c/p\u003e\u003cp\u003e(1.18\u0026ndash;1.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.73***\u003c/p\u003e\u003cp\u003e(11.23\u0026ndash;16.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.47***\u003c/p\u003e\u003cp\u003e(2.45\u0026ndash;4.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuite a lot\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.29***\u003c/p\u003e\u003cp\u003e(15.06\u0026ndash;27.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20.29***\u003c/p\u003e\u003cp\u003e(15.06\u0026ndash;27.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA lot\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47.03***\u003c/p\u003e\u003cp\u003e(29.91\u0026ndash;73.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e47.03***\u003c/p\u003e\u003cp\u003e(29.91\u0026ndash;73.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.50***\u003c/p\u003e\u003cp\u003e(1.27\u0026ndash;1.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003cp\u003e(0.84\u0026ndash;1.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u0026ndash;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003cp\u003e(0.93\u0026ndash;1.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003cp\u003e(0.93\u0026ndash;1.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.52*\u003c/p\u003e\u003cp\u003e(1.08\u0026ndash;2.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.52*\u003c/p\u003e\u003cp\u003e(1.08\u0026ndash;2.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45\u0026ndash;54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.56*\u003c/p\u003e\u003cp\u003e(1.11\u0026ndash;2.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.56*\u003c/p\u003e\u003cp\u003e(1.11\u0026ndash;2.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55\u0026ndash;67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003cp\u003e(0.94\u0026ndash;1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003cp\u003e(0.56\u0026ndash;1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLevel of education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary or missing information\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003cp\u003e(0.69\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.93*\u003c/p\u003e\u003cp\u003e(1.08\u0026ndash;3.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigher degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.65***\u003c/p\u003e\u003cp\u003e(0.55\u0026ndash;0.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.65***\u003c/p\u003e\u003cp\u003e(0.55\u0026ndash;0.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment contract\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePermanent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFixed term\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003cp\u003e(0.79\u0026ndash;1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003cp\u003e(0.79\u0026ndash;1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRemote work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot now, but before\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003cp\u003e(0.65\u0026ndash;1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003cp\u003e(0.65\u0026ndash;1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003cp\u003e(0.74\u0026ndash;1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003cp\u003e(0.74\u0026ndash;1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFossil intensive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUniversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003cp\u003e(0.81\u0026ndash;2.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003cp\u003e(0.81\u0026ndash;2.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePublic service\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.41**\u003c/p\u003e\u003cp\u003e(1.15\u0026ndash;1.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.41**\u003c/p\u003e\u003cp\u003e(1.15\u0026ndash;1.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrivate service\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.28**\u003c/p\u003e\u003cp\u003e(1.07\u0026ndash;1.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.28**\u003c/p\u003e\u003cp\u003e(1.07\u0026ndash;1.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e, Threshold 1\u0026thinsp;=\u0026thinsp;not at all vs. yes, to some extent\u0026thinsp;+\u0026thinsp;yes, significantly, Threshold 2\u0026thinsp;=\u0026thinsp;not at all +\u0026thinsp;yes, to some extent vs. yes, significantly\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo assess H5, we performed an ordered logit regression that included all AMO factors along with variables representing organizational context (ecological sustainability in workplace strategy, ecological sustainability in internal workplace interaction and supervisor support to promote ecological sustainability). Incorporating the three organizational variables into the regression model increased the Pseudo R2-value from 0.35 (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) only to 0.36 (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). A likelihood-ratio test was conducted and the increase in the Pseudo R2-value was found to be statistically significant. However, none of the individual organizational factors were statistically significant, except for the second threshold of \u0026lsquo;somewhat disagree\u0026rsquo; and the first threshold of \u0026lsquo;somewhat agree\u0026rsquo; option of the internal workplace interaction variable.\u003c/p\u003e\u003cp\u003eThe ORs for the AMO variables differed from those in the previous regression model for H4. The statistically significant ORs ranged from 1.60 to 2.22 for \u003cem\u003etime spent\u003c/em\u003e (ability). For \u003cem\u003eimportance\u003c/em\u003e (motivation), ORs ranged from 1.87 to 11.90, while for \u003cem\u003einfluence\u003c/em\u003e (opportunity), they ranged from 1.48 to 42.11.\u003c/p\u003e\u003cp\u003eIn this model, also age groups from 35 to 54 years, female gender, and employment in public and private services were in a statistically significant way associated with higher levels of our measure of EGB. Additionally, a statistically significant OR (0.67) was identified for employees with higher education, indicating that this group was less likely to engage in EGB.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHypothesis 5 (AMO and organizational factors): odds ratios of Partial Proportional Odds Model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThreshold 1 OR (CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eThreshold 2 OR (CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategories\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime spent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLess than \u0026frac14; of the time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.63***\u003c/p\u003e\u003cp\u003e(1.37\u0026ndash;1.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003cp\u003e(0.84\u0026ndash;1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbout \u0026frac14; of the time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.22***\u003c/p\u003e\u003cp\u003e(1.65\u0026ndash;2.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.22***\u003c/p\u003e\u003cp\u003e(1.65\u0026ndash;2.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbout \u0026frac12; of the time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.64*\u003c/p\u003e\u003cp\u003e(1.06\u0026ndash;2.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.64*\u003c/p\u003e\u003cp\u003e(1.06\u0026ndash;2.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbout \u0026frac34; of the time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.01*\u003c/p\u003e\u003cp\u003e(1.08\u0026ndash;3.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.01*\u003c/p\u003e\u003cp\u003e(1.08\u0026ndash;3.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAlmost all of the time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.60*\u003c/p\u003e\u003cp\u003e(1.10\u0026ndash;2.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.60*\u003c/p\u003e\u003cp\u003e(1.10\u0026ndash;2.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImportance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot particularly important\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuite important\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.33***\u003c/p\u003e\u003cp\u003e(4.16\u0026ndash;6.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.87**\u003c/p\u003e\u003cp\u003e(1.25\u0026ndash;2.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery important\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.90***\u003c/p\u003e\u003cp\u003e(9.01\u0026ndash;15.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.90***\u003c/p\u003e\u003cp\u003e(9.01\u0026ndash;15.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfluence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHave not thought about it\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.48**\u003c/p\u003e\u003cp\u003e(1.14\u0026ndash;1.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.48**\u003c/p\u003e\u003cp\u003e(1.14\u0026ndash;1.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.65***\u003c/p\u003e\u003cp\u003e(10.28\u0026ndash;15.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.18***\u003c/p\u003e\u003cp\u003e(2.23\u0026ndash;4.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuite a lot\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.08***\u003c/p\u003e\u003cp\u003e(12.45\u0026ndash;23.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.08***\u003c/p\u003e\u003cp\u003e(12.45\u0026ndash;23.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA lot\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.11***\u003c/p\u003e\u003cp\u003e(26.86\u0026ndash;66.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e42.11***\u003c/p\u003e\u003cp\u003e(26.86\u0026ndash;66.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorkplace strategy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDoes not apply to workplace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003cp\u003e(0.63\u0026ndash;1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003cp\u003e(0.63\u0026ndash;1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDon\u0026rsquo;t know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003cp\u003e(0.77\u0026ndash;1.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003cp\u003e(0.77\u0026ndash;1.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes, but no climate objectives or strategy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003cp\u003e(0.85\u0026ndash;1.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003cp\u003e(0.85\u0026ndash;1.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes, climate objectives written into strategy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003cp\u003e(0.72\u0026ndash;1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003cp\u003e(0.72\u0026ndash;1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes, a separate climate strategy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003cp\u003e(0.69\u0026ndash;1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003cp\u003e(0.69\u0026ndash;1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorkplace interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStrongly disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDoes not apply to my workplace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003cp\u003e(0.59\u0026ndash;1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003cp\u003e(0.59\u0026ndash;1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSomewhat disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003cp\u003e(0.87\u0026ndash;1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.43**\u003c/p\u003e\u003cp\u003e(0.26\u0026ndash;0.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDoes not agree nor disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003cp\u003e(0.87\u0026ndash;1.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003cp\u003e(0.87\u0026ndash;1.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSomewhat agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.51**\u003c/p\u003e\u003cp\u003e(1.12\u0026ndash;2.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003cp\u003e(0.69\u0026ndash;1.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStrongly agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003cp\u003e(0.90\u0026ndash;1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003cp\u003e(0.90\u0026ndash;1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSupervisor support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStrongly disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSomewhat disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003cp\u003e(0.61\u0026ndash;1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003cp\u003e(0.61\u0026ndash;1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDoes not agree nor disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003cp\u003e(0.52\u0026ndash;1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003cp\u003e(0.52\u0026ndash;1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSomewhat agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003cp\u003e(0.67\u0026ndash;1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003cp\u003e(0.67\u0026ndash;1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStrongly agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003cp\u003e(0.69\u0026ndash;1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003cp\u003e(0.69\u0026ndash;1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.51***\u003c/p\u003e\u003cp\u003e(1.28\u0026ndash;1.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003cp\u003e(0.84\u0026ndash;1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u0026ndash;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003cp\u003e(0.92\u0026ndash;1.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003cp\u003e(0.92\u0026ndash;1.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.50*\u003c/p\u003e\u003cp\u003e(1.06\u0026ndash;2.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.50*\u003c/p\u003e\u003cp\u003e(1.06\u0026ndash;2.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45\u0026ndash;54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.53*\u003c/p\u003e\u003cp\u003e(1.08\u0026ndash;2.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.53*\u003c/p\u003e\u003cp\u003e(1.08\u0026ndash;2.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55\u0026ndash;67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003cp\u003e(0.90\u0026ndash;1.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003cp\u003e(0.54\u0026ndash;1.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary or missing information\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003cp\u003e(0.83\u0026ndash;1.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003cp\u003e(0.83\u0026ndash;1.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigher degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.67***\u003c/p\u003e\u003cp\u003e(0.57\u0026ndash;0.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.67***\u003c/p\u003e\u003cp\u003e(0.57\u0026ndash;0.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment contract\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePermanent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFixed term\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003cp\u003e(0.77\u0026ndash;1.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003cp\u003e(0.77\u0026ndash;1.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRemote work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot now, but before\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003cp\u003e(0.64\u0026ndash;1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003cp\u003e(0.64\u0026ndash;1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003cp\u003e(0.73\u0026ndash;1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003cp\u003e(0.73\u0026ndash;1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFossil intensive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUniversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003cp\u003e(0.81\u0026ndash;2.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003cp\u003e(0.81\u0026ndash;2.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePublic service\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.46**\u003c/p\u003e\u003cp\u003e(1.18\u0026ndash;1.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.46**\u003c/p\u003e\u003cp\u003e(1.18\u0026ndash;1.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrivate service\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.31**\u003c/p\u003e\u003cp\u003e(1.09\u0026ndash;1.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.31**\u003c/p\u003e\u003cp\u003e(1.09\u0026ndash;1.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Threshold 1\u0026thinsp;=\u0026thinsp;not at all vs. yes, to some extent\u0026thinsp;+\u0026thinsp;yes, significantly, Threshold 2\u0026thinsp;=\u0026thinsp;not at all +\u0026thinsp;yes, to some extent vs. yes, significantly\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWorkplace strategy: \u0026lsquo;Is ecological sustainability taken into account in the objectives or strategy of your workplace?\u0026rsquo;, Internal workplace interaction: \u0026ldquo;In our workplace, we have been thinking together about how we can act in an ecologically sustainable manner\u0026rdquo;, Supervisor support: \u0026ldquo;My supervisor instructs her/his employees to act in an environmentally friendly manner at work\u0026rdquo;.\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eUsing a statistically representative Finnish employee survey, we investigated individual-level and organizational factors associated with the activity of employees to change their key working practices and methods to be more ecologically sustainable. The analysis gave support for all of our five hypotheses. Adding organizational factors to the regression model significantly increased its explanatory power, although the increase was modest. However, the individual organizational variables were not statistically significant, and the observed changes in OR suggest shared variance or possible mediation and moderation effects between the individual-level AMO factors and the organizational context.\u003c/p\u003e\u003cp\u003eFollowing the design of the study of Rayner and Morgan [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], we used the AMO framework as a high-level generalization and conceptual model for explaining our measure of EGB. Scholars have different views on whether AMO should be considered a multiplicative model, a summative model or a combinative model, in which one of the three factors (ability, motivation or opportunity) is believed to be more important than the others [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. We did not make such an assumption either. However, our empirical analysis showed that the importance of the different elements of the model was clearly different.\u003c/p\u003e\u003cp\u003eOf the three elements of the AMO framework, our measure of ability was less strongly linked to our measure of EGB than that of motivation and opportunity. One reason for this may be methodological. There was no question in the survey that directly addressed green skills and knowledge [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The variable used described time spent producing environmental products or services, and did not directly measure the respondent\u0026rsquo;s green skills and knowledge, thus not being ideal for analysis. Our assumption was that time spent producing such products or services as part of an employee\u0026rsquo;s job is positively associated with her/his green skills and knowledge. However, green skills and knowledge can also be acquired in the workplace through training or in various ways outside one\u0026rsquo;s job and workplace. The result can also be due to the fact that the dependent variable used in the analysis describes a rather low-intensity form of EGB, and does not necessarily require high-level green skills and knowledge to the same extent as some other forms of EGB, such as influencing others or taking initiative [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe importance of motivation and especially opportunity to influence one\u0026rsquo;s own work was emphasized in the models, also compared to factors describing the organizational context. This might suggest that people engage in EGB largely endogenously, largely regardless of how strongly organizations support it. However, such an interpretation is too far-reaching. One possible explanation for the results is that good opportunities to influence the ecological sustainability of one\u0026rsquo;s own work and the motivation to do so \u003cem\u003ein themselves\u003c/em\u003e reflect the organization\u0026rsquo;s positive attitude towards these issues and active green HRM [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In other words, the contextual, more indirect factors used in the analysis no longer have a significant direct and additional explanatory value, besides that of individual factors, for EGB [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. It is also possible, as in the case of ability, that motivation in particular arises from outside the workplace and not so much through the workplace context. Yuriev et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] refer to spillover effects between home and work in pro-environmental behaviours; a phenomenon, however, about which there is not much empirical research so far.\u003c/p\u003e\u003cp\u003eIn most statistical analyses, working outside of fossil-intensive industries increased the likelihood that an employee would have changed their key working practices or methods to be more ecologically sustainable. This suggests that in fossil-intensive industries with high GHG emissions, there are more profound system-level factors beyond the control of individual employees that constrain their sustainability agency compared to agency typically experienced in other industries. Such sectoral path dependencies can be structural (e.g. attachment to certain raw materials, tools, technologies or operational processes) or cognitive, cultural or institutional in nature [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The results of our statistical analysis suggest that the factors associated with EGB may be weighted quite differently across industries, a factor that should be paid attention to in future studies.\u003c/p\u003e\u003cp\u003eAge had positive relationships with our measure of EGB in all models, although the connection was not entirely linear. The result is consistent with those of previous research [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Women were also more active than men in changing their key working practices and methods to be more ecologically sustainable. The result is consistent with our own previous research results (Moilanen et al. 2024), but contradicts the findings of the meta-analysis of Katz et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A surprising result, which was contrary to our expectations and the results of many previous studies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], was the negative association found between level of education and our measure of EGB. We cannot provide an explanation for the deviating finding based on our data, but it is possible that the measure we used does not capture the more typical forms of green behaviour of the more highly educated. The type of employment relationship was not significant in any model.\u003c/p\u003e\u003cp\u003eWe also adjusted for the employees\u0026rsquo; current or previous remote work because some employees may think that remote work is in itself a significant change in work practices or methods, which has positive effects on ecological sustainability, for example through reduced commuting or use of office space. However, the respondent\u0026rsquo;s current remote work was positively connected to our measure of EGB only in one of the models. In any case, the increasing prevalence of remote and hybrid work is an important change in working practices that should also be taken into account in future studies on EGB.\u003c/p\u003e\u003cp\u003eThe main strength of our analysis was that it was based on a large, statistically representative dataset of Finnish employees, with a response rate of over 70%. Its key limitation was the cross-sectional nature of the study, which did not allow causal or bidirectional inference. This was the first time that the Quality of Work Life Survey included questions on ecological sustainability, so implementing a longitudinal study design was not feasible. Another limitation related to the novelty of the theme was that the functionality of the questions on ecological sustainability had not been tested for the survey to the same extent as many of the more traditional questions on working conditions and practices. We already mentioned above that the survey did not include a question that directly addressed the respondent\u0026rsquo;s green skills and knowledge (ability).\u003c/p\u003e\u003cp\u003eA methodological strength of this study was the application of the Partial Proportional Odds Model (PPOM), a type of ordinal logistic regression. While PPOM offers more flexibility than POM, it may not fully capture non-linear or interaction effects. Nevertheless, exploring alternative ordinal logistic regression models in future research could be pursued to enhance robustness and deepen insights.\u003c/p\u003e\u003cp\u003eRegarding studies on EGB, our analysis was limited by the fact that we focused on only one EGB category (working sustainably), and only one element (changing how work is done) within it. Results from a single category cannot be mechanically generalized to other categories, as demonstrated, for example, by the meta-analysis of Wiernik et al. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] on the association of age with EGB. We were unable to cover all EGB categories in our analysis due to the lack of suitable questions in the survey. At the same time, we consciously wanted to focus on a form of EGB which explicitly addresses concrete changes in one\u0026rsquo;s own work. In future studies, it is important to shed more light on the connections and dependencies between different EGB categories.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eOur study demonstrates the importance of employee motivation, and in particular, job-related influence, in fostering more ecologically sustainable ways of working.\u0026nbsp;In contrast, the direct connection of organizational factors to the promotion of more ecologically sustainable working practices and methods remained limited by our findings. However, organizational context, such as the extent to which ecological sustainability is integrated into the\u0026nbsp;workplace strategy or as part of internal workplace interaction and supervisor support, may have an important indirect effect on sustainable ways of working and EGB as a whole through individual-level factors included in the AMO framework.\u003c/p\u003e\n\u003cp\u003eThe results highlight the importance of gaining a deeper understanding of the specific industry characteristics that shape the mechanisms driving EGB. More detailed research is also needed on the connections and dependencies between different categories of EGB. For example, of the components of the AMO framework, the importance of ability may be more emphasized in some other forms of EGB than in changing one’s own work in a more ecologically sustainable direction. Furthermore, to better understand the significance of organizational contextual factors on EGB, more empirical research is also needed on possible spillover effects between home and work.\u003c/p\u003e\n\u003cp\u003eA key practical implication of the study concerns the importance of employees’ influence over their own work in bringing about more ecologically sustainable working practices and methods. Work autonomy and self-direction have traditionally been considered important dimensions of quality of work life [45]. Our results suggest that increasing self-direction and autonomy at work could also be an important means of promoting EGB and, thereby, the ecological sustainability of organizations in many industries – with the partial exception of fossil-intensive industries, where the focus obviously should be placed on system-level changes to a greater degree.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding declaration\u003c/strong\u003e: Funding provided by the Finnish Institute of Occupational Health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate declaration:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics and consent to publish declarations:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e The data that support the findings of this study are available from Statistics Finland but restrictions apply to the availability of these data, which were used under licence for the current study, and so are not publicly available.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eTA: Wrote the main manuscript text. VS: Made the statistical analyses and prepared the tables. JT: Contributed to the statistical analyses and editing of the text. FT: Contributed to editing of the text. AA-L: Contributed to editing of the text.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlasoini, T. Twin transition as a transformer of work and an opportunity for development. Helsinki: Finnish Institute of Occupational Health; 2024. \u003c/li\u003e\n\u003cli\u003eBessant, J. High-involvement innovation: building and sustaining competitive advantage through continuous change. Chichester: John Wiley; 2003.\u003c/li\u003e\n\u003cli\u003eH\u0026oslash;yrup, S, Bonnafous-Boucher, M, Hesse, C, Lotz, M, M\u0026oslash;ller, K (eds). Employee-driven innovation: a new approach. Houndmills: Palgrave Macmillan; 2012.\u003c/li\u003e\n\u003cli\u003eLawler, E. High-involvement management. San Francisco: Jossey-Bass; 1986.\u003c/li\u003e\n\u003cli\u003eMelkas, H, Harmaakorpi, V (eds). 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Fostering employee\u0026rsquo;s pro-environmental behavior through green transformational leadership, green human resource management and environmental knowledge. Technological Forecasting and Social Change. 2022;179:121643. https://doi.org/10.1016/j.techfore.2022.121643.\u003c/li\u003e\n\u003cli\u003eLange, F. Behavioral paradigms for studying pro‑environmental behavior: a systematic review. Behavior Research Methods. 2023;55:600\u0026ndash;622. https://doi.org/10.3758/s13428-022-01825-4.\u003c/li\u003e\n\u003cli\u003eSaifulina, N, Carballo-Penela, A, Ruzo-Sanmart\u0026iacute;n, E. Sustainable HRM and green HRM: the role of green HRM in influencing employee pro-environmental behavior at work. Journal of Sustainability Research. 2020;2(3):e200026. https://doi.org/10.20900/jsr20200026.\u003c/li\u003e\n\u003cli\u003eSong, W, Deng, J, Zhang, F, Peng, X, Jin, X. 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The missing opportunity in organizational research: some implications for a theory of work performance. Academic Management Review. 1982;7(4):560\u0026ndash;569. https://doi.org/10.5465/amr.1982.4285240.\u003c/li\u003e\n\u003cli\u003eBos-Nehles, A, Townsed, K, Cafferkey, K, Trullen, J. Examining the ability, motivation and opportunity (AMO) framework in HRM research: conceptualization, measurement and interactions. International Journal of Management Reviews. 2023;25(4):725\u0026ndash;739. https://doi.org/10.1111/ijmr.12332.\u003c/li\u003e\n\u003cli\u003eMarin-Garcia, J, Martinez Tomas, J. Deconstructing AMO framework: a systematic review. Intangible Capital. 2016;12(4):1040\u0026ndash;1087. https://doi.org/10.3926/ic.838.\u003c/li\u003e\n\u003cli\u003eRayner, J, Morgan, D. An empirical study of \u0026lsquo;green\u0026rsquo; workplace behaviours: ability, motivation and opportunity. 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Brussels: CEPS; 2023.\u003c/li\u003e\n\u003cli\u003eGiddens, A. The constitution of society: outline of the theory of structuration. Cambridge: Polity Press; 1984.\u003c/li\u003e\n\u003cli\u003eMoilanen, F, Toikka, A. Measuring employees\u0026rsquo; perceptions of sustainability transitions at work: a novel survey with findings from Finland. Discover Sustainability. 2023;4(45). https://doi.org/10.1007/s43621-023-00163-5.\u003c/li\u003e\n\u003cli\u003eSutela, H, Viinikka, J, P\u0026auml;rn\u0026auml;nen, A. Ty\u0026ouml;olot murrosten keskell\u0026auml; \u0026ndash; ty\u0026ouml;olotutkimuksen tuloksia 1977\u0026ndash;2023. Helsinki: Statics Finland; 2024.\u003c/li\u003e\n\u003cli\u003eMoilanen, F, Ala-Laurinaho, A, Alasoini, T. Climate change and everyday work: a survey of the views of Finnish employees. Helsinki: Finnish Institute of Occupational Health; 2024. \u003c/li\u003e\n\u003cli\u003eKoene, B, van Riemsdijk, M. Managing temporary workers: work identity, diversity and operational HR choices. Human Resource Management Journal. 2005;15(1):76\u0026ndash;92. https://doi.org/10.1111/j.1748-8583.2005.tb00141.x.\u003c/li\u003e\n\u003cli\u003eMcCullagh, P. Regression models for ordinal data. Journal of the Royal Statistical Society: Series B. 1980;42(2):109\u0026ndash;127.\u003c/li\u003e\n\u003cli\u003eBender, R, Grouven, U. Using binary logistic regression models for ordinal data with non-proportional odds. Journal of Clinical Epidemiology. 1998;51(10):809\u0026ndash;816. https://doi.org/10.1016/s0895-4356(98)00066-3.\u003c/li\u003e\n\u003cli\u003eFullerton, AS, Xu, J. The proportional odds with partial proportionality constraints model for ordinal response variables. Social Science Research. 2012;41(1):182\u0026ndash;198. https://doi.org/10.1016/j.ssresearch.2011.09.003.\u003c/li\u003e\n\u003cli\u003eLelisho, ME, Wogi, AA, Tareke, SA. Ordinal logistic regression analysis in determining factors associated with socioeconomic status of household in Tepi Town, Southwest Ethiopia. The Scientific World Journal. 2022;Article ID:2415692. https://doi.org/10.1155/2022/2415692.\u003c/li\u003e\n\u003cli\u003eLong, JS, Freese, J. Regression models for categorical dependent variables using Stata. College Station, TX: Stata Press; 2006.\u003c/li\u003e\n\u003cli\u003eBrant, R. Assessing proportionality in the proportional odds model for ordinal logistic regression. Biometrics. 1990;46(4):1171\u0026ndash;1178.\u003c/li\u003e\n\u003cli\u003eWilliams, R. Generalized ordered logit/partial proportional odds models for ordinal dependent variables. The Stata Journal. 2006;6(1):58\u0026ndash;-82. http://dx.doi.org/10.1177/1536867X0600600104.\u003c/li\u003e\n\u003cli\u003eEuropean Commission. Green skills and knowledge concepts: labelling the ESCO classification. Brussels: European Commission, Employment, Social Affairs and Inclusion; 2025. \u003c/li\u003e\n\u003cli\u003eSchienstock. G. From path dependency to path creation: Finland on its way to the knowledge-based economy. Current Sociology. 2007;55(1):92\u0026ndash;109. https://doi.org/10.1177/0011392107070136.\u003c/li\u003e\n\u003cli\u003eWiernik, BM, Ones, DS, Dilchert, S. Age and employee green behaviors: a meta-analysis. Frontiers in Psychology. 2016;7:194. https://doi.org/10.3389/fpsyg.2016.00194.\u003c/li\u003e\n\u003cli\u003eGuest, D, Knox, A, Warhurst, C. Humanizing work in the digital age: lessons from socio-technical systems and quality of working life initiatives. Human Relations. 2022;75(8):1461\u0026ndash;1482. https://doi.org/10.1177/00187267221092674.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"AMO framework, ecological sustainability, employee green behaviour, sustainability agency","lastPublishedDoi":"10.21203/rs.3.rs-7533093/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7533093/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe role of employees in the green transition in their workplaces is attracting increasing academic interest. This paper examines the individual and organizational determinants of employee green behaviour (EGB) using the classical ability\u0026ndash;motivation\u0026ndash;opportunity (AMO) behavioural model as a theoretical framework. The study draws on a large and statistically representative survey sample of 5,742 Finnish employees. The results of the ordinal logistic regression models show that, of the factors included in the models, employee motivation, and especially the opportunity to influence one\u0026rsquo;s work, had a strong positive association with the activity of employees to change their key working practices and methods to be more ecologically sustainable. Adding organizational factors \u0026ndash; the extent to which ecological sustainability is integrated into workplace strategy or as part of internal workplace interaction and supervisor support \u0026ndash; to the model increased the explanatory power in a statistically significant way, although only by one percentage point. The findings suggest that increasing self-direction and autonomy at work could be an important means of promoting EGB and the ecological sustainability of organizations in many industries \u0026ndash; with the partial exception of fossil-intensive industries, where the focus obviously should be placed on system-level changes to a greater degree.\u003c/p\u003e","manuscriptTitle":"Antecedents of sustainable working among Finnish employees in light of the ability–motivation–opportunity framework","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 17:19:14","doi":"10.21203/rs.3.rs-7533093/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-14T10:41:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-12T18:49:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-08T14:25:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-29T06:39:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100785339837841134552945889325426696552","date":"2025-09-26T05:19:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"41848787343269236144508446691620233937","date":"2025-09-24T12:59:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43462376644819276590817301254922724527","date":"2025-09-19T10:43:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-19T08:54:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-18T16:48:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-17T15:32:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-17T10:26:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2025-09-17T10:22:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4935f7fd-fd67-480c-bcd2-6fa01186ca05","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:15:09+00:00","versionOfRecord":{"articleIdentity":"rs-7533093","link":"https://doi.org/10.1007/s43621-025-02371-7","journal":{"identity":"discover-sustainability","isVorOnly":false,"title":"Discover Sustainability"},"publishedOn":"2025-12-12 15:59:03","publishedOnDateReadable":"December 12th, 2025"},"versionCreatedAt":"2025-09-30 17:19:14","video":"","vorDoi":"10.1007/s43621-025-02371-7","vorDoiUrl":"https://doi.org/10.1007/s43621-025-02371-7","workflowStages":[]},"version":"v1","identity":"rs-7533093","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7533093","identity":"rs-7533093","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}