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Article

Role of Individual Environmental Consciousness in Industrial Decarbonization Transition

by
Alina Köhler
* and
Simone Kauffeld
Department of Industrial, Organizational and Social Psychology, Institute of Psychology, Technische Universität Braunschweig, 38106 Braunschweig, Germany
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(13), 5462; https://doi.org/10.3390/su16135462
Submission received: 4 May 2024 / Revised: 11 June 2024 / Accepted: 18 June 2024 / Published: 27 June 2024
(This article belongs to the Special Issue Climate Change, Environmental Behavior, and Sustainable Development in Social and Cultural Psychology)
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Abstract

:
Decarbonization leads to significant transition processes in industrial companies with the aim of achieving sustainable production. The consequences are far-reaching and can affect, among other things, the workplace and the activities of the employees. The readiness for change among employees is seen as a central success factor for the success of the transition to sustainable production. However, it has been unclear to what extent the environmental consciousness of individual employees influences how open employees are to this transition. A total of 427 employees from a company in the steel industry, currently undergoing a transition aimed at sustainable production, were surveyed. It was found that affective environmental consciousness influences the stages of the Transtheoretical Model in which employees find themselves regarding the company’s transition. The results emphasize, among other things, the importance of encouraging individual environmental consciousness in decarbonization transitions for the successful management of these transitions.

1. Introduction

Companies are currently facing profound changes driven by trends such as digitalization, demographic change, decentralization, and decarbonization. Decarbonization, in particular, is gaining importance due to climate policy requirements [1,2]. The goal is to minimize carbon-intensive emissions, which significantly contribute to global warming. [2]. The German Climate Action Plan pursues the ambitious goal of reducing greenhouse gas emissions by 80 to 95 percent by 2050 compared to the reference year 1990 by strategies including sustainable production plans and promoting hydrogen production to decarbonize energy demand [3,4,5,6]. The steel industry is particularly affected, as its decarbonization transformation (DECT) has significant ecological, economic, and social impacts. A successful transition in steel production could positively influence the entire industrial value chain and contribute to achieving global climate goals. For this, a large part of the production infrastructure must be transformed within the next years [6].
This transformation poses not only a technical but also a personnel challenge. It implies the loss of traditional jobs and the creation of new, technologically different employment opportunities [7]. Crucial for maintaining the company’s competitiveness and achieving climate policy goals is the acceptance and commitment of employees to these new technologies [8,9,10]. In this context, the readiness of employees to change is a critical success factor [11,12]. Readiness for change is often understood as the individual inclination to engage with and actively support or initiate upcoming changes, including a component of motivational readiness for change as well as the perceived ability to implement changes, which encompasses beliefs, attitudes, and intentions regarding change [12]. Various researchers emphasize that organizational change readiness is a multidimensional construct that includes affective, cognitive, and behavioral components [13,14].
To enable companies to specifically strengthen employees’ readiness for change before and during DECT, it is essential to understand what influences this readiness. Some studies show which factors generally affect readiness for change in transformation processes (e.g., leadership and justice), distinguishing between content, process, context, and individual variables [8,11]. None of these studies, however, examines the scenario of DECT. Since DECT, unlike many transformations, is not voluntary but task-related, obligatory, and very content-specific, previous findings from these research areas may not fully apply to DECT [15].
Concerning green transformation processes, an important research focus is on employee green behavior (EGB) and technology acceptance. EGB refers to the environmentally friendly behavior of employees in the workplace, and technology acceptance results from perceived usefulness and ease of use [15,16]. For example, it has been shown that technology acceptance and EGB are influenced by attitudes, social norms, and perceived behavioral control along the Theory of Planned Behavior [17,18,19,20]. Other studies emphasize that EGB is promoted by environmental awareness and environmental knowledge [21,22,23,24]. Of course, EGB is relevant in the context of DECT because e. g. the use of the new technologies represents green behavior [15]. As DECT involves the introduction of new technologies, the acceptance of these technologies is obviously crucial, too [16]. However, in the context of DECT, it is not only about ETUC and technology acceptance but primarily about employees’ readiness for change. This is significant, as change readiness increases the success probability of DECT and moreover leads to increased work satisfaction, organizational commitment, motivation, and performance as well as to increased well-being and health [8,11,12].
Our study extends previous findings by focusing on the influence of environmental consciousness on employees’ readiness for change, concretely meaning their readiness to actively support DECT. Until now, EGB has been the focus of research on green innovations, pointing out the relevance of environmental consciousness. While EGB exclusively involves employee behavior, readiness for change additionally includes cognition and affect, providing a more holistic picture. To understand the intrapsychic processes involved in the influence of affective environmental consciousness on readiness for change, we use a mediation model that extends previous models focusing on EGB and technology acceptance. Furthermore, we examine the specific scenario of DECT for the first time, which will affect many companies in the future due to climate policies. To investigate the role of environmental consciousness, we focus on the steel industry, one of the most critical sectors in the context of decarbonization.

Theoretical Background

Environmental consciousness refers to the understanding and sensitivity towards environmental issues, combined with a positive attitude and active engagement in protecting the natural environment [25]. Affective environmental consciousness, characterized by emotional reactions such as concern and evaluative emotional expressions, is a fundamental component of broader environmental consciousness. It extends beyond mere knowledge and refers to deep emotional concern and worry about environmental problems [26,27].
Figure 1 shows the models from research on EGB, technology acceptance, and general readiness for change, which the model of this study builds upon [11,17,18,19]. The model of this study will be derived in the following sections.
Studies show that sustainable innovations can trigger intense emotional reactions [28,29,30]. The Value-Innovation-Congruence model of emotional reactions (VICE model) suggests that emotions towards innovations depend on whether the characteristics of the innovation are congruent with personal values [31,32]. It posits that positive emotions towards change arise when the characteristics of the transformation align with personal values, and negative emotions arise when they do not. The VICE model has already been applied to the context of sustainable innovations, where findings were replicated [32]. Based on this, people with a strong affective environmental consciousness can be expected to have a high congruence with the characteristics of DECT and therefore stronger positive and weaker negative emotions than employees with a low affective environmental consciousness. Based on the Broaden-And-Build Theory, which indicates that positive and negative emotions should be considered separately because they influence different emotional and cognitive aspects, we distinguish between the effects of positive and negative emotions [33]. We assume that affective environmental consciousness has a positive influence on positive emotions (H1a) and a negative influence on negative emotions (H1b) in the context of DECT.
Further, studies show that both positive and negative affect can independently predict attitudes towards change [28,34,35,36]. This is also shown by the model which predicts technology acceptance in Figure 1 [17,18]. For example, it has been found that affect influences attitudes towards hydrogen technology, carbon capture and storage, and nuclear power plants [17,34,36]. The Broaden-And-Build Theory suggests considering positive and negative affect separately because it has different impacts [33]. Based on this, we assume that positive emotions positively influence attitudes towards DECT (H2a) and that negative emotions negatively influence attitudes towards DECT (H2b).
According to the Theory of Planned Behavior, attitudes, social norms, and perceived behavioral control influence behavioral intentions, which are directly related to employees’ readiness for change [18,19,28,37,38]. Subjective norms refer to the social pressure a person perceives to perform a behavior [20]. Perceived behavioral control refers to the perceived difficulty or ease of performing the behavior [20]. Previous studies in the context of green change processes showed, for example, that the attitude of activists against nuclear power predicts their behavioral intention and that the attitude towards the use of hydrogen predicts the willingness to use hydrogen technologies [39,40]. In this study, we focus on employees’ behavioral intention to actively support DECT and assume that this can be predicted by the components of the Theory of Planned Behavior equivalent to the described studies. We assume that employees initially have no intention of actively supporting the change, which is also the assumption of the Transtheoretical Model [41]. Therefore, we focus on employees’ change readiness to support DECT actively. We use the Transtheoretical Model to depict various stages of readiness for change. This model assumes that individuals go through different stages (precontemplation, contemplation, preparation, action, and maintenance) during a change process, each associated with a different degree of willingness to change [41]. Figure 2 shows an overview of the stages in the context of DECT. The maintenance stage is not part of the study, as the employees considered are currently in the early stages of DECT, and maintenance could not occur yet. In connection with the Theory of Planned Behavior, it is expected that attitude (H3a), social norm (H3b), and perceived behavioral control (H3c) influence the stages of change. The higher these are, the more advanced stages of change are expected.
In summary, we assume that affective environmental consciousness influences the affect, that the affect influences the attitude, and that this, along with social norms and perceived behavioral control, influences readiness for change. This also means that affective environmental consciousness should be related to readiness for change. We expect that affective environmental consciousness will influence the stages of change. The higher the affective environmental consciousness, the more advanced stages of change are expected (H4).

2. Materials and Methods

An amount of 427 employees of a German manufacturing company at the initial stage of DECT participated in an anonymous survey. All employees significantly affected or to be affected by the transition received a questionnaire. The average age of participants is 40 years, with 48% of respondents younger than 40 years and 34% younger than 50 years. 98% of participants are male, and 1% each are female and diverse, as is common in the considered industry sector. 42% of respondents have leadership responsibilities.
Affective environmental consciousness was measured using a questionnaire “Skala zur Messung von zentralen Kenngrößen des Umweltbewusstseins” with seven items on a five-point scale ranging from ‘Strongly disagree’ to ‘Strongly agree’. An example item is ‘It worries me when I think about the environmental conditions in which future generations will probably have to live’ [27] (p. 66). Due to a Cronbach’s Alpha, which was not acceptable (<0.60), two items were removed.
Positive and negative affect was measured using the German version of the Positive And Negative Affect Schedule (PANAS) on a five-point scale ranging from ‘not at all’ to ‘extremely’. Ten items each for negative affect and ten items for positive affect were answered. Example items are ‘upset’ and ‘interested’ [42] (p. 2).
To measure attitude, social norms, and perceived behavioral control, the Planning Aid for Constructing a Questionnaire within the Framework of the Theory of Planned Behavior was used [43]. An example item for attitude, which was assessed through five items on a seven-point scale from ‘very disadvantageous/unpleasant’ to ‘very advantageous/pleasant/good/valuable/comfortable’, is ‘Supporting the change is disadvantageous to me’. An example for two items for social norm is ‘It is very unlikely/likely that I am expected to support the change’, and an example for one of the two items for perceived control is ‘Supporting the change is impossible/possible for me’. As Cronbach’s Alpha of social norm was not acceptable (<0.60), this scale was not integrated into our path model.
To measure the stages of change in the TTM, we proceeded in accordance with other studies that use non-clinical samples [44,45,46]. Contemplation was defined as ‘no intention of supporting change’, contemplation was defined as ‘considering supporting change as a vocational possibility’, preparation was operationalized as ‘decision to support change’, and action was defined as ‘an active supporter of change’.
To test the hypotheses, a complex path model was calculated using MPlus [47]. All possible paths were included in the model, as it is not assumed that the other variables of the model have a correlation of r = 0. A χ2 test was calculated to test the model fit. In addition, the Comparative Fit Index (CFI), the Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMR) were assessed [47].

3. Results

Table 1 shows descriptive statistics as well as manifest product–moment correlations of scales and scale reliabilities. The data are not normally distributed, as indicated by Kolmogorov–Smirnov tests (p ≥ 0.05). However, for a sample size of over 30 individuals, the Central Limit Theorem applies, allowing for the assumption of normal distribution [47]. Nevertheless, we used the Weighted Least Squares Mean and Variance adjusted estimator (WLSMV), which is robust against the assumption of normality [47].
Among the 427 participants in the study, 28 individuals (6.6%) were in the precontemplation stage, 135 individuals (31.6%) in the contemplation stage, 172 individuals (40.3%) in the preparation stage, and 83 individuals (19.4%) in the action stage. For nine individuals (2.1%), the relevant questions in the questionnaire were not answered, so their stages of change could not be determined.
Figure 3 descriptively shows the mean values of affective environmental consciousness, positive and negative affect, attitude, and perceived behavioral control according to stages of change. It becomes evident that the mean values for affective environmental consciousness, positive affect, attitude, and perceived behavioral control are lowest in the precontemplation phase and increase progressively, reaching their highest values in the action phase. For negative affect, the mean values are inversely lowest in the action phase. Additional one-way ANOVAs also revealed that individuals in the four stages of change significantly differ in their affective environmental consciousness (F = 9.47; p ≤ 0.001), positive affect (F = 47.37; p ≤ 0.001), negative affect (F = 22.67; p ≤ 0.001), attitude (F = 43.85; p ≤ 0.001), and perceived behavioral control (F = 33.27; p ≤ 0.001).
The hypothetical path model shows a significant difference from the observed model with χ2 ≤ 0.05. Due to the limited interpretability of this test, additional fit indices were captured, which, by incorporating factors such as model complexity and sample size, offer a more realistic representation of how well the model fits the data. The fit indices show excellent values with CFI ≥ 0.95, RMSEA ≤ 0.05, and SRMR ≤ 0.05 [40]. Figure 4 and Table 2 show the results of the path model. Affective environmental consciousness has a significant influence on positive affect (γ = 0.399, p ≤ 0.05) and negative affect (γ = −0.223, p ≤ 0.05). Positive affect, in turn, has a significant influence on attitude (γ = 0.846, p ≤ 0.05), and negative affect also significantly influences attitude (γ = −0.312, p ≤ 0.05). Attitude has a significant impact on the stages of change with an alpha level of p ≤ 0.1 (γ = 0.067, p = 0.073). The stages of change are also significantly influenced by perceived behavioral control (γ = 0.252, p ≤ 0.05).

4. Discussion and Conclusions

4.1. Summary and Interpretation

The study shows that affective environmental awareness has a positive influence on positive affect towards DECT and a negative influence on negative affect towards DECT. So, employees with a high level of environmental consciousness have stronger positive and weaker negative affect towards DECT than employees with a low environmental consciousness. Thus, the results of this study are consistent with previous research findings, which, however, were not in the context of DECT [28,29,30]. As hypothesized in the VICE model, the assumed value congruence could be a relevant factor for the manifestation of the affect, which was not specifically investigated in this study [32].
Furthermore, the results show that positive affect has a positive influence on attitudes towards DECT and that negative affect has a negative influence on attitudes towards DECT. These results support previous research findings, including those in the research field of technology acceptance, which have shown similar results but not in the context of DECT [28,35,36].
Furthermore, the study shows that attitude and perceived behavioral control influence the stages of change. Specifically, individuals with strong affect and high perceived behavioral control are in more advanced stages of change than individuals with low affect and low perceived behavioral control. In opposite to previous studies focusing on this relationship, the effect of attitude on the stages of change is only significant at an alpha level of p ≤ 0.1 [17,18,19]. One explanation could be that the dependent variable is categorical in this study, which leads to a reduction in variance and thus to a lower significance [48]. Nevertheless, the results support previous models showing this relationship based on the Theory of Planned Behavior [17,18,19,20].
It also became clear that affective environmental awareness has an overall positive influence on the stages of change in DECT. Previous studies never investigated readiness for change in DECT and instead focused on environmental consciousness in the relationship with EGB [21,22,23,24].
The descriptive results show that affective environmental consciousness, positive affect, attitude, and perceived behavioral control generally increase as the stages of change progress, while negative affect decreases as the stages progress. This means that people in the action phase, for example, on average, have the highest affective environmental consciousness. Additional analyses show that people in different stages of change differ significantly in affective environmental consciousness, positive affect, negative affect, attitude, and perceived behavioral control.
Among the relationships considered, the influence of positive affect on attitude is the strongest, followed by the influence of affective environmental consciousness on positive affect. The influence of environmental consciousness on negative affect and its influence on attitude are significantly lower than these. So, positive affect appears to be more relevant for attitude and thus readiness for change than negative affect in DECT. This could be explained by the fact that positive emotions can lead to stronger motivation and higher commitment due to their connection to the reward system, which is why the effect could be stronger [49]. Moreover, positive affect can act as a source of resilience, enhancing the ability to adapt to stress and, in this case, to DECT, which could also explain our result [50].
Earlier studies also showed that readiness for change influences actual behavior [15]. We thus conclude that, for example, employees in the precontemplation phase will support DECT less actively than people in other phases.
Furthermore, the Transtheoretical Model assumes that the stages are changeable and that people can move from the precontemplation phase to the contemplation phase, for example [41]. This means that their actual behavior is likely to adjust as well. This would mean that people in the precontemplation phase, for example, can develop into the action stage over time through increased environmental consciousness and thus actively support DECT.
Moreover, it was shown how the stages of change can also be influenced independently by affective environmental consciousness. They can also be influenced by an increase in positive affect, a decrease in negative affect, as well as by an increase in attitude and an increase in perceived behavioral control. We can thus interpret that people whose positive affect, attitude, and perceived behavioral control increase and/or whose negative affect decreases can also develop into the next stage of change.

4.2. Implications

The study underlines the role of environmental consciousness in DECT. By identifying the affective environmental consciousness as an influencing factor of readiness for change, we extend previous framework models of readiness for change that provide an overview of antecedents by establishing affective environmental awareness as an individual attribute [8,11].
In general, the study also emphasizes the relevance of values in change processes. While values and norms have previously been reported to be relevant to readiness for change, these have not been specifically examined [8,11]. In this study, we focused on the value affective environmental consciousness and identified its significant influence on readiness for change in the context of environmental transformation. This could mean, that those values, which are relevant in the context of a particular transformation, strengthen the readiness for change in that transformation. This would conceptually fit with value–belief–norm theory, which states that values influence beliefs, beliefs influence norms, and these, in turn, influence behavior [51]. Value congruence and its influence on affect are part of the VICE model described above, which we did not specifically examine in this study [32]. In any case, the results suggest the concrete integration of norms into models of readiness for change [8,11].
Our study also shows that intrapsychic affective and cognitive processes are relevant to infer the influence of environmental awareness as a norm on readiness to change. This emphasizes the role of affect and cognition as influencing factors on readiness for change. Although many variables, such as leadership and justice, have been recorded as antecedents, the intrapsychic processes that bring this about have not yet been explored [8,11]. This study provides a first approach, complementing previous research on readiness for change, in that we found that affect as well as cognition, and perceived behavioral control are mediating factors [8,11].
In addition, the results show that the Theory of Planned Behavior can be applied to the context of readiness for change [20]. It also becomes clear that the stages of change can be used to systematically map readiness for change in organizations [41].
The results also provide important practical implications for companies that want to conduct or have DECT conducted. They highlight affective environmental awareness as a starting point for strengthening readiness for change. By specifically promoting environmental awareness, companies can ensure that affect is more positive and the attitude towards DECT is also more positive. This, in turn, increases the readiness to actively support DECT and thus has an overall impact on DECT [11,12].
Additionally, the study shows that independent of environmental awareness, affect, cognition, and perceived behavioral control can also be starting points for promoting readiness for change. The latter emphasizes that companies should give employees the opportunity to actually support DECT.
Furthermore, the study emphasizes that it is more important to strengthen positive affect than to reduce negative affect. It is therefore advisable to strengthen positive affect through targeted measures.
It can also be implied that different interventions for employees in different stages of change are useful. For example, it is recommended to especially promote affective environmental consciousness in the precontemplation and contemplation stages. The same applies to affect, attitude, and perceived behavioral control, e.g., employees in the action stage already have high values in these variables and do not need to be promoted.
In addition to encouraging the environmental consciousness of existing employees, it is advisable to additionally focus on the environmental consciousness of employees during the recruitment process. If new employees have a high level of environmental consciousness, this also increases the likelihood of DECT’s success through a high willingness to actively support DECT.
Finally, this study underscores the need for a holistic approach and shows that socio-psychological dynamics in DECT should be considered.

4.3. Limitations and Future Research

There are limitations to consider. First, it should be noted that predominantly male respondents were examined, which, however, is industry-specific. Women, however, are often attributed to a higher environmental consciousness than men, limiting the generalizability to other industries where the female ratio is higher [52,53]. Additionally, the respondents are unequally distributed across the four stages of change. However, this reflects real differences in population and is unproblematic as all stages of change are representative with a share of at least 5% of the sample size with N = 427 [54]. Moreover, it must be considered that this is a cross-sectional design, which does not allow for an examination of the development of the stages of change over time and does not enable causality. Nonetheless, the stages reflect differences in readiness for change, which is sufficient for confirming the hypotheses. In further investigations, therefore, a longitudinal design should be preferred, from which it can be seen whether there is an improvement in the stages of change on an individual level if environmental consciousness increases. Additionally, only affective environmental consciousness was focused on. The cognitive and conative environmental consciousness and the intentional environmental behavior were not considered. While cognitive environmental consciousness significantly correlates positively with affective environmental consciousness, it may still be useful to additionally capture their influence on readiness for change [27]. The data are based exclusively on self-assessment. Future studies should also capture actual change behavior and/or include additional external assessments by colleagues or supervisors [54]. Overall, this study emphasizes the influence of affective environmental consciousness on the stages of change of TTM in the context of DECT and shows relevant theoretical implications as well as important measures for companies that are in DECT.

Author Contributions

Conceptualization, A.K. and S.K.; methodology, A.K. and S.K.; software, A.K.; validation, A.K. and S.K.; formal analysis, A.K. and S.K.; investigation, A.K. and S.K.; resources, A.K. and S.K.; data curation, A.K.; writing—original draft preparation, A.K. writing—review and editing, S.K.; visualization, A.K.; supervision, S.K.; project administration, A.K. and S.K.; funding acquisition, S.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research and development project was funded by the German Federal Ministry of Education and Research (BMBF) within the ‘The Future of Value Creation–Research on Production, Services and Work’ program (funding number 02L22C100) and managed by the Project Management Agency Karlsruhe (PTKA). The author is responsible for the content of this publication.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Technische Universität Braunschweig (protocol code FV_2022-08, date of approval: 4 May 2022).

Informed Consent Statement

Patient consent was waived due to high anonymity.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Demary, V.; Matthes, J.; Plünnecke, A.; Schaefer, T. Gleichzeitig: Wie Vier Disruptionen die Deutsche Wirtschaft Verändern; IW-Studien: Köln, Germany, 2021; pp. 1–111. [Google Scholar]
  2. Bickenbach, F.; Bode, E.; Dohse, D.; Fehrenbacher, S.; Gold, R.; Stolzenburg, U.; Vehrke, J. Digitalisierung. Dekarbonisierung. Demografie. Wandel Gestalten: Mittelstandsbericht Thüringen 2020; Kieler Beiträge zur Wirtschaftspolitik: Kiel, Germany, 2022; pp. 1–146. [Google Scholar]
  3. Roth, S.; Sultani, D.; Artmann, I.; Klement, T.; Angerer, K.; Bank, L. Dekarbonisierung der Produktion: Ansätze und Anwendungsfälle für die nachhaltige Produktionsplanung und Brennstoffversorgung. Z. Für Wirtsch. Fabr. 2022, 117, 109–116. [Google Scholar] [CrossRef]
  4. Meyer-Ohlendorf, N.; Voß, P.; Velten, E.; Görlach, B. EU Greenhouse Gas Emission Budget: Implications for EU Climate Policie; Ecologic: Berlin, Germany, 2018; pp. 1–22. [Google Scholar]
  5. Gores, T.; Graichen, V. Ansätze zur Bewertung und Darstellung der Nationalen Emmissionsentwicklung unter Berücksichtigung des EU-ETS; Umweltbundesamt: Desslau-Rossau, Germany, 2017; pp. 1–63.
  6. Blöcker, A. Grüner Stahl—Wie Geht Das? Rosa Luxemburg Stiftung: Berlin, Germany, 2022. [Google Scholar]
  7. Kirpal, S.; Biele Mefebue, A. ‘Ich Habe Einen Sicheren Arbeitsplatz, Aber Keinen Job’: Veränderung Psychologischer Arbeitsverträge unter Bedingung von Arbeitsmarktflexibilisierung und Organisationaler Transformation; Universität Institut Technik und Bildung: Bremen, Germany, 2007; pp. 1–40. [Google Scholar]
  8. Oreg, S.; Vakola, M.; Armenakis, A. Change recipients’ reactions to organizational change: A 60-year review of quantitative studies. J. Appl. Behav. Sci. 2011, 47, 461–524. [Google Scholar] [CrossRef]
  9. Oreg, S.; Bartunek, J.M.; Lee, G.; Do, B. An affect-based model of recipients’ responses to organizational change events. Acad. Manag. Rev. 2018, 43, 65–86. [Google Scholar] [CrossRef]
  10. Rafferty, A.E.; Jimmieson, N.L.; Armenakis, A.A. Change readiness: A multilevel review. J. Manag. 2013, 39, 110–135. [Google Scholar] [CrossRef]
  11. Holt, D.T.; Armenakis, A.A.; Field, H.S.; Harris, S.G. Readiness for organizational change: The systematic development of a scale. J. Appl. Behav. Sci. 2007, 43, 232–255. [Google Scholar] [CrossRef]
  12. Weiner, B.J.; Amick, H.; Lee, S.Y.D. Conceptualization and measurement of organizational readiness for change: A review of the literature in health services research and other fields. Med. Care Res. Rev. 2008, 65, 379–436. [Google Scholar] [CrossRef] [PubMed]
  13. Gräfe, H.; Kauffeld, S. ORC-Q: Toward the Multidimensional Measurement of Organizational Readiness for Change. Diagnostica 2004, 70, 77–87. [Google Scholar] [CrossRef]
  14. Piderit, S.K. Rethinking resistance and recognizing ambivalence: A multidimensional view of attitudes toward an organizational change. Acad. Manag. Rev. 2000, 25, 783–794. [Google Scholar] [CrossRef]
  15. Bissing-Olson, M.J.; Iyer, A.; Fielding, K.S.; Zacher, H. Relationships between daily affect and pro-environmental behavior at work: The moderating role of pro-environmental attitude. J. Organ. Behav. 2013, 34, 156–175. [Google Scholar] [CrossRef]
  16. Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 13, 319–340. [Google Scholar] [CrossRef]
  17. Huijts, N.M.; Molin, E.J.; Steg, L. Psychological factors influencing sustainable energy technology acceptance: A review-based comprehensive framework. Renew. Sustain. Energy Rev. 2012, 16, 525–531. [Google Scholar] [CrossRef]
  18. Huijts, N.M.; Molin, E.J.; van Wee, B. Hydrogen fuel station acceptance: A structural equation model based on the technology acceptance framework. J. Environ. Psychol. 2014, 38, 153–166. [Google Scholar] [CrossRef]
  19. Katz, E.; Lazarsfeld, P.F.; Roper, E. Personal Influence: The Part Played by People in the Flow of Mass Communications; Transaction Publishers: New Brunswick, NJ, USA, 2022; pp. 299–310. [Google Scholar]
  20. Ajzen, I.; Driver, B.L. Prediction of leisure participation from behavioral, normative, and control beliefs: An application of the theory of planned behavior. Leis. Sci. 1991, 13, 185–204. [Google Scholar] [CrossRef]
  21. Zhang, Y.; Luo, Y.; Zhang, X.; Zhao, J. How green human resource management can promote green employee behavior in China: A technology acceptance model perspective. Sustainability 2019, 11, 5408. [Google Scholar] [CrossRef]
  22. Zhang, W.; Xu, R.; Jiang, Y.; Zhang, W. How environmental knowledge management promotes employee green behavior: An empirical study. Int. J. Environ. Res. Public Health 2021, 18, 4738. [Google Scholar] [CrossRef] [PubMed]
  23. Albrecht, S.L.; Bocks, A.; Dalton, J.; Lorigan, A.; Smith, A. Pro-environmental employee engagement: The influence of pro-environmental organizational, job and personal resources. Sustainability 2021, 14, 43. [Google Scholar] [CrossRef]
  24. Omarova, L.; Jo, S.J. Employee pro-environmental behavior: The impact of environmental transformational leadership and GHRM. Sustainability 2022, 14, 2046. [Google Scholar] [CrossRef]
  25. Sánchez, J.; Lafuente, R. Defining and Measuring Environmental Consciousness. Rev. Int. Sociol. 2010, 68, 731–755. [Google Scholar] [CrossRef]
  26. Spada, H. Umweltpsychologie; Klett-Cotta: Stuttgart, Germany, 1990. [Google Scholar]
  27. Geiger, S.M.; Holzhauer, F.J. Environmental consciousness and behavior in Suriname. Sustain. Sci. 2022, 17, 91–102. [Google Scholar]
  28. Merk, C.; Pönitzsch, G. The role of affect in attitude formation toward new technologies: The case of stratospheric aerosol injection. Risk Anal. 2017, 37, 2289–2304. [Google Scholar] [CrossRef]
  29. Scott, S.E.; Inbar, Y.; Rozin, P. Evidence for absolute moral opposition to genetically modified food in the United States. Perspect. Psychol. Sci. 2016, 11, 315–324. [Google Scholar] [CrossRef] [PubMed]
  30. Rozin, P.; Haddad, B.; Nemeroff, C.; Slovic, P. Psychological aspects of the rejection of recycled water: Contamination, purification and disgust. Judgm. Decis. Mak. 2015, 10, 50–63. [Google Scholar] [CrossRef]
  31. Perlaviciute, G.; Steg, L. The influence of values on evaluations of energy alternatives. Renew. Energy 2015, 77, 259–267. [Google Scholar] [CrossRef]
  32. Contzen, N.; Perlaviciute, G.; Sadat-Razavi, P.; Steg, L. Emotions toward sustainable innovations: A matter of value congruence. Front. Psychol. 2021, 12, 661314. [Google Scholar] [CrossRef] [PubMed]
  33. Fredrickson, B.L.; Branigan, C. Positive emotions broaden the scope of attention and thought-action repertoires. Cogn. Emot. 2005, 19, 313–332. [Google Scholar] [CrossRef] [PubMed]
  34. Midden, C.J.; Huijts, N.M. The role of trust in the affective evaluation of novel risks: The case of CO2 storage. Risk Anal. Int. J. 2009, 29, 743–751. [Google Scholar] [CrossRef] [PubMed]
  35. Cropanzano, R.; Weiss, H.M.; Hale, J.M.; Reb, J. The structure of affect: Reconsidering the relationship between negative and positive affectivity. J. Manag. 2003, 29, 831–857. [Google Scholar]
  36. Montijn-Dorgelo, F.N.; Midden, C.J. The role of negative associations and trust in risk perception of new hydrogen systems. J. Risk Res. 2008, 11, 659–671. [Google Scholar] [CrossRef]
  37. Shih-Chih, C.; Chung-Wen, H. The effects of environmental regulation on technology diffusion: The case of chlorine manufacturing. Am. Econ. Rev. 2016, 106, 317–352. [Google Scholar]
  38. Ogbodoakum, N.; Abiddin, N.Z. Theory of planned behaviour and readiness for changes: Implication for organisations. J. Indones. Untuk Kaji. Pendidik. 2017, 2, 1–18. [Google Scholar] [CrossRef]
  39. Fox-Cardamone, L.E.E.; Hinkle, S.; Hogue, M. The correlates of antinuclear activism: Attitudes, subjective norms, and efficacy. J. Appl. Soc. Psychol. 2000, 30, 484–498. [Google Scholar] [CrossRef]
  40. Molin, E. Causal analysis of hydrogen acceptance. Transp. Res. Rec. 2005, 1941, 115–121. [Google Scholar] [CrossRef]
  41. DiClemente, C.C.; Prochaska, J.O.; Fairhurst, S.K.; Velicer, W.F.; Velasquez, M.M.; Rossi, J.S. The process of smoking cessation: An analysis of precontemplation, contemplation, and preparation stages of change. J. Consult. Clin. Psychol. 1991, 59, 295. [Google Scholar] [CrossRef] [PubMed]
  42. Breyer, B.; Bluemke, M. Positive and negative affect schedule (PANAS): Construct validity, measurement properties and normative data in a large non-clinical sample. Br. J. Clin. Psychol. 2016, 55, 398–417. [Google Scholar]
  43. Graf, A. Planning aid for constructing a questionnaire within the framework of the theory of planned behavior. J. Appl. Soc. Psychol. 2007, 37, 2670–2690. [Google Scholar]
  44. Grant, A.M.; Franklin, J. The transtheoretical model and study skills. Behav. Change 2007, 24, 99–113. [Google Scholar] [CrossRef]
  45. Grant, A.M. It takes time: A Stages of Change perspective on the adoption of workplace coaching skills. J. Chang. Manag. 2010, 10, 61–77. [Google Scholar] [CrossRef]
  46. Klonek, F.E.; Isidor, R.; Kauffeld, S. Different stages of entrepreneurship: Lessons from the transtheoretical model of change. J. Chang. Manag. 2015, 15, 43–63. [Google Scholar] [CrossRef]
  47. Muthén, B.O.; Muthén, L.K.; Asparouhov, T. Regression and Mediation Analysis Using Mplus; Muthén & Muthén: Los Angeles, CA, USA, 2017. [Google Scholar]
  48. Royston, P.; Altman, D.G.; Sauerbrei, W. Dichotomizing continuous predictors in multiple regression: A bad idea. Stat. Med. 2006, 25, 127–141. [Google Scholar] [CrossRef]
  49. Chiew, K.S.; Braver, T.S. Positive affect versus reward: Emotional and motivational influences on cognitive control. Front. Psychol. 2011, 2, 279. [Google Scholar] [CrossRef]
  50. Strand, E. The impact of positive affect: Two weekly assessment studies on benefits of positive emotions in patients with a chronic painful disease. In The Human Pursuit of Well-Being: A Cultural Approach; Springer: Dordrecht, The Netherlands, 2011; pp. 71–81. [Google Scholar]
  51. Stern, P.C.; Dietz, T.; Abel, T.; Guagnano, G.A.; Kalof, L. A value-belief-norm theory of support for social movements: The case of environmentalism. Hum. Ecol. Rev. 1999, 6, 81–97. [Google Scholar]
  52. Katzenstein, P.J. Crumbling structures: Social and political change in Germany. Int. Secur. 2003, 28, 93–109. [Google Scholar]
  53. Karls, J.M. Women and environmentalism: A psychological analysis. J. Soc. Issues 1996, 52, 139–153. [Google Scholar]
  54. Vazire, S. Who knows what about a person? The self–other knowledge asymmetry (SOKA) model. J. Personal. Soc. Psychol. 2010, 98, 281. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Overview of existing models and the mediation model of the current study [11,17,18,19].
Figure 1. Overview of existing models and the mediation model of the current study [11,17,18,19].
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Figure 2. Stages of change in DECT.
Figure 2. Stages of change in DECT.
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Figure 3. Differences in Stages of change.
Figure 3. Differences in Stages of change.
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Figure 4. Path model showing coefficients; ** p ≤ 0.05; * p ≤ 0.1.
Figure 4. Path model showing coefficients; ** p ≤ 0.05; * p ≤ 0.1.
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Table 1. Item characteristics and manifest product–moment correlations of scales.
Table 1. Item characteristics and manifest product–moment correlations of scales.
NMSD(1)(2)(3)(4)(5)(6)
(1) Affective environmental consciousness4213.070.650.77-----
(2) Positive affect4113.210.840.27 **00.85----
(3) Negative affect4071.770.77−0.21 **−0.21 **0.90---
(4) Attitude4234.941.170.28 **0.59 **−0.31 **0.92--
(5) Perceived behavior control4224.871.560.16 **0.43 **−0.13 **0.46 **0.78
(6) Stages of Change4192.750.840.23 **0.51 **−0.35 **0.47 **0.46 **-
Note. Reliabilities (Cronbach’s alpha) are indicated in the diagonal; ** p < 0.05 (2-sided); N = sample size; M = mean value, SD = standard deviation.
Table 2. Results of path model.
Table 2. Results of path model.
PathCoefficientStandard Errorp-Value
Affective environmental consciousness → Positive affect 0.339 0.056 0.000
Affective environmental consciousness → Negative affect −0.223 0.053 0.000
Positive affect → Attitude 0.846 0.048 0.000
Negative affect → Attitude −0.312 0.045 0.000
Attitude → Stages of change 0.067 0.038 0.073
Perceived behavior control → Stages of change 0.252 0.025 0.000
Affective environmental consciousness → Stages of change 0.260 0.070 0.018
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Köhler, A.; Kauffeld, S. Role of Individual Environmental Consciousness in Industrial Decarbonization Transition. Sustainability 2024, 16, 5462. https://doi.org/10.3390/su16135462

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Köhler A, Kauffeld S. Role of Individual Environmental Consciousness in Industrial Decarbonization Transition. Sustainability. 2024; 16(13):5462. https://doi.org/10.3390/su16135462

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Köhler, Alina, and Simone Kauffeld. 2024. "Role of Individual Environmental Consciousness in Industrial Decarbonization Transition" Sustainability 16, no. 13: 5462. https://doi.org/10.3390/su16135462

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