Understanding the Awareness Action Gap in Sustainable Clothing Consumption

Understanding the Awareness Action Gap in Sustainable Clothing Consumption | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Understanding the Awareness Action Gap in Sustainable Clothing Consumption Arunesh Ghosh, Seeboli Ghosh Kundu, Divya Gogia, Avisek Kundu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8578408/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The global fashion industry is a major contributor to environmental degradation, making sustainable clothing consumption increasingly essential. However, in emerging economies, the adoption of sustainable apparel remains inconsistent despite growing awareness. This study examines how environmental consciousness (EC) and perceived consumer empowerment (PCE) influence sustainable consumer behaviour (SCB), drawing on the Value–Belief–Norm (VBN) theory to explain the joint effects of environmental motivation and perceived agency. Using data from 505 respondents, the study employed a two-stage analytical approach. Multiple regression analysis showed that both EC and PCE significantly and positively predict SCB, though the variance explained is modest. To capture complex behavioural patterns, ensemble machine learning models—Decision Tree and Random Forest—were applied. The Random Forest model demonstrated superior predictive performance, and feature importance results revealed that EC and PCE dimensions are among the strongest contributors to sustainable behaviour. The findings highlight the pivotal role of psychological processes, specifically environmental consciousness and perceived agency, in influencing sustainable apparel consumption. The study extends psychology and sustainability literature by integrating empowerment into the VBN framework and demonstrating its relevance in an emerging economy context. Practically, the findings underscore the need to strengthen environmental awareness and consumer agency to enhance sustainable clothing adoption in India. Sustainable clothing consumption psychology perceived consumer empowerment environmental consciousness Machine learning approach Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The fashion industry has emerged as one of the most significant environmental polluters worldwide. The industry produces mass consumption, short-lived clothes, and high-speed production cycles, emitting enormous amounts of carbon, textile waste, and chemicals, making it one of the least sustainable industries in the world results in climate change (Köksal & Strähle, 2021 ). Such issues are also present in the rapidly emerging economies, where the continuous expansion of the apparel industry, increasing consumer demand, and highly resource-intensive manufacturing processes have heightened ecological anxieties (Shilby and Hoque, 2025). Although sustainable fashion can be linked to the United Nations Sustainable Development Goals (SDGs), particularly in terms of responsible production and consumption, the process of transitioning to sustainability is slow and disjointed. Recent research suggests that involvement in collective climate change mitigation efforts may alleviate distress while fostering adaptive engagement (Carlson et al, 2025). Emotions predict the likelihood that people will engage in behaviors to mitigate climate change (Ortner et al, 2025 ). Promoting sustainable fashion has several structural issues, among them consumer awareness, green-skilled labour, and supply chain complexities (Legere & Kang, 2020 ). Since consumers slowly start to become aware of the environmental impact of their wardrobe preferences and are interested in signs of eco-friendly options (Kim et al., 2020 ; Malik and Guptha, 2013 ), the necessity to define the factors that can translate the awareness into action is becoming more urgent. Much of the initial studies on sustainable consumption concentrated on environmental values, attitudes, and concerns, and stressed the importance of the environmentally conscious consumer considering that such consumers will have a higher propensity to embrace environmentally friendly behaviour (Joshi and Rahman, 2015 ; Paul et al., 2016 ). These researches increased the knowledge on the drivers of sustainable fashion decisions, indicating that environmental awareness is highly influential on consumer intentions. Nevertheless, with the development of the field, researchers have noted that even with the high degree of environmental awareness, the process of the implementation of sustainable clothes (particularly in the emerging economies) is inconsistent and, in some cases, dishearteningly low (Legere and Kang, 2020 ; Kim et al., 2020 ). This inconsistency indicates that awareness is not always enough to encourage sustainable behaviour. Recent empirical evidence in manufacturing and apparel shows the tremendous influence that stakeholder commitment has on the adoption of sustainable practices and the improvement of market competitiveness, thus highlighting the role of collective power and responsibility in the transitions towards sustainability (Gupta et al., 2025 ). Likewise, consumer-oriented research in the fashion industry that engages the use of sustainable materials points out that the consumers’ perception of sustainability greatly influences the perceived quality, value and purchase intentions amongst the consumers, thus illustrating how the informed and empowered consumers affect the success of the sustainable product (Li et al., 2024 ). Furthermore, the research on second-hand clothing consumption points out that the behavioural changes are mainly influenced by the promotional, social and environmental–economic factors, thus implying that the empowerment through communication and awareness mechanisms is the most important determinant in the adoption of sustainable clothing by the consumers (Jaheer Mukthar et al., 2024 ). In spite of these findings, the notion of consumer empowerment is still relatively unexplored within the realm of sustainable fashion research, specially in developing countries like India. The studies that have been conducted so far have mostly overlooked the interaction of the consumers’ sense of agency with the enabling conditions. The new research was initiated to integrate another dimension of psychology perceived consumer empowerment, the perception that personal consumption choice can affect environmental and social results (Lee et al., 2020 ; Kucuk, 2009). Ethical and sustainable consumption studies reveal that empowered people tend to avoid unethical brands, patronize responsibly-oriented businesses, and have long-term environmentally sustainable behaviors (Carrington et al., 2010 ; Armstrong et al., 2016 ). Nevertheless, the concept of empowerment has received little coverage in research in the field of sustainable fashion. Few of the literature have examined the impact of the sense of agency on consumers on the commitment to sustainable clothing and the limited ones, which have also not explored the interaction of empowerment with environmental consciousness to influence behaviour. Moreover, the literature is highly biased toward the West. A large part of the information about sustainable clothing behaviour is provided by nations that are more mature in terms of sustainability, bear stricter regulations, and have more transparent supply chains (Gwozdz et al., 2017 ). By contrast, the evidence of developing economies is still rather limited, even though its market share in apparel is increasing vigorously, and there is an urgent need to address sustainability problems. The few researches that could be located are mainly on younger consumers and it is not clear how various age groups react to sustainability measures. Furthermore, methodologically, the majority of research remains based on the models of linear regression, which might not be able to reflect the complexity and non-lineal nature of consumer choice in sustainable fashion scenarios (Liu et al., 2024). Consequently, there is much behavioural variation that cannot be accounted. According to these gaps, the research question that drives the study is as follows: RQ: What is the relationship between the environmental consciousness and perceived consumer empowerment and sustainable consumer behaviour within the clothing industry? The study is based on one of the theories of Value Belief Norm (VBN), according to which people engage in pro-environmental behaviors due to their values, their understanding of environmental effects, and their sense of personal responsibility (Shang et al., 2023 ). The environmental consciousness in this context denotes the awareness and concern that drives personal norms to sustainable behaviour (Gogia et al., 2025 ). Nevertheless, the awareness might not be translated into action unless people also believe that they can create the impact. The study draws on the perceived consumer empowerment concept to represent this element of the agency in order to get the efficacy aspect that is implicit in the norm activation process of VBN. The VBN perspective provides a coherent reason behind the decision to purchase sustainable clothes: it is the combination of environmental awareness (motivation) and consumer empowerment (perceived capability), which allows a person to think and act in a specific manner, as these actions are guided by ethical standards and can be shaped with the help of purchasing. Therefore, this paper has made some significant contributions to the sustainable fashion and consumer behaviour literature. First, it proposes a combined psychological perspective, which simultaneously studies environmental consciousness and perceived consumer empowerment, two constructs that have not been investigated in tandem, despite their theoretical applicability in sustainability choices. The placement of empowerment and consciousness contributes to the development of knowledge about the fact that awareness alone is not necessarily sufficient to sustain sustainable clothing behaviour. Second, the study offers context-sensitive evidence based on the emerging economies, including India, and resolves the geographical bias in the available literature, which mainly considers the western markets and is not sensitive to the structural, cultural and behavioural peculiarities of developing states. Third, methodologically, the study makes a significant step forward in the field by combining traditional regression analysis with ensemble machine learning models to provide more insights into linear and non-linear relationships that may be overlooked by conventional analysis methods. Lastly, the results provide useful implications to policymakers, fashion retailers, and sustainability activists through the need to ensure that environmental awareness and consumer empowerment are enhanced to foster the long-term adoption of sustainable clothing in India. 2. Literature Review and Research gap 2.1 Drivers and Barriers influencing Sustainable Clothing Behaviour The study of sustainable clothing consumption is becoming more intense within the past decade, and there is a growing interest in the environmental impact of fast fashion (Gazzola et al., 2020; Han et al., 2024; Legere and Kang, 2020 ; Yadav and Sijoria, 2024 ). Some of the studies highlight that environmental consciousness of consumers is at the centre of their reaction towards sustainable fashion (Gozzola et al., 2020). Han et al. (2024) demonstrate that those consumers who are aware of the environmental effects of fast fashion are more likely to buy the sustainable alternatives of the clothing. The same observation is reflected by Gazzola et al. (2020), who state that a young consumer is particularly encouraged by ethical and identity-related factors to wear sustainable clothes. Legere and Kang ( 2020 ) also state that consumers have regarded sustainability as a wider scope of value package which encompasses durability, exclusivity, and the overall quality of the product. These new attitudes suggest that sustainable clothing is no longer perceived only through the ethical prism, but is becoming a sign of quality and fashion, and it is possible to capitalise on that with the help of targeted marketing strategies. Another social factor that contributes to sustainable consumption is social influence (Kim and Lee, 2020; Mandarić et al., 2022 ). Kim and Lee (2020) mention that consumers prefer sustainable clothing widely due to the influence of digital platforms, social capital, and engagement with influencers. The more the social media advocacy and the use of online word-of-mouth, the more the intention to purchase responsibly is formed in the minds of consumers. Nevertheless, in spite of such incentives, there are still several structural and psychological obstacles that interfere with the popularization of sustainable fashion. Among those, there is the cost factor; sustainability materials are actively viewed as more expensive than traditional fast-fashion items, which makes them unaffordable to consumers focus on price (Mandarić et al., 2022 ). At the industry level, the opposing institutional logic profit maximization and environmental responsibility is a challenge to the sustainable production and reuse model (Dagilienė et al., 2024 ). Koeksal and Straehle (2021) also note that lack of supply chain conflicts of interest and insufficient social sustainability adoption are another constraint to the availability and quality of sustainable clothing. There are also barriers to consumer psychology. As Wiederhold and Martinez ( 2018 ) note, a large portion of consumers do not believe they can make a difference in the environment, which is why they are sceptical about the benefits of buying eco-friendly clothing. Such a perception forms an efficacy gap, a gap wherein the consumers might be aware of environmental problems but feel that they are incompetent to do anything worthwhile to help. According to Matsapola ( 2021 ), low awareness and the lack of access to sustainability information are also issues in the emerging economies. Sustainable clothing choices are also influenced by broader social, cultural, and economic forces. According to Sesini et al. (2019), sustainable consumption should be approached comprehensively, encompassing environmental motives, cultural factors, price, and social impact. Sajjad and Tappin (2020) reinforce this view by demonstrating that the issue of environmental concern and personal values is the main motivation, while scepticism, cost, and supply chain constraints still impede sustainable adoption at a global level. Generally, the literature gives an in-depth insight into the factors and obstacles that affect sustainable clothing behaviour, which encompass environmental awareness, social pressure, value perceptions, structural barriers, and psychological barriers. Nonetheless, even with the helpful insights, there are still several significant gaps, particularly in emerging market conditions. 2.2 Research Gap Even though environmental consciousness, values, attitudes, and social influence have been addressed extensively and identified as the motivators behind sustainable clothing consumption (Joshi and Rahman, 2015 ; Paul et al., 2016 ), these aspects do not completely reveal the reasons why a significant portion of consumers are not using sustainable clothing despite being environmentally aware. According to recent research in the new economies, awareness does not always lead to behaviour (Legere and Kang, 2020 ; Kim et al., 2020 ), which is why other psychological variables could be involved. Perceived consumer empowerment is one of these variables, i.e. the perception that individual consumption decisions may affect the environmental or social consequences (Lee et al., 2020 ; Kucuk, 2009). Although ethical consumption, boycotting, and sustainability-oriented behaviours have been associated with empowerment (Carrington et al., 2010 ; Armstrong et al., 2016 ), the latter is a little studied in the context of sustainable fashion, and its interaction with environmental consciousness has been understudied. The other gap is the geographical concentration of the current research. A significant part of the literature is Western-oriented in which sustainability practices, institutional provisions, and consumer demands vary widely between the emerging economies (Gwozdz et al., 2017 ; Sharma, 2023). Cultural orientations, cost-effectiveness, and less advanced sustainability infrastructures define the situation in sustainable clothing adoption in places like India, but little empirical research studies the importance of psychological factors in shaping sustainable behaviour in the various classes of consumers. Furthermore, most of the works are based on linear modelling methodologies despite the reality that sustainable consumption choices are often, complex and non-linear psychological events (Liu et al., 2024). This results in much behavioural variance that cannot be accounted and limits understanding of the interaction between different predictors. All these gaps demonstrate the necessity of a more holistic approach that would explore the interplay between the environmental awareness and the perceived consumer empowerment in the context of sustainable clothing behaviour, especially in the sphere of emerging economies where the practices are not uniform. 2.3 Theoretical Lens This paper uses the Value–Belief-Norm (VBN) theory as the theoretical frameworks used to explain sustainable clothing behaviour. The VBN theory argues that when people have good environmental values, when they are aware of the adverse effects of unsustainable practices, and when they have an individual sense of moral responsibility to act, pro-environmental actions can be achieved (Stern, 2000). In this respect the environment awareness coincides with the belief element, the environmental awareness of people and their concern about the damage to the environment. According to previous studies, an increase in awareness contributes to the emergence of a feeling of environmental responsibility among consumers and makes them more likely to wear sustainable clothes (Paul et al., 2016 ; Han et al., 2024). VBN theory, however, also suggests that awareness can never induce behaviour unless people feel they can contribute. To reflect this agency aspect, the study will use the perceived consumer empowerment (PCE), which is the belief that consumers hold that they can impact the environment and social outcomes by their purchasing decisions (Lee et al., 2020 ; Kucuk, 2009). Empowerment compares to the efficacy element within the norm activation dimension whereby individuals can be more willing to act in a sustainable manner when they believe they can make a difference. Research in ethical consumption supports this connection, showing that empowered consumers are more likely to boycott unethical brands, support responsible companies, and adopt pro-environmental consumption patterns (Carrington et al., 2010 ; Armstrong et al., 2016 ). This paper enhances the VBN model and provides a fuller explanation of sustainable clothing behaviour by combining environmental awareness (motivation) and consumer empowerment (capability), especially in the context of emerging markets where structural, cultural and informational constraints tend to undermine the relationship between awareness and action. The integrated VBN-empowerment lens recognizes that consumers do not just have to be concerned with environmental matters but they should also feel that they can change something. This adds more theoretical insight into the study therefore a solid conceptual base of analyzing the interplay of psychological factors in sustainable consumer behaviour. 2.4 Hypothesis development and Conceptual Framework Environmental consciousness shows environmental awareness of people and their intention to reduce ecological damage (Gogia et al., 2024 ; Rupa and Saif, 2021). The previous research is unanimous that pro-environmental consumers are more likely to find information about eco-friendly ones, refuse wasteful activities, and use eco-friendly products (Han et al., 2024; Gazzola et al., 2020). Therefore, the increased environmental awareness should result in the increased sustainable clothing behaviour. On the other hand, perceived consumer empowerment is the opinion that individual consumption decisions can affect the environmental and social consequences (Lee et al., 2020 ; Kucuk, 2009). Feeling more empowered, consumers are more inclined to make decisions that resonate with their values, such as the uptake of sustainable products, giving a favorable vote to ethical brands, and avoiding unsustainable options (Carrington et al., 2010 ; Armstrong et al., 2016 ). Hence, sustainable clothing behaviour should be empowered and we hypothesized that: H1: Environmental consciousness has a positive effect on sustainable consumer behaviour. H2: Perceived consumer empowerment has a positive effect on sustainable consumer behaviour. 3. Methodology 3.1 Research Design This study is divided into two stages; the first stage employed a survey to investigate the sustainable consumer behaviour towards clothing. Using literature, a structured questionnaire was developed as the data collection instrument. This study adopts quantitative designs as they are suitable for studies examining behavioural tendencies (Creswell & Creswell, 2018 ). In the second stage, the study used machine learning tools, enabling a deeper understanding of the linear and non-linear relationships between the variables. To capture the complex feature interactions and non-linear dependencies, this study employs random forest (RF) and decision tree (DT) regression models. 3.2 Sample and Data Collection Data were collected using a structured questionnaire, which served as the primary data collection instrument. The questionnaire consisted of 10 statements, measured on a 5-point Likert scale, ranging from 1 to 5, where 1 means strongly disagree and 5 means strongly agree. These types of scales are widely used in behavioural research because they allow respondents to express varying degrees of agreement or disagreement (Joshi et al., 2015). The survey instrument was divided into two sections: Demographic information – capturing respondents' age. Sustainable consumer behaviour towards clothing – assessing Environmental Consciousness (EC), Perceived Consumer Empowerment (PCE), and Sustainable Consumer Behaviour (SCB). Each construct was operationalized using validated items from prior studies (Emekci, 2019 ; Paul et al., 2016 ; Ali et al., 2021 ). The items and sources are presented in Table 1 . Table 1 Measurement Scale Construct Scale/Item Reference Sustainable Consumer Behaviour • I feel capable of helping solve the environmental problems. • I have started to buy more environmentally friendly products during the COVID-19 pandemic • I plan to spend more on environmentally friendly products than conventional ones. (Emekci, 2019 ); (Ali et al., 2021 ); (Paul et al., 2016 ) Environmental Consciousness • I see myself as capable of purchasing green products in the future. • If it were entirely up to me, I am confident I would purchase green products. • I can protect the environment by buying products that are friendly to the environment. (Emekci, 2019 ); (Paul et al., 2016 ) Perceived Consumer Empowerment • I feel capable of helping solve the environmental problems. • I have started to buy more environmentally friendly products during COVID-19. • I plan to spend more on environmentally friendly products than conventional ones. (Emekci, 2019 ); (Ali et al., 2021 ); (Paul et al., 2016 ) The questionnaire started with an introductory statement outlining the study's goals, voluntary participation, and confidentiality guarantees in order to guarantee respondent comprehension. To improve wording and clarity, a small sample of respondents pre-tested the instrument. Customers of eco-friendly apparel between the ages of 18 and 55 made up the target demographic. The study used this age group as they are the most active consumer segment in the clothing and fashion market. Additionally, according to the literature, they have awareness of the sustainability related issues, purchasing autonomy and economic capacity (Joshi & Rahman, 2015 ). Moreover, prior research on consumption of ethical and sustainable fashion highlights that this age group is one of the most likely to interact with, purchase, and influence the demand for sustainable clothing (e.g., Emekci, 2019 ; McNeill & Moore, 2015). Therefore, this range was thought to be suitable for capturing the sustainable consumer behaviour. A random sampling technique was adopted for this study to minimize selection bias, ensuring that all respondents had an equal chance of being chosen (Saunders, Lewis, & Thornhill, 2019). 3.3 Data Screening and Preparation The data's completeness, accuracy, and consistency were examined. The few missing values were handled by mean imputation. Normality diagnostics revealed mild deviations, supporting the use of ensemble tree models for added robustness in addition to regression. To summarise the demographic traits and variable distributions of the respondents, descriptive statistics (mean, median, and standard deviation) were calculated. Additionally, three key constructs were measured, namely, EC, PCE, and SCB, using multiple indicators adapted from validated scales. Table 2 provides the operationalization of each construct and the variable characteristics. First, EC, which defines the level of awareness and concern for environmental impact. Furthermore, PCE refers to the extent to which consumers feel they have an impact on environmental outcomes through their purchasing decisions. Lastly, SCB is the degree of promise to buy or prefer sustainable clothing products. Table 2 Variable Characteristics Variable Name Variable description Age The age of the respondents. EC1 Extent of concern regarding environmental issues. EC2 Extent of actively seeking information on how to reduce environmental impact. EC3 Level of importance of personal values EC4 Level of consideration of ethical values when buying products PCE1 Level of difficulty to buy sustainable products PCE2 Extent the respondent felt that buying sustainable products require significant extra efforts. PCE3 Extent the respondent believed that his/her actions can make a significant environmental impact. SCB1 Extent the respondent is sceptical that individual efforts can lead to significant environmental change SCB2 Extent the respondent buys products labelled as sustainable. SCB3 Extent the respondent prefers buying sustainable products over conventional ones 3.4 Analytical Framework To provide thorough insight into the predictors of SCB, a two-stage analytical approach was employed, which included both conventional regression analysis and machine learning-based ensemble modeling. Stage 1: Multiple Regression Analysis To observe the linear relationship between the independent and dependent variables, a multiple regression model was estimated. The general form of the regression equation is expressed as: $$\:{SCB}_{i}={\beta\:}_{0}+{\beta\:}_{1}{EC}_{i}+{\beta\:}_{2}{PCE}_{i}+{\epsilon\:}_{i}$$ Where \(\:{SCB}_{i}\) was the Sustainable consumer behaviour score of respondents i , \(\:{EC}_{i}\) Environmental consciousness score of respondent i, and \(\:{PCE}_{i}\) Perceived consumer empowerment score of respondent i. Stage 2: Ensemble Tree-Based Machine Learning Models Decision Tree (DT) and Random Forest (RF) regression models were used to capture complex feature interactions and non-linear dependencies. The predictive performance and interpretability of ensemble tree-based algorithms are improved due to their resilience to outliers and non-normal data distributions (Liu et al., 2024; Zhu et al., 2025). Training (75%) and validation (25%) subsets of the dataset were randomly selected. While the Random Forest model, which combines several decision trees, reduced overfitting and enhanced generalisation, the Decision Tree model was used as the baseline (Pious et al., 2024). 4. Results and Discussion 4.1 Descriptive Statistics Descriptive statistics for EC, PCE, and SCB are summarized in Table 3 . All variables had 505 valid responses. The mean scores were 3.93 (SD = 0.50) for EC, 2.66 (SD = 0.43) for PCE, and 3.18 (SD = 0.65) for SCB. Table 3 Descriptive Statistics for Study Variables N Min Max 25% 50% 75% Mean Std. Deviation EC 505 2.50 5.00 3.75 4 4.25 3.92 0.49 PCE 505 1.67 5.00 2.33 2.67 3 2.66 0.42 SCB 505 1.67 5.00 2.67 3 3.67 3.18 0.65 Moreover, Table 4 presents the Pearson correlation coefficients among EC, PCE, and SCB. EC exhibited a small but statistically significant positive correlation with SCB (r = 0.22, p < 0.01), suggesting that higher EC is associated with greater SCB. The correlation between PCE and SCB was positive and significant. However, weaker (r = 0.092, p < 0.05). Additionally, EC and PCE demonstrated a significant negative correlation (r = − 0.187, p<.01), indicating that as EC increases, PCE tends to decrease slightly. Table 4 Correlations among EC, PCE, and SCB EC PCE SCB EC 1 -0.19** 0.22** PCE -0.19** 1 .092* SCB 0.22** 0.09* 1 *Note: p-values in parentheses. **p < 0.01, p < 0.05 (2-tailed). 4.2 Regression Analysis A multiple regression analysis was conducted with SCB as the dependent variable and EC and PCE as predictors to further examine the predictive relationships (Table 5 ). The resulting model was statistically significant (F(2,502) = 18.29, p < 0.001 ) , accounting for approximately 7% of the variance in SCB, as shown in Table 3 . Examination of the standardized coefficients revealed that EC was the stronger predictor, while PCE also made a significant, though smaller, positive contribution. Table 5 Regression Model Summary Variables Coefficients (standard error) t-test Environmental Consciousness 0.32(0.05)** 5.65 Perceived Consumer Empowerment 0.21 (0.06)** 3.15 R square 0.07 Adjusted R square 0.06 F-test 18.29 **Significant at 0.01 level Overall, both EC and PCE were significant, positive predictors of SCB, with EC presenting a relatively larger effect. The model, however, explains only a modest proportion of the variance in SCB, suggesting that additional factors not considered in this analysis may also play a substantive role. Further investigation using ensemble learning techniques was prompted by the relatively low R 2 that indicated the possibility of non-linear or higher-order interactions, even though the regression model offered interpretative insights. 4.3 Ensemble Tree Based Models to capture the non-linear relationships and insights Ensemble Tree based models are the cutting-edge machine learning approach that has the capability of capturing non-linear relationships between the dependent variable (SCB) and all other independent variables (Zhu et al.,2025). Tree based models also handle outliers, missing values and non- normal data better, with higher explanability and ease of adoption ensuring the patterns and the insights are robust and actionable (Liu et al.,2024). We have used Decision Trees (Mienye et al., 2024) and Random Forest (Muna et al.,2023) as the two most popular, robust performing and deployed ensemble techniques used worldwide. One of the challenges considered for decision tree is the overfit of the model. This challenge of decision tree has been mitigated by using the performance of the ensemble tree (the performance being ensembled over a range of trees or forests) thus showcasing better model performance reducing any probable model overfit (Pious et al.,2024) .The data is being split into training and validation in the 75:25 ratio, wherein the training data was used to build the model and the validation data was used to test the model. The output of the decision tree and the random forest is showcased below (Fig. 2 ). The feature importance table from decision tree showcases Age to be most dominant feature explaining about 16% of the variance of the aggregated SCB score. In other way about 16% of the predictive power of the aggregated SCB score comes from the variable age This is followed by EC2 which explains about 15% of the variability of the aggregated SCB score, followed by PCE1 explaining 13% of the variability, PCE3, explaining 12.5% of the variability, EC3 explaining 12.2% of the variability, EC4 explaining 12% of the variability, PCE2 explaining 11% of the variability and EC1 explaining approximately 8.1% of the variability (Fig. 3 ). The feature importance table from random showcases EC2 to be most dominant feature explaining about 17% of the variance of the aggregated SCB score. In other way about 17% of the predictive power of the aggregated SCB score comes from the variable EC2. This is followed by Age which explains about 15.50% of the variability of the aggregated SCB score, followed by EC4 explaining 14.75% of the variability, PCE3, explaining 13.75% of the variability, PCE1 explaining 12% of the variability, PCE2 explaining 10.75% of the variability, EC3 explaining 10.5% of the variability and EC1 explaining approximately 7% of the variability. The feature importance comparison of the two machine learning models namely the decision tree and the ensembled random forest is showcased below (Table 6 ): Table 6 Comparison of the two machine learning tools Feature Feature Importance As par Random Forest (%) Feature Importance as par Decision Tree (%) Rank as par Random Forest Rank as par Decision Tree Age 15.50% 16% 2nd 1st EC2 17% 15% 1st 2nd PCE1 12% 13% 5th 3rd PCE3 13.75% 12.5% 4th 4th EC3 10.50% 12.2% 7th 5th EC4 14.75% 12% 3rd 6th PCE2 10.75% 11% 6th 7th EC1 7% 8.1% 8th 8th The data set was divided into training and validation in the ratio 75%: 25%. The model performance of both decision tree and random forest on the holdout validation data (25%) is showcased below (Table 7 ). Table 7 Holdout RMSE results Model Holdout RMSE Decision Tree Regressor 2.25 Random Forest Regressor 1.56 The tuned Random Forest clearly outperforms the Decision Tree in predicting the aggregated SCB score wherein the RMSE (Root Mean Square Error) of the holdout data of the Random Forest is lower than the Decision Tree. This can be further substantiated and visualized from the graph below which slows the plotting of actual vs predicted for both the models. Predictions for the decision tree deviate widely from the diagonal, showing weak fit. Predictions cluster from the tuned Random Forest are closer to the diagonal, indicating a better approximation of actual values. 5. Implications 5.1 Theoretical Implications This study offers several important theoretical contributions to the sustainable consumption and fashion literature. First, it advances understanding of sustainable clothing behaviour by integrating environmental consciousness and perceived consumer empowerment within a single behavioural framework. Existing research has typically examined environmental consciousness in isolation, assuming that awareness naturally leads to action. By showing that empowerment also plays a significant role, the study challenges the linear assumption embedded in attitude–behaviour models and supports the argument that pro-environmental behaviour requires both motivation (consciousness) and perceived capability (empowerment). This strengthens the application of Value–Belief–Norm (VBN) theory to the fashion domain by highlighting the importance of efficacy-related beliefs in activating personal norms. Second, the results allow a theoretical clarity to be established as a result of the empirical evidence that environmental consciousness and empowerment have independent yet complementary effects on sustainable clothing behaviour. This two-way interpretation brings nuances to the sustainability literature that usually views consumers as passive receivers of environmental information. However, in the current research, the sense of agency of consumers proves just to be central, thus, empowerment cannot be only a product of sustainability marketing but a psychological precondition of sustainable consumption. Third, the study advances the existing theory by including non-linear modelling methods (Decision Tree and Random Forest) to examine the predictors of behaviour. Conventional research on sustainability is based on the use of linear regression which can be inadequate in capturing the complexity of consumer decision-making. Making use of the opportunity of validating findings by means of ensemble machine learning models, the study proves that sustainable clothing behaviour is conditioned by the multi-dimensional interactions, which go beyond the suppositions of linear behavioural theories. Such sophistication of the methodology helps to create a more precise theoretical insight into the effects of psychological factors of the environmentally responsible behaviour. Finally, the study expands geographical relevance by contributing context-specific evidence from an emerging economy. Theoretical models often rely on Western data, limiting cross-cultural applicability. By using data from India, where sustainable fashion adoption is still evolving, the study provides empirical support for adapting psychological models to contexts with different socio-economic, cultural, and infrastructural realities. 5.2 Practical Implications The findings of this research can provide meaningful advice to fashion businesses, retailers, policymakers and sustainability activists aiming at encouraging sustainable clothing behaviour. To begin with, the high impact of environmental awareness implies that education and awareness creation should continue to be at the heart of environmental sustainability efforts. One way through which fashion brands can improve engagement is by informing people about the actual environmental impact of fast fashion, through transparent sustainability labels, stories, and data-driven graphics of ecological results they can make consumers relate to. Second, perceived consumer empowerment plays an important role, and the strategies should support the perceptions of consumers that their decisions do count. Empowerment can be enhanced and long-term behavioural commitment can be established by campaigning that emphasises the collective contribution (every purchase count), presents consumer-led movements, or demonstrates the benefits of sustainable purchases. The interactive tools that retailers may also include to enhance the perceived agency of consumers comprise carbon-footprint calculators, sustainability scorecards, or apps that demonstrate the effect of environmentally responsible decisions. Third, policymakers and industry bodies in emerging economies like India can play a pivotal role by improving accessibility and affordability of sustainable clothing. Financial incentives for sustainable manufacturers, guidelines for waste reduction, and support for green supply chains can reduce production costs, making sustainable products more competitive. Awareness campaigns at community or regional levels can also help overcome information gaps, especially among consumers with limited sustainability exposure. Fourth, since it has been demonstrated that the behaviour of sustainable clothing by different demographic groups is different, a specific approach is essential. Younger buyers can be very sensitive to social media promotion and collaboration with influencers, and older age groups can need more straightforward data about quality, durability, and long-lasting value. The sustainability communications should be as relevant and as possible by making brands customize the messages to the various consumer groups. Lastly, the application of machine learning models as methodology demonstrates the significance of using data to make decisions in the fashion industry. Advanced analytics allow retailers to understand behavioural trends, classify consumers, use sustainability efforts tailored to consumers, and anticipate future preferences. This technological convergence can assist with a more strategic and evidence-based shift to sustainable consumption. 6. Conclusion and Future Research: In this paper, the psychological factors that define sustainable clothing purchase within an emerging economy have been investigated and their importance on environmental consciousness (EC) and their perceived consumer empowerment (PCE) have been analyzed. The research was based on the Value–Belief-Norm (VBN) framework and it was revealed that the idea of sustainable behaviour is contributed not only by the awareness of environmental concerns by consumers but also by the belief that they can also impact environmental performance by the choice of their purchase. Based on the 505 respondents and two-stage analysis, it was found that EC and PCE are significant and positive predictors of sustainable consumer behaviour (SCB). Though the results of regression showed a low explanatory power, the use of machine learning models showed more significant non-linear trends, with the Random Forest being superior to Decision Tree and pointing to the high predictive value of EC and empowerment-related variables. Such outcomes contribute to the theoretical framework by incorporating the concept of empowerment into the decision-making process of sustainability by providing a more comprehensive way of explaining the reasons why consumers select sustainable clothing. The methodological contribution of the study is also to demonstrate the importance of integrating regression with ensemble machine learning in order to explain more complicated behaviour dynamics. In practice, the results highlight the need by fashion companies, politicians, and sustainability activists to concurrently raise social awareness and empower consumers with a feeling of agency should they wish to hasten the process of sustainable clothing adoption in more developing economies such as India. The study has its limitations although it has made some contributions, which provide openings to future research. To begin with, the data used are cross-sectional and thus the establishment of causal relationships becomes difficult. The longitudinal studies would be more effective in terms of the way consciousness and empowerment changes with time. Second, the research is based on two psychological constructs; further studies may include more factors like perceived value, trust of sustainability claims, social influence or cultural norms to achieve better explanatory force. Third, the sample group is limited to consumers between 18 and 55 years; it can be beneficial to broaden future research to cover the older generations or rural residents to gain a better understanding of demographic differences. Lastly, although machine learning models were useful in giving powerful predictive information, future research could focus on newer and more predictive algorithms like Gradient Boosting or XGBoost to provide a higher predictive accuracy and capture a more detailed behavioural pattern. Declarations Clinical trial number: Not applicable. Ethics Statement This study was conducted in accordance with the ethical standards of the “Research Conduct and Ethics Committee (Institutional Review Board), Christ University” and (Symbiosis International University's Independent Ethics Committee (IEC)) and with the principles outlined in the Declaration of Helsinki. Prior to data collection, informed consent was obtained from all participants. Ethics and Guidelines Full name of the ethical committee that has approved the study: “Research Conduct and Ethics Committee (Institutional Review Board), Christ University.” Consent to Participate Participation was voluntary, and respondents were assured of anonymity and confidentiality. No clinical trials or medical interventions were involved in this research. Informed consent was obtained from all participants. Consent for Publication Not applicable. The study does not include any individual person’s data in any identifiable form. Competing Interests There are no conflicts of interest relevant to the content of this review. Funding No funding was received by any organisation or person. Author Contribution Author Contributions StatementAuthor 1 Conceptualized the study, Data Collection, Initial draft writing.Author 2 Conceptualized the study, Contributed to the research design, Supervised the study and reviewed the draft.Author 3 Performed data analysis, Interpretation of results and assisted in drafting and revising the manuscript. Author 4 Performed machine learning analysis, provided critical intellectual input and reviewed and edited the manuscript for important academic content. All authors read and approved the final manuscript. Acknowledgements: The authors gratefully acknowledge all participants who generously shared their time, perspectives, and experiences through interviews. Their valuable insights were instrumental in enriching the depth and quality of this research. Data Availability The datasets generated and/or analysed during the current study are not publicly available due to ethical considerations and participant confidentiality, but are available from the corresponding author on reasonable request. References Ali BJ, Saleh PF, Akoi S, Abdulrahman AA, Muhamed AS, Noori HN, Anwar G. Impact of service quality on customer satisfaction: Case study at online meeting platforms. Int J Eng Bus Manage. 2021;5(2):65–77. Armstrong CM, Niinimäki K, Kujala S, Karell E, Lang C. Sustainable product-service systems for clothing: Exploring consumer perceptions of consumption alternatives in Finland. J Clean Prod. 2016;124:236–46. https://doi.org/10.1016/j.jclepro.2015.10.013 . Carrington MJ, Neville BA, Whitwell GJ. Why ethical consumers don’t walk their talk: Toward a framework for understanding the gap between ethical purchase intentions and actual buying behaviour. J Bus Ethics. 2010;97(1):139–58. https://doi.org/10.1007/s10551-010-0501-6 . Connell KYH. Internal and external barriers to eco-conscious apparel acquisition. Int J Consumer Stud. 2010;34(3):279–86. https://doi.org/10.1111/j.1470-6431.2010.00865.x . Creswell JW, Creswell JD. Research design: Qualitative, quantitative, and mixed methods approaches. 5th ed. Sage; 2018. Carlson JM. Increased attention to climate change mitigation approaches leads to lower levels of climate anxiety. Discover Psychol. 2025;5(1):179. Dagilienė L, Varaniūtė V, Banionienė J. Cultivating a sustainable and circular economy: The role of institutional logics in manufacturing companies. J Clean Prod. 2024;434:140363. https://doi.org/10.1016/j.jclepro.2022.140363 . Emekci S. Green consumption behaviours of consumers within the scope of TPB. J Consumer Mark. 2019;36(3):410–7. Gleim MR, Smith JS, Andrews D, Cronin JJ Jr.. Against the green: A multi-method examination of barriers to green consumption. J Retail. 2013;89(1):44–61. https://doi.org/10.1016/j.jretai.2012.10.001 . Gogia D, Gupta SK, Rathi P. Impact of various structural attributes on B2B sustainability positioning. J Bus Industrial Mark. 2024;39(2):388–406. Gogia D, Virk N, Choudhry V. (2025). Green purchase behavior: A pathway to corporate sustainability through bibliometric lens. [Journal name forthcoming] . Gupta S, Joshi D, Jamwal A, Bhanot N, Jagtap S. Unveiling the synergy of sustainable manufacturing practices and lean for sustainable manufacturing competitiveness. Discover Appl Sci. 2025;7(9):1005. Gwozdz W, Nielsen KS, Müller T. An environmental perspective on clothing consumption: Consumer segments and their behavioural patterns. Sustainability. 2017;9(7):762. https://doi.org/10.3390/su9070762 . Hair JF, Risher JJ, Sarstedt M, Ringle CM. When to use and how to report the results of PLS-SEM. Eur Bus Rev. 2019;31(1):2–24. Johnstone ML, Tan LP. Exploring the gap between consumers’ green rhetoric and purchasing behaviour. J Bus Ethics. 2015;132(2):311–28. https://doi.org/10.1007/s10551-014-2316-3 . Joshi Y, Rahman Z. Factors affecting green purchase behaviour and future research directions. Int Strategic Manage Rev. 2015;3(1–2):128–43. Jaheer Mukthar KP, Nagadeepa C, Selvaratnam DP, Pushpa A, Shukla N. Sustainable wardrobe: recycled clothing towards sustainability and eco-friendliness. Discover Sustain. 2024;5(1):151. Kim J, Kang S, Lee KH. How social capital impacts the purchase intention of sustainable fashion products. J Bus Res. 2020;117:596–603. https://doi.org/10.1016/j.jbusres.2018.07.018 . Köksal D, Strähle J. Social sustainability in fashion supply chains: Understanding social standard implementation failures in Vietnam and Indonesia using agency theory. Sustainability. 2021;13(4):2159. https://doi.org/10.3390/su13042159 . Kundu A, Kundu SG, Sahu SK, Badgayan ND. Leveraging Azure automated machine learning and CatBoost gradient boosting algorithm for service quality prediction in hospitality. Computers. 2025;14(2):32. Kundu SG, Ghosh A, Kundu A, GP G. A ML-AI enabled ensemble model for predicting agricultural yield. Cogent Food Agric. 2022;8(1):2085717. Lee E-J, Choi H, Han J, Kim DH, Ko E, Kim KH. How to nudge your consumers toward sustainable fashion consumption: An fMRI investigation. J Bus Res. 2020;117:642–51. https://doi.org/10.1016/j.jbusres.2018.07.008 . Legere A, Kang J. The role of consumers in sustainable fashion consumption. J Bus Res. 2020;117:681–8. https://doi.org/10.1016/j.jbusres.2019.09.027 . Li M, Choe YH, Gu C. How perceived sustainability influences consumers’ clothing preferences. Sci Rep. 2024;14(1):28672. Malik G, Guptha A. An empirical study on behavioural intent of consumers in online shopping. Bus Perspect Res. 2013;2(1):13–28. Mandarić D, Hunjet A, Vuković D. The impact of fashion brand sustainability on consumer purchasing decisions. J Risk Financial Manage. 2022;15(4):176. Matsapola E. (2021). Consumer behaviour towards sustainable clothing (Master’s thesis, University of South Africa). Niinimäki K, Peters G, Dahlbo H, Perry P, Rissanen T, Gwilt A. The environmental price of fast fashion. Nat Reviews Earth Environ. 2020;1(4):189–200. https://doi.org/10.1038/s43017-020-0039-9 . Nulty DD. The adequacy of response rates to online and paper surveys: What can be done? Assess Evaluation High Educ. 2008;33(3):301–14. Ortner CN, Armstrong M, Ulmer EJ. Emotion regulation, climate distress, and climate action in climate activist and student samples. Discover Psychol. 2025;5(1):1–12. Paul J, Modi A, Patel J. Predicting green product consumption using the theory of planned behavior and reasoned action. J Retailing Consumer Serv. 2016;29:123–34. Rupa RA, Saif ANM. Impact of green supply chain management on business performance and environmental sustainability: Case of a developing country. Bus Perspect Res. 2022;10(1):140–63. Sajjad A, Eweje G, Tappin D. Managerial perspectives on drivers for and barriers to sustainable supply chain management implementation: Evidence from New Zealand. Bus Strategy Environ. 2020;29(2):592–604. Sesini G, Castiglioni C, Lozza E. New trends and patterns in sustainable consumption: A systematic review and research agenda. Sustainability. 2020;12(15):5935. Shang D, Wu W, Schroeder D. Exploring determinants of green smart technology product adoption from a sustainability-adapted value–belief–norm perspective. J Retailing Consumer Serv. 2023;70:103169. Sharma H. Using topic modeling for extracting customers’ expectations: A case of women apparel. Bus Perspect Res. 2025;13(3):454–66. Thøgersen J. Green shopping: For selfish reasons or the common good? Am Behav Sci. 2011;55(8):1052–76. https://doi.org/10.1177/0002764211407903 . Umit Kucuk S. Consumer empowerment model: From unspeakable to undeniable. Direct Marketing: Int J. 2009;3(4):327–42. https://doi.org/10.1108/17505930911000892 . Weyant E. (2022). Review of Research design: Qualitative, quantitative, and mixed methods approaches (J. W. Creswell & J. D. Creswell, 2018). Business Education Innovation Journal . Wiederhold M, Martinez LF. Ethical consumer behaviour in Germany: The attitude–behaviour gap in the green apparel industry. Int J Consumer Stud. 2018;42(4):419–29. Yadav N, Sijoria C. Consumer shift from fast fashion to thrift fashion: An application of goal framing theory. Business Perspectives and Research. Advance online publication; 2024. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8578408","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595158721,"identity":"d014d472-058d-4cfe-ab30-782308ad2bc1","order_by":0,"name":"Arunesh Ghosh","email":"","orcid":"","institution":"Symbiosis International (Deemed) University","correspondingAuthor":false,"prefix":"","firstName":"Arunesh","middleName":"","lastName":"Ghosh","suffix":""},{"id":595158722,"identity":"efcd9881-c7a3-414e-ac4b-847754b9bb29","order_by":1,"name":"Seeboli Ghosh Kundu","email":"data:image/png;base64,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","orcid":"","institution":"Symbiosis Centre of Management Studies","correspondingAuthor":true,"prefix":"","firstName":"Seeboli","middleName":"Ghosh","lastName":"Kundu","suffix":""},{"id":595158723,"identity":"f9328af4-797c-4eea-9a68-7b41305738c0","order_by":2,"name":"Divya Gogia","email":"","orcid":"","institution":"Symbiosis Centre of Management Studies","correspondingAuthor":false,"prefix":"","firstName":"Divya","middleName":"","lastName":"Gogia","suffix":""},{"id":595158724,"identity":"501e3033-dc13-4b77-9fc1-fefd72488e6e","order_by":3,"name":"Avisek Kundu","email":"","orcid":"","institution":"Christ University: BRC ( Bannerghatta Road Campus),","correspondingAuthor":false,"prefix":"","firstName":"Avisek","middleName":"","lastName":"Kundu","suffix":""}],"badges":[],"createdAt":"2026-01-12 07:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8578408/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8578408/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103506082,"identity":"5c2ca1d8-07e6-4f1b-b2a6-25e774c057c0","added_by":"auto","created_at":"2026-02-26 13:34:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":20539,"visible":true,"origin":"","legend":"\u003cp\u003eProposed Conceptual Framework\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8578408/v1/d16db80b5c26056f72eb832b.png"},{"id":103209538,"identity":"a820b932-c281-4054-aaf3-033a286be43a","added_by":"auto","created_at":"2026-02-23 08:17:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64245,"visible":true,"origin":"","legend":"\u003cp\u003eFeature Importance from Decision Tree\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8578408/v1/848447f68da89d55587574d1.png"},{"id":103209537,"identity":"26c0ae4c-2e99-414a-9ccd-2f4cd52fcfd3","added_by":"auto","created_at":"2026-02-23 08:17:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":79672,"visible":true,"origin":"","legend":"\u003cp\u003eFeature Importance from Random Tree\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8578408/v1/40fcc5725b7e06e25bfa1800.png"},{"id":103505607,"identity":"0188cd3f-587c-4f96-bc17-cf320b228a56","added_by":"auto","created_at":"2026-02-26 13:32:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":134670,"visible":true,"origin":"","legend":"\u003cp\u003eDecision Tree vs. Random Forest\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8578408/v1/7c57ee2689f7df28e7fceb2b.png"},{"id":105387473,"identity":"a1e891c2-fc43-423d-bc0a-db69e9d450e0","added_by":"auto","created_at":"2026-03-25 12:43:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1293260,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8578408/v1/afe74f65-12cb-4936-938f-90bde16d7aa0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Understanding the Awareness Action Gap in Sustainable Clothing Consumption","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe fashion industry has emerged as one of the most significant environmental polluters worldwide. The industry produces mass consumption, short-lived clothes, and high-speed production cycles, emitting enormous amounts of carbon, textile waste, and chemicals, making it one of the least sustainable industries in the world results in climate change (K\u0026ouml;ksal \u0026amp; Str\u0026auml;hle, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such issues are also present in the rapidly emerging economies, where the continuous expansion of the apparel industry, increasing consumer demand, and highly resource-intensive manufacturing processes have heightened ecological anxieties (Shilby and Hoque, 2025). Although sustainable fashion can be linked to the United Nations Sustainable Development Goals (SDGs), particularly in terms of responsible production and consumption, the process of transitioning to sustainability is slow and disjointed.\u003c/p\u003e \u003cp\u003eRecent research suggests that involvement in collective climate change mitigation efforts may alleviate distress while fostering adaptive engagement (Carlson et al, 2025). Emotions predict the likelihood that people will engage in behaviors to mitigate climate change (Ortner et al, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Promoting sustainable fashion has several structural issues, among them consumer awareness, green-skilled labour, and supply chain complexities (Legere \u0026amp; Kang, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Since consumers slowly start to become aware of the environmental impact of their wardrobe preferences and are interested in signs of eco-friendly options (Kim et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Malik and Guptha, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), the necessity to define the factors that can translate the awareness into action is becoming more urgent. Much of the initial studies on sustainable consumption concentrated on environmental values, attitudes, and concerns, and stressed the importance of the environmentally conscious consumer considering that such consumers will have a higher propensity to embrace environmentally friendly behaviour (Joshi and Rahman, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Paul et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These researches increased the knowledge on the drivers of sustainable fashion decisions, indicating that environmental awareness is highly influential on consumer intentions. Nevertheless, with the development of the field, researchers have noted that even with the high degree of environmental awareness, the process of the implementation of sustainable clothes (particularly in the emerging economies) is inconsistent and, in some cases, dishearteningly low (Legere and Kang, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This inconsistency indicates that awareness is not always enough to encourage sustainable behaviour.\u003c/p\u003e \u003cp\u003eRecent empirical evidence in manufacturing and apparel shows the tremendous influence that stakeholder commitment has on the adoption of sustainable practices and the improvement of market competitiveness, thus highlighting the role of collective power and responsibility in the transitions towards sustainability (Gupta et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Likewise, consumer-oriented research in the fashion industry that engages the use of sustainable materials points out that the consumers\u0026rsquo; perception of sustainability greatly influences the perceived quality, value and purchase intentions amongst the consumers, thus illustrating how the informed and empowered consumers affect the success of the sustainable product (Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, the research on second-hand clothing consumption points out that the behavioural changes are mainly influenced by the promotional, social and environmental\u0026ndash;economic factors, thus implying that the empowerment through communication and awareness mechanisms is the most important determinant in the adoption of sustainable clothing by the consumers (Jaheer Mukthar et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In spite of these findings, the notion of consumer empowerment is still relatively unexplored within the realm of sustainable fashion research, specially in developing countries like India. The studies that have been conducted so far have mostly overlooked the interaction of the consumers\u0026rsquo; sense of agency with the enabling conditions.\u003c/p\u003e \u003cp\u003eThe new research was initiated to integrate another dimension of psychology perceived consumer empowerment, the perception that personal consumption choice can affect environmental and social results (Lee et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kucuk, 2009). Ethical and sustainable consumption studies reveal that empowered people tend to avoid unethical brands, patronize responsibly-oriented businesses, and have long-term environmentally sustainable behaviors (Carrington et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Armstrong et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Nevertheless, the concept of empowerment has received little coverage in research in the field of sustainable fashion. Few of the literature have examined the impact of the sense of agency on consumers on the commitment to sustainable clothing and the limited ones, which have also not explored the interaction of empowerment with environmental consciousness to influence behaviour.\u003c/p\u003e \u003cp\u003eMoreover, the literature is highly biased toward the West. A large part of the information about sustainable clothing behaviour is provided by nations that are more mature in terms of sustainability, bear stricter regulations, and have more transparent supply chains (Gwozdz et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). By contrast, the evidence of developing economies is still rather limited, even though its market share in apparel is increasing vigorously, and there is an urgent need to address sustainability problems. The few researches that could be located are mainly on younger consumers and it is not clear how various age groups react to sustainability measures. Furthermore, methodologically, the majority of research remains based on the models of linear regression, which might not be able to reflect the complexity and non-lineal nature of consumer choice in sustainable fashion scenarios (Liu et al., 2024). Consequently, there is much behavioural variation that cannot be accounted. According to these gaps, the research question that drives the study is as follows:\u003c/p\u003e \u003cp\u003e \u003cem\u003eRQ: What is the relationship between the environmental consciousness and perceived consumer empowerment and sustainable consumer behaviour within the clothing industry?\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe study is based on one of the theories of Value Belief Norm (VBN), according to which people engage in pro-environmental behaviors due to their values, their understanding of environmental effects, and their sense of personal responsibility (Shang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The environmental consciousness in this context denotes the awareness and concern that drives personal norms to sustainable behaviour (Gogia et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Nevertheless, the awareness might not be translated into action unless people also believe that they can create the impact. The study draws on the perceived consumer empowerment concept to represent this element of the agency in order to get the efficacy aspect that is implicit in the norm activation process of VBN. The VBN perspective provides a coherent reason behind the decision to purchase sustainable clothes: it is the combination of environmental awareness (motivation) and consumer empowerment (perceived capability), which allows a person to think and act in a specific manner, as these actions are guided by ethical standards and can be shaped with the help of purchasing.\u003c/p\u003e \u003cp\u003eTherefore, this paper has made some significant contributions to the sustainable fashion and consumer behaviour literature. First, it proposes a combined psychological perspective, which simultaneously studies environmental consciousness and perceived consumer empowerment, two constructs that have not been investigated in tandem, despite their theoretical applicability in sustainability choices. The placement of empowerment and consciousness contributes to the development of knowledge about the fact that awareness alone is not necessarily sufficient to sustain sustainable clothing behaviour. Second, the study offers context-sensitive evidence based on the emerging economies, including India, and resolves the geographical bias in the available literature, which mainly considers the western markets and is not sensitive to the structural, cultural and behavioural peculiarities of developing states. Third, methodologically, the study makes a significant step forward in the field by combining traditional regression analysis with ensemble machine learning models to provide more insights into linear and non-linear relationships that may be overlooked by conventional analysis methods. Lastly, the results provide useful implications to policymakers, fashion retailers, and sustainability activists through the need to ensure that environmental awareness and consumer empowerment are enhanced to foster the long-term adoption of sustainable clothing in India.\u003c/p\u003e"},{"header":"2. Literature Review and Research gap","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Drivers and Barriers influencing Sustainable Clothing Behaviour\u003c/h2\u003e \u003cp\u003eThe study of sustainable clothing consumption is becoming more intense within the past decade, and there is a growing interest in the environmental impact of fast fashion (Gazzola et al., 2020; Han et al., 2024; Legere and Kang, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yadav and Sijoria, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Some of the studies highlight that environmental consciousness of consumers is at the centre of their reaction towards sustainable fashion (Gozzola et al., 2020). Han et al. (2024) demonstrate that those consumers who are aware of the environmental effects of fast fashion are more likely to buy the sustainable alternatives of the clothing. The same observation is reflected by Gazzola et al. (2020), who state that a young consumer is particularly encouraged by ethical and identity-related factors to wear sustainable clothes. Legere and Kang (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) also state that consumers have regarded sustainability as a wider scope of value package which encompasses durability, exclusivity, and the overall quality of the product. These new attitudes suggest that sustainable clothing is no longer perceived only through the ethical prism, but is becoming a sign of quality and fashion, and it is possible to capitalise on that with the help of targeted marketing strategies.\u003c/p\u003e \u003cp\u003eAnother social factor that contributes to sustainable consumption is social influence (Kim and Lee, 2020; Mandarić et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Kim and Lee (2020) mention that consumers prefer sustainable clothing widely due to the influence of digital platforms, social capital, and engagement with influencers. The more the social media advocacy and the use of online word-of-mouth, the more the intention to purchase responsibly is formed in the minds of consumers. Nevertheless, in spite of such incentives, there are still several structural and psychological obstacles that interfere with the popularization of sustainable fashion. Among those, there is the cost factor; sustainability materials are actively viewed as more expensive than traditional fast-fashion items, which makes them unaffordable to consumers focus on price (Mandarić et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). At the industry level, the opposing institutional logic profit maximization and environmental responsibility is a challenge to the sustainable production and reuse model (Dagilienė et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Koeksal and Straehle (2021) also note that lack of supply chain conflicts of interest and insufficient social sustainability adoption are another constraint to the availability and quality of sustainable clothing.\u003c/p\u003e \u003cp\u003eThere are also barriers to consumer psychology. As Wiederhold and Martinez (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) note, a large portion of consumers do not believe they can make a difference in the environment, which is why they are sceptical about the benefits of buying eco-friendly clothing. Such a perception forms an efficacy gap, a gap wherein the consumers might be aware of environmental problems but feel that they are incompetent to do anything worthwhile to help. According to Matsapola (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), low awareness and the lack of access to sustainability information are also issues in the emerging economies. Sustainable clothing choices are also influenced by broader social, cultural, and economic forces. According to Sesini et al. (2019), sustainable consumption should be approached comprehensively, encompassing environmental motives, cultural factors, price, and social impact. Sajjad and Tappin (2020) reinforce this view by demonstrating that the issue of environmental concern and personal values is the main motivation, while scepticism, cost, and supply chain constraints still impede sustainable adoption at a global level. Generally, the literature gives an in-depth insight into the factors and obstacles that affect sustainable clothing behaviour, which encompass environmental awareness, social pressure, value perceptions, structural barriers, and psychological barriers. Nonetheless, even with the helpful insights, there are still several significant gaps, particularly in emerging market conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Research Gap\u003c/h2\u003e \u003cp\u003eEven though environmental consciousness, values, attitudes, and social influence have been addressed extensively and identified as the motivators behind sustainable clothing consumption (Joshi and Rahman, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Paul et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), these aspects do not completely reveal the reasons why a significant portion of consumers are not using sustainable clothing despite being environmentally aware. According to recent research in the new economies, awareness does not always lead to behaviour (Legere and Kang, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which is why other psychological variables could be involved. Perceived consumer empowerment is one of these variables, i.e. the perception that individual consumption decisions may affect the environmental or social consequences (Lee et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kucuk, 2009). Although ethical consumption, boycotting, and sustainability-oriented behaviours have been associated with empowerment (Carrington et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Armstrong et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), the latter is a little studied in the context of sustainable fashion, and its interaction with environmental consciousness has been understudied.\u003c/p\u003e \u003cp\u003eThe other gap is the geographical concentration of the current research. A significant part of the literature is Western-oriented in which sustainability practices, institutional provisions, and consumer demands vary widely between the emerging economies (Gwozdz et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sharma, 2023). Cultural orientations, cost-effectiveness, and less advanced sustainability infrastructures define the situation in sustainable clothing adoption in places like India, but little empirical research studies the importance of psychological factors in shaping sustainable behaviour in the various classes of consumers.\u003c/p\u003e \u003cp\u003eFurthermore, most of the works are based on linear modelling methodologies despite the reality that sustainable consumption choices are often, complex and non-linear psychological events (Liu et al., 2024). This results in much behavioural variance that cannot be accounted and limits understanding of the interaction between different predictors. All these gaps demonstrate the necessity of a more holistic approach that would explore the interplay between the environmental awareness and the perceived consumer empowerment in the context of sustainable clothing behaviour, especially in the sphere of emerging economies where the practices are not uniform.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Theoretical Lens\u003c/h2\u003e \u003cp\u003eThis paper uses the Value\u0026ndash;Belief-Norm (VBN) theory as the theoretical frameworks used to explain sustainable clothing behaviour. The VBN theory argues that when people have good environmental values, when they are aware of the adverse effects of unsustainable practices, and when they have an individual sense of moral responsibility to act, pro-environmental actions can be achieved (Stern, 2000). In this respect the environment awareness coincides with the belief element, the environmental awareness of people and their concern about the damage to the environment. According to previous studies, an increase in awareness contributes to the emergence of a feeling of environmental responsibility among consumers and makes them more likely to wear sustainable clothes (Paul et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Han et al., 2024).\u003c/p\u003e \u003cp\u003eVBN theory, however, also suggests that awareness can never induce behaviour unless people feel they can contribute. To reflect this agency aspect, the study will use the perceived consumer empowerment (PCE), which is the belief that consumers hold that they can impact the environment and social outcomes by their purchasing decisions (Lee et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kucuk, 2009). Empowerment compares to the efficacy element within the norm activation dimension whereby individuals can be more willing to act in a sustainable manner when they believe they can make a difference. Research in ethical consumption supports this connection, showing that empowered consumers are more likely to boycott unethical brands, support responsible companies, and adopt pro-environmental consumption patterns (Carrington et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Armstrong et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis paper enhances the VBN model and provides a fuller explanation of sustainable clothing behaviour by combining environmental awareness (motivation) and consumer empowerment (capability), especially in the context of emerging markets where structural, cultural and informational constraints tend to undermine the relationship between awareness and action. The integrated VBN-empowerment lens recognizes that consumers do not just have to be concerned with environmental matters but they should also feel that they can change something. This adds more theoretical insight into the study therefore a solid conceptual base of analyzing the interplay of psychological factors in sustainable consumer behaviour.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Hypothesis development and Conceptual Framework\u003c/h2\u003e \u003cp\u003eEnvironmental consciousness shows environmental awareness of people and their intention to reduce ecological damage (Gogia et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rupa and Saif, 2021). The previous research is unanimous that pro-environmental consumers are more likely to find information about eco-friendly ones, refuse wasteful activities, and use eco-friendly products (Han et al., 2024; Gazzola et al., 2020). Therefore, the increased environmental awareness should result in the increased sustainable clothing behaviour. On the other hand, perceived consumer empowerment is the opinion that individual consumption decisions can affect the environmental and social consequences (Lee et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kucuk, 2009). Feeling more empowered, consumers are more inclined to make decisions that resonate with their values, such as the uptake of sustainable products, giving a favorable vote to ethical brands, and avoiding unsustainable options (Carrington et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Armstrong et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Hence, sustainable clothing behaviour should be empowered and we hypothesized that:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH1: Environmental consciousness has a positive effect on sustainable consumer behaviour.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eH2: Perceived consumer empowerment has a positive effect on sustainable consumer behaviour.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Research Design\u003c/h2\u003e \u003cp\u003eThis study is divided into two stages; the first stage employed a survey to investigate the sustainable consumer behaviour towards clothing. Using literature, a structured questionnaire was developed as the data collection instrument. This study adopts quantitative designs as they are suitable for studies examining behavioural tendencies (Creswell \u0026amp; Creswell, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the second stage, the study used machine learning tools, enabling a deeper understanding of the linear and non-linear relationships between the variables. To capture the complex feature interactions and non-linear dependencies, this study employs random forest (RF) and decision tree (DT) regression models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Sample and Data Collection\u003c/h2\u003e \u003cp\u003eData were collected using a structured questionnaire, which served as the primary data collection instrument. The questionnaire consisted of 10 statements, measured on a 5-point Likert scale, ranging from 1 to 5, where 1 means strongly disagree and 5 means strongly agree. These types of scales are widely used in behavioural research because they allow respondents to express varying degrees of agreement or disagreement (Joshi et al., 2015). The survey instrument was divided into two sections:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDemographic information \u0026ndash; capturing respondents' age.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSustainable consumer behaviour towards clothing \u0026ndash; assessing Environmental Consciousness (EC), Perceived Consumer Empowerment (PCE), and Sustainable Consumer Behaviour (SCB).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eEach construct was operationalized using validated items from prior studies (Emekci, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Paul et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ali et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe items and sources are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeasurement Scale\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScale/Item\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSustainable Consumer Behaviour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; I feel capable of helping solve the environmental problems.\u003c/p\u003e \u003cp\u003e\u0026bull; I have started to buy more environmentally friendly products during the COVID-19 pandemic\u003c/p\u003e \u003cp\u003e\u0026bull; I plan to spend more on environmentally friendly products than conventional ones.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Emekci, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); (Ali et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); (Paul et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental Consciousness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; I see myself as capable of purchasing green products in the future.\u003c/p\u003e \u003cp\u003e\u0026bull; If it were entirely up to me, I am confident I would purchase green products.\u003c/p\u003e \u003cp\u003e\u0026bull; I can protect the environment by buying products that are friendly to the environment.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Emekci, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); (Paul et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Consumer Empowerment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; I feel capable of helping solve the environmental problems.\u003c/p\u003e \u003cp\u003e\u0026bull; I have started to buy more environmentally friendly products during COVID-19.\u003c/p\u003e \u003cp\u003e\u0026bull; I plan to spend more on environmentally friendly products than conventional ones.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Emekci, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); (Ali et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); (Paul et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\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 questionnaire started with an introductory statement outlining the study's goals, voluntary participation, and confidentiality guarantees in order to guarantee respondent comprehension. To improve wording and clarity, a small sample of respondents pre-tested the instrument.\u003c/p\u003e \u003cp\u003eCustomers of eco-friendly apparel between the ages of 18 and 55 made up the target demographic. The study used this age group as they are the most active consumer segment in the clothing and fashion market. Additionally, according to the literature, they have awareness of the sustainability related issues, purchasing autonomy and economic capacity (Joshi \u0026amp; Rahman, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Moreover, prior research on consumption of ethical and sustainable fashion highlights that this age group is one of the most likely to interact with, purchase, and influence the demand for sustainable clothing (e.g., Emekci, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; McNeill \u0026amp; Moore, 2015). Therefore, this range was thought to be suitable for capturing the sustainable consumer behaviour. A random sampling technique was adopted for this study to minimize selection bias, ensuring that all respondents had an equal chance of being chosen (Saunders, Lewis, \u0026amp; Thornhill, 2019).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Data Screening and Preparation\u003c/h2\u003e \u003cp\u003eThe data's completeness, accuracy, and consistency were examined. The few missing values were handled by mean imputation. Normality diagnostics revealed mild deviations, supporting the use of ensemble tree models for added robustness in addition to regression. To summarise the demographic traits and variable distributions of the respondents, descriptive statistics (mean, median, and standard deviation) were calculated.\u003c/p\u003e \u003cp\u003eAdditionally, three key constructs were measured, namely, EC, PCE, and SCB, using multiple indicators adapted from validated scales. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides the operationalization of each construct and the variable characteristics. First, EC, which defines the level of awareness and concern for environmental impact. Furthermore, PCE refers to the extent to which consumers feel they have an impact on environmental outcomes through their purchasing decisions. Lastly, SCB is the degree of promise to buy or prefer sustainable clothing products.\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\u003eVariable Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable description\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe age of the respondents.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtent of concern regarding environmental issues.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtent of actively seeking information on how to reduce environmental impact.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel of importance of personal values\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel of consideration of ethical values when buying products\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel of difficulty to buy sustainable products\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtent the respondent felt that buying sustainable products require significant extra efforts.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtent the respondent believed that his/her actions can make a significant environmental impact.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtent the respondent is sceptical that individual efforts can lead to significant environmental change\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtent the respondent buys products labelled as sustainable.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCB3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtent the respondent prefers buying sustainable products over conventional ones\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 \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Analytical Framework\u003c/h2\u003e \u003cp\u003eTo provide thorough insight into the predictors of SCB, a two-stage analytical approach was employed, which included both conventional regression analysis and machine learning-based ensemble modeling.\u003c/p\u003e \u003cp\u003e \u003cem\u003eStage 1: Multiple Regression Analysis\u003c/em\u003e \u003c/p\u003e \u003cp\u003eTo observe the linear relationship between the independent and dependent variables, a multiple regression model was estimated. The general form of the regression equation is expressed as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{SCB}_{i}={\\beta\\:}_{0}+{\\beta\\:}_{1}{EC}_{i}+{\\beta\\:}_{2}{PCE}_{i}+{\\epsilon\\:}_{i}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{SCB}_{i}\\)\u003c/span\u003e\u003c/span\u003e was the Sustainable consumer behaviour score of respondents \u003cem\u003ei\u003c/em\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{EC}_{i}\\)\u003c/span\u003e\u003c/span\u003e Environmental consciousness score of respondent i, \u003cem\u003eand\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{PCE}_{i}\\)\u003c/span\u003e\u003c/span\u003e Perceived consumer empowerment score of respondent i.\u003c/p\u003e \u003cp\u003e \u003cem\u003eStage 2: Ensemble Tree-Based Machine Learning Models\u003c/em\u003e \u003c/p\u003e \u003cp\u003eDecision Tree (DT) and Random Forest (RF) regression models were used to capture complex feature interactions and non-linear dependencies. The predictive performance and interpretability of ensemble tree-based algorithms are improved due to their resilience to outliers and non-normal data distributions (Liu et al., 2024; Zhu et al., 2025). Training (75%) and validation (25%) subsets of the dataset were randomly selected. While the Random Forest model, which combines several decision trees, reduced overfitting and enhanced generalisation, the Decision Tree model was used as the baseline (Pious et al., 2024).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Descriptive Statistics\u003c/h2\u003e \u003cp\u003eDescriptive statistics for EC, PCE, and SCB are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. All variables had 505 valid responses. The mean scores were 3.93 (SD\u0026thinsp;=\u0026thinsp;0.50) for EC, 2.66 (SD\u0026thinsp;=\u0026thinsp;0.43) for PCE, and 3.18 (SD\u0026thinsp;=\u0026thinsp;0.65) for SCB.\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\u003eDescriptive Statistics for Study Variables\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.65\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\u003eMoreover, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the Pearson correlation coefficients among EC, PCE, and SCB. EC exhibited a small but statistically significant positive correlation with SCB (r\u0026thinsp;=\u0026thinsp;0.22, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting that higher EC is associated with greater SCB. The correlation between PCE and SCB was positive and significant. However, weaker (r\u0026thinsp;=\u0026thinsp;0.092, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, EC and PCE demonstrated a significant negative correlation (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.187, p\u0026lt;.01), indicating that as EC increases, PCE tends to decrease slightly.\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\u003eCorrelations among EC, PCE, and SCB\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePCE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSCB\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.19**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.19**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.092*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.22**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\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*Note: p-values in parentheses. **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (2-tailed).\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Regression Analysis\u003c/h2\u003e \u003cp\u003eA multiple regression analysis was conducted with SCB as the dependent variable and EC and PCE as predictors to further examine the predictive relationships (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The resulting model was statistically significant (F(2,502)\u0026thinsp;=\u0026thinsp;18.29, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003cem\u003e)\u003c/em\u003e, accounting for approximately 7% of the variance in SCB, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Examination of the standardized coefficients revealed that EC was the stronger predictor, while PCE also made a significant, though smaller, positive contribution.\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\u003eRegression Model Summary\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficients (standard error)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003et-test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental Consciousness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.32(0.05)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Consumer Empowerment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21 (0.06)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e18.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e**Significant at 0.01 level\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOverall, both EC and PCE were significant, positive predictors of SCB, with EC presenting a relatively larger effect. The model, however, explains only a modest proportion of the variance in SCB, suggesting that additional factors not considered in this analysis may also play a substantive role.\u003c/p\u003e \u003cp\u003eFurther investigation using ensemble learning techniques was prompted by the relatively low R\u003csup\u003e2\u003c/sup\u003e that indicated the possibility of non-linear or higher-order interactions, even though the regression model offered interpretative insights.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Ensemble Tree Based Models to capture the non-linear relationships and insights\u003c/h2\u003e \u003cp\u003eEnsemble Tree based models are the cutting-edge machine learning approach that has the capability of capturing non-linear relationships between the dependent variable (SCB) and all other independent variables (Zhu et al.,2025). Tree based models also handle outliers, missing values and non- normal data better, with higher explanability and ease of adoption ensuring the patterns and the insights are robust and actionable (Liu et al.,2024). We have used Decision Trees (Mienye et al., 2024) and Random Forest (Muna et al.,2023) as the two most popular, robust performing and deployed ensemble techniques used worldwide. One of the challenges considered for decision tree is the overfit of the model. This challenge of decision tree has been mitigated by using the performance of the ensemble tree (the performance being ensembled over a range of trees or forests) thus showcasing better model performance reducing any probable model overfit (Pious et al.,2024) .The data is being split into training and validation in the 75:25 ratio, wherein the training data was used to build the model and the validation data was used to test the model.\u003c/p\u003e \u003cp\u003eThe output of the decision tree and the random forest is showcased below (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe feature importance table from decision tree showcases Age to be most dominant feature explaining about 16% of the variance of the aggregated SCB score. In other way about 16% of the predictive power of the aggregated SCB score comes from the variable age This is followed by EC2 which explains about 15% of the variability of the aggregated SCB score, followed by PCE1 explaining 13% of the variability, PCE3, explaining 12.5% of the variability, EC3 explaining 12.2% of the variability, EC4 explaining 12% of the variability, PCE2 explaining 11% of the variability and EC1 explaining approximately 8.1% of the variability (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe feature importance table from random showcases EC2 to be most dominant feature explaining about 17% of the variance of the aggregated SCB score. In other way about 17% of the predictive power of the aggregated SCB score comes from the variable EC2. This is followed by Age which explains about 15.50% of the variability of the aggregated SCB score, followed by EC4 explaining 14.75% of the variability, PCE3, explaining 13.75% of the variability, PCE1 explaining 12% of the variability, PCE2 explaining 10.75% of the variability, EC3 explaining 10.5% of the variability and EC1 explaining approximately 7% of the variability.\u003c/p\u003e \u003cp\u003eThe feature importance comparison of the two machine learning models namely the decision tree and the ensembled random forest is showcased below (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e):\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\u003eComparison of the two machine learning tools\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 \u003cp\u003eFeature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFeature Importance As par Random Forest (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFeature Importance as par Decision Tree (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRank as par Random Forest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRank as par Decision Tree\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2nd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1st\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1st\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2nd\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3rd\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4th\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5th\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3rd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6th\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7th\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8th\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 data set was divided into training and validation in the ratio 75%: 25%. The model performance of both decision tree and random forest on the holdout validation data (25%) is showcased below (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\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\u003eHoldout RMSE results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHoldout RMSE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecision Tree Regressor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom Forest Regressor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.56\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 tuned Random Forest clearly outperforms the Decision Tree in predicting the aggregated SCB score wherein the RMSE (Root Mean Square Error) of the holdout data of the Random Forest is lower than the Decision Tree. This can be further substantiated and visualized from the graph below which slows the plotting of actual vs predicted for both the models. Predictions for the decision tree deviate widely from the diagonal, showing weak fit. Predictions cluster from the tuned Random Forest are closer to the diagonal, indicating a better approximation of actual values.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Implications","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Theoretical Implications\u003c/h2\u003e \u003cp\u003eThis study offers several important theoretical contributions to the sustainable consumption and fashion literature. First, it advances understanding of sustainable clothing behaviour by integrating environmental consciousness and perceived consumer empowerment within a single behavioural framework. Existing research has typically examined environmental consciousness in isolation, assuming that awareness naturally leads to action. By showing that empowerment also plays a significant role, the study challenges the linear assumption embedded in attitude\u0026ndash;behaviour models and supports the argument that pro-environmental behaviour requires both motivation (consciousness) and perceived capability (empowerment). This strengthens the application of Value\u0026ndash;Belief\u0026ndash;Norm (VBN) theory to the fashion domain by highlighting the importance of efficacy-related beliefs in activating personal norms.\u003c/p\u003e \u003cp\u003eSecond, the results allow a theoretical clarity to be established as a result of the empirical evidence that environmental consciousness and empowerment have independent yet complementary effects on sustainable clothing behaviour. This two-way interpretation brings nuances to the sustainability literature that usually views consumers as passive receivers of environmental information. However, in the current research, the sense of agency of consumers proves just to be central, thus, empowerment cannot be only a product of sustainability marketing but a psychological precondition of sustainable consumption.\u003c/p\u003e \u003cp\u003eThird, the study advances the existing theory by including non-linear modelling methods (Decision Tree and Random Forest) to examine the predictors of behaviour. Conventional research on sustainability is based on the use of linear regression which can be inadequate in capturing the complexity of consumer decision-making. Making use of the opportunity of validating findings by means of ensemble machine learning models, the study proves that sustainable clothing behaviour is conditioned by the multi-dimensional interactions, which go beyond the suppositions of linear behavioural theories. Such sophistication of the methodology helps to create a more precise theoretical insight into the effects of psychological factors of the environmentally responsible behaviour.\u003c/p\u003e \u003cp\u003eFinally, the study expands geographical relevance by contributing context-specific evidence from an emerging economy. Theoretical models often rely on Western data, limiting cross-cultural applicability. By using data from India, where sustainable fashion adoption is still evolving, the study provides empirical support for adapting psychological models to contexts with different socio-economic, cultural, and infrastructural realities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Practical Implications\u003c/h2\u003e \u003cp\u003eThe findings of this research can provide meaningful advice to fashion businesses, retailers, policymakers and sustainability activists aiming at encouraging sustainable clothing behaviour. To begin with, the high impact of environmental awareness implies that education and awareness creation should continue to be at the heart of environmental sustainability efforts. One way through which fashion brands can improve engagement is by informing people about the actual environmental impact of fast fashion, through transparent sustainability labels, stories, and data-driven graphics of ecological results they can make consumers relate to.\u003c/p\u003e \u003cp\u003eSecond, perceived consumer empowerment plays an important role, and the strategies should support the perceptions of consumers that their decisions do count. Empowerment can be enhanced and long-term behavioural commitment can be established by campaigning that emphasises the collective contribution (every purchase count), presents consumer-led movements, or demonstrates the benefits of sustainable purchases. The interactive tools that retailers may also include to enhance the perceived agency of consumers comprise carbon-footprint calculators, sustainability scorecards, or apps that demonstrate the effect of environmentally responsible decisions.\u003c/p\u003e \u003cp\u003eThird, policymakers and industry bodies in emerging economies like India can play a pivotal role by improving accessibility and affordability of sustainable clothing. Financial incentives for sustainable manufacturers, guidelines for waste reduction, and support for green supply chains can reduce production costs, making sustainable products more competitive. Awareness campaigns at community or regional levels can also help overcome information gaps, especially among consumers with limited sustainability exposure.\u003c/p\u003e \u003cp\u003eFourth, since it has been demonstrated that the behaviour of sustainable clothing by different demographic groups is different, a specific approach is essential. Younger buyers can be very sensitive to social media promotion and collaboration with influencers, and older age groups can need more straightforward data about quality, durability, and long-lasting value. The sustainability communications should be as relevant and as possible by making brands customize the messages to the various consumer groups. Lastly, the application of machine learning models as methodology demonstrates the significance of using data to make decisions in the fashion industry. Advanced analytics allow retailers to understand behavioural trends, classify consumers, use sustainability efforts tailored to consumers, and anticipate future preferences. This technological convergence can assist with a more strategic and evidence-based shift to sustainable consumption.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion and Future Research:","content":"\u003cp\u003eIn this paper, the psychological factors that define sustainable clothing purchase within an emerging economy have been investigated and their importance on environmental consciousness (EC) and their perceived consumer empowerment (PCE) have been analyzed. The research was based on the Value\u0026ndash;Belief-Norm (VBN) framework and it was revealed that the idea of sustainable behaviour is contributed not only by the awareness of environmental concerns by consumers but also by the belief that they can also impact environmental performance by the choice of their purchase. Based on the 505 respondents and two-stage analysis, it was found that EC and PCE are significant and positive predictors of sustainable consumer behaviour (SCB). Though the results of regression showed a low explanatory power, the use of machine learning models showed more significant non-linear trends, with the Random Forest being superior to Decision Tree and pointing to the high predictive value of EC and empowerment-related variables.\u003c/p\u003e \u003cp\u003eSuch outcomes contribute to the theoretical framework by incorporating the concept of empowerment into the decision-making process of sustainability by providing a more comprehensive way of explaining the reasons why consumers select sustainable clothing. The methodological contribution of the study is also to demonstrate the importance of integrating regression with ensemble machine learning in order to explain more complicated behaviour dynamics. In practice, the results highlight the need by fashion companies, politicians, and sustainability activists to concurrently raise social awareness and empower consumers with a feeling of agency should they wish to hasten the process of sustainable clothing adoption in more developing economies such as India.\u003c/p\u003e \u003cp\u003eThe study has its limitations although it has made some contributions, which provide openings to future research. To begin with, the data used are cross-sectional and thus the establishment of causal relationships becomes difficult. The longitudinal studies would be more effective in terms of the way consciousness and empowerment changes with time. Second, the research is based on two psychological constructs; further studies may include more factors like perceived value, trust of sustainability claims, social influence or cultural norms to achieve better explanatory force. Third, the sample group is limited to consumers between 18 and 55 years; it can be beneficial to broaden future research to cover the older generations or rural residents to gain a better understanding of demographic differences. Lastly, although machine learning models were useful in giving powerful predictive information, future research could focus on newer and more predictive algorithms like Gradient Boosting or XGBoost to provide a higher predictive accuracy and capture a more detailed behavioural pattern.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eClinical trial number: Not applicable.\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics Statement\u003c/h2\u003e \u003cp\u003eThis study was conducted in accordance with the ethical standards of the \u0026ldquo;Research Conduct and Ethics Committee (Institutional Review Board), Christ University\u0026rdquo; and (Symbiosis International University's Independent Ethics Committee (IEC)) and with the principles outlined in the Declaration of Helsinki. Prior to data collection, informed consent was obtained from all participants.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics and Guidelines\u003c/strong\u003e \u003cp\u003eFull name of the ethical committee that has approved the study: \u0026ldquo;Research Conduct and Ethics Committee (Institutional Review Board), Christ University.\u0026rdquo;\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent to Participate\u003c/h2\u003e \u003cp\u003eParticipation was voluntary, and respondents were assured of anonymity and confidentiality. No clinical trials or medical interventions were involved in this research. Informed consent was obtained from all participants.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for Publication\u003c/strong\u003e \u003cp\u003eNot applicable. The study does not include any individual person\u0026rsquo;s data in any identifiable form.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting Interests\u003c/strong\u003e \u003cp\u003eThere are no conflicts of interest relevant to the content of this review.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo funding was received by any organisation or person.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions StatementAuthor 1 Conceptualized the study, Data Collection, Initial draft writing.Author 2 Conceptualized the study, Contributed to the research design, Supervised the study and reviewed the draft.Author 3 Performed data analysis, Interpretation of results and assisted in drafting and revising the manuscript. Author 4 Performed machine learning analysis, provided critical intellectual input and reviewed and edited the manuscript for important academic content. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003eThe authors gratefully acknowledge all participants who generously shared their time, perspectives, and experiences through interviews. Their valuable insights were instrumental in enriching the depth and quality of this research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to ethical considerations and participant confidentiality, but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAli BJ, Saleh PF, Akoi S, Abdulrahman AA, Muhamed AS, Noori HN, Anwar G. Impact of service quality on customer satisfaction: Case study at online meeting platforms. Int J Eng Bus Manage. 2021;5(2):65\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArmstrong CM, Niinim\u0026auml;ki K, Kujala S, Karell E, Lang C. Sustainable product-service systems for clothing: Exploring consumer perceptions of consumption alternatives in Finland. J Clean Prod. 2016;124:236\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2015.10.013\u003c/span\u003e\u003cspan address=\"10.1016/j.jclepro.2015.10.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarrington MJ, Neville BA, Whitwell GJ. Why ethical consumers don\u0026rsquo;t walk their talk: Toward a framework for understanding the gap between ethical purchase intentions and actual buying behaviour. J Bus Ethics. 2010;97(1):139\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10551-010-0501-6\u003c/span\u003e\u003cspan address=\"10.1007/s10551-010-0501-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConnell KYH. Internal and external barriers to eco-conscious apparel acquisition. Int J Consumer Stud. 2010;34(3):279\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1470-6431.2010.00865.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1470-6431.2010.00865.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCreswell JW, Creswell JD. Research design: Qualitative, quantitative, and mixed methods approaches. 5th ed. Sage; 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarlson JM. Increased attention to climate change mitigation approaches leads to lower levels of climate anxiety. Discover Psychol. 2025;5(1):179.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDagilienė L, Varaniūtė V, Banionienė J. Cultivating a sustainable and circular economy: The role of institutional logics in manufacturing companies. J Clean Prod. 2024;434:140363. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2022.140363\u003c/span\u003e\u003cspan address=\"10.1016/j.jclepro.2022.140363\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEmekci S. Green consumption behaviours of consumers within the scope of TPB. J Consumer Mark. 2019;36(3):410\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGleim MR, Smith JS, Andrews D, Cronin JJ Jr.. Against the green: A multi-method examination of barriers to green consumption. J Retail. 2013;89(1):44\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jretai.2012.10.001\u003c/span\u003e\u003cspan address=\"10.1016/j.jretai.2012.10.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGogia D, Gupta SK, Rathi P. Impact of various structural attributes on B2B sustainability positioning. J Bus Industrial Mark. 2024;39(2):388\u0026ndash;406.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGogia D, Virk N, Choudhry V. (2025). Green purchase behavior: A pathway to corporate sustainability through bibliometric lens. \u003cem\u003e[Journal name forthcoming]\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta S, Joshi D, Jamwal A, Bhanot N, Jagtap S. Unveiling the synergy of sustainable manufacturing practices and lean for sustainable manufacturing competitiveness. Discover Appl Sci. 2025;7(9):1005.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGwozdz W, Nielsen KS, M\u0026uuml;ller T. An environmental perspective on clothing consumption: Consumer segments and their behavioural patterns. Sustainability. 2017;9(7):762. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su9070762\u003c/span\u003e\u003cspan address=\"10.3390/su9070762\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHair JF, Risher JJ, Sarstedt M, Ringle CM. When to use and how to report the results of PLS-SEM. Eur Bus Rev. 2019;31(1):2\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnstone ML, Tan LP. Exploring the gap between consumers\u0026rsquo; green rhetoric and purchasing behaviour. J Bus Ethics. 2015;132(2):311\u0026ndash;28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10551-014-2316-3\u003c/span\u003e\u003cspan address=\"10.1007/s10551-014-2316-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoshi Y, Rahman Z. Factors affecting green purchase behaviour and future research directions. Int Strategic Manage Rev. 2015;3(1\u0026ndash;2):128\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJaheer Mukthar KP, Nagadeepa C, Selvaratnam DP, Pushpa A, Shukla N. Sustainable wardrobe: recycled clothing towards sustainability and eco-friendliness. Discover Sustain. 2024;5(1):151.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim J, Kang S, Lee KH. How social capital impacts the purchase intention of sustainable fashion products. J Bus Res. 2020;117:596\u0026ndash;603. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jbusres.2018.07.018\u003c/span\u003e\u003cspan address=\"10.1016/j.jbusres.2018.07.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK\u0026ouml;ksal D, Str\u0026auml;hle J. Social sustainability in fashion supply chains: Understanding social standard implementation failures in Vietnam and Indonesia using agency theory. Sustainability. 2021;13(4):2159. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su13042159\u003c/span\u003e\u003cspan address=\"10.3390/su13042159\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKundu A, Kundu SG, Sahu SK, Badgayan ND. Leveraging Azure automated machine learning and CatBoost gradient boosting algorithm for service quality prediction in hospitality. Computers. 2025;14(2):32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKundu SG, Ghosh A, Kundu A, GP G. A ML-AI enabled ensemble model for predicting agricultural yield. Cogent Food Agric. 2022;8(1):2085717.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee E-J, Choi H, Han J, Kim DH, Ko E, Kim KH. How to nudge your consumers toward sustainable fashion consumption: An fMRI investigation. J Bus Res. 2020;117:642\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jbusres.2018.07.008\u003c/span\u003e\u003cspan address=\"10.1016/j.jbusres.2018.07.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLegere A, Kang J. The role of consumers in sustainable fashion consumption. J Bus Res. 2020;117:681\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jbusres.2019.09.027\u003c/span\u003e\u003cspan address=\"10.1016/j.jbusres.2019.09.027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi M, Choe YH, Gu C. How perceived sustainability influences consumers\u0026rsquo; clothing preferences. Sci Rep. 2024;14(1):28672.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalik G, Guptha A. An empirical study on behavioural intent of consumers in online shopping. Bus Perspect Res. 2013;2(1):13\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMandarić D, Hunjet A, Vuković D. The impact of fashion brand sustainability on consumer purchasing decisions. J Risk Financial Manage. 2022;15(4):176.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsapola E. (2021). \u003cem\u003eConsumer behaviour towards sustainable clothing\u003c/em\u003e (Master\u0026rsquo;s thesis, University of South Africa).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiinim\u0026auml;ki K, Peters G, Dahlbo H, Perry P, Rissanen T, Gwilt A. The environmental price of fast fashion. Nat Reviews Earth Environ. 2020;1(4):189\u0026ndash;200. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s43017-020-0039-9\u003c/span\u003e\u003cspan address=\"10.1038/s43017-020-0039-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNulty DD. The adequacy of response rates to online and paper surveys: What can be done? Assess Evaluation High Educ. 2008;33(3):301\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrtner CN, Armstrong M, Ulmer EJ. Emotion regulation, climate distress, and climate action in climate activist and student samples. Discover Psychol. 2025;5(1):1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaul J, Modi A, Patel J. Predicting green product consumption using the theory of planned behavior and reasoned action. J Retailing Consumer Serv. 2016;29:123\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRupa RA, Saif ANM. Impact of green supply chain management on business performance and environmental sustainability: Case of a developing country. Bus Perspect Res. 2022;10(1):140\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSajjad A, Eweje G, Tappin D. Managerial perspectives on drivers for and barriers to sustainable supply chain management implementation: Evidence from New Zealand. Bus Strategy Environ. 2020;29(2):592\u0026ndash;604.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSesini G, Castiglioni C, Lozza E. New trends and patterns in sustainable consumption: A systematic review and research agenda. Sustainability. 2020;12(15):5935.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShang D, Wu W, Schroeder D. Exploring determinants of green smart technology product adoption from a sustainability-adapted value\u0026ndash;belief\u0026ndash;norm perspective. J Retailing Consumer Serv. 2023;70:103169.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma H. Using topic modeling for extracting customers\u0026rsquo; expectations: A case of women apparel. Bus Perspect Res. 2025;13(3):454\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTh\u0026oslash;gersen J. Green shopping: For selfish reasons or the common good? Am Behav Sci. 2011;55(8):1052\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0002764211407903\u003c/span\u003e\u003cspan address=\"10.1177/0002764211407903\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUmit Kucuk S. Consumer empowerment model: From unspeakable to undeniable. Direct Marketing: Int J. 2009;3(4):327\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/17505930911000892\u003c/span\u003e\u003cspan address=\"10.1108/17505930911000892\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeyant E. (2022). Review of \u003cem\u003eResearch design: Qualitative, quantitative, and mixed methods approaches\u003c/em\u003e (J. W. Creswell \u0026amp; J. D. Creswell, 2018). \u003cem\u003eBusiness Education Innovation Journal\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWiederhold M, Martinez LF. Ethical consumer behaviour in Germany: The attitude\u0026ndash;behaviour gap in the green apparel industry. Int J Consumer Stud. 2018;42(4):419\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYadav N, Sijoria C. Consumer shift from fast fashion to thrift fashion: An application of goal framing theory. Business Perspectives and Research. Advance online publication; 2024.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sustainable clothing consumption psychology, perceived consumer empowerment, environmental consciousness, Machine learning approach","lastPublishedDoi":"10.21203/rs.3.rs-8578408/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8578408/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe global fashion industry is a major contributor to environmental degradation, making sustainable clothing consumption increasingly essential. However, in emerging economies, the adoption of sustainable apparel remains inconsistent despite growing awareness. This study examines how environmental consciousness (EC) and perceived consumer empowerment (PCE) influence sustainable consumer behaviour (SCB), drawing on the Value\u0026ndash;Belief\u0026ndash;Norm (VBN) theory to explain the joint effects of environmental motivation and perceived agency. Using data from 505 respondents, the study employed a two-stage analytical approach. Multiple regression analysis showed that both EC and PCE significantly and positively predict SCB, though the variance explained is modest. To capture complex behavioural patterns, ensemble machine learning models\u0026mdash;Decision Tree and Random Forest\u0026mdash;were applied. The Random Forest model demonstrated superior predictive performance, and feature importance results revealed that EC and PCE dimensions are among the strongest contributors to sustainable behaviour. The findings highlight the pivotal role of psychological processes, specifically environmental consciousness and perceived agency, in influencing sustainable apparel consumption. The study extends psychology and sustainability literature by integrating empowerment into the VBN framework and demonstrating its relevance in an emerging economy context. Practically, the findings underscore the need to strengthen environmental awareness and consumer agency to enhance sustainable clothing adoption in India.\u003c/p\u003e","manuscriptTitle":"Understanding the Awareness Action Gap in Sustainable Clothing Consumption","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 08:17:13","doi":"10.21203/rs.3.rs-8578408/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"16b68f40-a67c-4d2f-9cba-6b65298647c5","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-25T12:41:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 08:17:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8578408","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8578408","identity":"rs-8578408","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}