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Article

Sustainable Transition through Circular Textile Products: An Empirical Study of Consumers’ Acceptance in India

by
Mushahid Ali Shamsi
1,
Imran Anwar
2,
Asiya Chaudhary
1,
Samreen Akhtar
3,* and
Alam Ahmad
3
1
Department of Commerce, Aligarh Muslim University, Aligarh 202002, India
2
School of Management, Sir Padampat Singhania University, Udaipur 313601, India
3
Department of Finance, College of Administrative and Financial Sciences, Saudi Electronic University, Riyadh 11673, Saudi Arabia
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(18), 13656; https://doi.org/10.3390/su151813656
Submission received: 7 August 2023 / Revised: 4 September 2023 / Accepted: 7 September 2023 / Published: 13 September 2023
(This article belongs to the Special Issue Data Analysis of Brand Sustainability and Consumer Satisfaction)
Browse Figures
Versions Notes

Abstract

:
The robust Indian textile industry results in prodigious consumption followed by equivalent waste, leading to environmental deterioration. The solution is circular textile production/products (CTPs), but their existence is almost absent in the Indian market due to a lack of focus on consumers’ acceptance of CTPs. To bridge the literature gap, the TPB model was extended to explore the antecedents that directly and indirectly influence consumers’ attitudes and purchase intentions towards CTPs. The PLS-SEM (using Smart-PLS) was used to assess the structural model with the 409 samples collected through an online survey from the NCR of India. The findings revealed that personal benefits, environmental consciousness (except risk perception), perceived behavioral control, and subjective norms significantly and directly influence consumers’ attitudes and purchase intentions towards CTPs. In addition, attitude significantly and indirectly mediates the relationship between “environmental consciousness, personal benefits (except risk perception)” and purchase intention towards CTPs. The finding offers pertinent information about the antecedents of CTPs that help the companies, marketers, and government to promote CTP acceptance and attain sustainability in the production and consumption of textile products in the Indian economy. Despite having product-specific and regional limitations, this research contributes significantly to the current literature on CTPs and their acceptance.

1. Introduction

The colossal Indian textiles industry stands firmly with its domestic Textile and Apparel (T&A) market at USD 75 billion in 2020–2021 and is estimated to grow at 10% CAGR and reach USD 190 billion by 2025–2026. Apparel holds as large as a 73% share of the total T&A market in India [1]. The fashion supply chain is diverse and complex, resulting in an explosion in consumption and equivalent waste, forming the leading cause of environmental deterioration. Past research has determined that cotton production involves high usage of pesticides [2], creating environmental pollution and unsafe chemicals [3], emitting solid waste as discarded apparel, and forming landfills [4]. Hence, concerns such as depleting natural resources, pesticide usage, waste management, and alarming environmental degradation are gaining the attention of government, researchers, and academia [5].
A circular textile economy (CTE) is a solution to the current concerns, as it is recuperative and rejuvenating by design [6], doing away with the limitations of the linear economy model (take–make–use–dispose). The CTE confronts issues like resource scarcity, exhaustion, and the recycling of used products [7]. In this CTE process, the discarded textiles are dismantled, adequately cleansed and organized, revamped, and, after adding some new parts, reassembled and tested for quality and market [8]. The circular economy (CE) protects the environment (up to 80% fewer emissions) by reducing energy (up to 60% less) and raw material consumption (up to 70% less) and offers low-cost products (up to 60% lower) consequently [9,10].
Envisaging the benefits of textile production and consumption, the CE has been endorsed by nations around the globe, including the USA, the European Union, and China [11]. On the contrary, developing countries such as India, Bangladesh, Brazil, Sri Lanka, and Bhutan have yet to implement it successfully in the textile industry [12,13,14,15]. Considering the urgency for environmental protection, any economy must adhere to the CE to address the needs of the ever-growing population while preserving natural resources [13]. In India, a few companies like Larsen and Toubro, Hewlett Packard, Volvo, Caterpillar, General Electric, Diesel Loco Modernization works, etc., follow the CE model [16], but the textiles producers are almost absent. Some units recycle textiles into domestic crafts like wipes, rugs, dusters, mats, blankets, bags, etc., from old used clothes and woolens, but these practices are insufficient, and textile waste is increasing continuously [16].
The textile industry can only be sustainable when an economy focuses on sustainable production and consumption strategies. According to Muranko, et al. [17], understanding consumers’ acceptance of circular products is essential, without which circular production would never bear the desired fruits. Hence, it becomes essential to investigate consumers’ purchase behavior and intentions towards circular textile products (CTPs) to understand, do away with the constraints, and promote their acceptance to achieve a sustainable environment.
Consumers’ ability to differentiate between new and recycled textiles and clothing is impacted by various social factors, resulting in varying attitudes and behaviors [18] and influencing demand for CTPs. However, it has been found that very scanty exploration has been carried out on Indian consumers’ attitudes and purchase intentions toward eco-friendly, organic, and green clothing [19,20,21], including CTPs, due to which the acceptance of CTPs remains a significant challenge in India [17]. The prior literature has examined consumer awareness, motivation, and perceptions towards eco-friendly, organic, and green clothing/apparel [22,23,24,25] and explained the relationships between product attributes, credibility, practicality, and convenience [26,27]. Most of the available research covers fiber performance [28], premium prices [29], choices or availability, aesthetics [30], fashion image, credibility [26], and convenience and practicality [31]. Further, it is more focused on remanufacturing techniques and recycled products than on the CE and circular products (a broader concept) [3,32], paying little attention to the factors influencing end-user demand for such products. This demonstrates the research gap and highlights the significance of in-depth studies in the related context. Since consumer adoption and acceptability are essential for the success of any product, this study makes efforts to add to the existing body of knowledge by understanding cognitive factors influencing the adoption and acceptability of Indian consumers in terms of attitudes and intentions towards CTPs.
Therefore, the current study develops a conceptual model by extending the widely accepted theory of planned behavior (TPB) with variables like personal benefits (PB), environmental consciousness (EC), and risk perception (RP) to explore the cognitive factors that directly and indirectly influence consumers’ attitudes (AT) and purchase intention (PI) towards CTPs. The primary objective of this research is to evaluate the influence of PB, EC, RP, and the underlying variables of the TPB, i.e., “Attitude (AT), Perceived Behavior Control (PBC), and Subjective Norm (SN)”, on consumers’ purchase intentions towards CTPs (PIC). The second is determining the influence of PB, EC, and RP on consumers’ AT towards CTPs. The third is to estimate whether AT towards CTPs positively or negatively mediates (indirectly influences) the relationship of PB, EC, and RP with the PIC of consumers towards CTPs.

2. Theoretical Framework and Conceptual Model Development

2.1. Theoretical Framework

Necessitating the implementation of CE practices [13] and understanding AT and PI towards CTPs, academics, researchers, and environmentalists have focused on sustainable consumption and production.
According to earlier research by Kumar, et al. [33], Singhal, et al. [34], and Wang, et al. [35], risk perception (RP) and personal benefits (PB) are the most common factors influencing consumer intentions for remanufactured products. Consumers are more likely to make decisions when they perceive PB more than risk [9,34,36]. Additionally, previous studies have demonstrated that consumers’ environmental consciousness (EC) is crucial in determining their intentions for eco-friendly, remanufactured, and green products [36,37]. This is because one’s awareness and, consequently, decision-making ability are greatly influenced by consciousness [38]. Ajzen [39] also asserts that the cognitive components of the theory of planned behavior (TPB) (viz. attitude (AT), social norms (SN), and perceived behavior control (PBC)) have an essential influence on shaping a consumer’s behavioral intention. This is because a resourceful individual with a positive attitude, the support of friends and family, and a perception of ease vigorously develops desires for specific behavior. The TPB has thus gained widespread adoption throughout the behavioral and intentional domains, solidifying its position as the leading expectancy–value theory [40,41].

2.2. Conceptual Model Development

The TPB has gained popularity in recent decades [42,43,44,45]. It is one of the best theories for analyzing global green behavior [46]. Consumers’ behavior towards remanufactured products, food choices/leftover sharing, healthy produce, green hotels, and green packaging have been investigated using the TPB in different regions like the USA, Iran, Australia, the UK, Russia, Brazil, the Czech Republic, China, and India [47,48,49,50]. Due to its proven value in multiple domains, we used constructs of the TPB in our study.
According to experts, adding field- or product-specific factors to the model can increase the TPB’s predictability. Most recent psychosomatic research added variables to the TPB model to predict consumer intentions in different domains [51]. Therefore, based on a detailed literature review, the present study developed a conceptual model by integrating the constructs of the TPB with additional variables (i.e., PB, EC, and RP) to increase the predictive power of the study’s conceptual model and to evaluate the PIC. In addition, the model’s uniqueness lies in the fact that earlier research never used or extended the TPB in the context of CTPs (especially in the Indian context), and a model that understands the influence of cognitive factors on the end-user demand towards CTPs has never been developed.
The previous literature supports the notion that PB, like low cost, incentives, discounts, and the cost of circular products (40–60% less than new products), may induce customers to form an intention for the product or services [52,53]. Further, environmentally conscious consumers satisfy their wants while contributing to society and the environment. Risks and uncertainties (including product reliability, lifespan, financial and societal repercussions, and physical performance or quality) discourage consumers’ purchasing intention and behavior toward products. Therefore, we included PB, EC, and RP in this study to determine whether they influence AT and PIC.
The model consists of seven constructs: PB, RP, and EC, with underlying variables of the TPB, i.e., SN, PBC, AT, and PIC. In the study, PB, RP, EC, SN, and PBC were independent variables; AT acted as an independent as well as mediating variable on the relationships of PB, RP, and EC with PIC, where PIC was the dependent variable. In order to fulfill the objectives of the study, the conceptual model hypothesized twelve relationships that were empirically tested in the below sections (see Figure 1).

3. Hypotheses Development

3.1. Purchase Intention towards Circular Textile Products (PIC)

Gilg, et al. [54] inferred that green behavior is based on green consumption/purchase decisions, practice (water and energy conservation), and recycling. It is a preventive step against the further degradation of Mother Nature [55]. The decision to make an ecological purchase supports reducing waste pollutants as they are substituted with green products [56]. Prior studies have deduced that product circularity positively affects consumers’ perceived environmental value [57]. They know such consumption will contribute to a sustainable environment [58]. Therefore, PIC is explained as the willingness and probability of a person to prefer eco-friendly or recyclable products or CTPs [59] in their purchase considerations, consequently leading to actual eco-friendly behavior.

3.2. Environmental Consciousness (EC), Attitude (AT), and Purchase Intention of Consumers towards CTPs (PIC)

The term “Environmental Consciousness” (EC) was coined by Roberts [60], and it refers to “consumers who base purchasing decisions on information about the negative effects of consumer behavior on the environment”. This definition was confirmed by Brochado, et al. [61]. According to Peattie and Collins [62], consumers need to have an awareness/consciousness of the environment in order to engage in sustainable consumption. Samarasinghe [63] found that green consumers’ consumption activities include acquiring, consuming, and disposing of eco-friendly products, reflecting their EC. Hence, EC may be interpreted as a factor affecting how people feel, what they buy, and their PI and AT [64].
Anderson Jr and Cunningham [65] performed the initial research on environmentally conscious consumers, focusing more on those inclined to consider environmental issues while making purchase decisions. According to the current study, environmentally conscious consumers seek to meet their needs while contributing to environmental sustainability and social well-being. EC refers to factors influencing one’s tendency to engage in environmentally friendly actions [66,67]. Many people, even those who did not care about or know little about the environment, are now interested in how human consumption affects sustainability. This is because research in the field has come a long way, and much information is available to consumers. Hence, EC improves consumers’ AT and PIC [62,68]. People who buy environmentally friendly products possess some EC [69,70], which leads to the following hypothesis:
Hypothesis 1. 
EC will positively influence the PI of consumers towards CTPs.
Hypothesis 2. 
EC will positively influence the purchase AT of consumers towards CTPs.

3.3. Risk Perception (RP), Attitude (AT), and Purchase Intention of Consumers towards CTPs (PIC)

The term RP refers to a person’s perceptions of a product’s reliability and possible adverse consequences, including the product’s lifespan, financial and societal consequences, and physical performance or quality [47,71]. Due to unknown factors, including past product uses, a lack of prior trials, and a producer’s lack of market familiarity [35], purchasing circular products is inherently riskier for consumers than purchasing new products from the market. RP and uncertainties are obstacles that deter consumers from purchasing circular products. Featherman and Pavlou [72] study verified that RP had a detrimental impact on a consumer’s PI and purchase AT. According to Chang and Tseng [73], RP is a crucial factor that adversely affects consumers’ PI. Likewise, Chen and Chang [74] determined that RP has a detrimental impact on the PI of products related to information and technology. De Medeiros, et al. [75] discovered a negative association between RP and intentions to purchase circular products. To further investigate CTPs, we hypothesize that:
Hypothesis 3. 
RP will negatively influence the PI of consumers towards CTPs.
Hypothesis 4. 
RP will negatively influence the purchase AT of consumers towards CTPs.

3.4. Personal Benefits (PB), Attitude (AT), and Purchase Intention of Consumers towards CTPs (PIC)

Consumers’ value proposition has attracted considerable interest in research and been adopted due to its importance in evaluating AT and PI and achieving potential market benefits [76,77]. The benefits of circular products can be classified into two categories: (1) environmental advantages such as lower energy or resource usage, solid or hazardous waste, and pollution [78,79]; (2) lower pricing in contrast to producing fresh products [37,80]. Companies use green/circular production strategies to reduce the negative impact of their operations on the natural environment [81,82] and to increase production and consumption efficiency.
When a consumer recognizes the personal benefits (PB), such as low cost, incentives, and discounts [12], as well as the fact that the price of circular products typically decreases by 40–60% in contrast to new items [52,53], they may be persuaded to purchase the CTP. It has been noted that manufacturers/companies usually provide different kinds of incentives and discounts to consumers to increase the sales of remanufactured, eco-friendly, green, and circular products [83]. As a result, the more significant CTPs are perceived as environmental and beneficial, the greater their value and consumption will be. Previous research, like Singhal, Jena and Tripathy [34], Choi, Lee and Ok [36], and Wang, Wiegerinck, Krikke and Zhang [37], has also shown that PB significantly impacts consumers’ purchase AT and PIC. Hence, we developed the following hypothesis to explore how PB influences consumers’ AT and PIC:
Hypothesis 5. 
PB will positively influence the PI of consumers towards CTPs.
Hypothesis 6. 
PB will positively influence the purchase AT of consumers towards CTPs.

3.5. Attitude (AT) and Purchase Intention of Consumers towards CTPs (PIC)

As per the TPB, an attitude determines a person’s behavioral intentions [84]. Positive or negative evaluations of cognitive conceptions about the idea, people, things, events, or actions under discussion are called “attitudes” [85]. They may also be positive or negative. Previous research has found that because AT is a psychological feeling, it is one of the most important predictors of green purchases [86]. Several studies have investigated the importance of attitude, notably in purchasing green, recycled, or remanufactured products, eco-friendly packaging, and waste management in various countries [87,88,89]. Most studies revealed that consumers concerned about the sustainability of the environment have an AT that significantly influences their intentions. As a result, attitude may considerably influence Indian consumers’ intention to purchase a CTP. As a result, the following hypothesis is proposed:
Hypothesis 7. 
AT will positively influence the PI of consumers towards CTPs.

3.6. Subjective Norms (SNs) and Purchase Intention of Consumers towards CTPs (PIC)

According to Bong Ko and Jin [90], subjective norms are a person’s beliefs or perceptions strongly influenced by society or essential persons, such as teachers, friends, family members, and others [91]. They are the pressure on the individual from the social environment to perform a particular behavior or purchase a specific product. SNs are a crucial TPB factor in forecasting behavioral intention [86]. They can frequently serve as an excellent indicator of intention. It is commonly seen that people’s purchase decisions are dramatically altered as a result of social pressure [91]. Considering this, Indian society is one of the most culturally diverse and prosperous societies; it has various faiths and other traditions that may influence a person’s lifestyle or purchase decisions [21]. Therefore, to forecast the influence of SNs on Indian consumers’ PI regarding CTPs, we propose the following:
Hypothesis 8. 
SNs will positively influence the PI of consumers towards CTPs.

3.7. Perceived Behaviour Control (PBC) and Purchase Intention of Consumers towards CTPs (PIC)

Perceived behavioral control (PBC) is also crucial for determining consumers’ PI [82] because it is defined as “the ability to carry out the behavior which can be controlled by an individual’s perception or individual beliefs’’ [92,93]. Due to their direct effect, it is essential to understand how these beliefs can alter intention and behavior [23]. Several earlier research works have been undertaken to investigate the influence of PBC in purchasing organic foods, green or remanufactured products, etc. [94]. It was discovered that PBC has a significant positive effect on consumers’ purchasing intentions. In contrast, Khor and Hazen [95] found that PBC has a negligible effect on the PI of Malaysian consumers towards remanufactured laptops. This leads to the conclusion that these beliefs can easily alter consumers’ perceptions and abilities. As a result, we attempt to investigate PBC’s impact on the PI for CTPs by proposing the below hypothesis:
Hypothesis 9. 
PBC will positively influence the PI of consumers towards CTPs.

3.8. Attitude as a Mediator between EC 🡪 PIC, RP 🡪 PIC, and PB 🡪 PIC

The current study also examines how attitudes towards CTPs mediate the correlations between “PB to PIC”, “EC to PIC”, and “RP to PIC”. The interaction between factors like trust (Chetioui, et al. [96]), PBC (Ayu, et al. [97]), and other factors influencing purchase intention have been the subject of investigations on the mediation impact of AT by many researchers in the context of e-commerce, m-commerce, m-banking, entrepreneurship, etc. The role of AT in mediating the links between past purchases, materialism, social factors, economic incentives, and repurchase intention was investigated by Harun, et al. [98]. The authors supported the notion that materialism and repurchase behavior are related, and that attitude has a mediating effect between previous purchases and repurchase behavior. Sun, et al. [99] tested the mediating role of AT, SSN, and PBC in the link between mobile functioning, mobile usability, and customer satisfaction in mobile payment. The authors confirmed a mediating relationship between consumer satisfaction and mobile usability. Chawla and Joshi [100] asserted that AT mediates the relationships between PU and BI, trust and BI, facilitating conditions and BI, and lifestyle compatibility and BI concerning mobile wallet services. Tyrväinen and Karjaluoto [101] used the TAM technique to compile online grocery shopping research published before and after COVID-19. By proposing perceived utility and attitude as mediators, the authors confirmed the association between attributes (trust, ease of use, price value, PR, etc.) and intention to buy food online. However, there is a lack of literature on AT’s function as a mediating factor in the relationships between PB, EC, PR, and PIC. The mediating effect of attitude on the relationships of “PB to PIC”, “EC to PIC”, and “RP to PIC” adds to the limited body of research on CTPs and a thorough understanding of the factors mentioned with the PI. In order to assess AT’s mediating role, the current study suggests the following hypotheses:
Hypothesis 10. 
Attitude toward CTPs will positively mediate the relationship between EC and PIC.
Hypothesis 11. 
Attitude toward CTPs will negatively mediate the relationship between RP and PIC.
Hypothesis 12. 
Attitude toward CTPs will positively mediate the relationship between PB and PIC.

4. Methods and Design

4.1. Developing and Pretesting the Questionnaire

The research instrument was divided into two parts. The first part had twenty-five items evaluated on a 7-point Likert scale. In this part, the study’s seven constructs were supposed to be measured. These constructs were EC (4 items) taken from Shamsi, et al. [102] and adapted initially from Suki [103] and Singhal, et al. [104]; RP (3 items) taken from Shamsi, Chaudhary, Anwar, Dasgupta and Sharma [102] and adapted initially from Wang, Wiegerinck, Krikke and Zhang [37] and Singhal, Tripathy and Jena [104]; PB (4-items) taken from Shamsi, Chaudhary, Anwar, Dasgupta and Sharma [102] and adapted initially from Singhal, Tripathy and Jena [104] and Forsythe, et al. [105]; AT towards CTP (3 items) taken from Anwar, et al. [106] and adapted initially from Kumar, Prakash and Kumar [33]; SN (4 items) taken from Anwar, Shamsi, Khatoon, Saleem and Chaudhary [106] and adapted initially from Kumar, Prakash and Kumar [33]; PBC (3 items) taken from Anwar, Shamsi, Khatoon, Saleem and Chaudhary [106] and adapted initially from Kumar, Prakash and Kumar [33]; and PI towards CTPs (4 items) taken from Anwar, Shamsi, Khatoon, Saleem and Chaudhary [106] and adapted initially from Calvo-Porral and Lévy-Mangin [107] and Singhal, Tripathy and Jena [104].
In the second part, the demographic information of the respondents was recorded. The variables measured were gender (male or female), age (below 20 years, 21–30 years, 31–40 years, 41–60 years, and above 60 years), occupation (unemployed/homemaker, student, employed, business, agriculture, and others), educational qualification (below intermediate, intermediate, graduation, postgraduation, and above), and monthly income in INR (below ≤30,000, 31,000–60,000, 61,000–90,000, and above 90,000). We also generated data on whether respondents were aware of CTPs and whether they had ever purchased them. We provided a dichotomous scale for these two questions, i.e., yes or no.
The scale items were altered to fit the CTP context. Therefore, it was deemed that pretesting the study instrument to validate the content of scale items was essential. Five professors and researchers in related fields from major Indian universities in the National Capital Region (NCR) were approached for this. The study’s objective was described to them, and their feedback on the language, ease of understanding, and relevance of the questionnaire items was solicited. Based on the comments, minor changes were made to the questionnaire to make it more relevant in the study context.
We conducted a pilot study on a sample of 50 respondents and computed Cronbach’s alpha to ensure whether the measurement scales were unidimensional and held internal consistency or not. Cronbach’s alpha coefficients for each measurement scale were found well above the threshold of 0.70, thus ensuring the unidimensionality and internal consistency of the scales [108].

4.2. Participants and Final Data Collection

We chose the National Capital Region (NCR) for the data collection because of the heterogeneity of the population in this region as it includes districts of Delhi (Union Territory) and several other districts from the states of Haryana, Uttar Pradesh, and Rajasthan [109]. The authors selected the clothing and apparel markets of New Delhi, Ghaziabad, Faridabad, Gurgaon, and Noida of the NCR because the combined population of these cities is estimated to be over 28 million, which makes it the largest metropolitan area in India and the second-largest in the world after Tokyo [110]. Moreover, these cities were selected because they have the highest interstate migration rate and attract people from all across India for various reasons [111], making an additional justification for better sample representation than other areas. To ensure a substantial representation of our population in the NCR, we identified prominent clothes and apparel markets in the mentioned cities and filtered them based on their popularity. We visited these markets and used a convenience sampling approach to reach respondents.
First, we physically approached around 600 participants to brief them about the study and seek their consent to fill out the survey. Those who agreed to participate in the survey were asked to scan a QR code to access the questionnaire (on Google Forms) and fill it out in their leisure time. The data collection took place from 22 February 2022 to 31 March 2022 and we received 467 filled-out questionnaires.

4.3. Data Preparation

Before moving ahead with any statistical analyses, we checked the data for missing, unengaged, and outlier responses. Upon observation, we found 16 missing and 23 unengaged responses and deleted them from the dataset. Further, we also checked for statistical outlier responses using Cook’s distance. The results of Cook’s distance revealed that 19 responses showed Cook’s distance above the threshold of 1 [112]; thus, they were also removed from the dataset and we obtained a final sample of 409 respondents (see Table 1). Given that the current study has 24 observed items reflecting seven latent constructs, a sample of 409 responses seems adequate enough as Hair Jr, et al. [113] suggest having a minimum of 10 responses for each observed item taken into the study, which equates to a minimum sample of 240 responses.

4.4. Checking Method Bias

Using the full collinearity approach suggested by Kock [114], the study also checked for Common Method Bias (CMB) as the study adopted a convenience sampling technique and the data might be affected by method bias. Kock [114] suggests checking VIF values for every relationship path while making each latent variable an outcome variable. The results affirmed that the VIF values for each relationship path were well below the threshold of 3.3, thus ensuring that the data were not affected by CMB.

5. Results

The findings were based on a PLS-SEM (Structural Equation Modeling) analysis using Smart-PLS 3.0. According to Tabachnick, et al. [115], when there is more than one dependent variable, PLS-SEM runs multiple regression and provides overall fit statistics. First-generation statistical analysis approaches, such as linear regression, can only test one dependent variable simultaneously, unlike SEM. According to Hair Jr, Sarstedt, Ringle and Gudergan [113], PLS-SEM considers the measurement errors of the observed variables. For these reasons, we employed PLS-SEM to produce our survey results.

5.1. Assessment of the Outer Model and Measurement Model

A CFA of the seven constructs was performed using the PLS algorithm in Smart-PLS 3.0. For the adopted scales, we obtained the acceptable range of model fit indices, viz. SRMR = 0.056 (recommended range < 0.08; Hu and Bentler [116]) and NFI = 0.964 (recommended threshold > 0.90; Lohmöller and Lohmöller [117]). The item loadings of all observed variables were also found to be above 0.70 [113], except for one item each from RP and AT; hence, they were not considered in the assessment. Each latent variable’s AVE value (Average Variance Extracted) represents a convergent validity parameter, 0.5 or higher, indicating the “degree to which two measures of the same concept are associated” [118,119] was above the recommended limit of 0.50, proving that the outer model was valid and convergent. The reported values of CR (to examine how effectively all indicators were linked to the construct, with an appropriate cut-off value of 0.70 or above [119,120]) and Cronbach’s alpha values (to assess the model’s internal reliability, with a recommended value of 0.70 [108]) were found above the thresholds (see Table 2). Hence, the measurement model’s convergent validity was established.
We also checked for the model’s discriminant validity following the Fornell and Larcker criterion [118], and the HTMT ratio criterion says that discriminant validity is guaranteed when the square root of each construct’s AVEs is higher than its correlation with the other constructs, which is presented (bold numbers on diagonals) in Table 3. The results from Table 3 confirm that the square root values (bold diagonal values) of each latent variable were greater than the correlation coefficients (below off-diagonal values); thus, discriminant validity was established as per Fornell and Larcker criterion [118].
Following the HTMT criterion for establishing the measurement model’s discriminant validity, we calculated HTMT ratios. Henseler, et al. [121] suggest that a measurement model tends to hold divergent validity if the HTMT ratios among the latent constructs are below 0.85. Table 4 affirms that the HTMT ratios among the latent constructs were below the threshold of 0.85, thus ensuring divergent validity.

5.2. Assessment of the Structural Model

The structural model of the study consisted of twelve hypotheses, and they were evaluated through PLS-SEM (a superior version of multiple regression, providing overall fit statistics and effectively dealing with multicollinearity issues [119] at 5% and 1% levels of significance by running bootstrapping at 5000 samples in Smart-PLS 3.0). It explored nine direct relationships (from H1 to H9) and three indirect ones (from H10 to H12). The model assessed the direct influence of EC (H1), RP (H3), PB (H5), AT (H7), SN (H8), and PBC (H9) on PIC towards CTPs, followed by assessing the direct influence of EC (H2), RP (H4), and PB (H6) on AT towards CTPs. Further, it also assessed the indirect influence (mediating effect) of AT on the three relationships between EC (H10), RP (H11), PB (H12), and the PIC of consumers towards CTPs.

5.2.1. Hypothesis Testing (Direct Effects)

The results showed that the constructs “EC” (β = 0.332; p-value < 0.01), “PB” (β = 0.325; p-value < 0.01), “AT towards CTP” (β = 0.291; p-value < 0.01), “SN” (β = 0.211; p-value < 0.01), and “PBC” (β = 0.515; p-value < 0.01) had a significant and positive influence on the “PIC” of consumers towards CTPs. Thus, hypotheses H1, H5, H7, H8, and H9 were supported. However, a significant influence of “RP” on consumers’ “PIC” towards CTPs was not observed (β = −0.050; p-value > 0.05). Hence, hypothesis H3 was not supported. The R-square value for PI was observed to be 0.726, suggesting that more than 72.60 percent variance in the PI of consumers towards CTPs was explained by factors such as EC, PB, AT towards CTPs, SN, and PBC.
Further, the constructs “EC” (β = 0.698; p-value < 0.01) and “PB” (β = 0.372; p-value < 0.01) positively influenced the “AT” of consumers toward CTPs. However, “RP” did not significantly influence the attitude (β = −0.051; p-value > 0.05). Therefore, H4 was not supported, while H2 and H6 were supported. The R-square value for ATC was observed to be 0.614 (61.40%), while it was found to be 0.726 (72.60%) for PIC, suggesting moderate and high explanatory power, respectively, for both the models [119]. Further, the study also reports Q2 values of 0.435 (43.50%) for ATC and 0.472 (47.20%) for PIC, respectively, inferring that the predictive relevance of the models was high as the Q2 value was greater than zero [119]. A summary of the study findings can be observed in Table 5 and Figure 2.

5.2.2. Mediation Analysis (Indirect Effects)

The results of the mediation analysis (indirect effect) evidenced that “AT” positively mediated the direct relations of “EC” and “PB” with the “PI” of consumers towards CTPs (the mediating effect of AT on EC 🡪 PI (β = 0.203, BootSE = 0.186; LLCI = 0.162; ULCI = 0.244), and on PB 🡪 PI: β = 0.108, BootSE = 0.079; LLCI = 0.047; ULCI = 0.169, respectively). Hence, hypotheses H10 and H12 were accepted, whereas the result referring to the mediating effect of “AT” between “RP” and “PIC” towards CTPs did not confirm hypothesis H11 (β = −0.015; BootSE = 0.06; LLCI = −0.109; ULCI = 0.079). The findings also deduced that the positive influence of “EC” and “PB” on the “PI” of consumers towards CTPs was significantly increased when AT mediated. On the other hand, when AT mediated, the negative influence of “RP” on “PIC” towards CTPs was reduced insignificantly.
Finally, the results also revealed that hypotheses related to direct relationships, viz. H1, H2, H5, H6, H7, H8, and H9, and indirect relationships, i.e., H10 and H12, were supported, whereas hypotheses H3 and H4 (verifying direct relationships) and H11 (indirect relationships) were not supported (see Table 6).

6. Discussion

The study of CE or circular products has recently acquired immense traction. Prior research has identified the essential variables for accepting circular products in various economies [122,123,124]. The RP, PB, EC, and TPB are considered the most significant of these essential factors [106,125].
The results agree with Aagerup and Nilsson [69], Nuttavuthisit and Thøgersen [70], and Brochado, Teiga and Oliveira-Brochado [61] that environmental consciousness strongly affects Indian consumers’ attitude and their purchase intention in turn, especially the younger generation (51.33% of the participants in the survey were young). Supporting the relevance of EC, different authors concluded that consumers form a positive attitude and purchase intentions towards circular products because they believe that the production and consumption of these products would contribute to minimizing the negative environmental consequences, leading to sustainability [63,64].
Consumers’ attitudes and intentions for purchase are greatly influenced by their apprehensions regarding the quality, effectiveness, and safety of remanufactured/green/eco-friendly products [71,72]. In our study though, consumer risk perceptions had a negative impact on Indian consumers, but it was insignificant, suggesting that consumers’ presumptions for risk while considering circular textiles are not strong enough. The finding does not agree with Featherman and Pavlou [72], Wang, Wiegerinck, Krikke and Zhang [37], and De Medeiros, Ribeiro and Cortimiglia [75], implying that Indian consumers are more conscious and aware of the benefits of consuming circular textiles [60,61] and would be more inclined towards buying them if given an opportunity.
Contrary to risk perception, when consumers become familiar with the benefits of circular textiles, their attitude and intentions become positively significant, indicating that economic benefits like low cost, discounts, nonmonetary incentives like exchange offers, and environmental sustainability attract consumers’ attention and encourage them to purchase circular textiles. The findings of the present investigation are supported by Singhal, Jena and Tripathy [34] and Choi, Lee and Ok [36].
The findings also reflect that attitude significantly influences Indian customers’ purchase intentions, suggesting that customers favoring circular textiles are more inclined to purchase them. The study is consistent with other research identifying AT as the most essential and fundamental component affecting PIC [17,88].
Coinciding with the studies by Hazen, et al. [126] and Khor and Hazen [95], the findings determine that subjective norms, i.e., the opinions of family, friends, and other important people in one’s life, positively and distinctly control consumers’ purchase intentions for any commodity. Indian families and friends, who are usually unaware of circular textiles, opt to acquire them after learning about their benefits from others. Similarly, the study postulates a positive association between perceived behavioral control and intentions to purchase circular textiles, expressing that consumers’ self-efficacy, knowledge (understanding) of the products, and availability (or lack thereof) influence their choice and decision making. The findings align with Jaiswal and Kant [127] and Kumar, Prakash and Kumar [33]. Further, the study examined the effect of attitude when it mediates the relationship between EC, RP, PB, and PIC of consumers. The results of the mediating effect of AT on “PB-PIC” and “EC-PIC” relationships are significant and are in line with those of Sun, Law and Schuckert [99] and Chawla and Joshi [100], but the results of mediation between “RP 🡪 EC” are contrary to them. However, it was concluded that attitude strongly and positively mediated between “EC and PIC” and “PB and PIC”, improving the relationship significantly. While meditating, attitude amplified the association between constructs. Conversely, the mediation of AT between “RP and PIC” remained insignificant, demonstrating a weaker impact. However, it reduced the negative impact of risk perception on consumers’ intentions. In other words, one is less likely to purchase circular textiles when one perceives related risks, but if one develops an attitude toward the same, the possibility of purchasing them will be increased. Hence, it may be inferred that behavioral attitude acted as a unique mediator, enhancing the direct influence of PB and EC on PIC, while it had no intervening role to play between RP and PIC.

7. Conclusions and Implications

7.1. Conclusions

Circular textile products and their awareness are almost absent in the Indian market, resulting in very few studies on consumers’ behavioral intentions and attitudes toward CTPs. Therefore, the current study bridged the literature gap by exploring the influence of cognitive factors in the acceptance of CTPs. For this, researchers of the study extended the TPB model by adding environmental consciousness, risk perception, and personal benefits to existing variables of the TPB (attitude, subjective norms, and perceived behavioral control). The findings revealed that personal benefits, environmental consciousness (except risk perception), perceived behavioral control, and subjective norms significantly and directly influenced consumers’ attitudes and purchase intentions towards CTPs. In addition, attitude significantly and indirectly mediated the relationship between “environmental consciousness, personal benefits (except risk perception)” and purchase intention towards CTPs. The findings offer pertinent information about the antecedents of CTPs that help companies, marketers, managers, and government to promote the acceptance of CTPs among consumers and to attain sustainability in the production and consumption of textile products in the textile industry and sustainable transition of the Indian economy through CTPs.

7.2. Theoretical Implications

The first significant contribution of the current study was to evaluate the behavioral intention of Indian consumers toward accepting CTPs using a TPB model. Second, the study included three external variables, namely the personal benefits, risk perception, and environmental consciousness of consumers associated with CTPs, and checked their direct and indirect effect (via attitude) on consumers’ purchase intention towards CTPs. The current body of literature is deficient in understanding both the direct and indirect contributions of PB, RP, and EC to consumers’ behavioral intentions toward accepting CTPs. Another significant contribution made by this study was the inclusion of attitude as a mediating variable in the model that predicted its role in the relationship between PB, RP, EC, and PIC.
The research makes theoretical contributions to the circular economy, the circular-product-related literature in general, and the circular textiles literature in particular. It will help implement CBM in the textile industry by giving empirical support on consumers’ PI and AT towards CTPs, indicating the significance of understanding the factors responsible for consumers’ acceptance for ensuring the success of CTP and CBM.

7.3. Practical Implications

The present study confirms that circular textiles are a good option for tackling environmental pollution and reducing carbon emissions, chemicals, and waste. After understanding consumers’ intentions and attitudes towards CTPs, which are significantly influenced by their perceptions of the harmful environmental effects, associated benefits, and risks, the government must work on encouraging and motivating them to acquire such products. Publicity and education campaigns can be used to enlighten CTPs’ benefits. The government should use social media and promotional strategies to provide information about the downsides of using non-CTPs, including environmental contamination, textile waste, and the depletion of virgin resources. Repeatedly receiving this unfavorable information will surely influence AT, SN, and PBC.
The government, industry managers, and retailers must develop effective strategies to promote the environmentally friendly features of CTPs to strengthen positive attitudes towards CTPs. They must evoke a sense of authority and pride among consumers for consuming circular textiles.
Moreover, the study offers pertinent information to the relevant companies and managers, who can highlight the sustainable aspect of circular textiles in their advertisements, public relations campaigns, sponsorships, and social networks and emphasize ecological benefits, comparatively lower prices, etc.
It provides the foresight to textile managers, suppliers, manufacturers, and businesses in India and other emerging markets, where circular textiles are still in their infancy, that they stand to benefit significantly if they proactively implement CE in their textile manufacturing, which in turn complies with the objectives of the government’s Sustainable Development Goals (SDGs).

8. Limitations and Future Direction

Like others, this research work also possesses a few limitations. Although it mostly approximately predicts actual behavior and may be widely employed as a proxy variable in scientific investigations, the behavioral intention may not reflect the actual behavior exactly. A future study must make efforts to investigate actual purchasing behavior. Second, the explained variances in PI and AT towards CTPs in the current study are 72.6% and 61.40%, respectively, indicating that additional factors may affect consumers’ PI and AT towards CTPs. Thirdly, this study focuses exclusively on CTPs; still, future research may include other relevant circular products, such as plastic, electrical devices, etc., in addition to other influencing factors, such as willingness to pay, the price of circular products, customer involvement with circularity, product usefulness, or the ease of CTP adoption. Fourth, our 409 samples came from India’s National Capital Region (NCR). In the future, researchers may examine additional or different cities and states with larger sample sizes. Lastly, the scope of our study was restricted to India; however, other countries also emphasize the sustainable consumption of textile products. Consequently, future research could involve additional testing and validation of our model utilizing samples from different nations.

Author Contributions

Conceptualization, M.A.S. and A.C.; methodology, I.A. and S.A.; data curation, I.A. and A.A.; writing—original draft, M.A.S. and I.A.; writing—review and editing, A.C., S.A. and A.A.; supervision, A.C.; funding acquisition, S.A. and All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Data were anonymized (not even personal information like contact numbers or/and email addresses was solicited), so this study was exempt from ethical approval by the Institutional Ethics Committee of Aligarh Muslim University.

Informed Consent Statement

Informed consent was obtained from all individual participants included in the study. Respondents were informed in advance about the theme for the questionnaire and a set of sample questions was provided to them before sending the full survey instrument.

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Acknowledgments

The authors sincerely acknowledge the respondents’ effort and time for this study.

Conflicts of Interest

The authors state that they have no conflict of interest related to this publication and that no financial assistance for this work influenced its outcome.

References

  1. DGCI&S; Wazir Analysis. ANNUAL REPORT, Indian Textile and Apparel Industry; Wazir Advisors: Gurgaon, Inida, 2021; p. 47. [Google Scholar]
  2. Badekhan, A.; Devi, K. Perception of Cotton Farmers on the Effects of Pesticide Use. Asian J. Agric. Ext. Econ. Sociol. 2018, 23, 1–6. [Google Scholar] [CrossRef]
  3. Sandin, G.; Peters, G.M. Environmental impact of textile reuse and recycling—A review. J. Clean. Prod. 2018, 184, 353–365. [Google Scholar] [CrossRef]
  4. Taqui, S.N.; Yahya, R.; Hassan, A.; Nayak, N.; Syed, A.A. A novel sustainable design to develop polypropylene and unsaturated polyester resin polymer composites from waste of major polluting industries and investigation on their physicomechanical and wear properties. Polym. Compos. 2019, 40, 1142–1157. [Google Scholar] [CrossRef]
  5. Kumar, A.; Moktadir, A.; Liman, Z.R.; Gunasekaran, A.; Hegemann, K.; Khan, S.A.R. Evaluating sustainable drivers for social responsibility in the context of ready-made garments supply chain. J. Clean. Prod. 2020, 248, 119231. [Google Scholar] [CrossRef]
  6. Jabbour, C.J.C.; de Sousa Jabbour, A.B.L.; Sarkis, J.; Godinho Filho, M. Unlocking the circular economy through new business models based on large-scale data: An integrative framework and research agenda. Technol. Forecast. Soc. Chang. 2019, 144, 546–552. [Google Scholar] [CrossRef]
  7. Tunn, V.S.; Bocken, N.M.; van den Hende, E.A.; Schoormans, J.P. Business models for sustainable consumption in the circular economy: An expert study. J. Clean. Prod. 2019, 212, 324–333. [Google Scholar] [CrossRef]
  8. Pinheiro, M.A.P.; Seles, B.M.R.P.; Fiorini, P.D.C.; Jugend, D.; de Sousa Jabbour, A.B.L.; da Silva, H.M.R.; Latan, H. The role of new product development in underpinning the circular economy: A systematic review and integrative framework. Manag. Decis. 2018, 57, 840–862. [Google Scholar] [CrossRef]
  9. Wang, Y.; Hazen, B.T. Consumer product knowledge and intention to purchase remanufactured products. Int. J. Prod. Econ. 2016, 181, 460–469. [Google Scholar] [CrossRef]
  10. Urbinati, A.; Chiaroni, D.; Chiesa, V. Towards a new taxonomy of circular economy business models. J. Clean. Prod. 2017, 168, 487–498. [Google Scholar] [CrossRef]
  11. Treat, A.; Honnold, V.; Wise, J. Remanufactured Goods: An Overview of the U.S. and Global Industries, Markets, and Trade; United States International Trade Commission: Washington, DC, USA, 2012; p. 284. [Google Scholar]
  12. Govindan, K.; Shankar, K.M.; Kannan, D. Application of fuzzy analytic network process for barrier evaluation in automotive parts remanufacturing towards cleaner production—A study in an Indian scenario. J. Clean. Prod. 2016, 114, 199–213. [Google Scholar] [CrossRef]
  13. Mishra, S.; Singh, S.P.; Johansen, J.; Cheng, Y.; Farooq, S. Evaluating indicators for international manufacturing network under circular economy. Manag. Decis. 2019, 57, 811–839. [Google Scholar] [CrossRef]
  14. Chaudhary, A.; Shamsi, M. Need of post-consumer textile waste management via circular economy. Int. J. Mod. Trends Sci. Technol. 2020, 6, 197–213. [Google Scholar]
  15. Saha, K.; Dey, P.K.; Papagiannaki, E. Implementing circular economy in the textile and clothing industry. In Supply Chain Sustainability in Small and Medium Sized Enterprises; Routledge: London, UK, 2022; pp. 239–276. [Google Scholar]
  16. Sharma, V.; Garg, S.K.; Sharma, P. Identification of major drivers and roadblocks for remanufacturing in India. J. Clean. Prod. 2016, 112, 1882–1892. [Google Scholar] [CrossRef]
  17. Muranko, Z.; Andrews, D.; Newton, E.J.; Chaer, I.; Proudman, P. The pro-circular change model (P-CCM): Proposing a framework facilitating behavioural change towards a circular economy. Resour. Conserv. Recycl. 2018, 135, 132–140. [Google Scholar] [CrossRef]
  18. Wagner, M.M.; Heinzel, T. Human perceptions of recycled textiles and circular fashion: A systematic literature review. Sustainability 2020, 12, 10599. [Google Scholar] [CrossRef]
  19. Khare, A.; Sadachar, A.; Manchiraju, S. Investigating the role of knowledge, materialism, product availability, and involvement in predicting the organic clothing purchase behavior of consumers in the Indian market. J. Int. Consum. Mark. 2020, 32, 228–242. [Google Scholar] [CrossRef]
  20. Goswami, P. Is the urban Indian consumer ready for clothing with eco-labels? Int. J. Consum. Stud. 2008, 32, 438–446. [Google Scholar] [CrossRef]
  21. Khare, A.; Varshneya, G. Antecedents to organic cotton clothing purchase behaviour: Study on Indian youth. J. Fash. Mark. Manag. Int. J. 2017, 21, 51–69. [Google Scholar] [CrossRef]
  22. Albers, A. Handweaving Today: Textile Work at Black Mountain College. Weaver 1941, 6, 1. [Google Scholar]
  23. Wiederhold, M.; Martinez, L.F. Ethical consumer behaviour in Germany: The attitude-behaviour gap in the green apparel industry. Int. J. Consum. Stud. 2018, 42, 419–429. [Google Scholar] [CrossRef]
  24. Kirchherr, J.; Piscicelli, L.; Bour, R.; Kostense-Smit, E.; Muller, J.; Huibrechtse-Truijens, A.; Hekkert, M. Barriers to the circular economy: Evidence from the European Union (EU). Ecol. Econ. 2018, 150, 264–272. [Google Scholar] [CrossRef]
  25. Nilssen, R.; Bick, G.; Abratt, R. Comparing the relative importance of sustainability as a consumer purchase criterion of food and clothing in the retail sector. J. Brand Manag. 2019, 26, 71–83. [Google Scholar] [CrossRef]
  26. Lorek, S.; Lucas, R. Towards Sustainable Market Strategies: A Case Study on Eco Textiles and Green Power; Wuppertal Institut für Klima Energie: Wuppertal, Germany, 2003. [Google Scholar]
  27. Vehmas, K.; Raudaskoski, A.; Heikkilä, P.; Harlin, A.; Mensonen, A. Consumer attitudes and communication in circular fashion. J. Fash. Mark. Manag. Int. J. 2018, 22, 286–300. [Google Scholar] [CrossRef]
  28. Forsythe, R.; Isaac, R.M.; Palfrey, T.R. Theories and Tests of “Blind Bidding” in Sealed-bid Auctions. RAND J. Econ. 1989, 20, 214–238. [Google Scholar] [CrossRef]
  29. Wan, M.; Zhang, Y.; Ye, W. Consumer willingness-to-pay a price premium for eco-friendly children’s furniture in Shanghai and Shenzhen, China. For. Prod. J. 2018, 68, 317–327. [Google Scholar]
  30. Meyer, A. What’s in it for the customers? Successfully marketing green clothes. Bus. Strategy Environ. 2001, 10, 317–330. [Google Scholar] [CrossRef]
  31. Selvefors, A.; Rexfelt, O.; Renström, S.; Strömberg, H. Use to use—A user perspective on product circularity. J. Clean. Prod. 2019, 223, 1014–1028. [Google Scholar] [CrossRef]
  32. Rapsikevičienė, J.; Gurauskienė, I.; Jučienė, A. Model of industrial textile waste management. Environ. Res. Eng. Manag. 2019, 75, 43–55. [Google Scholar] [CrossRef]
  33. Kumar, A.; Prakash, G.; Kumar, G. Does environmentally responsible purchase intention matter for consumers? A predictive sustainable model developed through an empirical study. J. Retail. Consum. Serv. 2021, 58, 102270. [Google Scholar] [CrossRef]
  34. Singhal, D.; Jena, S.K.; Tripathy, S. Factors influencing the purchase intention of consumers towards remanufactured products: A systematic review and meta-analysis. Int. J. Prod. Res. 2019, 57, 7289–7299. [Google Scholar] [CrossRef]
  35. Wang, Y.; Hazen, B.T.; Mollenkopf, D.A. Consumer value considerations and adoption of remanufactured products in closed-loop supply chains. Ind. Manag. Data Syst. 2018, 118, 480–498. [Google Scholar] [CrossRef]
  36. Choi, J.; Lee, A.; Ok, C. The effects of consumers’ perceived risk and benefit on attitude and behavioral intention: A study of street food. J. Travel Tour. Mark. 2013, 30, 222–237. [Google Scholar] [CrossRef]
  37. Wang, Y.; Wiegerinck, V.; Krikke, H.; Zhang, H. Understanding the purchase intention towards remanufactured product in closed-loop supply chains: An empirical study in China. Int. J. Phys. Distrib. Logist. Manag. 2013, 43, 866–888. [Google Scholar] [CrossRef]
  38. Corral-Verdugo, V. A structural model of reuse and recycling in Mexico. Environ. Behav. 1996, 28, 665–696. [Google Scholar] [CrossRef]
  39. Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211. [Google Scholar] [CrossRef]
  40. Echegaray, F.; Hansstein, F.V. Assessing the intention-behavior gap in electronic waste recycling: The case of Brazil. J. Clean. Prod. 2017, 142, 180–190. [Google Scholar] [CrossRef]
  41. Shaw, D.; Shiu, E.; Clarke, I. The contribution of ethical obligation and self-identity to the theory of planned behaviour: An exploration of ethical consumers. J. Mark. Manag. 2000, 16, 879–894. [Google Scholar] [CrossRef]
  42. Anwar, I. An Assessment of Behavioral Intention of Budding Small Scale Entrepreneurs in Select Universities of Uttar Pradesh; Aligarh Muslim University: Aligarh, India, 2021. [Google Scholar]
  43. Anwar, I.; Saleem, I.; Islam, K.B.; Thoudam, P.; Khan, R. Entrepreneurial intention among female university students: Examining the moderating role of entrepreneurial education. J. Int. Bus. Entrep. Dev. 2020, 12, 217–234. [Google Scholar] [CrossRef]
  44. Thoudam, P.; Anwar, I.; Bino, E.; Thoudam, M.; Chanu, A.M.; Saleem, I. Passionate, motivated and creative yet not starting up: A moderated-moderation approach with entrepreneurship education and fear of failure as moderators. Ind. High. Educ. 2023, 37, 294–308. [Google Scholar] [CrossRef]
  45. Anwar, I.; Jamal, M.T.; Saleem, I.; Thoudam, P. Traits and entrepreneurial intention: Testing the mediating role of entrepreneurial attitude and self-efficacy. J. Int. Bus. Entrep. Dev. 2021, 13, 40–59. [Google Scholar] [CrossRef]
  46. Verma, V.K.; Chandra, B. An application of theory of planned behavior to predict young Indian consumers’ green hotel visit intention. J. Clean. Prod. 2018, 172, 1152–1162. [Google Scholar] [CrossRef]
  47. Wang, S.; Wang, J.; Yang, F.; Li, J.; Song, J. Determinants of consumers’ remanufactured products purchase intentions: Evidence from China. Int. J. Prod. Res. 2020, 58, 2368–2383. [Google Scholar] [CrossRef]
  48. Taufique, K.M.R.; Vaithianathan, S. A fresh look at understanding Green consumer behavior among young urban Indian consumers through the lens of Theory of Planned Behavior. J. Clean. Prod. 2018, 183, 46–55. [Google Scholar] [CrossRef]
  49. Spence, M.; Stancu, V.; Elliott, C.T.; Dean, M. Exploring consumer purchase intentions towards traceable minced beef and beef steak using the theory of planned behavior. Food Control 2018, 91, 138–147. [Google Scholar] [CrossRef]
  50. Kirmani, M.D.; Uddin, S.F.; Sadiq, M.A.; Ahmad, A.; Haque, M.A. Food-leftover sharing intentions of consumers: An extension of the theory of planned behavior. J. Retail. Consum. Serv. 2023, 73, 103328. [Google Scholar] [CrossRef]
  51. Prakash, G.; Singh, P.K.; Yadav, R. Application of consumer style inventory (CSI) to predict young Indian consumer’s intention to purchase organic food products. Food Qual. Prefer. 2018, 68, 90–97. [Google Scholar] [CrossRef]
  52. Jena, S.K.; Sarmah, S. Price and service co-opetiton under uncertain demand and condition of used items in a remanufacturing system. Int. J. Prod. Econ. 2016, 173, 1–21. [Google Scholar] [CrossRef]
  53. Rathore, P.; Kota, S.; Chakrabarti, A. Sustainability through remanufacturing in India: A case study on mobile handsets. J. Clean. Prod. 2011, 19, 1709–1722. [Google Scholar] [CrossRef]
  54. Gilg, A.; Barr, S.; Ford, N. Green consumption or sustainable lifestyles? Identifying the sustainable consumer. Futures 2005, 37, 481–504. [Google Scholar] [CrossRef]
  55. Ergen, A.; Bekoglu, F.B.; Giray, C. Activism: A strong predictor of proactive environmentally friendly buying behavior in Turkey. Int. J. Res. Bus. Soc. Sci. 2014, 3, 130–142. [Google Scholar] [CrossRef]
  56. Moser, A.K. Thinking green, buying green? Drivers of pro-environmental purchasing behavior. J. Consum. Mark. 2015, 32, 167–175. [Google Scholar] [CrossRef]
  57. Steenis, N.D.; Van Herpen, E.; Van Der Lans, I.A.; Ligthart, T.N.; Van Trijp, H.C. Consumer response to packaging design: The role of packaging materials and graphics in sustainability perceptions and product evaluations. J. Clean. Prod. 2017, 162, 286–298. [Google Scholar] [CrossRef]
  58. Kong, W.; Harun, A.; Rini, S.S.; Lily, J. The influence of consumers’ perception of green products on green purchase intention. Int. J. Asian Soc. Sci. 2014, 4, 924–939. [Google Scholar]
  59. Rashid, N. Awareness of eco-label in Malaysia’s green marketing initiative. Int. J. Bus. Manag. 2009, 4, 132–141. [Google Scholar] [CrossRef]
  60. Roberts, J.A. Green consumers in the 1990s: Profile and implications for advertising. J. Bus. Res. 1996, 36, 217–231. [Google Scholar] [CrossRef]
  61. Brochado, A.; Teiga, N.; Oliveira-Brochado, F. The ecological conscious consumer behaviour: Are the activists different? Int. J. Consum. Stud. 2017, 41, 138–146. [Google Scholar] [CrossRef]
  62. Peattie, K.; Collins, A. Guest editorial: Perspectives on sustainable consumption. Int. J. Consum. Stud. 2009, 33, 107–112. [Google Scholar]
  63. Samarasinghe, D.R. Socio-Psychological Analysis of Green Consumer Behaviour: With Special Reference to Marketing Information on Attitudinal Behaviour. Ph.D. Thesis, University of Sri Jayewardenepura, Nugegoda, Sri Lanka, 2014. [Google Scholar]
  64. Cerri, J.; Testa, F.; Rizzi, F. The more I care, the less I will listen to you: How information, environmental concern and ethical production influence consumers’ attitudes and the purchasing of sustainable products. J. Clean. Prod. 2018, 175, 343–353. [Google Scholar] [CrossRef]
  65. Anderson, W.T., Jr.; Cunningham, W.H. The socially conscious consumer. J. Mark. 1972, 36, 23–31. [Google Scholar] [CrossRef]
  66. Martínez García de Leaniz, P.; Herrero Crespo, Á.; Gómez López, R. Customer responses to environmentally certified hotels: The moderating effect of environmental consciousness on the formation of behavioral intentions. J. Sustain. Tour. 2018, 26, 1160–1177. [Google Scholar] [CrossRef]
  67. de Lima, E.B.; Costa, C.S.R.; Félix, G.R. Guilt and pride emotions and their influence on the intention of purchasing green products. Consum. Behav. Rev. 2019, 3, 70–84. [Google Scholar] [CrossRef]
  68. Kushwah, S.; Dhir, A.; Sagar, M. Ethical consumption intentions and choice behavior towards organic food. Moderation role of buying and environmental concerns. J. Clean. Prod. 2019, 236, 117519. [Google Scholar] [CrossRef]
  69. Aagerup, U.; Nilsson, J. Green consumer behavior: Being good or seeming good? J. Prod. Brand Manag. 2016, 25, 274–284. [Google Scholar] [CrossRef]
  70. Nuttavuthisit, K.; Thøgersen, J. The importance of consumer trust for the emergence of a market for green products: The case of organic food. J. Bus. Ethics 2017, 140, 323–337. [Google Scholar] [CrossRef]
  71. Slovic, P. Perceived risk, trust, and democracy. Risk Anal. 1993, 13, 675–682. [Google Scholar] [CrossRef]
  72. Featherman, M.S.; Pavlou, P.A. Predicting e-services adoption: A perceived risk facets perspective. Int. J. Hum.-Comput. Stud. 2003, 59, 451–474. [Google Scholar] [CrossRef]
  73. Chang, E.-C.; Tseng, Y.-F. Research note: E-store image, perceived value and perceived risk. J. Bus. Res. 2013, 66, 864–870. [Google Scholar] [CrossRef]
  74. Chen, Y.-S.; Chang, C.-H. Greenwash and green trust: The mediation effects of green consumer confusion and green perceived risk. J. Bus. Ethics 2013, 114, 489–500. [Google Scholar] [CrossRef]
  75. De Medeiros, J.F.; Ribeiro, J.L.D.; Cortimiglia, M.N. Influence of perceived value on purchasing decisions of green products in Brazil. J. Clean. Prod. 2016, 110, 158–169. [Google Scholar] [CrossRef]
  76. Bolton, R.N.; Drew, J.H. A multistage model of customers’ assessments of service quality and value. J. Consum. Res. 1991, 17, 375–384. [Google Scholar] [CrossRef]
  77. Cronin, J.J., Jr.; Brady, M.K.; Hult, G.T.M. Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments. J. Retail. 2000, 76, 193–218. [Google Scholar] [CrossRef]
  78. Abdulrahman, M.D.-A.; Subramanian, N.; Liu, C.; Shu, C. Viability of remanufacturing practice: A strategic decision making framework for Chinese auto-parts companies. J. Clean. Prod. 2015, 105, 311–323. [Google Scholar] [CrossRef]
  79. Anwar, I.; Ahmad, A.; Saleem, I.; Yasin, N. Role of entrepreneurship education, passion and motivation in augmenting Omani students’ entrepreneurial intention: A stimulus-organism-response approach. Int. J. Manag. Educ. 2023, 21, 100842. [Google Scholar] [CrossRef]
  80. Michaud, C.; Llerena, D. Green consumer behaviour: An experimental analysis of willingness to pay for remanufactured products. Bus. Strategy Environ. 2011, 20, 408–420. [Google Scholar] [CrossRef]
  81. Zhu, Q.; Sarkis, J.; Lai, K.-H. Supply chain-based barriers for truck-engine remanufacturing in China. Transp. Res. Part E Logist. Transp. Rev. 2014, 68, 103–117. [Google Scholar] [CrossRef]
  82. Mollenkopf, D.; Stolze, H.; Tate, W.L.; Ueltschy, M. Green, lean, and global supply chains. Int. J. Phys. Distrib. Logist. Manag. 2010, 40, 14–41. [Google Scholar] [CrossRef]
  83. Liu, Z.; Tang, J.; Li, B.-Y.; Wang, Z. Trade-off between remanufacturing and recycling of WEEE and the environmental implication under the Chinese Fund Policy. J. Clean. Prod. 2017, 167, 97–109. [Google Scholar] [CrossRef]
  84. Kuo, T.C.; Tseng, M.-L.; Lin, C.H.; Wang, R.-W.; Lee, C.-H. Identifying sustainable behavior of energy consumers as a driver of design solutions: The missing link in eco-design. J. Clean. Prod. 2018, 192, 486–495. [Google Scholar] [CrossRef]
  85. Maio, G.R.; Verplanken, B.; Haddock, G. The Psychology of Attitudes and Attitude Change; Sage Publications Ltd.: London, UK, 2018; p. 400. [Google Scholar]
  86. Nguyen, T.N.; Lobo, A.; Nguyen, B.K. Young consumers’ green purchase behaviour in an emerging market. J. Strateg. Mark. 2018, 26, 583–600. [Google Scholar] [CrossRef]
  87. Indriani, I.A.D.; Rahayu, M.; Hadiwidjojo, D. The influence of environmental knowledge on green purchase intention the role of attitude as mediating variable. Int. J. Multicult. Multirelig. Underst. 2019, 6, 627–635. [Google Scholar] [CrossRef]
  88. Paul, J.; Modi, A.; Patel, J. Predicting green product consumption using theory of planned behavior and reasoned action. J. Retail. Consum. Serv. 2016, 29, 123–134. [Google Scholar] [CrossRef]
  89. Kumar, A. Exploring young adults’e-waste recycling behaviour using an extended theory of planned behaviour model: A cross-cultural study. Resour. Conserv. Recycl. 2019, 141, 378–389. [Google Scholar] [CrossRef]
  90. Bong Ko, S.; Jin, B. Predictors of purchase intention toward green apparel products: A cross-cultural investigation in the USA and China. J. Fash. Mark. Manag. Int. J. 2017, 21, 70–87. [Google Scholar] [CrossRef]
  91. Valaei, N.; Nikhashemi, S. Generation Y consumers’ buying behaviour in fashion apparel industry: A moderation analysis. J. Fash. Mark. Manag. Int. J. 2017, 21, 523–543. [Google Scholar] [CrossRef]
  92. Sreen, N.; Purbey, S.; Sadarangani, P. Impact of culture, behavior and gender on green purchase intention. J. Retail. Consum. Serv. 2018, 41, 177–189. [Google Scholar] [CrossRef]
  93. Mishal, A.; Dubey, R.; Gupta, O.K.; Luo, Z. Dynamics of environmental consciousness and green purchase behaviour: An empirical study. Int. J. Clim. Chang. Strateg. Manag. 2017, 9, 682–706. [Google Scholar] [CrossRef]
  94. Molinillo, S.; Vidal-Branco, M.; Japutra, A. Understanding the drivers of organic foods purchasing of millennials: Evidence from Brazil and Spain. J. Retail. Consum. Serv. 2020, 52, 101926. [Google Scholar] [CrossRef]
  95. Khor, K.S.; Hazen, B.T. Remanufactured products purchase intentions and behaviour: Evidence from Malaysia. Int. J. Prod. Res. 2017, 55, 2149–2162. [Google Scholar] [CrossRef]
  96. Chetioui, Y.; Lebdaoui, H.; Chetioui, H. Factors influencing consumer attitudes toward online shopping: The mediating effect of trust. EuroMed J. Bus. 2021, 16, 544–563. [Google Scholar] [CrossRef]
  97. Ayu, I.; Giantari, K.; Zain, D.; Rahayu, M. The role of perceived behavioral control and trust as mediator of experience on online purchasing intentions relationship a study on youths in denpasar city (Indonesia). Int. J. Bus. Manag. Invent. 2013, 2, 30–38. [Google Scholar]
  98. Harun, A.; Mahmud, M.; Othman, B.; Ali, R.; Ismael, D. Understanding experienced consumers towards repeat purchase of counterfeit products: The mediating effect of attitude. Manag. Sci. Lett. 2020, 10, 13–28. [Google Scholar] [CrossRef]
  99. Sun, S.; Law, R.; Schuckert, M. Mediating effects of attitude, subjective norms and perceived behavioural control for mobile payment-based hotel reservations. Int. J. Hosp. Manag. 2020, 84, 102331. [Google Scholar] [CrossRef]
  100. Chawla, D.; Joshi, H. Role of Mediator in Examining the Influence of Antecedents of Mobile Wallet Adoption on Attitude and Intention. Glob. Bus. Rev. 2023, 24, 609–625. [Google Scholar] [CrossRef]
  101. Tyrväinen, O.; Karjaluoto, H. Online grocery shopping before and during the COVID-19 pandemic: A meta-analytical review. Telemat. Inform. 2022, 71, 101839. [Google Scholar] [CrossRef] [PubMed]
  102. Shamsi, M.A.; Chaudhary, A.; Anwar, I.; Dasgupta, R.; Sharma, S. Nexus between Environmental Consciousness and Consumers’ Purchase Intention toward Circular Textile Products in India: A Moderated-Mediation Approach. Sustainability 2022, 14, 12953. [Google Scholar] [CrossRef]
  103. Suki, N.M. Consumer environmental concern and green product purchase in Malaysia: Structural effects of consumption values. J. Clean. Prod. 2016, 132, 204–214. [Google Scholar] [CrossRef]
  104. Singhal, D.; Tripathy, S.; Jena, S.K. Acceptance of remanufactured products in the circular economy: An empirical study in India. Manag. Decis. 2019, 57, 953–970. [Google Scholar] [CrossRef]
  105. Forsythe, S.; Liu, C.; Shannon, D.; Gardner, L.C. Development of a scale to measure the perceived benefits and risks of online shopping. J. Interact. Mark. 2006, 20, 55–75. [Google Scholar] [CrossRef]
  106. Anwar, I.; Shamsi, M.A.; Khatoon, S.; Saleem, I.; Chaudhary, A. Multivariate dataset on cognitive predictors of Indian consumers’ purchase intention toward circular textile products. Front. Psychol. 2022, 13, 1039435. [Google Scholar] [CrossRef]
  107. Calvo-Porral, C.; Lévy-Mangin, J.-P. The circular economy business model: Examining consumers’ acceptance of recycled goods. Adm. Sci. 2020, 10, 28. [Google Scholar] [CrossRef]
  108. Hair, J.F.; Black, W.C.; Babin, B.J.; Anderson, R.E. Multivariate Data Analysis; Prentice Hall: Hoboken, NJ, USA, 2010. [Google Scholar]
  109. National Capital Region Planning Board. NCR Constituent Areas. Available online: https://ncrpb.nic.in/ncrconstituent.html (accessed on 22 January 2022).
  110. United Nations, Department of Economic and Social Affairs, Population Division. The World’s Cities in 2018: Data Booklet; United Nations: New York, NY, USA, 2018. [Google Scholar]
  111. Kawoosa, V.M. Delhi Has Highest Share of Inter-State Migrants. Hindustan Times. 28 July 2019. Available online: https://www.hindustantimes.com/delhi-news/delhi-has-highest-share-of-inter-state-migrants/story-QCYXSWlnSYAJbNb25ljcPL.html (accessed on 15 January 2022).
  112. Stevens, J.P. Applied Multivariate Statistics for the Social Sciences; Routledge: London, UK, 2012. [Google Scholar]
  113. Hair, J.F., Jr.; Sarstedt, M.; Ringle, C.M.; Gudergan, S.P. Advanced Issues in Partial Least Squares Structural Equation Modeling; Sage Publications: London, UK, 2017. [Google Scholar]
  114. Kock, N. Common method bias in PLS-SEM: A full collinearity assessment approach. Int. J. e-Collab. 2015, 11, 1–10. [Google Scholar] [CrossRef]
  115. Tabachnick, B.G.; Fidell, L.S.; Ullman, J.B. Using Multivariate Statistics; Pearson: Boston, MA, USA, 2013; Volume 6. [Google Scholar]
  116. Hu, L.T.; Bentler, P.M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Model. Multidiscip. J. 1999, 6, 1–55. [Google Scholar] [CrossRef]
  117. Lohmöller, J.-B. Predictive vs. structural modeling: Pls vs. ml. In Latent Variable Path Modeling with Partial Least Squares; Springer: Berlin/Heidelberg, Germany, 1989; pp. 199–226. [Google Scholar]
  118. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  119. Hair, J., Jr.; Hair, J.F., Jr.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM); Sage Publications: London, UK, 2021. [Google Scholar]
  120. Bagozzi, R.P.; Yi, Y. On the evaluation of structural equation models. J. Acad. Mark. Sci. 1988, 16, 74–94. [Google Scholar] [CrossRef]
  121. Henseler, J.; Ringle, C.M.; Sarstedt, M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 2015, 43, 115–135. [Google Scholar] [CrossRef]
  122. Yoo, F.; Jung, H.J.; Oh, K.W. Motivators and barriers for buying intention of upcycled fashion products in China. Sustainability 2021, 13, 2584. [Google Scholar] [CrossRef]
  123. Koszewska, M. Circular economy in textiles and fashion—The role of a consumer. In Circular Economy in Textiles and Apparel; Elsevier: Amsterdam, The Netherlands, 2019; pp. 183–206. [Google Scholar]
  124. Kim, I.; Jung, H.J.; Lee, Y. Consumers’ value and risk perceptions of circular fashion: Comparison between secondhand, upcycled, and recycled clothing. Sustainability 2021, 13, 1208. [Google Scholar] [CrossRef]
  125. Dissanayake, D.; Weerasinghe, D. Towards circular economy in fashion: Review of strategies, barriers and enablers. Circ. Econ. Sustain. 2021, 2, 25–45. [Google Scholar] [CrossRef]
  126. Hazen, B.T.; Mollenkopf, D.A.; Wang, Y. Remanufacturing for the circular economy: An examination of consumer switching behavior. Bus. Strategy Environ. 2017, 26, 451–464. [Google Scholar] [CrossRef]
  127. Jaiswal, D.; Kant, R. Green purchasing behaviour: A conceptual framework and empirical investigation of Indian consumers. J. Retail. Consum. Serv. 2018, 41, 60–69. [Google Scholar] [CrossRef]
Sustainability 15 13656 g001
Figure 1. Hypothesized model of the study. Note: H10, H11, and H12 depict mediation via attitude.
Figure 1. Hypothesized model of the study. Note: H10, H11, and H12 depict mediation via attitude.
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Sustainability 15 13656 g002
Figure 2. Final tested model.
Figure 2. Final tested model.
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Table 1. Respondents’ demographic profile (N = 409).
Table 1. Respondents’ demographic profile (N = 409).
Demographic VariableFrequencyPercentage (%)
Age
   Below ≤ 20 years5613.69
   21–30 years21051.33
   31–40 years8019.57
   41–60 years5413.21
   Above 6092.20
Gender
   Male23056.23
   Female17943.77
Education
   Below intermediate81.95
   Intermediate286.85
   Graduation12430.32
   Postgraduation and above24960.88
Income (monthly) in INR
   Below ≤ 30,00022254.28
   31,000–60,00011227.38
   61,000–90,000358.56
   Above 90,000409.78
Consumers who are aware of circular textile
products.
   Yes26665.04
   No14334.96
Consumers who purchased circular textile
products.
   Yes14735.94
   No26264.06
Table 2. CFA loadings, AVE, CR, and Cronbach’s alpha.
Table 2. CFA loadings, AVE, CR, and Cronbach’s alpha.
Variable NameItem LoadingAVECRCronbach’s
Alpha
Environmental consciousness (EC)
Source: [102,103,104]		 0.6770.8930.840
“I am willing to make extraordinary efforts to purchase circular textile products to protect the environment.”0.746
“Given a choice, I will prefer to purchase a circular textile product because it is less harmful to the environment.”0.869
“I will purchase circular textile products because it contributes towards the sustainability of the environment.”0.847
“I would prefer circular textile products over fresh textile products because it helps in limiting environmental pollution.”0.823
Risk perception (RP)
Source: [37,102,104]		 0.6950.8720.780
“The frequent maintenance of circular textile products will waste my time and money.”0.813
“I apprehend that circular textile products will have poor performance.”0.869
“I fear that the use of circular textile products might lead to skin issues/problems.”0.818
Personal benefits (PB)
Source: [102,104,105]		 0.6880.8980.851
“I will buy circular textile products because of their lower price.”0.814
“I will purchase circular textile products because I will be able to buy more quantity of textile products at a low price.”0.837
“I will purchase circular textile products because they will be available at a discount.”0.846
“I will purchase circular textile products as I will be getting an exchange offer in return for my used textiles.”0.821
Attitude towards CTPs (ATC)
Source: [33,106] 		0.7530.9010.836
“I would prefer buying circular textile products because they follow an eco-friendly process in manufacturing.”0.887
“I would buy circular textile products because they are reproduced through circular production.”0.846
“I would purchase circular textile products because it is an important idea regarding a sustainable environment.”0.869
Subjective norms (SN)
Source: [33,106]		 0.6390.8760.813
“People with whom I get impressed influence me to purchase circular textile products.”0.719
“The people important to me think I should purchase circular textile products.”0.826
“My family and friends think purchasing circular textile products is a wise idea.”0.808
“People who are important to me would approve of my decision to buy circular textile products.”0.840
Perceived behavioral control (PBC)
Source: [33,106]		 0.7260.8880.811
“I will always try to purchase environmentally responsible circular textile products.”0.823
“I am confident that I will purchase circular textile products when I go for purchasing textile products.”0.871
“When I have the resources and opportunities, I will surely buy circular/remanufactured textile products.”0.861
Purchase intention towards CTPs (PIC)
Source: [104,106,107]		 0.6980.9020.856
“I will buy circular textile products in the future.”0.841
“I am likely to buy circular textile products.”0.822
“I will continue buying circular textile products.”0.845
“I am excited to buy circular textile products.”0.834
Table 3. Inter-construct correlations and divergent validity (Fornell and Larcker Criterion).
Table 3. Inter-construct correlations and divergent validity (Fornell and Larcker Criterion).
Variable NamesATCECPBPBCPICRPSN
ATC0.868
EC0.768 **0.823
PB0.418 **0.364 **0.830
PBC0.723 **0.682 **0.381 **0.852
PIC0.745 **0.663 **0.349 **0.815 **0.835
RP−0.124−0.1480.171 *−0.141−0.174 *0.833
SN0.551 **0.516 **0.481 **0.640 **0.609 **−0.0590.799
Note: Bold values on diagonals are square roots of AVE values for divergent validity. Correlations are significant at 0.01 level ** and 0.05 level *. EC = environmental consciousness; RP = risk perception; PB = personal benefits; ATC = attitude towards CTPs; SN = subjective norms; PBC = perceived behavioral control; PIC = purchase intention towards CTPs.
Table 4. Divergent validity using HTMT Ratios.
Table 4. Divergent validity using HTMT Ratios.
ATCECPBPBCPICRPSN
ATC
EC0.809
PB0.4810.418
PBC0.7870.8250.444
PIC0.7800.7760.3950.827
RP0.1540.1820.2120.1760.211
SN0.6530.6100.5620.7740.7150.099
Note: EC = environmental consciousness; RP = risk perception; PB = personal benefits; ATC = attitude towards CTPs; SN = subjective norms; PBC = perceived behavioral control; PIC = purchase intention towards CTPs.
Table 5. Standardized direct and indirect effects.
Table 5. Standardized direct and indirect effects.
Path (IV 🡪 DV) Std. Estimate (β)BootSEt-Valuep-ValueLLCIULCI
EC 🡪 PIC0.332 ***0.0462.697<0.0010.0550.128
EC 🡪 ATC0.698 ***0.03520.04<0.0010.6260.761
RP 🡪 PIC−0.050 NS0.0311.5970.110−0.1120.01
RP 🡪 ATC−0.051 NS0.0321.5780.115−0.1160.01
PB 🡪 PIC0.325 ***0.0332.766<0.0010.090.039
PB 🡪 ATC0.372 ***0.0364.731<0.0010.1070.248
ATC 🡪 PIC0.291 ***0.0624.683<0.0010.1670.412
SN 🡪 PIC0.211 ***0.0402.7420.0060.0310.188
PBC 🡪 PIC0.515 ***0.0588.8830.0040.3990.625
Model’s explanatory power (adjusted R2): ATC = 0.614 (61.40%); PIC = 0.721 (72.10%)
Model’s predictive relevance (Q2): ATC = 0.435 (43.50%); PIC = 0.472 (47.20%)
Note: Standardized effects are significant at 1%, i.e., *** p < 0.01 levels. LLCI and ULCI were calculated at 5000 bootstrap samples using bias-corrected percentile method. NS = Not significant; EC = environmental consciousness; RP = risk perception; PB = perceived benefits; ATC = attitude towards CTPs; SN = social norms; PBC = perceived behavioral control; PIC = purchase intention towards CTPs.
Table 6. Standardized indirect effects (mediation analysis).
Table 6. Standardized indirect effects (mediation analysis).
Path (IV 🡪 M 🡪 DV)Std. Estimate (β)BootSEt-Valuep-ValueLLCIULCI
EC 🡪 ATC 🡪 PIC0.203 ***0.0454.519<0.0010.1150.291
RP 🡪 ATC 🡪 PIC−0.015 NS0.0101.5310.126−0.0350.003
PB 🡪 ATC 🡪 PIC0.108 **0.0152.3210.0340.0230.083
Note: Standardized effects are significant at 5%, i.e., ** p < 0.05, and 1%, i.e., *** p < 0.01 levels. LLCI and ULCI were calculated at 5000 bootstrap samples using bias-corrected percentile method. NS = Not significant; EC = environmental consciousness; RP = risk perception; PB = perceived benefits; ATC = attitude towards CTPs; PIC = purchase intention towards CTPs.
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Shamsi, M.A.; Anwar, I.; Chaudhary, A.; Akhtar, S.; Ahmad, A. Sustainable Transition through Circular Textile Products: An Empirical Study of Consumers’ Acceptance in India. Sustainability 2023, 15, 13656. https://doi.org/10.3390/su151813656

AMA Style

Shamsi MA, Anwar I, Chaudhary A, Akhtar S, Ahmad A. Sustainable Transition through Circular Textile Products: An Empirical Study of Consumers’ Acceptance in India. Sustainability. 2023; 15(18):13656. https://doi.org/10.3390/su151813656

Chicago/Turabian Style

Shamsi, Mushahid Ali, Imran Anwar, Asiya Chaudhary, Samreen Akhtar, and Alam Ahmad. 2023. "Sustainable Transition through Circular Textile Products: An Empirical Study of Consumers’ Acceptance in India" Sustainability 15, no. 18: 13656. https://doi.org/10.3390/su151813656

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