A Holistic Consumer-Centric and Value Co-Creation Scale for E-Waste Management through the Extended Theory of Planned Behavior
Prof Kartik Dave
PhD, Professor
Dean - School of Management,
Dr. B.R Ambedkar University, Delhi
Email ID: kartik@aud.ac.in
ORCiD: 0000-0001-6581-8979
Corresponding Author
Dr Deeksha Dave
PhD, Associate Professor
Environmental Studies
School of Inter Disciplinary and Trans Disciplinary Studies
Indira Gandhi National Open University (IGNOU)
Maidan Garhi, New Delhi 110068
Email address: deekshadave@ignou.ac.in
ORCiD: 0000-0001-5578-6280
Dr Meenakshi Mohan Bhardwaj
PhD, Research Associate
School of Management,
Dr. B.R Ambedkar University, Delhi
Email ID: meenakshim89@gmail.com
ORCiD: 0000-0003-4264-9142
Bharti Sharma
PhD Scholar
School of Management,
Dr. B.R Ambedkar University, Delhi
Email ID: bsharma.20@stu.aud.ac.in
ORCiD: 0009-0006-3917-9425
Abstract
The growing problem of electrical and electronic waste (e-waste) poses a major environmental challenge, particularly in India, where limited management facilities and consumer reluctance aggravate the situation. Rising consumption and rapid technological advancements further intensify the issue. This study extends the Theory of Planned Behavior (TPB) by incorporating co-creation to examine consumer behavior in e-waste management. A comprehensive scale was developed to assess key constructs Attitude, Subjective Norms, Intention, Perceived Behavioral Control, Responsible Consumer Behavior, and Co-creation influencing consumer engagement. Data were collected from 445 participants across India and analyzed using SPSS ver. 26 and PLS-SEM ver. 4. Findings show that attitude, subjective norms, and perceived behavioral control significantly predict intentions for responsible e-waste management. Co-creation (t = 9.807) significantly moderates responsible consumer behavior, strengthening the positive effect of intention on sustainable practices. The scale demonstrated strong reliability and validity, underscoring the importance of collaboration in promoting sustainable e-waste management.
Keywords: E-waste management, Consumer behavior, Theory of Planned Behavior, Co-creation, Sustainability, Responsible consumer behavior, Scale development.
Introduction and Conceptual Background
Electrical and electronic waste (e-waste) has emerged as one of the fastest-growing categories of solid waste globally (Bhardwaj, Rath and Tokas, 2023; Venes et al., 2025). Over recent decades, the electrical and electronics industry has experienced significant growth driven by a consumer-centric market and rapid technological advancements. Consequently, electronic products are becoming obsolete at an accelerating pace, with many reaching the end of their lifecycle more quickly than in the past (Kumar, Holuszko and Espinosa, 2017). The continuous introduction of newer models has led consumers to frequently replace existing devices, contributing to overconsumption and resource depletion (Michael, Hungund and Sriram, 2024). This trend has resulted in environmental challenges such as pollution, land degradation, and climate change (Tansel, 2017; Ertz et al., 2019; Walke et al., 2025).
Globally, 62 million tonnes of e-waste were generated in 2022, of which only 22.3% was properly collected and recycled (Jaiswal and Mukti, 2024). India, one of the largest electronics markets in the Asia-Pacific region, has also witnessed a rise in manufacturing and consumption (Invest India, 2022a; Minister of State for Environment, Forest & Climate Change, 2023). While electronic products improve quality of life, their disposal poses serious environmental and health risks due to toxic components (Needhidasan, Samuel and Chidambaram, 2014; Bagwan, 2024). The presence of an unregulated informal recycling sector further aggravates these risks (Dutta and Goel, 2021). Although regulatory frameworks such as the E-Waste Rules (2011, amended in 2016, 2018, and 2023) have been introduced, challenges in effective implementation remain.
Effective e-waste management requires the collective effort of all stakeholders, particularly consumers, as emphasized under Sustainable Development Goal 12 (UN, 2021). Existing studies highlight factors influencing consumer participation; however, a comprehensive consumer-centered framework for evaluating behavior remains limited (Borthakur, 2015; Kumar, 2019; Wang et al., 2019). A major barrier to recycling is consumer reluctance, with many devices stored instead of being disposed of properly (Kumar, 2019). Although reverse logistics and recycling strategies are widely discussed, they often overlook the critical link between consumer behavior and effective implementation (Mohd Sharif and Soo, 2017). This gap is more pronounced in developing countries, where research on consumer intentions remains limited (Do Valle et al., 2004; Mohamad, Thoo and Huam, 2022).
The Theory of Planned Behavior (TPB) is widely used to examine consumer behavior, alongside models such as the Technology Acceptance Model (TAM) and Norm Activation Theory (NAT). TPB explains behavior through attitude, subjective norms, and perceived behavioral control; however, it has limitations in capturing broader contextual and motivational factors (Awasthi et al., 2018; Nguyen et al., 2019; Ramzan et al., 2021). Prior studies have examined determinants such as awareness, convenience and social influence in shaping e-waste recycling intentions (Laeequddin et al., 2022; Aboelmaged, 2021; Zhang et al., 2018). These studies highlight that attitudes, social norms, and perceived convenience significantly influence consumer intentions, although findings vary across contexts.
Despite these contributions, the role of co-creation in influencing consumer behavior in e-waste management remains underexplored. Co-creation refers to collaborative value generation between consumers and stakeholders, particularly in designing sustainable products and promoting recycling practices. While widely studied in other domains, its application in e-waste management is limited.
To address this gap, the present study proposes an extended TPB framework by incorporating co-creation as a moderating variable. The study develops a comprehensive scale integrating key constructs: Attitude (ATT), Subjective Norm (SN), Intention (INT), Perceived Behavioral Control (PBC), Responsible Consumer Behavior (RCB), and Co-creation (CoC). This framework aims to assess consumer willingness to engage in e-waste management and examine how co-creation strengthens sustainable practices. The study further seeks to validate this scale for use by policymakers, researchers and practitioners to improve e-waste management strategies.
Methodology and Results
To develop a comprehensive consumer-centric scale incorporating co-creation for e-waste management, established scale-development guidelines were followed (Churchill, 1979; Gerbing and Anderson, 1988; Bearden, Netemeyer and Teel, 1989; Nenkov, Inman and Hulland, 2008). The study was conducted in three phases: item development, scale development, and scale evaluation (Boateng et al., 2018). Phase 1 included domain identification, operationalization of constructs, hypothesis formulation, item generation, and content validity. Phase 2 included statistical analysis and pre-testing. Phase 3 included sample size determination, factor analysis, reliability and validity assessment, model fit, and hypothesis testing.
Phase I: Item Development/Generation
Step 1: Domain Identification
The first step was to identify the domain of the study, which defines the boundaries of the phenomenon under investigation and guides item generation and content validation (Haynes, Richard and Kubany, 1995; Boateng et al., 2018). In the present study, the constructs were finalized through an extensive literature review. The review indicated that Attitude (ATT), Subjective Norm (SN), Intention (INT), Responsible Consumer Behavior (RCB), Co-Creation (CoC), and Perceived Behavioral Control (PBC) have been widely studied individually, but not together in the context of e-waste management.
Through a systematic literature review (Registration ID: CRD42024623157), various theories related to e-waste management were examined (Dave K et al., 2025). Among these, the Theory of Planned Behavior (TPB), proposed by Ajzen (1991), was found to be frequently used to understand and predict recycling intentions (Ajzen, 1991). Accordingly, the above constructs were finalized and operationalized in line with the objectives of the present study.
Step 2: Operationalization of the Constructs and its Antecedents
The Theory of Planned Behavior (TPB), developed by Ajzen (1991), is one of the most widely used frameworks for understanding and predicting human behavior across various domains, including marketing, public health, and behavioral research(Ajzen, 1991; Wang, Guo and Wang, 2016; Kumar, 2019; Nguyen et al., 2019; Shevchenko, Laitala and Danko, 2019; Delcea et al., 2020). According to the TPB, an individual's behavior is influenced by their intention, which is, in turn, predicted by three core factors: Attitude ("What do I think?"), Subjective Norms ("What do my family and friends think?"), and Perceived Behavioral Control ("How difficult is it for me to perform the behavior?"). If any of these factors are unfavorable, the likelihood of forming the behavioral intention is reduced(Lou, 2022).
The TPB extends the Theory of Reasoned Action(Fishbein and Ajzen, 1977), originating from social psychology, and remains a robust tool for predicting individuals' intentions. It does so by assessing the interactions between attitude, perceived control, and subjective norms, which together influence behavioral intentions. Despite some criticisms, the TPB has proven effective and has been widely applied across diverse areas, including health, education, consumer behavior, environmental studies, and technology(Ajzen, 1991). Researchers have also adapted and expanded the model to enhance its predictive power(Conner and Armitage, 1998; Ajzen, 2012). In the context of e-waste management, several studies have extended the TPB by incorporating constructs such as moral norms, convenience, infrastructure, and a sense of duty(Wang et al., 2011; Ramayah, Lee and Lim, 2012; Kumar, 2017, 2019), as discussed in the earlier sections of this manuscript. These studies predominantly focus on household consumers in both developed and emerging economies. However, few studies have specifically investigated e-waste recycling behavior among young adults using the extended TPB framework.
Moreover, no studies have examined co-creation as a moderator influencing behavior in the context of e-waste management. Responsible Consumer Behavior (RCB) is another key construct influenced by intention, which underscores the significance of attitudes in driving sustainable practices (Figure 1).
Attitude is commonly defined as an individual’s tendency to rate a particular entity as favorable or unfavorable(Eagly and Chaiken, 2007). In the context of e-waste management, attitude refers to an individual's overall opinion or mindset about e-waste, shaped by their knowledge, feelings, and likelihood of taking action. This attitude influences whether they support or engage in actions like recycling electronics(Kumar, 2019).
Attitude is formed through behavioral beliefs and the evaluation of expected outcomes, particularly whether the behavior is perceived as favorable or unfavorable (Greaves, Zibarras and Stride, 2013). In the context of e-waste management, attitude is considered positive when recycling or safe disposal is viewed as good, useful, beneficial, sensible, and responsible (Cheung, Chan and Wong, 1999; Tonglet, Phillips and Bates, 2004; Kumar, 2019). Previous studies have linked environmental attitudes with moral responsibility, sense of duty, convenience, awareness of consequences, and pro-environmental values such as reducing waste and reusing materials (Corral-Verdugo and Frías-Armenta, 2006; Kumar, 2019). Accordingly, the attitude items in this study were derived to assess consumers’ evaluation of e-waste management as a responsible, beneficial, and environmentally relevant action.
Subjective Norm refers to the perceived social pressure to perform or avoid a particular behavior, shaped by the expectations of significant others such as family, friends, colleagues, and the broader community. In the context of e-waste management, it reflects how these social groups influence an individual’s intention to engage in responsible behaviors. According to the Theory of Planned Behavior (TPB), individuals are more likely to act when they believe that important others expect them to do so, making subjective norms a key predictor of pro-environmental actions.
Subjective norms are shaped by normative beliefs, which represent individuals’ perceptions of others’ expectations regarding their behavior (Ajzen and Fishbein, 1972; Greaves, Zibarras and Stride, 2013). The strength of these perceived expectations determines the extent to which individuals are motivated to comply. In addition to direct social influence, subjective norms may also reflect broader societal values and internalized moral expectations.
In the present study, subjective norms were operationalized through perceived expectations from family, friends, colleagues, workplace, and community (Greaves, Zibarras and Stride, 2013; Al-Swidi et al., 2014; Gonul Kochan et al., 2016). These influences are important in understanding e-waste management behavior, as recycling and disposal decisions are often shaped by social approval, moral norms, and perceived responsibility (Aboelmaged, 2021; Sabbir, Taufique and Nomi, 2023). Previous research also suggests that factors such as habits and convenience can interact with normative beliefs, further influencing individuals’ perceptions of social pressure and their intention to engage in responsible environmental practices (Tsai and Tan, 2022; Vijayan et al., 2023). Accordingly, the items for this construct were developed to capture the role of social expectations in shaping consumer behavior toward e-waste management.
Intention, as defined by Triandis (1980) and Söderlund and Öhman (2005)(Söderlund and Öhman, 2005), refers to an individual's self-directed plan or commitment to engage in a specific behavior in order to achieve a desired outcome. It reflects the individual’s readiness and determination to carry out the behavior in question. According to the Theory of Planned Behavior (TPB)(Fishbein and Ajzen, 1977; Ajzen, 2012), intentions are shaped by three key factors: attitude, subjective norms, and perceived behavioral control. In the context of this study, intention is conceptualized as the consumer’s willingness to manage their e-waste through actions such as returning, recycling, exchanging, or refurbishing(Laeequddin et al., 2022). Research has shown that a consumer’s intention to recycle significantly influences their actual behavior towards e-waste management(Gonul Kochan et al., 2016).
Perceived Behavioral Control refers to the extent to which individuals feel capable of performing a behavior. It includes both self-efficacy and perceived ease or difficulty of action. In this study, PBC is conceptualized as consumers’ confidence in their ability to manage e-waste disposal, including recycling and overcoming related barriers (Aboelmaged, 2021). PBC is influenced by control beliefs that determine whether individuals perceive a behavior as feasible (Ajzen, 1985; Greaves, Zibarras and Stride, 2013).
In the context of e-waste management, this construct is operationalized through various control factors that either facilitate or hinder recycling behavior. These control factors may include aspects like the availability of recycling schemes, storage space, and convenience, as well as situational barriers such as inconvenience and lack of infrastructure(Ajzen, 2002; Chen and Tung, 2010). The effectiveness of PBC in predicting consumer behavior towards e-waste management which largely depends on how accessible and convenient recycling options are to individuals(Tsai and Tan, 2022; Sabbir, Taufique and Nomi, 2023).
In the present study, behavior is examined as Responsible Consumer Behavior (RCB), which refers to the habitual, goal-oriented actions that individuals take to manage e-waste responsibly. These behaviors such as cleaning, storing, sorting, separating, disassembling, reusing, or properly disposing of e-waste become automatic over time with minimal conscious effort once they become habitual. These repetitive actions are motivated by a sense of responsibility toward environmental sustainability and are sustained over time(Knussen and Yule, 2008; Labrecque and Angeles, 2016). Prior research indicates that habits, once formed, significantly influence behavior by bypassing conscious decision-making processes, even in the presence of conflicting attitudes or social norms(Neal et al., 2012; Wood, 2024). In the context of e-waste management, RCB involves a sequence of learned actions that individuals incorporate into their routines to minimize the environmental impact of electronic waste. The antecedents of such behaviors are multi-faceted, encompassing psychological factors such as attitudes, subjective norms, perceived behavioral control (PBC), and habits. Attitudes toward responsible e-waste management, shaped by environmental concerns and personal values, significantly contribute to the formation of recycling habits(Ajzen, 1991; Tsai and Tan, 2022). Subjective norms, which reflect the influence of family, peers, and societal expectations, further drive individuals’ intentions to engage in e-waste management practices(Vijayan et al., 2023). Additionally, perceived behavioral control, which involves an individual’s belief in their ability to perform the recycling behavior, plays a crucial role in fostering habitual e-waste management actions(Sabbir, Taufique and Nomi, 2023). Together, these antecedents contribute to the development of RCB, reinforcing individuals’ consistent engagement in environmentally responsible behaviors toward e-waste.
This study examines co-creation through consumers' willingness to engage in activities that enhance e-waste management practices. Simply put, working together to create something valuable. (join forces to share ideas, skills, and resources). It focuses on consumers' involvement in product design, e-waste disposal, and management, as well as their readiness to share ideas for improvement. Key aspects of co-creation, such as design participation, information sharing, advocacy, decision-making, and engagement, were explored, highlighting co-creation as a collaborative process between consumers and companies. Co-creation is a dynamic, participatory process where value is jointly created through interactions between customers, firms, and other stakeholders, as defined by the service-dominant (S-D) logic framework(Vargo and Lusch, 2008). This perspective shifts from traditional value-exchange models, recognizing customers as active contributors to value creation. The antecedents of co-creation were studied in literature include customers' willingness to interact, share knowledge, feedback, advocacy and engage in responsive behaviors. In the literature, co-creation is studied from various perspectives, focusing on the roles customers play in the process. Studies highlight the importance of customer interaction, knowledge-sharing, and responsive attitudes in facilitating value co-creation. For example, Grönroos (2008) and Vargo and Lusch (2008)(Grönroos, 2008; Vargo and Lusch, 2008) argue that value is co-created through ongoing interactions, with firms acting as facilitators and customers playing an essential role in the service creation process. Customers are encouraged to share their ideas, preferences, and feedback, which influences the firm's product or service offerings. Studies, such as those by Shamim and Ghazali (2017)(Shamim, Ghazali and Albinsson, 2017), show that attitudes towards interaction and knowledge sharing significantly influence participation in co-creation. Environmental factors like communication and accessibility further encourage consumer involvement. Overall, co-creation represents a shift towards a more inclusive and collaborative approach to value creation, where active participation from customers is essential for both service providers and customers to derive meaningful value. This is particularly relevant in sectors like e-waste management, where consumer engagement is crucial to achieving successful outcomes.
Step 3: Hypothesis
Hypothesis 1: Attitude is positively associated with the intention to engage in responsible consumer behavior, which, in turn, influences e-waste management practices.
Hypothesis 2: Subjective norms are positively associated with the intention to engage in responsible consumer behavior, which, in turn, influences e-waste management practices.
Hypothesis 3: Individuals' perceived ability to manage e-waste is positively associated with their intention to engage in responsible consumer behavior, which, in turn, positively influences e-waste management practices.
Hypothesis 4: The intention to manage e-waste is positively associated with responsible consumer behavior.
Hypothesis 5: Co-Creation (CoC) moderates the positive relationship between individuals' intention to engage in responsible consumer behavior, such that the relationship is stronger when the level of Co-Creation is high.
Step 4: Item Generation
After finalizing the constructs, the item pool was developed through a comprehensive literature review using a deductive approach (Hinkin, 1995; Boateng et al., 2018). Initially, 20 to 35 items were developed for each construct to allow flexibility in selecting suitable items. For Co-Creation, 153 items were generated because this construct had not been previously examined as a moderator in consumer behavior toward e-waste management.
After reviewing and removing duplicate or redundant items, 67 items were selected for validation. A 5-point Likert scale was used, ranging from “Strongly Agree” (5) to “Strongly Disagree” (1), to capture participants’ attitudes, perceptions, and behaviors related to e-waste management.
Step 5: Content Validity
Content validity ensures that a measure adequately represents the intended domain and reflects the target population’s experiences (Morgado et al., 2017). In the present study, content validity was assessed through expert review using both qualitative and quantitative methods. Face validity was established through expert discussions and cognitive interviews to evaluate the clarity, relevance, and appropriateness of the scale items.
Experts provided detailed feedback, which was systematically recorded and incorporated. Based on their suggestions, redundant items and those not aligning well with the constructs were removed or revised. For instance, items such as ‘My goal is to make people aware about e-waste management practices’ and ‘Local media influences me to safely manage my e-waste’ were deleted. Minor modifications were also made to improve clarity, such as adding the phrase “As a Consumer” to Co-Creation items. These revisions ensured that the scale items were conceptually consistent, relevant, and easily understandable. Subsequently, both qualitative and quantitative evaluations were conducted to finalize the content validity of the scale.
The following three steps were taken to establish content validity for the current study:
A total of ten experts were invited to participate in the validation process, of which seven accepted. The panel comprised two subject experts from marketing, one industry professional, one environmentalist, one methodologist, and two consumers. The study materials and evaluation sheets were shared with the experts for detailed review and feedback.
Experts were requested to evaluate the scale in terms of the appropriateness of the title, clarity of directions, overall content, and individual items. They were also encouraged to provide additional comments and suggestions. Based on their feedback, redundant or unclear items were either removed or revised. For example, items such as ‘My goal is to make people aware about e-waste management practices’ and ‘Local media influences me to safely manage my e-waste’ were deleted. Minor modifications were also made to improve clarity and contextual relevance, such as adding the phrase “As a Consumer” to Co-Creation items.
Experts rated each item on relevance, clarity and essentiality. Relevance was measured on a 4-point scale (0 = Not relevant to 3 = Very relevant), clarity on a 3-point scale (0 = Not clear to 2 = Very clear), and essentiality on a 3-point scale (0 = Not essential, 1 = Useful but not essential, 2 = Essential). Responses were recorded and coded systematically.
The Content Validity Ratio (CVR) was calculated using Lawshe’s formula (Lawshe, 1975):
where ne represents the number of experts rating an item as “essential” and N is the total number of experts. Based on the panel size (N = 7), a minimum CVR value of 0.43 was adopted as the threshold. Items below this threshold were revised or removed to improve clarity, relevance, and construct alignment. For example, items reflecting social expectations were reworded to better capture broader influences (e.g., combining “family” and “friends”), while redundant items related to similar behavioral conditions were removed, and minor wording refinements were made to enhance clarity.
Following the first round, the revised scale was recirculated to the same panel for validation. After re-evaluation, all items met the minimum CVR requirement, and the overall CVR of the scale was 0.75, indicating acceptable content validity (Lawshe, 1975) (Table 1).
In addition, the Content Validity Index (CVI) was calculated based on relevance ratings (Yusoff, 2019). Ratings were recoded into binary values (1 = relevant, 0 = not relevant). The I-CVI values exceeded 0.85, while S-CVI/Ave (0.95) and S-CVI/UA (0.73) indicated satisfactory content validity. These values meet the recommended criteria for expert panels with six or more members (Lynn, 1986) (Table 2).
PHASE 2: SCALE DEVELOPMENT
Step 6: Statistical Analysis
Descriptive statistics were used to summarize the demographic characteristics and data distribution. SPSS version 26 was used for statistical analysis, and Excel was used for data organization. Measures such as means, standard deviations, frequencies, and percentages were calculated. Structural Equation Modeling (SEM) using SmartPLS 4 was employed to assess the reliability and validity of the constructs and to test the relationships among variables. The sample size was determined based on the commonly accepted respondent-to-item ratio of 10:1 to ensure adequate statistical power.
Step 7: Pre-testing Questions
Pre-testing was conducted with 100 consumers to assess the internal consistency and clarity of the scale items. The sample included male and female participants aged 18 years and above from the Delhi-NCR region, with varied demographic backgrounds.
The majority of participants were aged 18–30 years (65%), with a mean age of approximately 29 years. Most respondents were male (64%), and 61% were graduates. Socio-economic status was assessed using the Kuppuswamy Scale (Saleem and Jan, 2021), with most participants belonging to lower-middle and upper-middle categories. This diversity enhanced the representativeness of the pre-test sample.
Internal consistency was assessed using Cronbach’s alpha. All constructs demonstrated acceptable reliability, with values above 0.75 (Nunnally, 1978): Attitude (0.85), Intention (0.75), Subjective Norm (0.85), Co-Creation (0.93), Responsible Consumer Behaviour (0.82), and Perceived Behavioral Control (0.80). No items were removed at this stage, and a total of 51 items were retained for further analysis.
PHASE 3: SCALE EVALUATIONS
Step 8: Sample Size Determination for Factor Analysis
To ensure a heterogeneous sample for Confirmatory Factor Analysis (CFA), participants were selected from multiple regions and cities, representing diverse demographic backgrounds. Data were collected through hard-copy surveys administered across various locations, including colleges, markets, and residential networks, targeting individuals aged 18 years and above who could read and understand English.
For PLS-SEM analysis, sample size was determined using the “10 times indicators” and “5 times paths” rule to ensure adequate statistical power (Laeequddin et al., 2022). With six structural paths and 52 indicators, the minimum required sample size was 225.
Data collection was conducted from November 2024 to January 2025. A total of 449 responses were received, of which 445 valid responses were retained after excluding incomplete entries. This sample size is considered sufficient for PLS-SEM analysis.
Step 9: Data analysis and results
Descriptive Statistics
Table 3 presents the demographic characteristics of the sample. The majority of respondents were aged 18-30 years (77.3%), followed by 31-40 years (16.0%). The sample included a higher proportion of females (60.9%) than males (39.1%). In terms of education, most respondents were graduates (58.2%), followed by those with intermediate/diploma qualifications (14.8%). A large proportion of respondents were unemployed (52.4%), while professionals constituted 15.3% of the sample. Most participants were single (69.4%) and belonged to nuclear families (60.4%). Geographically, respondents were primarily from Punjab (47.0%) and Uttar Pradesh (31.0%), with smaller representation from Delhi-NCR (18.9%) and Madhya Pradesh (2.5%). The majority of respondents belonged to the upper-middle (52.6%) and lower-middle (22.7%) socioeconomic categories.
PLS-SEM
The collected data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 4.0 to assess the measurement properties of the constructs and validate the proposed model. Table 4 presents the factor loadings and cross-loadings of the items across their respective constructs.
Factor Loadings
Table 4 presents the factor loadings and cross-loadings of all items across their respective constructs. Most items demonstrated satisfactory loadings above the recommended threshold of 0.50, indicating strong associations with their respective constructs. Cross-loadings were comparatively lower, supporting discriminant validity. For instance, items such as ATT_7 (0.706) and CoC_9 (0.686) showed strong loadings on their respective constructs, while maintaining lower cross-loadings on other constructs.
Items with loadings below 0.50 or higher cross-loadings were excluded from further analysis. Based on this criterion, 7 items were removed, resulting in a final set of 45 items retained for subsequent analysis. However, INT_1, RCB_4, and RCB_9 (0.331) were retained due to their theoretical relevance and expert validation, despite lower loadings. The relatively lower loadings may be attributed to sample heterogeneity and variation in responses across different demographic groups (Figure 2).
The moderating role of Co-Creation was also supported. The interaction term between Co-Creation and Intention showed a positive path coefficient (β = 0.086), indicating that higher levels of Co-Creation strengthen the relationship between Intention and Responsible Consumer Behavior.
Reliability, Discriminant and Convergent Validity
All constructs were assessed for reliability, as well as discriminant and convergent validity, as presented in Tables 4 and 5. Discriminant validity was evaluated using the Heterotrait-Monotrait Ratio (HTMT), where values below 0.85 indicate acceptable validity. In the present model, most construct pairs met this criterion, supporting discriminant validity. For instance, HTMT values for SN-RCB (0.052), INT-CoC (0.57), and RCB-CoC (0.70) were within acceptable limits. The HTMT value between RCB and PBC (0.813) was comparatively higher, indicating some overlap, likely due to conceptual similarity between the constructs.
Reliability was assessed using Cronbach’s alpha and composite reliability. All constructs exceeded the recommended threshold of 0.70, indicating satisfactory reliability. For example, CoC showed a Cronbach’s alpha of 0.863, and RCB demonstrated composite reliability of 0.768.
During pre-testing, all constructs exhibited reliability values above 0.80. However, in the final sample, PBC showed a slightly lower reliability (0.62). Despite this, the construct was retained due to its theoretical importance within the model. The lower value may be attributed to sample heterogeneity and variability in responses across different demographic groups. This aspect may be further examined in future studies.
T-Statistics: Hypothesis testing
After validating the measurement model, hypothesis testing was conducted using bootstrapping in PLS-SEM. The results are presented in Table 6. All proposed hypotheses were supported, with t-statistics exceeding 1.96 and p-values below 0.05.
The findings indicate that Attitude positively influences Intention, which in turn significantly affects Responsible Consumer Behavior (RCB). Subjective Norms and Perceived Behavioral Control also showed significant positive effects on Intention. In addition, Co-Creation significantly moderates the relationship between Intention and RCB, strengthening the effect at higher levels of co-creation.
The standardized path coefficients were significant across all relationships (Table 6). For example, Attitude (β = 0.311, p < 0.001), Subjective Norms (β = 0.396, p < 0.001), Intention (β = 0.331, p < 0.001), Co-Creation (β = 0.418, p < 0.001), and Perceived Behavioral Control (β = 0.157, p = 0.001) showed positive effects on Responsible Consumer Behavior. The moderating effect of Co-Creation was also significant (β = 0.075, p = 0.006).
These findings support all proposed hypotheses, indicating that attitude, subjective norms and perceived behavioral control significantly influence responsible consumer behavior with Co-Creation strengthening this relationship.
Model Fitness
The continuous generation of e-waste has emerged as a major environmental challenge, affecting sustainable economic growth across countries (Rezaul et al., no date; Mohamad, Thoo and Huam, 2022). Addressing this issue requires an effective regulatory framework supported by efficient take-back mechanisms involving all stakeholders, particularly consumers and industries. Consumer participation is critical, as they are the primary users and initiators of the e-waste lifecycle, and their engagement at early stages significantly influences recycling and disposal outcomes.
Reverse logistics has been recognized as an effective approach to manage e-waste, however, studies linking it with consumer behavior remain limited (Mallick, P. K.,et al 2023,Singh et al 2025, Pongen I et al 2026). Since consumers act as the starting point of the reverse supply chain, their active involvement is essential for its success (Ravi and Shankar, 2015). The present study addresses this gap by examining the role of co-creation in shaping responsible consumer behavior in e-waste management.
Using an extended Theory of Planned Behavior (TPB) framework, this study incorporates co-creation as a moderating factor in the Indian context. A scale was developed to assess consumer participation through co-creation, emphasizing its contribution to sustainable development, particularly in achieving Sustainable Development Goals (SDGs) 11 and 12. The findings confirm that co-creation significantly enhances e-waste management outcomes.
The results indicate that attitude significantly influences intention, which in turn affects responsible consumer behavior. The path coefficient for attitude was significant (β = 0.311, p < 0.001), indicating that positive attitudes toward e-waste management contribute to stronger behavioral intentions. This finding is consistent with prior studies demonstrating that positive attitudes toward recycling are strong predictors of behavioral intention (Davies, Foxall and Pallister, 2002; Ramayah, Lee and Lim, 2012; Dixit and Badgaiyan, 2016; Gonul Kochan et al., 2016; Awasthi et al., 2018; Wang et al., 2018; Kumar, 2019). Attitudes toward e-waste management such as perceiving recycling as responsible, beneficial and necessary that plays a critical role in shaping consumer intentions. As noted by Greaves et al. (2013), attitudes are formed through favorable evaluations of behavior, while Echegaray and Hansstein (2017) emphasize the perceived environmental and health benefits of recycling. However, the findings also suggest that positive attitudes do not always translate into actual behavior. This gap may be attributed to limited awareness and knowledge in certain contexts, highlighting the need for targeted awareness and educational interventions.
The findings further indicate that subjective norms significantly influence intention with a strong positive effect (β = 0.396, p < 0.001), suggesting that social expectations from family, peers, colleagues, and communities play an important role in shaping e-waste management intentions as observed in recycling context (Laeequddin et al., 2022; Vijayan et al., 2023). However, some studies report mixed results, particularly in non-Western contexts, suggesting that the influence of subjective norms may vary depending on cultural and situational factors (Aboelmaged, 2021). In the present study, the strong effect of subjective norms highlights the importance of social influence in shaping responsible e-waste management practices.
Perceived Behavioral Control (PBC) also showed significant positive relationship with intention, although its effect was comparatively smaller (β = 0.157, p = 0.001). This finding is consistent with earlier research emphasizing the role of perceived control in facilitating recycling behavior (Nigbur, Lyons and Uzzell, 2010a; Pakpour et al., 2014a; Botetzagias, Dima and Malesios, 2015; Wang et al., 2018). PBC reflects individuals’ confidence in their ability to perform recycling-related actions, which is influenced by factors such as accessibility, infrastructure, and convenience. Enhancing these enabling conditions can strengthen consumers’ intention to engage in e-waste management. However, some studies have reported weaker or insignificant effects of PBC, particularly in contexts with limited infrastructure (Mohamad, Thoo and Huam, 2022), suggesting that structural barriers may constrain behavioral outcomes.
A significant positive relationship was observed between intention and responsible consumer behavior (β = 0.331, p < 0.001). This finding reinforces the central role of intention in driving pro-environmental behavior, as established in TPB-based research (Bamberg and Möser, 2007; Nigbur, Lyons and Uzzell, 2010b). Literature highlights that both intrinsic factors, such as moral norms and habits, and extrinsic factors, such as social pressure and incentives, contribute to shaping behavioral intentions (Sabbir, Taufique and Nomi, 2023; Vijayan et al., 2023). The present study confirms that stronger intentions lead to more consistent engagement in responsible e-waste management practices.
Finally, the moderating role of co-creation was significant (β = 0.075, p = 0.006), indicating that co-creation strengthens the relationship between intention and responsible consumer behavior. Co-creation also showed a direct positive association with responsible consumer behavior (β = 0.418, p < 0.001), further confirming its relevance in the proposed model. This finding highlights the importance of actively involving consumers in the value creation process, rather than treating them as passive participants. Although limited research has examined co-creation in the context of e-waste management (Martínez-Cañas et al., 2016), existing studies emphasize its role in enhancing consumer engagement and sustainability outcomes (Prahalad and Ramaswamy, 2004; Grönroos, 2008; Payne, Storbacka and Frow, 2008; Brodie et al., 2013).
Supporting studies also highlight the importance of collaboration and stakeholder engagement in improving environmental outcomes. For instance, Giampaolo et al. (2020) demonstrate how communication and proximity enhance responsible disposal behavior, while Gurauskienė (2008) emphasizes the dual role of consumers as both users and contributors in e-waste systems. Emotional and social dimensions of engagement, such as those discussed by Lodato and Loi (2014), further reinforce the importance of co-creation in fostering sustainable behavior. Overall, co-creation enhances consumer engagement through active participation, knowledge sharing, stakeholder collaboration, and feedback mechanisms, thereby promoting shared responsibility between consumers and organizations.
These findings provide empirical support for all proposed hypotheses and validate the extended TPB framework with co-creation in explaining responsible consumer behavior in e-waste management.
Limitations
This study has some limitations. Although the sample size is adequate, it may limit the generalizability of the findings. Future research can include a larger and more diverse sample from different regions and cultural contexts to improve external validity. The reliability of the Perceived Behavioral Control (PBC) construct was slightly below the recommended threshold. This may be due to differences in respondents’ understanding or the diversity of the sample. Further refinement of the scale and testing across different groups may help improve its reliability.
In addition, the study treats consumers as a single group. Future research may focus on specific segments such as socio-economic groups, age groups, gender or occupation to gain more detailed insights into responsible consumer behavior. Expanding the geographical scope and including more diverse populations may also help in better understanding contextual differences in e-waste management practices.
Declaration of Conflicting Interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
References
Aboelmaged, M. (2021) ‘E-waste recycling behaviour: An integration of recycling habits into the theory of planned behaviour’, Journal of Cleaner Production, 278, p. 124182. Available at: https://doi.org/10.1016/J.JCLEPRO.2020.124182.
Agrawal, A.K. and Rahman, Z. (2019) ‘CCV Scale: Development and Validation of Customer Co-Created Value Scale in E-Services’, Current Psychology, 38(3), pp. 720–736. Available at: https://doi.org/10.1007/s12144-017-9639-z.
Ajzen, I. (1985) ‘From Intentions to Actions: A Theory of Planned Behavior’, Action Control, pp. 11–39. Available at: https://doi.org/10.1007/978-3-642-69746-3_2.
Ajzen, I. (1991) The Theory of Planned Behavior, ORGANIZATIONAL BEHAVIOR AND HUMAN DECISION PROCESSES.
Ajzen, I. (2002) ‘Perceived Behavioral Control, Self-Efficacy, Locus of Control, and the Theory of Planned Behavior1’, Journal of Applied Social Psychology, 32(4), pp. 665–683. Available at: https://doi.org/10.1111/J.1559-1816.2002.TB00236.X.
Ajzen, I. (2012) ‘The theory of planned behavior’, Handbook of Theories of Social Psychology: Volume 1, pp. 438–459. Available at: https://doi.org/10.4135/9781446249215.n22.
Ajzen, I. and Fishbein, M. (1972) ‘Attitudes and normative beliefs as factors influencing behavioral intentions’, Journal of Personality and Social Psychology, 21(1), pp. 1–9. Available at: https://doi.org/10.1037/h0031930.
Al-Swidi, A. et al. (2014) ‘The role of subjective norms in theory of planned behavior in the context of organic food consumption’, British Food Journal, 116(10), pp. 1561–1580. Available at: https://doi.org/10.1108/BFJ-05-2013-0105/FULL/PDF.
Assiouras, I. et al. (2019) ‘Value co-creation and customer citizenship behavior’, Annals of Tourism Research, 78(May), p. 102742. Available at: https://doi.org/10.1016/j.annals.2019.102742.
Awasthi, A.K. et al. (2018) ‘E-waste management in India: A mini-review’, Waste Management and Research, 36(5), pp. 408–414. Available at: https://doi.org/10.1177/0734242X18767038.
Bagwan, W.A. (2024) ‘Electronic waste (E-waste) generation and management scenario of India, and ARIMA forecasting of E-waste processing capacity of Maharashtra state till 2030’, Waste Management Bulletin, 1(4), pp. 41–51. Available at: https://doi.org/10.1016/j.wmb.2023.08.002.
Bamberg, S. and Möser, G. (2007) ‘Twenty years after Hines, Hungerford, and Tomera: A new meta-analysis of psycho-social determinants of pro-environmental behaviour’, Journal of Environmental Psychology, 27(1), pp. 14–25. Available at: https://doi.org/10.1016/J.JENVP.2006.12.002.
Bearden, W.O., Netemeyer, R.G. and Teel, J.E. (1989) ‘Measurement of Consumer Susceptibility to Interpersonal Influence’, Journal of Consumer Research, 15(4), pp. 473–481. Available at: https://doi.org/10.1086/209186.
Bhardwaj, L.K., Rath, P. and Tokas, R. (2023) ‘E-Waste Management in Developing Countries: Current Practices, Challenges, Disposal, and Impact on Human Health and Environment’, https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-1018-2.ch007, pp. 90–102. Available at: https://doi.org/10.4018/979-8-3693-1018-2.CH007.
Boateng, G.O. et al. (2018) ‘Best Practices for Developing and Validating Scales for Health, Social, and Behavioral Research: A Primer’, Frontiers in Public Health, 6, p. 366616. Available at: https://doi.org/10.3389/FPUBH.2018.00149/ENDNOTE.
Borthakur, A. (2015) ‘Changes in composition of EEE and subsequent impacts on electronic waste’, Proceedings of Institution of Civil Engineers: Waste and Resource Management, 168(4), pp. 186–193. Available at: https://doi.org/10.1680/warm.14.00011.
Borthakur, A. and Govind, M. (2018) ‘Public understandings of E-waste and its disposal in urban India: From a review towards a conceptual framework’, Journal of Cleaner Production. Elsevier Ltd, pp. 1053–1066. Available at: https://doi.org/10.1016/j.jclepro.2017.10.218.
Botetzagias, I., Dima, A.F. and Malesios, C. (2015) ‘Extending the Theory of Planned Behavior in the context of recycling: The role of moral norms and of demographic predictors’, Resources, Conservation and Recycling, 95, pp. 58–67. Available at: https://doi.org/10.1016/J.RESCONREC.2014.12.004.
Botti, A., Grimaldi, M. and Vesci, M. (2018) ‘Customer value co-creation in a service-dominant logic perspective: Some steps toward the development of a measurement scale’, New Economic Windows, (9783319619668), pp. 137–157. Available at: https://doi.org/10.1007/978-3-319-61967-5_8.
Brodie, R.J. et al. (2013) ‘Consumer engagement in a virtual brand community: An exploratory analysis’, Journal of Business Research, 66(1), pp. 105–114. Available at: https://doi.org/10.1016/J.JBUSRES.2011.07.029.
Chan, L. and Bishop, B. (2013) ‘A moral basis for recycling: Extending the theory of planned behaviour’, Journal of Environmental Psychology, 36, pp. 96–102. Available at: https://doi.org/10.1016/J.JENVP.2013.07.010.
Chen, M.F. and Tung, P.J. (2010) ‘The moderating effect of perceived lack of facilities on consumers’ recycling intentions’, Environment and Behavior, 42(6), pp. 824–844. Available at: https://doi.org/10.1177/0013916509352833.
Cheung, S.F., Chan, D.K.S. and Wong, Z.S.Y. (1999) ‘Reexamining the Theory of Planned Behavior in Understanding Wastepaper Recycling’, Environment and Behavior, 31(5), pp. 587–612. Available at: https://doi.org/10.1177/00139169921972254.
Churchill, G.A. (1979) ‘A Paradigm for Developing Better Measures of Marketing Constructs’, Journal of Marketing Research, 16(1), p. 64. Available at: https://doi.org/10.2307/3150876.
Conner, M. and Armitage, C.J. (1998) ‘Extending the Theory of Planned Behavior: A Review and Avenues for Further Research’, Journal of Applied Social Psychology, 28(15), pp. 1429–1464. Available at: https://doi.org/10.1111/J.1559-1816.1998.TB01685.X.
Corral-Verdugo, V. and Frías-Armenta, M. (2006) ‘Personal normative beliefs, antisocial behavior, and residential water conservation’, Environment and Behavior, 38(3), pp. 406–421. Available at: https://doi.org/10.1177/0013916505282272.
Davies, J., Foxall, G.R. and Pallister, J. (2002) ‘Beyond the Intention–Behaviour Mythology’, Marketing Theory, 2(1), pp. 29–113. Available at: https://doi.org/10.1177/1470593102002001645.
Dave, K.,Dave, D.,Bhardwaj M.M.,Sharma B.,(2025) ' Global perspectives on e-waste: a systematic literature review of definitions, classifications, and challenges. International Journal of Environment and Waste Management (IJEWM), Accepted for publication.
Delcea, C. et al. (2020) ‘Determinants of Individuals’ E-Waste Recycling Decision: A Case Study from Romania’, Sustainability 2020, Vol. 12, Page 2753, 12(7), p. 2753. Available at: https://doi.org/10.3390/SU12072753.
Diletta, A., Linda, L. and Giampaolo, V. (no date) The impact of communication and proximity on citizens’ sustainable disposal of e-waste.
Dixit, S. and Badgaiyan, A.J. (2016) ‘Towards improved understanding of reverse logistics – Examining mediating role of return intention’, Resources, Conservation and Recycling, 107, pp. 115–128. Available at: https://doi.org/10.1016/J.RESCONREC.2015.11.021.
Dutta, D. and Goel, S. (2021) ‘Understanding the gap between formal and informal e-waste recycling facilities in India’, Waste Management, 125, pp. 163–171. Available at: https://doi.org/10.1016/j.wasman.2021.02.045.
Eagly, A.H. and Chaiken, S. (2007) ‘The advantages of an inclusive definition of attitude’, Social Cognition, 25(5), pp. 582–602. Available at: https://doi.org/10.1521/SOCO.2007.25.5.582.
Echegaray, F. and Hansstein, F.V. (2017) ‘Assessing the intention-behavior gap in electronic waste recycling: the case of Brazil’, Journal of Cleaner Production, 142, pp. 180–190. Available at: https://doi.org/10.1016/J.JCLEPRO.2016.05.064.
Ertz, M. et al. (2019) ‘Made to break? A taxonomy of business models on product lifetime extension’, Journal of Cleaner Production, 234, pp. 867–880. Available at: https://doi.org/10.1016/J.JCLEPRO.2019.06.264.
ESDM Market Research Report & Industry Analysis | Invest India (2022). Available at: https://www.investindia.gov.in/siru/electronic-systems-design-and-manufacturing-india-120-bn-market-opportunity (Accessed: 1 April 2025).
Fishbein, M. and Ajzen, I. (1977) ‘Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research’. Available at: https://philpapers.org/rec/FISBAI?all_versions=1 (Accessed: 1 April 2025).
Gerbing, D.W. and Anderson, J.C. (1988) ‘An Updated Paradigm for Scale Development Incorporating Unidimensionality and Its Assessment’, Journal of Marketing Research, 25(2), p. 186. Available at: https://doi.org/10.2307/3172650.
Gonul Kochan, C. et al. (2016) ‘Determinants and logistics of e-waste recycling’, International Journal of Logistics Management, 27(1), pp. 52–70. Available at: https://doi.org/10.1108/IJLM-02-2014-0021.
Greaves, M., Zibarras, L.D. and Stride, C. (2013) ‘Using the theory of planned behavior to explore environmental behavioral intentions in the workplace’, Journal of Environmental Psychology, 34, pp. 109–120. Available at: https://doi.org/10.1016/j.jenvp.2013.02.003.
Grönroos, C. (2008) ‘Service logic revisited: Who creates value? And who co-creates?’, European Business Review, 20(4), pp. 298–314. Available at: https://doi.org/10.1108/09555340810886585/FULL/PDF.
Gurauskienė, I. (2008) ‘Behaviour of Consumers as One of the Most Important Factors in E-Waste Problem’, Environmental Research, Engineering and Management, (4), pp. 56–65.
Haynes, S.N., Richard, D.C.S. and Kubany, E.S. (1995) ‘Content Validity in Psychological Assessment: A Functional Approach to Concepts and Methods’, Psychological Assessment, 7(3), pp. 238–247. Available at: https://doi.org/10.1037/1040-3590.7.3.238.
Hinkin, T. (1995) ‘A review of scale development practices in the study of organizations’, Journal of Management, 21(5), pp. 967–988. Available at: https://doi.org/10.1016/0149-2063(95)90050-0.
Jaiswal, S.K. and Mukti, S.K. (2024) ‘E-waste circularity in India: identifying and overcoming key barriers’, Journal of Material Cycles and Waste Management, 26(6), pp. 3928–3945. Available at: https://doi.org/10.1007/S10163-024-02050-1/TABLES/9.
Khan, F., Ahmed, W. and Najmi, A. (2019) ‘Understanding consumers’ behavior intentions towards dealing with the plastic waste: Perspective of a developing country’, Resources, Conservation and Recycling, 142, pp. 49–58. Available at: https://doi.org/10.1016/J.RESCONREC.2018.11.020.
Kiddee, P., Naidu, R. and Wong, M.H. (2013) ‘Electronic waste management approaches: An overview’, Waste Management, 33(5), pp. 1237–1250. Available at: https://doi.org/10.1016/J.WASMAN.2013.01.006.
Knussen, C. and Yule, F. (2008) ‘“I’m not in the habit of recycling”: The role of habitual behavior in the disposal of household waste’, Environment and Behavior, 40(5), pp. 683–702. Available at: https://doi.org/10.1177/0013916507307527.
Kumar, A. (2017) ‘Extended TPB model to understand consumer “selling” behaviour: Implications for reverse supply chain design of mobile phones’, Asia Pacific Journal of Marketing and Logistics, 29(4), pp. 721–742. Available at: https://doi.org/10.1108/APJML-09-2016-0159.
Kumar, A. (2019) ‘Exploring young adults’ e-waste recycling behaviour using an extended theory of planned behaviour model: A cross-cultural study’, Resources, Conservation and Recycling, 141(October 2018), pp. 378–389. Available at: https://doi.org/10.1016/j.resconrec.2018.10.013.
Kumar, A., Holuszko, M. and Espinosa, D.C.R. (2017) ‘E-waste: An overview on generation, collection, legislation and recycling practices’, Resources, Conservation and Recycling, 122, pp. 32–42. Available at: https://doi.org/10.1016/J.RESCONREC.2017.01.018.
Labrecque, J.S. and Angeles, L. (2016) ‘Habits in dual process models’, (January 2014).
Laeequddin, M. et al. (2022) ‘Factors That Influence the Safe Disposal Behavior of E-Waste by Electronics Consumers’, Sustainability (Switzerland), 14(9), pp. 1–16. Available at: https://doi.org/10.3390/su14094981.
Lawshe, C.H. (1975) ‘A quantitative approach to content validity”.Personnel Psychology’, Personnel Psychology, 28, pp. 563–575.
Lisi, G. (2025) ‘Growth, education and sustainability: an overview’, International Journal of Environment and Sustainable Development, 24(2), pp. 201–215. [Online] Available at: https://doi.org/10.1504/IJESD.2025.145334
Lodato, T.J. and Loi, D. (2014) ‘Where’s love in e-waste?’, in ACM International Conference Proceeding Series. Association for Computing Machinery, pp. 195–197. Available at: https://doi.org/10.1145/2662155.2667198.
Lou, J.E. (2022) ‘Developing an Extended Theory of Planned Behavior Model for Small E-Waste Recycling: An Analysis of Consumer Behavior Determinants’, Journal of Environmental Science and Engineering A, 11, pp. 71–86. Available at: https://doi.org/10.17265/2162-5298/2022.03.001.
Lynn, M.R. (1986) ‘Determination and quantification of content validity’, Nursing Research, 35(6), pp. 382–386. Available at: https://doi.org/10.1097/00006199-198611000-00017.
Mallick, P. K., Salling, K. B., Pigosso, D. C., & McAloone, T. C. (2023). Closing the loop: Establishing reverse logistics for a circular economy, a systematic review. Journal of Environmental Management, 328, 117017. https://doi.org/10.1016/j.jenvman.2022.117017
Manish and Chakraborty (2019) E-Waste Management in India: Challenges and Opportunities | TERI, The Energy and Resource Institute, TerraGreen. Available at: https://www.teriin.org/article/e-waste-management-india-challenges-and-opportunities (Accessed: 1 April 2025).
Martínez-Cañas, R. et al. (2016) ‘Consumer participation in co-creation: An enlightening model of causes and effects based on ethical values and transcendent motives’, Frontiers in Psychology, 7(MAY), pp. 1–17. Available at: https://doi.org/10.3389/fpsyg.2016.00793.
Michael, L.K., Hungund, S.S. and Sriram, K. V. (2024) ‘Factors influencing the behavior in recycling of e-waste using integrated TPB and NAM model’, Cogent Business and Management, 11(1), p. 2295605. Available at: https://doi.org/10.1080/23311975.2023.2295605/ASSET/2EB5C9FC-AA47-4EE5-9AAC-0E8F55FB0292/ASSETS/GRAPHIC/OABM_A_2295605_F0002_C.JPG.
Minister of State for Environment, Forest & Climate Change, S.A.K.C. (2023) Press Release:Press Information Bureau. Available at: https://pib.gov.in/PressReleasePage.aspx?PRID=1845822 (Accessed: 1 April 2025).
Mohamad, N.S., Thoo, A.C. and Huam, H.T. (2022) ‘The Determinants of Consumers’ E-Waste Recycling Behavior through the Lens of Extended Theory of Planned Behavior’, Sustainability (Switzerland), 14(15), pp. 1–27. Available at: https://doi.org/10.3390/su14159031.
Mohd Sharif, K.I. and Soo, W.K. (2017) ‘Factors influence consumer’s behaviour toward logistics e-waste recycling in Malaysia’, Pusat Pengajian Pengurusan Teknologi dan Logistik, Kolej Perniagaan, Universiti Utara Malaysia, pp. 90–99. Available at: http://stmlportal.net/stmlgogreen2016/pdf/p489.pdf (Accessed: 2 April 2025).
Morgado, F.F.R. et al. (2017) ‘Scale development: ten main limitations and recommendations to improve future research practices’, Psicologia, reflexao e critica : revista semestral do Departamento de Psicologia da UFRGS, 30(1), pp. 1–20. Available at: https://doi.org/10.1186/S41155-016-0057-1.
Neal, D.T. et al. (2012) ‘How do habits guide behavior? Perceived and actual triggers of habits in daily life’, Journal of Experimental Social Psychology, 48(2), pp. 492–498. Available at: https://doi.org/10.1016/J.JESP.2011.10.011.
Needhidasan, S., Samuel, M. and Chidambaram, R. (2014) ‘Electronic waste - an emerging threat to the environment of urban India’, Journal of environmental health science & engineering, 12(1). Available at: https://doi.org/10.1186/2052-336X-12-36.
Nenkov, G.Y., Inman, J.J. and Hulland, J. (2008) ‘Considering the future: The conceptualization and measurement of elaboration on potential outcomes’, Journal of Consumer Research, 35(1), pp. 126–141. Available at: https://doi.org/10.1086/525504/2/35-1-126-FG2.JPEG.
Nguyen, Hong Thi Thu et al. (2019) ‘Determinants of residents’ E-waste recycling behavioral intention: A case study from Vietnam’, Sustainability (Switzerland), 11(1), pp. 1–24. Available at: https://doi.org/10.3390/su11010164.
Nigbur, D., Lyons, E. and Uzzell, D. (2010a) ‘Attitudes, norms, identity and environmental behaviour: using an expanded theory of planned behaviour to predict participation in a kerbside recycling programme’, The British journal of social psychology, 49(Pt 2), pp. 259–284. Available at: https://doi.org/10.1348/014466609X449395.
Nigbur, D., Lyons, E. and Uzzell, D. (2010b) ‘Attitudes, norms, identity and environmental behaviour: using an expanded theory of planned behaviour to predict participation in a kerbside recycling programme’, The British journal of social psychology, 49(Pt 2), pp. 259–284. Available at: https://doi.org/10.1348/014466609X449395.
Pakpour, A.H. et al. (2014a) ‘Household waste behaviours among a community sample in Iran: an application of the theory of planned behaviour’, Waste management (New York, N.Y.), 34(6), pp. 980–986. Available at: https://doi.org/10.1016/J.WASMAN.2013.10.028.
Pakpour, A.H. et al. (2014b) ‘Household waste behaviours among a community sample in Iran: An application of the theory of planned behaviour’, Waste Management, 34(6), pp. 980–986. Available at: https://doi.org/10.1016/j.wasman.2013.10.028.
Parajuly, K. et al. (2020) ‘Behavioral change for the circular economy: A review with focus on electronic waste management in the EU’, Resources, Conservation & Recycling: X, 6, p. 100035. Available at: https://doi.org/10.1016/J.RCRX.2020.100035.
Patrao, G. and Karnik, A. (2023) ‘Identifying Drivers and Hindrances to the Disposal of Used Mobile Phones: A Study of User Behavior in the UAE’, SAGE Open, 13(3), pp. 1–19. Available at: https://doi.org/10.1177/21582440231196757.
Payne, A.F., Storbacka, K. and Frow, P. (2008) ‘Managing the co-creation of value’, Journal of the Academy of Marketing Science, 36(1), pp. 83–96. Available at: https://doi.org/10.1007/s11747-007-0070-0.
Pongen I, Ray P, Vijay T, Govindan K (2026), "Driving success in closed-loop supply chain: investigating consumer attitudes toward e-waste return". The International Journal of Logistics Management, Vol. 37 No. 2 pp. 430–451, doi: https://doi.org/10.1108/IJLM-09-2024-0576
Prahalad, C.K. and Ramaswamy, V. (2004) ‘Co-creation experiences: The next practice in value creation’, Journal of Interactive Marketing, 18(3), pp. 5–14. Available at: https://doi.org/10.1002/DIR.20015.
Rajaram, V. and Pekeur, S.W. (2014) ‘ASSESSING E-WASTE MANAGEMENT WITHIN THE MANGAUNG METROPOLITAN MUNICIPALITY, SOUTH AFRICA’, Arabian Journal of Business and Management Review (OMAN Chapter, 3(11).
Ramayah, T., Lee, J.W.C. and Lim, S. (2012) ‘Sustaining the environment through recycling: An empirical study’, Journal of Environmental Management, 102, pp. 141–147. Available at: https://doi.org/10.1016/j.jenvman.2012.02.025.
Ramzan, S. et al. (2021) ‘The adoption of online e-waste collection platform to improve environmental sustainability: an empirical study of Chinese millennials’, Management of Environmental Quality: An International Journal, 32(2), pp. 193–209. Available at: https://doi.org/10.1108/MEQ-02-2020-0028.
Ravi, V. and Shankar, R. (2015) ‘Survey of reverse logistics practices in manufacturing industries: An Indian context’, Benchmarking, 22(5), pp. 874–899. Available at: https://doi.org/10.1108/BIJ-06-2013-0066/FULL/PDF.
Rezaul, M. et al. (no date) ‘Electronic waste: present status and future perspectives of sustainable management practices in Malaysia’. Available at: https://doi.org/10.1007/s12665-014-3129-5.
Russell, S. V. et al. (2017) ‘Bringing habits and emotions into food waste behaviour’, Resources, Conservation and Recycling, 125(June), pp. 107–114. Available at: https://doi.org/10.1016/j.resconrec.2017.06.007.
Sabbir, M.M., Taufique, K.M.R. and Nomi, M. (2023) ‘Consumers’ reverse exchange behavior and e-waste recycling to promote sustainable post-consumption behavior’, Asia Pacific Journal of Marketing and Logistics, 35(10), pp. 2484–2500. Available at: https://doi.org/10.1108/APJML-07-2022-0647.
Saleem, S.M. and Jan, S.S. (2021) ‘Modified Kuppuswamy socioeconomic scale updated for the year 2021’, Indian Journal of Forensic and Community Medicine, 8(1), pp. 1–3. Available at: https://doi.org/10.18231/j.ijfcm.2021.001.
Shamim, A., Ghazali, Z. and Albinsson, P.A. (2017) ‘Construction and validation of customer value co-creation attitude scale’, Journal of Consumer Marketing, 34(7), pp. 591–602. Available at: https://doi.org/10.1108/JCM-01-2016-1664.
Shevchenko, T., Laitala, K. and Danko, Y. (2019) ‘Understanding Consumer E-Waste Recycling Behavior: Introducing a New Economic Incentive to Increase the Collection Rates’, Sustainability 2019, Vol. 11, Page 2656, 11(9), p. 2656. Available at: https://doi.org/10.3390/SU11092656.
Söderlund, M. and Öhman, N. (2005) ‘Assessing behavior before it becomes behavior: An examination of the role of intentions as a link between satisfaction and repatronizing behavior’, International Journal of Service Industry Management, 16(2), pp. 169–185. Available at: https://doi.org/10.1108/09564230510592298/FULL/PDF.
Singh, A., Goel, A., Chauhan, A., & Singh, S. K. (2025). Sustainability of electronic product manufacturing through e-waste management and reverse logistics. Sustainable Futures, 9, 100490. https://doi.org/10.1016/j.sftr.2025.100490
Tansel, B. (2017) ‘From electronic consumer products to e-wastes: Global outlook, waste quantities, recycling challenges’, Environment International. Elsevier Ltd, pp. 35–45. Available at: https://doi.org/10.1016/j.envint.2016.10.002.
Tonglet, M., Phillips, P.S. and Bates, M.P. (2004) ‘Determining the drivers for householder pro-environmental behaviour: Waste minimisation compared to recycling’, Resources, Conservation and Recycling, 42(1), pp. 27–48. Available at: https://doi.org/10.1016/j.resconrec.2004.02.001.
Tsai, A.Y.J. and Tan, A.Y.K. (2022) ‘The Expanded Theory of Planned Behavior in the Context of Environmental Protection Behaviors for Undergraduates: Roles of Moral Norms and University Class Standings’, International Journal of Environmental Research and Public Health, 19(15). Available at: https://doi.org/10.3390/ijerph19159256.
Do Valle, P.O. et al. (2004) ‘Behavioral determinants of household recycling participation: The Portuguese case’, Environment and Behavior, 36(4), pp. 505–540. Available at: https://doi.org/10.1177/0013916503260892.
Vargo, S.L. and Lusch, R.F. (2008) ‘Service-dominant logic: Continuing the evolution’, Journal of the Academy of Marketing Science, 36(1), pp. 1–10. Available at: https://doi.org/10.1007/s11747-007-0069-6.
Vijayan, R.V. et al. (2023) ‘Exploring e-waste recycling behaviour intention among the households: Evidence from India’, Cleaner Materials, 7(January), p. 100174. Available at: https://doi.org/10.1016/j.clema.2023.100174.
Venes, H., de Alvarenga Rosa, R. and Siman, R.R. (2025) ‘The impacts of municipal solid waste collection and transport technologies in smart cities: trends and challenges’, International Journal of Environment and Sustainable Development, 24(2), pp. 163–200. [Online] Available at: https://doi.org/10.1504/IJESD.2025.145338
Wang, C. et al. (2019) ‘How do policies take effect in the development of the urban mining industry? A local capability perspective: Evidence from Miluo, China (2000–2017)’, Journal of Cleaner Production, 240. Available at: https://doi.org/10.1016/j.jclepro.2019.118216.
Wang, F. et al. (2024) ‘Exploring consumer’s intention to recycle waste from household kitchen and bathroom appliances in a formal way: extending behavioral reasoning theory’, Journal of Material Cycles and Waste Management, 26(4), pp. 2226–2241. Available at: https://doi.org/10.1007/s10163-024-01963-1.
Wang, Z. et al. (2011) ‘Willingness and behavior towards e-waste recycling for residents in Beijing city, China’, Journal of Cleaner Production, 19(9–10), pp. 977–984. Available at: https://doi.org/10.1016/J.JCLEPRO.2010.09.016.
Wang, Z. et al. (2018) ‘How does information publicity influence residents’ behaviour intentions around e-waste recycling?’, Resources, Conservation and Recycling, 133, pp. 1–9. Available at: https://doi.org/10.1016/J.RESCONREC.2018.01.014.
Walke, S., Mandake, M. and Naniwadekar, M. (2025) ‘Review study of e-waste management and resource recovery system for controlling environmental pollution’, International Journal of Environment and Waste Management, 37(2), pp. 215–243. [Online] Available at: https://doi.org/10.1504/IJEWM.2025.146557
Wang, Z., Guo, D. and Wang, X. (2016) ‘Determinants of residents’ e-waste recycling behaviour intentions: Evidence from China’, Journal of Cleaner Production, 137, pp. 850–860. Available at: https://doi.org/10.1016/J.JCLEPRO.2016.07.155.
Waris, I., Khalil, S. and Dad, M. (2024) ‘Exploring household recycling participation e-wastes management: an application of TPB and NAM models’, International Journal of Environment and Waste Management, 35(4), pp. 457–477. [Online] Available at: https://doi.org/10.1504/IJEWM.2024.142738
Wood, W. (2024) ‘Habits, Goals, and Effective Behavior Change’, Current Directions in Psychological Science, 33(4), pp. 226–232. Available at: https://doi.org/10.1177/09637214241246480/ASSET/DD075459-EE23-45E8-81B4-E8ECD9DA7CAA/ASSETS/IMAGES/LARGE/10.1177_09637214241246480-FIG4.JPG.
Yusoff, M.S.B. (2019) ‘ABC of Content Validation and Content Validity Index Calculation’, Education in Medicine Journal, 11(2), pp. 49–54. Available at: https://doi.org/10.21315/eimj2019.11.2.6.
Zhang, B. et al. (2018) ‘Technological Forecasting & Social Change Motivation and challenges for e-commerce in e-waste recycling under “ Big data ” context : A perspective from household willingness in China’, (October 2017). Available at: https://doi.org/10.1016/j.techfore.2018.03.001.
TABLE LEGENDS
Table 1: The essentiality ratings on the item scale by seven experts (CVR)
Table 2: The relevance ratings on the item scale by seven experts
Table 3: Socio-demographic Details
Table 4: Summary of the measurement model.
Table 5: Discriminant validity assessment (HTMT).
Table 6: Hypothesis testing
Table 7: Model Fitness
FIGURE LEGEND
Figure 1: Research Framework
Figure 2: Measurement Model
Table 1: The essentiality ratings on the item scale by seven experts (CVR)
|
|
Essentiality |
|
|
|
|
||
|
Items |
Not essential (0) |
Useful, but not essential (1) |
Essential (2) |
N (total number of experts) |
Ne (number of experts indicating "essential") |
N/2 |
CVR |
|
Q1 |
|
|
7 |
7 |
7 |
3.5 |
1.00 |
|
Q2 |
|
|
7 |
7 |
7 |
3.5 |
1.00 |
|
Q3 |
|
1 |
6 |
7 |
6 |
3.5 |
0.71 |
|
Q4 |
|
1 |
6 |
7 |
6 |
3.5 |
0.71 |
|
Q5 |
|
|
7 |
7 |
7 |
3.5 |
1.00 |
|
Q6 |
|
1 |
6 |
7 |
6 |
3.5 |
0.71 |
|
Q7 |
|
|
7 |
7 |
7 |
3.5 |
1.00 |
|
Q8 |
|
1 |
6 |
7 |
6 |
3.5 |
0.71 |
|
Q9 |
|
0 |
7 |
7 |
7 |
3.5 |
1.00 |
|
Q10 |
|
|
7 |
7 |
7 |
3.5 |
1.00 |
|
Q11 |
|
2 |
5 |
7 |
5 |
3.5 |
0.43 |
|
Q12 |
|
0 |
7 |
7 |
7 |
3.5 |
1.00 |
|
Q13 |
|
1 |
6 |
7 |
6 |
3.5 |
0.71 |
|
Q14 |
|
1 |
6 |
7 |
6 |
3.5 |
0.71 |
|
Q15 |
|
1 |
6 |
7 |
6 |
3.5 |
0.71 |
|
Q16 |
|
2 |
5 |
7 |
5 |
3.5 |
0.43 |
|
Q17 |
|
|
7 |
7 |
7 |
3.5 |
1.00 |
|
Q18 |
|
|
7 |
7 |
7 |
3.5 |
1.00 |
|
Q19 |
|
2 |
5 |
7 |
5 |
3.5 |
0.43 |
|
Q20 |
|
1 |
6 |
7 |
6 |
3.5 |
0.71 |
|
Q21 |
|
1 |
6 |
7 |
6 |
3.5 |
0.71 |
|
Q22 |
1 |
|
6 |
7 |
6 |
3.5 |
0.71 |
|
Q23 |
|
|
7 |
7 |
7 |
3.5 |
1.00 |
|
Q24 |
|
1 |
6 |
7 |
6 |
3.5 |
0.71 |
|
Q25 |
|
|
7 |
7 |
7 |
3.5 |
1.00 |
|
Q26 |
|
1 |
6 |
7 |
6 |
3.5 |
0.71 |
|
Q27 |
|
1 |
6 |
7 |
6 |
3.5 |
0.71 |
|
Q28 |
|
1 |
6 |
7 |
6 |
3.5 |
0.71 |
|
Q29 |
|
|
7 |
7 |
7 |
3.5 |
1.00 |
|
Q30 |
|
|
7 |
7 |
7 |
3.5 |
1.00 |
|
Q31 |
|
2 |
5 |
7 |
5 |
3.5 |
0.43 |
|
Q32 |
|
2 |
5 |
7 |
5 |
3.5 |
0.43 |
|
Q33 |
|
1 |
6 |
7 |
6 |
3.5 |
0.71 |
|
Q34 |
|
2 |
5 |
7 |
5 |
3.5 |
0.43 |
|
Q35 |
|
1 |
6 |
7 |
6 |
3.5 |
0.71 |
|
Q36 |
1 |
1 |
5 |
7 |
5 |
3.5 |
0.43 |
|
Q37 |
|
2 |
5 |
7 |
5 |
3.5 |
0.43 |
|
Q38 |
|
|
7 |
7 |
7 |
3.5 |
1.00 |
|
Q39 |
|
|
7 |
7 |
7 |
3.5 |
1.00 |
|
Q40 |
|
1 |
6 |
7 |
6 |
3.5 |
0.71 |
|
Q41 |
|
2 |
5 |
7 |
5 |
3.5 |
0.43 |
|
Q42 |
|
1 |
6 |
7 |
6 |
3.5 |
0.71 |
|
Q43 |
|
|
7 |
7 |
7 |
3.5 |
1.00 |
|
Q44 |
|
|
5 |
7 |
5 |
3.5 |
0.43 |
|
Q45 |
|
1 |
6 |
7 |
6 |
3.5 |
0.71 |
|
Q46 |
|
1 |
6 |
7 |
6 |
3.5 |
0.71 |
|
Q47 |
|
1 |
6 |
7 |
6 |
3.5 |
0.71 |
|
Q48 |
|
1 |
6 |
7 |
6 |
3.5 |
0.71 |
|
Q49 |
|
2 |
5 |
7 |
5 |
3.5 |
0.43 |
|
Q50 |
|
|
7 |
7 |
7 |
3.5 |
1.00 |
|
Q51 |
|
1 |
6 |
7 |
6 |
3.5 |
0.71 |
|
0.75 |
|||||||
Table 2: The relevance ratings on the item scale by seven experts
|
Item Number |
Expert 1 |
Expert 2 |
Expert 3 |
Expert 4 |
Expert 5 |
Expert 6 |
Expert 7 |
|
|
Expert in Agreement (out of 7) |
I-CVI |
UA |
|
Q1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q2 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
6 |
0.857143 |
0 |
|
Q3 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
6 |
0.857143 |
0 |
|
Q4 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q5 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q6 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
6 |
0.857143 |
0 |
|
Q7 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q8 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
6 |
0.857143 |
1 |
|
Q9 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
6 |
0.857143 |
1 |
|
Q10 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q11 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
6 |
0.857143 |
0 |
|
Q12 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
6 |
0.857143 |
1 |
|
Q13 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q14 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q15 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
6 |
0.857143 |
0 |
|
Q16 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q17 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q18 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
6 |
0.857143 |
0 |
|
Q19 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
6 |
0.857143 |
0 |
|
Q20 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q21 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q22 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q23 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q24 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
6 |
0.857143 |
0 |
|
Q25 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q26 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q27 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
6 |
0.857143 |
0 |
|
Q28 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q29 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q30 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q31 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q32 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
6 |
0.857143 |
0 |
|
Q33 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q34 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
6 |
0.857143 |
0 |
|
Q35 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q36 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
6 |
0.857143 |
0 |
|
Q37 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
6 |
0.857143 |
0 |
|
Q38 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q39 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q40 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
6 |
0.857143 |
0 |
|
Q41 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q42 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q43 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q44 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q45 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q46 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q47 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q48 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q49 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q50 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
Q51 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
|
|
7 |
1 |
1 |
|
|
37 |
51 |
51 |
51 |
51 |
51 |
51 |
|
|
S-CVI /Ave |
0.95 |
37 |
|
|
0.71 |
0.98 |
0.98 |
0.98 |
0.98 |
0.98 |
0.98 |
|
0.942308 |
S-CVI/UA (Universal Agreement) |
|
0.71 |
Table 3: Socio-demographic details
|
Demographic |
Category |
Frequency |
Percent (%) |
|
Age (in years) |
18-30 |
344 |
77.3 |
|
|
31-40 |
71 |
16 |
|
|
41-50 |
25 |
5.6 |
|
|
51-60 |
4 |
0.9 |
|
|
60 and above |
1 |
0.2 |
|
Gender |
Male |
174 |
39.1 |
|
|
Female |
271 |
60.9 |
|
Education |
Illiterate |
1 |
0.2 |
|
|
Primary school |
1 |
0.2 |
|
|
Middle school |
3 |
0.7 |
|
|
High school |
47 |
10.6 |
|
|
Intermediate/Diploma |
66 |
14.8 |
|
|
Graduate |
259 |
58.2 |
|
|
Professional degree |
68 |
15.3 |
|
Occupation |
Unemployed |
233 |
52.4 |
|
|
Elementary occupation |
56 |
12.6 |
|
|
Plant and machine operators |
1 |
0.2 |
|
|
Craft and related trade workers |
9 |
2 |
|
|
Skilled agricultural workers |
7 |
1.6 |
|
|
Skilled workers, sales workers |
36 |
8.1 |
|
|
Clerk |
10 |
2.2 |
|
|
Technicians/Associate professionals |
11 |
2.5 |
|
|
Professional |
68 |
15.3 |
|
|
Legislators, managers |
14 |
3.1 |
|
Monthly Family Income |
≤ Rs. 6,767 |
41 |
9.2 |
|
|
Rs. 6,768 - Rs. 20,273 |
42 |
9.4 |
|
|
Rs. 20,274 - Rs. 33,792 |
51 |
11.5 |
|
|
Rs. 33,793 - Rs. 50,559 |
89 |
20 |
|
|
Rs. 50,560 - Rs. 67,586 |
92 |
20.7 |
|
|
Rs. 67,587 - Rs. 1,35,168 |
63 |
14.2 |
|
|
≥ Rs. 1,35,169 |
67 |
15.1 |
|
Marital Status |
Single |
309 |
69.4 |
|
|
Married |
132 |
29.7 |
|
|
Divorced |
2 |
0.4 |
|
|
Separated |
2 |
0.4 |
|
Family Type |
Nuclear |
269 |
60.4 |
|
|
Joint |
176 |
39.6 |
|
Socio-Economic Status (SES) |
Lower (V) |
12 |
2.7 |
|
|
Upper Lower (IV) |
55 |
12.4 |
|
|
Lower Middle (III) |
101 |
22.7 |
|
|
Upper Middle (II) |
234 |
52.6 |
|
|
Upper (I) |
43 |
9.7 |
Table 4: Summary of the measurement model.
|
Construct |
Indicator |
Factor Loading |
Cronbach’s Alpha |
Composite Reliability (rho_a) |
Composite reliability (rho_c) |
Square Root of Average Variance Extracted (SqrtAVE) |
|
ATT_Attitude |
ATT_1 |
0.529 |
0.811 |
0.815 |
0.808 |
0.615 |
|
ATT_2 |
0.672 |
|||||
|
ATT_3 |
0.587 |
|||||
|
ATT_4 |
0.538 |
|||||
|
ATT_5 |
0.566 |
|||||
|
ATT_6 |
0.682 |
|||||
|
ATT_7 |
0.706 |
|||||
|
CoC_Co-Creation |
CoC_1 |
0.585 |
0.863 |
0.863 |
0.858 |
0.566 |
|
CoC_2 |
0.514 |
|||||
|
CoC_3 |
0.567 |
|||||
|
CoC_4 |
0.532 |
|||||
|
CoC_5 |
0.467 |
|||||
|
CoC_6 |
0.543 |
|||||
|
CoC_7 |
0.583 |
|||||
|
CoC_8 |
0.584 |
|||||
|
CoC_9 |
0.686 |
|||||
|
CoC_10 |
0.451 |
|||||
|
CoC_11 |
0.533 |
|||||
|
CoC_12 |
0.675 |
|||||
|
CoC_13 |
0.596 |
|||||
|
INT_Intention |
INT_1 |
0.331 |
0.719 |
0.734 |
0.723 |
0.526 |
|
INT_2 |
0.616 |
|||||
|
INT_3 |
0.575 |
|||||
|
INT_4 |
0.525 |
|||||
|
INT_5 |
0.509 |
|||||
|
INT_6 |
0.527 |
|||||
|
INT_7 |
0.553 |
|||||
|
PBC_Perceived Behavioural Control |
PBC_1 |
0.687 |
0.616 |
0.630 |
0.616 |
0.593 |
|
PBC_2 |
0.586 |
|||||
|
PBC_3 |
0.493 |
|||||
|
RCB_Responsible Consumer Behaviour |
RCB_1 |
0.601 |
0.768 |
0.768 |
0.750 |
0.508 |
|
RCB_2 |
0.582 |
|||||
|
RCB_3 |
0.494 |
|||||
|
RCB_4 |
0.379 |
|||||
|
RCB_5 |
0.470 |
|||||
|
RCB_6 |
0.601 |
|||||
|
RCB_7 |
0.553 |
|||||
|
RCB_8 |
0.537 |
|||||
|
RCB_9 |
0.363 |
|||||
|
SN_Subjective Norms |
SN_1 |
0.638 |
0.819 |
0.819 |
0.817 |
0.653 |
|
SN_2 |
0.583 |
|||||
|
SN_3 |
0.663 |
|||||
|
SN_4 |
0.667 |
|||||
|
SN_5 |
0.691 |
|||||
|
SN_6 |
0.674 |
|||||
|
CoC_Co-Creation x INT_Intention |
|
1.000 |
|
|
|
|
Table 5: Discriminant validity assessment (HTMT).
|
ATT_Attitude |
CoC_Co-Creation |
INT_Intention |
PBC_Perceived Behavioural Control |
RCB_Responsible Consumer Behaviour |
SN_Subjective Norms |
CoC_Co-Creation x INT_Intention |
|
|
ATT_Attitude |
|
|
|
|
|
|
|
|
CoC_Co-Creation |
0.596 |
|
|
|
|
|
|
|
INT_Intention |
0.625 |
0.576 |
|
|
|
|
|
|
PBC_Perceived Behavioural Control |
0.558 |
0.525 |
0.697 |
|
|
|
|
|
RCB_Responsible Consumer Behaviour |
0.629 |
0.731 |
0.716 |
0.869 |
|
|
|
|
SN_Subjective Norms |
0.335 |
0.461 |
0.711 |
0.677 |
0.520 |
|
|
|
CoC_Co-Creation x INT_Intention |
0.128 |
0.319 |
0.194 |
0.085 |
0.283 |
0.063 |
|
Table 6: Hypothesis testing
|
|
Path |
Standard deviation (STDEV) |
Standard Error (SE) |
T statistics (|O/STDEV|) |
P values |
Results |
|
H1: ATT_Attitude -> INT_Intention |
0.371 |
0.043 |
0.00204 |
7.252 |
0.000 |
Supported |
|
H2: SN_Subjective Norms -> INT_Intention |
0.485 |
0.044 |
0.0021 |
9.078 |
0.000 |
Supported |
|
H3: PBC_Perceived Behavioural Control -> INT_Intention |
0.162 |
0.047 |
0.00223 |
3.373 |
0.001 |
Supported |
|
H4: INT_Intention -> RCB_Responsible Consumer Behaviour |
0.443 |
0.046 |
0.00219 |
7.220 |
0.000 |
Supported |
|
H5: CoC_Co-Creation -> RCB_Responsible Consumer Behaviour |
0.438 |
0.043 |
0.00204 |
9.807 |
0.000 |
Supported |
|
H6: CoC_Co-Creation x INT_Intention -> RCB_Responsible Consumer Behaviour |
0.086 |
0.027 |
0.00129 |
2.759 |
0.006 |
Supported |
Table 7: Model Fitness
|
Saturated model |
Estimated model |
|
|
SRMR |
0.081 |
0.083 |
|
d_ULS |
6.766 |
7.102 |
|
d_G |
1.674 |
1.721 |
|
Chi-square |
3654.822 |
3731.910 |
|
NFI |
0.574 |
0.565 |
Figure 1: Research Framework
|
ATTITUDE (ATT) |
|
SUBJECTIVE NORMS (SN) |
|
PERCEIVED BEHAVIORAL CONTROL (PBC) |
|
RESPONSIBLE CONSUMER BEHAVIOR (RCB) |
|
INTENTION (INT) |
|
Co-Creation (CoC) |
Figure 2: Mesurement Model
