How does consumer awareness of the waste of electrical and electronic equipment affect reverse logistics? Extended triple bottom line approach

Abstract

This study explores how government support and consumer awareness affect the waste of electrical and electronic equipment reverse logistics in the circular economy framework. To improve waste of electrical and electronic equipment reverse logistics consumer awareness, the extended triple bottom line concept introduces technology with traditional economic, social, and environmental aspects. The impact of government initiatives on consumer awareness and socioeconomic factors on the intention to reuse the waste of electrical and electronic equipment reverse logistics is examined. Partial least squares structural equation modeling was used to analyze survey data from 330 Chinese participants. The results revealed that government policies significantly influence extended triple bottom line awareness while socioeconomic factors impact waste of electrical and electronic equipment reverse logistics reuse intentions. However, direct government support does not significantly impact the expected service quality of waste of electrical and electronic equipment reverse logistics. These findings improve the literature by identifying relevant factors and boosting waste of electrical and electronic equipment reverse logistics comprehension in a circular economy environment, driving the development of efficient and sustainable waste management strategies.

Keywords

Chinese government circular economy extended triple bottom line reverse logistics waste of electrical and electronic equipment

Introduction

As a response to climate change and resource scarcity, circular economy (CE) is becoming an increasingly viable alternative to linear economy.¹ CE focuses on limiting resource consumption, reusing items, and managing old products to prolong the end-of-life (EoL) cycle.^2,3 The CE framework is deeply rooted in the cradle-to-cradle philosophy, which advocates for the design of products that are intended to re-enter the environment or be transformed into new products at the end of their lifecycle.⁴ Therefore, effective waste management is indispensable for a seamless transition to a CE.^5,6 The principles of the CE necessitate efficient reverse logistics (RL).^7,8 Rogers and Tibben-Lembke⁹ differentiated RL from traditional forward logistics, defining it as the process of planning, implementing, and managing the efficient return of materials and goods from consumption back to their origin to recover value or facilitate proper disposal. Expanding on this foundation, Rogers and Tibben-Lembke¹⁰ elaborated on RL as “the process of planning, implementing, and controlling the efficient, cost-effective flow of raw materials, in-process inventory, finished goods, and related information from the point of consumption to the point of origin for the purpose of recapturing or creating value or proper disposal.” RL manages product returns, refurbishment, remanufacturing, and recycling and disposal.¹¹

Efficient waste of electrical and electronic equipment (WEEE) management is crucial for a successful CE.^12,13 In 2019, global WEEE production reached 53.6 million metric tons, expected to rise to 74.7 Mt by 2030, a significant acceleration from 9.2 Mt generated in 2014.¹² Key to WEEE RL is consumers’ active involvement in recycling practices, which directly influences the process's efficiency and effectiveness.^14,15

Governments increasingly discern the vital consumer role in WEEE recycling and are intensifying efforts to raise awareness. China, as the global WEEE production leader generating about 10.1 million metric tons in 2019, nearly 20% of the global total, exemplifies government efforts to increase consumer awareness.¹² However, there is a notable lack of eco-consciousness among Chinese consumers regarding e-waste management. Ramzan et al.¹⁶ noted an increasing trend of eco-consciousness among Chinese consumers, highlighting heightened awareness of e-waste issues and the importance of sustainable management practices. Nonetheless, challenges persist in translating this awareness into meaningful participation in formal WEEE recycling processes, especially among younger demographics. Additionally, there is a lack of understanding regarding the risks associated with WEEE disposal, with consumers generally being less informed about these concerns.¹⁷

Unlike the bottom-up waste management policies adopted by many countries including the European Union, China has implemented top-down policies grounded in national political objectives.¹⁸ Thus, China has been a leader in applying the CE paradigm to WEEE and raising consumer awareness of WEEE recycling.¹⁹ This proactive role of the Chinese government illuminates how top-down policies might promote sustainable waste management and the CE paradigm shift.¹

The original triple bottom line (TBL) framework—economic, social, environmental—is significant in sustainable practices²⁰ but falls short in WEEE management due to rapid technological advancement. Incorporating technology as a fourth pillar in the extended TBL (ETBL) enables thorough RL initiative evaluation and corresponds with consumers’ desire for improving WEEE management practices. A better understanding of the ETBL sets customer expectations for RL service quality for WEEE. Effective WEEE management relies on technologically sophisticated techniques that balance economic viability, environmental sustainability, and social equality. Usages of remanufactured products face challenges due to consumers’ misinformation and unfamiliarity with the product which leads to a negative perception of remanufactured products.²¹ In this sense, this study observes the consumer's socioeconomic status (SES)—education and income levels—as a potential moderator influencing their reuse intentions. This study addresses the following research questions:

RQ1. Does government support enhance consumer awareness, consequently improving anticipated WEEE RL service quality?

RQ2. Do economic, social, environmental, and technological pillars of consumer awareness enhance the expected service quality in WEEE RL?

RQ3. How does SES, particularly education and income, moderate the consumer's intention to reuse remanufactured electronic goods?

Addressing the research gaps allows this study to provide the following contributions. First, this study integrates technology awareness into the TBL framework within WEEE management. Our contribution enriches the TBL framework by aligning it with contemporary sustainable practice challenges, contributing to WEEE RL initiatives, and filling a void in the existing literature.²² By acknowledging and incorporating this link, the study provides a more comprehensive understanding of how to drive effective and sustainable WEEE management strategies, ultimately supporting the broader goals of environmental sustainability and resource conservation. Second, comprehending the role of governmental support in shaping consumer awareness toward WEEE recycling practices is key. Evaluating the efficacy of top-down strategies in notable WEEE producers, such as China, furnishes substantial insights. Third, investigating the impact of SES on the intention to reuse remanufactured electronics adds a fresh dimension to existing research. While previous studies primarily accentuate service quality, this approach endeavors to encompass socioeconomic factors, potentially yielding additional implications for consumer behavior and sustainable practices in WEEE RL. Understanding how socioeconomic factors influence consumer behavior allows policymakers and companies to tailor recycling programs and marketing strategies more effectively to different demographic groups.

Theoretical framework and hypothesis development

Extended TBL

Elkington’s²⁰ TBL theory is a fundamental sustainability framework that integrates economic, social, and environmental factors to encourages organizations to balance economic viability, stakeholder welfare, and environmental impact. Despite its widespread use across sectors to promote sustainability, the conventional TBL paradigm seems to undervalue technology, especially in WEEE management, where technological awareness is important. Given that WEEE encompasses a diverse array of electronic appliances—from household devices to consumer electronics, as well as equipment used in industrial and commercial contexts—having a foundational understanding of technology is crucial to effectively grasp its RL processes and distinctive traits.^23,24

In WEEE management, consumers’ technology awareness fundamentally alters consumer participation with waste management systems, instilling a proactive attitude toward safe WEEE disposal.¹² Technology-savvy consumers may benefit from innovative WEEE management solutions including digital platforms for scheduling pickups and targeted notifications.²² Innovations such as transparency and convenience build confidence in service quality and their intention to reuse.²⁵ Studies show that individuals who are aware of WEEE technology are more likely to recycle and avoid incorrect disposal, reducing their environmental impact.²⁶ Thus, technology awareness in WEEE transforms passive consumers into active and responsible players in sustainable WEEE management.

This research proposes an extension to the original TBL theory by introducing the technology dimension as a fourth construct. Figure 1 illustrates the connection between WEEE's forward logistics and RL and highlights that consumer awareness initiates the start of RL.

Figure 1.

Conceptual of waste of electrical and electronic equipment (WEEE) logistics.

Expected service quality of WEEE RL

Service quality refers to the degree and extent to which a service meets customers’ expectations and requirements²⁷ and is a critical determinant of customer satisfaction and loyalty, influencing customers’ overall perceptions and evaluations of a service. The SERVQUAL model,²⁷ has been instrumental in assessing service quality. Since its inception, the SERVQUAL model has served as a foundation for further developments in service quality research. Recent research has extended the exploration of service quality beyond traditional industries, such as banking²⁸ and healthcare,²⁹ yet there remains a notable gap in the literature concerning its application specifically to WEEE RL.³⁰ Service quality in WEEE management is a new contribution to this field in that it tackles the particular difficulties and potentials of electronic waste recycling.

Government support and service quality of WEEE RL

Previous research highlights the need for both bottom-up and top-down approaches to completely enable CE and encourage RL.³¹ Among many factors that constitute a bidirectional strategy, environmental concerns are increasing desire for governments and institutions to be more commanding.³² The Paris Agreement (2015) and subsequent Conference of the Parties (COP) meetings have shown that governments have committed to reducing carbon emissions by adopting nationally determined contributions (NDCs). Previous studies argue that institutional pressure on all stakeholders by governments may reduce carbon emissions most effectively.³³ Heydari et al.³⁴ and Singh and Raj³⁵ studies have shown government support as the most critical element in fostering a CE system. According to institutional theory, governments may influence industry standards and create an atmosphere that encourages CE and RL adoption via coercive and normative constraints.³⁶ Bernstein and Hoffmann³⁷ have asserted that governments have the greatest potential to mitigate carbon emissions by exercising institutional pressure on all other stakeholders. As explored by Akadiri et al.,³⁸ governments of developing states must partake in the role of trailblazers—that compel firms to adopt sustainable business practices, as private firms inherently lack the motivation to do so. Based on this discussion, we hypothesize that a supportive government may improve WEEE RL service quality.

H1: Government support has a positive effect on the expected service quality of WEEE RL.

Government support and ETBL of WEEE RL

The TBL framework is frequently discussed in relation to WEEE RL, highlighting the importance of establishing a closed-loop supply chain to maximize financial, social, and economic performance.^39–41 Agrawal and Singh⁴² argued that RL and disposal decisions affect TBL performance in the Indian EEE sector. Similarly, Darbari et al.⁴³ employed fuzzy criteria programming to study WEEE RL TBL performance for consumer electronics. These researches focused on WEEE RL and TBL performance, while others have shown how TBL awareness might improve RL operations due to enhanced attention and expectations.⁴⁴

Building on these discussions, it is critical to understand how TBL awareness factors into WEEE RL. For instance, Zeng et al.⁴⁵ have confirmed that government mandates increase public knowledge of WEEE recycling. Government initiatives indirectly affect customers’ WEEE reuse intent via personal norms and recycling attitudes. Additionally, Iyer and Reczek⁴⁶ also noted that public policy, green marketing, and pro-environmental conduct promote TBL awareness. Previous research has linked government funding to public awareness, thus it seems sense to link it to TBL awareness. Government requirements may raise consumer TBL knowledge via public policy. Following this discovery, we suggest the following hypotheses:

H2a: Government support has a positive effect on consumers’ economic awareness of WEEE RL.

H3a: Government support has a positive effect on consumers’ social awareness of WEEE RL.

H4a: Government support has a positive effect on consumers’ environmental awareness of WEEE RL.

Technological awareness of consumers should be an extension of the TBL framework as it can positively affect WEEE RL. The RL process for WEEE is inherently complex, involving multiple operational stages due to the specific metals and design-based components it contains.⁴⁷ This underscores the importance of technological awareness in effectively navigating these complexities. Awareness of this layer is also important as it can lead to actions such as choosing sustainable products and advocating for policies that promote sustainability.^48,49 Gonul Kochan et al.⁵⁰ illustrated that the consumer perspective on recycling is important in participatory behavior. This technological awareness can come from government support. For example, a case study in Brazil illustrates the importance of consumer education regarding the proper handling and disposal of e-waste in improving waste management and sustainable practices.⁵¹ Islam and Huda²³ also support this claim by stating that government incentives and awareness are crucial to consumers’ attitudes toward WEEE recycling. According to a survey in Zhejiang province in China, consumers are unaware of formal and informal collectors of WEEE and often choose the informal collector for convenience; government support is required to provide incentives and build educational programs to raise technological awareness for an increase in the proper disposal of WEEE.⁵² As shown, technology is pivotal for WEEE, as its RL process is largely embedded with technological complexities. Given its widespread presence, studying ETBL with a focus on consumer awareness is academically valuable. This leads to our fifth hypothesis:

H5a: Government support has a positive effect on consumers’ technological awareness of WEEE RL.

ETBL and expected service quality of WEEE RL

The current research examines the TBL strategy from a consumer-centric viewpoint, focusing on WEEE RL attitudes and behaviors. Studies found that Zhejiang citizens choose informal EEE collecting routes owing to convenience and higher pricing.⁵² Qu et al.⁵³ highlighted how the Chinese government's “buy a new one with a used one” program pushed consumers to utilize commercial and government-owned recycling facilities instead of illicit scrap merchants. This shows that economically informed customers choose government recycling enterprises, which provide higher compensation and convenient service, since they value good service quality. In India, economically informed customers were motivated to properly dispose of WEEE for financial rewards.⁵⁴ Consumers with economic knowledge anticipate great service quality while recycling WEEE. This shows how important service quality is for addressing customer WEEE management needs. These studies show that economic knowledge affects WEEE service quality expectations. The following are the study's hypotheses.

H2b: Economic awareness has a positive effect on the expected service quality of WEEE RL.

Social norms, which govern the standards of behavior of individuals, may promote environmentally friendly actions. To modify an individual's attitude toward recycling, a societal norm must be established to promote awareness, which is then internalized into a personal standard. This psychological process increases awareness and responsibility for recycling.^55,56 Consumers are more inclined to recycle when it becomes a personal norm.⁵⁷ This increased awareness is crucial for raising WEEE RL awareness.⁵⁸ Meeting high service quality standards is crucial since satisfied customers tend to recycle more.

Individuals expect recycling companies to provide better service because societal standards encourage environmental friendliness and social awareness. Therefore, we suggest that consumers will be more inclined to engage in recycling behaviors due to the service that they are provided with. We propose the following hypothesis:

H3b: Social awareness has a positive effect on the expected service quality of WEEE RL.

Environmental awareness plays a role in fostering increased participation in WEEE recycling,⁵⁹ as evidenced by low participation in recycling practices as there is a relatively low level of awareness (9%) in Bangladesh.⁶⁰ Park et al.⁶¹ illustrated that an increase in environmental awareness is associated with the increase in the use of door-to-door WEEE recycling services and found that the majority (90.7%) of the consumers were satisfied with the service quality. From these findings, we assume that environmentally aware consumers have higher expectations regarding the quality of recycling infrastructure. This was also exemplified in China, where consumers preferred private or governmental organizations with quality certifications in remanufactured products.⁶² Thus, we propose the following hypotheses:

H4b: Environmental awareness has a positive effect on the expected service quality of WEEE RL.

Consumers who have technological awareness are willing to purchase and reuse second-hand WEEE products.⁶³ Consumer privacy concerns regarding electronics can be indicative of technological awareness. Consumers with such awareness were surprisingly more willing to participate in WEEE RL when provided with the trust that comes from the expectation of high service quality in the collection service.^64,65 In contrast, Gollakota et al.⁶⁶ have shown that low levels of technological awareness lead to lower participation in WEEE RL; it is therefore suggested to educate the population about EoL and provide a standard certificate for reusable EEE. For these reasons, we hypothesize that consumers will expect a higher service quality with higher technological awareness. Thus, we propose the following hypothesis:

H5b: Technological awareness has a positive effect on the expected service quality of WEEE RL.

Expected service quality and reuse intention of WEEE RL

The direct relationship between expected service quality and intention to reuse a service is a focal point in several marketing and consumer behavior studies.^67,68 In the context of WEEE RL, service quality encompasses several dimensions including the efficiency and effectiveness of WEEE collection, transparency of the recycling process,⁶² and the assurance of data security for electronic devices.⁶⁹ An increase in the expected service quality, driven by a heightened awareness of these various aspects, is posited to enhance the intention to reuse WEEE RL.

This is grounded in the theory of reasoned action (TRA)⁷⁰ which asserts that behavioral intentions (e.g. intention to reuse) are predominantly shaped by an individual's attitude toward the behavior and subjective norms. In this case, the consumer's attitude toward reusing WEEE RL is significantly influenced by their perception of service quality. It follows that services perceived as high-quality are more likely to engender positive attitudes and therefore, foster reuse intentions. Liu et al.⁷¹ highlighted the crucial role that enhancing the efficacy of WEEE recycling services plays in shaping consumer behavior. Thus, we posit the following hypothesis:

H6: Expected service quality has a positive effect on the intention to reuse WEEE RL.

Among SES dimensions, education level has continuously been linked to pro-environmental behaviors.⁷² Higher education levels correlate with heightened environmental awareness⁷³ and a greater propensity for recycling and reusing.⁷⁴ In the context of WEEE management, individuals with higher education levels not only demonstrate greater discernment regarding service quality but also exhibit a more comprehensive understanding of effective WEEE disposal and recycling practices.⁷⁵ This research hypothesizes that education moderates the effect of predicted service quality on WEEE RL reuse. Higher education levels may improve the positive relationship between expected service quality and reuse intentions. Accordingly, we propose the following hypothesis:

H7: Higher education levels have a positive effect on expected service quality on the intention to reuse WEEE RL.

Income level is also recognized as a significant determinant of consumer behaviors, particularly in relation to environmentally friendly practices such as recycling and reusing.⁷⁶ Individuals with higher income levels often have the financial capability to prioritize higher service quality in areas like WEEE RL, which is crucial in increasing their likelihood of engaging in reuse activities.⁷⁷ This demographic often emphasizes aspects like data security and the efficiency of recycling processes. Additionally, when service quality exceeds user expectations, these dynamics can significantly enhance reuse intentions.⁷⁸ Notably, higher-income individuals typically display a substantial willingness to pay (WTP) for green services, solidifying their commitment to reuse without the lure of financial incentives.⁷⁹

This study posits that income level serves as a moderator enhancing the effect of perceived service quality on reuse intentions. Specifically, higher-income consumers will respond more positively to high-quality service, thereby strengthening the impact of service quality on their intention to engage in WEEE RL. Therefore, we propose the following hypothesis:

H8: Higher income levels have a positive effect on expected service quality on the intention to reuse WEEE RL.

Based on these observations, we developed our assumptions and theoretical framework in Figure 2.

Figure 2.

Theoretical framework.

Data and methodology

Research context: Intention to reuse

China is the greatest manufacturer of EEE and WEEE, hence efforts must include customer desire to reuse WEEE RL.⁸⁰ Our research examines how government support raises awareness, expectations, and ultimately reuse intentions. While some studies underscore the importance of institutional pressure and government role in the RL narrative,⁸¹ others have emphasized the significance of citizen support and consumer intention.^52,82 Our study tries to bridge these two perspectives by identifying how government support can lead to improved awareness, higher expectations, and ultimately the intention to reuse. For this purpose, we have conducted a survey to identify the reuse intention of WEEE RL by Chinese residents. For this research, we have focused primarily on WEEE related to consumer electronics and home appliances to align with our research objective. Table 1 depicts the results of this survey in detail.

Table 1.

Sample demographic.

Variables	Category	Frequency (N)	Percent (%)
Gender	Male	153	46.36
	Female	177	53.64
Age	< 20	39	11.82
	20–29	161	48.79
	30–39	65	19.7
	40–49	38	11.52
	50–59	18	5.45
	Above 60	9	2.73
Education level	Less than high school diploma	39	11.82
	Graduated high school	161	48.79
	Graduated two-year bachelor course college	65	19.7
	Graduated bachelor course college	133	40.3
	Graduated master's course college or above	18	5.45
Occupation	Student	91	27.58
	Civil servant	51	15.45
	Employee	120	36.36
	Private business	57	17.27
	Housewife	11	3.33
Monthly income	< 3000 yuan (¥)	82	24.85
	3000–5000 yuan (¥)	91	27.58
	5000–9000 yuan (¥)	85	25.76
	9000–15,000 yuan (¥)	53	16.06
	Above, 15,000 yuan (¥)	19	5.76
Region	Dongbei region	68	20.61
	Huabei region	65	19.7
	Huazhong	63	19.09
	Huadong region	58	17.58
	Huanan region	35	10.61
	Xibei region	18	5.45
	Xinan region	23	6.97
Total		330	100

Data and sample

The survey data was collected using a Chinese survey service company. The sample consisted of 330 individuals, with gender distribution balanced between 153 males (46.36%) and 177 females (53.64%). Most respondents were in their 20 and 30 s, with individuals in their 20 s representing the largest group (48.79%). Most respondents held at least a vocational college degree or higher, with college graduates comprising the largest educational group (48.79%). Company employees constituted the largest occupational group (36.36%). Geographically, respondents were mainly located in the Huabei, Huazhong, and Dongbei regions of China.

Analysis methods

Partial least squares structural equation modeling (PLS-SEM) was used to establish constructs by combining observed variables and assessing the relationships between them.⁸³ It is a reliable method for identifying optimized factors by extracting and amplifying the variables’ influence on endogenous variables. The PLS-SEM analysis was conducted using the “plssem” command in STATA 17 syntax. The model fit test was initially assessed using a confirmatory factor analysis to ensure the validity and reliability of the research model, as well as to examine the convergence and discriminant validity. Subsequently, the hypothesis between constructs in the structural model was examined. Once significant results were obtained in the structural model, a re-sampling approach and 5000 iterations for bootstrapping, were employed to re-evaluate the significance of indirect effects. This process ensured the robustness and accuracy of the findings in the study.

Variables

The survey questions pertaining to the main constructs in this study were designed using a 5-point Likert scale. This study examines a comprehensive set of variables that have the potential to impact the intention to reuse WEEE. It encompasses nine distinct variables, namely government support, economic awareness, social awareness, environmental awareness, technological awareness, expected service quality, education level, income level, and the intention to reuse. Government support refers to the level of assistance and initiatives provided by the government to promote the reutilization of WEEE. Expected service quality is the dependent variable affected by government support and can be understood as the consumers’ anticipated perception of the level of WEEE RL. The ETBL framework introduces four variables that factor into to the connection between government support and expected service quality.

The ultimate dependent variable in this study is the intention to reuse WEEE RL, which is influenced by two socioeconomic norms: education level and income level. By examining these variables, this research aims to provide valuable insights into the factors that drive or hinder the intention to reuse.

Measurement assessment

The acceptability of this model was assessed by evaluating the reliability, convergent validity, and discriminant validity of the observed items associated with each construct. As per the data presented in Table 2, all the factor loading values surpass the established threshold of 0.7, which signifies a dependable level of reliability.⁸⁴ A total of 32 items have been selected for this analysis, and all values were observed to be higher than 0.7 at a significance level of 0.01.

Table 2.

Confirmatory factor loadings.

Construct	Code	Scale item	SFL	Mean	SD
GS	CA = 0.914, CR = 0.932, rho_A = 0.973, AVE = 0.726, Dixit and Badgaiyan³² and Islam et al.⁶⁰
	GS1	I believe that the WEEE collection should be publicly managed by authorized collection centers.	0.88	3.80	1.14
	GS2	I think that governments should inform people that it is incorrect to dispose of e-waste with regular trash.	0.75	3.62	1.15
	GS3	I think that governments need to improve their e-waste management system.	0.88	3.86	1.05
	GS4	I consider recycling e-waste as a valuable contribution to society and that government support should be provided for it	0.90	3.88	1.06
	GS5	I believe that laws and regulations should provide sufficient guidance for firms to protect the environment.	0.88	3.81	1.11
ECA	CA = 0.861, CR = 0.883, rho_A = 0.848, AVE = 0.727, Laitala et al.⁴⁰
	ECA1	I consider various factors and costs when deciding to use e-waste repair services.	0.81	3.68	1.10
	ECA2	I believe that high costs deter many consumers from opting for repair services.	0.90	3.84	1.04
	ECA3	I see repair services as integral to a business model.	0.89	3.82	0.99
SA	CA = 0.902, CR = 0.927, rho_A = 0.915, AVE = 0.783, Laitala et al.⁴⁰ and Marrucci et al.⁵⁶
	SA1	I believe organizations should create products from reclaimed and reused e-waste materials.	0.87	3.87	1.08
	SA2	I am likely to participate in company-sponsored e-waste collection programs in the future.	0.82	3.78	1.11
	SA3	I have avoided buying a product because of its potential harm to people and the environment.	0.89	3.91	1.03
	SA4	I believe that monitoring and supervision of businesses is essential to improve accountability.	0.83	3.75	1.06
ENA	CA = 0.908, CR = 0.935, rho_A = 0.921, AVE = 0.748, Amores-Salvadó et al.⁵⁹ and Echegaray and Hansstein⁴¹
	ENA1	I think eco-friendly products aid in improving recycling processes.	0.87	3.81	1.08
	ENA2	I favor products packaged in biodegradable materials for a lesser environmental impact.	0.90	3.82	1.10
	ENA3	I am aware that electronic waste contains valuable metals like copper, silver, gold, etc.	0.87	3.85	1.08
	ENA4	I give preference to products bearing green ecolabels.	0.90	3.84	1.02
TA	CA = 0.832, CR = 0.899, rho_A = 0.834, AVE = 0.708, Chang⁴⁸ and Wang et al.⁶⁵
	TA1	I was already aware of the concept of remanufacturing before this survey.	0.88	4.00	0.92
	TA2	I usually pay close attention to information about remanufactured products.	0.87	4.03	0.94
	TA3	I worry that the unregulated nature of the e-waste repair market could lead to the sale of low-quality products.	0.84	4.01	0.90
ESQ	CA = 0.948, CR = 0.956, rho_A = 0.951, AVE = 0.738, Xie et al.⁶⁸ and Roh et al.⁸⁵
	ESQ1	I expect the staff of the recycling company to be professional and tidy during service delivery.	0.82	3.92	1.04
	ESQ2	I believe the recycling company should fulfill their services as promised, and in a timely manner.	0.86	3.97	1.02
	ESQ3	I think my firm responsibly manages waste disposal and actively combats environmental pollution during production.	0.86	3.99	1.03
	ESQ4	I expect the recycling company to be responsive to customer requests.	0.85	4.02	0.96
	ESQ5	I feel secure in my transactions with the recycling company and find their responses to be knowledgeable.	0.83	3.87	1.02
	ESQ6	I consider the employees of the e-waste management service provider to be polite.	0.83	3.95	0.91
	ESQ7	I find the company dependable in handling customer service and in delivering as promised.	0.85	3.98	1.02
	ESQ8	I expect my cloud system to provide the necessary user reports.	0.86	3.94	1.02
	ESQ9	I feel the employees of the recycling company should understand my needs and offer personalized attention.	0.80	3.98	0.95
IR	CA = 0.885, CR = 0.919, rho_A =0.920, AVE = 0.805, Xie et al.⁶⁸ and van Riel et al.⁸²
	IR1	I am likely to continue using this firm's services.	0.90	3.92	1.01
	IR2	I believe that my customers are loyal to my products and services.	0.89	3.93	1.04
	IR3	I am likely to recommend this firm to someone seeking my advice.	0.89	3.92	1.05
	IR4	I am inclined to speak positively about this firm to others.	0.91	3.91	0.99

Note: (1) SFL: standardized factor loading; SD: standard deviation; CA: Cronbach's alpha; CR: composite reliability; AVE: average variance extracted; GS: government support; ECA: economic awareness; SA: social awareness; ENA: environmental awareness; TA: technological awareness; WEEE RL: waste of electrical and electronic equipment reverse logistics; ESQ: expected service quality of WEEE RL; IR: intention to reuse WEEE RL. (2) All SFLs are significant at 0.01.

Table 2 includes various metrics including Cronbach's alpha, composite reliability, correlations among latent variables, average variance extracted (AVE), and the square root of AVE. Hair et al.⁸³ posited that when Cronbach's alpha and composite reliability values surpass 0.7, the internal consistency can be deemed satisfactory. Considering the results in this context, all variables exhibited composite reliability values of 0.8 or higher, a value notably exceeding the standard threshold. This observation suggests the presence of satisfactory convergent validity.

Discriminant validity can be evaluated by comparing the AVE with the SQRT (AVE). All calculated SQRT (AVE) reached levels beyond 0.8. As these values are above the recommended benchmark of 0.5, the reliability of the analysis is well-secured. There is a noticeable gap between AVEs when compared with SQRT (AVE), suggesting that the AVE of each latent variable exhibits a stronger correlation with its own measure compared to other constructs.⁸⁴ Furthermore, with rho_A values all exceeding 0.7, PLS-SEM also demonstrates robust validity.⁸⁶ In this research, the minimum rho value identified among the latent variables within the model was 0.834, further substantiating the model's validity.⁸³

Common method bias (CMB) test

Table 3 presents the reliability and validity of the study model through the correlation analysis of constructs. The variance inflation factor, a measure utilized to quantify the severity of multicollinearity in a regression analysis, demonstrated minimum and maximum values of 1.000 and 1.053, respectively, well below the established threshold of 3.3.⁸⁷ The consequent risk of collinearity within the model is thus deemed minimal. Furthermore, the implementation of Harman's single factor test through principal component analysis yielded a value of 24.05%, falling notably short of the 50% threshold,⁸⁸ indicating the lack of a dominant single factor and thereby providing no substantial evidence of CMB.

Table 3.

Inter-construct correlations, convergent and discriminant validity.

Construct	1	2	3	4	5	6	7
1 GS	0 . 860	0.142	0.166	0.157	0.157	0.150	0.266
2 ECA	0.122	0.865	0.148	0.127	0.193	0.083	0.156
3 SA	0.155	0.121	0.853	0.067	0.152	0.211	0.261
4 ENA	0.146	0.110	0.042	0.884	0.150	0.227	0.162
5 TA	0.143	0.167	0.126	0.129	0.865	0.209	0.371
6 ESQ	0.152	0.076	0.202	0.215	0.189	0.841	0.404
7 IR	0.240	0.093	0.221	0.132	0.302	0.362	0.640

Note: (1) GS: government support; ECA: economic awareness; SA: social awareness; ENA: environmental awareness; TA: technological awareness; WEEE RL: waste of electrical and electronic equipment reverse logistics; ESQ: expected service quality of WEEE RL; IR: intention to reuse WEEE RL. (2) The bolded diagonals represent the square root of AVE. (3) Below the bold diagonal are inter-construct correlations, while above the bold diagonal are HTMT correlations.

Additionally, the correlation coefficients observed between a cost-focus strategy constituted marker variable (M1) and the original constructs spanned a range of 0.011–0.281. These correlations were found to be statistically insignificant, thereby suggesting an absence of a theoretical relationship between M1 and the existing constructs Lindell and Whitney.⁸⁹ Collectively, these findings provide reassurance concerning the integrity of the study's research model, indicating that the risk of CMB is highly unlikely.

Results and discussion

Structural model assessment using PLS-SEM

The outcomes of the PLS-SEM estimation are illustrated in Figure 3. It presents the hypotheses of this study, the standardized path coefficients connecting the constructs, and their respective statistical significance. First, the path from government support to the expected service quality of WEEE RL was not supported due to statistical insignificance (β = 0.082, p > 0.1). Therefore, the results were unable to support H1. However, all paths for government support to ETBL awareness were supported; government support → economic awareness (β = 0.122, p < 0.05), government support → social awareness (β = 0.155, p < 0.01), government support → environmental awareness (β = 0.146, p < 0.01), government support → technological awareness (β = 0.143, p < 0.01). Thus, this finding supports H2a, H3a, H4a, and H5a.

Figure 3.

Results of structural equation model. Note: (1) Path coefficients are standardized, (2) *p < 0.05, **p < 0.01, ***p < 0.001, ns = not supported.

Economic awareness was revealed to have no significant connection to the expected service quality of WEEE RL (β = 0.004, p > 0.1), effectively denying H2b. Regardless, the other three ETBL elements were shown to be relevant. By examining the paths to the expected service quality of WEEE RL, an interaction between social awareness (β = 0.165), environmental awareness (β = 0.179), and technological awareness (β = 0.133) can be observed at a significance level of 0.01. Therefore, we were able to confirm H3b, H4b, and H5b. The path from the expected service quality of WEEE RL to the intention to reuse WEEE RL was also proven to be significant (β = 0.250, p < 0.001). This path is influenced by two control variables, namely education level and income level. Both of these were proven to be statistically relevant: expected service quality of WEEE RL × education level → intention to reuse WEEE RL (β = 0.382, p < 0.001), expected service quality of WEEE RL × income level → intention to reuse WEEE RL (β = 0.354, p < 0.01). Collectively supporting H6, H7, and H8. The effects of social status on the expected service quality of WEEE RL can be seen in Figures 4 and 5.

Figure 4.

Interaction effect between education level and expected service quality on intention to reuse of waste of electrical and electronic equipment reverse logistics (WEEE RL).

Figure 5.

Interaction effect between income level and expected service quality on intention to reuse of waste of electrical and electronic equipment reverse logistics (WEEE RL).

Following the existing literature,^90–92 we identify if there were any indirect or mediating effects of ETBL awareness between government support and the expected service quality of WEEE RL. The test was repeated 5000 times to affirm the significance of the indirect effects. As per Hair et al.,⁸³ the bootstrap results can be considered significant if the bias-corrected confidence interval (BCCI) does not encompass 0. As depicted in Table 4, all of the BCCIs for the four indirect paths contained 0, and all p-values exceeded the 0.05 threshold, indicating that the results are statistically insignificant. Therefore, our study was unable to identify any mediation for government support → ETBL → expected service quality of WEEE RL. Through this, we were able to discover that government support leads to the expected service quality of WEEE RL solely through ETBL awareness, as a direct connection.

Table 4.

Significance testing of mediation effects with bootstrap.

Path	GS → ECA → ESQ		GS → SA → ESQ		GS → ENA → ESQ		GS → TA → ESQ
Statistics	Sobel	Bootstrap	Sobel	Bootstrap	Sobel	Bootstrap	Sobel	Bootstrap
Indirect effect	0.007	0.007	0.028	0.028	0.029	0.029	0.024	0.024
Standard error	0.007	0.008	0.013	0.015	0.013	0.016	0.012	0.014
z-statistic	0.960	0.860	2.169	1.880	2.155	1.790	2.012	1.740
p-value	0.337	0.389	0.030	0.060	0.031	0.073	0.044	0.082
BCCI	(−0.009, 0.023)		(−0.001, 0.058)		(−0.003, 0.060)		(−0.003, 0.052)
Baron and Kenny⁹⁰	Non-significant (p > 0.05)		Non-significant (p > 0.05)		Non-significant (p > 0.05)		Non-significant (p > 0.05)
Zhao et al.⁹²	4.7% mediated		18.5% mediated		18.8% mediated		16.0% mediated

Note: (1) GS: government support; ECA: economic awareness; WEEE RL: waste of electrical and electronic equipment reverse logistics; ESQ: expected service quality of WEEE RL; SA: social awareness; ENA: environmental awareness; TA: technological awareness. (2) 5000 iterations for bootstrapping. (3) Confidence level is 95%. (4) BCCI: bias-corrected confidence interval.

Discussion

This study examines how government support influences ETBL awareness, expected service quality, and intention to reuse in the context of WEEE RL. Analyzing 330 responses from various regions in China, we found that government support enhances ETBL awareness, which in turn increases reuse intentions. Notably, the expected service quality of WEEE RL correlated with government support only through enhanced ETBL awareness, likely due to complexities in information processing and application within groups. Dennis⁹³ noted that providing information does not ensure its effective use, and initial support impacts may involve delayed feedback, depending on consumer perception and implementation.

Theoretical implications

Based on our discoveries, we were able to interpret our hypotheses as follows. First, government support positively affected ETBL awareness, suggesting that governments may influence consumer perception. Based on Marshall and Farahbakhsh,⁹⁴ public awareness is a key driver for waste management in developing countries. Wheeler⁹⁵ stressed the importance of government and institutional involvement in regulatory and policy measures to raise public awareness. Our results confirmed the literature by linking government support to ETBL awareness.

Second, our research found that ETBL awareness—except economic awareness—increased WEEE RL service quality. Economic concepts often operate independently of social and environmental understandings.⁹⁶ This is peculiar to developing nations, where individuals favor financial gains over society and the environment.⁹⁷ Considering these disparities, we theorize that WEEE RL's path from economic awareness to expected service quality was not supported. Conversely, the other ETBL components are appropriately related to WEEE RL service quality, suggesting that awareness might raise service quality expectations.

Lastly, we examined how the predicted service quality of WEEE RL might favorably improve reuse intention, in conjunction with education and income levels. Using SERVQUAL frameworks, Griskevicius et al.⁹⁸ claimed that higher SES increases the likelihood of paying, participating, and partaking in sustainable actions. Our approach showed a favorable link between the variables, supporting this assertion.

Practical implications

The practical implications derived from our empirical study can provide two insights. First, for policymakers in developing countries, understanding the significant role of government support is paramount, especially in elevating the awareness of ETBL layers. Developing countries frequently face challenges in emulating the clean development blueprints of their developed counterparts, primarily due to a lack of awareness among their domestic populations.⁹⁹ Therefore, by proactively facilitating campaigns and initiatives to shed light on ETBL layers, governments can fill in this knowledge gap. Given the complex and rapidly evolving nature of the technological sector, improper management of electronic waste may easily lead to environmental and health hazards. Thus, developing an understanding of the technologies involved across the entire lifecycle of electronic products from design and manufacture to disposal and recycling is essential. This can create a foundation for sustainable growth and induce benefits such as reduced energy consumption in manufacturing new products and decreased reliance on raw material extraction are fully realized.

Second, for managers within the WEEE RL sector, a deeper comprehension of customer expectations and SES is crucial. Our results unveiled that higher expected service quality, education levels, and income levels were positively related to the intention to reuse WEEE RL services. Therefore, WEEE RL managers must recalibrate their marketing strategies, placing a particular emphasis on targeting consumers of a higher socioeconomic stratum.⁸⁵ Thus, managers must refine their marketing strategies specifically tailored to higher socioeconomic groups, who not only prefer high-quality WEEE RL services but also show commitment to sustainable practices. By targeting this demographic, WEEE RL services can leverage their preference for sustainability to drive engagement and build long-term loyalty.¹⁰⁰ Incorporating technological awareness into business strategies recognizes the variable technological landscapes across different neighborhoods, acknowledging that the density and presence of RL companies can significantly vary from one to another. By evaluating the technological understanding and readiness of the residents in each neighborhood, companies can address the specific needs and capabilities accordingly. For example, neighborhoods with higher technological awareness may focus on collection and recycling technologies along with digital platforms for consumer engagement while in areas with lower awareness, efforts might focus on basic education campaigns or easily accessible collection points. Such differentiation not only enhances the effectiveness of WEEE management but also contributes to cost savings by avoiding oversupply of services or underutilization of advanced technologies.

Limitations and directions for future research

Our research has unearthed new implications within the WEEE management domain, yet we acknowledge the existence of two limitations that warrant further exploration and offer fertile ground for future research. First, our results’ limited explanatory power is a major limitation. The contextual nature of our study, conducted exclusively in a region in China may have contributed to this result. Despite China's burgeoning technological advancements, the understanding of WEEE recycling among consumers is still in its infancy. This lack of awareness is not only reflective of the situation in China but also echoes a more pervasive global challenge.¹² Second, a more diverse and geographically representative sample might indicate consumer awareness and behavior across different regions and cultures. Consumers’ perspectives on WEEE recycling may differ between developed and developing nations or even between urban and rural regions within a country. Third, our findings suggest that technological awareness alone may not fully capture the range of factors influencing recycling behaviors and reuse intentions. Therefore, future research should aim to utilize a multi-dimensional framework, accommodating a broader spectrum of factors influencing WEEE management.

Footnotes

Acknowledgements

This work was supported by the Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), which was granted financial resources from the Ministry of Trade, Industry, & Energy, Republic of Korea (20214000000520).

Authors contributions

The specific contributions made by each author are as follows: T Roh and H Choi: conception and design of the study; K Park: data curation; T Kim, S Son, and D Kim: interpretation of data.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Hyeseung Choi

Taewoo Roh

Correction (August 2024):

This article has been updated with corrections in the section ‘Acknowledgements’.

Hyeseung Choi is an Assistant Professor at Asian Pacific Researh Center, Hanayng University, South Korea. Her research interests include circular economy, environmental behavior, green management, and green innovation.

Tongkyu Kim is a master student at Department of Global Climate Environmental Studies, Hanayng University, South Korea. His research interests include circular economy, environmental behavior, green management, and green innovation.

Hyeonyu Son is a master student at Department of Global Climate Environmental Studies, Hanayng University, South Korea. Her research interests include circular economy, environmental behavior, green management, and green innovation.

Donghyun Ken Kim is a bachelor student at School of International Studies, Hanayng University, South Korea. His research interests include management, international business, and information systems.

Keunsik Park is an Associate Professor of Department of International Logistics, Chung-Ang University, South Korea. His research interests include international logistics, maritime transportation, trade practice, and maritime insurance. He has published in such journals as Business Strategy and the Environment, Journal of Cleaner Production, Maritime Policy & Management, and International Journal of Logistics Research and Applications.

Taewoo Roh is an Associate Professor of Strategy and International Business at School of International Studies, Hanayng University, South Korea. His research interests center on institutional theory in environmental sustainability, green management, global strategy, knowledge and innovation, and business ethics. He has published in such journals as Journal of Business Ethics, Business Strategy and the Environment, Journal of International Management, Management International Review, Industrial Marketing Management, IEEE Transactions on Engineering Management, Technological Forecasting and Social Change, Journal of Environmental Management, European Management Journal, Journal of Cleaner Production, Journal of Retailing and Consumer Service, Journal of Innovation & Knowledge, Management Decision, Business Research Quarterly, Asian Business & Management, Managerial and Decision Economics, Cross Cultural & Strategic Management, Chinese Management Studies, Electronic Commerce Research, Journal of Electronic Commerce Research, Information Technology & Management, International Journal of Human-Computer Interaction, Telematics and Informatics, and Applied Economics Letters. Publications are available at: .

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