Residents’ Willingness to Participate in E-Waste Recycling: Evidence by Theory of Reasoned Action

Abstract

E-waste, a form of solid waste, contains many recyclable metals, but improper disposal can make it very harmful. Therefore, the recycling of e-waste is very important, and the willingness of residents to participate is crucial in e-waste recycling. Taking Jiangsu Province, China as an example, we used the theory of reasoned action (TRA) to construct a research model to investigate the factors influencing residents’ willingness to participate in e-waste recycling. The paper introduces impression management motivation and further reveals the application of the Hawthorne effect in e-waste recycling. The paper also introduces the awareness of benefits, which encompasses personal economic benefits, physical health benefits, and environmental benefits, with physical health benefits being ignored by most of the previous literature. In addition, knowledge and convenience are also introduced in this paper. A total of 400 valid responses were used to test the hypotheses of the structural equation model. It was found that all factors positively influenced residents’ willingness to engage in e-waste recycling. Attitude has a mediating role in the effects of convenience, knowledge, and awareness of benefits on willingness, and subjective norms have a mediating role in the effects of impression management motivation on willingness. The model explains 82.9% of the variance in residents’ willingness to recycle e-waste, surpassing the original TRA model’s explanatory power and confirming the strength of the extended framework. The study provides valuable policy implications for the government to promote e-waste recycling.

1. Introduction

Electronic products include many household items, such as TVs, refrigerators, washing machines, computers, smartphones, and so on. With the progress and development of technology, electronic products are increasingly used in the lives of residents, and the acceleration of their replacement has led to the generation of more and more electronic waste, i.e., e-waste. Report indicates the global consumer electronics market is projected to grow from USD 864.73 billion in 2025 to USD 1467.94 billion by 2032, exhibiting a Compound Annual Growth Rate (CAGR) of 7.85% during this forecast period [1]. E-waste refers to discarded or unusable electronic products that are no longer useful, also technically known as waste electrical and electronic equipment (WEEE), or “end-of-life” (EoL) electronic equipment, which is a mixture of plastic, metal, and composite materials. A total of 10% of e-waste comes from accessories, 14% from electronics, 34% from communication devices, and 42% from household appliances [2]. According to the Global E-waste Monitor 2024 report, a record 62 million tonnes (Mt) of e-waste was produced in 2022, an 82% increase from 2010. The report projects that this figure is on track to rise another 32% to 82 million tonnes by 2030. Worryingly, the global generation of electronic waste is rising five times faster than documented e-waste recycling. In 2022, less than a quarter (22.3%) of the year’s e-waste was documented as properly collected and recycled [3].

E-waste contains toxic metals that can pose a threat to both humans and the environment. Improper disposal of e-waste can pose a significant threat to the environment. In order to recover economically valuable metals from e-waste, such as copper, gold, and aluminum, open burning is the preferred option for improper disposal, which results in harmful compounds such as hydrocarbons (polycyclic aromatic hydrocarbons, halogenated hydrocarbons, dioxins, and furans) being released into the environment. Among them, copper is considered highly toxic and harmful to the ecosystem, which has been recognized by RoHS (the Restriction of the use of certain hazardous substances in electrical and electronic equipment) [4]. Plastics in e-waste contain flame-retardant polymers that, when burned, produce harmful polychlorinated dibenzofurans (PCDFs), polybrominated dibenzofurans (PBDFs), polychlorinated dibenzo-p-dioxins (PCDDs), and polybrominated dibenzo-p-dioxins (PBDDs) [5,6]. Next is chemical treatment. In order to recover the metal components of e-waste, such as Pd, Ag, Au, etc., chemicals such as H₂SO₄, HCL, cyanide, and HNO₃ are required, which are highly corrosive and toxic, and produce fumes that pollute the atmosphere during treatment [7]. Finally, direct landfills allow the heavy metal ions in e-waste to leach out and affect the fertility of the soil, and they may enter water sources through the soil, affecting the aquatic environment [8,9]. In addition to threatening the environment, it poses threats to human health, including respiratory effects, learning impairments, neurological disorders, hearing problems, cancer, skin diseases, cardiovascular diseases, and mutations due to deoxyribonucleic acid damage [10,11,12,13,14,15,16].

In addition to electronic waste, significant progress has been made in the recycling of various types of solid waste, particularly in the construction sector. Recycled construction materials, such as waste concrete and aggregates, are increasingly used to reduce environmental impacts and resource consumption. For instance, reference [17] demonstrated the potential of fiber-reinforced recycled aggregate concrete by conducting an in-situ 4D CT analysis to investigate microcrack evolution during carbonation, revealing promising structural integrity. Similarly, reference [18] developed a fully recycled alkali-activated mortar using waste concrete fines as substitutes for both binder and sand, achieving strong performance across multiple mechanical and durability metrics. These innovations exemplify how recycled materials can be effectively repurposed, reinforcing the relevance of recycling not only in construction but also in addressing the challenges of e-waste.

China, the most populous and largest developing country in the world, generated a total of 12 Mt of e-waste in 2019, making it one of the largest e-waste-producing countries in the world. According to predictions, China’s e-waste will grow to 27.2 Mt in 2030 and 51.6 Mt in 2050 [19]. Guiyu town in Guangdong province, China, is the e-waste capital of China, and 70% of the world’s e-waste goes to it, but only 25% of the e-waste is subjected to formal take-back, and most of the e-waste is improperly disposed of, leading to serious damage to the local ecological environment [20]. According to a report, the cost of e-waste utilization in China could be as high as USD 42 billion in 2020 and triple by 2030 [21]. As a result, the Chinese government has started to address this issue by strengthening the establishment of relevant regulations and supervision of e-waste recycling and disposal, announcing that it will no longer accept imported e-waste starting from 2018 [22]. As of 2015, 109 companies were qualified to formally recycle e-waste in China, and they disposed of 133 million pieces of e-waste [21]. China has also started to regulate the formal recycling of e-waste through various fund policies to promote sustainable development [23]. Benefiting from the national policies, formal recycling centers began to flourish, spreading across the country and using formal recycling technologies such as final treatment, mechanical treatment, and deep recycling to minimize environmental pollution. Jiangsu Province, the second largest province in China’s economy, is a major consumer of electronic products. According to estimates, during the “12th Five-Year Plan” period, the electronic waste of Jiangsu Province—particularly the “four machines and one brain” (TVs, refrigerators, washing machines, room air conditioners, and microcomputers)—is growing rapidly at the rate of 600,000 units per year. Therefore, this paper selects Jiangsu Province, China, as the research object.

The literature review in this paper is divided into three parts. Firstly, it covers the literature on the green treatment of e-waste from a technical point of view. Reference [24] found that two bacteria, Acidithiobacillus sp. and Leptospirillum sp., have good adsorption and precipitation effects for the recovery of metals from e-waste and that bio-hydrometallurgy is very ecologically friendly [24]. Reference [25] studied the role of plasma technology in e-waste treatment and suggested that plasma technology could be combined with flux agents to eliminate contaminants from e-waste. Given the respective limitations of chemical and biological methods, Pant et al. proposed a combined approach for better and safer extraction of metals from e-waste [26]. Reference [27] proposed the use of green methods of electrodeposition, bioleaching, and waste-to-treasure for the treatment of waste lithium-ion batteries. However, the prerequisite for e-waste to be treated green is that it can be formally recycled. Therefore, the second part will examine the literature on e-waste recycling from the perspective of government and enterprises.

Most of the existing literature focuses on how the government can increase the management of e-waste recycling and the social responsibility of enterprises. Reference [28] studied the extended producer responsibility (EPR) in e-waste management in Canada, as well as the policy drivers and challenges. Reference [29] reviewed the existing Chinese e-waste management framework, including regulatory policies and pilot projects. The challenges of implementing an e-waste reuse/recycling system tailored to the Chinese context were analyzed, and two alternative policy approaches were proposed to address these challenges. Reference [30] reviewed the development of e-waste recycling in China through a study of the technical system, laws, and recycling in China and proposed an integrated recycling system to achieve higher e-waste recovery rates. Reference [31] suggested that the success of recycling behavior depends largely on the participation of residents. The final part of the literature review focuses on residents’ willingness to participate in e-waste recycling. Reference [32] studied the behavioral intentions, attitudes, reasons, and values of Japanese residents on e-waste recycling through behavioral reasoning theory (BRT). Reference [33] studied the factors affecting e-waste recycling among Romanian residents.

Reference [34] studied the effects of demographic, socioeconomic, and personal preferences on e-waste recycling with Indian residents. Reference [35] added recycling habits to the theory of planned behavior and concluded that recycling habits and perceived attitudes have a significant positive impact on e-waste recycling among young people. Reference [36] studied the differences in the willingness of faculty, staff, and college students of different grades to recycle e-waste through a survey of a Midwestern university, concluding that the cost of recycling and knowledge are important factors that the impact people’s willingness to participate in e-waste recycling, the role of education is not sufficient, and more consideration should be given to consumer incentives in future research. There are not many studies on the willingness of Chinese residents to recycle e-waste. References [37,38] analyzed the relationship between factors such as convenience, living conditions, recycling habits, economic benefits, and environmental awareness and willingness to recycle by constructing regression models. References [39,40] both used TPB and analyzed data by constructing structural equation models. These studies provide implications for enterprises and governments to recycle e-waste, but there are not many studies in this area in China, and more case studies are needed to provide better suggestions and references.

To address this challenge, this paper attempts to explain the factors that influence Chinese residents’ willingness to participate in e-waste recycling using Jiangsu Province, China, as a case study. We will extend the theory of reasoned action and construct a structural equation model based on it, collect data through questionnaire collection, and finally validate the model with data in order to investigate the effects of convenience, knowledge, awareness of benefits, impression management motivation, attitude, and subjective norms on residents’ willingness to participate in e-waste recycling. Therefore, the study attempts to answer the following questions:

• What factors influence residents’ willingness to participate in e-waste recycling?
• Does attitude mediate the effects of convenience, knowledge, and awareness of benefits on residents’ willingness to participate in e-waste recycling?
• Does the subjective norm mediate the effect of impression management motivation on residents’ willingness to participate in e-waste recycling?

This paper makes the following contributions. First, the paper introduces impression management motivation and further reveals the application of the Hawthorne effect in e-waste recycling. The paper also introduces the awareness of benefits, which encompasses personal economic benefits, physical health benefits, and environmental benefits, with physical health benefits being ignored by most of the previous literature. Knowledge and convenience are also introduced in this paper, forming a new research model and providing a new perspective for subsequent studies. Second, the study assesses the mediating role of attitude between convenience, knowledge, awareness of benefits, and residents’ willingness to participate in e-waste recycling, and the mediating role of subjective norms between impression management motivation and residents’ willingness to participate in e-waste recycling. Third, the extended model has stronger explanatory power than the original TRA. Finally, the study provides valuable policy implications for policymakers to promote e-waste recycling.

2. Conceptual Framework and Hypotheses

2.1. Theory of Reasoned Action (TRA)

The theory of reasoned action was first proposed by Fishbein and Ajzen in 1975 with the aim of predicting human behavior. It argues that people’s willingness to engage in particular activities is determined by intentions [41]. Intentions are predicted by two main constructs: attitude and subjective norms. Attitude is the positive or negative feeling that people hold about engaging in a target behavior. Subjective norms refer to the degree to which people perceive that those who are important to them want them to use the new system [42]. In the existing literature using TRA, it is suggested to include other specific constructs to extend TRA to analyze domain-specific influences [43]. TRA has been widely used in pro-environmental behavior studies, such as green consumption, waste sorting, and energy conservation [44,45,46]. As a theoretical framework, TRA is appropriate for this paper because it is used to predict individuals’ intentions and behaviors under voluntary control conditions [41]. In this paper, convenience, knowledge, awareness of benefits, and impression management motivation were added to TRA to investigate their influence on residents’ willingness to participate in e-waste recycling, as shown in Figure 1.

In terms of pro-environmental behavior, attitude is defined as cognitive and affective evaluations of environmental protection objects [47]. Therefore, people who are more enthusiastic about e-waste recycling are more likely to participate in e-waste recycling. Similar to previous studies, this paper hypothesizes that there is a positive correlation between attitude and residents’ willingness to participate in e-waste recycling.

H1. 

There is a positive correlation between attitude and residents’ willingness to participate in e-waste recycling.

Subjective norms are influenced by normative beliefs and motivation to comply. A normative belief is the expectation that individuals anticipate from significant others or groups about whether they should perform a specific behavior. Motivation to comply refers to an individual’s intention to conform to the expectations held by significant others or groups [48]. The influence of subjective norms has been found in many studies on e-waste recycling [39,40]. Therefore, this paper makes the following hypothesis.

H2. 

There is a positive correlation between subjective norms and residents’ willingness to participate in e-waste recycling.

2.2. Convenience

Convenience is the sum of time and effort a person has to spend to engage in an activity. The less time and effort an activity requires, the more beneficial it is to increase people’s willingness to participate [49]. Reference [50] studied the relationship between convenience and mobile learning. Reference [51] studied the impact of service convenience on customer satisfaction and loyalty in the Indian retail banking industry. Reference [52] studied the extent to which convenience affects online shopping among women. In many previous studies on residents’ willingness to participate in e-waste recycling, the effect of convenience has been considered [53]. Therefore, this paper makes the following hypothesis.

H3. 

There is a positive correlation between convenience and residents’ willingness to participate in e-waste recycling.

H3.1. 

There is a positive correlation between convenience and residents’ attitudes toward participating in e-waste recycling.

2.3. Knowledge

Knowledge is an individual’s understanding and mastery of concepts, facts, patterns, and relationships acquired through continuous research, observation, or based on past experience on a specific topic [54]. Knowledge is considered to have a positive effect on behavioral attitude and willingness in existing studies [55]. Reference [56] studied the effect of product knowledge, green knowledge, and quality knowledge on consumers’ willingness to purchase remanufactured products and concluded that higher knowledge is more favorable for consumers to purchase remanufactured products, i.e., one of the green products. Reference [57] found that attitude played a sufficient mediating role in the effect of environmental knowledge on the willingness to purchase green products. Therefore, this paper makes the following hypothesis.

H4. 

There is a positive correlation between residents’ knowledge and willingness to participate in e-waste recycling.

H4.1. 

There is a positive correlation between residents’ knowledge and attitude toward participating in e-waste recycling.

2.4. Awareness of Benefits

Awareness of benefits refers to residents’ awareness of the benefits brought by e-waste recycling, and such benefits include personal benefits and environmental benefits. In the earliest research on green behavior, scholars focused more on the influence of outcome consciousness on green behavior. Outcome consciousness refers to people’s awareness of the results caused by their actions, and scholars believe that when a person realizes that his or her actions are damaging the environment, he or she will tend to avoid doing so [40]. It has also been argued that people engage in green behavior not only because they care about environmental outcomes, but also about personal outcomes [58]. Personal outcomes suggest that people care more about the indirect advantages that their behavior brings to benefit themselves. Later, it was argued that to motivate people to engage in green behavior, awareness of benefits rather than outcomes should be emphasized, because people are more willing to focus on the benefits of their behavior and less willing to face the negative outcomes [59]. Reference [60] found that after conveying the environmental hazards of private cars to subjects, there was less impact on reducing the use of private cars. However, after conveying to the subjects the economic benefits and physical health benefits of public transport trips for individuals, their choices changed significantly [60]. Therefore, this paper makes the following hypothesis.

H5. 

There is a positive correlation between residents’ awareness of benefits and willingness to participate in e-waste recycling.

H5.1. 

There is a positive correlation between residents’ awareness of benefits and attitude toward participating in e-waste recycling.

2.5. Impression Management Motivation

Impression management motivation is derived from the two-dimensional model constructed by Leary and Kowalski in 1990, and one of the dimensions is impression management motivation. Individuals want to use impression management to influence others’ perceptions and opinions of them in order to obtain good evaluations from others [61]. According to [62], impression management motivation can be divided into two types: defensive impression management motivation and positive impression management motivation. Defensive impression management motivation is an individual’s attempt to downplay or avoid a negative image of themselves in order to reduce negative ratings of others. Positive impression management motivation is an individual’s effort to show the positive side of themselves in order to enhance their ratings and image in the minds of others [62]. Reference [63] found that people are more inclined to present a positive self-image to the general public, and therefore they show more positive intentions and behaviors towards altruistic appeals than selfish appeals. Reference [64] argued that people purchase green products because such behavior is perceived as altruistic, and showing off and displaying their altruistic behavior can improve their pro-social reputation. By showing off their own green behavior, people indicate to the public that they are pro-social individuals in order to improve their positive image in the eyes of others. Therefore, this paper makes the following hypothesis.

H6. 

There is a positive correlation between residents’ impression management motivation and willingness to participate in e-waste recycling.

H6.1. 

There is a positive correlation between residents’ impression management motivation and subjective norms to participate in e-waste recycling.

3. Research Methodology

3.1. Study Area

The case study object of this paper is Jiangsu Province, China. Jiangsu Province is a provincial-level administrative region of the People’s Republic of China, located in the Yangtze River Delta region. It consists of thirteen cities in three regions: northern Jiangsu (Suqian, Lianyungang, Yancheng, Xuzhou, and Huaian), central Jiangsu (Nantong, Yangzhou, and Taizhou), and southern Jiangsu (Changzhou, Nanjing, Suzhou, Zhenjiang, and Wuxi). Jiangsu has a total area of 107,200 square kilometers and a resident population of 85,054,000 in 2021 (the seventh national census). GDP per capita has been the first in the country continuously since 2009, achieving a GDP of RMB 11636.42 billion in 2021 (China Economic Network). In 2009, “China Household Appliance Trade-in Implementation Measures” was first run on a pilot basis in 9 provinces, and Jiangsu Province became the first pilot province, and the selected enterprises became the first pilot enterprises for electronic waste recycling and treatment. The enterprises recycled about 1.57 million units of used electronics within 13 months [65]. After calculating the CO₂ emission reduction benefits generated by e-waste recycling in 29 provinces in China, the total emission reduction benefits from 2013 to 2017 were RMB 390 million, with Hubei in first place, Henan in second place, and Jiangsu in third place nationwide with RMB 32.256 million [66].

3.2. Data Collection

In this paper, the questionnaire was designed according to the content in the research framework and with reference to the previous literature. The questionnaire items were all derived from the existing literature and were adapted to fit the paper. Attitude and subjective norms have 5 and 4 items, respectively, adapted from [43,67,68,69]. Convenience has 3 items, adapted from [49,70,71]. Knowledge has 4 items, adapted from [40,72]. Awareness of benefits has 4 items, adapted from [73,74]. Impression management motivation has 4 items, adapted from [75]. Willingness to participate in e-waste recycling has 3 items, adapted from [76,77,78]. The items of the questionnaire were designed according to a five-point Likert scale, which ranges from 1 to 5, equating to strongly disagree, disagree, unsure, agree, and strongly agree, respectively. Before conducting the survey, the questionnaire was first written in Chinese and distributed to students who were pursuing their master’s and doctoral degrees for a preliminary survey after being reviewed by our supervisors. The questionnaire was adjusted and modified according to the guidance of the supervisors and experts. The questionnaire consisted of 32 items, including 5 demographic items and 27 structural items. We used Sojump (http://www.sojump.com/) for questionnaire design and distribution. We shared the URL of the questionnaire to media platforms and distributed a request message with the URL to the target group in the region, inviting them to forward this information to other audiences. We were looking for adult citizens over the age of 18 who lived in Jiangsu Province and had some knowledge of e-waste recycling.

A total of 493 questionnaires were collected from 20 October 2022 to 28 November 2022, and 400 questionnaires were accepted after sorting and screening the data.

3.3. Data Analysis

In the paper, the demographic data of the respondents were analyzed using IBM SPSS 26 software, and structural equation modeling was used to analyze using smartPLS 4. Structural equation modeling is a multivariate statistical method for analyzing the relationship between latent and observed variables, and the traditional method is based on the covariance matrix of the variables for analysis. In this paper, we chose to use partial least squares structural equation modeling (PLS-SEM), whose main advantages are that it does not require assumptions about the data distribution and that it is accurate, simple, and flexible [79]. PLS-SEM analysis is divided into two parts: the measurement model and the structural model, and the measurement model is used to analyze the relationship between observed and latent variables. The structural model is to analyze the relationships between latent variables [80].

4. Results

4.1. Profile of Respondents

The profile of the respondents shows that 167 men and 233 women participated in this survey. Most of the respondents were between 20 and 30 years old (38.5%), followed by 31 to 40 years old (38%), 41 to 50 years old (13.25%), older than 50 years old (6.25%), and younger than 20 years old (4%). The majority of respondents had a bachelor’s degree (38%), followed by junior college (33.75%), high school and below (16.5%), and graduate (10.75%). The majority of respondents earned RMB 6500 to 10,000 per month (37.5%), followed by RMB 3000 to 6500 (33.5%), less than RMB 3000 (17.75%), and more than RMB 10,000 (11.25%). There were more urban respondents (69.75%) than suburban or rural respondents (30.25%). The profile of the respondents is shown in

Table 1. An overview of respondents.

Demographics		Frequency	Percentage (%)
Gender	Male	167	41.75
	Female	233	58.25
Age (Years)	Under 20	16	4
	20–30	154	38.5
	31–40	152	38
	41–50	53	13.25
	Above 50	25	6.25
Educational Level	High School and Below	66	16.5
	Junior College	135	33.75
	Bachelor	156	39
	Postgraduate	43	10.75
Income Level	Below RMB 3000	71	17.75
	RMB 3000–6500	134	33.5
	RMB 6500–10,000	150	37.5
	Above RMB 10,000	45	11.25
Type of community	Urban	279	69.75
	Suburban or Rural	121	30.25

Source: compiled by the authors.

.

4.2. Measurement Model

The measurement model focuses on analyzing the reliability and validity of the model. The standardized loading factor of the indicator should be above 0.708, as this indicates that the structure explains more than 50% of the variance of the indicator and thus is considered reliable. The standardized loading factor for the paper is between 0.843 and 0.893, which is greater than the threshold value and should be considered reliable. Composite reliability (CR) and Cronbach’s alpha ($\mathsf{\alpha }$) were used to assess internal reliability [79]. Rho A is considered an approximate measure of structural reliability and usually lies between composite reliability and Cronbach’s alpha. In this paper, CR ranged from 0.903 to 0.930, Cronbach’s alpha ranged from 0.839 to 0.906, and Rho A ranged from 0.840 to 0.906, all greater than the threshold of 0.7 [81]. The average variance extracted value (AVE) was used to assess the convergent validity of each factor. In this paper, the AVE ranged from 0.726 to 0.766, all of which were greater than the threshold of 0.5. The variance inflation factor (VIF) is used to assess the degree of index collinearity, which increases as the VIF increases. In this paper, the VIF ranged from 1.909 to 2.706, all of which were less than the threshold of 3.3 [82], as shown in

Table 2. Construct reliability and validity.

Variable	Measurement Item	Factor Loadings	$\mathsf{\alpha }$	Rho A	CR	AVE	VIF
Attitude	ATT1: I think the idea of recycling e-waste is incredibly positive.	0.843	0.906	0.906	0.930	0.726	2.306
	ATT2: For me, recycling e-waste is favorable.	0.872					2.706
	ATT3: For me, recycling e-waste is rewarding.	0.847					2.365
	ATT4: I think recycling e-waste is useful in solving environmental problems like soil pollution.	0.845					2.310
	ATT5: I think recycling e-waste is extremely important.	0.854					2.413
Subjective Norms	SBN1: Most people who are vital to me think I should be involved in recycling e-waste.	0.856	0.892	0.893	0.925	0.756	2.230
	SBN2: My friends or peers expect me to be involved in recycling e-waste.	0.870					2.390
	SBN3: Friends and relatives around me will affect my participation in recycling e-waste.	0.888					2.648
	SBN4: The sense of social responsibility will influence my participation in recycling e-waste.	0.864					2.369
Convenience	CNV1: The e-waste recycling site is very close to me.	0.862	0.839	0.840	0.903	0.757	1.912
	CNV2: It is easy for me to find information on e-waste recycling.	0.864					1.944
	CNV3: For me, e-waste recycling is not complicated.	0.884					2.094
Knowledge	KNG1: I know e-waste contains a large number of heavy metals, which will pollute the environment if discarded carelessly.	0.853	0.877	0.878	0.916	0.731	2.167
	KNG2: I am aware that e-waste recycling reduces environmental pollution.	0.866					2.281
	KNG3: I know the importance of e-waste recycling.	0.855					2.188
	KNG4: I have sufficient knowledge of e-waste recycling.	0.846					2.089
Awareness of benefits	AOB1: Recycling e-waste can bring me financial benefits.	0.863	0.898	0.899	0.929	0.766	2.313
	AOB2: Recycling e-waste can reduce my risk of developing certain diseases.	0.888					2.684
	AOB3: Recycling e-waste can protect the environment.	0.882					2.594
	AOB4: Recycling e-waste can save resources.	0.869					2.455
Impression management motivation	IMM1: I care what other people think of me.	0.867	0.888	0.889	0.923	0.749	2.409
	IMM2: I want to present myself to others in a positive way.	0.877					2.466
	IMM3: I want to make a positive impression on others.	0.853					2.187
	IMM4: I want to look good to others.	0.864					2.298
Willingness to participate in e-waste recycling	WTP1: I am willing to participate in e-waste recycling.	0.858	0.840	0.842	0.904	0.758	1.926
	WTP2: I intend to participate in e-waste recycling in the near future.	0.893					2.215
	WTP3: I will participate in e-waste recycling more frequently.	0.861					1.909

.

The Fornell–Larcker criterion proposed by Fornell and Larcker was used to assess the discriminant validity of the structures, where the square root of the AVE of each structure should be higher than the correlation value of that structure with other structures [79], as shown in

Table 3. Fornell–Larcker criterion.

	AOB	ATT	CNV	IMM	KNG	SBN	WTP
AOB	0.875
ATT	0.504	0.852
CNV	0.314	0.291	0.870
IMM	0.487	0.371	0.596	0.865
KNG	0.676	0.522	0.447	0.563	0.855
SBN	0.430	0.505	0.619	0.313	0.567	0.870
WTP	0.504	0.574	0.475	0.667	0.647	0.659	0.871

.

4.3. Structural Model

The standardized root mean square residual (SRMR) of the model in the paper is 0.034, which is less than the threshold of 0.08. The normative fit index (NFI) is 0.901, which is greater than the threshold of 0.8 [83]. With the R-square of 0.829, the fit validity is considered significant [84]. We used the same data for the original TRA model and found that the R-square of the original TRA model is 0.815, which is less than 0.829, and therefore the current model is considered to have better explanatory power than the original TRA model.

According to

Table 4. Hypothetical path analysis.

Hypotheses	Items	Path Co-Efficient	T Statistics	Confidence Interval		p Values	Significance
				2.5%	97.5%
H1	ATT -> WTP	0.293	5.860	0.195	0.389	0.000	Supported
H2	SBN -> WTP	0.164	2.958	0.057	0.273	0.003	Supported
H3	CNV-> WTP	0.127	2.634	0.033	0.224	0.008	Supported
H3.1	CNV -> ATT	0.259	5.268	0.159	0.352	0.000	Supported
H4	KNG-> WTP	0.150	2.722	0.043	0.258	0.007	Supported
H4.1	KNG -> ATT	0.331	6.716	0.232	0.429	0.000	Supported
H5	AOB-> WTP	0.054	2.226	0.012	0.181	0.023	Supported
H5.1	AOB -> ATT	0.358	6.596	0.253	0.468	0.000	Supported
H6	IMM-> WTP	0.178	3.549	0.079	0.276	0.000	Supported
H6.1	IMM -> SBN	0.313	7.575	0.230	0.393	0.000	Supported

and Figure 2, it can be seen that all hypotheses are acceptable at a 5% significance level. H1 confirms that there is a positive correlation between attitude and residents’ willingness to participate in e-waste recycling (β = 0.293, p = 0.000). H2 confirms that there is a positive correlation between subjective norms and residents’ willingness to participate in e-waste recycling (β = 0.164, p = 0.003). H3 and H3.1 confirm a positive correlation between convenience and residents’ willingness (β = 0.127, p = 0.008) and attitude (β = 0.259, p = 0.000) to participate in e-waste recycling. H4 and H4.1 confirm a positive correlation between knowledge and residents’ willingness (β = 0.150, p = 0.007) and attitude (β = 0.331, p = 0.000) to participate in e-waste recycling. H5 and H5.1 confirm a positive correlation between awareness of benefits and residents’ willingness (β = 0.054, p = 0.023) and attitude (β = 0.358, p = 0.000) to participate in e-waste recycling. H6 and H6.1 confirm a positive correlation between impression management motivation and residents’ willingness (β = 0.178, p = 0.000) and subjective norms (β = 0.313, p = 0.000) to participate in e-waste recycling.

The analysis of mediating effects can be seen in

Table 5. Mediating effects analysis.

Path	Indirect Effect	T-Value	Confidence Intervals		p-Value	Significance
			2.5%	97.5%
KNG -> ATT -> WTP	0.097	4.474	0.057	0.142	0.000	Supported
IMM-> SBN -> WTP	0.143	2.955	0.051	0.239	0.003	Supported
AOB -> ATT -> WTP	0.105	4.265	0.061	0.156	0.000	Supported
CNV -> ATT -> WTP	0.076	3.914	0.040	0.117	0.000	Supported

. Attitude has a significant indirect effect on the influence of convenience (β = 0.076, p = 0.000), knowledge (β = 0.097, p = 0.000), and awareness of benefits (β = 0.105, p = 0.000) on residents’ willingness to participate in e-waste recycling. Subjective norms have a significant indirect effect on the influence of impression management motivation on residents’ willingness to participate in e-waste recycling (β = 0.143, p = 0.003).

4.4. Model Fit Analysis

As shown in

Table 6. Model fit analysis summary.

Fit Index	Value	Threshold	Interpretation
Standardized Root Mean Square Residual (SRMR)	0.034	<0.08	Good model fit
Normative Fit Index (NFI)	0.901	>0.80 (acceptable), >0.90 (good)	Indicates good fit
R2 (Willingness to Participate)	0.829	>0.75 (substantial)	Strong explanatory power
R2 (Attitude)	Not explicitly reported	>0.50 (moderate)	Should be reported for clarity
R2 (Subjective Norms)	Not explicitly reported	>0.50 (moderate)	Should be reported for clarity
Composite Reliability (CR)	0.903–0.930	>0.70	High internal consistency across constructs
Average Variance Extracted (AVE)	0.726–0.766	>0.50	Strong convergent validity
Cronbach’s Alpha	0.839–0.906	>0.70	High scale reliability
Variance Inflation Factor (VIF)	1.909–2.706	<3.3	No multicollinearity issue
Fornell–Larcker Criterion	Satisfied	AVE > inter-construct correlations	Good discriminant validity

, the research model demonstrates good overall fit, strong explanatory power, and robust reliability and validity. Specifically, the standardized root means square residual (0.034) and Normative Fit Index (0.901) confirm a good model fit, while the substantial R² value of 0.829 for “Willingness to Participate” signifies the model’s strong ability to explain the variance in e-waste recycling willingness. Furthermore, high composite reliability and Cronbach’s alpha values (all above 0.70) ensure high internal consistency, and average variance extracted values (all above 0.50) alongside a satisfied Fornell–Larcker criterion confirm strong convergent and discriminant validity. Lastly, variance inflation factor values below 3.3 indicate no multicollinearity issues.

4.5. One-Way ANOVA

This paper investigated the differences in the demographics of respondents’ willingness to participate in e-waste recycling through one-way ANOVA. The effect of gender on residents’ willingness to recycle e-waste was not significant. Individuals aged 31 to 40 years (mean = 3.908, p = 0.000) were more inclined to recycle e-waste, followed by those aged 20 to 30 years (mean = 3.883), under 20 years old (mean = 3.729), 41 to 50 years old (mean = 3.522), and above 50 years old (mean = 3.187). The higher the education level, the more willing residents are to recycle e-waste. In terms of income, higher income groups are more likely to recycle e-waste than lower income groups. Residents in urban areas (mean = 3.993, p = 0.000) were more willing to participate in e-waste recycling than those in suburban or rural areas (mean = 3.339), as shown in

Table 7. One-way ANOVA.

Demographics	Items	Mean	F-Value	p-Value	Significance
Gender	Male	3.754	1.299	0.222	Not Supported
	Female	3.824
Age (Years)	Under 20	3.729	5.898	0.000	Supported
	20–30	3.883
	31–40	3.908
	41–50	3.522
	Above 50	3.187
Educational Level	High School and Below	3.510	4.461	0.002	Supported
	Junior College	3.795
	Bachelor	3.812
	Postgraduate	4.171
Income Level	Below RMB 3000	3.606	3.812	0.005	Supported
	RMB 3000–6500	3.706
	RMB 6500–10,000	3.869
	Above RMB 10,000	4.111
Type of community	Urban	3.993	4.858	0.000	Supported
	Suburban or Rural	3.339

and Figure 3.

5. Discussion and Policy Implications

This paper focuses on exploring the factors influencing residents’ willingness to participate in e-waste recycling by extending the theory of reasoned action to include convenience, knowledge, awareness of benefits, and impression management motivation, using Jiangsu Province, China, as an example. Up to this point, the three questions raised in this paper have been addressed. The results of the hypothesis test indicated that all factors (attitude, subjective norm, convenience, knowledge, awareness of benefits, and impression management motivation) had a significant positive effect on residents’ willingness to participate in e-waste recycling, suggesting that strengthening all these factors could enhance residents’ willingness to participate in e-waste recycling. In addition, the results of the mediating effects analysis indicated that attitude had a mediating role in the effects of convenience, knowledge, and awareness of benefits on willingness, and subjective norms had a mediating role in the effects of impression management motivation on willingness.

Attitude is an important factor influencing residents’ willingness to participate in e-waste recycling, which means that residents are more likely to participate if they perceive e-waste recycling as beneficial and positive, as also shown in previous literature [32,35,39,40]. In addition, subjective norms also positively influence residents’ participation in e-waste recycling. China is a socialist country with a deeply rooted collectivist ideology, and if the idea of e-waste recycling is accepted by the majority of the population, those who do not follow it will be influenced by social pressure to change their minds. This has been corroborated by previous literature [35,39,40]. Thus, it can be seen that in addition to individual attitudinal factors, relatives, friends, neighbors, or opinion leaders can influence people’s willingness to participate in e-waste recycling. Therefore, we suggest that socially influential people such as stars, celebrities, microbloggers, and short video creators could join the campaign to encourage participation in e-waste recycling, which would create some social pressure. Similarly, in the Hawthorne effect, the extra attention people receive causes a rise in performance and effort; therefore, we introduce impression management motivation, i.e., residents would be more willing to show their favorable side in front of others. If participation in e-waste recycling is promoted as a good behavior by influential people, residents will be more willing to participate, and they will perceive a closer distance between themselves and their idols. While promoting the hazards of e-waste, the government can promote e-waste recycling behavior as a positive behavior through commercial or public service announcements. The government can establish advanced role models and individual awards for e-waste recycling and promote them on social media. Residents will thus believe that participating in e-waste recycling will be an important factor in improving their perception in the eyes of others.

The paper finds that convenience has a positive effect on residents’ willingness to participate in e-waste recycling, and similarly, previous literature has argued for the importance of convenience among the factors influencing green behavior [36]. The development of technology in China and the world at large can be attributed to the human quest for convenience, and people are more likely to be willing to participate in e-waste recycling when they find it to be a more convenient thing to do. The government can provide green subsidies for recycling companies to increase the construction of recycling outlets, as well as the design and rollout of recycling networks, to improve the convenience of recycling for residents. At the same time, they can increase the construction and promotion of online recycling. The development of the Internet and logistics has made online recycling a more popular and convenient way.

Knowledge has likewise been found to positively influence residents’ willingness to participate in e-waste recycling, and previous literature has argued for the role of knowledge in influencing green behavior [39,57], but this has rarely been considered in previous studies of factors influencing willingness to recycle e-waste. In this paper, we find that the more knowledgeable residents are about the environmental pollution caused by e-waste not being formally recycled, the more inclined they are to participate in e-waste recycling, and this is also reflected in the results of the one-way ANOVA, where residents with higher education are more willing to participate in e-waste recycling. Although education cannot be fully equated with knowledge of the hazards of e-waste, people with higher education are more likely to be knowledgeable. In this regard, the government should disclose more about the current state of e-waste hazards in the country and publicize the relevant knowledge through platforms that are better known to the public, such as Microblog and WeChat.

Awareness of benefits is also an important factor influencing residents’ participation in e-waste recycling, which includes personal benefits and environmental benefits, of which personal benefits include economic benefits and physical health benefits. Most of the existing literature considers residents’ awareness of environmental benefits and the cost of recycling (i.e., personal economic benefits) [39,40], but the emphasis that residents place on their own physical health cannot be ignored, and past literature has also argued that people tend to place more emphasis on personal benefits [60,85]. Therefore, this paper introduces awareness of benefits, taking into account personal economic benefits, physical health benefits, and environmental benefits. This variable plays a crucial role in providing policy implications for policymakers. In this regard, when the government publicizes the hazards of e-waste, in addition to reporting the pollution of e-waste to the environment, it should also publicize the hazards to the human body and raise the public’s attention to e-waste recycling. In addition, the government can provide recycling subsidies for residents who carry out e-waste recycling to improve their economic returns from participating in e-waste recycling, which may also be a good way to attract them as an incentive.

To situate these behavioral insights within the broader technological landscape of recycling, recent advances in AI-based e-waste sorting technologies further reinforce the shift toward intelligent, scalable recycling systems. For example, reference [86] proposed an IoT-enabled system combining lightweight CNN image recognition with digital weighing to classify real-time e-waste components, improving sorting efficiency and accuracy Another state-of-the-art computer vision model, EWasteNet a two-stream image transformer architecture achieved 96% accuracy in recognizing multiple electronic waste categories, demonstrating the capability of data-efficient AI models for classification tasks. These technological innovations complement the behavioral perspective of the present study, suggesting that increasing residents’ willingness to recycle (through psychological and contextual measures) may be effectively supported by AI-driven backend systems for sorting and processing [87]. Together, they form a unified strategy addressing both demand-side and supply-side challenges in building a more efficient, sustainable e-waste recycling ecosystem.

6. Conclusions

This study provides new insights into the determinants of residents’ willingness to participate in e-waste recycling by extending the theory of reasoned action (TRA) with four additional constructs: convenience, knowledge, awareness of benefits, and impression management motivation. Based on data collected from residents in Jiangsu Province through an online survey, the extended structural equation model demonstrated stronger explanatory power (R² = 0.829) compared to the original TRA model. Among the predictors, attitude (β = 0.293, p < 0.001) and impression management motivation (β = 0.178, p < 0.001) emerged as particularly influential, highlighting the significance of both internal evaluations and socially driven motivations in shaping recycling behavior. Notably, this study is among the first to incorporate impression management motivation as a psychological driver in the context of e-waste recycling, revealing the relevance of the Hawthorne effect in pro-environmental behavior. The findings suggest that all examined variables—attitudes, subjective norms, convenience, knowledge, awareness of benefits, and impression management motivation positively affect willingness to recycle. These results offer actionable guidance for policymakers aiming to enhance e-waste recycling through tailored interventions, public awareness, and incentive programs that align with psychological and contextual drivers. The limitations include a limited focus on self-reported willingness rather than observed behavior, and being geographically confined. Also, the findings are based on data from a single regional context, which may not capture variations in consumer behavior, cultural norms, and institutional frameworks. Therefore, future research should undertake cross-regional or cross-national studies to validate the generalizability of the results across diverse settings, as Jiangsu Province may limit generalizability. Future research should explore additional behavioral determinants, examine the intention–behavior gap, and validate the extended TRA model across diverse cultural and policy contexts to deepen understanding and support global e-waste recycling efforts.