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Article

Developing the Scale for Measuring the Service Quality of Internet-Based E-Waste Collection Platforms

by
Yan Fang
1,*,
Zhuowen Qu
2 and
Wenhua Wang
1
1
School of Economics and Management, Dalian University of Technology, Dalian 116024, China
2
School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(9), 7701; https://doi.org/10.3390/su15097701
Submission received: 2 March 2023 / Revised: 28 April 2023 / Accepted: 3 May 2023 / Published: 8 May 2023
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Abstract

:
As Internet-based e-waste collection moves from a novelty to a routine way of e-waste collection in China, the service quality of Internet-based e-waste collection platforms plays a crucial role in attracting users. Based on content analysis of 94 online reviews and data analysis of a questionnaire survey of 395 participants, this study develops a scale for measuring the service quality of Internet-based e-waste collection platforms with both reliability and validity tests. The scale comprises 12 items across four dimensions: efficiency, accuracy, customer service and price offerings. The study provides a sound theoretical basis for further researchers and practitioners to focus on the major aspects of the service quality of Internet-based e-waste collection platforms, and it achieves an extension of the existing dimensions of e-service quality.

1. Introduction

As the world’s second-largest producer of e-waste [1], China is entering the peak period of scraping, while there is a persistent problem with traditional e-waste collection, such as heavy contamination, high cost, obstructed channels of information and unreasonable allocation of resources [2,3]. Fortunately, e-waste collection, which is defined by the Organization for Economic Cooperation and Development (OECD) as equipment that uses electricity to reach the end of its lifecycle, has new opportunities that are emerging from Internet-based e-waste collection platforms with the rapid development of the Internet [2].
Internet-based e-waste collection is known as an O2O collection model, which can make collection easy for users, traceable for producers, profitable for recyclers, transparent and secure for the public, and friendly to the environment [1,4,5,6,7]. Internet-based e-waste collection is incredibly essential for the sustainable development of the e-waste collection industry [2] and enables the e-waste collection industry to be highly favored by the capital market and government.
The e-waste collection industry in China has been prompted by Internet-based e-waste collection platforms in recent years, and various Internet-based e-waste collection platforms were born from this. E-commerce giants such as Alibaba Group and JD.com have additionally joined endeavors to promote e-waste collection. Alibaba Group is the leading investor of Huishoubao.com, one of the China’s largest Internet-based e-waste collection platforms. JD.com has invested in Aihuishou.com, which is another Internet-based e-waste collection platform focusing on wasted digital products. By using these Internet-based e-waste collection platforms, users can make arrangements for unused e-waste with online orders and offline collection. Internet-based e-waste collection also helps combine the traditional e-waste collection system with the Internet in the form of online platforms and collection applications [7,8]. Therefore, it is necessary for the e-waste collection industry to achieve new vitality through Internet-based e-waste collection [9].
Meanwhile, the Chinese government has highly supported Internet-based e-waste collection through a series of laws and regulations, such as “Guidance on actively promoting the ‘Internet +’ action” issued by the State Council, “‘Internet +’ three-year action plan for green ecology” issued by the National Development and Reform and Commission [10] and ‘‘Opinions on promoting transformation and upgrading renewable resources recycling industry” jointly issued by the Ministry of Commerce and the Ministry of Environmental Protection and other government departments in 2016 [2]. All these laws and regulations aim at promoting e-waste collection by integrating the Internet and other advanced technologies [11].
With the continuous expansion of the scale of the service industry and the enhancement of public service awareness, enterprises have to improve service quality so as to enhance their competitiveness and profits [12]. Under Internet-based e-waste collection, many platforms have been striving to improve their service quality so as to better satisfy user experience. A case in point is Aihuishou, which had opened over 300 outlets and provided face-to-face communication and transactions in 35 cities by 2018 [4], which aims to make it easy for users to participate in Internet-based e-waste collection.
Although researchers have put forward dozens of suggestions and solutions for e-waste collection [13,14,15,16] and many countries have made great efforts to improve the quality of e-waste collection [14,17,18,19,20,21], the outcomes are not optimal [22]. Only 8.9 Mt of e-waste was recorded to be collected and recycled globally in 2016, which was equivalent to 20% of all the e-waste generated [23]. The lack of flexibility and convenience of Internet-based e-waste collection increases users’ reluctance to act [24]. Additionally, users’ complaints about inaccurate pricing and long mailing collection waiting time further hinder users’ participation. Therefore, it is essential for Internet-based e-waste collection platforms to provide their users with better service quality to promote the effectiveness and efficiency of Internet-based e-waste collection. For this purpose, it is necessary for managers to understand the connotation and constituent elements of the service quality of Internet-based e-waste collection. This will help them focus on the key aspects to meet the needs and desires of users [25].
A considerable number of scholars have tried to identify the key role of service quality in platform performance and users’ participation behavior under Internet-based e-waste collection. The effects of the service quality, such as the accessibility of Internet-based e-waste collection and the convenience of offline collection channels on users’ satisfaction, loyalty, or behavioral intentions, are significant according to the literature on Internet-based e-waste collection [26,27,28]. However, a holistic and methodical service-quality-oriented focus on Internet-based e-waste collection platforms has rarely been considered in existing studies. Service quality scales in the existing literature were mainly developed for online shopping websites and online hotel websites [29,30,31,32]. These special service quality scales might not be applicable to Internet-based e-waste collection platforms, and it might be troublesome to modify a general measurement scale on an ad-hoc basis [33]. Similarly, Riel et al. [34] also called attention to thoroughly deriving and reformulating scale items from the former literature on service quality.
Therefore, concretizing the conceptualization and measurement of the service quality of Internet-based e-waste collection platforms is of great necessity [35]. Without objective and comprehensive measures of the service quality of Internet-based e-waste collection platforms, it is impossible for managers and researchers to evaluate and improve the service quality properly [26]. Therefore, this paper probes into the components of service quality in the context of Internet-based e-waste collection platforms and develops a refined measurement scale. The rest of this paper is organized as follows: Section 2 introduces the literature on service quality, the scale of the development of service quality and influencing factors of Internet-based e-waste collection; Section 3 describes the method in detail and presents the data analysis; and Section 4 discusses the study results. Finally, the conclusions are given in the Section 5.

2. Literature Review

2.1. Service Quality

The concept of e-service quality was first referred to by Lemon [36], whereas Zeithaml et al. [37] defined e-service quality as “the extent to which a website facilitates efficient and effective shopping, purchasing and delivery of products and services”. Bressolles and Nantel [38] argued that the definition covers both pre- and post-service delivery experiences on the web. It is essential to examine the service quality (SERVQUAL) scale before discussing e-service quality because much existing e-service quality research is mainly based on SERVQUAL [39]. Parasuraman et al. [40,41] developed the SERVQUA scale, which has ten dimensions, at first and then modified the scale and reduced it to five dimensions (tangibles, reliability, responsiveness, assurance and empathy) in 1988. In 2005, a similar method was employed by Parasuraman et al. [42] in their work on SERVQUAL, and they proposed e-service quality instruments (E-S-QUAL), which have four dimensions (efficiency, system availability, fulfillment and privacy). This has been utilized and adapted in several empirical studies in different settings such as an e-supermarket and e-bank, subsequently [43,44,45].

2.2. The Scale Development of Service Quality

With further research into e-service quality, scholars have developed some measurement scales in various e-service contexts. Some scholars have adopted the traditional service quality dimensions to advance their research into electronic service quality [46,47] However, some researchers also criticized these adaptations because each electronic service environment dominated by people-machine interface is incredibly different and a new set of quality dimensions need to be established [42,48,49,50]. The dimensions for measuring e-service quality are mainly based on a modified SERVQUAL scale [51]; Parasuraman, Zeithaml and Malhotra [42] presented efficiency, system availability, fulfillment, and privacy as the dimensions of the E-S-QUAL scale, and the E-RecS-QUAL scale was consisted of three dimensions (responsiveness, ease of contact and financial compensation). The former mainly focused on e-service technology, while the latter mainly focused on customer service. Especially availability and responsiveness were highlighted with the rapid development of the e-service. It is easy to understand that some dimensions of the traditional scale could be retained in the e-service context while others could not.

2.3. Influencing Factors of Internet-Based E-Waste Collection

A number of studies have investigated the key factors influencing the intention of Internet-based e-waste collection and pointed out that users are much more likely to collect when the Internet-based e-waste collection platform provided high-quality services such as faster responses, safer transactions, and more functions. Chi et al. [52] investigated end-users’ participating behaviors in Taizhou of China and indicated that the economic benefit and convenience of collecting, which included being near to home, having flexible collection time, being easy to find and having pickup service with the proper value of e-waste, were the main concerning factors. Zhang et al. [53] conducted a questionnaire survey and suggested that perceived convenience, attitude and subjective norms were positively related to the users’ willingness to adopt platforms for e-waste collection. The price disadvantage was not conducive to encouraging users to participate in e-waste collection. Wang, Ren, Dong, Zhang, and Wang [1] acknowledged that the convenience of Internet-based e-waste collection was the most attractive factor for users to take part in online collection programs compared with traditional collection and that users who had a lower income expected higher returns.
As a result, many dimensions of service quality have been recognized as key factors influencing the intention of participating in Internet-based e-waste collection. However, a specific measurement scale for the service quality of Internet-based e-waste collection platforms has not been developed. The scales for other online websites or platforms cannot apply to Internet-based e-waste collection platforms appropriately, so it is urgently needed to develop a service quality measurement scale in this field to support the development of Internet-based e-waste collection.

3. Scale Development

The scale development of service quality is critical to Internet-based e-waste collection platforms but receives little attention. Therefore, this research develops a scale for the service quality of Internet-based e-waste collection platforms following this general process. Item generation, scale development, and scale evaluation are the three main phases of scale development [54]. In the first phase, a literature review, focus group, and expert interview are generally used to generate the initial items (Boudreau et al., 2001). Based on the important role of online reviews in users’ choice of a collection service [55], online reviews were used in the phase of item generation, together with a literature review in this research. In the second phase, we refined the items to extract the dimensionality of the scale with desirable reliability and validity through exploratory factor analysis and confirmatory factor analysis. Finally, in the evaluation phase, we examined the correlations of the scale with important customer outcomes to ensure the predictive validity of the scale.

3.1. Initial Dimensions

In this research, we considered online reviews from the users to develop the initial dimensions and items of the service quality of Internet-based e-waste collection platforms. The purpose of the online reviews was to uncover specific characteristics of the service quality of Internet-based e-waste collection platforms.
This research collected participants’ reviews from Zhihu (www.zhihu.com, accessed on 21 January 2021). Zhihu is a high-quality Q&A community and an original content platform for creators in China. People spontaneously gather to communicate with each other about topics of common interest, and millions of various types of groups were formed by 2015.
We mainly focused on the participants who had previously used an online collection platform and experienced the whole collection process. Such reviews would be retained if (1) they could explain the experience of participating in Internet-based e-waste collection, (2) they included an evaluation of the service quality of Internet-based e-waste collection platforms, (3) they constituted an independent and complete plot, and (4) they were specific and credible. According to this selection rule, 94 reviews were purposefully selected, with more than 44 thousand Chinese characters.
The online reviews were transcribed, analyzed, and converted into 54 codes using content analysis. Content analysis is defined as a method for systematic, objective, and quantitative analysis of information characteristics using a coding process to integrate quantitative thinking into qualitative research [56]. It has been successfully applied in a large number of studies on the development of initial service quality dimensions and indicators. Therefore, the initial code identification and theme refinement of the service quality of Internet-based e-waste collection platforms were completed using the content analysis method. In addition, the traditional (manual) content analysis method was applied to identify the common themes from online reviews because perceived service quality has a complex structure and different methods may produce different results. The initial codes are shown in Table 1. The sources refer to the number of online reviews containing the code. The number refers to the total number of text units coded as the corresponding codes in all online reviews.
The 54 initial codes were then summarized as four dimensions by thematic extraction according to the literature review on the connotation and dimensional division of the existing scale of service quality. The basic concept of the four dimensions were also identified, based on the main themes and initial codes. Table 2 provides a list of initial dimensions of the service quality of Internet-based e-waste collection platforms.

3.2. Item Generation

In this research, an initial pool of items was generated from a review of the previous literature that covered the four main dimensions proposed above. Then, the items such as quality inspection and price gaps, which were particularly relevant to Internet-based e-waste collection platforms from the initial codes based on online reviews, were added to fill the gaps left by previous studies. As a result, 89 scale items were gathered for further refinement.
During the focus group discussion conducted by a professor, two Ph.D. students in environmental management, and several undergraduate students, 39 items were eliminated because of ambiguity and redundancy. After this, eight experienced Ph.D., M.A., and undergraduate students evaluated these 50 items by rating how well each item reflected the corresponding dimension of the service quality of Internet-based e-waste collection platforms. The ratings used the following scale: 1 = clearly representative, 2 = somewhat representative, and 3 = not representative at all [57]. Only those items with an average score of two or less were retained [57]. This process reduced the number of items to 30. Then, a manager from one of the Internet-based e-waste collection platforms and a professor in this research field were interviewed for further refinement of the scale, and 18 items were retained according to their knowledge of this industry and user preferences. The items are shown in Table 3.

3.3. Generating Factor Structure

In order to determine the factor structure of the service quality of Internet-based e-waste collection platforms, for this research we collected survey data from 395 respondents who had used the Internet-based e-waste collection platforms at least one time during the past two years. In this process, a professional marketing research firm called Wenjuanxing was selected to collect the data. The online panel of Wenjuanxing has 2.6 million members, with a daily average of over 1 million questionnaire respondents. The questionnaire was distributed by Wenjuanxing due to the firm’s ability to quickly provide a sufficient number of suitable respondents. All respondents were asked to recall their recent experiences with Internet-based e-waste collection platforms from an introduction to the main process of Internet-based e-waste collection and then to complete the questionnaire as users. Respondents mentioned the last Internet-based e-waste collection platform they used and indicated their level of agreement on the service quality items using a 5-point Likert scale (1 = “Strongly disagree” and 5 = “Strongly agree”) [25,58,59].
The questionnaire was issued on 19 March 2021 and completed on 11 April 2021, and a total of 802 questionnaires were collected. After removing those samples not meeting the keeping standard of having experience with Internet-based e-waste collection in the last two years, 395 valid questionnaires were finally obtained, and the effective response rate was 49.3%.
Some important demographic variables including gender, age, income, region and collection channel were collected in this research to evaluate the representativeness of our sample. The demographic characteristics were similar to those reported in the study of second-hand transaction users conducted by QuestMobile, one of the leading Chinese Mobile Internet Big Data Research Institutes. Table 4 lists the demographic and regional characteristics of the sample. The age of the participants ranged from 19 to 60 years old, of which women accounted for 41% (n = 162), and participants younger than 40 years old accounted for 90% (n = 358). In terms of the geographic characteristics of the participants, the proportion of participants living in new first-tier cities and first-tier cities (70%, n = 277) was relatively large, which was consistent with the current development of Internet-based e-waste collection platforms. The first-tier cities include Beijing, Shanghai, Guangzhou and Shenzhen and the new first-tier cities include 15 cities from the ranking of the “2021 China City Business Attractiveness Ranking” released by First Financial. The business attractiveness of Chinese cities is calculated by the concentration of commercial resources, urban hub type, urban human activity, lifestyle diversity, and future plasticity. Every year, 15 new first-tier cities, 30 second-tier cities, 70 third-tier cities, 90 fourth-tier cities, and 128 fifth-tier cities are rated.
To begin with, we adopted principal component analysis as the extraction method and Varimax rotation as the rotation method to conduct an exploratory factor analysis of the items. Next, we examined the coefficient alpha and item-to-total correlations by dimension, and items were retained if (1) they loaded more than 0.50 on a factor, (2) they failed to load 0.50 or more on two factors, and (3) if the reliability analysis indicated an item to total correlation of more than 0.40 [60]. Then we reassigned the items, reconstructed the dimensions and conducted a series of iterations from the first step if necessary [42]. After such iterations, the final service quality scale came into being, consisting of 12 items in four dimensions. Four factors were identified with Eigen values greater than one, which totally explained 68.41% of the variance and they were labeled as efficiency (EF), accuracy (AC), customer service (CS) and price offerings (PO). The KMO test value of 0.82 indicated sampling adequacy. The Cronbach alpha values were between 0.69 and 0.81 (see Table 5), which was a range close to the commonly cited standard minimum value of 0.7 [61], indicating that each dimension had high internal consistency and reliability.
Based on the results of the exploratory factor analysis, the study calculated the correlation coefficient between dimensions to ensure whether there existed a second-order factor structure. As shown in Table 6, the correlation coefficient between the four factors was high, suggesting that it was necessary to carry out the analysis of the second-order factor structure. As a result, confirmatory factor analysis (CFA) was done to further evaluate the factor structure of the service quality scale.

3.4. Confirming Factor Structure

The final 12-item scale identified with four factors through EFA was then followed by CFA to evaluate the factor structure and its validity. There are CFA results and coefficient alpha values for the four dimensions and item loadings in Table 5. The average variances extracted (AVE) values as well as strong loadings of items on their relevant factors showed the scale’s component dimensions possessed convergent validity. Additionally, the combined reliability (CR) values of the four factors were higher than the recommended value of 0.67, indicating that the scale possessed high reliability. There was a good fit between the extracted dimensions and the scale through the fit indices. The fit criteria exceeded the recommended standard values proposed by Bagozzi and Baumgartner [62], providing strong evidence of the unidimensionality of each construct.
Following the method utilized by Doll and Xia [63], we compared the CFA model fitting with multiple factor models (see Table 7), and the one second-order factor model performed best on fitness measures. The results (χ2 = 44.36, df = 43, normed fit index [NFI] = 0.97, confirmatory fit index [CFI] = 1.00, root mean squared error of approximation [RMSEA] = 0.01, goodness-of-fit index [GFI] = 0.98, and adjusted goodness-of-fit index [AGFI] = 0.97) indicated that each dimension possessed a significant and positive loading on the higher-order factor (p ≤ 0.001). All correlations among the four constructs were significant at p < 0.001, revealing that they centered at a common underlying construct [64], which suggested that the data set fit a higher-order model well. The higher order measurement model is shown in Figure 1.
We split the data into two equal halves including a calibration and a validation sample for minimizing random capitalization [65]. The factor structure of the calibration sample that was formed by EFA was the same as that formed by CFA. In this light, the analysis of the validation sample would first consider the factor structure. Additionally, the fit statistics were well (χ2 = 52.72, df = 46, normed fit index [NFI] = 0.94, confirmatory fit index [CFI] = 0.99, root mean squared error of approximation [RMSEA] = 0.03, goodness-of-fit index [GFI] = 0.96, adjusted goodness-of-fit index [AGFI] = 0.92), and all path loadings were statistically significant.
To examine the discriminant validity, the conservative Fornell/Larcker test was adopted. Evident discriminant validity was established when the correlation between any two constructs was less than the square root of the AVE [66]. The square root of the AVE shown in Table 8 exceeded all the coefficients, which showed that the service quality measure model in our study had satisfactory discriminant validity.

3.5. Predictive Value of Scale

For testing the common method bias, Harman’s single factor test [67] was adopted. The basic principle of the test was that if the variance of the common method made a significant difference to the result of data analysis and interpretation, then a single potential factor would explain all the dominant variables [68]. The result of the single factor model’s worse fit indicated that common method variance failed to make a significant difference. The single factor model produced an χ2 = 621.27 with df = 54 (compared with the χ2 = 44.36 and df = 43 for the four-dimensional measurement model). For the unidimensional model, the fit was not well, showing that common method bias could not make a significant difference in the research.
For verifying the practical value as well as usability of the scale, we adopted its dimensions to investigate the relationship between the service quality of Internet-based e-waste collection platforms and customer satisfaction as well as loyalty. Satisfaction and loyalty are two of the most commonly used powerful predictors for service quality [59,69]. Customer satisfaction and loyalty were measured using the items from the existing literature [59,70] with a 5-point Likert scale (1 = “Strongly disagree” and 5 = “Strongly agree”). Compared with other statistical analysis methods, the adoption of multiple linear regression analyses was appropriate because it minimized the effects of multicollinearity, heteroscedasticity, and polynomial relationships [71]. Table 9 suggests the relationship of scale dimensions with customer satisfaction and loyalty. The average value of the relevant items represented the quality dimension of the scale in each regression model. The results suggested that all the service quality dimensions had significant impacts on customer satisfaction and loyalty. Service quality, which was measured in the scale, explained 40% of the variance in customer satisfaction and 37% of the variance in customer loyalty, indicating satisfactory external validity. In terms of customer satisfaction and loyalty, customer service provided the most significant determinant (β = 0.30, β = 0.36). The other quality dimensions appeared to conduce to customer satisfaction significantly, as shown by beta weights of 0.11 for efficiency, 0.25 for accuracy, and 0.07 for price offerings. For loyalty, customer service once again became the most significant determinant (β = 0.36), followed by accuracy (β = 0.26), and efficiency (β = 0.12). The importance of customer service in influencing customer satisfaction and loyalty, which are two major service outcomes in e-waste collection, was strengthened by these findings.

4. Discussion and Implication

This study develops and validates the measurement scale of service quality of Internet-based e-waste collection platforms. The scale conforms to a two-order factor model with four dimensions: efficiency, accuracy, customer service and price offerings. Each of these dimensions has three items. For one thing, the scale is consistent with the major service quality dimensions and factors influencing Internet-based e-waste collection intention and behavior discussed in the literature [3,16,72,73], and for another, it suggests the difference between Internet-based e-waste collection platforms and other online platforms such as shopping websites. For example, this study reflects that the users of Internet-based e-waste collection platforms are more concerned about soft indicators such as efficiency and customer service. The core dimensions in other online service quality measurement scales, such as website functionality [58], receive little attention in this research. The reason for such differences is probably that users only needed to follow the instructions to fill in the information about their e-waste, instead of taking plenty of time and effort to search for a product on shopping websites.

4.1. Efficiency

This study illustrates that efficiency should be one of the critical service quality dimensions in the measurement scale of Internet-based e-waste collection platforms. Efficiency means the ability to offer effective and convenient service during the collection stage and quality inspection stage, which is basically consistent with the previous studies [25,42,58,72,73]. Efficiency as defined in some studies [42,70] represents the rapidity of submitting orders and delivering products, while other studies [58] include diverse indicators such as efficiency of navigation, efficiency of online order processing, and timeliness of order delivery. In contrast, efficiency in our scale focuses on the collection stage and quality inspection stage, which reflects the necessity of exploring the connotation of the service quality under the Internet-based e-waste collection mode. The efficiency of the collection stage and quality inspection stage is quite different between diverse enterprises, different regions and distinct collection channels (including store collection, door-to-door collection and mailing collection). Thus, it is essential for Internet-based e-waste collection platforms to capture users’ requirements for efficiency to adopt appropriate service strategies in different regions or collection channels such as improving the speed of delivery, bettering quality inspection and payment processes, adding more offline collection points near the community, and expanding more collection channels.

4.2. Accuracy

Accuracy is identified as a vital dimension of the service quality of Internet-based e-waste collection platforms. Accuracy in this research represents the ability to identify an accurate price and reasonable explanation. Zuo, Wang and Sun [2] pointed out that the most attractive competitive advantage of online collection platforms is the legal collection at the highest market price based on its accurate pricing system. However, it is not easy for platforms to evaluate the price of e-waste correctly while differentiating the subjective understanding of users, finite evaluating options, and between online evaluating options and offline quality inspection standards. Typically, offline quality inspection standards are more detailed. Hence, it is necessary for Internet-based e-waste collection platforms to build accurate price evaluation systems while ensuring efficiency as well as the ability to offer appropriate explanations for possible price gaps. Similarly, Internet-based e-waste collection practices also highlight the importance of accuracy. For example, Aihuishou provides more detailed price assessment options and explanations for Apple mobile according to offline quality inspection standards, which could reduce the uncertainty of users’ subjective understanding from limited evaluating options.

4.3. Customer Service

The results indicate that high-level customer service at the stages of ordering, collection and quality inspection is the most important factor driving customer satisfaction and loyalty. The major elements of customer service in this research are the friendly service attitude of personnel, efficient ability to solve relevant problems actively and high professionalism.
In effect, various stages, such as online valuation, offline collection, quality inspection, final quotation and payment, comprise the customer service of Internet-based e-waste collection platforms. Additionally, these stages involve various customer service personnel, such as online, door-to-door, or store service personnel; pickup personnel; etc. Due to the fact that users might be dissatisfied or even angry about the existence of the gap between the evaluation and final price offerings, it is time for the customer service personnel at each stage to offer timely and professional customer service. This could not only make up for the inaccuracy of pricing but also enhance the satisfaction and loyalty of users. This view has also been confirmed in previous research [42,70]. However, customer service in Internet-based e-waste collection platforms also highlights professionalism and initiative. The reason might be that the quality inspection of e-waste involves complex professional knowledge and the price gap makes users conduct a great deal of online consultation and price negotiation. Consequently, users are more concerned about the professionalism and initiative of service personnel on Internet-based e-waste collection platforms compared with other e-service platforms such as shopping websites. It is necessary for managers to provide unified training for all service personnel, ensuring that they have a high level of professionalism and a good service attitude. When users encounter problems, personnel can actively provide help and suggestions, and when conflicts arise, they can negotiate and communicate with users in a meticulous and patient manner.

4.4. Price Offerings

Price offerings represents the capacity of Internet-based e-waste collection platforms to offer high collection prices in line with user expectations. Currently, in China, a payment would be offered by most Internet-based e-waste collection platforms to users for collecting their e-wastes. The relative price deviation among different e-waste collection platforms would affect the decision-making of users, especially those who are price sensitive. Accordingly, the appropriate collection price in line with users’ expectations needs to be offered to users to prompt them to voluntarily deliver their e-waste [53]. The existing studies on Internet-based e-waste collection [1,10,74] have also suggested that price offerings are a vital factor influencing users’ participation behavior and intention, and previous research [75,76,77] also proved that implementing economic incentives to foster e-waste collection is necessary. Therefore, the government should provide direct and reasonable subsidies to Internet-based e-waste collection platforms and propose some related tax reduction and exemption policies to help platforms offer high collection prices to their users. The existing research about price offerings is hard to apply to the practice without a clear connotation and measurement scale. This study offers the measurement indicators of price offerings, providing a theoretical basis for practitioners and researchers to explore ways of service quality improvement and innovation.

5. Conclusions and Limitation

This research probes into e-service quality in the context of Internet-based e-waste collection platforms with the aims of fully understanding of critical composition and developing an appropriate measurement scale for service quality. This research develops an instrument applicable for assessing the service quality of Internet-based e-waste collection platforms, providing guidance to managers focusing on the main elements of service quality. Efficiency, accuracy, high level services and optimal price are key dimensions of the Internet-based e-waste collection that happens between users and service providers, and the list of 12 service quality items used in our study would help them diagnose in detail. According to our conclusion, in the process of service management, managers should focus on these four core service quality dimensions. Comprehensive evaluation and analysis should be conducted on these four service quality dimensions, and managers should strive to improve collection prices with the support of the government, enhance collection convenience by increasing collection channels, improve evaluation accuracy by developing a valuation system, and improve customer service through systematic training for all staff.
The results contribute to extant research regarding Internet-based e-waste collection platforms in several ways. The first contribution is the development of a measurement scale, providing a sound theoretical basis for further researchers and practitioners to focus on the major aspects of the service quality of Internet-based e-waste collection platforms. Additionally, we achieve the extension of existing dimensions to e-service quality. Finally, the scale in our study predicts the satisfaction and loyalty of users well under Internet-based e-waste collection service settings, which enhances the reliability and validity of the research model for predicting user behavior in online service contexts. Although this research provides some useful insights, it is necessary for further research to address the limitations. One limitation lies in the samples used for item evaluation, as only users from the sample panel of Wenjuanxing are taken into account in this study. Future research could test the scale for richer samples to confirm the universality of our study. The second limitation is the screening question for the investigation, which lacks a specific classification of e-waste. So, future studies could explore the differences in service quality in different types of e-waste in order to reveal more facets of the service quality of Internet-based e-waste collection platforms. Thirdly, the service quality considered in this study is only from users’ perspectives, while enterprises’ perspectives should be taken into account for further effort.

Author Contributions

Formal analysis, Y.F. and W.W.; investigation, W.W. and Z.Q.; resources, W.W. and Z.Q.; data curation, W.W.; writing—original draft preparation, Z.Q. and W.W.; writing—review and editing, Y.F.; supervision, Y.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data used to support the findings of this study are available from the corresponding author by request.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 15 07701 g001 550
Figure 1. Second-order model of service quality.
Figure 1. Second-order model of service quality.
Sustainability 15 07701 g001
Table
Table 1. Initial codes of service quality.
Table 1. Initial codes of service quality.
IDCodeSourcesNumberIDCodeSourcesNumber
1the collection price matches with the valuation price7210328waiting time for door-to-door collection33
2differences between quality test results and actual situation396129transaction efficiency33
3high collection price202330quality test video33
4waiting time for payment192231logistics information22
5reliability in dealing with problems141832transaction progress information update22
6professionalism of service personnel111733complexity of order submission22
7offline store service121634valuation options22
8higher collection price than expected131535face-to-face service22
9politeness of service personnel131536product models available for collection22
10transaction speed111437product categories available for collection22
11higher collection price than other similar platforms121238timeliness in dealing with problems22
12accessibility of the collection channels101139regulations for price setting11
13waiting time for quality test91140accessibility of the platforms11
14collection price negotiation81041valuation-related user terms11
15overall convenience7842waiting time for quality test report11
16door-to-door pickup service6743transaction procedures11
17communication skills of service personnel6644implementation of quality test standards11
18reasonableness of the price reduction after quality test3645uniformity of platform valuation standards11
19offline transaction service5546truthfulness of platform valuation11
20responsiveness in dealing with problems5547returned goods logistics reminder11
21complaint handling5548returned products protection11
22door-to-door quality test4549price reduction subsidy11
23service personnel empathy4550product damage compensation11
24price subsidies4451free mailing back service11
25return waiting time4452human customer service accessibility11
26reasonable price3353data cleansing service11
27transaction progress information reminder3354privacy protection11
Table
Table 2. The initial dimensions of service quality.
Table 2. The initial dimensions of service quality.
DimensionsThemesConcepts
Price offeringsprice competitiveness, price reasonableness, price standardsThe ability of Internet-based e-waste collection platforms to provide users with a reasonable and competitive collection price
Efficiencyaccessibility, transaction efficiency, collection channels, information quality, diversityThe ability of Internet-based e-waste collection platforms to provide users with easily accessible and efficient collection service
Accuracygap between the valuation and collection price, difference between quality test results and actual situation, reasonableness for price reduction and valuation standardsThe ability of Internet-based e-waste collection platforms to provide users with professional and accurate valuation prices and reasonable explanations for the differences
Customer serviceattitude, professionalism, reliability, responsiveness, service guaranteeThe ability of Internet-based e-waste collection platforms to provide users with a high level of customer service with strong professionalism, good service attitude and rapid response
Table
Table 3. Items of primary service quality scales.
Table 3. Items of primary service quality scales.
DimensionItemsStatements
Price offeringsPO1The collection price offered by this Internet-based e-waste collection platform exceeds the ideal price in my mind.
PO2The collection price offered by this Internet-based e-waste collection platform is competitive among peers.
PO3The collection price offered by this Internet-based e-waste collection platform is higher than my expectation.
PO4The collection price offered by this Internet-based e-waste collection platform is generally higher.
EfficiencyEF1I can quickly deliver e-waste to this Internet-based e-waste collection platform for quality inspection.
EF2This Internet-based e-waste collection platform starts quality inspection quickly.
EF3This Internet-based e-waste collection platform completes quality inspection quickly.
EF4I can quickly get paid after this Internet-based e-waste collection platform finishes the quality inspection.
AccuracyAC1The valuation provided by this Internet-based e-waste collection platform is the same as the collection price.
AC2There is a slight price gap between valuation and collection price offered by this Internet-based e-waste collection platform.
AC3The quality inspection results provided by this Internet-based e-waste collection platform are in line with the actual situation of e-waste.
AC4The quality inspection results provided by this Internet-based e-waste collection platform don’t exaggerate the problems of e-waste.
AC5This Internet-based e-waste collection platform can offer a reasonable explanation for the price gap.
Customer serviceCS1The online (or offline) service staff of this Internet-based e-waste collection platform are polite.
CS2The online (or offline) service staff of this Internet-based e-waste collection platform have excellent service attitude.
CS3The online (or offline) service staff of this Internet-based e-waste collection platform solve problems proactively.
CS4The online (or offline) service staff of this Internet-based e-waste collection platform can offer reasonable negotiation plan of price.
CS5The online (or offline) service staff of this Internet-based e-waste collection platform have sufficient knowledge and skills.
Table
Table 4. Sample profile.
Table 4. Sample profile.
VariablesFrequency%
Gender
Male23359
Female16241
Age
≤1941
20–2918240
30–3917244
40–59339
≥6041
Income (CNY)
<250072
2500–60008121
6001–80008020
8001–10,0006115
10,001–20,00013133
>20,000359
City
First-tier cities12131
New first-tier cities15639
Second-tier cities5614
Third-tier cities and below6216
Collection channel
Door to door17745
Store379
Mailing18146
Table
Table 5. Result of EFA and CFA.
Table 5. Result of EFA and CFA.
ConstructItemCITCLoadingsCronbach’s αCRAVE
EfficiencyEF10.560.620.800.810.60
EF20.710.84
EF30.690.84
AccuracyAC10.530.670.710.720.46
AC20.580.72
AC30.500.65
Customer serviceCS20.480.500.690.670.41
CS30.520.83
CS50.530.55
Price offeringsPO10.670.780.810.810.59
PO20.600.69
PO40.700.83
All of the standard factor loadings are significant at p < 0.001.
Table
Table 6. Correlation matrix of the factors.
Table 6. Correlation matrix of the factors.
Customer ServiceAccuracyPrice OfferingsEfficiency
Customer service1.000
Accuracy0.3121.000
Price offerings0.3880.3961.000
Efficiency0.3180.4130.3071.000
All correlations are significant at p < 0.001.
Table
Table 7. Comparison of CFA model fitting between multiple factor models.
Table 7. Comparison of CFA model fitting between multiple factor models.
ModelChi-Square (df)Chi-Square/dfNFICFIRMSEAGFIAGFI
One first-order correlation four factors44.70(42)1.0640.9720.9980.0130.9820.967
One first-order uncorrelation four factors356.19(54)6.5960.7780.8040.1190.8490.782
One second-order factor44.36(43)1.0320.9720.9990.0090.9820.968
One first-order single factor621.27(54)11.5050.6130.6320.1630.7630.658
Table
Table 8. Fornell/Larcker test for the four quality dimensions.
Table 8. Fornell/Larcker test for the four quality dimensions.
Customer ServiceAccuracyPrice
Offerings		Efficiency
Customer service0.64
Accuracy0.620.68
Price offerings0.460.460.77
Efficiency0.510.520.380.77
Square root of AVE on diagonal.
Table
Table 9. Relationship of service quality dimensions with satisfaction and loyalty.
Table 9. Relationship of service quality dimensions with satisfaction and loyalty.
ConstructCustomer SatisfactionCustomer Loyalty
Efficiency0.11 **0.12 **
Accuracy0.25 ***0.26 ***
Customer service0.30 ***0.36 ***
Price offerings0.07 *0.06
R20.400.37
*** Significant at p < 0.001. ** Significant at p < 0.01. * Significant at p < 0.05.
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Fang, Y.; Qu, Z.; Wang, W. Developing the Scale for Measuring the Service Quality of Internet-Based E-Waste Collection Platforms. Sustainability 2023, 15, 7701. https://doi.org/10.3390/su15097701

AMA Style

Fang Y, Qu Z, Wang W. Developing the Scale for Measuring the Service Quality of Internet-Based E-Waste Collection Platforms. Sustainability. 2023; 15(9):7701. https://doi.org/10.3390/su15097701

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Fang, Yan, Zhuowen Qu, and Wenhua Wang. 2023. "Developing the Scale for Measuring the Service Quality of Internet-Based E-Waste Collection Platforms" Sustainability 15, no. 9: 7701. https://doi.org/10.3390/su15097701

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