Developing the scale for measuring the service quality of Internet-based e-waste collection platforms

Abstract

As Internet-based electronic waste (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 is playing a crucial role in attracting users. Based on the 94 online reviews and the survey data 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. On the basis of this scale, some significant implications to the service quality management of Internet-based e-waste collection platforms have been proposed to both practitioners and researchers.

1. Introduction

As the world’s second-largest producer of electronic waste (e-waste) (Wang, Ren et al. 2019), China is entering the peak period of scraping, while there is persistent problem with the traditional e-waste collection, such as heavy contamination, high cost, obstructed channels of information and unreasonable allocation of resources (Zuo, Wang et al. 2020). Fortunately, e-waste collection has new opportunities that Internet-based e-waste collection platforms are emerging with the rapid development of the Internet (Zuo, Wang et al. 2020).

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 environment (Xin, Tao et al. 2017, Sun, Wang et al. 2018, Wujie, Zhang et al. 2018, Wang, Ren et al. 2019). Internet-based e-waste collection is incredibly essential for the sustainable development of e-waste collection industry (Zuo, Wang et al. 2020) 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, various Internet-based e-waste collection platforms are born out. 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 help combine traditional e-waste collection system with Internet in form of online platforms and collection applications (Gu, Ma et al. 2017). Therefore, it is necessary for e-waste collection industry to get new vitality through Internet-based e-waste collection (Qu, Sheng et al. 2019).

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 (Jian, Xu et al. 2019) 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 (Zuo, Wang et al. 2020). All these laws and regulations aim at promoting e-waste collection by integrating Internet and other advanced technologies (Zhu, Chao et al. 2016).

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 (Wu, Chen et al. 2020). Under Internet-based e-waste collection, many platforms have been striving to improve their service quality so as to better satisfy user experience. As a case in point is Aihuishou, it has opened over 300 outlets and provided face-to-face communication and transactions in 35 cities by 2018 (Sun, Wang et al. 2018), 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 (Song, Li et al. 2015, Zeng, Yang et al. 2017, Fu, Zhang et al. 2018), and many countries have made great efforts to improve the quality of e-waste collection (Yu, Williams et al. 2010, Tanskanen and Pia 2013, Yu, He et al. 2014, Zeng, Yang et al. 2017, Sousa, Agante et al. 2018), the outcomes are not optimal (Sergio, Jaume et al. 2018). Only 8.9 Mt of e-waste was recorded to be collected and recycled all over the world in 2016, which was equivalent to 20% of all the e-waste generated (Baldé, Forti et al. 2017). The lack of flexibility and convenience of Internet-based e-waste collection increases users’ reluctance to act (Wang, Qu et al. 2019). Additionally, users’ complaint 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. With this purpose, it is necessary for the 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 desire of users (Blut 2016).

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 (Tabernero, Hernandez et al. 2015, Hong, Hung et al. 2018, Xiao, Dong et al. 2018). However, a holistic and methodical service quality-oriented focus on Internet-based e-waste collection platforms has rarely been considered in existing studies (Schulte, Gellenbeck et al. 2017). Service quality scales in existing literatures were mainly developed for online shopping websites and online hotel websites (Narteh 2013, Hahn, Sparks et al. 2017). These special service quality scales might not be applicable for Internet-based e-waste collection platforms, and it might be troublesome to modify a general measurement scale on ad-hoc basis (Francis and Julie 2007). Similarly, Riel, Liljander et al. (2001) also called attention to thoroughly deriving and reformulating scale items from former service quality literatures.

Therefore, concretizing the conceptualization and measurement of service quality of Internet-based e-waste collection platforms are of great necessity (Schulte, Gellenbeck et al. 2017). Without objective and comprehensive measures of the service quality of Internet-based e-waste collection platforms, it is impossible for the managers and researchers to evaluate and improve the service quality properly (Tabernero, Hernandez et al. 2015). 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 service quality literature, the scale 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. Section 4 discusses the study results. Finally, the conclusions are given in the last section.

2. Literature Review

2.1 Service quality

The concept of e-service quality was first referred by Lemon (2001), whereas Zeithaml, Parasuraman et al. (2002) 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 (2010) argued that the definition covers both pre-and post-service delivery experiences on the web. It’s essential to examine the service quality (SERVQUAL) scale before discussing e-service quality, because many existing e-service quality researches are mainly based on SERVQUAL (Zhou, Wang et al. 2018). Parasuraman, Zeithaml et al. (1985) 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, Zeithaml et al. (2005) in their work on SERVQUAL and they proposed e-service quality instruments (E-S-QUAL), which has four dimensions (efficiency, system availability, fulfillment and privacy). It had been utilized and adapted in several empirical studies in different settings like e-supermarket, e-bank subsequently (Boshoff 2007, Fuentes-Blasco, Saura et al. 2010, Marimon, Vidgen et al. 2010).

2.2 The scale development of service quality

With the further research of e-service quality, scholars have developed some measurement scales under various e-service contexts. Some scholars have adopted the traditional service quality dimensions to advance their researches into electronic service quality (Gefen 2002, Gwo-Guang and Lin 2005) 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 (Parasuraman, Zeithaml et al. 2005, Nusair and Kandampully 2008, Shachaf, Oltmann et al. 2008, Khan 2010). The dimensions for measuring e-service quality are mainly based on a modified SERVQUAL scale (Parasuraman., Berry. et al. 1991) And Parasuraman, Zeithaml et al. (2005) presented efficiency, system availability, fulfillment, and privacy as the dimensions of E-S-QUAL scale and 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’s easy to know that some dimensions of traditional scale could be retained in e-service context while other 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 that users were much more likely to collect when the Internet-based e-waste collection platform provided high quality services of faster response, safer transaction and more functions. Chi, Wang et al. (2014) investigated end-users’ participating behaviors in Taizhou of China and indicated that economic benefit and convenience of collecting which included near to home, flexible collection time, easy to find and pickup service with proper value of e-waste were the main concerning factors. Zhang, Du et al. (2018) conducted a questionnaire survey and suggested that perceived convenience, attitude and subjective norms were positively related to the users’ willingness towards adopting platforms for e-waste collection. The price disadvantage was not conducive for users to participate in e-waste collection. Wang, Ren et al. (2019) 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 users who had lower income were expecting higher returns.

As a result, many dimensions of service quality have been recognized as the key factors influencing the intention of participating Internet-based e-waste collection. However, the specific measurement scale for the service quality of Internet-based e-waste collection platforms hasn’t been developed. The scales for other online websites or platforms could not apply to Internet-based e-waste collection platforms appropriately, so it’s urgent to develop the service quality measurement scale in this field to support the development of Internet-based e-waste collection.

3. Scale Development

The scale development of the service quality is critical to Internet-based e-waste collection platforms but receives little attention. Therefore, this research develops the scale of service quality of Internet-based e-waste collection platforms following the general process. Item generation, scale development, and scale evaluation are the three main phases of scale development (Churchill and Jr. 1979). In the first phase, literature review, focus group, and expert interview are generally used to generate initial items (Boudreau et al., 2001). Based on the important role of online reviews in users’ choice of a collection service (Mansuy, Verlinde et al. 2020), online reviews are used in the phase of item generation, together with literature review in this research. In the second phase, we refine 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 examine 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 the 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). 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 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 the evaluation of the service quality of Internet-based e-waste collection platforms; (3) they constituted an independent and complete plot; (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 (Krippendorff 1980). It has been successfully applied to 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 were 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.

Table 1

Initial codes of service quality
ID	Code	Sources	Number	ID	Code	Sources	Number
1	the collection price matches with the valuation price	72	103	28	waiting time for door-to-door collection	3	3
2	differences between quality test results and actual situation	39	61	29	transaction efficiency	3	3
3	high collection price	20	23	30	quality test video	3	3
4	waiting time for payment	19	22	31	logistics information	2	2
5	reliability in dealing with problems	14	18	32	transaction progress information update	2	2
6	professionalism of service personnel	11	17	33	complexity of order submission	2	2
7	offline store service	12	16	34	valuation options	2	2
8	higher collection price than expected	13	15	35	face-to-face service	2	2
9	politeness of service personnel	13	15	36	product models available for collection	2	2
10	transaction speed	11	14	37	product categories available for collection	2	2
11	higher collection price than other similar platforms	12	12	38	timeliness in dealing with problems	2	2
12	accessibility of the collection channels	10	11	39	regulations for price setting	1	1
13	waiting time for quality test	9	11	40	accessibility of the platforms	1	1
14	collection price negotiation	8	10	41	valuation-related user terms	1	1
15	overall convenience	7	8	42	waiting time for quality test report	1	1
16	door-to-door pickup service	6	7	43	transaction procedures	1	1
17	communication skills of service personnel	6	6	44	implementation of quality test standards	1	1
18	reasonableness of the price reduction after quality test	3	6	45	uniformity of platform valuation standards	1	1
19	offline transaction service	5	5	46	truthfulness of platform valuation	1	1
20	responsiveness in dealing with problems	5	5	47	returned goods logistics reminder	1	1
21	complaint handling	5	5	48	returned products protection	1	1
22	door-to-door quality test	4	5	49	price reduction subsidy	1	1
23	service personnel empathy	4	5	50	product damage compensation	1	1
24	price subsidies	4	4	51	free mailing back service	1	1
25	return waiting time	4	4	52	human customer service accessibility	1	1
26	reasonable price	3	3	53	data cleansing service	1	1
27	transaction progress information reminder	3	3	54	privacy protection	1	1

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 provided a list of initial dimensions of the service quality of Internet-based e-waste collection platforms.

Table 2

The initial dimensions of service quality
Dimensions	Themes	Concepts
Price offerings	price competitiveness, price reasonableness, price standards	The ability of Internet-based e-waste collection platforms to provide users with a reasonable and competitive collection price
Efficiency	accessibility, transaction efficiency, collection channels, information quality, diversity	The ability of Internet-based e-waste collection platforms to provide users with easily accessible and efficient collection service
Accuracy	gap between the valuation and collection price, difference between quality test results and actual situation, reasonableness for price reduction and valuation standards	The ability of Internet-based e-waste collection platforms to provide users with professional and accurate valuation price and reasonable explanations for the differences
Customer service	attitude, professionalism, reliability, responsiveness, service guarantee	The 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

3.2 Item generation

In this research, an initial pool of items was generated from a review of previous literature which 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.

Table 3

Items of primary service quality scales
Dimension	Items	Statements
Price offerings	PO1	The collection price offered by this Internet-based e-waste collection platform exceeds the ideal price in my mind.
	PO2	The collection price offered by this Internet-based e-waste collection platform is competitive among peers.
	PO3	The collection price offered by this Internet-based e-waste collection platform is higher than my expectation.
	PO4	The collection price offered by this Internet-based e-waste collection platform is generally higher.
Efficiency	EF1	I can quickly deliver e-waste to this Internet-based e-waste collection platform for quality inspection.
	EF2	This Internet-based e-waste collection platform starts quality inspection quickly.
	EF3	This Internet-based e-waste collection platform completes quality inspection quickly.
	EF4	I can quickly get paid after this Internet-based e-waste collection platform finishes the quality inspection.
Accuracy	AC1	The valuation provided by this Internet-based e-waste collection platform is the same as the collection price.
	AC2	There is a slight price gap between valuation and collection price offered by this Internet-based e-waste collection platform.
	AC3	The quality inspection results provided by this Internet-based e-waste collection platform are in line with the actual situation of e-waste.
	AC4	The quality inspection results provided by this Internet-based e-waste collection platform don’t exaggerate the problems of e-waste.
	AC5	This Internet-based e-waste collection platform can offer a reasonable explanation for the price gap.
Customer service	CS1	The online (or offline) service staff of this Internet-based e-waste collection platform are polite.
	CS2	The online (or offline) service staff of this Internet-based e-waste collection platform have excellent service attitude.
	CS3	The online (or offline) service staff of this Internet-based e-waste collection platform solve problems proactively.
	CS4	The online (or offline) service staff of this Internet-based e-waste collection platform can offer reasonable negotiation plan of price.
	CS5	The online (or offline) service staff of this Internet-based e-waste collection platform have sufficient knowledge and skills.

During the focus group discussion conducted by a professor and two Ph.D. students in Environment Management, 39 items were eliminated because of ambiguous and redundant. After this, eight experienced Ph.D. and M.A. students evaluated these 50 items by rating how well each item reflected the corresponding dimension of service quality of Internet-based e-waste collection platforms. The rating used the following scale: 1 = clearly representative, 2 = somewhat representative, and 3 = not representative at all (Bearden., Hardesty. et al. 2001). Only those items with an average score of two or less were retained (Bearden., Hardesty. et al. 2001). This process reduced the number of items to 30. Then, a manager from one of Internet-based e-waste collection platforms and a professor in this research field were interviewed for a 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, this research 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 its 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 of 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 the level of agreement on the service quality items using a 5-point Likert scale (1 = “Strongly disagree” and 5 = “Strongly agree”) (Bauer, Falk et al. 2006, Ding, Hu et al. 2011, Blut 2016).

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 experiences of 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 included 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 the participants less 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 “2021 China City Business Attractiveness Ranking” released by First Financial.

Table 4

Sample profile
Variables		%
Gender
Male	233	59
Female	162	41
Age
<=19	4	1
20–29	182	40
30–39	172	44
40–59	33	9
>=60	4	1
Income
< 2500	7	2
2500–6000	81	21
6001–8000	80	20
8001–10000	61	15
10001–20000	131	33
> 20000	35	9
City
First-tier cities	121	31
New first-tier cities	156	39
Second-tier cities	56	14
Third tier cities and below	62	16
Collection channel
Door to door	177	45
Store	37	9
Mailing	181	46

To begin with, we adopted principal component analysis as the extraction method and Varimax rotation as the rotation method to conduct exploratory factor analysis of the items. Next we examined coefficient alpha and item-to-total correlations by dimension and items were retained if (1) they loaded more than .50 on a factor, (2) fail to load .50 or more on two factors, and (3) if the reliability analysis indicated an item to total correlation of more than .40 (Hair, Black et al. 2011). Then we reassigned the items and reconstructed the dimensions and conducted a series iterations from the first step if necessary (Parasuraman, Zeithaml et al. 2005). After such iterations, the final service quality scale came into being, consisting of 12 items on four dimensions. Four factors were identified with Eigen values greater than one, which totally explained 68.41% of the variance and 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 close to the commonly cited standard minimum value of 0.7 (Nunnally 1978), indicating that each dimension had high internal consistency and reliability.

Table 5

Result of EFA and CFA
Construct	Item	CITC	loadings	Cronbach'sα	CR	AVE
Efficiency	EF1	0.56	0.62	0.80	0.81	0.60
	EF2	0.71	0.84
	EF3	0.69	0.84
Accuracy	AC1	0.53	0.67	0.71	0.72	0.46
	AC2	0.58	0.72
	AC3	0.50	0.65
Customer service	CS2	0.48	0.50	0.69	0.67	0.41
	CS3	0.52	0.83
	CS5	0.53	0.55
Price offerings	PO1	0.67	0.78	0.81	0.81	0.59
	PO2	0.60	0.69
	PO4	0.70	0.83
All of the standard factor loadings are significant at p < 0.001.

Based on the results of exploratory factor analysis, the study calculated the correlation coefficient between dimensions to ensure whether there existed 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 second-order factor structure. As a result, confirmatory factor analysis (CFA) had been done to further evaluate the factor structure of the service quality scale.

Table 6

Correlation matrix of the factors
	Customer service	Accuracy	Price offerings	Efficiency
Customer service	1.000
Accuracy	0.312	1.000
Price offerings	0.388	0.396	1.000
Efficiency	0.318	0.413	0.307	1.000
All correlations are significant at p < 0.001.

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 were 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. And 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 (1994), providing strong evidence of the unidimensionality of each construct.

Table 7

Comparison of CFA model fitting between multiple factor models
Model	Chi-square(df)	Chi-square/df	NFI	CFI	RMSEA	GFI	AGFI
One first-order correlation four factors	44.70(42)	1.064	0.972	0.998	0.013	0.982	0.967
One first-order uncorrelation four factors	356.19(54)	6.596	0.778	0.804	0.119	0.849	0.782
One second-order factor	44.36(43)	1.032	0.972	0.999	0.009	0.982	0.968
One first-order single factor	621.27(54)	11.505	0.613	0.632	0.163	0.763	0.658

Following the method utilized by Doll. and Xia (1997), we compared CFA model fitting between multiple factor models (see Table 7), 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, 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). Besides, all correlations among the four constructs were significant at p < 0.001, revealing that they centered at a common underlying construct (Lages, Lages et al. 2005), which suggested that the data set fitted a higher-order model well. The higher order measurement model was shown in Fig. 1.

We split the data into two equal halves including a calibration and a validation sample for minimizing random capitalization (Ma Cc Allum, Roznowski et al. 1992). The factor structure of the calibration sample which was formed by EFA was the same as by CFA. In this light, the analysis of the validation sample would first consider the factor structure. And 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.

Table 8

Fornell/Larcker test for the four quality dimensions
	Customer service	Accuracy	Price offerings	Efficiency
Customer service	0.64
Accuracy	0.62	0.68
Price offerings	0.46	0.46	0.77
Efficiency	0.51	0.52	0.38	0.77
Square root of AVE on diagonal.

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 AVE (Bagozzi. and P. 1981). The square root of AVE 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 (Mcfarland and Sweeney 1992) was adopted. The basic principle of the test was that if variance of common method made a terrible difference to the result of data analysis and interpretation, then a single potential factor would explain all the dominant variables (Podsakoff. and M. 2016). The result of single factor model’s worse fit indicated that common method variance failed to make a terrible difference. The single factor model produced a 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 couldn’t make a terrible 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 (Ding, Hu et al. 2011, Miranda, Tavares et al. 2017). Customer satisfaction and loyalty were measured using the items from existing literature (Wolfinbarger and Gilly 2003, Ding, Hu et al. 2011) 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 appropriative because it minimized the effects of multicollinearity, heteroscedasticity, and polynomial relationships (Neter and Wassermann 1974). Table 9 suggested the relationship of scale dimensions to customer satisfaction and loyalty. The average value of 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 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 were two major service outcomes in e-waste collection was strengthened by these findings.

Table 9

Relationship of service quality dimensions to satisfaction and loyalty
Construct	Customer satisfaction	Customer loyalty
Efficiency	0.11^⁎⁎	0.12^⁎⁎
Accuracy	0.25^⁎⁎⁎	0.26^⁎⁎⁎
Customer service	0.30^⁎⁎⁎	0.36^⁎⁎⁎
Price offerings	0.07^⁎	0.06
R²	0.40	0.37
⁎⁎⁎ Significant at p < 0.001; ⁎⁎ Significant at p < 0.01; ⁎ Significant at p < 0.05.

4. Discussion

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, and for another, it suggests the difference between Internet-based e-waste collection platforms and other online platforms like shopping websites. For example, this study reflects that the users of Internet-based e-waste collection platforms are more concerned about soft indicators like efficiency and customer service. The core dimensions in other online service quality measurement scales like website functionality (Bauer, Falk et al. 2006) are receiving little attention in this research. The reason for such differences is probably that users only need 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 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 (Wolfinbarger and Gilly 2003, Parasuraman, Zeithaml et al. 2005, Bauer, Falk et al. 2006, Blut 2016). Efficiency as defined in some studies (Wolfinbarger and Gilly 2003, Parasuraman, Zeithaml et al. 2005) represents rapidity of submitting orders and delivering products, while others (Bauer, Falk et al. 2006) include diverse indicators like 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 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’s 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.

4.2 Accuracy

Accuracy is identified as a vital dimension of service quality of Internet-based e-waste collection platforms. Accuracy in this research represents the ability of accurate price and reasonable explanation. Zuo, Wang et al. (2020) pointed that the most attractive competitive advantage of the online collection platforms was the legal collection at the highest market price based on its accurate pricing system. However, it’s not easy for platforms to evaluate the price of e-waste correctly for differentiating subjective understanding of users, finite evaluating options and difference between online evaluating options and offline quality inspection standards. Typically, offline quality inspection standards are more detailed. Hence, it’s necessary for Internet-based e-waste collection platforms to build accurate price evaluation systems while ensuring efficiency as well as the ability of offering appropriate explanation for possible price gaps. Similarly, Internet-based e-waste collection practice also highlights the importance of accuracy. For example, Aihuishou provides more detailed price assessment options and explanations for Apple mobile according to the offline quality inspection standards, which could reduce the uncertainty of users’ subjective understanding from limited evaluating options.

4.3 Customer service

The results indicate 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 cover the customer service of Internet-based e-waste collection platforms. And these stages involve various customer service personnel like 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’s time for customer service personnel of each stage to offer timely and professional customer service. It could not only make up for the inaccuracy of pricing, but also enhance satisfaction and loyalty of users. This view was also confirmed in previous researches (Wolfinbarger and Gilly 2003, Parasuraman, Zeithaml et al. 2005). However, customer service in Internet-based e-waste collection platforms also highlights the professionalism and initiative. The reason might be that the quality inspection of e-waste involves complex professional knowledge and the price gap make 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 the Internet-based e-waste collection platforms compared with other e-service platforms like shopping websites.

4.4 Price offerings

Price offerings represents the capacity of Internet-based e-waste collection platforms to offer high collection price 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 the users' expectations need to be offered for users to prompt them to voluntarily deliver their e-waste (Zhang, Du et al. 2018). The existing studies of Internet-based e-waste collection (Jian, Xu et al. 2019, Liu., Baia. et al. 2019, Wang, Ren et al. 2019) also suggested that price offerings were a vital factor influencing users’ participation behavior and intention. And previous researches (Ongondo and Williams 2011, Ylae-Mella, Keiski et al. 2015, Baxter and Gram-Hanssen 2016) also proved that implementing economic incentives to foster e-waste collection was necessary. Therefore, the government should provide direct and reasonable subsidies to the Internet-based e-waste collection platforms, and propose some related tax reduction and exemption policies to help platforms offer high collection price to their users. The existing researches about price offerings are hard to be applied 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

This research probes into the 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 of 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 in the Internet-based e-waste collection which happens between users and service providers. And the list of 12 service quality items used in our study would help them diagnose in detail.

The results contribute to extant researches regarding Internet-based e-waste collection platforms in several ways. The first contribution is the development of measurement scale, providing a sound theoretical basis for further researches and practitioners to fucus 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 (Ding, Hu et al. 2011), which enhances the reliability and validity of research model for predicting user behavior in online service contexts.

Although this research provides some useful insights, it is necessary for further researches to address the limitations. One limitation lies in the samples used for item evaluation, 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 of specific classification of e-waste. So future studies could explore the differences of 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’ perspective should be taken into account for further efforts.

Declarations

Acknowledgements

This work is supported two projects funded by the National Natural Science Foundation of China (71473029, 71320107006) and one project funded by the University Innovative Talent Program of Liaoning Province (ZX20180003).

Author Contributions

This paper was written by Wenhua Wang, Ying Qu and Yan Fang. W. Wang collected and analyzed the data, and wrote the paper. Y. Qu guided the research direction and proposed the framework of analysis. Y. Fang was responsible for data processing and soft operation.

Conflicts of Interest

The authors declare no conflict of interest. And the founding sponsors had no role in the design of the study; in the collection, analysis, and interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

Acknowledgement

This research is supported by National Nature Science Foundation of China (71974028).

References

Bagozzi, R. P. and H. Baumgartner (1994). "The Evaluation of Structural Equation Models and Hypothesis Testing." principles in marketing research.
Bagozzi. and R. P. (1981). "Evaluating Structural Equation Models With Unobservable Variables and Meas Error: A Comment, in: Journal of Marketing Research." Journal of Marketing Research 18(3): 375–381.
Baldé, C., V. Forti, V. Gray, R. Kuehr and P. Stegmann (2017). "The Global E-waste Monitor 2017: Quantities, Flows, and Resources."
Bauer, H. H., T. Falk and M. Hammerschmidt (2006). "eTransQual: A transaction process-based approach for capturing service quality in online shopping." Journal of Business Research 59(7): 866–875.
Baxter, J. and I. Gram-Hanssen (2016). "Environmental message framing: Enhancing consumer recycling of mobile phones." Resources Conservation & Recycling 109: 96–101.
Bearden., W. O., D. M. Hardesty. and R. L. Rose. (2001). "Consumer Self-Confidence Refinements in Conceptualization and Measurement." Journal of Consumer Research 28(1): 121–134.
Blut, M. (2016). "E-Service Quality: Development of a Hierarchical Model." Journal of Retailing 92(4): 500–517.
Boshoff, C. (2007). "A Psychometric Assessment of E-S-Qual: A Scale to Measure Electronic Service Quality." Journal of Electronic Commerce Research 8.
Bressolles, G. and J. Nantel (2010). The Measurement of Electronic Service Quality, Electronic Services.
Chi, X., M. Wang and M. A. Reuter (2014). "E-waste collection channels and household recycling behaviors in Taizhou of China." Journal of Cleaner Production 80(oct.1): 87–95.
Churchill, G. A. and Jr. (1979). "A Paradigm for Developing Better Measures of Marketing Constructs." Journal of Marketing Research 16(1): 64–73.
Ding, D. X., P. J.-H. Hu and O. R. L. Sheng (2011). "e-SELFQUAL: A scale for measuring online self-service quality." Journal of Business Research 64(5): 508–515.
Doll., W. J. and W. Xia (1997). "Confirmatory Factor Analysis of the End-User Computing Satisfaction Instrument: A Replication." journal of organizational & end user computing.
Francis and E. Julie (2007). "Internet retailing quality: one size does not fit all." Journal of Service Theory & Practice 17(3): 341–355.
Fu, J., H. Zhang, A. Zhang and G. Jiang (2018). "E-waste Recycling in China: A Challenging Field." Environmental Science and Technology 52(12): 6727–6728.
Fuentes-Blasco, M., I. G. Saura, G. Berenguer-Contrí and B. Moliner-Velázquez (2010). "Measuring the antecedents of e-loyalty and the effect of switching costs on website." Service Industries Journal 30(11): 1837–1852.
Gefen, D. (2002). "Customer Loyalty in E-Commerce." AIS Educator Journal 3(1): 27–51.
Gu, F., B. Ma, J. Guo, P. A. Summers and P. Hall (2017). "Internet of things and Big Data as potential solutions to the problems in waste electrical and electronic equipment management: An exploratory study." Waste Management: 434.
Gwo-Guang, L. and H. F. Lin (2005). "Consumer perceptions of e-Service quality in online shopping." International Journal of Retail & Distribution Management 33(2): 161–176.
Hahn, S.-E., B. Sparks, H. Wilkins and X. Jin (2017). "E-service Quality Management of a Hotel Website: A Scale and Implications for Management." Journal of Hospitality Marketing & Management 26(7): 694–716.
Hair, J. F., B. Black, B. J. Babin and R. Anderson (2011). Multivariate Data Analysis, Multivariate Data Analysis.
Hong, T. T. N., R. J. Hung, C. H. Lee and T. T. N. Hang (2018). "Determinants of Residents' E-Waste Recycling Behavioral Intention: A Case Study from Vietnam." Sustainability 11(1).
Jian, H., M. Xu and L. Zhou (2019). "Collaborative collection effort strategies based on the "Internet + recycling" business model." Journal of Cleaner Production 241: 118120.
Khan, M. A. (2010). "An Empirical Study of Automated Teller Machine Service Quality and Customer Satisfaction in Pakistani Banks." European Journal of Social Sciences 13(3).
Krippendorff, K. (1980). Content analysis: An introduction to its methodology. Beverly Hills CA: Sage Publications.
Lages, C., C. R. Lages and L. F. Lages (2005). "The RELQUAL scale: a measure of relationship quality in export market ventures." Journal of Business Research 58(8): 1040–1048.
Lemon, R. (2001). "E-Service and the Consumer." International Journal of Electronic Commerce 5(3): 85–101.
Liu., J., H. Baia., Q. Zhang., Q. Jing. and H. Xu. (2019). "Why are obsolete mobile phones difficult to recycle in China?" Resources, Conservation and Recycling 141: 200–210.
Ma Cc Allum, R. C., M. Roznowski and L. B. Necowitz (1992). "Model modifications in covariance structure analysis: the problem of capitalization on chance." Psychological Bulletin 111(3): 490–504.
Mansuy, J., S. Verlinde and C. Macharis (2020). "Understanding preferences for EEE collection services: A choice-based conjoint analysis." Resources, Conservation and Recycling 161.
Marimon, F., R. Vidgen, S. Barnes and E. Cristobal (2010). "Purchasing behaviour in an online supermarket The applicability of E-S-QUAL." International Journal of Market Research 52(1).
Mcfarland, D. B. and P. Sweeney (1992). "Distributive and procedural justice as predictors of satisfaction with personal and organizational outcomes." The Academy of Management Journal 35(3): 626–637.
Miranda, S., P. Tavares and R. Queiró (2017). "Perceived service quality and customer satisfaction: A fuzzy set QCA approach in the railway sector." Journal of Business Research 89(aug.): 371–377.
Narteh, B. (2013). "Service quality in automated teller machines: an empirical investigation." Managing Service Quality: An International Journal 23(1): 62–89.
Neter, J. and W. Wassermann (1974). "Applied linear statistical models. Regression, analysis of variance, and experimental designs. 4th printing." R.D. Irwin.
Nunnally, J. C. (1978). "Psychometric theory." American Educational Research Journal 5(3): 83.
Nusair, K. K. and J. Kandampully (2008). "The antecedents of customer satisfaction with online travel services: a conceptual model." European Business Review 20(1): 4–19.
Ongondo, F. O. and I. D. Williams (2011). "Greening academia: Use and disposal of mobile phones among university students." Waste Management 31(7): 1617–1634.
Parasuraman, A., V. A. Zeithaml and L. L. Berry (1985). "A Conceptual Model of Service Quality and Its Implications for Future Research." Journal of Marketing 49(4).
Parasuraman, A., V. A. Zeithaml and L. L. Berry (1988). "SERVQUAL: A Multiple-Item Scale for Measuring Consumer Perceptions of Service Quality." Journal of Retailing 64(1): 12–40.
Parasuraman, A., V. A. Zeithaml and A. Malhotra (2005). "E-S-QUAL - A multiple-item scale for assessing electronic service quality." Journal of Service Research 7(3): 213–233.
Parasuraman., A., L. Berry. and V. Zeithaml. (1991). "Refinement and Reassessment of SERVQUAl scale." Journal of Retailing 67(4): 420–450.
Podsakoff. and P. M. (2016). "Self-Reports in Organizational Research: Problems and Prospects." Journal of Management 12(4): 531–544.
Qu, J., H. Sheng, J. Yang, H. Wu and Z. Yuan] (2019). "Urban mining potentials of university: In-use and hibernating stocks of personal electronics and students’ disposal behaviors." Resources, Conservation and Recycling.
Riel, A., V. Liljander and P. Jurriens (2001). "Exploring Consumer Evaluations of e-Services: A Portal Site." International Journal of Service Industry Management 12(4): 359–377.
Schulte, N. A., K. Gellenbeck and M. Nelles (2017). "Operationalisation of service quality in household waste collection." Waste Manag 62: 12–23.
Sergio, S., L. Jaume and P. V. Ignasi (2018). "Mind the gap: A model for the EU recycling target applied to the Spanish regions." Waste Management 79(SEP.): 415–427.
Shachaf, P. Oltmann, S. Horowitz and Sarah (2008). "Service Equality in Virtual Reference." John Wiley & Sons, Inc.
Song, Q., J. Li, W. Yuan, H. Duan and L. Liu] (2015). "Solving e-waste problem using an integrated mobile recycling plant." Journal of Cleaner Production.
Sousa, R., E. Agante, J. Cerejeira and M. Portela (2018). "EEE fees and the WEEE system – A model of efficiency and income in European countries." Waste Management 79(SEP.): 770–780.
Sun, Q., C. Wang, L. S. Zuo and F. H. Lu (2018). "Digital empowerment in a WEEE collection business ecosystem: A comparative study of two typical cases in China." Journal of Cleaner Production 184(MAY 20): 414–422.
Tabernero, C., B. Hernandez, E. Cuadrado, B. Luque and C. R. Pereira (2015). "A multilevel perspective to explain recycling behaviour in communities." J Environ Manage 159: 192–201.
Tanskanen and Pia (2013). "Management and recycling of electronic waste." Acta Materialia 61(3): 1001–1011.
Wang, B., C. Ren, X. Dong, B. Zhang and Z. Wang (2019). "Determinants shaping willingness towards on-line recycling behaviour: An empirical study of household e-waste recycling in China." Resources Conservation & Recycling 143: 218–225.
Wang, W., Y. Qu, Y. Liu and Y. Zhang (2019). "Understanding the barriers for Internet-based e-waste collection system in China." Journal of Environmental Planning and Management 63(4): 629–650.
Wolfinbarger, M. and M. C. Gilly (2003). "eTailQ: dimensionalizing, measuring and predicting etail quality." Journal of Retailing 79(3): 183–198.
Wu, D., J. Chen, P. Li and R. Zhang (2020). "Contract coordination of dual channel reverse supply chain considering service level." Journal of Cleaner Production 260.
Wujie, Zhang, Jianfeng, Guo, Philip and Hall (2018). "Exploring "Internet + Recycling": Mass balance and life cycle assessment of a waste management system associated with a mobile application." The Science of the total environment.
Xiao, S., H. Dong, G. Yong and M. Brander (2018). "An overview of China's recyclable waste recycling and recommendations for integrated solutions." Resources, Conservation and Recycling 134: 112–120.
Xin, T., D. Tao and R. Lifset (2017). "Varieties of Business Model for Post-Consumer Recycling in China." Journal of Cleaner Production 170.
Ylae-Mella, J., R. L. Keiski and E. Pongracz (2015). "Electronic waste recovery in Finland: Consumers' perceptions towards recycling and re-use of mobile phones." Waste Manag 45(NOV.): 374–384.
Yu, J., E. Williams, M. Ju and C. Shao (2010). "Managing e-waste in China: Policies, pilot projects and alternative approaches." Resources, Conservation and Recycling 54(11): 991–999.
Yu, L., W. He, G. Li, J. Huang and H. Zhu (2014). "The development of WEEE management and effects of the fund policy for subsidizing WEEE treating in China." Waste Management 34(9): 1705–1714.
Zeithaml, V. A., A. Parasuraman and A. Malhotra (2002). "Service quality delivery through web sites: A critical review of extant knowledge." Journal of the Academy of Marketing Science 30(4): 362–375.
Zeng, X., C. Yang, J. F. Chiang and J. Li (2017). "Innovating e-waste management: From macroscopic to microscopic scales." Science of The Total Environment 575: 1–5.
Zhang, B., Z. Du, B. Wang and Z. Wang (2018). "Motivation and challenges for e-commerce in e-waste recycling under "Big data" context: A perspective from household willingness in China." Technological Forecasting and Social Change 144(JUL.): 436–444.
Zhou, R., X. Wang, Y. Shi, R. Zhang, L. Zhang and H. Guo (2018). "Measuring e-service quality and its importance to customer satisfaction and loyalty: an empirical study in a telecom setting." Electronic Commerce Research 19(3): 477–499.
Zhu, W., C. Chao, B. Guo, Z. Yu and X. Zhou (2016). "Internet Plus in China." It Professional 18(3): 5–8.
Zuo, L., C. Wang and Q. Sun (2020). "Sustaining WEEE collection business in China: The case of online to offline (O2O) development strategies." Waste Management 101: 222–230.