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Article

Enhancing Continuous Usage Intention in E-Commerce Marketplace Platforms: The Effects of Service Quality, Customer Satisfaction, and Trust

by
Jongnam Kim
1 and
Kyeongmin Yum
2,*
1
Division of Computer Engineering and Artificial Intelligence, Pukyong National University, Busan 48513, Republic of Korea
2
College of Business, Seoul National University, Gwanak-gu Gwanak-ro 1, Seoul 08826, Republic of Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(17), 7617; https://doi.org/10.3390/app14177617
Submission received: 1 August 2024 / Revised: 20 August 2024 / Accepted: 27 August 2024 / Published: 28 August 2024
(This article belongs to the Special Issue Human-Computer Interaction in Smart Factory and Industry 4.0)
Browse Figure
Versions Notes

Abstract

:
E-commerce marketplace platforms have evolved into integral digital intermediaries that shape online transactions in competitive environments. Companies continuously endeavor to improve e-service quality, customer satisfaction, and e-trust to gain a competitive advantage. This study aimed to identify the relationships between e-service quality, customer satisfaction, e-trust, and continuous usage intention in e-commerce marketplace platforms. Moreover, this study examined the roles of customer satisfaction and e-trust as mediators. We estimated nine hypothesized relationships using a structural equation modeling technique. Data from 311 users were used in the data analysis. The results are as follows: First, e-service quality significantly and positively affects customer satisfaction, e-trust, and continuous usage intention. Second, customer satisfaction has a significant and positive impact on e-trust and continuous usage intention. Third, e-trust has a significant and positive impact on continuous usage intention. Finally, both customer satisfaction and e-trust serve as significant mediating factors in the relationship between e-service quality and continuous usage intention. These insights hold strategic importance for e-commerce marketplace platform operators, allowing them to formulate service strategies and policies tailored to enhance user experience, foster trust, and drive continued usage, thereby strengthening their market position and ensuring sustained success.

1. Introduction

In the contemporary digital landscape, e-commerce marketplace platforms serve as important intermediaries that facilitate seamless online transactions between buyers and sellers [1]. Designed to streamline transactions and enhance user experience, these platforms integrate various features, such as online storefronts, secure payment systems, and responsive customer support. By catering to participants across different industries, e-commerce marketplace platforms have garnered widespread adoption, supporting transactions ranging from traditional e-commerce to specialized professional services and secondhand markets. This versatility attracts a broad user base, fuels economic activity, and fosters innovation within the platform ecosystem.
Operating as virtual commerce, e-commerce marketplace platforms such as Amazon and eBay aggregate sellers from diverse backgrounds and locations, thereby offering consumers a wide selection of products and services within a single digital space. User-generated content, including ratings and reviews, plays a crucial role in informing purchasing decisions and building trust among buyers [2]. Flexible business models enable various revenue streams while advanced technological integration enhances user experience and streamlines operations. With their global reach and community-building features, e-commerce marketplace platforms offer convenience, choice, and value to sellers and buyers, thereby shaping the landscape of modern commerce.
At the core of e-commerce marketplace platforms lies a robust technical infrastructure that facilitates seamless transaction processing, secure payments, and intelligent recommendation algorithms. To remain competitive, these platforms continuously pursue technological advancements, including prioritizing e-service quality (e-SQ) and customer satisfaction (CS) amid rising competition. Recognizing their pivotal role, e-commerce marketplace platforms understand that e-SQ and CS are essential for building e-trust and fostering repeat usage as they influence customer retention, brand reputation, and market differentiation. By focusing on e-SQ and CS, e-commerce marketplace platforms can effectively navigate intense competition, strengthen their market position, and ensure sustained success.
Exploring the interplay between service quality, CS, trust, and continuous usage intention (CUI) offers valuable insights for platform operators and customers. Several studies have demonstrated that service quality is a crucial factor influencing CS, trust, and CUI, while CS and trust are key predictors of CUI across various fields. For instance, Al-Nasser et al. [3] and Rita et al. [4] examined this dynamic through online shopping, Cristobal et al. [5] focused on websites, Song et al. [6] explored community group buying platforms, Haghkhah et al. [7] studied business-to-business contexts, and Keating et al. [8] analyzed online services.
However, applying insights from existing research on the relationships between these factors to e-commerce marketplace platforms presents challenges because of their unique diversity and complexity, which distinguish them from traditional and online platforms [9]. Consequently, addressing these challenges requires new research tailored specifically to the distinct characteristics of e-commerce marketplace platforms.
To address this gap, this study aimed to examine the structural relationships between e-SQ, CS, e-trust, and CUI, as well as the mediating roles of CS and e-trust. The findings of this study can provide valuable insights for establishing service strategies and policies tailored specifically to e-commerce marketplace platforms.
The following sections of this paper are organized as follows: First, relevant literature is reviewed to provide a foundation for the study. Next, the research model and hypotheses are presented. Then, an empirical analysis is conducted using the collected data. Finally, the results and implications of the study are discussed.

2. Literature Review

2.1. E-Commerce Marketplace Platforms

The e-commerce marketplace platform is a fundamental business model in the digital era and serves as an online intermediary facilitating the exchange of goods and services between diverse sellers and buyers [10]. Emerging from the advent of the Internet and the evolution of digital technologies, it has rapidly expanded alongside e-commerce. Initially conceived as a means for companies to sell products online and for consumers to make purchases conveniently, e-commerce marketplace platforms have undergone continuous innovation and enhancement in terms of user experience. Through technological advancements, these platforms now offer improved services, including robust transaction processing, secure payment systems, and dedicated user support.
E-commerce marketplace platforms are, thus, poised for further innovation and advancement as a result of the integration of emerging technologies, such as artificial intelligence, big data, and blockchain. These advancements promise to make transactions more intelligent and efficient, resulting in the delivery of personalized recommendations and services to users while strengthening reliability and security [11]. Meanwhile, intensifying global competition is expected to spur the increased adoption of e-commerce marketplace platforms across various industries. Consequently, platform operators may be empowered to drive ongoing innovation and development and thereby enrich user experiences and benefits.
E-commerce marketplace platforms exhibit the following characteristics: First, they create a dynamic and vibrant environment in which sellers compete directly and, thus, they offer a diverse range of products tailored to individual preferences and needs. Second, these platforms act as reliable intermediaries that prioritize the safety and security of transactions to foster trust and confidence among customers [12]. Finally, flexible fee structures accommodate sellers of all sizes, thereby promoting inclusivity, fostering healthy competition, and ultimately enhancing the overall customer experience [13]. These characteristics collectively enrich the online shopping journey, empower customers to make well-informed purchasing decisions, and contribute to the continued success of e-commerce marketplace platforms.

2.2. E-Service Quality

E-SQ is central to e-commerce and online services as it profoundly shapes CS and the overall experience within the digital marketplace. Defined as customers’ evaluations and opinions related to service quality in the virtual marketplace, e-SQ encompasses various facets, such as convenience, accessibility, and user experience tailored to online environments, as well as traditional factors such as speed, accuracy, and reliability [14]. As customers have high expectations of their online service encounters, they anticipate a quality level commensurate with these expectations.
The success of online commerce has been intricately linked to e-SQ enhancement. Components such as swift and reliable payment systems, accurate product information dissemination, and responsive and friendly customer support play crucial roles in augmenting CS. Failure to meet quality standards or align with customer expectations can result in customer dissatisfaction and erode their trust in the company. This effect underscores the importance of enhancing e-SQ to boost CS and loyalty [15].
E-SQ also significantly enhances companies’ competitiveness in the online landscape. Businesses offering exemplary service quality are more likely to overtake their competitors by securing customer preference and trust, which potentially leads to increased sales and market share. Hence, companies are increasingly focusing on improving e-SQ to remain competitive and drive growth in the e-commerce and online service industries [4].
The strategic importance of e-SQ extends beyond immediate customer interactions to broader business outcomes. Optimizing e-SQ not only drives customer retention and satisfaction, but also catalyzes long-term business success [16,17]. By consistently delivering superior digital service experiences, companies can establish themselves as leaders in their respective markets and attract and retain a loyal customer base [18]. This strategic focus on e-SQ is crucial as digital interactions continue to dominate consumer behavior and expectations. It also underscores the role of e-SQ as a cornerstone of competitive advantage in the evolving landscape of digital commerce.

2.3. Customer Satisfaction

CS is a key metric for evaluating the extent to which customers’ expectations align with the actual fulfillment delivered by products or services [19]. It encompasses a subjective assessment based on customers’ experiences, perceptions, and emotions toward business offerings. Factors influencing CS include functionality, convenience, reliability, trustworthiness, and overall interaction experience [20,21]. This comprehensive evaluation reflects not only the quality of products or services, but also the effectiveness of customer service, and offers support in meeting consumer needs and preferences.
Businesses recognize CS as a strong basis for fostering loyalty and cultivating enduring relationships with their customers. Satisfied customers are more likely to not only make repeat purchases, but also become advocates for the brand by sharing positive experiences with others. This advocacy contributes significantly to brand reputation and attracts new customers, thereby driving growth and profitability [22]. Consequently, organizations prioritize strategies aimed at continually monitoring and enhancing CS with the understanding that customer satisfaction is directly correlated with long-term business success and sustainability.
To manage and improve CS effectively, businesses employ robust feedback mechanisms and proactive measures. Customer feedback provides invaluable insights into areas where improvements are needed, thereby allowing companies to promptly address issues and adapt their strategies to better meet customer expectations [23]. Moreover, proactive measures such as personalized customer service, streamlined processes, and innovative solutions further enhance the overall customer experience, reinforce positive perceptions, and foster stronger bonds between customers and brands [24].
In today’s competitive commerce landscape, characterized by abundant consumer choices, maintaining high levels of CS is imperative for retaining customers and differentiating oneself from competitors. Organizations that consistently deliver exceptional customer experiences not only mitigate the risk of customer churn, but also establish a solid foundation for growth and market leadership [25]. By prioritizing CS and continuously refining their approaches, businesses can build a loyal customer base that serves as a driving force for sustained business growth and resilience in a dynamic economic environment.

2.4. E-Trust

E-trust is a critical concept in the realm of digital commerce and interaction and represents the foundation of users’ confidence in online platforms, services, and transactions. It encompasses their perceptions of reliability, security, and integrity, and reflects users’ willingness to entrust digital environments with their personal information and transactional activities [26,27]. Essentially, e-trust may shape user behavior and loyalty within the digital landscape, thereby influencing how individuals choose and interact with online platforms.
In the absence of traditional trust cues such as physical presence or face-to-face interaction, e-trust becomes even more significant. Users rely heavily on indicators of digital security and reliability to assess whether they can safely engage in online transactions and activities. Factors contributing to e-trust include robust security measures, transparent data handling practices, and effective communication of privacy policies [28]. Collectively, these elements contribute to building a trustworthy digital environment in which users feel secure and confident about their interactions [29].
The impact of e-trust extends beyond mere perception; it profoundly influences user engagement and loyalty. Customers who perceive a high level of e-trust in an online platform or service are more likely to engage frequently and conduct transactions without hesitation. E-trust fosters a sense of security and assurance, mitigating concerns related to privacy breaches, fraudulent activities, and the misuse of personal data. By cultivating a trustworthy digital presence, businesses not only enhance customer satisfaction, but also strengthen long-term relationships with their user base [30].
Moreover, positive customer experiences significantly contribute to e-trust and the overall reputation of digital platforms. Satisfied users are more likely to perceive platforms as reliable and secure, and thereby exhibit increased e-trust [31]. This enhanced e-trust fosters customer retention because users who trust platforms are more likely to return for future transactions and recommend them to others. Such positive word of mouth promotion can drive organic growth, expand a platform’s user base, and strengthen its position in the world of competitive digital commerce [32,33].
For businesses, understanding and cultivating e-trust is paramount for sustained success in digital commerce. Investing in technologies and practices that enhance security, transparency, and user confidence can yield substantial benefits in the form of customer loyalty and business growth [26]. As digital interactions continue to evolve, maintaining a strong foundation of e-trust will become essential for businesses aiming to thrive amid ever-changing consumer expectations and competitive pressures.

2.5. Continuous Usage Intention

CUI is a vital concept in consumer behavior as it indicates the likelihood or intention of customers to persistently use products or services over extended periods. It serves as a key metric for measuring customer loyalty and commitment to a brand or offering [34]. Customers who demonstrate strong CUI are inclined to not only make repeat purchases, but also advocate for a product or service, contributing positively to brand reputation and market presence.
Understanding CUI involves delving into the factors that influence each customer’s decision to continue using a product or service. These factors range from functional benefits and performance reliability to emotional attachment and perceived value. By identifying these drivers, businesses can formulate targeted strategies to enhance customer satisfaction and loyalty and, consequently, increase the likelihood of continuous usage and reduce customer churn.
CUI has significant strategic importance for businesses aiming to foster long-lasting relationships with customers. It provides insights into customer preferences and behaviors, thus enabling companies to tailor their offerings and services to better meet evolving consumer needs. By proactively addressing the factors that influence CUI, such as product innovation, service excellence, and personalized customer experiences, organizations can cultivate a loyal customer base that contributes to sustained revenue growth and market competitiveness [35].
Moreover, cultivating high levels of CUI can enhance customer lifetime value (CLV) as loyal customers tend to generate more revenue over the course of their relationship with the brand. This increased CLV not only strengthens financial performance, but also reduces the cost of acquiring new customers, thus improving overall profitability. Additionally, loyal customers are more likely to recommend the brand to others through positive word of mouth, further expanding the brand’s reach and influence in the market [36].
Strategically managing CUI involves continuous engagement with customers throughout their journey with the brand. Such engagement includes ongoing communication, proactive customer support, and meaningful interactions that reinforce trust and satisfaction [34]. By consistently delivering good value and addressing customer concerns, businesses can reinforce their positions as trusted partners in consumers’ lives, as well as their commitment to continued usage and loyalty.

3. Research Method

3.1. Development of the Research Hypotheses

3.1.1. Relationship between E-Service Quality and Customer Satisfaction

The relationship between e-SQ and CS is fundamental in digital environments. Conventionally, e-SQ has been identified as a crucial determinant of CS. When a business consistently delivers high-quality services that meet or exceed customer expectations, it positively influences CS [37].
Numerous studies have demonstrated that e-SQ is an antecedent to CS in various domains [38,39]. Cristobal et al. [5] highlighted the effects of e-SQ on CS in websites. Chang et al. [40] noted a significant impact of e-SQ on CS within e-marketing, while Szymanski and Hise [41] found this relationship in the context of e-retail. Consequently, the current study hypothesizes the following relationship between e-SQ and CS in e-commerce marketplace platforms:
Hypothesis 1 (H1).
E-SQ is positively related to CS.

3.1.2. Relationship between E-Service Quality and E-Trust

Several studies have explored the intricate relationship between e-SQ and e-trust, finding that customers who perceive high-quality services tend to have greater confidence in the company. Harris and Goode [42] noted positive correlations between different aspects of e-SQ and e-trust in online interactions. Similarly, Al-Nasser et al. [3] and Rita et al. [4] observed the substantial impact of e-SQ on e-trust in online shopping, suggesting that enhanced e-SQ is linked to increased e-trust among online shoppers. Haghkhah et al. [7] found that e-SQ influences e-trust in a business-to-business (B2B) context. Consequently, the current study hypothesizes the following relationship between e-SQ and e-trust in e-commerce marketplace platforms:
Hypothesis 2 (H2).
E-SQ is positively related to e-trust.

3.1.3. Relationship between E-Service Quality and Continuous Usage Intention

The relationship between e-SQ and CUI is typically positive and significant. Higher levels of e-SQ tend to lead to greater CS, e-trust, and loyalty, thereby increasing customer intentions to continue using a service over time. Conversely, lower levels of e-SQ can result in dissatisfaction, distrust, and eventual discontinuation of usage. Several studies have explored the correlation between e-SQ and CUI. Hu et al. [43] examined the association between e-SQ and continuance intention specifically within eTax services. Similarly, Akter et al. [44] observed the impact of e-SQ on continuance intentions in the business-to-consumer mHealth domain. Song et al. [6] demonstrated how e-SQ influences CUI in community group buying platforms. Consequently, the current study hypothesizes the following relationship between e-SQ and CUI in e-commerce marketplace platforms:
Hypothesis 3 (H3).
E-SQ is positively related to CUI.

3.1.4. Relationship between Customer Satisfaction and E-Trust

CS serves as a cornerstone for building e-trust between customers and companies and lays the groundwork for lasting relationships. Numerous studies have highlighted the strong correlation between CS and e-trust across diverse industries. Chu et al. [45] and Hwang and Kim [46] found a significant association between CS and e-trust in websites. Flavian et al. [47] supported this correlation using e-banking platforms. Rita et al. [4] and Ribbink et al. [48] explored the positive effects of e-SQ on e-trust through online shopping and e-commerce, respectively. Consequently, the current study hypothesizes the following relationship between CS and e-trust in e-commerce marketplace platforms:
Hypothesis 4 (H4).
CS is positively related to e-trust.

3.1.5. Relationship between Customer Satisfaction and Continuous Usage Intention

CS has emerged as a pivotal determinant of various behavioral outcomes, including loyalty, intention to recommend, and CUI [49]. Satisfied customers are inclined to cultivate stronger bonds with specific service providers, whereas dissatisfaction often triggers a re-evaluation of existing relationships and an exploration of alternatives [50]. This relationship is evident across diverse e-service domains. Al Amin et al. [51] and Wang et al. [52] found that CS significantly influences CUI in food delivery and social applications, respectively. Miao et al. [53] presented further evidence of this relationship using online shopping. Consequently, the current study hypothesizes the following relationship between CS and CUI in e-commerce marketplace platforms:
Hypothesis 5 (H5).
CS is positively related to CUI.

3.1.6. Relationship between E-Trust and Continuous Usage Intention

Trust is pivotal in fostering robust customer relationships and driving sustained success because it encourages customer loyalty, CUI, and positive word-of-mouth promotion of products and services [26]. Numerous studies have emphasized its impact on various facets of customer behavior in online environments. Ribbink et al. [48] explored the positive effect of e-trust on customer loyalty in e-commerce. Shao et al. [54] confirmed the positive relationship between e-trust and CUI in mobile payment systems. Rita et al. [4] and Miao et al. [53] investigated the positive effect of e-trust on CUI in online shopping. Consequently, the current study hypothesizes the following relationship between e-trust and CUI in e-commerce marketplace platforms:
Hypothesis 6 (H6).
E-trust is positively related to CUI.

3.1.7. Customer Satisfaction and E-Trust as Mediators of the Relationship between E-Service Quality and Continuous Usage Intention

Understanding how CS and trust mediate the relationship between SQ and CUI is crucial for understanding consumer behavior dynamics. Previous research has indicated that CS frequently acts as a mediator in various service industries [55]. Cronin et al. [56] and He and Song [57] found that SQ influences repurchase intentions through CS in multiple service industries and packaged tour services, respectively. Additionally, Keating et al. [8] and Song et al. [6] demonstrated that e-SQ affects CUI through CS in the contexts of online services and community group buying platforms, respectively. However, limited research has explored e-trust as a mediator. Cuong [58] investigated the mediating role of trust in the relationship between CS and repurchase intention in laptop shops. Moreover, Haghkhah et al. [7] found that e-trust mediates the relationship between e-SQ and customer loyalty in a B2B context. Nevertheless, the mediating effects of CS and e-trust on the relationship between e-SQ and CUI remain unexplored in terms of e-commerce marketplace platforms. Therefore, the current study hypothesizes CS and e-trust as mediators in the relationship between e-SQ and CUI in e-commerce marketplace platforms.
Hypothesis 7 (H7).
CS mediates between e-SQ and CUI.
Hypothesis 8 (H8).
E-trust mediates between e-SQ and CUI.
Hypothesis 9 (H9).
CS and e-trust mediate between e-SQ and CUI.
This study investigates the relationships between e-SQ, CS, e-trust, and CUI in e-commerce marketplace platforms, focusing on the roles of CS and e-trust as mediating variables. Figure 1 presents the research model and the corresponding hypotheses.

3.2. Measurement of Variables and Data Collection

We established the variables in this study by adapting measurement variables from previous research, specifically focusing on e-SQ, CS, e-trust, and CUI. The measurement items for e-SQ comprised three questions. Similarly, the measurement items for CS, e-trust, and CUI consisted of four questions for each construct. The measurement items are presented in Table A1, found in Appendix A. We used a five-point Likert scale for all measurements.
Conducted in November 2023, our survey targeted the users of e-commerce marketplace platforms based in South Korea. The study focused on four major e-marketplace platforms: Coupang, Naver Shopping, Gmarket, and 11st Street. These platforms offer a similar range of products, including electronics, travel services, apparel, beauty products, groceries, and home appliances. In 2023, Opensurvey, a consumer data analysis platform operating in South Korea, conducted a survey on major online shopping platforms. The results showed that Coupang received 37.7% of the responses, Naver Shopping garnered 27.2%, Gmarket attracted 6.8%, and 11th Street was noted as having 5.5% [59].
We distributed questionnaires online, ensuring that respondents were fully informed about the study’s purpose and content before obtaining their consent. The survey was conducted using a self-administered method. Out of 330 questionnaires distributed, we collected 316 responses. For the final analysis, we used 311 responses, excluding 5 responses due to a high number of missing answers or lack of sincerity. Data analysis was performed using SPSS 29.0 and Amos 29.0. First, a frequency analysis was conducted to examine the respondents’ demographic characteristics. Second, confirmatory factor analysis was performed to assess the reliability and validity of the constructs. Finally, structural equation modeling was employed to investigate the relationships among the variables.

3.3. Demographic Characteristics of the Sample

The gender, age, occupation, and educational background of each respondent were recorded as demographic items. Table 1 presents the respondents’ characteristics. In terms of sex, 54.0% of the respondents were male and 46.0% were female. In terms of age distribution, most respondents were in their 20s (34.0%) or 30s (31.8%). Employed workers constituted the largest group, accounting for 54.3% of the respondents. Regarding education level, over half of the respondents (58.5%) held a bachelor’s degree.

4. Empirical Analysis

4.1. Reliability and Validity Analysis

In this study, we established the variables by adapting measurement items from previous research. We then employed confirmatory factor analysis to assess the fit of the predefined model. This type of analysis is intended to validate a specified factor model, rather than merely estimating correlations between items. Specifically, the confirmatory factor analysis in this study assessed how well the measurement items aligned with their designated factors [60,61].
During the confirmatory factor analysis, one item from the e-SQ scale was found to be unsuitable for measuring the intended construct. Consequently, this item was removed, and the fit of the revised model was re-evaluated to ensure its validity. Following this, we assessed the reliability and validity of the constructs in the model. First, we examined the reliability coefficient using Cronbach’s α. As shown in Table 2, the Cronbach’s α values of all variables were 0.765 or higher, thereby exceeding the reference value (≥0.7) [62] and indicating satisfactory internal consistency. Second, confirmatory factor analysis was conducted to evaluate the validity of the variables, and we found that χ2 = 182.829, d.f. = 84, χ2/d.f. = 2.177, TLI = 0.962, CFI = 0.970, RMR = 0.022, and RMSEA = 0.062; these results adequately met the criteria for good model fit as proposed by Hair et al. [48]. The factor loading values of the measurement items ranged from 0.636 to 0.953, thus satisfying the concept validity above the standard value (≥0.5). Composite reliability (CR) was 0.907–0.960, which exceeded the standard value (≥0.7), while the average variance extracted (AVE) was 0.761–0.859, which exceeded the threshold of 0.50 [63]. These indicators revealed the good convergent validity of the measures.
Table 3 presents the correlation coefficients between the constructs and the discriminant validity results. All correlations were statistically significant (p < 0.001). The square root of the AVE of each construct was greater than the correlation coefficients between the constructs, thereby implying discriminant validity [64]. These results demonstrate that the reliability and validity criteria were satisfied.

4.2. Hypothesis Test Results

The structural equation modeling technique was employed to estimate the hypothesized relationships, with the adequacy of the structural model confirmed by the good fit indices (χ2/d.f. = 2.177, TLI = 0.956, CFI = 0.962, RMR = 0.022, RMSEA = 0.062) that satisfied the recommended thresholds. Table 4 lists the parameter estimates for the hypothesized relationships.
The standardized coefficient (β) of e-SQ on CS was 0.394 (p = 0.000), thus supporting H1 and indicating the significant and positive effect of e-SQ on CS. The coefficient of e-SQ on e-trust was 0.195 (p = 0.000), thus supporting H2 and revealing the significant and positive effect of e-SQ on e-trust. Similarly, the coefficient of e-SQ on CUI was 0.213 (p = 0.000), thus supporting H3 and revealing the significant and positive effect of e-SQ on CUI. Moreover, the coefficient of CS on e-trust was 0.579 (p = 0.000), thereby supporting H4 and indicating the significant and positive effect of CS on e-trust. Additionally, the coefficient of CS on CUI was 0.466 (p = 0.000), thus supporting H5 and highlighting the significant and positive effect of CS on CUI. Furthermore, the coefficient of e-trust on CUI was 0.194 (p = 0.005), thereby supporting H6 and demonstrating the significant and positive effect of e-trust on CUI. These findings underscore the need for further experiments to explore the mediating roles of CS and e-trust.
The bootstrap method, developed for non-parametric resampling tests, was employed to identify the roles of CS and e-trust as mediators [65], with 500 bootstrapping procedures conducted at a 95% confidence level. Table 5 illustrates the indirect effects of the three paths between e-SQ and CUI. The indirect effects through CS (β = 0.183, p = 0.014) and e-trust (β = 0.038, p = 0.004) and through the sequential influences of CS and e-trust (β = 0.044, p = 0.004) were significant. Therefore, H7–H9 were supported. This result indicates that both CS and e-trust serve as significant mediating factors in the relationship between e-SQ and CUI within e-commerce marketplace platforms.

5. Conclusions

5.1. Findings and Discussion

The results of this study elucidate the complex relationships among e-SQ, CS, e-trust, and CUI within e-commerce marketplace platforms. First, the findings confirm the significant positive impact of e-SQ on CS, e-trust, and CUI, thereby supporting hypotheses 1, 2, and 3, respectively. Consistent with the studies by Akter et al. [44], Al-Nasser et al. [3], Cristobal et al. [5], Olorunniwo et al. [39], Rita et al. [4], and Song et al. [6], our study demonstrates that e-SQ is a critical antecedent to CS, e-trust, and CUI. This outcome underscores the important role of e-SQ in shaping customer perceptions and behaviors in digital commerce.
Additionally, the study demonstrates a strong positive association between CS and e-trust, thus supporting hypothesis 4. This finding is in line with those of the studies by Flavian et al. [47], Hwang and Kim [46], Ribbink et al. [48], and Rita et al. [4], all of which emphasize the foundational role of CS in fostering trust and confidence to sustain long-term relationships and encourage repeat usage. The significant positive impact of CS on CUI supports hypothesis 5, as corroborated by the results of studies by Miao et al. [53] and Wang et al. [52], which highlight the importance of prioritizing CS as a key driver of CUI within e-commerce marketplace platforms.
Moreover, this study confirms the positive influence of e-trust on CUI; hence, hypothesis 6 is supported. This finding is consistent with those of Miao et al. [53], Rita et al. [4], and Shao et al. [54], who underscore the role of e-trust in enhancing customer commitment and fostering sustained usage over time.
Furthermore, this study reveals the mediating roles of CS and e-trust in the relationship between e-SQ and CUI. The significant indirect effects identified through bootstrap analysis support these mediations; hence, hypotheses 7, 8, and 9 are verified. These findings align with those of Haghkhah et al. [7], Keating et al. [8], and Song et al. [6], who highlight the importance of CS and e-trust in linking e-SQ to CUI. This result underscores the sequential impacts of CS and e-trust on translating e-SQ into CUI.

5.2. Theoretical Implications

The theoretical implications of this study extend our understanding of key concepts in the context of e-commerce marketplace platforms and contribute to the existing literature in several ways. First, by empirically validating the relationships between e-SQ, CS, e-trust, and CUI, this study provides empirical support for theoretical frameworks that posit the importance of these factors in shaping customer behavior within digital commerce. The findings corroborate and extend previous theoretical propositions by demonstrating the sequential roles of CS and e-trust in translating e-SQ into CUI.
Second, this study advances theoretical understanding by highlighting the interplay between CS, e-trust, and CUI within the context of e-commerce platforms. By elucidating the mediating roles of CS and e-trust in the relationship between e-SQ and CUI, this study contributes to a deeper understanding of the mechanisms through which CS and e-trust influence CUI. These insights offer theoretical foundations for future research exploring the dynamics of customer behavior within digital commerce. They also provide a basis for developing comprehensive theoretical frameworks to guide further inquiry in this domain.
Third, the findings underscore the multidimensional nature of customer perceptions and behaviors within e-commerce marketplace platforms, thereby emphasizing the importance of the interplay between e-SQ, CS, and e-trust in the understanding of CUI. This holistic perspective enriches the theoretical discussions surrounding CS and e-trust in digital environments and provides a nuanced understanding of the factors that drive CUI.
Fourth, the study underscores the strategic importance of e-SQ enhancements in the context of emerging technologies like artificial intelligence, big data, and blockchain. As these technologies continue to integrate into e-commerce marketplace platforms, they promise to further refine e-SQ dimensions by offering personalized recommendations, strengthening security, and improving overall service quality. This perspective not only enriches the theoretical discourse on digital commerce, but also provides actionable insights for businesses aiming to leverage these advancements to enhance user trust and satisfaction.

5.3. Managerial Implications

The managerial implications of this study offer actionable insights for platform operators and stakeholders seeking to enhance CS, e-trust, and CUI within e-commerce marketplace platforms. First, the findings underscore the importance of prioritizing e-SQ to cultivate positive customer experiences and drive CUI. Platform operators should invest in enhancing the key aspects of e-SQ, such as transaction processing, payment security, and user support, to meet customer expectations and foster trust and satisfaction.
Second, this study highlights the pivotal roles of CS and e-trust in shaping customer perceptions and driving CUI within e-commerce marketplace platforms. Platform operators should focus on building strong customer relationships through personalized interactions, responsive support, and transparent communication to foster CS and e-trust. Additionally, efforts to enhance transparency, security, and integrity within platforms can help reinforce e-trust, ultimately driving long-term loyalty and retention.
Third, the findings suggest that strategies for improving CS and e-trust can greatly influence CUI in e-commerce marketplace platforms. Platform operators should leverage customer feedback mechanisms and analytics to monitor CS and e-trust levels and identify areas for improvement. By proactively addressing customer concerns and enhancing overall customer experience, platform operators can cultivate a loyal customer base and drive sustained usage over time.

5.4. Limitations and Future Directions

Although this study provides valuable insights into the relationships among e-SQ, CS, e-trust, and CUI within e-commerce marketplace platforms, several limitations warrant consideration. First, it focused solely on users of e-commerce marketplace platforms that are based in South Korea, thus limiting the generalizability of the findings to other cultural contexts and platform types. Second, the cross-sectional nature of the study precludes causal inferences. Hence, longitudinal research must be conducted to ascertain the temporal relationships between the variables. Third, while this study examined the relationships between factors, it did not explore the specific components of each factor. Future research should conduct a more granular analysis at the component level to understand how each element contributes to the overall relationships, providing a deeper insight into their nuanced roles and interactions. Fourth, this study did not consider the duration of platform operation as a factor. However, future research should include the duration as a moderating variable, to analyze its impact on the relationships between the variables. Finally, the reliance on self-reported data may introduce response biases, and future research could incorporate objective measures or behavioral data to complement subjective perceptions.

Author Contributions

Conceptualization, J.K. and K.Y.; Methodology, J.K. and K.Y.; Validation, K.Y.; Formal analysis, J.K.; Investigation, J.K. and K.Y.; Data curation, J.K. and K.Y.; Writing—original draft, J.K.; Writing—review and editing, K.Y.; Supervision, K.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

All procedures performed in the study were in accordance with the ethical guidelines of the Korean Psychological Association and the 1964 Helsinki Declaration.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Measurement items.
Table A1. Measurement items.
ItemDescriptionSource
e-SQ[66]
Item 1Overall, my purchase experience with the platform is excellent.
Item 2It is easy to do what I want to do on the platform.
Item 3Using the platform makes me more effective at completing my tasks.
CS[48]
Item 1 I am generally pleased with the platform.
Item 2I enjoy using the platform.
Item 3I am very satisfied with the services of the platform.
Item 4I am happy with the platform.
e-trust[17,67]
Item 1If the platform makes a claim or promise about its service, I trust that it is truthful.
Item 2I trust the platform to protect my personal information and ensure security.
Item 3Based on my experience, I consider the platform to be highly reliable.
Item 4If issues arise, I expect to be treated fairly by the platform.
CUI[68]
Item 1I will continue to use the platform in the future.
Item 2I intend to continue using the platform.
Item 3I plan to make my next purchase through the platform.
Item 4I will increase the frequency of my purchases through the platform.

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Applsci 14 07617 g001
Figure 1. Research model.
Figure 1. Research model.
Applsci 14 07617 g001
Table 1. Characteristics of respondents.
Table 1. Characteristics of respondents.
VariableCategoryFrequencyPercentage
Gendermale16854.0
female14346.0
Agebelow 20299.3
20–2910634.0
30–399931.8
40–494514.5
over 503210.3
Occupationemployed16954.3
self-employed7223.2
student4815.4
homemaker134.2
other92.9
Education levelhigh school graduate3110.0
some university education4815.4
bachelor’s degree18258.5
postgraduate degree5016.1
Table 2. Reliability and convergent validity.
Table 2. Reliability and convergent validity.
ConstructNo. of ItemsFactor LoadingCRAVECronbach’s α
e-SQ30.9530.9070.7690.798
0.863
0.636
CS40.8290.9600.8590.781
0.844
0.915
0.859
e-Trust40.7910.9270.7610.865
0.776
0.772
0.800
CUI40.9040.9420.8050.765
0.931
0.753
0.717
Indices of the measurement model: χ2 = 182.829; d.f. = 84; χ2/d.f. = 2.177; TLI = 0.962; CFI = 0.970; RMR = 0.022; RMSEA = 0.062. Abbreviations: e-SQ = e-service quality; CS = customer satisfaction; CUI = continuous usage intention; CR = composite reliability; AVE = average variance extracted.
Table 3. Correlation coefficients between constructs and discriminant validity.
Table 3. Correlation coefficients between constructs and discriminant validity.
Constructe-SQCSe-TrustCUI
e-SQ0.877
CS0.394 ***0.927
e-Trust0.424 ***0.656 ***0.872
CUI0.479 ***0.677 ***0.590 ***0.897
Note: *** p < 0.001. The diagonal value is the square root of the AVE.
Table 4. Parameter estimates for hypothesized relationships.
Table 4. Parameter estimates for hypothesized relationships.
RelationshipβSE.CR.pResult
H1. e-SQ → CS0.3940.0716.1160.000 ***Supported
H2. e-SQ → e-trust0.1950.0613.4060.000 ***Supported
H3. e-SQ → CUI0.2130.0563.9340.000 ***Supported
H4. CS → e-trust0.5790.0609.2090.000 ***Supported
H5. CS → CUI0.4660.0676.5630.000 ***Supported
H6. E-trust → CUI0.1940.0682.8070.005 **Supported
Note: ** p < 0.01, *** p < 0.001.
Table 5. Indirect effects used for analyzing mediation.
Table 5. Indirect effects used for analyzing mediation.
RelationshipIndirect EffectResult
βp
H7. e-SQ → CS → CUI0.1830.014 *Supported
H8. e-SQ → e-Trust → CUI0.0380.004 **Supported
H9. e-SQ → CS → e-Trust → CUI0.0440.004 **Supported
Note: * p < 0.05, ** p < 0.01.
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Kim, J.; Yum, K. Enhancing Continuous Usage Intention in E-Commerce Marketplace Platforms: The Effects of Service Quality, Customer Satisfaction, and Trust. Appl. Sci. 2024, 14, 7617. https://doi.org/10.3390/app14177617

AMA Style

Kim J, Yum K. Enhancing Continuous Usage Intention in E-Commerce Marketplace Platforms: The Effects of Service Quality, Customer Satisfaction, and Trust. Applied Sciences. 2024; 14(17):7617. https://doi.org/10.3390/app14177617

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Kim, Jongnam, and Kyeongmin Yum. 2024. "Enhancing Continuous Usage Intention in E-Commerce Marketplace Platforms: The Effects of Service Quality, Customer Satisfaction, and Trust" Applied Sciences 14, no. 17: 7617. https://doi.org/10.3390/app14177617

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