Sage Journals: Discover world-class research

Abstract

E-commerce, one of the most important methods in modern business, has improved organizational efficiency and contributed to countries’ economic growth by fostering customer satisfaction and trust. The purpose of the study is to examine the relationships among these variables (payment security, information quality, website Usability, e-trust, and perceived-to-actual performance) and consumer satisfaction in the online shopping context. Primary data were collected from 245 respondents via a quantitative survey, and the analysis was conducted using Smart PLS 4 and IBM SPSS 27, employing structural equation modeling. The findings show that payment security, information quality, and perceived actual performance contribute to e-commerce satisfaction, while website usability enhances perceived performance, which does not directly improve satisfaction. E-trust directly impacted satisfaction, but it did not moderate the relationship between payment security and customer satisfaction. The research is also crucial for aligning customer expectations with the e-commerce site’s actual performance. The integrated models, including the DeLone & McLean IS Success Model and Expectation Confirmation Theory, offer promise for an improved understanding of e-commerce satisfaction, reflecting conceptual value through their merger. E-commerce platforms in emerging markets like Bangladesh should improve website usability, payment security, and information quality to build trust and enhance customer satisfaction. Applying insights from the literature and study findings can help them refine user experience across demographics and strengthen customer loyalty.

Keywords

e-commerce satisfaction payment security information quality website usability perceived to actual performance e-trust

Introduction

Over the past decade, e-commerce has experienced unprecedented growth, becoming a cornerstone of the global economy (Wang et al., 2024). In 2025, worldwide e-commerce retail sales are projected to reach approximately $6.42 trillion, marking a 6.9% increase from 2024 and accounting for about 20.5% of total global retail sales (Statistics, 2025). This expansion is driven by increased internet penetration, the proliferation of mobile commerce, and the growing adoption of digital payment technologies. More than 2.77 billion people are expected to shop online in 2025, reflecting the integration of e-commerce into everyday life worldwide (Shopify, 2025).

Kulkarni and Fadewar (2017) highlight the significance of the Internet and web development as key information and communication technologies. By 2021, 4.66 billion people, 92.6% via mobile devices, accessed the Internet, driving substantial growth in e-commerce globally and locally (Langdale, 2019). However, B2C e-commerce is rapidly evolving, and e-retailers are facing challenges in maintaining customer loyalty. Research highlights the importance of drivers like e-customer satisfaction and e-trust, examining whether website usability, information quality, and payment security concerns predict trust and satisfaction in B2C e-commerce (Gevers et al., 2024). Anggraini and Sari (2024) defined e-commerce as trade between parties via networks or digital technology. Their study examines service quality, customer satisfaction, and trust, focusing on how cultural differences shape perceptions of service quality. Da Costa and Casais (2020) highlighted that e-commerce encompasses diverse activities, such as shopping, investments, meal ordering, and ticketing, using websites, email, mobile apps, and social media as platforms.

Online shopping provides convenient and efficient access to a wide range of products worldwide. Driven by advancements in information technology and high-speed internet, e-commerce facilitates virtual transactions and business interactions (Wu, 2022). As a key business strategy, it enhances organizational efficiency and contributes to national economies through the service sector. Lam (2020) notes that e-commerce is growing at an average global rate of 20% annually, despite challenges faced by physical retail. Operating 24/7, e-commerce platforms provide instant access to information, hassle-free shopping, price comparisons, and freedom from time and location constraints, significantly boosting economies worldwide.

Yousuf et al. (2021) noted that in competitive markets, companies strive to establish lasting customer relationships, as unmet needs frequently prompt customers to switch to competitors. Enhancing e-customer satisfaction depends on improving product or service quality, as well as factors such as consumer preferences and market conditions. This research aims to identify key drivers of e-customer satisfaction in e-commerce (Amadea et al., 2022). Given the relatively low adoption of e-commerce, it is essential to explore how various factors influence e-consumer satisfaction and trust, ultimately fostering customer loyalty (Anggraini & Sari, 2024).

The study begins by reviewing existing literature on key theories underpinning the proposed research model, focusing on the relationships between e-commerce satisfaction, e-trust, and their influencing factors to construct a conceptual framework (Xie et al., 2024). In addition, the study concludes with key findings, implications, limitations, and recommendations for future research. Prior studies, such as H. Zhou and Xing (2022) and Sastika et al. (2023), primarily examined the effects of service quality and website design on customer satisfaction in e-commerce, suggesting that other influencing factors remain underexplored. Extensive research has identified several determinants of e-commerce satisfaction, including website usability (Gevers et al., 2024), information quality (Widjaja et al., 2020), and e-trust (Kustiani et al., 2022). Among these, payment security stands out as a critical factor influencing consumers’ trust and satisfaction, as secure transactions reduce perceived risks and foster confidence in online shopping (Kim et al., 2008; Pavlou, 2003; Stamkou et al., 2025). However, the existing literature offers fragmented insights and limited exploration of the interplay among these factors, particularly the moderating role of e-trust and the mediating role of perceived-to-actual performance (Asawawibul et al., 2025; Wang et al., 2024).

Research Gap

H. Zhou and Xing (2022) stated that e-commerce customer satisfaction reflects a consumer’s willingness to repurchase from the same merchant. However, recent studies suggest satisfaction alone may not ensure long-term loyalty (Savastano et al., 2025). Customer satisfaction mediates the perceived-to-actual performance gap, directly influencing satisfaction and positively correlating with repurchase intent (Asawawibul et al., 2025; Stamkou et al., 2025). To date, no research has explored these interactions in the context of e-commerce consumer loyalty (Sinaga & Rizal, 2024; H. Zhou & Xing, 2022). The Theory of Reasoned Action (TRA) predicts individuals’ behavior and tendencies but is limited to actions within their control. Research highlights that e-satisfaction and e-trust significantly influence e-loyalty (Khowjoy et al., 2023). Trust is linked to positive purchase intentions and repeat purchases, influenced by consumers’ evaluations of e-service quality in virtual markets that lack face-to-face interactions (S.-J. Lee et al., 2018). Limited research exists on how institutional mechanisms of e-commerce moderate the impact of interactivity and trust on repurchase intentions in Bangladesh (Bashar et al., 2024; Sayem et al., 2025). Data collection was conducted directly from respondents without manipulation, ensuring authenticity and reliability (Rahman et al., 2022). While prior studies have focused on individual influences on e-commerce satisfaction, gaps remain regarding how payment security’s impact on satisfaction functions independently of customers’ trust levels or in conjunction with perceptions of usability and performance (Miao et al., 2022). Furthermore, contextual understudies are evident, especially in emerging e-commerce markets such as Bangladesh (Bashar et al., 2024)

Research Objective

RO1: To determine and analyze the key factors, including website usability, payment security, and information quality, which influence and contribute to customer satisfaction in E-commerce in Bangladesh.

RO2: To explore the impact of e-trust as a moderating factor and perceived-to-actual performance as a mediating factor on customer satisfaction.

Literature Review

H. Zhou and Xing (2022) conducted a study focusing on the factors influencing customer satisfaction and trust in electronic commerce. They found that younger consumers’ perceptions of product excellence and service assurance significantly influenced their well-being and satisfaction with electronic shopping experiences. E-commerce, defined as conducting commercial transactions through electronic media and telecommunications networks, has become a pivotal force in reshaping business operations and consumer behavior. While numerous studies have aimed to enhance e-commerce service quality and understand factors impacting customer satisfaction and purchasing intentions, few have delved into the role of system quality in influencing customer behavior (V. Lee et al., 2022).

Website Usability (WU)

Website Usability (WU) is based on the DeLone and McLean (2003) and literature on Human-Computer Interaction (HCI). Website usability, or system quality, refers to the extent to which a system (e-commerce website) enables users to accomplish their tasks effectively through attributes such as reliability, ease of use, and ease of navigation. It leads users to be satisfied with e-commerce and to use it further. Website design encompasses elements that aim to create visually appealing and engaging user experiences (Chan & Seong-won, 2021). Effective designs prioritize fast loading times and clear product information, enhancing post-purchase satisfaction and interest in the platform. Usability refers to how easily users navigate and interact with a site to achieve their goals. A user-focused e-commerce site typically includes explicit content, comprehensive product categorization, intuitive navigation, personalized services, and strong security measures to ensure a positive user experience and high satisfaction (Meiyantika & Rusdianto, 2024). Maguire (2022) emphasizes that website usability, essential for evaluating user-friendliness, involves analyzing how users interact with features and interfaces. In a competitive market, usability is crucial for differentiating websites and retaining customers, as it drives sales and supports the survival and competitiveness of e-commerce businesses (Arshad et al., 2021).

Information Quality (IQ)

Information Quality can be defined as the extent to which the information transmitted by the e-commerce site covers all necessary data, allowing consumers to obtain information about product/brand quality (DeLone & McLean, 2003; W. Gao & Li, 2019). Information quality refers to how customers perceive the value of product evidence provided on electronic commerce websites, including its relevance and usefulness. The higher the quality of the information customers recognize about products or services offered on the site, the more likely high-quality online buyers are to show interest in purchasing them. W. Gao and Li (2019) noted that information, defined as processed data with real value for decision-making, plays a fundamental role in various online activities, such as learning, shopping, and marketing. The dissemination of information through the internet has become essential in today’s society. Research suggests that high-quality information significantly impacts website selection and overall business performance. Research suggests a positive correlation between information quality and customer satisfaction (Widjaja et al., 2020). H. Zhou and Xing (2022) argue that user-friendly systems encourage repeat e-commerce usage. Studies highlight the impact of logistics facility value on customer satisfaction and repurchase intentions. Customers often browse product information before making an online purchase. Therefore, it is essential for the smooth transmission of high-quality, accurate, transparent, trustworthy, and honest information.

Payment Security (PS)

L. Gao and Waechter (2017) and Pavlou (2003) develop a risk/trust model that focuses on payment security measures that reduce consumers’ perceived risks, thereby fostering trust and enabling transaction completion. Payment security is crucial in online shopping, where direct payments rely on systems such as bank transfers, credit cards, and mobile banking, raising concerns about privacy and security. Payment gateways ensure secure transactions by encrypting sensitive data (Yousuf et al., 2021). Strategies to enhance security focus on safeguarding communication between clients and gateways. The rise of e-payment systems reflects the growing popularity of online banking and shopping, driven by technological advancements (Basbeth & Sumapraja, 2021).

E-Trust (ET)

Trust, particularly in online contexts such as e-trust, reflects consumers’ confidence in digital environments despite the risks (Lin & Zhu, 2024). L. Gao and Waechter (2017) and Pavlou (2003) propose a risk–trust framework suggesting that strong payment security mechanisms lower consumers’ perceived risk, which, in turn, builds trust and facilitates successful transactions. E-trust, centered on websites and sellers, is crucial for online transactions and differs from traditional trust by involving interactions with digital platforms (Ryan, 2019). The lack of trust can deter online engagement, highlighting its importance for platform success. Security concerns significantly impact e-trust and purchase intentions, driving the development of technical measures to ensure safety (Lăzăroiu et al., 2020). E-trust reduces uncertainty in electronic markets, enabling transactions mediated by electronic devices. Trust-building efforts, such as clear customer service information, office locations, and contact details, enhance consumer confidence in e-commerce. Studies consistently show a positive correlation between e-trust and e-satisfaction, underscoring its role in fostering strong customer relationships (Kustiani et al., 2022). In e-commerce, trust is critical for sustaining relationships, especially when transactions precede product receipt.

Perceived to Actual Performance (PAP)

Reynaldi and Wuisan (2023) define Perceived-to-Actual Performance as customers’ cognitive evaluation of their purchase experience, while Satisfaction reflects their emotional response. PAP, focusing on tangible attributes like competence and reliability, contrasts with satisfaction, which compares performance to expectations (Sastika et al., 2023). PAP is based on ECT (also known as Expectation Confirmation Theory; AlSokkar et al., 2024), which is the extent to which the consumer’s expectations about the experience on an e-commerce website correspond to the actual experience, making it satisfactory. In e-commerce, factors such as website usability, transaction speed, security, and customer service influence satisfaction. Customer feedback and reviews provide insights into perceptions of e-commerce performance, aiding platform improvement (Marlina, 2024). Perception reflects subjective judgments of goods and services, while expectations, shaped by past experiences and advertisements, serve as benchmarks before service interaction.

E-Commerce Satisfaction (ECS)

Customer satisfaction occurs when a product or service meets consumers’ needs, desires, and expectations. Batheja (2020) identifies key metrics for measuring satisfaction across all stages of the shopping journey, from need perception to post-purchase behavior, including quality, pricing, and convenience. It represents a customer’s overall evaluation of a company’s performance, emphasizing the importance of improving logistical processes to enhance satisfaction. Daulay (2022) highlights satisfaction as a critical indicator of firm-customer relationships, driven by meeting expectations, which fosters repeat purchases. In e-commerce, activities like online transactions and customer relationship management rely on factors such as website usability, information quality, payment security, e-trust, and perceived versus actual performance, all of which are vital to understanding and improving customer satisfaction (Sastika et al., 2023).

Conceptual Framework and Hypothesis Development

This Conceptual Framework integrates constructs from many foundational models, including the DeLone and McLean (2003), W. Gao and Li (2019), and Pavlou’s trust and risk model (Pavlou, 2003), and the Expectation Confirmation theory by AlSokkar et al. (2024), to understand the direct and indirect impacts of website usability, payment security, information quality, e-trust, and perceived-to-actual performance on e-commerce satisfaction (Miao et al., 2022) in Figure 1. E-trust and PAP serve as intermediaries that facilitate the transmission of influence from antecedent factors to satisfaction. The e-trust construct has also been employed more recently in the literature to moderate the effect of satisfaction on outcomes such as repurchase intention (L. Gao & Waechter, 2017; Pavlou, 2003). The developed hypotheses aim to test these relationships, with a focus on the mediating and moderating roles of perceived-to-actual performance and e-trust in satisfaction. An integrated model can effectively explain e-commerce satisfaction and success by drawing on the strengths of each foundation (Rustandi et al., 2024). Figure 1 illustrates the underlying assumptions within this conceptual framework.

Figure 1.

Conceptual framework and hypotheses.

Payment Security (PS) With E-Commerce Customer Satisfaction (ECS)

Payment security refers to the extent to which consumers feel confident that online payment methods are safe from risks like fraud and unauthorized use of their personal information (L. Gao & Waechter, 2017) where PS is the perceived reliability and importance of secure transactions, influencing user trust in e-commerce platforms. The rise of digital payment methods, such as electronic money, has streamlined retail payment systems, driven by technological advancements (Hisyam & Manuaba, 2022). These gateways enable secure, cashless transactions and are valued for their efficiency, service quality, convenience, speed, enjoyment, and security (Zeynalov, 2023). Halim and Orleans (2023) noted that e-commerce platforms are adopting diverse digital payment systems, including e-wallets, PayPal, Visa, and MasterCard, to enhance customer satisfaction and simplify purchasing. Payment systems significantly influence online shopping decisions, as customers prefer options that meet their needs. A lack of suitable payment methods may prevent transactions, while a wider variety positively impacts satisfaction. This study posits that implementing secure and convenient digital payment systems on e-commerce platforms enhances customer satisfaction. Payment security reduces risk perception. It also enhances trust on the consumer end (Pavlou, 2003). When customers feel secure while paying, they are more likely to be satisfied with the e-commerce transaction.

H1: There is a positive relationship between PS and ECS.

Information Quality (IQ) With E-Commerce Customer Satisfaction (ECS)

Information Quality (IQ) refers to the desirable characteristics of the system’s output, specifically its accuracy, relevance, completeness, timeliness, and format (DeLone & McLean, 2003). In contrast, system quality focuses on technical features and usability. Consumer satisfaction is positively linked to IQ, with secure payment options and data privacy playing critical roles (Ludwina et al., 2022). Factors such as product quality, reliability, shipping methods, and satisfaction with e-commerce services also influence customer satisfaction. Transaction security, data protection, and effective use of consumer information for targeted marketing further enhance satisfaction. Organizations must improve their IQ to help customers make informed purchase decisions by providing detailed product information, reviews, and service details (Sudharshan, 2020). Well-designed shopping pages simplify information retrieval, reducing customer confusion and effort and boosting post-purchase satisfaction. This study aims to test the assumption that higher IQ positively affects consumer satisfaction in online shopping (Novialeta & Slamet, 2020). DeLone and McLean (2003) argue that high-quality information, for example, accurate, relevant, and timely, helps build user satisfaction. Consumers tend to be satisfied when they are appropriately informed about the product they are purchasing, and they have reliable information.

H2: IQ has a positive correlation with ECS.

Website Usability (WU) With Perceive to Actual Performance (PAP)

Cai (2022) and Merve Demirci (2021) suggest that perceived performance significantly enhances the user experience on e-commerce websites. Website design plays a crucial role in usability, user satisfaction, and performance, with high usability and user-centered design being essential for success (Jabbari & Bussracumpakorn, 2022). When actual performance meets or exceeds expectations, satisfaction increases. Usability reflects a website’s ability to fulfill user needs and facilitate goal achievement effectively, influencing consumer willingness to return (Bartels et al., 2024). Djulianto et al. (2022) highlight that user-centered design ensures website effectiveness and usability. In e-commerce, performance and usability mediate the relationships among design elements, customer satisfaction, and loyalty, with satisfaction closely tied to usability and the overall site experience.

H3: WU Has a Positive Connection With PAP.

Website Usability (WU) With E-Commerce Customer Satisfaction (ECS)

Website usability is a website that is easy to navigate, user-friendly, and efficient in helping users achieve their goals with minimal effort, error, and confusion (Flavián et al., 2006). Gevers et al. (2024) examined B2C e-commerce as a competitive marketing channel, highlighting the role of website effectiveness in driving customer satisfaction. Similarly, Amadea et al. (2022) found that website features, product availability, and the quality of customer support directly impact satisfaction among online pharmacy users. Sudira et al. (2019) identified additional factors boosting satisfaction, including suitable payment platforms, quick feedback, effective advertising, personalized promotions, and robust security measures. Research suggests that system design quality and customer interaction are crucial to a website’s success. Enhancing system quality is thus proposed to increase consumer satisfaction, with website quality being pivotal to a company’s digital progress in e-commerce (Sastika et al., 2023). User satisfaction is affected by the system quality (which includes usability) suggested by DeLone and McLean (2003). A simple, functional website means a better user experience, making users satisfied with e-commerce activities.

H4: WU positively affects ECS.

Perceived to Actual Performance (PAP) With E-Commerce Customer Satisfaction (ECS)

Customer satisfaction reflects users’ evaluations of products and services, indicating how website content aligns with customer expectations and influencing satisfaction (Ingale et al., 2024). When customers are satisfied, they are more likely to trust the company. Perceived performance refers to the belief that using a system enhances productivity and efficiency. Satisfaction arises from comparing actual performance to expectations, with alignment leading to higher satisfaction (Tan, 2019). Dissatisfaction occurs when performance falls short (Suchanek & Kralova, 2023). Perceived performance directly influences online shopping intentions, encouraging engagement and overcoming challenges (Lăzăroiu et al., 2020). Key factors affecting satisfaction include service quality, product quality, and price (Naini et al., 2022). Customer satisfaction improves when the actual performance of an online platform aligns with customer expectations (AlSokkar et al., 2024). The closer the actual performance is to expectations, the greater the satisfaction.

H5: There is a positive relationship between PAP and ECS.

E-Trust (ET) with E-Commerce Customer Satisfaction (ECS)

Juwaini et al. (2022) emphasize that strong customer trust fosters relationships within a company’s network and is vital for maintaining business partnerships. In e-commerce, where upfront payments and product images are common, trust is crucial. Haryono et al. (2024) highlight that e-trust positively influences e-satisfaction, with its foundation rooted in e-service quality and operational security. Apidana and Prasetyo (2023) noted that consumer satisfaction in online environments depends on trust in e-commerce services, as satisfied customers view the company as reliable and committed to their interests. Trustworthy websites enhance user experience, sales, and overall business performance. Prasojo and Sari (2023) define e-trust as the mental state of users when navigating risks in digital interactions, underscoring its role in the success of online systems. E-trust is important to maintain long-term relationships. When users trust the platform’s reliability and security, they are satisfied with the service (Pavlou, 2003)

H6: ET Positively Affects ECCS.

Perceive to Actual Performance (PAP) With E-Commerce Customer Satisfaction (ECS), Website Usability (WU)

Research shows that repurchase intention is strongly influenced by perceived value, defined as the gap between what customers receive (quality, benefits, and utility) and what they pay (price and effort) (Azhar & Wuryan, 2024). Positive perceived value boosts repurchase intention, while negative value reduces it. Customer perceptions of value evolve, emphasizing the need for consistent product and service quality. High-quality products significantly enhance customer satisfaction (Efendi & Dirgantara, 2023). In e-commerce, transactions depend on suitable payment options and user-friendly platforms. The user interface (UI) is critical to the user experience, with its design significantly shaping perceptions (Shao et al., 2020). This study proposes that a well-designed UI enhances user experience and purchase decisions, while perceived value and product quality positively impact repurchase intention and customer satisfaction. The Expectation Confirmation Theory (ECT) states that perceived-to-actual performance mediates the relationship between user experience (e.g., website usability) and satisfaction (Abd Ghani et al., 2017).

H7: PAP mediates the effects of WU on ECS.

E-Trust (ET) With E-Commerce Customer Satisfaction (ECS), Payment Security (PS)

Octadyla and Mismiwati (2024) emphasize that e-customer trust is crucial for cultivating loyalty in e-commerce, where customer satisfaction fosters trust, which is vital for both online and offline transactions. According to Pavlou (2003), a secure payment system will enhance a platform’s trustworthiness and build confidence in the platform’s commitment to customer data safety. Trust in a company influences e-trust among customers (Abd Ghani et al., 2017). Trust influences consumer decisions, with security and privacy concerns playing a significant role. Satisfied customers perceive e-commerce as efficient and helpful, while loyalty encourages repeat purchases. Good information reduces uncertainty and enhances e-trust, thereby positively affecting satisfaction with services such as home delivery (Joseph & Sajikumar, 2024). Huddin et al. (2024) highlight a positive link between e-trust and loyalty, showing that trust fosters comfort, security, and recurring business. Trust is the foundation of lasting customer relationships (Sari & Darma, 2022). This study posits that improving e-customer trust through enhanced service, privacy, and security protocols will positively impact satisfaction and loyalty in e-commerce. E-trust is likely to improve the effect of payment safety on e-commerce satisfaction. As e-trust increases, the positive effect of payment security on satisfaction is expected to be greater, according to trust-based models (Miao et al., 2022).

H8: ET moderates the effects of PS on ECS.

Research Methodology

Research Design

This study employs a quantitative research design, focusing on collecting numerical data and using statistical techniques to test hypotheses, ensuring objectivity and explanatory analysis to determine the e-commerce satisfaction of Bangladesh (Nikose, 2022). Hypotheses were developed from existing literature and experimental studies. An online structured survey method was chosen for its efficiency in gathering extensive data on customer trust and satisfaction in e-commerce (Lipp et al., 2020). Responses were measured using a 5-point Likert scale, from “strongly disagree” to “strongly agree.” Demographic and socioeconomic questions placed at the start addressed sensitive topics such as ethnicity, gender, age, marital status, education, and income, reflecting respondents’ reserved nature in a conservative market. The details of the study instruments are provided in Appendix A (Table A1).

Sampling Strategy and Sample Size Justification

The study employed convenience sampling, collecting data from 245 respondents via an online survey. This approach was selected to efficiently gather preliminary insights into e-commerce satisfaction in a rapidly evolving market. While convenience sampling may limit the generalizability of findings, the sample reflects a diverse demographic profile, including variation in age, education, and income levels. The sample size of 245 respondents was deemed adequate for the structural equation modeling (SEM) analysis based on multiple established criteria, including the 10-times rule, which states that the minimum sample size should exceed 10 times the maximum number of inner model paths directed at any latent variable (Hair, 2014). Given the model structure with six variables, each with four items (a total of 24), multiply by 10 to obtain a data size of 240. Our usable data set was 245, above the minimum requirement of 240, so this criterion was met by Jacoby (1972). Furthermore, power analysis considerations suggest that for moderate-to-strong effect sizes and factor loadings above 0.6, samples of approximately 200 to 300 participants typically achieve sufficient statistical power (≥0.80) to detect significant path relationships. Simulation studies corroborate that sample sizes around 200 provide robust parameter estimates and acceptable levels of bias for models of comparable complexity (Muthén & Muthén, 2002). While acknowledging the benefits of larger samples, the chosen sample size is consistent with field norms and appropriate for the analytic techniques employed.

Data Collection

Primary data were collected via an online Google Form questionnaire. The sample consisted of 245 individuals selected through convenience sampling, based on the distribution of 350 data, with 70% of respondents. Key variables included security, information quality, e-trust, and website usability as independent variables, e-commerce satisfaction as the dependent variable, and moderating and mediating factors.

Analysis

To assess the effectiveness of our proposed model, we analyzed the collected data using structural equation modeling (SEM), focusing on both the measurement and structural models. SEM was selected for this study due to its advantages over methods such as linear regression, which have limitations for evaluating path relationships among constructs. Data obtained from Google Forms in CSV format was processed using SmartPLS version 4.00, while IBM SPSS Statistics 27 was utilized to analyze the demographic profile of the respondents.

Findings

The study findings reveal that 73.50% of respondents are male, while 26.50% are female. In addition, the predominant age group is 20 to 30 years, comprising 93.06% of the sample, followed by 3.67% aged 30 to 40 years, 2.86% aged under 20 years, and 0.41% aged 50 years or older. Moreover, most respondents (84.08%) are highly educated, having completed graduation, while 13.06% have secondary-level education, 2.04% have primary education, and 0.82% are illiterate. A significant majority (81.63%) earn a monthly income below 10,000, with 11.43% earning between 10,000 and 20,000, and 5.31% between 20,000 and 30,000. Only 1.63% earn above 30,000. Internet access is primarily through smartphones or tablets (88.16%), with 11.84% using desktops or laptops. Online shopping habits vary: 40.82% of individuals shop once a year, 27.35% every 3 months, 22.04% every 6 months, 7.76% weekly, and 2.04% daily, as shown in Table 1.

Table 1.

Demographic Profile of the Respondents.

Demographic profile	Frequency	Percent
Gender
Female	65	26.50
Male	180	73.50
Age
Less than 20 years	7	2.86
Between 20 and 30 years	228	93.06
Between 30 and 40 years	9	3.67
50 years or above	1	0.41
Education Qualification
Illiterate	2	0.82
Primary Level	5	2.04
Secondary Level	32	13.06
Graduate	206	84.08
Monthly Income
Below 10,000	200	81.63
10,000–20,000	28	11.43
20,000–30,000	13	5.31
30,000–40,000	1	0.41
More than 40,000	3	1.22
How do you have access to the Internet?
Smart Phone/Tablet	216	88.16
Desktop/Laptop	29	11.84
General Online Shopping
Daily	5	2.04
Weekly	19	7.76
Once every 3 months	67	27.35
Once every 6 months	54	22.04
Once a year	100	40.82

Measurement Model

We evaluated the adequacy of the measurement model and the reliability of the measurement items using various parameters of convergent and discriminant validity (Hulland, 1999). To assess model reliability, we employed Cronbach’s alpha and composite reliability. For convergent validity, we examined item loadings and Average Variance Extracted (AVE). Discriminant validity was assessed using the Fornell and Larcker correlation matrix, the square root of AVE, and the Heterotrait-Monotrait ratio of correlations (HTMT). In social science research, Cronbach’s alpha is typically used to assess internal consistency, with a recommended standard of .7 (Ab Hamid et al., 2017). Ideally, measurement items should have a factor loading of 0.70 or higher; a loading of 0.40 or higher is acceptable in exploratory studies (Hulland, 1999). Composite reliability (CR) should be 0.70 or higher, but a CR of 0.60 or more is acceptable, and the Average Variance Extracted (AVE) should be 0.50 or higher (Bagozzi & Yi, 1988). In this study, Table 2 presents factor loadings, Cronbach’s alpha, rho_A, CR, and AVE. All items have factor loadings above 0.7, and Cronbach’s alpha for all constructs is also above .7. Additionally, the AVE values for all constructs exceed 0.5, indicating good internal consistency and convergent validity. This study used VIFs to assess the potential for common-method bias. A VIF score below 5 is considered acceptable, suggesting that multicollinearity is not an issue (Kock, 2015). As shown in Table 3, the VIF values for all independent and dependent variables were within the acceptable threshold, indicating that multicollinearity is not a concern in this analysis. Discriminant validity was evaluated using methods proposed by Fornell and Larcker (1981) and the Heterotrait-Monotrait (HTMT) ratio of correlations (Sarstedt et al., 2021). Table 4 shows that the square roots of the AVEs were higher than the correlations with other constructs. Furthermore, Table 5 shows that all HTMT values fall within an acceptable range. Therefore, these results suggest adequate discriminant validity.

Table 2.

Measurement Model.

Constructs	Items	Loadings	Cronbach’s alpha	rho_A	CR	AVE
E-commerce Satisfaction	ECS1	0.848	.876	.882	0.915	0.729
	ECS2	0.866
	ECS3	0.877
	ECS4	0.823
E-Trust	ET1	0.888	.908	.912	0.936	0.785
	ET2	0.910
	ET3	0.903
	ET4	0.840
Information Quality	IQ1	0.902	.895	.896	0.935	0.827
	IQ2	0.918
	IQ3	0.907
Perceived to Actual Performance	PAP1	0.913	.917	.919	0.941	0.801
	PAP2	0.861
	PAP3	0.889
	PAP4	0.915
Payment Security	PS1	0.936	.928	.928	0.954	0.874
	PS2	0.930
	PS3	0.938
Website Usability	WU1	0.919	.930	.931	0.950	0.827
	WU2	0.907
	WU3	0.900
	WU4	0.912

Table 3.

Collinearity Statistics.

Constructs	PAP	ECS
PS		3.835
IQ		3.418
WU	1.000	4.222
PAP		3.409
ET		2.426
ECS

Table 4.

Correlation Matrix and Square Root of the AVE.

Constructs	PS	IQ	WU	PAP	ET	ECS
PS	0.935
IQ	0.755	0.909
WU	0.831	0.770	0.909
PAP	0.714	0.785	0.762	0.895
ET	0.701	0.655	0.698	0.708	0.886
ECS	0.778	0.771	0.761	0.784	0.731	0.854

Note. PS = payment security; IQ = information quality; WU = website usability; PAP = perceived to actual performance; ET = e-trust; ECS = e-commerce satisfaction.

Table 5.

Heterotrait–Monotrait Ratio.

Constructs	PS	IQ	WU	PAP	ET
PS
IQ	0.828
WU	0.894	0.843
PAP	0.773	0.866	0.823
ET	0.763	0.724	0.757	0.775
ECS	0.859	0.864	0.837	0.867	0.813

Note. PS = payment security; IQ = information quality; WU = website usability; PAP = perceived to actual performance; ET = e-trust; ECS = e-commerce satisfaction.

Structural Model

In this study, we employed the bootstrap method to test hypotheses at the 5% significance level, using 5,000 bootstrap samples from the primary dataset (N = 245). Bootstrapping involves repeatedly sampling subsamples from the original dataset to calculate model parameters iteratively. T-values are then derived to assess the significance of the weights. We analyzed path coefficients (β), t-statistics, and p-values to explore the relationships between explanatory and endogenous variables. The results of the structural model analysis, including path coefficients (β), t-statistics, p-values, and hypothesis outcomes, are detailed in Table 6. Additionally, Figure 2 illustrates the path diagram and loadings along with their p-values.

Table 6.

Structural Model Assessment for Direct Connections.

Hypothesis	Relationship	β	SE	t-Statistics	f ²	p Values	Decisions
H1	PS → ECS	.241	0.083	2.913	0.065	.004	Yes
H2	IQ → ECS	.189	0.075	2.529	0.045	.011	Yes
H3	WU → PAP	.762	0.029	26.157	1.381	.000	Yes
H4	WU → ECS	.061	0.082	0.743	0.004	.457	No
H5	PAP → ECS	.261	0.076	3.420	0.078	.001	Yes
H6	ET → ECS	.196	0.066	2.959	0.061	.003	Yes

Note. PS = payment security; IQ = information quality; WU = website usability; PAP = perceived to actual performance; ET = e-trust; ECS = e-commerce satisfaction.

Figure 2.

Structural model with path coefficients and p value.

We identified the direct relationship between PS and ECS (β = .241; t-statistics = 2.913; p-value < .05), IQ and ECS (β = .189; t-statistics = 2.529; p-value < .05), WU and PAP (β = .762; t-statistics = 26.157; p-value < .05), WU and ECS (β = .061; t-statistics = 0.743; p-value > .05), PAP and ECS (β = .261; t-statistics = 3.420; p-value < .05), ET and ECS (β = .196; t-statistics = 2.959; p-value < .05). Therefore, our findings indicate that all hypotheses were supported except for H4. Table 6 also reports the f² values, which indicate the effect of each independent variable on its corresponding dependent variable, ranging from very small to large. Table 7 displays the R² values for PAP and ECS, which were .580 and .745, respectively, indicating the model’s explanatory power. Additionally, Table 7 reports Q² values greater than 0, indicating that predictive relevance has been established in this study.

Table 7.

R and Q Square.

Constructs	R-square	R-square adjusted	Q square
PAP	.580	.578	0.460
ECS	.745	.739	0.526

Note. PAP = perceived to actual performance; ECS = e-commerce satisfaction.

Mediation and Moderation Analysis

PAP mediates the direct relationship between WU and ECS. In this study, we employed SmartPLS version 4 to assess the mediating effect of PAP on the relationship between WU and ECS. In this study, 5,000 bootstrap samples were randomly extracted from the dataset (N = 245). Table 8 shows that PAP mediates the relationship between WU and ECS (β = .199; t-statistic = 3.414; p-value < .05). Our analysis indicates that PAP mediates the relationship between WU and ECS (complete mediation). Additionally, we also performed the Sobel test to validate the full mediation effect of PAP in the relationship between WU and ECS (Sobel, 1982). Thus, our hypothesis H7 is supported. In addition, we also examined the moderating effect of ET in the relationship between PS and ECS (β = −.046; t-statistic = 1.441; p-value > .05). Therefore, our hypothesis H8 is rejected, as illustrated in Table 8. Consistent with our findings, the effect of payment security on e-commerce satisfaction is direct and significant. However, it is not moderated by individual differences in e-trust, indicating that the influence of payment security on customer satisfaction operates independently of the level of trust customers have in the platform.

Table 8.

Mediation and Moderation Analysis.

Hypothesis	Relationship	β	SE	t-Statistics	p Values	Sobel test	Decisions
H7	WU → PAP → ECS	.199	0.058	3.414	.001	3.405	Yes
H8	ET × PS → ECS	−.046	0.032	1.441	.150	NA	No

Note. PS = payment security; IQ = information quality; WU = website usability; PAP = perceived to actual performance; ET = e-trust; ECS = e-commerce satisfaction.

Discussion

According to the objectives of the study, the findings show that payment security significantly and positively influences e-customer satisfaction, which is consistent with the previous studies conducted by Kim et al. (2008) and Pavlou (2003) who found that secure payment systems are crucial for establishing trust and mitigating perceived risks, both of which are directly related to customer satisfaction in e-commerce. This outcome suggests that payment security is a crucial factor in enhancing e-commerce customer satisfaction. When customers perceive that a platform has secure payment processes, it reduces their concerns about potential fraud or financial risk, building trust in the platform. This increased trust leads to a more positive shopping experience, as customers feel more comfortable and confident in their transactions. The finding suggests that e-commerce businesses can enhance customer satisfaction by prioritizing and clearly communicating robust payment security measures, as this not only alleviates customer anxiety but also fosters repeat purchases and loyalty.

In addition, the outcomes of this study reveal that information quality significantly impacts e-commerce customer satisfaction, which is strongly supported by the previous studies of Wixom and Todd (2005) and T. Zhou (2013). They found that information quality is an important factor in e-commerce customer satisfaction. The result indicates that higher information quality directly enhances customers’ satisfaction with e-commerce platforms. When customers perceive that product information is accurate, reliable, complete, and up-to-date, they tend to feel more confident and satisfied with their shopping experience.

Moreover, the study’s findings reveal that Website usability, as perceived by users, aligns with actual performance, as noted in previous studies by Tandon et al. (2018). These studies found that website usability enhances both perceived and actual performance in online shopping by reducing perceived risk and improving customer satisfaction through task efficiency and ease of use. The result indicates that website usability positively influences the alignment between perceived and actual performance, implying that a well-designed, user-friendly website not only enhances users’ perceptions of their efficiency but also improves actual task performance. This alignment occurs because usability features such as intuitive navigation, clear layout, and responsive design reduce cognitive load, making it easier for users to complete tasks effectively. As a result, users feel more capable and efficient, and their experience validates this perception. This outcome highlights the importance of prioritizing usability in website design, ensuring that users’ expectations of ease and performance align with their experience, thereby enhancing user satisfaction and overall site effectiveness.

However, this study shows that Website usability does not positively influence e-customer satisfaction, which is supported by the studies of Ashiq and Hussain (2024) and Guo et al. (2023), who suggest that usability does not always translate into affective satisfaction. In the context of highly standardized, competitive e-commerce platforms, website usability appears to serve as a hygiene factor. It is necessary to prevent dissatisfaction, but it is insufficient to generate satisfaction in itself. Customers now expect basic usability features such as easy navigation, clarity, and functional responsiveness; thus, only exceptional or highly optimized usability performance is likely to enhance satisfaction. Instead, more psychologically meaningful factors such as trust, security, personalization, and emotional engagement play a stronger role in shaping customers’ satisfaction and loyalty in contemporary e-commerce environments.

Furthermore, the study’s findings indicate that perceived actual performance has a significant and positive influence on e-commerce customer satisfaction. This finding is aligned with previous studies, such as those in the field of online retail experience (Aljohani, 2024), and online purchase intention (Saleem et al., 2022). This outcome recommends that businesses should focus on aligning customer expectations with the real performance of their products or services. When customers find that the experience meets or exceeds their expectations, it builds trust and enhances satisfaction, which can lead to increased loyalty, positive reviews, and word-of-mouth recommendations. This alignment is critical in e-commerce, where perceived quality and actual performance, such as product accuracy, delivery speed, and service reliability, play a central role in customer retention and satisfaction.

Besides, this study also reveals that e-trust significantly influences e-commerce customer satisfaction, which is strongly supported by the study of Quintus et al. (2024) and Saoula et al. (2023), who found that e-trust has a positive and significant impact on e-commerce customer satisfaction, indicating that customer satisfaction in online shopping is strongly tied to the level of trust consumers place in the platform. This relationship suggests that when consumers trust an e-commerce site, they feel more secure and confident about the transaction’s reliability and the platform’s ability to protect their data and deliver products as promised. This trust mitigates common online shopping concerns such as fraud, poor product quality, or data security issues, making customers more satisfied with their experience.

In examining the mediating role of perceived actual performance (PAP) between website usability (WS) and e-commerce customer satisfaction (ECS), the results indicate that PAP fully mediates this relationship. Specifically, website usability alone does not have a significant direct impact on ECS; however, it significantly contributes to enhancing PAP, which, in turn, positively influences customer satisfaction. Thus, WS is instrumental in shaping PAP, and it is through this perceived alignment of performance with expectations that customer satisfaction in e-commerce is ultimately achieved.

Finally, the study does not find any moderating effect of e-trust on payment security and e-commerce customer satisfaction. The absence of a moderating effect of e-trust on the relationship between payment security and e-commerce customer satisfaction may stem from several key factors. Firstly, many customers may already expect a baseline level of payment security, rendering e-trust a less impactful factor in shaping their satisfaction if that expectation is met. Additionally, the direct influence of payment security may be so strong that it overshadows any additional contribution of e-trust, as secure transactions alone may be sufficient for customer satisfaction.

Theoretical Implications

By integrating multiple foundational theories, this study enhances understanding of e-commerce customer satisfaction. These theories include the DeLone and McLean IS Success Model, Pavlou’s Trust and Risk Model, and Expectation Confirmation Theory. This integrated conceptual framework explains the direct and indirect effects of a website’s usability, payment security, information quality, e-trust, and perceived-to-actual performance on e-commerce satisfaction (H. Zhou & Xing, 2022). A key conceptual insight is that perceived-to-actual performance serves as a full mediator between website usability and customer satisfaction. This finding highlights that website usability does not directly lead to satisfaction. It influences satisfaction, though, through perceptions about performance. This research shows that website usability does not elevate customer satisfaction, contrary to the usability-performance experience.

Moreover, the significant role of payment security in determining e-commerce satisfaction underscores the need to build trust in secure transactions and in accurate, relevant product information. Although e-trust influences satisfaction directly, it does not moderate the effect of payment security on satisfaction, as it is a given set by customers. Payment security is a prerequisite for customers and not dependent upon overall trust (Rico et al., 2019). The findings are theoretically significant as they extend e-commerce satisfaction research beyond expert-oriented consumer behavior observed in developed countries to a less developed country, Bangladesh. Also, it shows that we need to integrate psychological and technological factors. These findings extend theoretical frameworks by contextualizing e-commerce satisfaction in emerging markets, such as Bangladesh, and highlight the need to integrate psychological and technical factors to understand consumer behavior in digital commerce better. This comprehensive approach offers an enriched conceptual model that can inform future research and practical applications to improve customer satisfaction and loyalty in competitive online markets.

Practical Implications

The implications of this study offer many suggestions for e-commerce practitioners and businesses to improve customer satisfaction and loyalty. To begin, the website’s usability should be improved alongside an assurance that performance meets or exceeds customer expectations, as perceived-to-actual performance mediates satisfaction (Cho & Moon, 2023). So, investments in user-friendly design need to go hand in hand with the service delivery system. Secondly, payment security appears to be another driver of customer satisfaction. Thus, e-commerce platforms will also have to enhance transactional security and clearly communicate this to customers.

Thirdly, if online shoppers have high-quality, accurate, and relevant information, it will reduce their uncertainty and increase their confidence in their purchasing decisions. It shows that companies need to improve the information on their websites (Mogea, 2022). In the end, e-trust enhances satisfaction; however, people already expect a secure payment. Hence, trust-building activities must go beyond transaction security to include brand credibility, customer support responsiveness, and post-purchase engagement, among others (Kedaton et al., 2024). Businesses and policymakers in Bangladesh – and other emerging economies – can use the insights from this study to allocate resources and design policies that help achieve sustainability and improved experiences in the online environment (Sihombing et al., 2023).

Limitations and Future Research

This research has certain limitations that have to be considered. To begin with, the study targets a specific group (young adults in Bangladesh). In that case, the findings may not be that helpful for other ages. To enhance the external validity of the results, a wider range of separate samples could be included in future work (e.g., age groups, regions, cultural backgrounds). The second limitation is the use of self-reported data, which is subject to bias that could influence the results. To address this limitation, potential future research could expand on the use of mixed-methods research to explore consumers’ behavior in greater depth.

Another limitation is the exclusive reliance on convenience sampling, which may not fully represent the broader e-commerce customer base He (2023). To achieve greater representativeness, future studies may benefit from random or stratified sampling. Also, the study looks at a limited number of variables, that is, website usability, payment security, quality of information, e-trust, and perceived-to-actual performance, which may miss other factors that can also be important. The scope of future research should include additional variables, such as product quality, customer service, and logistics performance, to better understand customer satisfaction in the e-commerce context. Ultimately, the research offers no insight into long-term consumer behavior, such as repurchase intention or brand loyalty, which may change over time (Mahsyar & Surapati, 2020; Sari & Darma, 2022). Future studies could examine the long-term effects of the factors discussed in this study on client satisfaction and loyalty. Furthermore, how leading technologies like artificial intelligence and personalization influence satisfaction is another interesting area of e-commerce research that future researchers could focus on.

Conclusion

This study examined the primary factors that influence e-commerce customer satisfaction. It uncovers several significant outcomes. Payment security and information quality emerged as robust and substantial indicators of satisfaction, underscoring that buyers prioritize secure transactions and precise, dependable product information as fundamental elements of a favorable online buying experience. The perceived-to-actual performance gap had a significant impact, suggesting that satisfaction rises when customers’ expectations align with the platform’s actual performance in terms of product accuracy, delivery reliability, and service quality. While website usability markedly improved perceived actual performance, its direct impact on satisfaction was minimal, indicating that usability acts as a hygiene factor: it mitigates dissatisfaction but fails to elicit satisfaction unless performance is exceptionally elevated. Furthermore, e-trust was found to affect satisfaction directly; however, it did not reduce the correlation between payment security and contentment, likely because customers’ pre-existing expectations for a fundamental level of transactional security remained. Notably, the perceived-to-actual performance gap entirely mediated the association between website usability and happiness, underscoring the importance of performance-driven perceptions of usability in consumer evaluations. Ultimately, these conclusions must be regarded with caution, given that the data were obtained from 245 young Bangladeshi customers. Consequently, the results may not entirely apply to older users, individuals from other cultural backgrounds, or consumers in non-mobile or more expansive e-commerce environments. Subsequent research should corroborate these associations utilizing more heterogeneous samples and cross-cultural settings.

Footnotes

Appendix A

Table A1.

Items and Constructs of the Research on E-Commerce.

Construct	Items	Options	Source
Website Usability	1. Able to access the website easily. 2. Quickly find the products or information 3. To work the website across different devices (e.g., desktop, mobile) 4. To navigate through the website	• Much Weaker • Weaker • Neutral • Strong • Much Stronger	Jogoo et al. (2019)
Payment Security	1. The overall satisfaction with the payment security measures provided by the e-commerce platform 2. In the e-commerce platform’s ability to protect the payment information 3. Important is payment security when making online purchases 4. The perception of payment security influences overall trust in an e-commerce website	• Strongly low • Low • Neutral • High • Strongly high	Pohjanen (2019)
Information quality	1. To get error-free information from the website 2. Up-to-date information on website 3. Sufficient information from the website for the task at hand 4. Getting information from the website at the right time	• Strongly disagree • Disagree • No opinion • Agree • Strongly agree	Saha et al. (2012)
Perceived to Actual Performance	1. Efficient and fast customer service performance 2. Customer service without reservation 3. Timeliness of delivery 4. Respond to the request	• Strongly disagree • Disagree • No opinion • Agree • Strongly agree	Gajewska et al. (2020)
E-customer Satisfaction	1. The performance of the website meets expectations. 2. The website has never had sufficient experience marketing its products and services. 3. The website knows the users well enough to offer products and services adapted to their needs. 4. The website hasn’t the necessary resources to carry out the activities successfully.	• Much Weaker • Weaker • Neutral • Strong • Much Stronger	Eid (2011)
E-Trust	1. Readily provide the personal information to the online retailer of choice for convenience in future transactions. 2. Confidence in recommendations made by the preferred online retailer. 3. Trust the claims and promises this online retailer makes about a product. 4. There is a risk of the loss of privacy while purchasing online.	• Strongly disagree • Disagree • No opinion • Agree • Strongly agree	Miao et al. (2022)

Acknowledgements

We used Grammarly Premium to enhance sentence quality and improve the overall readability of the manuscript. Moreover, we want to express our sincere gratitude to all the individuals and organizations that supported us throughout this project.

ORCID iDs

Mohammad Rakibul Islam Bhuiyan

Md Al Amin

Md. Deluar Hossen

Ethical Considerations

This study was conducted with the ethical approval of the Ethical Committee of Noakhali Science and Technology University (NSTU), Noakhali, Bangladesh (Ref: NSTU/FBS/EC/2024/02a). The ethical approval was issued on 18 March 2024.

Consent to Participate

Informed consent was obtained from all respondents before participation through a structured questionnaire. Participants were fully informed about the study’s purpose, their rights, and the voluntary nature of their involvement. They were also allowed to withdraw from the survey at any stage. The authors confirm that respondents provided consent for their responses to be used exclusively for academic research purposes.

Consent for Publication

Each author has reviewed and approved the final manuscript, indicating collective agreement on the work and findings presented. In addition, the authors give their full consent to publish the manuscript in Sage Open.

Author Contributions

The development and execution of this study were the result of a collaborative effort, with each author making unique contributions. Maimuna Khatun, Mohammad Rakibul Islam Bhuiyan, and Md Al Amin initiated the project, designed the study framework, collected the data, analyzed the data, and drafted the initial manuscript. In addition, Md Al Amin and Mohammad Rakibul Islam Bhuiyan significantly deepened the proposed theory by providing critical insights that informed the development of the research model and the discussion sections. The research findings and theories were enriched through collective discussions and brainstorming sessions with all authors; moreover, Dr Md. Abdul Hannan Mia conceptualized and supervised the whole work and edited the final manuscript. Md. Deluar Hossen has contributed to analyzing data, reviewing, and addressing the reviewer’s comments to make quality work.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

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

Data Availability Statement

The data used in this study cannot be publicly shared due to confidentiality concerns. However, the data can be made available upon reasonable request from the corresponding author.*

References

Ab Hamid

M. R.

Sami

Sidek

M. M

. (2017). Discriminant validity assessment: Use of Fornell & Larcker criterion versus HTMT criterion. Journal of Physics: Conference Series, 890, Article 012163.

Abd Ghani

Rahi

Yasin

N. M.

Alnaser

. (2017). Adoption of internet banking: Extending the role of technology acceptance model (TAM) with e-customer service and customer satisfaction. World Applied Sciences Journal, 35(9), 1918–1929. https://doi.org/10.5829/idosi.wasj.2017.1918.1929

Aljohani

(2024). The role of last-mile delivery quality and satisfaction in online retail experience: An empirical analysis. Sustainability, 16(11), Article 4743. https://doi.org/10.3390/su16114743

AlSokkar

A. A.

Law

E. L.-C.

AlMajali

D. A.

Al-Gasawneh

J. A.

Alshinwan

(2024). An indexed approach for expectation-confirmation theory: A trust-based model. Electronic Markets, 34(1), Article 12. https://doi.org/10.1007/s12525-024-00694-3

Amadea

Suryaputra

Sondakh

(2022). The effect of product quality, service quality, environment quality, and product assortment on customer loyalty trough customer satisfaction of BCA mobile application. Journal of Economics, Finance and Management Studies, 5(3), 633–654. https://doi.org/10.47191/jefms/v5-i3-17

Anggraini

H. J.

Sari

D. K.

(2024). Can e–service quality, e–trust and e–customer satisfaction influence the repurchase intention on e–commerce shopee? Dapatkah e–service quality, e–trust dan e–customer satisfaction berpengaruh terhadap repurchase intention pada e–commerce shopee? Advance online publication. https://doi.org/10.21070/ups.4965

Apidana

Prasetyo

(2023). The influence of e-trust and e-servqual on e-repurchase intention with e-satisfaction as an intervening variable. Jurnal Ilmiah Manajemen Kesatuan, 11(3), 1075–1086.

Arshad

Yasir

Shakeel

(2021). Web usability and user experience for Pakistani e-commerce websites. 2021 International Conference on Innovative Computing (ICIC) (pp. 1–7). IEEE.

Asawawibul

Na-Nan

Pinkajay

Jaturat

Kittichotsatsawat

(2025). The influence of cost on customer satisfaction in e-commerce logistics: Mediating roles of service quality, technology usage, transportation time, and production condition. Journal of Open Innovation: Technology, Market, and Complexity, 11(1), Article 100482. https://doi.org/10.1016/j.joitmc.2025.100482

10.

Ashiq

Hussain

(2024). Exploring the effects of e-service quality and e-trust on consumers’e-satisfaction and e-loyalty: Insights from online shoppers in Pakistan. Journal of Electronic Business & Digital Economics, 3(2), 117–141. https://doi.org/10.1108/JEBDE-09-2023-0019

11.

Azhar

M. R.

Wuryan

(2024). Pengaruh customer engagement, perceived quality, dan perceived value Terhadap repurchase intention pada Produk Mcdonald’s. MES Management Journal, 3(1), 186–199. https://doi.org/10.56709/mesman.v3i1.173

12.

Bagozzi

R. P.

(1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16, 74–94.

13.

Bartels

Kesternich

Löschel

(2024). The demand for voluntary carbon dioxide removal: Experimental evidence from an afforestation project in Germany. Land Economics, 100(4), 621–638. https://doi.org/10.1257/rct.12278-3.0

14.

Basbeth

I. F.

Sumapraja

I. R.

(2021). The moderating role of perceived security mobile payment in online shopping. SSRN. https://doi.org/10.2139/ssrn.3918142

15.

Bashar

Wang

Babu

M. S. I.

Rafiq

(2024). Adoption of E-commerce in developing nations: Insights from low-and middle-income consumers. International Journal of Innovation and Technology Management (IJITM), 21(07), 1–29. https://doi.org/10.1142/S0219877024500548

16.

Batheja

(2020). Customer satisfaction, trust & re-purchase for online shopping behavior for consumers: An empirical investigation. SSRN. https://doi.org/10.2139/ssrn.3636350

17.

Cai

(2022). Examining social e-commerce platforms by mediating the effect of perceived usefulness and perceived trust using the technology acceptance model. Journal of Organizational and End User Computing (JOEUC), 34(8), 1–20. https://doi.org/10.4018/joeuc.315621

18.

Chan

Seong-won

(2021). A study on the impact of clothing design elements on purchase intention. Korean Design Research Society, 6(2), 197–209. https://doi.org/10.46248/kidrs.2021.2.197

19.

Cho

N. J.

Moon

S. M.

(2023). A study of the impact of online retailers’ direct overseas purchases on shopping attitudes and repurchase intentions. e-Business Research, 24(2), 209–222. https://doi.org/10.20462/tebs.2023.4.24.2.209

20.

Da Costa

Casais

. (2020). Social media and E-commerce: A study on motivations for sharing content from E-commerce websites. In Alavi

Ahuja

(Eds.), Managing social media practices in the digital economy (pp. 121–149). IGI Global. https://doi.org/10.4018/978-1-7998-2185-4.ch006

21.

Daulay

(2022). Analysis of customer loyalty models influenced by customer relationship management and satisfaction in transportation services. Journal of International Conference Proceedings, 5(4), 324–332.

22.

DeLone

W. H.

McLean

E. R.

(2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748

23.

Djulianto

M. V.

Wardhanie

A. P.

Candraningrat

(2022). The role of perceived usability, satisfaction, and customer trust in design and developing user loyalty Edutech website. Business and Finance Journal, 7(2), 183–194. https://doi.org/10.33086/bfj.v7i2.3493

24.

Efendi

S. C.

Dirgantara

I. M. B.

(2023). Analysis of the effect of perceived service quality and perceived value on E loyalty through customer satisfaction and customer trust as intervening variables: Study on Wardah beauty products e-commerce consumers in Semarang city. International Journal of Science and Society (IJSOC), 5(5), 591–608. https://doi.org/ttps://doi.org/10.54783/ijsoc.v5i5.924

25.

Eid

M. I.

(2011). Determinants of e-commerce customer satisfaction, trust, and loyalty in Saudi Arabia. Journal of Electronic Commerce Research, 12(1), 78–93. https://doi.org/10.1787/eco_surveys-chn-2013-graph6-en

26.

Flavián

Guinalíu

Gurrea

(2006). The role played by perceived usability, satisfaction and consumer trust on website loyalty. Information & Management, 43(1), 1–14. https://doi.org/10.1016/j.im.2005.01.002

27.

Fornell

Larcker

D. F.

(1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104

28.

Gajewska

Zimon

Kaczor

Madzík

(2020). The impact of the level of customer satisfaction on the quality of e-commerce services. International Journal of Productivity and Performance Management, 69(4), 666–684.

29.

Gao

Waechter

K. A.

(2017). Examining the role of initial trust in user adoption of mobile payment services: An empirical investigation. Information Systems Frontiers, 19, 525–548. https://doi.org/10.1007/s10796-015-9611-0

30.

Gao

(2019). Building presence in an online shopping website: The role of website quality. Behaviour & Information Technology, 38(1), 28–41. https://doi.org/10.1080/0144929x.2018.1509127

31.

Gevers

Millemann

J. A.

Schnellbaecher

(2024). Factors affecting customer satisfaction in the customer journey in B2B and B2C e-commerce environment. International Journal of Technology Marketing, 18(3), 241–268. https://doi.org/10.1504/ijtmkt.2024.10064508

32.

Guo

Zhang

Xia

(2023). Impact of shopping website design on customer satisfaction and loyalty: The mediating role of usability and the moderating role of trust. Sustainability, 15(8), Article 6347. https://doi.org/10.3390/su15086347

33.

Hair

J. F.

(2014). A primer on partial least squares structural equation modeling (PLS-SEM). Sage.

34.

Halim

Orleans

(2023). The impact of digital payment on customer purchasing satisfaction in online market transaction. 2023 International Conference on Digital Business and Technology Management (ICONDBTM) (pp. 1–6). IEEE.

35.

Haryono

Setiawan

A. I.

Wahyudi

Amperawati

E. D.

(2024). E-trust and e-service quality on e-loyalty: Role of e-satisfaction and customer privacy. Journal of Ecohumanism, 3(4), 3130–3143. https://doi.org/10.62754/joe.v3i4.3831

36.

(2023). Research on the fuzzy comprehensive evaluation of consumer satisfaction with mobile e-commerce platforms. International Journal of Web Based Communities, 19(1), 28–38. https://doi.org/10.1504/ijwbc.2023.128407

37.

Hisyam

Manuaba

I. B. K.

(2022). Integration model of multiple payment gateways for online split payment scenario. 2022 International Conference on Information Management and Technology (ICIMTech) (pp. 122–126). IEEE.

38.

Huddin

M. N.

Kurnia

Deviyantoro

Nafiudin

(2024). The relationship between customer experience, customer satisfaction, customer trust, and customer loyalty in tourism destination. Diponegoro International Journal of Business, 6(2), 142–154. https://doi.org/10.14710/dijb.6.2.2023.142-154

39.

Hulland

(1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195–204.

40.

Ingale

Paliwal

Jha

Kodlekere

Shedge

(2024). Delighting customers: Evaluating service quality and customer satisfaction of self-checkout users in sports retail. Marketing, 20(3), 97–109. https://doi.org/10.21511/im.20(3).2024.08

41.

Jabbari

Bussracumpakorn

(2022). The effects of website design quality on the success rate of online web design agency. Usability and User Experience, 39, 153–160. https://doi.org/10.54941/ahfe1001703

42.

Jacoby

(1972). Is there an optimal number of alternatives for Likert-scale items? Effects of testing time and scale properties. Journal of Applied Psychology, 56(6), 506–509. https://doi.org/10.1037/h0033601

43.

Jogoo

Golamgous

I. S.

Chuttur

(2019). From desktop to mobile view: A simplified approach to mobile website development. 2019 Conference on Next Generation Computing Applications (NextComp) (pp. 1–6,). IEEE.

44.

Joseph

Sajikumar

(2024). Electronic banking attributes, customer trust and loyalty on customer retention-a case study among the customers of commercial banks. International Journal of Management, Economics and Commerce, 1(2), 54–66. https://doi.org/10.62737/hkr1gg87

45.

Juwaini

Chidir

Novitasari

Iskandar

Hutagalung

Pramono

Maulana

Safitri

Fahlevi

Sulistyo

(2022). The role of customer e-trust, customer e-service quality and customer e-satisfaction on customer e-loyalty. International Journal of Data and Network Science, 6(2), 477–486. https://doi.org/10.5267/j.ijdns.2021.12.006

46.

Kedaton

N. R. S.

Sadat

A. M.

Sari

D. A. P.

(2024). The effect of e-trust, information quality and user interface quality on e-customer loyalty through e-satisfaction as an intervening variable:(Study on Tokopedia Users in Jabodetabek). International Journal of Education, Social Studies, And Management (IJESSM), 4(2), 720–733. https://doi.org/10.52121/ijessm.v4i2.350

47.

Khowjoy

Petmee

Phakamach

Sriplang

Kaewsrem

Chayomchai

(2023). Factors influencing brand loyalty: The mediating effect of brand satisfaction and trust. Polish Journal of Management Studies, 27(2), 136–154. https://doi.org/10.17512/pjms.2023.27.2.09

48.

Kim

D. J.

Ferrin

D. L.

Rao

H. R.

(2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544–564. https://doi.org/10.1016/j.dss.2007.07.001

49.

Kock

(2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of E-Collaboration (IJEC), 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101

50.

Kulkarni

Fadewar

(2017). Pedometric CAPTCHA for mobile Internet users. 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 600–604). IEEE.

51.

Kustiani

Setyawati

Widiawati

Lestari

Endayani

(2022). Service quality and customer trust for customer satisfaction: The mediating role of homestay corporate image. International Journal of Business and Administrative Studies, 8(3), 168–175. https://doi.org/10.20469/ijbas.8.10004-3

52.

Lam

(2020). Why has income inequality increased while education inequality has decreased in many developing countries? In Asefa

Huang

W.-C.

(Eds.), The political economy of inequality: US and global dimensions (pp. 79–116). WE Upjohn Institute for Employment Research. https://doi.org/10.17848/9780880996730.ch5

53.

Langdale

(2019). Electronic commerce: Global-local relationships in financial services. In Felsenstein

Taylor

(Eds.), Promoting local growth (pp. 209–226). Routledge. https://doi.org/10.4324/9781315186849-14

54.

Lăzăroiu

Neguriţă

Grecu

Mitran

P. C.

(2020). Consumers’ decision-making process on social commerce platforms: Online trust, perceived risk, and purchase intentions. Frontiers in Psychology, 11, Article 890. https://doi.org/10.3389/fpsyg.2020.00890

55.

Lee

S.-J.

You

Y.-Y.

Kim

S.-H.

(2018). The mediation effect of technical support service quality between product quality and relationship performance in B2B marketplace-focus on face to face service quality and non-face to face service quality. Indian Journal of Public Health Research & Development, 9(8), 432–439. https://doi.org/10.5958/0976-5506.2018.00772.6

56.

Lee

Park

Lee

(2022). The effect of e-commerce service quality factors on customer satisfaction, purchase intention, and actual purchase in Uzbekistan. Global Business & Finance Review, 27(3), Article 56. https://doi.org/10.17549/gbfr.2022.27.3.56

57.

Lin

Zhu

(2024). The impact of online negative hotel reviews and merchant response strategies on potential consumers’ purchase intentions: A perceived trust perspective. International Journal of Social Science Studies 12, 1–16. https://doi.org/10.11114/ijsss.v12i2.6756

58.

Lipp

Rudack

Vroomen

Bührig-Polaczek

(2020). When to collect what? Optimizing data load via process-driven data collection. In Proceedings of the 22nd International Conference on Enterprise Information Systems (ICEIS 2020) (Vol. 1, pp. 220–225). SciTePress.

59.

Ludwina

E. P.

Dimastuty

Despina

(2022). The influence of information quality, system quality, service quality towards customer satisfaction and trust and their impact towards Finpay money continuous use intention. 2022 7th International Conference on Business and Industrial Research (ICBIR) (pp. 46–50). IEEE.

60.

Maguire

(2022). Interface design and usability evaluation of voice-based user interfaces. In Soares

M. M.

Rebelo

Ahram

T. G.

(Eds.), Handbook of usability and user-experience (pp. 117–134). CRC Press. https://doi.org/10.1201/9780429343513-10

61.

Mahsyar

Surapati

(2020). Effect of service quality and product quality on customer satisfaction and loyalty. International Journal of Economics, Business and Accounting Research (IJEBAR), 4(01), 204–211. https://doi.org/10.54660/anfo.2022.3.2.1

62.

Marlina

W. A.

(2024). Investigating the determinants of online fashion purchases: Insights into consumer intention in the e-commerce marketplace. Jurnal Manajemen dan Kewirausahaan, 12(1), 89–99. https://doi.org/10.26905/jmdk.v12i1.12289

63.

Meiyantika

A. S.

Rusdianto

(2024). The influence of service quality, security, and product features on user customer satisfaction BRImo application (Study of BRImo Application Users in Madiun Regency). Formosa Journal of Multidisciplinary Research, 3(3), 843–854. https://doi.org/10.55927/fjmr.v3i3.8582

64.

Merve Demirci

. (2021). Online shopping web sites’ perceived usability: A case study with Turkish shopping related web sites. In Ahram

T. Z.

Falcão

C. S.

(Eds.), Advances in Usability, User Experience, Wearable and Assistive Technology: Proceedings of the AHFE 2021 Virtual Conferences on Usability and User Experience, Human Factors and Wearable Technologies, Human Factors in Virtual Environments and Game Design, and Human Factors and Assistive Technology (Vol. 275, pp. 719–726). Springer.

65.

Miao

Jalees

Zaman

S. I.

Khan

Hanif

N.-u.-A.

Javed

M. K.

(2022). The influence of e-customer satisfaction, e-trust and perceived value on consumer’s repurchase intention in B2C e-commerce segment. Asia Pacific Journal of Marketing and Logistics, 34(10), 2184–2206. https://doi.org/10.1108/APJML-03-2021-0221

66.

Mogea

G. E.

(2022). The effect of customer perceived value, customer trust, and customer satisfaction on repurchase intention of services provided by shopee online shop. Jurnal EMBA: Jurnal Riset Ekonomi, Manajemen, Bisnis dan Akuntansi, 10(2), 531–540. https://doi.org/10.35794/emba.v10i2.40268

67.

Muthén

L. K.

Muthén

B. O.

(2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, 9(4), 599–620. https://doi.org/10.1207/S15328007SEM0904_8

68.

Naini

N. F.

Santoso

Andriani

T. S.

Claudia

U. G.

Nurfadillah

(2022). The effect of product quality, service quality, customer satisfaction on customer loyalty. Journal of Consumer Sciences, 7(1), 34–50. https://doi.org/10.29244/jcs.7.1.34-50

69.

Nikose

(2022). Use of IT in information gathering of schedule tribe researchers: A study. International Journal of Research in Library Science, 8(4), 105–109. https://doi.org/10.26761/ijrls.8.4.2022.1596

70.

Novialeta

Slamet

M. R.

(2020). The effect of security, information availability, delivery, quality, price and time on online shopping consumer satisfaction. Proceedings of the 2nd International Conference on Applied Economics and Social Science - Shaping a Better Future Through Sustainable Technology (pp. 5–11). SciTePress.

71.

Octadyla

M. M.

Mismiwati

P. C. A.

(2024). The mediating role of e-trust between online customer review, online customer rating, and e-service quality on customer loyalty on the e-commerce site shopee (Case Study of the Seberang Ulu Community of Palembang City). International Journal of Multidisciplinary Research and Analysis, 7(3), 896–905. https://doi.org/10.47191/ijmra/v7-i03-06

72.

Pavlou

P. A.

(2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134. https://doi.org/10.1080/10864415.2003.11044275

73.

Pohjanen

(2019). The benefits of search engine optimization in Google for businesses [Master’s thesis, University of Oulu]. https://doi.org/10.4018/9781591403456.ch002

74.

Prasojo

Sari

D. K.

(2023). Peran brand image, online trust, dan customer satisfaction dalam membangun repurchase intention pada konsumen e-commerce shopee di Sidoarjo. Innovative Technologica: Methodical Research Journal, 2(1), 13–13. https://doi.org/10.21070/ups.703

75.

Quintus

Mayr

Hofer

K. M.

Chiu

Y. T.

(2024). Managing consumer trust in e-commerce: Evidence from advanced versus emerging markets. International Journal of Retail & Distribution Management, 52(10), 1038–1056. https://doi.org/10.1108/IJRDM-10-2023-0609

76.

Rahman

Ahmed

Daiyan

A. I.

Al Mamun

M. A.

(2022). Impact of intermediary and seller trust on consumer repurchase and E-WOM intentions: Demographics moderating trust transference. Journal of Electronic Commerce in Organizations (JECO), 20(1), 1–19. https://doi.org/10.4018/jeco.292471

77.

Reynaldi

Wuisan

(2023). Pengaruh service quality, perceived value, customer satisfaction Terhadap post-purchase intention pada apple di Indonesia [The effect of service quality, perceived value, customer satisfaction on post-purchase intention for apple in Indonesia]. Indonesian Marketing Journal, 3(1), 50. https://doi.org/10.19166/imj.v3i1.7020

78.

Rico

M. T.

Wahyoedi

Purnama

E. D.

(2019). The effects of trust, service quality and perceived value on satisfaction and their impact on loyalty. Proceedings of the 7th International Conference on Entrepreneurship and Business Management (ICEBM Untar) (pp. 325–330). SciTePress.

79.

Rustandi

Hindarsah

Jamaludin

(2024). Electronic customer relationship management systems in e-commerce platforms: Exploring the antecedents of technology acceptance and customer satisfaction. Media Ekonomi dan Manajemen, 39(1), 1–22. https://doi.org/10.56444/mem.v39i1.3771

80.

Ryan

(2019). Trust and distrust in digital economies. Routledge. https://doi.org/10.4324/9781351104845-9

81.

Saha

Nath

A. K.

Salehi-Sangari

(2012). Evaluation of government e-tax websites: An information quality and system quality approach. Transforming Government: People, Process and Policy, 6(3), 300–321. https://doi.org/10.1108/17506161211251281

82.

Saleem

Bilal

Topor

D. I.

Căpu⋅neanu

(2022). The impact of website quality on customer satisfaction and eWOM in online purchase intention: The moderating role of gender in risk-taking. Frontiers in Psychology, 13, Article 945707. https://doi.org/10.3389/fpsyg.2022.945707

83.

Saoula

Shamim

Suki

N. M.

Ahmad

M. J.

Abid

M. F.

Patwary

A. K.

Abbasi

A. Z.

(2023). Building e-trust and e-retention in online shopping: The role of website design, reliability and perceived ease of use. Spanish Journal of Marketing-ESIC, 27(2), 178–201. https://doi.org/10.1108/SJME-07-2022-0159

84.

Sari

N. K. M.

Darma

G. S.

(2022). Website quality, brand image, brand trust, repurchase intentions, and customer satisfaction. Jurnal Manajemen Bisnis, 19(2), 260–283. https://doi.org/10.38043/jmb.v19i2.4611

85.

Sarstedt

Ringle

C. M.

Hair

J. F.

(2021). Partial least squares structural equation modeling. In Homburg

Klarmann

Vomberg

(Eds.), Handbook of market research (pp. 587–632). Springer.

86.

Sastika

Kusumahadi

Hanifa

F. H.

Marcelino

(2023). E-commerce website quality: Usability, information, service interaction & visual quality on customer satisfaction. 2023 11th International Conference on Cyber and IT Service Management (CITSM) (pp. 1–7). IEEE.

87.

Savastano

Anagnoste

Biclesanu

Amendola

(2025). The impact of e-commerce platforms’ quality on customer satisfaction and repurchase intention in post COVID-19 settings. The TQM Journal, 37(7), 2074–2093. https://doi.org/10.1108/TQM-04-2024-0143

88.

Sayem

Islam

Uddin

M. R.

Promy

J. S.

(2025). Determinants of e-commerce customer satisfaction: Mediating role of IT innovation acceptance. International Journal of Quality & Reliability Management, 42(1), 86–106. https://doi.org/10.1108/IJQRM-10-2023-0332

89.

Shao

Yang

(2020). The study of relationships between user experience, perceived value and purchase intention of sportswear online customer. In Li

Yuan

Kerre

E. E.

(Eds.), Developments of Artificial Intelligence Technologies in Computation and Robotics: Proceedings of the 14th International FLINS Conference (FLINS 2020) (pp. 540–549). World Scientific.

90.

Shopify. (2025). Global ecommerce sales growth report. https://shopify.com

91.

Sihombing

Sari

D. K.

Games

(2023). Analisis service quality Dan customer perceived value Terhadap customer satisfaction Melalui customer trust Sebagai Variabel Mediasi. Journal Publicuho, 6(4), 1301–1310. https://doi.org/10.35817/publicuho.v6i4.270

92.

Sinaga

B. D.

Rizal

(2024). Satisfaction positively impacts customer loyalty and mediates the relationship between perceived service quality, perceived relationship quality, and customer loyalty, but does not mediate the relationship between perceived value and customer loyalty. Journal of Economic, Bussines and Accounting (COSTING), 7(4), 8713–8732. https://doi.org/10.31539/costing.v7i4.10490

93.

Sobel

M. E.

(1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290–312. https://doi.org/10.2307/270723

94.

Stamkou

Saprikis

Fragulis

G. F.

Antoniadis

(2025). User experience and perceptions of ai-generated e-commerce content: A survey-based evaluation of functionality, aesthetics, and security. Data, 10(6), Article 89. https://doi.org/10.3390/data10060089

95.

Statistics

(2025). Sales & user growth trends. https://capitaloneshopping.com/research/ecommerce-statistics/

96.

Suchanek

Kralova

(2023). Relationship between customer expectations and financial performance of food industry businesses in a customer satisfaction model. Economic and Business Review, 25(2), 103–117. https://doi.org/10.15458/2335-4216.1320

97.

Sudharshan

(2020). Customer information processing and decision-making in CTEs. In Marketing in customer technology environments (pp. 17–47). Emerald Publishing Limited. https://doi.org/10.1108/978-1-83909-600-620201003

98.

Sudira

Diar

A. L.

Ruldeviyani

(2019). Instagram sentiment analysis with naive bayes and KNN: Exploring customer satisfaction of digital payment services in Indonesia. 2019 International Workshop on Big Data and Information Security (IWBIS) (pp. 21–26). IEEE.

99.

Tan

(2019). The roles of information technology in customer relationship performance, employee user satisfaction, service quality and customer satisfaction [Master’s thesis, Iowa State University]. https://doi.org/10.31274/cc-20240624-1490

100.

Tandon

Kiran

Sah

A. N.

(2018). The influence of website functionality, drivers and perceived risk on customer satisfaction in online shopping: An emerging economy case. Information Systems and E-Business Management, 16, 57–91. https://doi.org/10.1007/s10257-020-00472-1

101.

Wang

Pang

(2024). Research on influence mechanism of consumer satisfaction evaluation behavior based on grounded theory in social e-commerce. Systems, 12(12), Article 572. https://doi.org/10.3390/systems12120572

102.

Widjaja

Astuti

Manan

(2020). The relationship between e-service quality and customer satisfaction evidence on online transportation services in Indonesia. In Proceedings of the 1st International Conference on IT, Communication and Technology for Better Life (ICT4BL 2019) (pp. 183–188). SciTePress. https://doi.org/10.5220/0008931501830188

103.

Wixom

B. H.

Todd

P. A.

(2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85–102. https://doi.org/10.1287/isre.1050.0042

104.

(2022). Research on platform loyalty and influencing factors of cross-border online shopping consumers. In Proceedings of the International Conference on Big Data Economy and Digital Management (BDEDM 2022) (pp. 47–54). SciTePress. https://doi.org/10.5220/0011156600003440

105.

Xie

C. X.

De Simoni

Eldridge

Pinnock

Relton

(2024). Development of a conceptual framework for defining trial efficiency. PLOS ONE, 19(5), e0304187. https://doi.org/10.1371/journal.pone.0304187

106.

Yousuf

Khan

D. A.

Jeelani

(2021). Security and privacy concerns for blockchain while handling healthcare data. In Idrees

S. M.

Agarwal

Alam

M. A.

(Eds.), Blockchain for healthcare systems (pp. 177–192). CRC Press. https://doi.org/10.1201/9781003141471-12

107.

Zeynalov

(2023). The impact of digital payments on the growth of cashless payments and the factors limiting access to financial services. Agora International Journal of Economical Sciences, 17(2), 181–186. https://doi.org/10.15837/aijes.v17i2.6455

108.

Zhou

Xing

(2022). Study on the impact of fresh food e-commerce logistics service quality on customer satisfaction, customer trust, and repurchase intention. Journal of International Trade and Commerce, 18(5), 19–35. https://doi.org/10.16980/jiyc.22.5.202210.19

109.

Zhou

(2013). An empirical examination of continuance intention of mobile payment services. Decision support systems, 54(2), 1085–1091. https://doi.org/10.1016/j.dss.2012.10.034

Evaluating E-Commerce Satisfaction: The Impact of Trust,Information Quality,Usability,and Perceived-to-Actual Performance

Abstract

Keywords

Introduction

Research Gap

Research Objective

Literature Review

Website Usability (WU)

Information Quality (IQ)

Payment Security (PS)

E-Trust (ET)

Perceived to Actual Performance (PAP)

E-Commerce Satisfaction (ECS)

Conceptual Framework and Hypothesis Development

Payment Security (PS) With E-Commerce Customer Satisfaction (ECS)

Information Quality (IQ) With E-Commerce Customer Satisfaction (ECS)

Website Usability (WU) With Perceive to Actual Performance (PAP)

Website Usability (WU) With E-Commerce Customer Satisfaction (ECS)

Perceived to Actual Performance (PAP) With E-Commerce Customer Satisfaction (ECS)

E-Trust (ET) with E-Commerce Customer Satisfaction (ECS)

Perceive to Actual Performance (PAP) With E-Commerce Customer Satisfaction (ECS), Website Usability (WU)

E-Trust (ET) With E-Commerce Customer Satisfaction (ECS), Payment Security (PS)

Research Methodology

Research Design

Sampling Strategy and Sample Size Justification

Data Collection

Analysis

Findings

Measurement Model

Structural Model

Mediation and Moderation Analysis

Discussion

Theoretical Implications

Practical Implications

Limitations and Future Research

Conclusion

Footnotes

Appendix A

Acknowledgements

ORCID iDs

Ethical Considerations

Consent to Participate

Consent for Publication

Author Contributions

Funding

Declaration of Conflicting Interests

Data Availability Statement

References