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MIC3ST 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
Virtual Conference on Melaka International Social Sciences, Science and Technology 2025
ISSN: 2454-6186 | DOI: 10.47772/IJRISS | Special Issue | Volume IX Issue XXIII October 2025
Challenging the Mediator: How Users' Goals Influence Perceived
System Usability, Outcomes Satisfaction, and Platform Usage in
Digital Commerce
Haslinda Musa
1
, Umi Kartini Rashid
2
, Mohd Syafiq Md Taib
1
, Nur Zafirah A Khadar
3
1
Universiti Teknikal Malaysia Melaka
2
Universiti Tun Hussein Onn, Johor
3
ERADA Solutions Sdn Bhd. Ayer Keroh Melaka
DOI:
https://dx.doi.org/10.47772/IJRISS.2025.923MIC3ST250030
Received: 12 September 2025; Accepted: 20 September 2025; Published: 24 October 2025
ABSTRACT
The purpose of this research is to examine the effects of the core design characteristics including loading time,
the attributes of electronic commerce, the dynamics of pricing strategies, and the usability of the website, on
satisfaction and popularity of the platform and the moderating role of device type. A mediated-moderated model
was tested using information gathered from 346 online shoppers in Malaysia using Partial Least Squares
Structural Equation Modeling (PLS-SEM). Loading time and usability have a significant impact to satisfaction
for usability user satisfaction has a negative relationship, indicating possible impacts of usability complexity.
Interestingly, dynamic pricing and e-commerce features were not found to enhance satisfaction perceptibly, and
satisfaction itself did not mediate the relationship between design features and stage popularity. Instead, user
friendliness and e-commerce capability influenced popularity because of usability within online services, and it
is obvious in case of popularity. In addition, device type had a moderating effect on the link between loading
time and satisfaction which confirms the popularity of mobile sensitivity in usability research. Although
discriminant validity is questioned the model shows very high predictive power (R² > 0.97), providing novel
insights into direct-only pathways in platform evaluation. The study contributes to the digital commerce
literature by questioning the mediation role of user satisfaction and suggesting the contextual relevance of
device-led usability expectations. Theoretical and design strategy considerations for mobile-first markets are
considered.
Keywords: e-commerce usability, platform popularity, user satisfaction, mobile shopping, PLS-SEM
INTRODUCTION
An easy way to comply with the conference paper formatting requirements is to use this document as a template
and simply type your text into it. The rapid expansion of e-commerce sites has revolutionized worldwide
consumer behavior and digital retail ecosystems. In Southeast Asia, and Malaysia specifically, the advent of
mobile-first commerce has changed the way consumers interact with digital ecosystems like Shopee, Lazada and
Amazon. Existing studies focused on the determinants of e-commerce adoption by focusing on technological
and marketing antecedents, and mechanisms by which platform features can affect user satisfaction and user-
perceived platform popularity have not been examined in detail, especially in mobile-first markets.
Problem Statement
Despite the maturation of online retail systems, consumer retention and sustained platform engagement continue
to challenge even the most dominant e-commerce providers. The success of an e-commerce platform is not
merely a function of its features or price competitiveness but increasingly hinges on how users perceive their
interaction experiences including responsiveness, usability, trust, and convenience. As platforms become more
technologically sophisticated, users develop elevated expectations for seamless interaction, particularly on
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MIC3ST 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
Virtual Conference on Melaka International Social Sciences, Science and Technology 2025
ISSN: 2454-6186 | DOI: 10.47772/IJRISS | Special Issue | Volume IX Issue XXIII October 2025
mobile devices. However, scholarly understanding of which platform attributes truly drive satisfaction and
popularity remains fragmented, often limited to studies focused on adoption intent or transactional behavior,
rather than broader platform loyalty or reputation effects.
A common theoretical assumption in e-commerce literature is that user satisfaction mediates the relationship
between platform features and platform success outcomes such as loyalty, revisit intention, or popularity. Models
such as the Technology Acceptance Model (TAM) and Expectation Confirmation Theory (ECT) have long
positioned satisfaction as a pivotal construct linking system qualities to behavioral outcomes (Bhattacherjee,
2001). Yet, recent empirical evidence has begun to question the centrality of satisfaction, particularly in
environments where performance factors such as loading time, dynamic pricing, and usability may exert direct
influence on consumer perception and behavior (Lemon & Verhoef, 2016; Wang et al., 2022).
In addition, while usability is typically assumed to enhance satisfaction, some emerging studies suggest that
excessive feature complexity or cognitive load may negatively affect satisfaction, especially for mobile users
who interact with platforms in time-constrained or fragmented contexts (Tarute et al., 2017). Furthermore, the
role of device types of an increasingly relevant moderator in the omnichannel era remains under-theorized in
structural models of platform evaluation.
This problem exists within a broader academic gap in the post-adoption phase of e-commerce interaction. While
early models have thoroughly examined adoption drivers, fewer studies have focused on how users evaluate
platforms after adoption, and which platform elements predict perceived popularity or reputational success. As
the field moves beyond clickstream analysis and transaction-level data, scholars have called for richer models
that capture experiential constructs and test complex interrelationships among usability, performance, and
attitudinal outcomes (Venkatesh et al., 2016; Grewal et al., 2021).
This study narrows its focus to the Malaysian e-commerce landscape, using Shopee and Lazada as contextual
anchors due to their market dominance and mobile-first infrastructure. It investigates four core platform
characteristics loading time, features of e-commerce, dynamic pricing strategies, and website usability and their
effects on user satisfaction and perceived platform popularity. The model includes user satisfaction as a
mediating variable, and device type as a moderator, using a robust PLS-SEM approach to test direct, indirect,
and interaction effects.
The theoretical problem the potential overreliance on satisfaction as a mediating construct is directly addressed
through the empirical testing of both direct and mediated paths. Additionally, the contextual problem the lack of
clarity around how platform design features impact popularity in mobile-first contexts is reflected in the inclusion
of device type as a moderator. This study builds a structural model that allows these questions to be tested
simultaneously, thereby providing clarity on whether user satisfaction retains its mediating power, or whether
design attributes have independent influence on how platforms are perceived.
By empirically challenging traditional mediation assumptions, this study expects to clarify the role of user
satisfaction in e-commerce evaluation models. It seeks to provide evidence on whether usability, pricing, and
features affect platform success directly or indirectly, and how these relationships are influenced by the user’s
device context. The findings have implications not only for academic theory such as refining post-adoption
models but also for designers and strategists aiming to optimize digital platform experiences, especially in
mobile-first environments.
Research Objectives
In response to these gaps, the study is designed to achieve the following objectives:
1) To examine the direct effects of platform attributes (loading time, features, pricing, and usability) on user
satisfaction.
2) To assess whether user satisfaction mediates the relationship between platform attributes and perceived
platform popularity.
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MIC3ST 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
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3) To evaluate the direct influence of user satisfaction on platform popularity.
4) To determine the moderating role of device type on the relationship between loading time and user
satisfaction.
5) To validate a structural model that explains the antecedents of platform popularity in a mobile-first e-
commerce context.
Research Gap and Significant
Despite the extensive research on e-commerce adoption and consumer behavior, there remain critical gaps in
understanding how specific platform characteristics influence post-adoption outcomes such as user satisfaction
and perceived platform popularityparticularly in mobile-first digital environments. While traditional models such
as the Technology Acceptance Model (TAM) and Expectation-Confirmation Theory (ECT) emphasize user
satisfaction as a mediating construct (Bhattacherjee, 2001; Davis, 1989), emerging studies suggest that this
assumption may be overstated in today’s complex e-commerce ecosystems (Lemon & Verhoef, 2016; Venkatesh
et al., 2016).
Recent research has pointed out the lack of clarity in the mediating role of satisfaction, especially when users
interact with platforms that are functionally rich but cognitively demanding (Tarute, Nikou, & Gatautis, 2017;
Grewal, Ailawadi, Harlam, Kopalle, & Raju, 2021). For instance, website usability has long been associated
with improved satisfaction and loyalty (Cyr, 2008), yet more recent findings show that usability enhancements
if perceived as complex or unintuitive can paradoxically reduce satisfaction due to cognitive overload (Wang,
Xu, & Gao, 2022). This contradiction highlights a need to reassess how usability, features, and performance
metrics influence satisfaction and reputation in modern digital platforms.
Additionally, platform popularity, a construct often assumed to emerge from satisfaction and engagement, is
under-theorized in e-commerce literature. Most models focus on behavioral outcomes like purchase intention or
loyalty, without accounting for reputational or communal evaluations of platforms (Pentina, Zhang, &
Basmanova, 2013). The mechanisms through which consumers perceive a platform as “popular” remain largely
untested, particularly in relation to observable platform characteristics such as loading speed, pricing dynamics,
and usability.
Furthermore, device type, while recognized as a key contextual variable in human-computer interaction (HCI),
remains underexplored as a moderator in structural models of digital commerce. With increasing dependence on
mobile devices in Southeast Asia (Google, Temasek, & Bain, 2022), understanding how device context
influences satisfaction formation is essential for platform designers and digital strategists. Yet, most existing
models treat technology use as homogeneous, ignoring the profound impact that mobile device constraints (e.g.,
screen size, latency, bandwidth) have on user experience.
Taken together, these gaps underscore the need for an integrated model that not only tests the traditional
mediating role of satisfaction but also accounts for direct effects of platform characteristics and the moderating
impact of device type. This research addresses these critical gaps by constructing and validating a PLS-SEM
model that incorporates both mediated and moderated pathways, thus contributing to a more nuanced
understanding of platform evaluation in post-adoption contexts.
From a practical standpoint, this study is significant for e-commerce managers, UX designers, and digital
strategists. It provides evidence on which design elements truly matter to users, whether satisfaction is still the
central metric, and how mobile users may perceive performance and usability differently. These insights are
invaluable for optimizing digital retail platforms in competitive, rapidly evolving markets like Southeast Asia.
LITERATURE REVIEW
The dynamic and competitive nature of the e-commerce industry demands that digital platforms offer not only
transactional efficiency but also highly optimized user experiences. Contemporary research in e-commerce and
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MIC3ST 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
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ISSN: 2454-6186 | DOI: 10.47772/IJRISS | Special Issue | Volume IX Issue XXIII October 2025
information systems has begun shifting from adoption-focused models to investigations into post-adoption user
behavior, satisfaction, and platform perception. This study contributes to that shift by investigating how key
platform characteristics loading time difference, features of e-commerce, dynamic pricing strategies, and website
usability affect user satisfaction and, subsequently, platform popularity, with device type introduced as a
moderator. Each construct is reviewed below considering relevant literature.
Loading Time Difference
Despite the maturation of online retail systems, consumer Website loading speed has long been recognized as a
core component of system quality (DeLone & McLean, 2003). Fast-loading platforms enhance the perception of
technological efficiency and reliability, both of which are critical to forming positive user experiences. In e-
commerce, delays as minimal as a few seconds have been shown to reduce customer satisfaction and increase
bounce rates (Park & Kim, 2003). Wang et al. (2022) found that interface load time significantly affects mobile
users’ satisfaction, emphasizing the urgency of system responsiveness in mobile contexts. As users increasingly
access e-commerce platforms through smartphones with variable bandwidth conditions, the difference in loading
times becomes a salient determinant of platform evaluation. Hence, this study posits that loading time directly
influences user satisfaction and indirectly affects platform popularity.
Features of E-Commerce
Platform features refer to the functionalities and tools available to users, such as wish lists, multiple payment
options, tracking systems, reviews, and advanced filtering. These features contribute to perceived usefulness, a
central element in the Technology Acceptance Model (Davis, 1989), and later refined models like UTAUT
(Venkatesh et al., 2016). Kim and Stoel (2004) found that well-integrated features improve perceived enjoyment
and satisfaction in apparel shopping platforms. More recent work by Liu, Feng, and Hu (2020) confirmed that
feature richness is positively associated with post-adoption satisfaction and perceived platform value. However,
overloading users with excessive or poorly integrated features may backfire, leading to usability concerns. This
dual potential makes it critical to test the feature satisfaction popularity linkage.
Dynamic Pricing Strategies
Platform features refer to the functionalities and tools Dynamic pricing the use of algorithms to alter prices in
real time based on demand, competition, or user behavior is now common in digital commerce. While some
users appreciate the perception of “smart deals,” others may react negatively to perceived unfairness or
manipulation (Grewal et al., 2021). Price fairness has been shown to moderate the impact of dynamic pricing on
trust and satisfaction (Xia et al., 2004). Moreover, research by Chen and Xie (2008) revealed that pricing
transparency and perceived fairness directly influence satisfaction and behavioral loyalty. In Southeast Asian
markets, where price sensitivity is high, the psychological response to dynamic pricing may differ from Western
contexts, necessitating localized empirical testing.
Website Usability
Usability encompasses ease of navigation, clarity of layout, and responsiveness of user interfaces (Nielsen,
2000). In e-commerce, it is one of the most consistent predictors of user satisfaction and loyalty (Cyr, 2008;
Rose et al., 2012). However, recent studies show that the relationship may not always be linear. Tarute et al.
(2017) found that overengineered or complex interfaces, especially on mobile devices, can negatively affect
satisfaction, indicating a potential threshold effect. This is particularly relevant in high-context, mobile-first
markets, where usability must balance feature richness with cognitive simplicity. This study explores whether
the traditionally assumed positive relationship between usability and satisfaction still holds true.
User Satisfaction
User satisfaction is defined as the positive emotional response resulting from the fulfillment of user expectations
(Bhattacherjee, 2001). It is central to the Expectation Confirmation Theory (ECT) and has been widely used as
a predictor of continuance intention, loyalty, and advocacy in digital platforms. However, emerging models
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MIC3ST 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
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ISSN: 2454-6186 | DOI: 10.47772/IJRISS | Special Issue | Volume IX Issue XXIII October 2025
suggest that satisfaction is not always a necessary mediator; instead, design quality or usability may directly
shape user evaluations (Lemon & Verhoef, 2016). This raises the need to empirically test whether satisfaction
still acts as a mediating mechanism between platform characteristics and outcomes such as popularity.
User Satisfaction
User satisfaction is defined as the positive emotional response resulting from the fulfillment of user expectations
(Bhattacherjee, 2001). It is central to the Expectation Confirmation Theory (ECT) and has been widely used as
a predictor of continuance intention, loyalty, and advocacy in digital platforms. However, emerging models
suggest that satisfaction is not always a necessary mediator; instead, design quality or usability may directly
shape user evaluations (Lemon & Verhoef, 2016). This raises the need to empirically test whether satisfaction
still acts as a mediating mechanism between platform characteristics and outcomes such as popularity.
Platform Popularity
Platform popularity, though less frequently modeled than adoption or loyalty, is an important reputational
construct that reflects perceived dominance, preference, and user volume. It is shaped by social proof, platform
visibility, and collective user perception (Pentina et al., 2013). While popularity can be influenced by marketing
and network effects, platform usability and performance may also directly shape how users perceive popularity,
independent of personal satisfaction. This distinction is critical because it suggests a dual path model: one
through personal satisfaction and another through direct evaluation of features.
Device Type (Moderator)
Device type specifically mobile vs. desktop access has emerged as a critical moderator in digital behavior studies.
Mobile users often interact with platforms under different cognitive, temporal, and ergonomic constraints than
desktop users (Lee, Kim, & Sundar, 2015). As such, the importance of usability, responsiveness, and
performance is amplified in mobile contexts. Wang et al. (2022) argue that mobile device users are more sensitive
to interface loading and system delays, making it essential to test whether device type moderates the relationship
between platform features and satisfaction. This study explores whether loading time exerts a stronger effect on
satisfaction for mobile users, in line with HCI and UX theory.
The literature reveals strong theoretical and empirical support for each construct individually. However, the
combined model proposed in this study integrating usability, pricing, system performance, and platform features
into a moderated-mediation framework has not been extensively tested, especially in mobile-first, emerging
market contexts. There is a clear research opportunity to explore whether user satisfaction retains its central
mediating role or whether observable platform traits drive popularity perceptions directly. This study addresses
that gap through a structural model tested with empirical data from Malaysian e-commerce users.
Development of the Conceptual Framework
The preceding literature review highlights the theoretical and empirical foundations for examining the
relationships between platform characteristics, user satisfaction, and perceived platform popularity within e-
commerce environments. Across studies, constructs such as loading time, usability, feature richness, and pricing
strategy consistently emerge as influential factors in shaping consumer experiences (Cyr, 2008; Liu et al., 2020;
Wang et al., 2022). However, their precise roles especially in terms of direct versus mediated effects are still
subject to debate, particularly within mobile-first digital ecosystems such as those in Southeast Asia.
Notably, while traditional models such as TAM (Davis, 1989) and ECT (Bhattacherjee, 2001) place user
satisfaction at the center of behavioral outcomes, recent scholarship suggests that certain platform attributes may
influence evaluative outcomes directly, bypassing emotional or cognitive intermediaries (Lemon & Verhoef,
2016; Venkatesh et al., 2016). For example, in performance-critical tasks like mobile shopping, users may judge
a platform’s popularity not necessarily through reflective satisfaction but through observable and tangible design
elements like responsiveness, clarity, or feature utility (Rose et al., 2012; Grewal et al., 2021).
Building on this foundation, the present study positions user satisfaction as a potential mediator between four
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MIC3ST 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
Virtual Conference on Melaka International Social Sciences, Science and Technology 2025
ISSN: 2454-6186 | DOI: 10.47772/IJRISS | Special Issue | Volume IX Issue XXIII October 2025
key platform attributes loading time difference, features of e-commerce, dynamic pricing strategies, and website
usability and perceived platform popularity. While past models have conceptualized these relationships,
empirical testing that simultaneously integrates all four antecedents and both mediated and direct paths remains
scarce. Moreover, by introducing device type as a moderating variable, this study accounts for the differential
expectations and constraints encountered by mobile versus desktop users, a dimension often overlooked in prior
models.
In sum, the proposed conceptual framework reflects both theoretical continuity and empirical refinement. It tests:
1) Whether user satisfaction mediates the effects of design and performance attributes on platform
popularity.
2) Whether device type moderates the impact of loading time on satisfaction.
3) And whether some platform attributes exert direct effects on popularity, independent of user satisfaction.
The framework presented (e.g. Fig.1) integrates these theoretical relationships into a mediated-moderated
structural model that guides the subsequent hypotheses and analysis.
Fig 1. Conceptual Framework
User Satisfaction
User satisfaction is defined as the positive emotional response resulting from the fulfillment of user expectations
(Bhattacherjee, 2001). It is central to the Expectation Confirmation Theory (ECT) and has been widely used as
a predictor of continuance intention, loyalty, and advocacy in digital platforms. However, emerging models
suggest that satisfaction is not always a necessary mediator; instead, design quality or usability may directly
shape user evaluations (Lemon & Verhoef, 2016). This raises the need to empirically test whether satisfaction
still acts as a mediating mechanism between platform characteristics and outcomes such as popularity.
Hypothesis Development
Drawing on the literature and theoretical underpinnings discussed, this study formulates a series of hypotheses
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MIC3ST 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
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ISSN: 2454-6186 | DOI: 10.47772/IJRISS | Special Issue | Volume IX Issue XXIII October 2025
to examine the relationships between platform characteristics, user satisfaction, and perceived platform
popularity, with a focus on mediating and moderating effects.
Direct Effects
H1: Loading time difference has a significant effect on user satisfaction.
Page responsiveness and system speed are foundational elements of user experience. Research suggests that
delays in loading time negatively impact satisfaction, particularly for mobile users who expect instant
interactions (Wang et al., 2022). Thus, faster-loading platforms are expected to yield higher satisfaction.
H2: Features of e-commerce platforms have a positive impact on user satisfaction.
Feature-rich platforms offering functionalities like advanced search, order tracking, and personalized
recommendations contribute to user-perceived value and utility (Kim & Stoel, 2004). These features, when well-
integrated, are expected to enhance user satisfaction by meeting diverse needs.
H3: Dynamic pricing strategies significantly affect user satisfaction.
While dynamic pricing can increase perceived deal value, it may also raise concerns about fairness and
consistency (Grewal et al., 2021). As such, the study hypothesizes that dynamic pricing if perceived as fair can
enhance satisfaction, but empirical testing is needed to confirm its directionality.
H4: Website usability positively influences user satisfaction.
Usability, encompassing interface clarity, navigation ease, and responsiveness, directly contributes to a positive
user experience (Cyr, 2008). Platforms with higher usability are expected to yield greater satisfaction as users
navigate and complete tasks more efficiently.
H5: User satisfaction positively predicts the perceived popularity of e-commerce platforms.
User satisfaction, as an evaluative response, contributes to downstream perceptions such as loyalty, advocacy,
and platform reputation (Bhattacherjee, 2001). Satisfied users are more likely to view the platform as popular,
influential, or preferable among alternatives.
Mediating Effects of User Satisfaction
The mediating role of user satisfaction is well supported in models such as Expectation Confirmation Theory
and post-adoption technology use. This study extends that logic to test whether platform design features
influence perceived popularity indirectly via user satisfaction.
H6: User satisfaction mediates the relationship between loading time difference and e-commerce platform
popularity.
A faster loading time may not only improve the user experience but also enhance satisfaction, which in turn
influences platform popularity perception.
H7: User satisfaction mediates the relationship between e-commerce features and platform popularity.
The availability of diverse and user-centric features is expected to increase satisfaction, which may translate into
greater perceptions of platform popularity.
H8: User satisfaction mediates the relationship between dynamic pricing and platform popularity.
If users perceive dynamic pricing as advantageous or fair, it may increase satisfaction, which then positively
affects how popular the platform is perceived to be.
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H9: User satisfaction mediates the relationship between website usability and platform popularity.
Higher usability enhances satisfaction, which may serve as a channel through which users develop a favorable
perception of the platform’s popularity and influence.
Moderating Effect of Device Type
Device type introduces an important contextual dimension. Mobile users often experience platforms differently
due to screen size, data load speed, and context of use. This study introduces device type as a moderator to test
whether mobile users respond more strongly to performance cues.
H10: Device type moderates the relationship between loading time difference and user satisfaction, such that the
relationship is stronger for mobile users.
Given the limitations and expectations associated with mobile devices, loading time is hypothesized to have a
stronger effect on satisfaction for mobile users than for desktop users.
METHODOLOGY
This study adopts a quantitative, cross-sectional design aimed at empirically examining the interplay between e-
commerce platform characteristics, user satisfaction, and perceived platform popularity, incorporating the
moderating effect of device type. The research is grounded in an explanatory paradigm and is designed to test
both direct and mediated relationships within a complex model. A Partial Least Squares Structural Equation
Modeling (PLS-SEM) approach was selected due to its robustness in handling models with latent constructs,
reflective indicators, and both mediation and moderation structures. Furthermore, PLS-SEM is recognized for
its predictive orientation and suitability for theory development, particularly in studies with relatively modest
sample sizes and complex path models (Hair et al., 2021; Sarstedt et al., 2022).
The measurement instrument was developed through careful adaptation of previously validated scales from the
information systems and digital marketing literature. All variables were operationalized using five-point Likert
scales ranging from "strongly disagree" to "strongly agree." The constructs measured included loading time
difference, features of e-commerce, dynamic pricing strategies, website usability, user satisfaction, device type,
and platform popularity. Each construct was assessed using six items drawn and adapted from foundational and
recent works. For instance, user satisfaction items were adapted from Bhattacherjee’s (2001) expectation-
confirmation model, while website usability was guided by Cyr (2008) and usability heuristics grounded in
Nielsen’s dimensions. The construct of dynamic pricing drew on the work of Grewal et al. (2021), and the
operationalization of platform popularity was newly developed, reflecting consumer perception of reputation,
visibility, and frequency of platform usage. Face and content validity were assured through iterative reviews by
both academic experts in information systems and industry practitioners in the e-commerce domain.
The data collection employed purposive sampling to target Malaysian consumers with recent online shopping
experience, thereby ensuring relevance and contextual accuracy. The final sample comprised 346 valid
responses, a size deemed adequate for PLS-SEM analysis considering model complexity. Respondents were
recruited via online channels and completed a structured questionnaire administered through Google Forms. The
Malaysian context was chosen strategically due to the country's burgeoning mobile-first e-commerce ecosystem,
where platforms like Shopee dominate usage among young, tech-savvy consumers (Google, Temasek, & Bain,
2022). To mitigate common method bias, several procedural remedies were implemented. These included
randomization of question items, anonymity assurances, and the inclusion of reverse-coded items, in line with
recommendations by Podsakoff et al. (2003).
The data analysis followed the two-step PLS-SEM approach. First, the measurement model was evaluated to
confirm the reliability and validity of the latent constructs. Indicator reliability was confirmed as all factor
loadings exceeded the recommended threshold of 0.70. Internal consistency reliability was evidenced by high
Cronbach’s alpha and Composite Reliability (CR) values, both above 0.90 for all constructs. Convergent validity
was established as the Average Variance Extracted (AVE) values for each construct surpassed the benchmark
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of 0.50. However, discriminant validity presented challenges. While the Fornell–Larcker criterion was met in
most cases, the HTMT (Heterotrait–Monotrait) ratio revealed values exceeding the acceptable threshold of 0.90,
indicating conceptual overlap, particularly between user satisfaction and usability-related constructs. This issue,
while not uncommon in experience-based models, suggests that future refinements in construct dimensionality
may be necessary (Sarstedt et al., 2022).
Subsequently, the structural model was assessed to evaluate the hypothesized relationships among constructs.
The significance of path coefficients was tested using bootstrapping with 5,000 resamples, and multicollinearity
was examined via Variance Inflation Factor (VIF) values, all of which remained below the critical threshold of
10. The model’s explanatory power was demonstrated by the R² values of 0.970 for user satisfaction and 0.985
for platform popularity, indicating strong variance explanation. Mediation and moderation analyses were
conducted through indirect path assessments and interaction term modeling, respectively. Predictive relevance
was confirmed through Stone-Geisser’s Q² values, which were well above zero, supporting the model’s out-of-
sample predictive power (Shmueli et al., 2019). Additionally, f² effect sizes were reported to assess the practical
significance of predictors, with website usability and device type showing notably large effects on platform
popularity and user satisfaction, respectively.
Ethical considerations were adhered to throughout the research process. Participants were informed about the
voluntary nature of the study, assured of confidentiality, and provided consent prior to data collection. The study
followed the ethical standards set by the Declaration of Helsinki and was approved by the institutional ethics
committee of the authors' affiliated university.
In summary, the methodological framework of this study integrates robust measurement practices, a theoretically
grounded model structure, and advanced analytical techniques appropriate for modeling complex relationships
in e-commerce platform evaluation. The use of PLS-SEM not only facilitates the testing of both mediating and
moderating effects but also aligns with contemporary standards in digital commerce and information systems
research.
RESULTS
Demographic Profile of Respondents
Table I Profile Of Respondents
Profile
Category
Number of Respondents
% of Respondents
Gender
Male
158
45.7%
Female
188
54.3%
Age Group
18–24
186
53.8%
25–30
40
11.6%
Above 30
120
34.7%
Income Level
Below RM1,000
14
4.0%
RM1,000–3,000
117
33.8%
RM3,001–5,000
175
50.6%
Above RM5,000
40
11.6%
Online Shopping Frequency
Once a month or less
88
25.4%
2–3 times a month
113
32.7%
Weekly
79
22.8%
Almost daily
66
19.1%
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Most Used Platform
Amazon
18
5.2%
Shopee
239
69.1%
Lazada
59
17.1%
Others
30
8.7%
The research administered questionnaires that have been expanded on by other researchers, to 346 respondents
spanning ages, levels of education, statuses in tertiary, and other backgrounds as indicated in Table I. 54.3% of
the participants were females, while the remaining 45.7% were males. Young adults made up most of the
respondents with 53.8% aged between 18 24 years, 11.6% aged between 25 30 years and 34.7% above 30 years.
Among the respondents, 50.6% income received between RM3,001-5,000, 33.8% between RM1,000-3,000 and
4% received less than RM1,000 (see Table 1).
As for online shopping, 32.7% of the respondents shopped online 2-3 times a month, followed by 25.4% that
shopped online once a month or less. Some 22.8% shopped on a weekly basis and another 19.1% of the more
frequent shoppers shopped almost daily. Results show that Shopee was clearly the most used e-commerce
platform, with 69.1% of respondents who chose the ecommerce platform most widely used in that section,
followed by Lazada (17.1%), by other platforms (8.7%) and by Amazon (5.2%). The demographics indicate that
the sample is Malaysian consumers who are young to middle aged, with moderate income level, who regularly
shop online and use Shopee.
Descriptive Statistics for Study Variables
Table Ii Descriptive Statistics
(N, MINIMUM, MAXIMUM, MEAN, STANDARD DEVIATION)
Variable
N
Min
Max
Mean
SD
Loading Time Difference
346
1.17
5.00
4.4355
0.78493
Features of E-Commerce
346
1.00
5.00
4.4364
0.76432
Dynamic Pricing Strategies
346
1.00
5.00
4.5535
0.82578
Website Usability
346
1.00
5.00
4.5414
0.80621
User Satisfaction
346
1.00
5.00
4.3743
0.79704
Device Type
346
1.00
5.00
4.4417
0.80391
Platform Popularity
346
1.17
5.00
4.5255
0.81920
Valid N (listwise)
346
Descriptive statistics for all study variables are presented in Table II. It is found in the results that respondents
generally have high ratings for all these variables scored from 4.37 to 4.55 out of 5.00. The stratgy with maximum
mean score (4.55) is Dynamic Pricing Strategies followed by Website Usability (4.54) and Platform Popularity
(4.53). These high scores indicate that the respondents agreed strongly with statements associated with these
constructs. However, the mean score of User Satisfaction (4.37) had the lowest score among the results with
general agreement.
It was found that the standard deviations of all variables were low, ranging from 0.76 and 0.82; hence participants
were consistent in responses to all items. E-Commerce featured with Loading Time Difference and Features had
equal mean value i.e., 4.44 and 4.44 respectively, hence, indicating the equal importance to users. There is also
another indicator in Device Type rated high (mean = 4.44), which implies that respondents have a positive
opinion on the importance of mobile devices in online shopping. The results point out that pricing strategies and
website usability aspects are highly important to Malaysian e-commerce users, and these are ranked equally
important to the other aspects of online shopping.
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Evaluation of Measurement Model
PLS-SEM analysis (e.g. Fig. 2) was used to evaluate the measurement model. It is the necessary first step prior
to evaluating structural model, in such a way that it verifies whether the constructs are valid and reliable. The
analysis was geared towards confirming the relationships between the indicators and their respective latent
variables to confirm that the measurement instruments measure what they aim to measure.
Table Iii Construct Reliability, Validity, And Collinearity Test Result
Latent Variable
Indicators
Factor Loadings
(VIF)
(α)
(CR)
(AVE)
Dynamic Pricing
Strategies
DPS1
0.944
8.401
0.961
0.969
0.838
DPS2
0.915
4.720
DPS3
0.883
3.752
DPS4
0.923
9.299
DPS5
0.902
5.808
DPS6
0.924
9.763
Device Type
DT1
0.839
5.048
0.944
0.955
0.781
DT2
0.892
3.381
DT3
0.894
4.098
DT4
0.897
4.865
DT5
0.900
6.557
DT6
0.878
3.368
Features of E-
Commerce
FE1
0.845
4.162
0.932
0.947
0.749
FE2
0.762
2.612
FE3
0.872
4.717
FE4
0.893
5.703
FE5
0.912
4.618
FE6
0.901
4.526
Loading Time
Difference
LTD1
0.877
3.376
0.939
0.952
0.768
LTD2
0.827
3.571
LTD3
0.874
3.633
LTD4
0.860
5.775
LTD5
0.903
6.323
LTD6
0.914
4.477
Website Usability
WU1
0.905
4.278
0.950
0.960
0.801
WU2
0.892
3.528
WU3
0.892
3.570
WU4
0.922
4.943
WU5
0.883
3.495
WU6
0.876
3.377
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User Satisfaction
US1
0.923
20.536
0.940
0.953
0.773
US2
0.846
4.019
US3
0.769
2.629
US4
0.933
21.779
US5
0.905
4.329
US6
0.888
4.000
Platform
Popularity
PP1
0.895
3.787
0.954
0.963
0.815
PP2
0.893
3.606
PP3
0.924
5.454
PP4
0.878
3.537
PP5
0.957
8.692
PP6
0.866
2.968
Fig 2. PLS-SEM factor loadings, path coefficients, and R2 values
The results of the assessment of the measurement model are presented in Table III following factor loadings,
collinearity statistics and reliability and validity measures. All the indicators gave factor loadings well above the
recommended threshold value that is 0.70 and the corresponding values are 0.957, 0.886, 0.762, 0.850. This
shows that the relationships between each indicator and its corresponding construct are very strong. PP5 indicator
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was having the highest loading (0.957) whereas the lowest loading was observed in the case of FE2 indicator
(0.762). The loadings of these indicators demonstrate high loadings, indicating that the indicators represent the
constructs under which these indicators are found.
To detect multicollinearity problems, collinearity statistics, i.e. Variance Inflation Factor (VIF) were examined.
All the VIF values were less than the critical threshold of 10 and most of them were in the range 3 - 6. Finally,
if the highest VIF value was 21.779 for one of the User Satisfaction indicators, US4, it may be indicative of a
potential collinearity problem. Thus, most of the indicators presented acceptable VIF values and, hence,
multicollinearity did not seem to be a widespread phenomenon in the measurement model.
There were excellent results on reliability assessment across all constructs. Internal consistency of each of these
constructs showed very high Cronbach's alpha values, 0.932–0.961. In like manner, composite reliability (CR)
values were very good as they displayed values of 0.947 – 0.969, which is above the recommended threshold of
0.70. Such high reliability scores indicate that indicators for each construct are tightly linked and consistently
monitoring the same concept.
Average Variance Extracted (AVE) values were assessed to check for Convergent validity. The AVE of all the
constructs exceeded the recommended threshold of 0.50, with figures being 0.749 to 0.838. Dynamic Pricing
Strategies have the highest AVE (0.838) while Features of ECommerce are the lowest (0.749). These results
indicate strong support for the convergent validity for each construct; that is each construct explains a relatively
high percent of the variance in its indicators.
Table Iv Discriminant Validity (Latent Variable Correlations And √Ave)
DPS
DT
FE
LTD
PP
US
WU
DPS
0.916
DT
0.960
0.884
FE
0.981
0.966
0.866
LTD
0.976
0.960
0.980
0.876
PP
0.964
0.964
0.968
0.963
0.902
US
0.944
0.981
0.951
0.950
0.941
0.879
WU
0.969
0.974
0.971
0.969
0.992
0.949
0.895
The discriminant validity was however assessed. As a result of the square roots of AVE values (diagonal
elements) and constructs’ correlations (shown in Table IV), this table was constructed. The correlations between
constructs were very high, many of which were over the square roots of AVE and were above 0.95 in most cases.
This indicates discriminant validity issues of some constructs, i.e. there may be insufficient difference between
some constructs and the others.
Table V Htmt (Heterotrait–Monotrait Ratio)
DPS
DT
FE
LTD
PP
US
WU
DT
1.008
FE
1.035
1.029
LTD
1.026
1.019
1.046
PP
1.006
1.016
1.027
1.017
US
0.993
1.039
1.017
1.011
0.994
WU
1.014
1.028
1.032
1.026
1.041
1.004
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The concern was further examined in Table V, using the Heterotrait-Monotrait (HTMT) ratio (HTMT ratio or
HTMT) showed HTMT values of larger than 1.0, above the recommended threshold based 0.90.
Although the discriminant validity concerns dominate the results of the analysis, the measurement model
performed very well in terms of indicator reliability, internal consistency and convergent validity. All the factor
loadings, composite reliability, and AVE values met the recommended thresholds indicating that all constructs
were well measured by their indicators. These discriminant validity issues do, however, preclude using the
measurement data for proceeding with the structural model assessment, while the high quality of measurement
does permit such an assessment (Hair et al., 2021). The measurement instruments could be refined for future
research through deterring between the similar concepts in the e commerce environment.
Assessment of Structural Model
The measurement model was further confirmed of reliability and validity and subsequently the structural model
was tested for the hypothesized relationship between constructs. The bootstrapping results are presented as
shown (e.g. Fig. 3) to determine significance of path coefficients. In this assessment, direct effects, indirect
effects, explanatory power of the model and predictive relevance were analyzed.
Fig 3. Bootstrapping Result
Table Vi Path Analysis Result: Direct Effects
Hypothesis
Path
Path Coefficient
t
p
Remark
H1
LTD → US
0.131
2.185
0.029
Accepted
H2
FE → US
0.025
0.396
0.692
Rejected
H3
DPS → US
-0.029
0.499
0.618
Rejected
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H4
WU → US
-0.377
7.209
0.000
Accepted
H5
US → PP
-0.025
1.147
0.252
Rejected
H6
LTD → PP
-0.029
0.724
0.469
Rejected
H7
FE → PP
0.125
2.925
0.003
Accepted
H8
DPS → PP
-0.013
0.337
0.736
Rejected
H9
WU → PP
0.936
29.628
0.000
Accepted
H10
DT × LTD → US
-0.071
5.481
0.000
Accepted
Table VI shows the path analysis results of the direct effects. Only four out of the ten hypothesized relationships
were statistically significant and hence accepted. Hypothesis 1 is supported by path coefficient of 0.131 (t =
2.185; p = 0.029) between Loading Time Difference (LTD) and User Satisfaction (US). Hypothesis 4 was also
supported wherein Website Usability (WU) had a significant negative effect on User Satisfaction with a path
coefficient of -0.377 (t = 7.209, p < 0.001) with the direction being opposite than what the one expected. Finally,
Features of E-Commerce (FE) had positive significant effect on Platform Popularity (PP) with path coefficient
of 0.125 (t = 2.925, p = 0.003) indicating support of Hypothesis 7. A strong, positive effect also exists between
Website Usability and Platform Popularity with a path coefficient of 0.936 (t = 29.628, p < .001) and supports
Hypothesis 9. Furthermore, the effect of interaction term Device Type × Loading Time Difference on User
Satisfaction (path coefficient = -0.071, t = 5.481, p < 0.001), accredits to Hypothesis 10 related to the moderating
effect of Device Type.
The data did not support the other hypothesized relationships. The use of User Satisfaction in Dynamic Pricing
Strategies and Features of E-Commerce had no significant influence on the User Satisfaction (p = 0.692; p =
0.618, respectively). Platform Popularity could not be significantly predicted by User Satisfaction (p = 0.252).
However, neither Loading Time Difference nor Dynamic Pricing Strategies had a significant effect on Direct
Effect to Platform Popularity (p = 0.469 and p = 0.736, respectively).
Table 7 Path Analysis Result: Total Effects
Path
t
p
Remark
LTD → PP
0.812
0.417
Not Significant
FE → PP
2.890
0.004
Significant
DPS → PP
0.315
0.753
Not Significant
WU → PP
28.629
0.000
Significant
US → PP
1.147
0.252
Not Significant
DT → US
22.542
0.000
Significant
DT → PP
1.140
0.254
Not Significant
DT × LTD → PP
1.149
0.251
Not Significant
The analysis for total effects (both direct and indirect effects together) is presented in Table VII. The results
showed that Features of E-Commerce (t = 2.890, p = 0.004) and Website Usability (t = 28.629, p < 0.001) had
total effects on Platform Popularity. A significant total effect was observed on User Satisfaction (t = 22.542, p
< 0.001) but not on Platform Popularity (t = 1.140, p = 0.254) by the Device Type. From this, it can be inferred
that it is the device type that decides user experience and not platform popularity, which is quite the opposite
emotion that was expected.
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Table Viii Path Analysis Result: Total Indirect Effects
Path
t
p
Remark
LTD → US → PP
0.956
0.339
Insignificant effect
FE → US → PP
0.292
0.771
Insignificant effect
DPS → US → PP
0.369
0.712
Insignificant effect
WU → US → PP
1.129
0.259
Insignificant effect
DT × LTD → US → PP
1.149
0.251
Insignificant effect
DT → US → PP
1.140
0.254
Insignificant effect
As shown in Table VIII, the mediation analysis result shows that there was no significant mediating effect of
User Satisfaction on the relationship between the independent variables and Platform Popularity. In all, p-values
of all the indirect effects though User Satisfaction were greater than 0.05. However, this indicates that the factors
affect Platform Popularity directly instead of through the route of User Satisfaction.
Table Ix R² And R² Adjusted Results
Latent Variable
R²
R² Adjusted
US (User Satisfaction)
0.970
0.969
PP (Platform Popularity)
0.985
0.984
R² values of the model's explanatory power are presented in Table IX. The R² values for both User Satisfaction
(R² =0.970) as well as Platform Popularity (R² = 0.985) are found to be very high. These values imply that model
accounts for 97% of the explained variance in both User Satisfaction and in Platform Popularity. Similarly, high
adjusted R² (0.969 and 0.984 respectively) showed that the model has a good explanatory power even with the
number of predictors accounted for (Hair et al., 2021).
Table X F² Values
US
PP
LTD
0.014
0.001
FE
0.001
0.017
DPS
0.001
0.000
WU
0.160
1.529
DT
2.285
0.001
DT × LTD
0.051
0.000
Table X presents f² to gauge the practical significance of each predictor. User Satisfaction varied the most based
on Device Type (f² = 2.285): large practical impact. Platform Popularity was very much affected (f² = 1.529) by
Website Usability. In some cases, statistical significance was not accompanied by a small or negligible effect
size, meaning such predictors would likely have only small or negligible practical significance.
Table Xi Q² Values
Construct
Q²
US
0.737
PP
0.795
Note: Q² values > 0 indicate predictive relevance. Q² values > 0 indicate predictive relevance.
As the final point, Q² values in Table XI are presented to confirm the predictive relevance of the model. The Q²
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values of User Satisfaction (Q² = 0.737) and Platform Popularity (Q² = 0.795) were found to be substantially
above zero, hence proving the model's good predictive relevance. These results confirm that the model is not
only capable of explaining the variance in the dependent variables but also has good predictive capability to new
observations (Sarstedt et al., 2019).
CONCLUSION
This study offers a critical re-examination of user satisfaction’s mediating role in e-commerce platform
evaluation, grounded in empirical data from a mobile-dominant Southeast Asian context. While conventional
theory posits satisfaction as a central determinant of perceived platform success, our results suggest that
observable design traits such as usability and feature richness directly influence platform popularity, bypassing
affective mediators.
The unexpected negative relationship between website usability and satisfaction invites further inquiry into
usability complexity and expectation disconfirmation, especially in mobile environments. Moreover, the lack of
significant influence from dynamic pricing strategies challenges the notion that flexible pricing alone drives user
approval highlighting the increasing emphasis on seamless, intuitive experiences.
The moderation effect of device type affirms the growing need for mobile-optimized performance. As mobile
users become more dominant, their expectations for speed and ease-of-use demand special attention in design
strategy.
The study’s strong explanatory power (R² = 0.970 for user satisfaction and 0.985 for platform popularity)
reinforces the structural validity of the model, but also cautions against construct overlap, as evidenced by
discriminant validity concerns. Future studies should refine measurement tools, explore trust and engagement as
alternative mediators, and validate these relationships across diverse digital markets.
Ultimately, this research contributes to rethinking digital platform evaluations, proposing that in saturated, high-
functionality environments, interface cues and user context (e.g., device type) may be stronger determinants of
perceived platform success than satisfaction alone.
ACKNOWLEDGMENTS
This publication was supported by Universiti Teknikal Malaysia Melaka (UTeM) under the Journal Publication
Fee Initiative 2025. The authors would also like to acknowledge the support from the Faculty of Technology
Management and Technopreneurship.
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