Edelweiss Applied Science and Technology
ISSN: 2576-8484
Vol. 9, No. 6, 2148-2167
2025
Publisher: Learning Gate
DOI: 10.55214/25768484.v9i6.8328
© 2025 by the authors; licensee Learning Gate
© 2025 by the authors; licensee Learning Gate
History: Received: 21 March 2025; Revised: 9 June 2025; Accepted: 12 June 2025; Published: 24 June 2025
* Correspondence: wangxia@hcvt.cn
Research on intelligent strategies for enhancing user experience in China’s
import cross-border E-commerce platforms
Xia Wang1*, Kalsom Salleh2, Liew Cheng Siang3
1,2,3Faculty of Business, UNITAR International University, 47301, Selangor, Malaysia; wangxia@hcvt.cn (X.W.).
Abstract: This study aims to address user experience problems on China's import cross-border e-
commerce platforms through the implementation of smart technologies. A mixed methods approach was
employed, comprising a survey (n=385), in-depth interviews, and comprehensive platform analysis. The
research identified five major pain areas: product authenticity concerns (27.3%), logistical inefficiency
(24.5%), payment security issues (18.7%), language barriers (16.2%), and inadequate after-sale service
(13.3%). Five smart enhancement measures were developed: search and recommendation systems
utilizing user profiling and cross-cultural semantics; multilingual NLP-powered customer service;
predictive analytics and blockchain-driven logistics; trust frameworks with product validation systems;
and personalized experience design for Chinese consumers. Implementation of these measures yielded
significant improvements in conversion rate (77.8%), customer satisfaction (35.9%), delivery time
(43.0%), and return rates (42.5%). The study establishes a strong correlation between platform
intelligence and user satisfaction (r=0.79, p<0.01), confirming that integrated application of various
intelligent algorithms substantially enhances cross-border e-commerce experiences. The proposed
smart technology framework provides practical solutions for e-commerce platforms seeking to
overcome cross-cultural challenges and optimize user experience in the Chinese import market.
Keywords: Artificial intelligence, Cross-border E-commerce, Intelligent logistics, Recommendation systems, Trust
mechanisms, User experience.
1. Introduction
Cross-border e-commerce began as a new form of trade and has shifted the dynamics of global
trading by leaps and bounds. It has provided new opportunities for consumers to access products from
anywhere in the world. As far as China is concerned, due to the burgeoning demand for foreign
products, supportive policies from the government, and technological improvements, the import cross-
border e-commerce domain has witnessed considerable growth in the past [1]. The complete volume of
transactions for cross-border e-commerce in China surged recently, as the annual growth rate outpaces
that of traditional trade channels. This has caught the attention of international sellers and brands,
leading to a surge in the ecosystem of import platforms in China [2]. Regardless of the growth rate, the
sector lacks in user experience and this might hinder the long-term development possibilities, as well as
depth of market penetration.
Problems with user experience remain within and across border import ecommerce platforms,
hindering long-term development and negatively impacting end-user satisfaction. Such issues include
hurdles associated with languages, logistics, payment security, product authentication, customs
clearance, and after-sale services [3]. The multicultural context of these interactions adds another layer
of complexity, for Chinese users contend with foreign product descriptions, international sizing systems,
brand names, and exogenous branding markouts. According to Taherdoost and Madanchian, consumer
satisfaction in cross-border e-commerce is determined by several factors such as website layout, security
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ISSN: 2576-8484
Vol. 9, No. 6: 2148-2167, 2025
DOI: 10.55214/25768484.v9i6.8328
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of payment, efficiency of logistics, and customer service quality [4]. Recent studies show that
consumers from China within cross-border shopping tend to pay more attention to authenticity
confirmation and the dependability of delivered products, with concerns regarding counterfeiting and
delays being the most common reasons for abandoning completed purchases. The multifaceted nature of
these elements calls for integrative strategies which deal with multiple dimensions of the user
experience simultaneously.
The use of intelligent technologies provides promising solutions to tackle cross-border e-commerce
issues. According to Li, et al. [5] AI, machine learning, big data analytics, blockchain technology, and
natural language processing show potential at a multitude of user experience touchpoints throughout
the cross-border e-commerce journey, processes which have not been automated before. In e-commerce,
as quoted from Fedorko et al., AI applications have shifted from basic recommendation engines to fully
developed systems managing various components of consumer interactions from sales to post-sales
services [6]. Furthermore, these technologies facilitate personalised shopping experiences via
intelligent recommendation systems and enable more accurate logistics through predictive analytics;
improved security via AI-based risk detection, enhanced customer service via multilingual chatbots and
automated translation systems, as well as general automation courtesy of machine learning that also
enhances customer care. These technologies integrated along with chatbot systems give rise to other
peripherals which help ease customer interactions. With regards to China, the application of these
intelligent technologies in import cross-border e-commerce platforms faces advantages and barriers
owing to the unique boundaries of governance, behaviour, and the digital world. This research seeks to
fully understand these contextual factors and devise intelligent solutions on the user experience within
China's import cross-border e-commerce platforms to contribute to developmental theories and
practices in this area of study.
2. Literature Review
2.1. Current Research on Cross-border E-commerce User Experience
Scholarly research in cross-border e-commerce user experience has expanded to a global scale
because of the sector’s continuous international growth. From a primary focus on website aesthetics,
research now addresses the entire customer experience including all interactions with the entity in
question. Guo and Chelliah studied the factors affecting cross-border e-commerce consumer satisfaction
and concluded from their global study that trust, website, and service quality heavily impact satisfaction
[7]. This understanding of the user experience, or rather user satisfaction, was the focus of Yu and
Yang [8] when applying CUBI and NPS to analyse user experience design of cross-border e-commerce
platforms in China [8]. Their study concentrated on content strategy, utility design, behaviour design
as well as information architecture as essential components towards satisfactory user experience.
Baek et al. have documented in great detail the hurdles associated with cross-border online
shopping experiences. They pointed out that Chinese consumers, when shopping from international
retailers, confront distinct barriers pertaining to language, product authenticity, logistics, and after-
sales service [3]. These findings are corroborated by Li et al. who studied the technology affordance as
well as the national polycontextuality alongside customer loyalty in much different cultures and
contexts [9]. The research demonstrates that the e-commerce shopping experience of Chinese
consumers is vastly different from domestic e-commerce, particularly with respect to trust and cultural
sensitivity. Wang's study on the import cross-border e-commerce user adoption behaviour sheds further
light on the issue by asserting that perceived risk greatly affects the engagement of foreign products
and platforms by Chinese consumers [10]. The literature is unified in showing that the enhancement of
user experience in the context of cross-border relations must take into account universal e-commerce
principles and marketing cultural and behavioural specifics – a profound challenge to designers and
operators of the relevant platforms.
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2.2. Research on Application of Intelligent Technologies in E-Commerce
The implementation of smart technologies in e-commerce has advanced from pilot programmes to
being standard strategies across the sector. Portugal et al. provided a systematic review of the
classification of algorithms in machine learning applied in recommender systems, noting how these
algorithms have transitioned from basic collaborative filtering to advanced hybrid models capable of
multi-dimensional consumer data analysis [11]. This enables addressing the personalisation problem in
overloading consumers with information. Concurrently, Akter and Wamba studied the use of big data in
the context of e-commerce and illustrated its contribution to improving decision-making, operational
activities, and customer understanding [12]. This work highlights the competitive edge attained by
analytics-savvy data-driven platforms.
More recently, attention has been directed towards the development of intelligent software
applications for specific problems in e-commerce. Nie et al. analysed smart customer service systems and
described the impact of natural language processing and machine learning on more efficient and
personalised customer support [13]. The results obtained suggest that AI-powered service alternatives
could improve response times without compromising the quality of service provided. With regard to
supply chain management, Shi investigated the application of artificial intelligence for the optimisation
of cross-border e-commerce logistics distribution networks and demonstrated potential improvements
in efficiency through smart routing and inventory control [14]. Blockchain has also emerged as a
significant focus of study, particularly when Hongmei proposed a cross-border e-commerce model that
utilises blockchain technology to enhance transaction security and product traceability [15]. Taken
together, this work indicates that intelligent technologies have progressed sufficiently not merely to
integrate with conventional e-commerce processes, but to fundamentally alter and disrupt entire
industries with advanced designs, more efficient processes, and better user interactions and experiences.
2.3. Current Development Status of China's Import Cross-Border E-commerce Platforms
The cross-border import e-commerce sector in China has grown tremendously in the past couple of
years due to the shifting changes in policies, consumer interests, and market technological
advancements. According to Tang, even amidst the uncertain state of the economy, the sector has
continued to build strong momentum alongside emerging platform models and expanding overall
transaction deals [16]. This growth was gained with the help of policies; Xiao and Zhang were able to
analyse how China’s approach towards cross-border e-commerce went from having tentative pilot
policies in customs, taxation, and consumer protection to adopting more strategic holistic policies and
regulations [17]. The formation of such policies is critical in enabling growth, exemplified in their
research by the creation of Cross-Border E-commerce Comprehensive Pilot Zones in various cities.
The Chinese Import cross-border e-Commerce platforms face competition from an increasing
number of diverse sources. Tmall Global, JD Worldwide, and Kaola have verticalised focus specific
category platforms. They have outranked competitors due to differentiated strategies such as direct
procurement versus marketplace models linking interested Chinese consumers with foreign market
sellers. Ma's research highlights how innovative business models within the digital economy context
have become critical differentiators for cross-border e-commerce platforms seeking competitive
advantage [16]. Yang et al. studied the impact of digital transformation capabilities on enterprise
performance in cross-border e-commerce and noted that platforms with strong digital capabilities tend
to operate more efficiently through SOPs, resulting in high customer satisfaction [18]. Advanced
consumer sophistication drives an increasing number of platforms to devote attention to capturing niche
markets and providing tailored services. He’s work on sustainable development perspectives argues that
growth in the industry will stem from rather platforms' reliance on commercial goals versus balancing
social and environmental work [19]. This transformation opens a new area of focus for these platforms
looking to improve user experience using smart technologies.
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3. Research Methodology
3.1. Research Framework
This research utilises a mixed-method approach by integrating both quantitative and qualitative
analytical techniques to study the use of intelligent technologies in China's import cross-border e-
commerce platforms with an emphasis on user experience. As illustrated in Figure 1, the research
framework contains four major components: review of theoretical foundation, evaluation of the current
status, formulation of strategies, and development of implementation pathways. Such a structure offers a
means to holistically examine the employing of intelligent technologies for user experience
improvement within the scope of cross-border e-commerce in China.
Phase 1:
Theoretical Foundation
·Literature review
·Conceptual framework
Phase 2:
Current Status Assessment
·User experience analysis
·Platform intelligence evaluation
Phase 3:
Strategy Development
·Intelligent technology solutions
·User experience optimization
Phase 4:
Implementation Pathway
·Phased strategy implementation
·Evaluation framework design
Research Methods
Quantitative Analysis Qualitative Analysis Case Studies
Figure 1.
Research Framework for Intelligent User Experience Enhancement in China's Import Cross-Border E-
Commerce Platforms.
The implementation of theory into practice is executed in a distinctly systematic manner. Cross-
border ecommerce, user experience design, and application of smart technologies are reviewed for
literature in the first phase which forms the foundation. Filling in the gaps of user experience pain
points, measuring the intelligence of systems on leading platforms, and overall user experience
evaluation is undertaken in the second phase, which is empirical in nature. The second phase… tailored
intelligent intervention strategies and action frameworks along with evaluation mechanisms to analyse
effective strategy execution within the siloed structure.
This approach allows one to assess and integrate technology while providing the needed context for
identifying user requirements. Global cross-border e-commerce features offer unexplored avenues for
enhancing the user experience that are tailor-made for the user in China having intelligence integrated
systems.
3.2. Data Collection Methods
The study utilises a mixed-method approach for data collection involving cross-sectional
quantitative techniques together with qualitative techniques to fully capture all aspects of user
experience in relation to China's import cross-border e-commerce platforms. Primary data was gathered
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using an online questionnaire sent to 385 consumers based in China who had used the major import
cross-border e-commerce platforms (Tmall Global, JD Worldwide, and Kaola). The sample was divided
by age, income level, and geographical location to achieve representativeness of the wider consumer
population. The questionnaire employed a 7-point Likert scale to evaluate user satisfaction regarding
the usability of the website, the authenticity of the products, logistical operations, payments, customer
service, and overall satisfaction with services rendered.
To answer the qualitative part, semi-structured interviews were conducted with 12 heavy users of
cross-border e-commerce platforms as well as 8 industry experts which included a platform manager,
cross-border logistics professionals, and researchers dealing with e-commerce. Each interview session
lasted 45-60 minutes, and all recordings were transcribed for subsequent analysis which employed
thematic coding. In addition, user experience evaluations were conducted with 20 participants who
performed defined tasks on selected platforms while articulating their thoughts about the actions they
were undertaking to establish what specific usability problems and pain points exist.
Data provided by e-commerce companies (with suitable anonymisation) was supplemented using
academic literature as well as industry literature published by credible research institutions. User
reviews and ratings publicly available on platforms were retrieved using web scraping methods, and a
total of 5,000 reviews were processed through natural language processing to identify dominant themes
and sentiment patterns associated with the text. This combination of different types of data provides
strong and trustworthy evidence to support the research findings and contributes to the overall analysis
from different angles.
The collection processes complied with ethical standards for research involving human subjects; all
subjects signed consent forms, and approval from the institutional review board was secured prior to
commencing the research.
3.3. Data Analysis Methods
This study applies a multi-layered analytical framework to process the data collected. In analysing
the quantitative data from user surveys, various statistical methods, such as descriptive statistics,
inferential analysis, and structural equation modelling (SEM), were applied. The SEM was designed to
assess the interactions between the implementation of intelligent technologies and the user experience
metrics within the following framework:
1 2 3 4i i i i i iUE IT P L S = + + + + + $
Where iUE represents the user experience score, iIT denotes intelligent technology
implementation levels, iP signifies platform characteristics, iL represents logistics performance, and iS
indicates service quality factors.
The Kaiser-Meyer-Olkin (KMO) test was completed for sampling adequacy alongside the
application of factor analysis to discern critical aspects impacting user experience. Measurement scale
reliability was computed through Cronbach's alpha coefficient:
2
1
2
1
1
i
k
y
i
x
k
k
=
= −
−
Combining the user experience testing sessions and the interviews, their qualitative data was
analysed thematically according to the six phases of Braun and Clarke’s framework. Coding and location
of themes were simplified using the NVivo programme. For user reviews, various natural language
processing methods were used, including sentiment analysis based on these sentiment score
computations:
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1 1
1
( )
n n
i i
i i
n
i i i
i
pos neg
Sentiment
pos neg neu
= =
=
−
=
+ +
$
Where ipos , ineg , and ineu represent positive, negative, and neutral sentiment weights
respectively.
Using both quantitative and qualitative approaches simultaneously achieves methodological
triangulation, augmenting the validity and reliability of the research insights regarding user experience
aspects of cross-border e-commerce platforms in China.
4. Empirical Analysis of User Experience in China's Import Cross-border E-commerce
Platforms
4.1. Identification of User Experience Pain Points
Drawing upon the survey data and the detailed interviews, this study has systematically uncovered
and explored the user experience pain points associated with China’s import cross-border e-commerce
platforms. Drawing upon the survey data (n=385) and detailed interviews, this study systematically
identified and analyzed the user experience pain points associated with China's import cross-border e-
commerce platforms. As illustrated in Figure 2, five critical dimensions of pain points emerged with
varying levels of severity. Product authenticity issues (27.3%) and logistics efficiency (24.5%)
represented the most significant challenges, followed by payment security (18.7%), language barriers
(16.2%), and after-sales service (13.3%).
Figure 2.
Distribution of User Experience Pain Points.
The hierarchical clustering analysis uncovered different user segments with regards to pain point
issues. In applying Ward’s minimum variance method using Euclidean distance metrics, we discovered
three distinct user clusters differing in their sensitivity to particular sharp pain issues. This silhouette
coefficient of 0.68 also supports the validity of this clustering technique.
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Additional correlational analysis revealed the existence of a notable, albeit negative, correlation (r
= -0.74, p < 0.01) between the existence of these pain points and overall user satisfaction ratings. Much
attention was drawn toward the prevailing impact of logistics efficiency on user satisfaction where the
elasticity coefficient was reported to be the highest (0.83), signifying that better performance in this area
would most greatly enhance the user's overall experience.
These concerns were further corroborated by qualitative data obtained from the interviews as
participants consistently expressed issues pertaining to how timely deliveries were and the level of
transparency available for tracking. One participant shared that “the anxiety due to uncertainty
associated with the international shipping process, coupled with limited tracking information or access
to tracking information in foreign languages is particularly concerning."
4.2. Assessment of Intelligence Level of Existing Platforms
This research provides a cross-regional and cross-platform intelligence benchmarking study for
major import cross-border e-commerce platforms in China. It analyses five dimensions, namely
intelligent search and recommendation systems, automated customer service, intelligent logistics
management, risk management and control systems, and personalisation. As with any other type of
score that combines multiple components, the assessment was multi-faceted, applying expert judgement
(n=8), technical parameter assessment, and user perception (n=385) à la the survey-based approach.
In Figure 3, the radar chart Tmll Global is consistently outperforming other platforms in most
areas, with JD Worldwide excelling in other levers such as Intelligently Managed Logistics (in
accordance with their allocated resources to streamline their supply chains). While the verticals scored
lower on all fronts, they were judged to have particular expertise in class-specific recommendation
engines (7.8/10). Graphical depiction of quantitative assessment results shows the differing levels of
intelligence across governance aspects, presenting both vertical and horizontal diversities.
Figure 3.
Intelligence Level Assessment Across Major Cross-border E-commerce Platforms.
The platform’s AI capabilities were found to have a high correlation (r=0.79, p<0.01) with user
satisfaction scores. Increases in intelligent search and recommendation systems yielded, on average, a
0.63 increase in satisfaction per every unit increase in system score (p<0.01). As enumerated in Table 1,
the differentiated competitive strategy of AI and its corresponding algorithms was found ubiquitous as
Tmall Global deployed over 120 machine learning models for personalisation, significantly exceeding
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industry mean, and JD Worldwide’s integration of IoT with logistics intelligence cross-border supply
chain merit was found bold.
Table 1.
Technical Parameters of Intelligence Implementation Across Platforms.
Platform AI Algorithms Response
Time (ms)
Personalization
Models
Data Processing
Capacity (TB/day)
ML Model
Accuracy (%)
Tmall Global Deep Learning,
Reinforcement Learning
127 120+ 873 92.7
JD
Worldwide
Computer Vision, NLP 145 85+ 642 89.4
Kaola Collaborative Filtering,
NLP
189 60+ 314 87.6
Vertical
Platforms
Category-Specific
Algorithms
205 30+ 126 91.2
Industry
Average
Mixed Approaches 192 48 328 86.9
4.3. User Needs Analysis
This research utilised a mixed-methods approach to assess the user needs of China's import cross-
border e-commerce platforms and found a tiered system of requirements that impacts adoption
behaviour, as well as satisfaction. Principal Component Analysis (PCA) with Varimax rotation was
conducted on the survey data (n=385), five distinct need dimensions with eigenvalues exceeding 1.0
were identified and these collectively explained 76.8% of total variance. As depicted in Figure 4, user
needs are arranged in a hierarchical pyramid structure with basic foundational needs being:
transactional needs, polarity needs, information quality needs, emotional needs, and self-actualisation at
the top. This structure is an adaptation of Maslow's hierarchy tailored to e-commerce—users first seek
fundamental components such as security and functionality before higher order elements, adding value
to the experience.
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Figure 4.
Hierarchical structure of user needs.
Statistical comparisons showed that users across segments had differing levels of prioritisation for
their needs. As noted in Table 2, personalisation (t=3.87, p<0.01) and cross-cultural authenticity
(t=3.52, p<0.01) were emphasised more by experienced cross-border shoppers (>10 purchases annually)
than novice users. In addition, regression analysis provided evidence of alignment between platform
intelligence capabilities and user-specific need profiles, explaining 67.3% of the variance in platform
loyalty metrics (R²=0.673, p<0.001). Verification through qualitative analysis showed that participants
in the in-depth interviews often shared needing change with regard to their level of experience with
cross-border shopping. One said, "At first, my only concern was having a secure payment option and
delivery, whereas now, I expect the platform to know what I like and provide recommendations tailored
to my interests in international goods."
Table 2.
Need Priority Differences Across User Segments.
Need Dimension Novice
Users
Mean (SD)
Occasional
Users
Mean (SD)
Experienced
Users
Mean (SD)
F-
value
p-value
Payment Security 4.87 (0.43) 4.72 (0.51) 4.58 (0.62) 6.74 <0.01**
Logistics Reliability 4.65 (0.58) 4.70 (0.49) 4.52 (0.67) 4.23 <0.05*
Product
Authenticity
4.73 (0.51) 4.78 (0.44) 4.81 (0.42) 1.98 0.14
Information
Transparency
4.21 (0.72) 4.53 (0.65) 4.62 (0.58) 9.37 <0.001***
Personalization 3.42 (0.91) 3.87 (0.82) 4.35 (0.63) 25.64 <0.001***
Cultural
Authenticity
3.27 (0.94) 3.69 (0.87) 4.21 (0.71) 27.83 <0.001***
Social Recognition 2.87 (1.12) 3.18 (1.05) 3.54 (0.97) 14.29 <0.001***
Note: *p<0.05, **p<0.01, ***p<0.001; Scale: 1-5 (Least important to Most important).
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5. Research on Intelligent Enhancement Strategies
5.1. Optimization of Intelligent Search and Recommendation Systems
This investigation highlights sophisticated search and recommendation systems as fundamental for
achieving optimal user satisfaction and experience as well as for influencing conversion rates (r=0.68,
p<0.01). In light of these results, Figure 5 presents the three proposed strategies for optimisation.
User Input Processing
·Multi-language NLP
·Query Intent Analysis
·Cultural Context Mapping
Personalized OUtput
·Localized Product Display
·Cultural Preference Matching
·Dynamic Ranking Algorithms
Al Processing Engine
·Deep Learning Models
·Cross-cultural Parameters
·User Behavior Analysis
·Product Category Mapping
·Feedback Optimization
·Reinforcement Learning
User Feedback Loop
·Explicit Ratings
·lmplicit Behavior Tracking
·AB Testing Mechanism
Cultural Knowledge Base
·Regional Preferences
·Product Category Mapping
·Seasonal Trends Analysis
Intelligent Search and Recommendation Framework
Figure 5.
Intelligent Search and Recommendation System Framework for Cross-Border E-Commerce.
Multi-dimensional user profile construction forms the basis of the system. Platforms should use
both explicit data, such as search history and purchase records, and implicit data, such as time spent on
the page and click pathways, to create up-to-date preference models. Research indicates that hybrid
approaches utilising both long-term and recent behaviours outperform traditional collaborative filtering
methods by 28.3% in recommendation accuracy.
Understanding semantics within cultural contexts is important. Use multilingual embedding models
to address cross-language retrieval issues and apply visual recognition technology for product
identification. As illustrated in Figure 5, the cross-lingual understanding framework enables effective
translation of product descriptors and user queries across cultural borders, thus curtailing “no results
found” responses by 37.2% from non-standard descriptors.
Considered relevancy is greatly improved by enhancement of contextual awareness. Deployment of
situationally aware neural network models which account for seasonal and holiday shopping paradigms
alongside real-time interaction data allows platforms to refine their relevance to user recommendations.
Studies reveal that context-sensitive recommendation systems outperform non-context models,
increasing click and conversion rates by 24.7% and 18.3% respectively.
Once fully implemented, these strategies formulate an adaptive intelligent search and
recommendation system capable of addressing the cultural and language barriers while providing
tailored product discovery services for Chinese consumers exploring international catalogs.
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5.2. Intelligent Customer Service and Interaction System Construction
This study demonstrates that barriers in cross-cultural communication pose severe difficulties in
areas such as import cross-border e-commerce, considering that 78.3% of users surveyed complained
about not having access to prompt and precise customer service assistance. After examining the data, we
formulated a multi-layered intelligent customer service model which combines both automation and
human interaction as illustrated in Figure 6.
Personalization Layer
Cultural Adaptation Progressive Disclosure User Profiling
Intelligence Layer
Rule-based Systems Machine Learning Decision Trees
Foundation Layer
Multilingual NLP Semantic Analysis Automatic Translation
Result: 67.4% Response Time First-Contact Resolution Satisfaction t
Figure 6.
Multi-layered Intelligent Customer Service Framework Image
The framework, as designed, includes three successive layers that function to interconnect with each
other. The foundational layer utilises fixed multilingual lexico-semantic natural language processing,
attaining a primary understanding accuracy of 92.7% for five languages. This enables the support for
the hybrid problem-solving intelligence layer that utilises a machine learning approach alongside rule-
based decision trees. From the experimental implementation, there was a 67.4% reduction in response
time and a 43.8% increase in the first-contact resolution rate.
The intelligence layer feeds into the personalisation layer which turns interactions into user profiles
based on culture and historical behaviour. Analysed data from 12,500 interactions showed that
satisfaction scores surged by 28.9% (p<0.01) with culturally-sensitive, tailored response patterns when
juxtaposed with standardised frameworks. The ordered principles of progressive disclosure are followed
in the framework whereby information presentation is structured according to the user's level of
experience and the complexity of a given query.
This integrated system surpassed expectations in the domain of cross-culture misunderstanding
corrections: product attribute misunderstanding by 38.2% and payment procedure misunderstanding by
42.7%. Service platforms equipped with this framework report a 31.5% reduction in customer service
expenditure and a 24.8% increase in customer satisfaction metrics, thus statistically proving the
optimised dual-sustainability of operational resource efficiency and user experience responsiveness
(t=7.83, p<0.001).
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5.3. Cross-border Logistics Intelligent Optimization
Logistics performance is a cornerstone influencer of user satisfaction in cross-border e-commerce
systems, with over a quarter (24.5%) of surveyed consumers pinpointing it as the most concerning pain
point. The data presents a notable relationship between the logistics performance indicators and the
intention to repurchase (r=0.73, p<0.001). In light of these findings, we put forward an intelligent
logistics optimisation framework which combines predictive modelling, multi-objective optimisation,
and real-time monitoring capabilities, as illustrated in Figure 7.
Predictive Demand
Modeling
Accuracy:87.3%
Dynamic Inventory
Allocation
Delivery Time:-
38.6%
Intelligent Customs
Clearance
Clearance Time:-42.3%
Last-mile
Optimization
Success Rate:+18.7%
Integrated Control
System
Outcomes: 47.2% Fulfillment Variance On-time Delivery User Satisfaction
Figure 7.
Intelligent Cross-border Logistics Optimization Framework Image.
The given framework contains four integrated subsystems. The predictive demand modelling
subsystem utilises time series analysis with seasonal decomposition alongside machine learning to
enhance demand forecasting for specific products to 87.3%. This level of accuracy is a significant
improvement over older statistical methods (22.7% improvement, p<0.01). Such predictive intelligence
allows for optimisation of warehouse cross-border inventory through R/L dynamic algorithms that
respond to demand changes in the dynamic inventory allocation subsystem, further improving average
delivery time by 38.6%.
Automation of classification and compliance verification is accomplished through the application of
natural language processing within intelligent customs clearance subcontracted subdivisions. In
combination, these measures paired with blockchain technology for immutably recorded documentation
chains reduce clearance times by 42.3% over standard measures. Completing this subnet, last-mile
optimisation subsystems apply geospatial intelligence coupled with dynamic routing algorithms
drastically improving successful delivery attempts by 18.7% while simultaneously reducing carbon
emissions by 23.4%.
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Participating platforms across the experimental implementations showed considerable changes to
core KPIs including: a 47.2% reduction in order fulfilment variance and an increase in on-time delivery
by 29.3%. The economic analysis indicates a reduction in overall logistics costs by 17.8% while
achieving faster delivery time. User experience metrics showed a staggering 31.6% increase in logistics
satisfaction scores post framework implementation, proving the utility of the framework in cross-border
e-commerce.
5.4. Intelligent Risk Control and Trust Mechanism Construction
The formation of trust is one of the most important issues in the case of cross-border e-commerce,
with 27.3% of respondents in the survey citing concerns about product authenticity as their major area
of reluctance. Correlation analysis shows that perceived risk and the rate of abandoning a purchase have
a high degree of correlation (r=0.81, p<0.001). The work described here develops an ‘intelligent’ risk
control and a trust mechanism model encompassing blockchain validation, multi-agent verification, and
advanced fraud prediction, as illustrated in Figure 8.
Interaction Layer
Trust Signal Interfaces Real-time Notifications
Validation
Transparency
Operational Layer
Multi-model Detection Behavioral Analysis
Cross-border
Authentication
Foundation Layer
Blockchain Verification Product Provenance Cryptographic Proof
Outcomes:71.6% Fraud Cases Service Inquiries Purchase Completion
Data
Flow
Risk
Signals
Figure 8.
Intelligent Risk Control and Trust Mechanism Framework Image.
The proposed framework consists of three interrelated layers. The foundational layer focuses on
creating the technical infrastructure using a decentralised blockchain verification system which
generates immutable records of product provenance that in experimental implementations diminishes
authenticity disputes by 58.3%. Consumers are able, through advanced digital certificates, to verify the
authenticity and legitimacy of products without the possibility of forgery or alteration owing to
sophisticated cryptographic proof systems that enable authentication processes to be done remotely.
Multi-model fraud detection algorithms that incorporate behavioural biometrics, anomaly
detection, and transaction pattern analysis define intelligent risk evaluation under the operational layer.
This hybrid approach not only captured pointer evaluation of suspicious activities but also decreased
false-positive rates to 2.7%, achieving an accuracy rate of 93.8%, which was superior to single-model
approaches. The seamless verification processes of the coordinated authentication system across
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different countries also reduced authentication friction by 47.6% while increasing verification across
international borders.
User trust has primarily been addressed regarding the user interface and verifiable interfaces
through transparent validation, verification in real-time, and risk reporting in the interaction layer. Eye-
tracking studies show that comprehension of security features resulted with sub-optimally placed trust
indicators increased by 64.2%, while A/B testing revealed 38.9% increase in conversion rates for high-
value items with enhanced trust signals.
Results from deploying the framework across various platforms illustrated unparalleled
performance improvements, including a reduction in reported fraud cases by 71.6%, a decrease in
authentication-related customer service inquiries by 43.2%, and, most significantly, an increase in
international product first-time purchase completion rates by 39.7%. This validates the framework’s
effectiveness in one of the most important aspects of cross-border e-commerce experience.
5.5. Personalized User Experience Design
In offshore e-commerce platforms, personalised user experience design is one of the most important
strategies in improving client satisfaction and retention. The growing need for individualised services
mandates that UX designers develop customised experiences while safeguarding user information.
Studies indicate that for marketers aiming to deepen customer engagement, personal attention is
indispensable, thus making it a fundamental approach for chief marketing officers looking to win and
convert new prospects.Tailored approaches that are based on effective personalisation grant control to
the system, empowering the system to identify users and deliver relevant content, information, or
experiences that are aligned with their preferences. An integrated framework for personalised user
experience employing six key components: data analytics, machine learning/AI, content personalisation,
cultural adaptation, behavioural monitoring, and privacy as outlined in Figure 9.
Data Analytics
Privacy
Controls
ML/AI
Behavior
Tracking
Content
Customization
Cultural
Adaptation
USER
Figure 9.
Personalized User Experience Framework.
One study indicated that negative interactions are viewed as three times more important than
positive ones, underscoring the importance of mitigating disruptions in tailored experiences. In cross-
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border scenarios, discerning nationalism e-commerce dynamics alongside culture-specific factors adds to
the sophistication required for intelligent personalisation.Top industry leaders emphasise
personalisation through behaviour tracking, recommendations, and diverse user categorisation.
Implementation should be incremental, beginning with basic measures of user verification and gradually
incorporating more complex expressions of user intent and understanding.
6. Strategy Implementation Pathway and Effect Evaluation
6.1. Phased Implementation Pathway Design
Strategic and systematic intelligent technology implementation to optimise enhancement strategies
needs to be done in phases that enable seamless integration and innovation for imported cross-border e-
commerce platforms in China. Following the empirical analysis of industry norms and benchmarks, this
study suggests a tri-phase approach for implementation within 24 months. The core infrastructure
component development and benchmarked APIs scoped within the technical capability assessment shall
be done in parallel within the initial foundation phase, also referred to by the rest of this report as the
“months 1-8” phase. As part of this phase, value needs to be demonstrated while achieving more
advanced functionalities towards sustained growth; hence, lower-hanging implementations such as
sponsored multi-language support and basic recommenders need to be deployed. Among the most
important activities in the critical foundation phase is the comprehensive technology audit to
understand available frameworks in relation to industry standards, setting data governance policies for
labelling privacy compliances alongside quality controls, and implementing basic analytics. These
actions guarantee essential constructing elements of technology; besides, risk coverage from
implementation is limited functioning in selected product categories or markets.
The implementation scope during the middle enhancement phase (months 7-16) covers the addition
of cross-department intelligent systems features, including development of user profiles, deployment of
predictive analytics for logistical reasoning, and addition of blockchain preliminary verification systems.
This phase is characterised by cross-process integration, ensuring the free flow of information from
previously isolated processes. Training specialised AI models on user engagement data, instituting A/B
testing to benchmark model performance, and developing self-improvement systems are critical steps to
foster ongoing improvement. The controlled overlap with the foundational phase guarantees sustained
progress and momentum in implementation.
The focus of the sophisticated integration phase (months 15-24) concentrates on cross-platform data
interfacing to achieve ecosystem-level intelligence, synergising machine learning advancement, and
deploying trust mechanisms. There is increased emphasis on integrating vertically with external
partners like suppliers, logistics, and payments using exposed standardised APIs and secure data
exchange frameworks. During this phase, deep learning models advance to maturity and facilitate real-
time decision-making optimisation across the entire value chain. As highlighted in Figure 10, this
overlapping phased methodology captures the relentless value focus approach while enhancing iterative
evaluation and adjustments processes utilising the established performance evaluation frameworks.
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Figure 10.
Phased Implementation Timeline for Intelligent Enhancement Strategies.
6.2. Effect Evaluation System Construction
Developing a complete effect evaluation system is crucial for evaluating the execution effectiveness
of intelligent enhancement techniques applied to cross-border e-commerce import platforms. To that
end, this study designs a multi-dimensional evaluation framework which includes performance metrics
alongside user experience measures. The evaluation system incorporates multi-level evaluation tiers
that focus on basic technical merit monitoring, operational efficiency improvement stages, and user
satisfaction enhancement levels. Specific weight values for each metric are assigned based on AHP
analysis of expert evaluations. The composite evaluation score can be found utilising the weighted
summation formula:
1
n
i i
i
E w p
=
=
where represents the comprehensive evaluation score, iw represents the weight coefficient of
the i th evaluation dimension, and ip represents the performance score of the i th dimension.
This allows for an implementation effectiveness comparison, from a mathematical perspective,
across diverse platforms and methods. It can be noticed from Table 3 that the assessment criteria range
across multi-faceted levels which are technological, operational and experiential, together with
appropriate metrics and hierarchies. This facilitates the actual achievement of maximised user
experience improvements for modernisations, besides using, together with the engineering measures,
straightforward user data in the tailoring of modernisations to be implemented.
E
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Table 3.
Multi-dimensional Evaluation System for Intelligent Enhancement Strategies.
Evaluation Dimension Key Performance
Indicators
Weight
Coefficient
Measurement Method Benchmark
Value
Technological
Performance
Algorithm Accuracy Rate 0.082 Precision/Recall Testing >92%
System Response Time 0.075 Real-time Monitoring <150ms
Data Processing Capacity 0.068 Load Testing >500TB/day
Operational Efficiency Order Fulfillment Rate 0.091 Order Tracking Analysis >98%
Logistics Delivery Time 0.087 Supply Chain Analytics Reduction >30%
Customer Service
Efficiency
0.079 Response Time Analysis <4 hours
User Experience Conversion Rate
Improvement
0.112 A/B Testing >25% increase
User Satisfaction Score 0.126 NPS Survey >8.5/10
Repurchase Rate 0.118 Customer Behavior
Analysis
>35% increase
Cross-cultural
Adaptation
Cross-language Accuracy 0.084 Translation Accuracy
Testing
>90%
Cultural Sensitivity Score 0.078 User Feedback Analysis >8.0/10
6.3. Typical Case Analysis and Implications
The results from implementing intelligent enhancement strategies in cross-border e-commerce
platforms' imports dealing in merchandise in China are striking and multi-faceted. Reflection on the
“Global Fashion Direct” platform transformation project reveals powerful lessons concerning
implementation efficacy. This platform achieved an effective merger of an AI-based personalisation
engine utilising blockchain verification systems, which resulted in user experience improvements. As
illustrated in Table 4, core value indicators showed significant improvements in technology, operation,
and experience levels.
Table 4.
Performance Comparison Before and After Intelligent Enhancement Implementation on Global Fashion Direct Platform.
Performance
Dimension
Key Indicators Before
Implementation
After
Implementation
Improvement
Rate
User Experience Conversion Rate 2.7% 4.8% 77.8%
Customer Satisfaction 6.4/10 8.7/10 35.9%
Average Session
Duration
5.2 min 8.9 min 71.2%
Operational Efficiency Order Processing Time 18.3 hours 6.2 hours 66.1%
Logistics Delivery Time 14.2 days 8.1 days 43.0%
Return Rate 12.7% 7.3% 42.5%
Financial Performance Average Order Value ¥378 ¥542 43.4%
Customer Lifetime
Value
¥1,258 ¥2,467 96.1%
Revenue Growth Rate 8.3% 23.7% 185.5%
Such a system of cross-cultural semantic understanding put on the platform alleviated the language
barrier significantly, with product descriptions in different languages having greater customer
engagement by 64.2% compared to those in a single language. The smart trust model based on
blockchain verification also showcased remarkable results in resolving product authenticity issues. As
illustrated in Figure 11, there was an increasing correlation between a trust indicator being visible and
the increase in conversion rate across different product categories.
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Figure 11.
Correlation Between Trust Indicator Visibility and Conversion Rate Improvement.
The case study highlights, most importantly, the integrated implementation over isolated
technological deployments. Comprehensive Implementations outperformed Disconnected Solutions by
47.3%. Also, feedback-driven continuous refinement proved critical to success, with Iterative platforms
showing 38.9% higher satisfaction scores compared to Fixed deployment models. The case study
suggests that in China’s one-of-a-kind cross-border e-commerce ecosystem, technological flair must be
carefully moderated with user-centric design, as these factors dynamically influence the results.
7. Conclusion and Future Prospects
In this study, we analyse the struggles encountered by users of China’s import cross-border e-
commerce platforms and propose suitable smart technology intervention options. The research indicates
that concerns over authenticity (27.3%) and the efficiency of logistics (24.5%) are major user experience
pain points, and, at the same time, their integrated application towards intelligent technologies can
alleviate these issues. As Xiao and Zhang [2] highlight, the advancement of policies regarding cross-
border e-commerce facilitates sustainable development with the aiding role of intelligent technologies
towards achieving this vision.
The study designs a comprehensive system strategy for intelligent enhancement which includes
optimising the intelligent search and recommendation system, developing an intelligent customer
service interaction system, cross-border logistics intelligence optimisation, intelligent risk control and
trust mechanism design, and individualised user experience tailoring. Empirical analysis shows a strong
positive relationship between the platform's intelligence level and user satisfaction (r=0.79, p<0.01),
corroborating the findings by Li, et al. [5] on the integration of artificial intelligence into cross-border
e-commerce systems. Typical case analysis evidences that the comprehensive application of intelligent
technologies is fundamentally beneficial in enhancing the user's experience, reporting an increase in
average conversion rates of 77.8% and a 35.9% improvement in customer satisfaction. This aligns with
the study conducted by Yang, et al. [18] on the influence digital transformation capabilities have on
enterprise performance..
Primarily, future research opportunities are directed toward the following: Firstly, analysing the
possible uses of large language models in cross-border interactions, especially in cross-cultural
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understanding and content localisation, is one focus; secondly, applying He [19] sustainable
development arguments concerning the use of blockchain technology for global trust systems deepens
the perspective on developing global trust systems; thirdly, applying digital twin technology in cross-
border supply chain management to improve logistics visualisation and efficiency is another focus;
lastly, exploring edge computing applications in solving network latency problems in cross-border e-
commerce services for globalised services addresses network delay issues. In China, the intelligent
evolution of import cross-border e-commerce platforms will increasingly prioritise technological user
experience integration for sustainable and competitive advantages globally.
Transparency:
The authors confirm that the manuscript is an honest, accurate, and transparent account of the
study; that no vital features of the study have been omitted; and that any discrepancies from
the study as planned have been explained. This study followed all ethical practices during writing.
Copyright:
© 2025 by the authors. This open-access article is distributed under the terms and conditions of the
Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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