THE EFFECT OF AI-DRIVEN FEATURES ON CUSTOMER
SATISFACTION AND REPEAT PURCHASE AMONG LAZADA USERS IN THAILAND
*Naw Htoo Aung, **Dr. Siriwan Kitcharoen, ***Dr.
Bhumiphat Gilitwala
*Graduate School of Business and Advanced Technology
Management, **Assistant Vice President for Educational Innovation
and Graduate Studies ***Program Director MBA, Assumption University, Thailand
Artificial intelligence (AI) has
become a key driver in shaping today’s e-commerce platforms and customer
experiences. The current study aims to analyze the effect of AI-driven features
on customer repeat purchase among Lazada users in Thailand, and the mediating
effect of customer satisfaction. A quantitative research method was used, where
primary data were collected from Lazada users through an online questionnaire.
A total of 409 Lazada users actively participated in the survey. The data were
analyzed using SmartPLS. The findings showed that AI-driven personalization,
customer control over personalization, and recommendation algorithm
transparency have no statistically significant effect on customer repeat
purchases, while customer satisfaction has a significant impact on customer
repeat purchases at Lazada. Customer control over personalization and
transparency in recommendations has a statistically significant effect on
customer satisfaction; however, AI-driven personalization has no significant
impact on customer satisfaction. Moreover, the results indicated that customer
satisfaction served as a significant mediator between customer control over
personalization and customer repeat purchase. Lazada customers are satisfied
when they have the authority to control their personalized data, and the
e-commerce recommendation algorithm is transparent and trustworthy. Satisfied
customers become loyal customers. E-commerce sectors can implement the findings
to improve customer satisfaction and retain customers.
Keywords:
AI-Driven
Personalization, Recommendation Algorithm Transparency, Customer Control over
Personalization, Customer Satisfaction, Repeat Purchase, Lazada, Thailand
Artificial
intelligence (AI) becomes an essential part of a modern e-commerce development
due to AI transformation affecting customer engagement on digital platforms
including customized product recommendations through an algorithmic system's
visibility. Over the past few years, e-commerce such as Lazada in Thailand has
implemented cutting-edge technology to meet consumer expectations and stay competitive
with a growing market (Turatti, 2025).
The COVID-19
pandemic created Thailand's shift to online shopping platforms, allow more
consumers to understand online shopping spaces. Due to the shifting landscape
of online shopping, consumers began demanding a personalized and seamless
shopping experience (Weiler & Gilitwala, 2023; Oh et al., 2023). AI is one
critical tool in personalizing the digital shopping experience by allowing
consumers to find suitable products effectively and with improved accuracy (Agoro
et al., 2021; Ahmed et al., 2025).
The deployment of AI technology is
now an essential component of contemporary e-commerce platforms. As one of the
premier online platforms in Thailand, Lazada's utilizes some AI functions to
enhance the online shopping experience and enjoy a technological advantage in
the online marketplace (Oh et al., 2025). Several studies regarding algorithms
have examined AI functions in isolation from each other. Previous research
regarding AI personalization described production improvements and customer
purchase behavior (Ahmed et al., 2025; Agoro et al., 2021b). AI transparency
studies have focused on how user trust develops based on their understanding of
AI processes (Owen et al., 2023; Akbar et al., 2024). In addition, several
studies have analyzed user modal control methods, which highlight that user
control over AI-generated results improves user satisfaction (Bok, 2023; Geetha
et al., 2024). AI tools assist consumers through shopping decision processes,
although some consumers question the recommendations, algorithmic fairness, and
use of their data collection (Bok, 2023; Owen et al., 2023; Akbar et al.,
2024). Although AI is becoming increasingly common in e-commerce, Thai
consumers have limited knowledge regarding AI functions. This study fills the
gap of previous studies where the researchers implemented the single AI feature
for each study; thus, the current study analyzes the effect of AI
personalization, customer control over personalization, and recommendation
transparency on customer satisfaction and repeat purchases at Lazada e-commerce
in Thailand. The results will contribute to an in-depth understanding of how
AI-driven features effect on e-commerce users in Thailand.
The following research questions support the purpose of the study:
1. How does AI-driven personalization affect customer satisfaction among Lazada users in Thailand?
2. How does recommendation algorithm transparency affect customer satisfaction?
3. How does customer control over personalization affect customer satisfaction?
4. To what extent does customer satisfaction affect repeat purchases in the context of AI-enabled e-commerce?
E-commerce businesses must know what
makes customers reorder. Theoretical frameworks that outline the ways
individuals appraise their experience can better facilitate identifying
customer satisfaction and long-term loyalty, or even patronage. Thus, these two
models are helpful for our study, which explores the influence of AI-enabled
features on customer satisfaction and intention to repeat purchases:
Expectation Confirmation Theory (ECT) and the Technology Acceptance Model
(TAM).
Expectation Confirmation Theory (ECT)
specifies satisfaction because of comparing expectations to outcomes.
Intuitively speaking, AI-enabled features (personalization, automation, and
transparency) can succeed (satisfaction) when the system performs according to
expectations or exceeds the user's expectations. Faster, relevant, and
well-timed interactions can make customers feel satisfied and therefore want to
return shopping. With ECT, satisfaction outcomes can be contextualized in set
standards (i.e., expectations), which makes for an especially good fit in the
e-commerce environment that relies heavily on expectations of speed,
convenience, and personalization (Ahmed et al., 2025; Hardcastle et al., 2025).
More specifically, if an AI tool is designed to do something, like uncover
hidden patterns in customer data, users expect that it delivers exactly what it
is designed to do. The outcomes are critical for maintaining user satisfaction
and therefore fostering loyalty (Ahmed et al., 2025; Hardcastle et al., 2025).
Technology Acceptance Model (TAM)
describes a relationship that exists between users and technology. TAM has two
components, such as perceived usefulness and perceived ease of use. The two
components define whether a user will adopt and continue to use a respective
technology. In the context of AI-enabled personalization, if customers perceive
the AI tool to be useful and feel they can easily interact with it, they will
have a positive experience and are likely to remain a loyal customer.
Therefore, meaningful AI, combined with usability, was a significant factor of
overall experience and intention to repurchase (Alkudah & Almomani, 2024;
Turatti, 2025). ECT defines the importance of meeting and exceeding
expectations, while TAM highlights usefulness and ease of use. When combined,
both theories help explain the preference for returning to AI-enabled
experiences and platforms that showcase trust, speed, and customer satisfaction.
Repeat purchase behavior indicates
that a consumer is willing to purchase from that same platform again, based on
prior experience that was favorable. In e-commerce platforms like Lazada,
consumers are likely to return because they trust that platform, they are
familiar with the interface, and they see a good perceived value in their
purchases. Previous studies reported that customer satisfaction is a
significant predictor of repeat purchase behavior. If customers' needs are met
and/or exceeded, they are likely to return for future purchases (Tufahati et
al., 2021; Jiradilok et al., 2013).
AI-driven platforms facilitate repeat
purchases by recommending relevant products at appropriate times. The impact of
AI-powered personalization improves satisfaction and has a significant effect
on customers' willingness to repeat purchase behavior. The relevance increases
customers perceived efficiency and ease of use the platform (Ahmed et al.,
2025). Personalization minimizes search time and tailor’s users’ experience,
which creates loyalty (Hardcastle et al., 2025). Consumers expect their
personalized experience are ethical and reliable. Transparency and control
offer a reduction in uncertainty and build their confidence over time (Owen et
al, 2023; Akbar et al, 2024). Demonstrating transparency into the algorithms
used can enhance users’ confidence. When consumers know how their
recommendations are generated and, importantly, feel the personalization system
is fair, their trust in the platform becomes much stronger. In turn, their
trust relates positively to their willingness to repeat purchase behavior.
This section
discusses the relationships between independent variables; understanding how
these variables interact is essential for identifying which AI features most
effectively enhance customer satisfaction and drive repeat purchases on
e-commerce platforms.
AI-driven
personalization increases customer satisfaction through product
recommendations, promotional offerings and outstanding navigation flows by
utilizing an individual user’s interactions, preferences, and past purchases.
When users are provided with suggestions that mirror their interests and
individual preferences, they identify with the personalized approach and feel
validated and recognized. This feeling can help decrease decision fatigue and
improve browsing convenience, leading to contentment. (Ahmed et al., 2025)
highlight that personalization done accurately and respectfully increases
customers' enjoyment, perceived convenience, and loyalty. (Hardcastle et al.,
2025) also illustrate that personalization helps develop emotional engagement
and emotional comfort, promoting contentment as well as a longer-term
preference for using the platform.
Owen et al.
(2023) highlighted that personalization is considered ethical and transparent
to avoid relationship concerns regarding manipulation and privacy.
Personalization can positively influence contentment with relationships;
however, it is highly relevant in terms of how it is respectful and
transparent. On a platform like Lazada, making personalization ethical without
undue burden is effective in simplifying shopping by improving the user
experience, while strengthening the user-platform relationship through enhanced
trust resulting from a positive user experience.
Transparent
recommendation algorithm improves customer satisfaction by making the reasons
why AI suggests an offering more visual and understandable. When customers
understand where the recommendation came from, they can view the platform in a
way that contains fair analysis, offers reliability, and provides a user
perspective (Owen et al., 2023). Akbar et al. (2024) also stated that transparent
user systems can increase user’s satisfaction by increasing the appropriateness
and reliability of AI-generated suggestions. Alkudah and Almomani (2024)
mentioned the drawback of having overly technical and complicated explanation
instructions, as it can confuse customers with low digital literacy. Thus,
transparency needs to be designed responsibly, having just enough transparency
for the neediest of customers to be satisfied but not disrupting their
connotative equilibrium
Customer
satisfaction can be correlated to whether users are able to take control of
their personalization settings, such as data preferences, recognition of
product types, and suggestions. Control features provide users with the ability
to shape their shopping experience, offering a sense of freedom and autonomy,
which can effectively reduce user frustration with suggestions that may seem
either irrelevant or invasive. Alkudah and Almomani (2024) stated that digital
control empowers users and can improve users’ perceptions of fairness and trust
in the system. Turatti (2025) echoes the sentiment by indicating that when
customers feel a sense of control in their digital ecosystem, they may become
more emotionally committed to the process, which can lead them to return.
Turatti (2025) suggests that excessive personalization options may confuse
users or deter them from using any personalization, thereby minimizing the
benefits. It highlights that control systems should be designed to be simple,
intuitive, user-centered, and easy to use. For e-commerce platforms, a sensible
approach is to offer users meaningful control, ensuring improvements in user
satisfaction by reflecting a sense of participation and personalization in a
relevant and respectful manner.
Customer
satisfaction is a key factor in growing repeat purchase behavior in e-commerce.
A user with an expectation of a satisfactory shopping experience from a platform
is likely to return to make purchases. Satisfied users develop emotional and
psychological commitments that lead to loyalty and contribute to the
development of habitual buying behavior over time. Hardcastle et al. (2025)
claim that emotional engagement resulting from satisfaction often translates
into trust and familiarity, which fosters repeated interactions. Tufahati et
al. (2021) caution that satisfied users are also more likely to elicit positive
word-of-mouth behaviors, leading to repeat purchases and new users to the
platform. Furthermore, Jiradilok et al. (2013) found that satisfaction was one
of the strongest indicators of repurchase intention, particularly when users
experienced ease, responsiveness, and value in Thailand. Maintaining customer
satisfaction for e-commerce platforms is essential for an individual's
retention on the platform and the long-term growth of the market through repeat
purchase and referrals.
Repeat Purchases (RP) Recommendation
Algorithm Transparency (RAT) AI-driven Personalization (AIP) Customer Control over Personalization
(CCP) Customer Satisfaction (CS) H1 H2 H3 H4
Figure (1)
Conceptual framework
In this study, a cross-sectional
design was used as data collection occurred at a single time point. The data
collection proceeded on across-sectional basis for the inclusion of
investigating the naturally occurring, which may have influenced the three AI-driven
features: personalization, transparency of the algorithm, and control by
customer (McCarthy et al., 2022), which imposed customer satisfaction and their
consequent behavior in repeat purchasing. The goal of the research was not to
manipulate the research participants but to measure their perceptions of AI
systems in the Lazada environment as it existed in real-time.
The research instrument was a
self-administered online questionnaire that contained structured items that
were measured on a five-point Likert scale to ensure that access to the study
was meaningful, to reach as many potential respondents as possible, and to
normalize options provided to respondents. The final data file was run through
the SEM technique for analysis. The design was aligned to the study without
influencing the outcomes, suggesting it was appropriate to explore consumer
experiences in AI-enabled e-commerce contexts.
The target population for this study
encompasses Lazada users in Thailand who have bought at least one product on
Lazada (online) within the last three months before data collection and who
have experienced the AI features of Lazada. Research conducted by SEMrush
(2025) states that Lazada Thailand receives around 37.6 million site visitors
per month, which indicates a large and active customer group online. Despite no
public number for unique users, this statistic strongly supports the viability
of collecting data using an online survey and provides assurance there are
enough unique users in the target population to apply a statistical analysis
approach like Structural Equation Modeling (SEM).
This study collected responses from
409 valid participants, which exceeds the minimum sample size required for
conducting statistical analysis using structural equation modeling.
The study employed an online convenience
sampling method, inviting participants who met the defined screening criteria
for this study. Respondents were permitted to participate if they had made at
least one purchase from Lazada within the last three months and had interacted
with the AI features on Lazada in some way, such as a consumer of personalized
recommendations or algorithm-generated suggestions. The questionnaire was
administered online through the researcher distributing the questionnaire using
commonly accessible online platforms such as Facebook groups and through LINE
messaging apps, as these are platforms frequently used by Lazada shoppers in
Thailand, thereby allowing the researcher to conveniently obtain responses from
a convenient but relevant subset of Lazada users.
The screening questions were
incorporated at the beginning of the questionnaire so that only qualified
respondents could complete the full survey. The participation component of the
research was completely voluntary and anonymous, ensuring the ethical standards
in relation to data collection and participation remained intact.
The construct
reliability and validity are measured by Cronbach’s alpha and composite
reliability. The reliability results in Table (1) show that all constructs
achieved Cronbach’s alpha and composite reliability (CR) values above the
recommended threshold (0.70). AI-driven Personalization (AIP) had the highest
alpha (.922) and CR (.942), indicating excellent internal consistency. Customer
Control over Personalization (CCP) also performed strongly (α = .846, CR =
.891). The lowest reliability values were for Customer Satisfaction (CS)
(α = .786, CR = .854), but these are still acceptable. The Average
Variance Extracted (AVE) values for all constructs were greater than 0.50,
ranging from .540 (CS) to .766 (AIP), which confirms convergent validity. These
results indicate that the constructs explain more than half of the variance in
their items and can be considered reliable and valid for further testing.
|
Cronbach's alpha 𝛼 |
Composite reliability (rho_a) |
Composite reliability (rho_c) |
Average variance
extracted (AVE) |
|
|
AIP |
0.922 |
0.924 |
0.942 |
0.766 |
|
CCP |
0.846 |
0.846 |
0.891 |
0.621 |
|
CR |
0.816 |
0.819 |
0.872 |
0.576 |
|
CS |
0.786 |
0.788 |
0.854 |
0.540 |
|
RAT |
0.808 |
0.810 |
0.867 |
0.566 |
Table (1).
Construct reliability and validity for measurement model
(Source:
survey data 2025)
Gender of the
respondent
Out of 409
respondents, 242 were female, making up 59.17% of the total sample. Male
respondents accounted for 153, or 37.41%, while 14 participants (3.42%)
preferred not to disclose their gender. These results show that female
participants form the majority, which may reflect the strong presence of women
in online shopping. At the same time, the significant number of male
respondents ensures that the findings reflect opinions across both genders.
Consumers also expect their personalized experience to be ethical and reliable.
Demonstrating transparency into algorithms used can enhance users’ confidence
of how it works. When consumers know how their recommendations are generated,
and, importantly, feel the personalization system is fair, their trust becomes much
stronger in the platform. In turn, their trust relates positively to their
willingness to repeat purchase behavior. Transparency and control offer a
reduction in uncertainty and build their confidence over time (Owen et al,
2023; Akbar et al, 2024).
Figure (2) Gender
(Source: survey data 2025)
Age of the
respondents
The age
distribution shows that most respondents are young adults. The largest group is
those aged 26–30 years (167 respondents, 40.83%), followed by 31–35 years (104
respondents, 25.43%) and 20–25 years (102 respondents, 24.94%). A smaller group
of respondents were aged 18–20 years (16 respondents, 3.91%) and 36 years or
above (20 respondents, 4.89%). Together, these results confirm that most
participants are within the prime working and shopping ages, which makes them
highly relevant for online retail studies.
Figure (3) Gender
(Source: survey data 2025)
Current Occupation
The respondents also come from
diverse occupational backgrounds. Students make up the largest portion, with
172 participants (42.05%), followed by employees with 128 participants (31.30%).
Freelancers represent 73 respondents (17.85%), while business owners account
for 36 respondents (8.80%). This mix indicates that Lazada is widely used
across different occupational groups, from young students to working
professionals and entrepreneurs. The high number of students suggests that
younger users are particularly active in online shopping.
Figure (4)
Gender
(Source:
survey data 2025)
Purchasing
Frequency
Purchasing frequency results show how
often respondents shop on Lazada. The highest proportion, 169 respondents
(41.30%), reported shopping twice per month. Another 136 respondents (33.25%)
shop once per month, while 104 respondents (25.43%) reported purchasing three
times or more per month. These results suggest that most participants are
frequent and engaged shoppers, with the majority making at least one to two
purchases each month. This confirms that the respondents are active online
consumers who can reliably evaluate Lazada’s AI-driven features and their
influence on satisfaction and repeat purchase.
Figure (5)
Gender
The outer loading model presents the
factor loadings of the structural equation modeling. The minimum acceptable
threshold of factor loading score is 0.7. If the item scores less than 0.7, it
needs to be removed from the model. According to the results shown in table
(2), the factor loadings of AI-driven personalization (AIP1 to AIP5); customer
control over personalization (CCP1 to CCP5); customer repeat purchase (CR1 to
CR5); customer satisfaction (CS1 to CS5), and recommendation algorithm
transparency (RAT1 to RAT5) are more than 0.7 which means that the items are
reliable.
|
|
AIP |
CCP |
CR |
CS |
RAT |
|
AIP1 |
0.926 |
|
|
|
|
|
AIP2 |
0.796 |
|
|
|
|
|
AIP3 |
0.924 |
|
|
|
|
|
AIP4 |
0.794 |
|
|
|
|
|
AIP5 |
0.924 |
|
|
|
|
|
CCP1 |
|
0.722 |
|
|
|
|
CCP2 |
|
0.848 |
|
|
|
|
CCP3 |
|
0.848 |
|
|
|
|
CCP4 |
|
0.767 |
|
|
|
|
CCP5 |
|
0.746 |
|
|
|
|
CR1 |
|
|
0.758 |
|
|
|
CR2 |
|
|
0.808 |
|
|
|
CR3 |
|
|
0.773 |
|
|
|
CR4 |
|
|
0.730 |
|
|
|
CR5 |
|
|
0.723 |
|
|
|
CS1 |
|
|
|
0.720 |
|
|
CS2 |
|
|
|
0.778 |
|
|
CS3 |
|
|
|
0.755 |
|
|
CS4 |
|
|
|
0.711 |
|
|
CS5 |
|
|
|
0.706 |
|
|
RAT1 |
|
|
|
|
0.743 |
|
RAT2 |
|
|
|
|
0.783 |
|
RAT3 |
|
|
|
|
0.732 |
|
RAT4 |
|
|
|
|
0.764 |
|
RAT5 |
|
|
|
|
0.737 |
Table
(2). SEM results outliner
The table (3) shows the direct effect
of AI-driven personalization, customer control over personalization, and
recommendation algorithm transparency on customer satisfaction and repeat
purchase. The results indicate that AI-driven personalization has a weak no
statistically significant effect on repeat purchase (path coefficient 0.109,
p-value 0.275). Moreover, AI-driven personalization has no statistically
significant effect on customer satisfaction (path coefficient 0.09, p-value
0.244). Customer control over personalization has no statistically significant
effect on customer repeat purchase (path coefficient 0.068, p-value 0.286),
although it has statistically significant direct effect on customer
satisfaction (path coefficient 0.306, p-value < 0.01). Customer satisfaction
also has direct significant effect on customer repeat purchase (path
coefficient 0.703, p-value <0.01). Recommendation Algorithm Transparency has
no statistically significant effect on customer repeat purchase (path
coefficient -0.027, p-value 0.811), although it has direct significant effect
on customer satisfaction (path coefficient 0.40, p-value < 0.01).
The results indicate that customer
satisfaction is the critical factor of repeat purchase. When customer satisfy
with customer control over personalization and recommendation algorithm
transparency, they repurchase products.
|
|
Path Coefficient |
P values |
|
AIP Þ CRP |
0.109 |
0.275 |
|
AIP Þ CS |
0.090 |
0.244 |
|
CCP Þ CRP |
0.068 |
0.286 |
|
CCP Þ CS |
0.306 |
0.000 |
|
CS Þ CRP |
0.703 |
0.000 |
|
RAT Þ CRP |
-0.027 |
0.811 |
|
RAT Þ CS |
0.400 |
0.000 |
Table
(3) - Direct Effect
Table (4) shows the mediating effect
of customer satisfaction. The results indicate that the AI-driven
personalization has no statistically significant indirect effect on repeat
purchase through customer satisfaction (path coefficient 0.0634, p-value
0.2331), which means that customer satisfaction does not perform as a
significant mediator between the relationship of AI-driven personalization and
repeat purchase. However, the indirect effects of customer control over
personalization and recommendation algorithm transparency on repeat purchase
are statistically significant through customer satisfaction (path coefficient
0.2154 and 0.2815, respectively, p-value < 0.01), which means that customer
satisfaction has mediating effect between the relationships of customer control
over personalization, recommendation algorithm transparency, and customer
repeat purchase.
|
|
Path coefficient |
P values |
|
AIPÞCSÞCRP |
0.0634 |
0.2331 |
|
CCPÞCS ÞCRP |
0.2154 |
0.0002 |
|
RATÞCSÞCRP |
0.2815 |
0.0010 |
Table (4) -
Indirect effect (Mediating effect of customer satisfaction
The total effect presents the overall
effect of AI-driven personalization, customer control over personalization, and
recommendation algorithm transparency on customer repeat purchases. Table (5)
presents the total effects, showing that AI-driven personalization has no statistically
significant total effect on customer repeat purchases (path coefficient 0.172,
p-value 0.111). Customer control over personalization has a statistically
significant total effect on customer repeat purchase (path coefficient 0.2832,
p-value < 0.01). However, the transparency of the recommendation algorithm
has no statistically significant total effect on customer repeat purchases
(path coefficient 0.2541, p-value 0.0793).
Table (5) Total effect
(Direct effect + Indirect effect)
|
Direct effect |
Indirect effect |
Total Effect |
P-value |
||
|
AIPÞCRP |
0.109 |
AIPÞCSÞCRP |
0.0634 |
0.1720 |
0.1111 |
|
CCPÞCRP |
0.068 |
CCPÞCSÞCRP |
0.2154 |
0.2832 |
0.0018 |
|
RATÞCRP |
-0.027 |
RATÞCSÞCRP |
0.2815 |
0.2541 |
0.0793 |
|
AIPÞCS |
0.090 |
- |
- |
0.090 |
0.2442 |
|
CCPÞCS |
0.306 |
- |
- |
0.306 |
0.0001 |
|
RSTÞCS |
0.400 |
- |
- |
0.400 |
0.0003 |
|
CSÞCRP |
0.703 |
- |
- |
0.7033 |
0.0000 |
(Source: survey data 2025)
|
β 0.400(P-value 0.0003) |
|
β 0.306 (P-value 0.0001) |
|
β 0.09 (P-value 0.244) |
|
β 0.703 (P-value 0.000) |
|
Repeat Purchases (RP) |
|
Recommendation
Algorithm Transparency (RAT) |
|
AI-driven Personalization (AIP) |
|
Customer Control over Personalization (CCP) |
|
Customer Satisfaction (CS) |
The results indicate that consumer
satisfaction is the strongest driver of repeat purchases in Lazada. The path of
satisfaction and repeat purchase is significant (β = 0.703, p < .001),
so if a consumer is satisfied with the purchase experience, then they are
likely to buy again. When people are satisfied with the buying experience in
e-commence platform, they develop trust in the system, recognize the system as
trustworthy, and are confident that future purchases are smooth and rewarding.
Satisfaction reduces uncertainty about the experience and switching intentions
because it is easier to buy again within comfortable system that is familiar
than search for a new one. Consequently, customers satisfaction builds loyalty
by converting a one-time customer into a repeat purchaser.
The results also indicated the
primary factors influenced on customer satisfaction were recommendation
transparency and user control. Recommendation Algorithm Transparency was the
primary influence on satisfaction, having the significant effect size (β =
0.400, p < .001). When AI system explains why it is recommending a product,
customers see it as fair. This fairness builds trust, and trust supports
confidence to buy. The second strongest influence was customer control over personalization
(β = 0.306, p < .001). Customers who can change their settings feel
more in control and less worried about privacy. This also builds trust, may
lead them to switch from old retailers, and finally gives comfort while
improving satisfaction. However, AI based personalization was not statistically
significant (β = 0.090, p = .244). This shows that in current times
personalization has become an essential expectation of online shopping and does
not create any additional satisfaction unless the process includes transparency
and a sense of control.
The findings reveal that AI-driven
personalization has no statistically significant effect on customer
satisfaction or repeat purchases among Lazada users in Thailand. However, Ahmed
et al. (2025) and Hardcastle et al. (2025) found that personalization can
enhance enjoyment, reduce decision fatigue, and lead to emotionally engaged
users. Moreover, Owen et al. (2023) noted that personalization builds trust
when deployed ethically and transparently. In the case of Lazada, users likely
see personalization as an essential feature of browsing at an e-commerce site,
but not the creative and innovate feature. For Lazada users, the value of
personalization depends more on whether it is transparent and gives them
control than on personalization alone.
The findings demonstrate strong
positive effect of recommendation algorithm transparency on customer
satisfaction of Lazada users in Thailand. Owen et al. (2023) have suggested
that trust builds beliefs of transparency that minimize feelings of
manipulation, while Akbar et al. (2024) report that satisfied customers are
more likely to accept that the appropriateness of the recommendation.
Transparent recommendation system is perceived as a source of credibility also
as an element of fairness enhances customer satisfaction.
The study found that customer control
over personalization has a significant and positive impact on customer
satisfaction. Alkudah and Almomani (2024) emphasized that control over user's
digital interactions fosters a sense of fairness and trust in the system.
Turatti (2025) similarly found that control also enhanced the emotional
commitment from users, as it made them feel more invested and valued in the
experience.
This study’s finding confirmed that
customers' satisfaction was a powerful tool on Lazada users repeat purchase
behavior in Thailand. Hardcastle et al. (2025) highlighted those positive
experiences the user has creates trust and familiarity, which encourages users
to engage repeatedly. Jiradilok et al. (2013) demonstrated that satisfaction
had the most decisive influence on repurchase intention. Customer satisfaction
was the most important factor to ensure long-term customer loyalty. Lazada must
ensure that their users' experience is satisfactory through good quality
service, transparent communication, and providing reliable experiences.
Lazada should prioritize customer
satisfaction as a key factor in building repeat purchase behavior. Lazada
should develop strategies that enhance customers’ shopping experience through
AI features to improve satisfaction and, consequently, repeat purchases. Lazada
needs to improve its personalization by making the system transparent.
Moreover, Lazada should develop simple and user-friendly control functions that
allow customers to adjust recommendation levels and decide what personal data
can be used. Giving users this sense of autonomy will strengthen their feeling
of being respected and valued, which directly contributes to satisfaction.
Lazada should also ensure that the overall service experience is reliable and
efficient. Fast shipping, accurate delivery, and responsive customer service
are important for building positive emotions toward the platform. When
customers enjoy a smooth and trustworthy shopping journey, they are more likely
to recommend the platform to others and continue purchasing repeatedly. By
focusing on transparency, user control, and reliable service, Lazada can
strengthen satisfaction, increase loyalty, and secure long-term growth through
repeat purchases.
The current study targeted only
Lazada to find the impact of AI features on customer satisfaction and repeat
purchase. Further studies should do in other e-commerce platforms as Shopee,
and SHEIN. This study implemented the quantitative research method, and used
three AI features (personalization, transparency, customer control over
personalization) to study how AI features on customer satisfaction and repeat
purchase. Further researchers should use other AI features as Chatbot, voice
assistants, and visual search tools. Moreover, future research should apply
different research methods as qualitative and mixed-method to get in-depth
understanding on AI-features of e-commerce platform. Future studies should
collect data from different e-commerce users and nations to understand the
different consumer perception on e-commerce.
This study shows that customer
satisfaction is the most powerful predictor of repeat purchase for Lazada consumers
in Thailand. While AI-driven personalization did not establish any significant
direct effect, recommendation transparency and customer control were both found
to explain customer satisfaction significantly in this research. Satisfied
customers are more likely to repurchase, be loyal, and recommend others to buy
products through Lazada, thus making their purchasing experience and have
positive retention effects. Consequently, the results demonstrate that
satisfaction is a major mediator of AI recommendations on customer retention.
Overall, the current research provides insights into how e-commerce AI-driven
features could be better designed and managed. By focusing on customer
satisfaction, Lazada and similar businesses can reap the dividends of sustainable
growth through repeated purchases and long-term loyal customers.
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