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Customer Service Communication as a Driver of Satisfaction: A
Comparative Analysis of Shopee and Taobao
Melody Trixie, Sazzad Kadir Zim*
Information Science and Technology (NUIST), China
*
Corresponding Author
DOI: https://dx.doi.org/10.51584/IJRIAS.2025.10100000126
1016 Published: 14 November 2025
ABSTRACT
This study explores how customer service communication attributes influence customer satisfaction on Shopee
and Taobao. Based on survey data from 134 Indonesian users and analyzed using ordered logistic regression,
the study examines the effects of perceived service quality, responsiveness, communication quality, and
empathy. (1) On Shopee, responsiveness (γ₂ = 0.921) and communication quality (γ₃ = 1.674) significantly drive
satisfaction, highlighting the value of timely and clear interactions. (2) On Taobao, perceived service quality (β₁
= 0.906) and empathy (β₄ = 0.907) are key, reflecting the importance of informative and emotionally supportive
communication. (3) Control variables such as age and frequency show limited influence, with age negatively
affecting satisfaction only on Shopee. These findings highlight the important role of strong communication skills
in customer service, which can enhance customer satisfaction across different e-commerce platforms.
Keywords: Communication, Customer satisfaction, Customer service, E-commerce, Shopee, Taobao.
INTRODUCTION
Research Background
E-commerce has become a dominant mode of transaction in recent years, offering greater efficiency by reducing
both time and cost. According to Statista
1
, Indonesia's e-commerce market is the largest in Southeast Asia, valued
at approximately 65 billion USD. Since 2019, the country has experienced significant growth in its e-commerce
sector, driven by the increasing number of internet users and improvements in digital infrastructure. The outlook
for Indonesia’s e-commerce market remains highly positive. In this competitive environment, customer service
quality has become the key determinant of customer satisfaction. Consumers increasingly evaluate platforms not
only based on product price or quality but also on the overall service experience.
Shopee has emerged as the leading e-commerce platform in Southeast Asia, including Indonesia, where it
outpaces competitors like Tokopedia and Bukalapak. With a Gross Merchandise Value (GMV) reaching $23.3
billion in 2024
2
. In parallel, Indonesia has seen a growing connection with China, particularly through education.
As one of the top five countries for Indonesian students studying abroad, China has become increasingly familiar
to Indonesians (Paterson, 2019). This exposure has introduced many Indonesian consumers to Chinese platforms
like Taobao, operated by Alibaba Group, which offers competitive prices, product variety, and cross-border
access.
These trends present a unique opportunity to explore how customer service communication differs across these
two platforms from the perspective of Indonesian users. With a balanced sample from both Shopee and Taobao
users, this study aims to examine how communication skills of customer service agents influence customer
satisfaction in the evolving e-commerce environment.
1
https://www.statista.com/topics/5742/e-commerce-in-indonesia/#topicOverview
2
https://cube.asia/read/shopee-gmv-growth-2023-2024-shows-bright-future/
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Research Questions
Based on the preceding analysis, the upcoming discussion will delve into 2 research questions:
1. How do perceived service quality, responsiveness, communication quality, and empathy influence
customer satisfaction in the context of Shopee and Taobao?
2. Are there significant differences between Shopee and Taobao in terms of how these service attributes
impact customer satisfaction among Indonesian consumers?
Objective/Significance of Study/ Level 2 Heading
LITERATURE REVIEW/ METHODS
This literature review focuses on previous research related to customer satisfaction and the role of customer
service within e-commerce platforms. The discussion is organized into six core categories: platform-based
comparative design, customer satisfaction, perceived service quality, responsiveness, communication quality,
and empathy.
Platform-based Comparative Design
Although customer satisfaction in e-commerce is broadly shaped by perceived service quality, responsiveness,
communication quality, and empathy, the salience of each driver can differ by platform type. Cross-border
marketplaces (e.g., Taobao for overseas users) embed more uncertainty and friction, longer lead times, customs,
returns across borders, and multi-currency/payment issues, so customers place greater weight on risk-reducing
service cues (fast dispute handling, transparent returns, proactive updates). Evidence shows perceived risk
directly depresses e-commerce intentions and behaviours, underscoring the value of risk-mitigating service and
communication in cross-border contexts (Ma et al., 2025). In parallel, return policy design matters more when
cross-border logistics make exchanges harder; lenient/clear policies materially raise purchase intentions in cross-
border retailing (Shao et al., 2021).
By contrast, regional platforms like Shopee typically provide denser local logistics, COD and local wallets, and
localized customer support. In such settings, operational execution (on-time delivery, accurate info, consistent
messaging) and day-to-day responsiveness tend to dominate satisfaction. Finally, Taobao’s strong live-streaming
commerce ecosystem makes interactive communication quality especially pivotal: live video builds trust and
engagement, amplifying satisfaction pathways relative to static chat or ticketing (Wongkitrungrueng & Assarut,
2020).
Perceived Service Quality
Service quality has consistently been linked to satisfaction across many industries. While it may not always be
the strongest driver, it remains a fundamental factor in shaping overall customer perceptions (Asawawibul et al.,
2025). Several studies emphasize that perceived service quality affects satisfaction indirectly, by shaping
expectations, emotional responses, and the perceived value of the service received (Yang et al., 2024). In time-
sensitive sectors like fresh product e-commerce, aligning service design with customer emotions and
expectations becomes even more important. Trust toward delivery personnel has also been identified as a key
component of perceived service quality, with significant influence on overall satisfaction levels (Uzir et al.,
2021).
H1 = Perceived service quality of customer service has a significant effect on customer satisfaction in e-
commerce.
Responsiveness
Responsiveness, the ability to respond to customers promptly and effectively, is widely regarded as an essential
aspect of quality service. Research has shown that AI-powered chatbots can contribute positively to customer
satisfaction, particularly when handling routine inquiries quickly and clearly (Chen et al., 2022). However, while
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helpful, automated responses still need to be complemented by human assistance in more complex or emotionally
sensitive cases. Studies have shown that perceived responsiveness in e-commerce indicates staff competency
and empathy, which are crucial for relational quality and customer satisfaction (Zhang et al., 2024). Furthermore,
research indicates that responsiveness, along with reliability, plays a pivotal role in enhancing customer
satisfaction in e-commerce settings. The ability of service providers to promptly address customer needs and
concerns contributes to a positive customer experience and fosters long-term relationships (Rodriguez-Diaz et
al., 2022). These findings underscore the importance of responsiveness as a key factor in delivering high-quality
customer service in the digital marketplace.
H2 = Responsiveness of customer service has a significant effect on customer satisfaction in e-commerce.
Communication Quality
Communication quality is essential in shaping customer satisfaction, particularly in e-commerce where
interactions are often mediated through digital platforms. Clear, empathetic, and responsive communication
helps reduce perceived risk and builds trust (Ma et al., 2025). AI-powered chatbots have also been shown to
improve satisfaction when they provide prompt and coherent responses, though their effectiveness depends on
the complexity of the customer's inquiry (Chen et al., 2022).
Additionally, ensuring consistency between product descriptions and customer reviews minimizes semantic
gaps, which can positively influence customer perception and experience (Rodriguez-Diaz et al., 2022). These
findings affirm that effective communication, whether human or automated, plays a critical role in delivering
satisfying e-commerce experiences. Based on the literature review and research objectives, the following
hypotheses are proposed:
H3 = Communication quality has a significant effect on customer satisfaction in e-commerce.
Empathy
Empathy plays a vital role in shaping customer service quality, particularly through its influence on emotional
engagement and personalized interaction. Emotional responses have been shown to mediate the relationship
between perceived service quality and overall satisfaction, underscoring the need for emotionally attuned service
delivery (Yang et al., 2024). This emphasis on emotional understanding has led to the development of the
Customer-Oriented Perspective Taking (COPT) theory, which defines COPT as an employee’s ability to adopt
the customer’s perspective by recognizing their emotions, needs, and motivations. Higher levels of COPT have
been found to significantly improve service quality by fostering more empathetic and tailored communication
(Dong & Hon, 2025). In particular, empathy is essential when addressing younger consumers, whose emotional
needs strongly influence how they perceive and evaluate their service experiences (Dai et al., 2024).
Furthermore, empathetic interaction does not occur in isolation—it is supported by broader organizational factors
such as social expertise and institutional mechanisms. These elements contribute to a positive service climate,
enabling more meaningful and emotionally responsive customer interactions (Tuan & Doan, 2025).
H4 = Empathy of customer service has a significant effect on customer satisfaction in e-commerce
Customer Satisfaction
Customer satisfaction plays a central role in influencing consumer loyalty and future purchasing behavior. As
trust in an e-commerce platform grows, so does the customer’s likelihood of continued engagement and
satisfaction, often shaped by effective communication throughout the service experience (Taheri et al., 2024).
Machine learning approaches have also been used to predict online retail satisfaction, with key factors such as
delivery time, pricing, and review content found to be highly influential (Zaghloul et al., 2024). In line with this,
implementing dynamic pricing strategies has been shown to not only optimize revenue but also improve
customer satisfaction by offering fair and competitive pricing (Datta et al., 2025). Furthermore, delivery updates,
return procedures, and cross-border service information all require accurate and timely communication have a
significant impact on how satisfied customers feel after their transactions (Hui et al., 2025).
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METHODOLOGY
Conceptual Framework
The conceptual framework illustrates the overall structure of this research. It includes four independent variables
(perceived service quality, responsiveness, communication quality, and empathy) examined in relation to the
dependent variable, customer satisfaction. Additionally, control variables such as gender, shopping frequency,
age, and user tenure are included to assess their influence on customer satisfaction.
Figure 1 Research Framework
Research Method
This research employs quantitative data analysis to examine the distribution of variables within numerically-
based data or data that can be easily converted into numerical form. The data collected for this study comes from
responses to questionnaires, which are organized into five categories: demographic information, customer
satisfaction, perceived service quality, responsiveness, communication quality and empathy. The author will use
a descriptive approach to outline the population's characteristics, followed by an ordered logistic regression
analysis. Finally, the author will compare the results of the regression between two platform.
Population and Sample
The study's population includes Indonesians aged 18 or above who ever buy a product from Taobao or Shopee
platform. Data was collected through purposive sampling from 134 Indonesian citizens.
Data Collection Method
This study uses primary data to directly address its objectives. Data was collected through closed-ended
questionnaires distributed via Google Form to understand what kind of customer service qualities that could
affect customer satisfaction towards that e-commerce. A Likert Scale questionnaire was used in this research to
assess attitudes, behaviours, and opinions, with responses ranging from "strongly disagree" to "strongly agree,"
including a neutral option (Jebb et al., 2021).
Research Model
Ordered Logistic Regression (OLR)
Ordered Logistic Regression (OLR) is a statistical method designed for analyzing categorical data with a ranked
order (Soshnikova, 2021). Unlike standard logistic regression, which focuses on binary outcomes (e.g., yes/no),
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OLR is suited for dependent variables with multiple ordered categories, such as customer satisfaction levels
(dissatisfied, neutral, satisfied) or Likert scale responses (strongly disagree to strongly agree). It enables the
evaluation of relationships between the dependent variable and independent variables.
Measurement Model
Validity Test
A validity test using Pearson's r table evaluates the correlation between scores from a measurement instrument
and scores from a criterion measure. Pearson's correlation coefficient (r) indicates the strength and direction of
the relationship between the two variables. The calculated r value is compared to the critical value from the
Pearson's r table to determine validity (df=n-2). If the r value meets or exceeds the table's value, the variable is
valid; if not, it is invalid (Cheung et al., 2024). Table 3 below displays the critical values for a two-tailed test.
Table 1 Pearson’s r Table for Two-tailed Test
df:
0.1
0.05
0.02
1
.9877
.9969
.9995
10
.4973
.576
.6581
100
.1638
.1946
.2301
120
.1496
.1779
.2104
132
.1427
.1697
.2008
134
.1416
.1684
.1993
Reliability Test
Adapt to the previous literature (Asawawibul et al., 2025; Uzir et al., 2021; Yang et al., 2024) , this research use
Cronbach’s alpha to test the reliability and assess the internal consistency of a measurement tool. It ranges from
0 to 1, with lower values indicating weaker consistency and higher values reflecting stronger reliability. A score
between 0.0 and 0.20 is “Less reliable,” 0.20 to 0.40 is “Rather reliable,” 0.40 to 0.60 is “Quite reliable,” 0.60
to 0.80 is “Reliable,” and 0.80 to 1.00 is “Very reliable.” Higher values suggest better consistency, while lower
values indicate the need for adjustments to improve reliability. This test helps researchers ensure the
dependability of their measurement instruments.
RESULTS AND DISCUSSION
Descriptive Statistics
This study involved 134 Indonesian respondents, equally divided between users of Taobao and Shopee. Most
participants (82.84%) were aged between 18 and 24 years old. The sample consisted of 54.48% male and 45.52%
female respondents.
When comparing users by platform, several differences are observed. Taobao users tend to shop more frequently,
with 20.90% purchasing more than five times per month, compared to only 8.96% of Shopee users. On the other
hand, 58.21% of Shopee users shop less than once a month, higher than Taobao's 44.78%. Both platforms are
mainly used by younger users, especially those in the 18–24 age group, which accounts for 80.60% of Shopee
users and 85.07% of Taobao users. Shopee also has a slightly higher proportion of male users (55.2%) compared
to Taobao (53.7%), in the other hand, Taobao has a slightly higher proportion of female users (46.3%). In terms
of platform usage duration, Shopee users are more likely to have used the platform for a longer period, with
46.3% using it for over three years, compared to 20.9% of Taobao users. These results indicate that Taobao tends
to attract more frequent buyers, while Shopee users show higher platform loyalty over time.
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Figure 2 Respondent Demographic Information
List of Variable
The table 2 contain the list of variables followed by its description used within the structural equation modelling
within this research.
𝑌
=
𝑎
+
1
PSQ+ γ
2
R+ γ
3
CQ
+ γ
4
E
+ f(x) +ε (1)
𝑌
=
𝑎
+ β
1
PSQ+ β
2
R+ β
3
CQ
+ β
4
E
+ f(x) +ε (2)
Table 2 Descriptive Statistics
Variable
Description
Mean
S.D.
Min
Max
Outcome Variable
Customer Satisfaction
(Y) – CS
Level of satisfaction a customer experiences toward a
seller’s customer service on the Shopee or Taobao
platform.
16.45
2.35
4
20
Explanatory Variables
Perceived Service Quality
(X
1
) – PSQ
Customer service quality towards the product knowledge
and purchase support
16.50
2.55
5
20
Responsiveness (X
2
)-R
Captures how promptly and effectively the customer
service responded to inquiries
16.23
2.84
4
20
Communication Quality
(X
3
) - CQ
Measures how clearly the customer service communicates
information about the product and transaction.
16.49
2.44
4
20
37
30
36
31
0
5
10
15
20
25
30
35
40
Male Female
Gender
Shopee Taobao
54
11
0
2
57
9
1
0
0
10
20
30
40
50
60
18-24 years 25–34 years 35–44 years >=45 years
Age
Shopee Taobao
39
11 11
6
30
10
13
14
0
5
10
15
20
25
30
35
40
45
less than once 1-2 times 3-5 times > 5 times
Shopping Frequency (Monthly)
Shopee Taobao
28
6
2
31
27
9
25
6
0
5
10
15
20
25
30
35
< 6 month 6 month - 1
year
1-3 years > 3 years
Tenure
Shopee Taobao
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Empathy (X
4
) - E
Customer service’s ability to show friendliness,
understand customer needs, express concern, and build
confidence through supportive and personalized
interaction.
16.46
2.66
4
20
Control Variables (𝒇(𝒙))
Platform – P
Indicates whether the respondent used Shopee or Taobao
as the transaction platform.(P=0 represents Shopee, P=1
represents Taobao)
0.50
0.50
0
1
Frequency – F
Refers to how often the respondent makes purchases on the
e-commerce platform.
1.96
1.14
1
4
Gender – G
The respondent’s gender identity
0.45
0.50
0
1
Age – A
Age category of the respondent
1.21
0.52
1
4
Tenure - T
Length of time the respondent has used the e-commerce
platform
2.34
1.27
1
4
Other Symbols
𝑎
Intercept
1
,
2
,
3,
4
Coefficients for independent variables in model 1
β
1
, β
2
, β
3
，
β
4
Coefficients for independent variables in model 2
ε
Error terms
Measurement Test
Validity Test
This study employed a validity test using Cronbach’s Alpha. After conducting the analysis in STATA, the item-
test correlation values were compared with the critical value from Pearson’s r table. The degrees of freedom (df)
were calculated as n-2, resulting in df = 132. Based on table 1, the critical r-value was determined to be 0.1697.
Any item with a correlation above this threshold is considered valid. The results indicated that all questionnaire
indicators exceeded this value, with minimum value of 0.6913 (See Appendix B.1.1), confirming their validity
for further analysis.
Reliability Test
To assess the reliability of the research instrument, the Cronbach’s Alpha test was conducted using STATA. The
results showed high reliability scores for all variables: Customer Service (0.9680), Perceived Service Quality
(0.9729), Responsiveness (0.9640), Communication Quality (0.9657), and Empathy (0.9696). Since all values
exceed the commonly accepted threshold of 0.8 (See Appendix B.1.2), it can be concluded that the data
demonstrates a high level of internal consistency and is therefore considered very reliable.
Ordered Logistic Regression
Regression Analysis of Customer Service Communication Factors Influencing Satisfaction on Shopee
Table 3 Model 1 Analysis (See Appendix B.1.3)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
VARIABLES
cs
cs
cs
cs
cs
cs
cs
cs
PSQ
1.382***
0.783**
0.340
0.194
0.196
0.157
0.188
0.231
(0.247)
(0.384)
(0.324)
(0.233)
(0.250)
(0.217)
(0.290)
(0.284)
R
1.441***
1.027***
0.980***
0.982***
0.949***
1.027***
0.921**
(0.260)
(0.264)
(0.282)
(0.280)
(0.305)
(0.343)
(0.407)
CQ
1.437***
0.734
0.795*
1.155**
1.521***
1.674***
(0.493)
(0.467)
(0.468)
(0.560)
(0.576)
(0.541)
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E
1.396***
1.408***
1.346***
1.059*
1.245
(0.426)
(0.423)
(0.485)
(0.576)
(0.759)
F
0.180
0.471*
1.154**
1.142**
(0.252)
(0.286)
(0.460)
(0.448)
A
-1.262***
-1.341***
-1.242**
(0.475)
(0.489)
(0.502)
G
-1.616*
-0.153
(0.858)
(1.752)
T
-0.565
(0.551)
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
In the model 1, where the platform is set as Shopee (p = 0) and the population consists of Indonesian consumers,
the analysis reveals that Responsiveness and Communication Quality are the most important customer service
communication factors shaping customer satisfaction. Responsiveness has a coefficient of
2 =0.921 and is
significant at the 1% level, indicating that Indonesian Shopee users highly value fast and effective replies from
sellers. Communication Quality also demonstrates a strong and significant effect, with a coefficient of
3 =1.674
(1% level), showing that clear and understandable communication plays an important role in enhancing
satisfaction for Indonesian users.
Other communication-related aspects like Empathy (
4 =1.245) and Perceived Service Quality (
1 =0.231) show
positive signs but they are not statistically significant in this model. This suggests that although Indonesian
consumers appreciate these aspects, their influence is less prominent compared to responsiveness and
communication. Among the control variables, Frequency of purchase has a significant positive impact (1.142,
5% level), meaning that Indonesian users who shop more frequently on Shopee tend to report higher satisfaction
towards customer service communication skills. In contrast, Age has a significant negative effect (–1.242, 1%
level), indicating that older Indonesian users are less likely to be satisfied. Gender and Platform Tenure show no
significant effects. Hence, the findings suggest that for Indonesian consumers on Shopee, timely responses and
clear communication are the main drivers of customer satisfaction towards the customer service.
Regression Analysis of Customer Service Communication Factors Influencing Satisfaction on Taobao
Table 4 Model 2 Analysis (See Appendix B.1.4)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
VARIABLES
cs
cs
cs
cs
cs
cs
cs
cs
PSQ
1.853***
1.121***
1.053***
0.673***
0.700***
0.692***
0.667**
0.906***
(0.321)
(0.392)
(0.401)
(0.240)
(0.230)
(0.231)
(0.281)
(0.333)
R
0.789***
0.439
0.356
0.323
0.333
0.372
0.0810
(0.246)
(0.416)
(0.304)
(0.332)
(0.336)
(0.432)
(0.485)
CQ
0.617
0.671*
0.693*
0.675*
0.598
0.680
(0.555)
(0.379)
(0.385)
(0.390)
(0.654)
(0.630)
E
0.738***
0.756***
0.749***
0.750***
0.907***
(0.213)
(0.222)
(0.228)
(0.219)
(0.246)
F
-0.0713
-0.0565
-0.130
-0.123
(0.312)
(0.322)
(0.521)
(0.497)
A
0.135
0.107
0.375
(0.526)
(0.524)
(0.585)
G
0.445
0.828
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(2.315)
(2.383)
T
-0.632
(0.460)
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
In the final model (Column 8), where the platform is set as Taobao (p = 1) and the respondents are Indonesian
users, the results show that Perceived Service Quality (PSQ) and Empathy (E) are the most important customer
service communication factors in determining customer satisfaction. PSQ has a coefficient of β1 = 0.906 and is
significant at the 1% level, indicating that Indonesian users on Taobao place strong importance on how
informative, helpful, and respectful the seller is. Likewise, Empathy shows a high coefficient of β4 = 0.907, also
significant at the 1% level, suggesting that customers feel more satisfied when sellers show care, concern, and
emotional understanding during service interactions.
Communication Quality (CQ) has a positive coefficient (β3 = 0.680) but is not statistically significant in this
model, implying that while clear communication is appreciated, it may not be a decisive factor for satisfaction
on Taobao when other variables like PSQ and empathy are already present. Interestingly, Responsiveness (R),
although traditionally considered important, shows a very low and insignificant coefficient (β2 = 0.0810). This
pattern is consistent with the cross-border nature of Taobao transactions where users anticipate longer service
cycles due to time zones, translation, and customs, so speed alone becomes a hygiene factor, whereas accurate,
complete, and actionable resolution (PSQ) delivered with care and perspective-taking (Empathy) is what
meaningfully shifts satisfaction. In practice, many behaviors perceived as “responsive” (e.g., proactive updates,
detailed guidance) are evaluated by users as quality or empathy, so their variance is absorbed by PSQ and
Empathy in the multivariate model. Moreover, once these two are high, additional reply speed offers diminishing
returns.
Among control variables, none show statistically significant effects. Frequency (F), Age (A), Gender (G), and
Tenure (T) all have non-significant coefficients, indicating that satisfaction levels on Taobao are not strongly
influenced by how often users shop, their age, gender, or how long they’ve used the platform.
In summary, for Indonesian consumers using Taobao, Perceived Service Quality and Empathy are the key drivers
of customer satisfaction, while Responsiveness and Communication Quality play more secondary roles in this
context.
CONCLUSION AND RECOMMENDATION
This research emphasizes the critical role of customer service communication skills in influencing customer
satisfaction. Based on survey data from 134 Indonesian respondents who have used either Shopee or Taobao,
and analyzed through ordered logistic regression, the study identifies which communication aspects are most
effective on each platform. For Shopee, Indonesian users value timely responses and clear communication as the
primary drivers of satisfaction. In contrast, for Taobao, Perceived Service Quality and Empathy play a more
significant role. This sample also has limitiations, the sample is small in size and geographically concentrated in
Indonesia. Future work should employ a larger, more diverse, multi-country sample (and, where feasible,
probability-based recruitment or platform log data) to improve generalizability and to test whether the platform-
specific patterns observed here replicate across user segments and market contexts.
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