ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 315
www.rsisinternational.org
The Role of User Behavior as a Mediator of Personalized Digital
Marketing and Trust on Customer Satisfaction: A Case of
Indonesian Local Products
Elena Suhadi
1
, Budi Suprapto
2*
, Mohd Fazli Mohd Sam
3
1,2
Faculty of Business and Economics, Universitas Atma Jaya Yogyakarta
2,3
Fakulti Pengurusan Teknologi dan Teknousahawanan, Universiti Teknikal Malaysia Melaka
*Corresponding Author
DOI: https://dx.doi.org/10.47772/IJRISS.2025.92800031
Received: 08 November 2025; Accepted: 14 November 2025; Published: 19 December 2025
ABSTRACT
The objective of this research is to examine the influence of personalized digital marketing and trust on customer
satisfaction, with user behavior as a mediating variable. The subject of this research is local Indonesian products.
The research employs a purposive sampling method. The number of respondents obtained was 240. The method
used to analyze the questionnaire results is Smart PLS software version 4.1.0.0, using the Partial Least Square
method. The results of the research indicate that personalized digital marketing and trust have a positive influence
on customer satisfaction both directly and indirectly. The indirect influence occurs through the mediating
variable of user behavior. The mediation relationship between personalized digital marketing, user behavior, and
customer satisfaction, as well as trust, is found to be complementary.
Keywords: customer satisfaction, personalized digital marketing, trust, user behavior.
INTRODUCTION
Everyday products of Indonesian society are produced by various manufacturers and brands. For groups of
people who are active on social media or at least have social media applications, they tend to buy their daily
necessities through online shops. This is because online shops offer convenience in the shopping and product
delivery processes. This phenomenon is evidenced by the increasing number of e-commerce users daily (Davis
et al., 2021). Although online purchases in Indonesia are subject to taxes and administrative fees, these costs are
still lower compared to the operational costs of managing an offline store. Therefore, entrepreneurs, especially
those just starting their businesses, often choose to begin online and later expand to offline stores. This is contrary
to the past, where offline stores were established first before considering online marketing.
In the current digital era, e-marketing is a crucial element in e-commerce marketing efforts (Johnson et al., 2001).
E-commerce has become a significant tool for gaining a competitive advantage in the market (Cunha et al.,
2023). Customer trust in a brand or manufacturer can significantly influence customer behavior. Trust is
something that needs to be continuously built and nurtured. There are many cases where even a slight breach of
trust can cause a brand to lose almost all of its loyal customers. Potential consumers may choose to buy a new
product from a brand because they trust that the product will be beneficial to them, without needing to try it first.
Customer behavior is a process where customers think about buying a product or choosing to use a service.
Customers in the online world have a different approach compared to offline customers. Online customers pay
attention to the website’s appearance (UI/UX), the interaction between the seller and buyer (even if its online),
and various online advertising methods (Anderle et al., 2016). Customer satisfaction is essential for building the
branding of a trademark. This branding can generate trust or lead to impulse buying due to the established trust
in a particular brand, causing even unnecessary products to be purchased (Cunha et al., 2023).
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 316
www.rsisinternational.org
Understanding the process of information dissemination and the use of customer data among manufacturers can
generate insights into customer satisfaction preferences and help develop strategies to become market leaders
(Lee et al., 2022). Previous research has discussed variables such as general marketing, but the current study
uses personalized marketing techniques to meet customer satisfaction. (Davis et al., 2021; Kushwaha, 2020).
This study will identify whether the personalization of tools (personalized digital marketing) affects customer
behavior. Additionally, it will analyze the impact of customer trust in manufacturers (trust) on product selection
and purchase. The novelty of this research lies in using personalized digital marketing as an independent variable
and examining its relationship with the mediating variable of user behavior on customer satisfaction. The subjects
of this study are individuals who have shopped on e-commerce platforms at least twice in the past three months
and have social media accounts.
Personalized digital marketing strategies can influence the behavior of potential buyers or consumers of local
Indonesian products. This digital marketing content can be informative, subscription-based, or interactive.
Strategies can also be implemented by combining several existing content types. In addition to content, it is
possible to maximize the selection of digital marketing media such as social media, email, visual ads, and videos.
User behavior follows the content they see and encounter in the media they frequently observe. Marketing teams
must use digital marketing strategies that do not offend any market segment, as digital marketing has a very
broad reach (Davis et al., 2021).
The feeling of trust can become a competitive advantage for a brand. Gaining trust can lead to impulse buying
from consumers. The concept of impulse buying can be very beneficial for producers. For instance, some people
have established trust in the Chanel brand; when a new Chanel product is released, people tend to believe that it
is of high quality, even if there are no reviews and the price is relatively high. However, for some customers,
reviews from other customers are crucial for building their trust in purchasing a product, especially if it is bought
online or cannot be tried beforehand. Such behaviors present challenges for producers in marketing their products
and finding effective marketing strategies (Davis et al., 2021).
Customer satisfaction can lead to Repurchase Intention, which is the desire to buy again from the same producer.
The intention to repurchase is itself a competitive advantage. Loyalty is one of the attitudes that consumers
develop after being nurtured by both the producer and the customer. Building customer satisfaction is crucial for
developing a brand. The decision to purchase a product result from customer behavior, which trusts and responds
positively to the digital marketing efforts provided by the producer (Davis et al., 2021).
Customer satisfaction is crucial for creating a sustainable relationship between a brand and its customers.
Resources should be focused on specific content or platforms, achieved by analyzing target customers and
identifying factors that influence their behavior. These insights are then applied to personalized content to
achieve the desired customer satisfaction (Sri et al, 2025, Chan, 2022, Davis et al., 2021).
Increasing trust can also be achieved by ensuring the security of customer data. In digital marketing, when
customers believe that their personal data is safe and used wisely, they tend to feel more comfortable and satisfied
with their experience with the brand. Additionally, business owners should strive for quick and responsive
feedback. When customers feel that a brand responds promptly to their needs or inquiries, it can enhance
customer satisfaction by providing a smooth and efficient Q&A experience (Davis et al., 2021).
Personalized digital marketing enables a brand to gain deeper insights into user preferences and behaviors
through potential customers’ interactions with personalized content. This feedback is extremely valuable and
useful for the brand to continuously improve marketing strategies and create a more satisfying shopping
experience for customers in the future (Sri et al, 2025, Chan, 2022, Davis et al., 2021).
When the content presented by the marketing team aligns with the interests and needs of the target customers at
the right time, they are likely to feel more understood, which fosters trust and enhances customer satisfaction.
Prospective customers who actively engage with digital marketing content, such as interacting with ads,
participating in polls about upcoming products, and so on, tend to have a higher level of trust in the brand, which
improves their satisfaction. Personalization in digital marketing often involves providing product or service
recommendations that match the preferences and behaviors of the target users. If these recommendations are
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 317
www.rsisinternational.org
accurate and relevant, users are more likely to trust the brand and be satisfied with their experience (Sri et al,
2025, Chan, 2022, Davis et al., 2021).
Figure 1. Research Framework
H1: There is a positive association between personalized digital marketing and user behavior
H2: There is a positive association between trust and user behavior
H3: There is a positive association between user behavior and customer satisfaction
H4: There is a positive association between personalized digital marketing and customer satisfaction
H5: There is a positive association between trust and customer satisfaction
H6: User behavior mediates the effect of personalized digital marketing and customer satisfaction
H7: User behavior mediates the effect on trust and customer satisfaction
METHODS
The study investigates causality using a quantitative method and is classified as explanatory research. The
research is conducted cross-sectionally (one-shot). This study uses Structural Equation Modeling with the
SmartPLS software version V4.1.0.0. The questionnaire is distributed digitally using social media applications
such as Instagram. The digital questionnaire is created using Google Forms. From the Distribution of the
questionnaire, the data received were 242 entries, with 240 of these entries being valid.
Table 1. Variable Measurement
Indicator
Statements
Personalized
Digital
Marketing
- Most ads for local Indonesian products provide comprehensive details about the
products. - Personalized ads for local Indonesian products tend to provide more
motivation to purchase. - Ads for local Indonesian products on search engines are
disruptive but offer important information about the products and limited-time offers or
discounts.
Ads for local Indonesian products via email are disruptive but offer important information about the products
and limited-time offers or discounts.
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 318
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Ads for local Indonesian products on social media are disruptive but offer important information about the
products and limited-time offers or discounts.
Ads for local Indonesian products in video ads are disruptive but offer important information about the products
and limited-time offers or discounts.
Generally, there is no significant difference between the quality of products purchased online and offline.
I feel it is important that the product information provided by the marketing team is accurate and unbiased.
Trust, I expect that the quality of goods purchased from distributors or third parties is the same as when buying
directly from the original.
I want online providers to assure customers that reviews and ratings from other customers are genuine and
reliable.
When I shop online, I purchase products that I have planned in advance.
I always buy products that I need. I decided to purchase local Indonesian products after comparing them has
similar products from other brands.
User Behavior I am easily influenced by marketing content through search engine marketing.
I am easily influenced by marketing content through social media marketing. I am easily influenced by marketing
content through video ads. I am satisfied with the local Indonesian products I purchaseonline. I feel that the price
of local Indonesian products is justified by their quality. I am satisfied with the post-purchase service from online
stores, as a Customer It is better than offline stores. Satisfaction
I am satisfied with the customer service during and after online purchases. I am satisfied with the delivery of
products after online purchases. I would recommend local Indonesian products to others.
Demographic Data
Based on data from 240 respondents, regarding gender, 62 (25,83%) were males, and 178 (74,17%) were
females. Regarding of the age, 145 (60,42%) were aged between 18 and 25, 70 (29,17%) were aged between 26
and 34, 25 (10,42%) were aged above 35 years old. Regarding shopping frequency within the last three months,
with frequency 1-2 times, 15 (6,25%), with frequency 3-5 times, 36 (15.0%); with frequency more than 5 times,
189 (78,75%).
Table 2. Respondents Characteristics
Characteristics
Category
N
Percentage (%)
Gender
Male
Female
62
178
240
25,83
74,17
100
Age
18 - 25 years old
26 - 34 years old
≥35 years old
145
70
25
240
60,42
29,17
10,42
100
Shopping frequency (within the last three
months)
1 – 2 times
3 – 5 times
>5 times
15
36
189
240
6,25
15
78,75
100
Source: Data Processed (2024)
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
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Validity and Reliability
The study performs convergent validity testing by examining outer loading and AVE values. A value of > 0,60
is regarded as valid, and all indicators used in the study have met this criterion. Additionally, all AVE values for
each variable are > 0,5, indicating their validity (Hamid & Anwar, 2019).
Table 3. Convergent Validity
Variable
Indicator
Outer Loading
AVE
Descripti
on
Personalized Digital
Marketing
PDM 1
PDM 2
PDM 3
PDM 4
PDM 5
PDM 6
0.822
0.872
0.921
0.936
0.93
0,928
0.814
Valid
Valid
Valid
Valid
Valid
Valid
Trust
T 1
T 2
T 3
T 4
0.807
0.88
0.829
0.888
0.725
Valid
Valid
Valid
Valid
User Behavior
UB 1
UB 2
0.806
0.779
0.699
Valid
Valid
Source: Research Data (2025)
The reliability test aims to confirm the precision and consistency of the research instruments. According to
Hamid and Anwar 2019, Cronbach’s alpha and composite reliability values of > 0,7 are required. Each latent
variable’s Cronbach’s alpha and composite reliability values meet these minimum requirements.
Table 4. Reliability
Variable
Cronbach’s Alpha
Composite Reliability
Description
Customer Satisfaction
0.938
0.939
Reliable
Personalized Digital Marketing
0.954
0.957
Reliable
Trust
0.874
0.885
Reliable
User Behavior
0.913
0.921
Reliable
Source: Research Data (2025)
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
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The discriminant validity test evaluates how well a construct represents its latent variable and how distinct it is
from other constructs. According to Hamid and Anwar (2019), a construct must have a cross-loading value of >
0,6 and a higher comparison value than others. The test results show that the construct’s cross-loading value
meets the minimum expected limit of > 0,60 and is higher than other constructs, confirming the validity of the
discriminant test. Additionally, the square root value of AVE from the analysis is greater than the correlation
value between variables. Therefore, the discriminant validity test results are considered valid.
Table 5. Convergent Validity
Indicator
PDM
T
UB
CS
Descriptio
n
PDM1
PDM2
PDM3
PDM4
PDM5
PDM6
0.822
0.872
0.921
0.936
0.93
0.928
-0,099
-0,111
-0,174
-0,119
-0,136
-0,129
0.247
0.272
0,258
0.264
0.266
0,216
0.628
0.753
0.761
0.756
0.773
0.758
Valid
Valid
Valid
Valid
Valid
Valid
T1
T2
T3
T4
-0,156
-0,188
-0,099
-0,063
0.807
0.88
0.829
0.888
0.295
0.323
0.369
0.446
-0.152
-0.163
-0.056
-0.002
Valid
Valid
Valid
Valid
UB1
UB2
UB3
UB4
UB5
UB6
0,262
0.278
0,186
0.148
0.268
0,261
0.202
0.364
0.383
0.46
0.303
0.413
0.806
0.890
0.779
0.829
0.83
0.877
0.332
0.384
0.232
0.22
0.355
0.36
Valid
Valid
Valid
Valid
Valid
Valid
CS1
0.695
-0.059
0.354
0.872
Valid
CS2
0.718
-0.115
0.286
0.857
Valid
CS3
0.742
-0.150
0.308
0.879
Valid
CS4
0.729
-0.027
0.396
0.880
Valid
CS5
0.723
-0.108
0.313
0.885
Valid
CS6
0.682
-0.071
0.329
0.873
Valid
Source: Research Data (2025)
Table 6. Fornell Lecker
Variable
CL
ER
ET
EWB
Customer Satisfaction
0.874
Personalized Digital Marketing
0.818
0.902
Trust
-0,101
-0,143
0.852
User Behavior
0.379
0.281
0.427
0.836
Source: Research Data (2025)
Hypothesis Testing
This research examines the impact on employee training and employee well-being models. The study finds that
39.8% of coaching leadership and employee relationship influence employee training, while 60.2% is influenced
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
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by other variables. On the other hand, employee well-being is influenced by 66.7% of coaching leadership,
employee relationships, and employee training. The remaining 33.3% is influenced by other variables. The
research's R-Square values indicate that employee training is in the low category, while the employee well-being
model is in the moderate category.
Table 6. R-Square and R-Square Adjusted
Variable
R-Square
R-Square Adjusted
Description
Employee Training
0.398
0.390
Weak
Employee Well-being
0.667
0.660
Moderate
Source: Research Data (2025)
Hypothesis testing is conducted for both direct and indirect relationships. The path coefficient values, t-statistics,
and p-values will serve as the criteria for making hypothetical decisions.
Table 7. Hypothesis Result
Construct
Path Coefficient
T-Statistics
P-Values
Description
PDM --> UB
0.349
7,126
0.000
Significant
T --> UB
0.477
8,436
0.000
Significant
UB --> CS
0.202
5,377
0.006
Significant
PDM --> CS
0.750
20,065
0.001
Significant
T --> CS
0.016
2,5
0.012
Significant
PDM --> UB -->
CS
0.071
3.777
0.000
Significant
T --> UB --> CS
0.097
4,364
0.000
Significant
Source: Research Data (2025)
The first hypothesis (H1) states that personalized digital marketing has a positive impact on user behavior.
According to Table 12, the Path Coefficient value is 0.349, the T-statistic value is 7.126, and the P-value is 0.000,
indicating positive and significant results. These three values suggest that the hypothesis can be accepted with
an alpha value of 5%. Therefore, it can be concluded that personalized digital marketing has a positive and
significant impact on user behavior.
The second hypothesis (H2) states that trust has a positive impact on user behavior. According to Table 12, the
Path Coefficient value is 0.477, the T-statistics value is 8.436, and the P-value is 0.000, indicating positive and
significant results. These three values suggest that the hypothesis can be accepted with an alpha value of 5%.
Therefore, it can be concluded that trust has a positive and significant impact on user behavior.
The third hypothesis (H3) states that user behavior has a positive impact on customer satisfaction. According to
Table 12, the Path Coefficient value is 0.202, the T-statistics value is 5.377, and the P-value is 0.000, indicating
positive and significant results. These three values suggest that the hypothesis can be accepted with an alpha
value of 5%. Therefore, it can be concluded that user behavior has a positive and significant impact on customer
satisfaction.
The fourth hypothesis (H4) states that personalized digital marketing has a positive impact on customer
satisfaction. According to Table 12, the Path Coefficient value is 0.75, the T-statistics value is 20.065, and the
P-value is 0.000, indicating positive and significant results. These three values suggest that the hypothesis can
be accepted with an alpha value of 5%. Therefore, it can be concluded that personalized digital marketing has a
positive and significant impact on customer satisfaction.
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
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The fifth hypothesis (H5) states that trust has a positive impact on customer satisfaction. According to Table 12,
the Path Coefficient value is 0.016, the T-statistics value is 2.500, and the P-value is 0.012, indicating positive
and significant results. These three values suggest that the hypothesis can be accepted with an alpha value of
5%. Therefore, it can be concluded that trust has a positive and significant impact on customer satisfaction.
The sixth hypothesis (H6) states that user behavior mediates the relationship between personalized digital
marketing and customer satisfaction. According to Table 13, the path coefficient value is 0.071, the T-statistics
value is 3.777, and the P-value is 0.000. These three values indicate that the hypothesis can be accepted.
Subsequently, an analysis was conducted to determine the type of mediation occurring. The first path analysis
(a) confirmed a significant influence between personalized digital marketing and user behavior. The second path
analysis (b) showed a significant relationship between user behavior and customer satisfaction. The third path
analysis (c) revealed a significant relationship between personalized digital marketing and customer satisfaction.
These results indicate that the mediation effect is complementary because the relationships a x b x c are
significant. Therefore, the hypothesis that user behavior mediates the relationship between personalized digital
marketing and customer satisfaction is accepted with a complementary mediation effect.
The seventh hypothesis (H7) states that user behavior mediates the relationship between trust and customer
satisfaction. According to Table 13, the path coefficient value is 0.097, the T-statistic value is 4.364, and the P-
value is 0.000. These three values indicate that the hypothesis can be accepted. Subsequently, an analysis was
conducted to determine the type of mediation occurring. The first path analysis (a) confirmed a significant
influence between trust and user behavior. The second path analysis (b) showed a significant relationship
between user behavior and customer satisfaction. The third path analysis (c) revealed a significant relationship
between trust and customer satisfaction. These results indicate that the mediation effect is complementary
because the relationships a x b x c are significant. Therefore, the hypothesis that user behavior mediates the
relationship between trust and customer satisfaction is accepted with a complementary mediation effect.
DISCUSSION
Research shows that personalized digital marketing influences user behavior. This is because consumer behavior
is greatly affected by the right approach from marketers or marketing teams. Nowadays, verbal or oral
explanations alone are not sufficient. While a few years ago product explanations via radio might have sufficed,
today many aspects can influence consumers' desire or interest in a product. Even billboards on the street can no
longer rely on simple posters; they need more innovative video content.
Social media also has the advantage due to its massive use in Indonesia. One of the initial steps that local
Indonesian products should take is to manage social media, provide information, and conduct branding on
platforms like Instagram and TikTok. However, with the abundance of brands emerging today, it is important
for every local Indonesian product to have its own unique identity that sets it apart from other local brands as
well as foreign products.
Research shows that trust has a positive and significant impact on user behavior. Trust can arise from both
internal and external factors. Internal factors are those that originate from within oneself and involve pre-existing
beliefs, such as purchasing a product because it aligns with one's personal values. This factor is often the primary
reason someone buys a product. If a brand's values do not align with what the consumer believes in, the consumer
is unlikely to purchase the product offered.
External factors are influences from outside oneself, such as family, friends, celebrities, or influencers, and the
surrounding environment. These influences also have a significant impact on an individual. Especially today,
many people place great importance on others' opinions of them, feeling the need to satisfy others as a form of
biased self-satisfaction.
Trust also needs to be fostered if companies wish to use consumer data as a basis for their business operations.
The use of consumer data can provide a significant advantage for the brand. However, it is essential to obtain
consent or permission from the consumers to avoid discomfort or potential harm to them. Clear and transparent
explanations regarding the use and collection of data should be provided from the outset. Several strategies can
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
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be employed to encourage consumers to share their data. For instance, some local Indonesian products offer free
services or products as feedback rewards in exchange for the data provided by consumers.
This study demonstrates a positive correlation between user behavior and customer satisfaction. Ads placed on
search engine marketing can be connected to a form box where visitors can enter their email addresses to become
subscribers. Collecting email data can also be achieved by offering promotions or information about new
products through chats to existing customers or those who have previously transacted with the brand. This
approach is more likely to capture consumer attention as they have already purchased from the brand.
Another effective strategy is to combine conventional marketing with email data collection, such as at beauty
fairs where a skincare brand's marketing team offers free samples in exchange for filling out personal data forms.
Additionally, video ads are a technique that can influence consumers to make purchase transactions. This
behavior can be driven by videos with compelling taglines that attract potential customers to buy the product.
Besides specific taglines, promotions conveyed in the video, such as "prices go up tomorrow," can create urgency
for immediate purchases.
However, there are cases where local Indonesian brands collaborate with foreign cartons to differentiate
themselves from other brands that collaborate with South Korean actors and actresses. Unfortunately, the results
did not meet expectations; they failed to stand out because their target market did not resonate with the cartoon,
preferring brands with South Korean actors and actresses as Brand Ambassadors. Therefore, it is crucial to
understand the brand's strengths and align them with the target market to stand out from similar products.
Research shows that personalized digital marketing has a positive and significant impact on customer
satisfaction. The transaction process is divided into three stages: pre-purchase, during the transaction, and post-
transaction. Marketing plays a crucial role throughout the entire transaction process. In the first phase, or pre-
transaction process, the marketing team creates attractive promotions and provides product information to
targeted customers. These marketing offers can be delivered through various channels, both online and offline,
to better reach potential customers.
The role of personalized digital marketing here is to understand which channels consumers prefer for receiving
product information and promotions. Video ads, for example, give consumers an idea of how the product might
be used, which is why consumers favor this marketing technique. Creating high-quality video content requires
collaboration with various parties. This collaboration benefits the brand by securing high-quality videos and
leveraging the engagement that influencers have with their followers, rather than trying to build that engagement
from scratch.
The engagement that influencers have is built on the trust their followers place in them. This trust can be
leveraged by new brands to attract customers quickly and effectively. However, choosing influencers should not
be based solely on their viral status; in-depth research is needed to ensure that their followers align with the
brand's target market.
This research proves that there is a positive correlation between trust and customer satisfaction. Efforts to build
trust require actions from marketers or brands before the purchase, during the transaction, and after the
transaction. Before the transaction, the marketing team needs to carefully consider the language used in
promotions. The choice of words is important to avoid biased information or potential ambiguities, which can
help maintain and build consumer trust (Davis et al., 2021)
During the transaction process, sellers, whether first-party or third-party, need to maintain consumer trust by
acting honestly and avoiding deceit. Price competition among distributors is inevitable, but they must conduct
this competition fairly and follow the guidelines from the producers (if there are set price standards). Market
monopolies, if pursued, can pose future challenges (Boufim & Barka, 2021)
After the transaction, since the shopping is done online, consumers can only evaluate the purchased product once
it arrives. If there is any discrepancy between the received product and the provided information, customers will
feel disappointed, significantly affecting their satisfaction level. Low customer satisfaction can negatively impact
the product's reputation (Boufim & Barka, 2021).
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
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Special Issue | Volume IX Issue XXVIII November 2025
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Research indicates that personalized digital marketing has a positive and significant impact on customer
satisfaction, with user behavior acting as a mediating variable. The mediation model identified is complementary
mediation (Zhao et al., 2010). Complementary mediation occurs because personalized digital marketing, user
behavior, and customer satisfaction all significantly influence one another. Additionally, a significant impact is
observed from the relationship between personalized digital marketing and customer satisfaction through user
behavior. The study's findings reveal that user behavior can mediate the relationship between personalized digital
marketing and customer satisfaction for local Indonesian products. This is supported by previous research
indicating that user behavior can mediate the relationship between marketing efforts and customer satisfaction
(Davis et al., 2021)
Selecting the appropriate digital marketing channels should be preceded by customer research, which is a critical
activity during the brainstorming phase of developing marketing concepts. Digital marketing channels such as
search engine marketing, email marketing, social media marketing, and video ads impact customer satisfaction
and user behavior. However, the choice of channel should align with the marketing team's goals and the budget
available for marketing activities (Boufim & Barka, 2021)
Various factors can influence the success of digital marketing efforts. These factors include the completeness of
information about local Indonesian products. The information provided must be accurate and relevant to the
product being offered, highlighting the product's advantages or uniqueness. For example, for apparel products,
it is important to provide a size guide, material types, detailed images, and all other relevant product information.
Additionally, the marketing team should consider and articulate additional value points in the product
information (Boufim & Barka, 2021).
Research indicates that trust has a positive and significant impact on customer satisfaction, with user behavior
acting as a mediating variable. Consumer behavior in response to information provided by the marketing team
shows that prospective customers do not immediately trust the information. This lack of trust is due to frequent
discrepancies that occur. Prospective customers expect the same quality of goods whether they purchase from a
distributor as a third party or directly from the first party. Quality differences can occur due to dishonest practices
by distributors, such as repackaging or mixing with other products. Quality differences can also arise from
improper storage of goods. Therefore, despite price differences between purchasing from the first party and the
third party, consumers might still prefer to buy directly from the first party to avoid disappointment or
dissatisfaction after the transaction (Devine et al., 2021).
CONCLUSION
The foundation of this research is to understand the influence of personalized digital marketing and trust on
customer satisfaction for local Indonesian products, with user behavior serving as a mediating variable. Based
on the data collected and processed using SmartPLS V4.1.0.0, both directly and indirectly, several conclusions
can be drawn. The choice of digital marketing platforms significantly impacts social media user behavior.
Consumer behavior in responding to the transaction process affects their satisfaction after completing the
transaction. Effective management of consumer behavior can increase customer satisfaction levels. Trust
correlates with customer satisfaction, as fostering trust can help customers feel safe and comfortable during
online transactions. Based on the conclusions obtained, personalized digital marketing, trust, and user behavior
need to be prioritized by companies to enhance customer satisfaction for local Indonesian products.
The limitation of this research is that the research object this time is not specific to just one area so it has a
specific scope. Furthermore, the research has questionnaire questions using closed questions so that there is no
feedback from questionnaire fillers (not open questions). There are dual indicators in the research question
(requiring online and offline purchases).
Suggestions for future research are that personalized digital marketing and trust variables influence customer
satisfaction directly and indirectly, so it is necessary to carry out research using the same model as the other
variables. Then, further research can be carried out in one particular field or category so as to obtain more specific
results in the context of the research object.
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 325
www.rsisinternational.org
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