Jurnal Humaniora Vol.
No.
407 - 416
http://jurnal.
id/index.
php/humaniora p-ISSN: 2684-9275 e-ISSN: 2548-9585 Research Paper Loyalty: The Use of Chatbots and Social Media Marketing with User Satisfaction as a Mediating Variable Aida Fitri1 .
Muhammad Jaka Wiratama1.
Daffa Azka1.
Harbiyah G2 Faculty of Economics.
Universitas Muhammadiyah Aceh.
Banda Aceh 23123.
Indonesia Faculty of Vocational Studies.
Universitas Muhammadiyah Aceh.
Banda Aceh 23123.
Indonesia fitri@unmuha.
https://doi.
org/10.
30601/humaniora.
v%vi%i.
Published by Universitas Abulyatama Artikel Info Online first:
23/10/2025
Abstract Chatbots and social media marketing (SMM) have emerged as two primary tools utilized by companies to enhance customer experience and foster customer loyalty.
The objective of this quantitative study is to examine the influence of chatbot usage and social media marketing on customer loyalty, with user satisfaction serving as a mediating variable.
total of 96 respondents from the city of Banda Aceh participated in the survey, selected using a random sampling technique based on individuals actively engaged in business Data were collected through questionnaires and documentation review.
Hypothesis testing was conducted using multiple linear regression analysis, including both F-tests .
and t-tests .
, at a 95% confidence level ( = 0.
The findings indicate that chatbots have a strong influence on customer loyalty, and social media marketing demonstrates a close relationship with customer loyalty.
Furthermore, user satisfaction significantly mediates the relationship between these digital tools and loyalty.
The implication of this research is that the integration of chatbots and social media marketing, mediated by user satisfaction, significantly influences customer loyalty.
Keywords: Chatbot usage.
Social media marketing.
User satisfaction.
Customer loyalty Introduction The advancement of digital technology has transformed the way businesses interact with In the digital era, chatbots and social media marketing (SMM) have emerged as two primary tools used by companies to enhance customer experience and foster loyalty.
Chatbots, as a representation of artificial intelligence (AI), enable fast and personalized interactions with users, while social media marketing leverages social media platforms to reach a broader These two tools not only facilitate communication between brands and consumers but This work is licensed under a Creative Commons Attribution-NonCommercial 0 International License.
Jurnal Humaniora A Aida Fitri.
Muhammad Jaka Wiratama.
Daffa Azka.
Harbiyah G also play a significant role in shaping user satisfaction, which ultimately influences customer loyalty .
Customer loyalty is a key factor in the long-term success of a business.
Loyal customers tend to make repeat purchases, recommend the brand to others, and are less sensitive to price changes.
However, building customer loyalty in the digital era is not an easy task.
TodayAos consumers have access to vast amounts of information and can easily switch to other brands if their experience is unsatisfactory.
Therefore, companies need to understand the factors that can enhance customer loyalty, particularly in the context of technology adoption such as chatbots and social media marketing .
Chatbots have become a popular solution for companies to enhance the efficiency of customer With the ability to provide instant responses and operate 24/7, chatbots can meet the needs of consumers who demand convenience and speed in interactions.
In addition, chatbots can be personalized according to user preferences, thereby creating a more relevant and satisfying experience.
However, the effectiveness of chatbots in building customer loyalty still requires further investigation, particularly in relation to user satisfaction as a mediating variable .
On the other hand, social media marketing has become a key strategy for companies to build relationships with consumers.
Social media enables brands to interact directly with audiences, share engaging content, and obtain real-time feedback.
Through social media marketing, companies can create loyal communities that are actively engaged with the brand.
However, the main challenge in social media marketing lies in maintaining consistency and relevance of content to meet user expectations .
User satisfaction serves as a critical factor in the relationship between chatbot usage, social media marketing, and customer loyalty.
User satisfaction can be defined as the level of pleasure or disappointment experienced by consumers after comparing the performance of a product or service with their expectations.
When users are satisfied with their interactions through chatbots or with the content presented via social media marketing, they are more likely to develop stronger emotional bonds with the brand, which in turn can enhance loyalty .
Chatbot systems are designed to provide responses to users based on the questions posed.
A chatbot is capable of generating replies by adapting to the words contained in its training phrases .
To address the challenges mentioned above, innovation is required to minimize the role of human administrators by employing automatic chat systems, commonly known as chatbots.
With the implementation of chatbots, a website can remain active around the clock without concerns about customersAo inquiries regarding the products offered, since responses are automatically provided by the programmed chatbot integrated into the website.
Several previous studies have revealed the relationship between the use of digital technology and customer loyalty.
However, there remains a research gap that needs to be explored, particularly in the context of the simultaneous use of chatbots and social media marketing.
Moreover, the role of user satisfaction as a mediating variable in this relationship has not been extensively examined.
Therefore, this study aims to fill this gap by analyzing how the use of chatbots and social media marketing can influence customer loyalty through user satisfaction .
This research is also grounded in current industry phenomena, where many companies have begun adopting chatbots and social media marketing as part of their marketing strategies.
However, not all companies have succeeded in utilizing these tools optimally.
Some face challenges in integrating chatbots with their social media marketing strategies, while others struggle to ensure that interactions through chatbots and social media content meet user Hence, this study is expected to provide valuable insights for companies in optimizing the use of chatbots and social media marketing to enhance customer loyalty .
Jurnal Humaniora A Aida Fitri.
Muhammad Jaka Wiratama.
Daffa Azka.
Harbiyah G In addition, this study is also relevant to the growing consumer trend of seeking personalized and instant experiences.
TodayAos consumers are not only looking for quality products or services but also expect interactions that are simple, fast, and tailored to their needs.
Chatbots and social media marketing can serve as solutions to meet these expectations, provided that they are implemented with the right strategies.
By understanding how these two tools can influence user satisfaction and customer loyalty, companies can design more effective strategies to gain a competitive advantage in the digital marketplace .
From a theoretical perspective, this study is expected to contribute to the development of literature on customer loyalty, particularly in the context of digital technology adoption.
The findings of this research may enrich the understanding of how chatbots and social media marketing can interact to create a satisfying customer experience, which ultimately enhances Furthermore, this study can also serve as a reference for other researchers interested in exploring similar topics in the future.
Practically, this study is expected to provide recommendations for companies in optimizing the use of chatbots and social media marketing.
By understanding the factors that influence user satisfaction and customer loyalty, companies can design more targeted and effective strategies.
For example, firms may integrate chatbots with social media platforms to create more seamless interactions or develop more personalized and relevant content to enhance user satisfaction.
Based on the above discussion, this research is urgent to conduct to better understand the dynamics between chatbot usage, social media marketing, user satisfaction, and customer Thus, this study is expected to contribute both theoretically and practically within the context of digital marketing and customer relationship management.
The research will be conducted through the distribution of questionnaires to respondents.
The title of this study is loyalty: the use of chatbots and social media marketing with user satisfaction as a mediating Method 1 Research location and object This study was conducted in Banda Aceh.
The research objects are Artificial Intelligence (AI) applications and business strategies utilizing AI technology in Banda Aceh.
2 Research design The research design begins with identifying quantitative problems and formulating them into research questions.
The problems are then addressed using relevant theories.
According to .
, research design is a process of finding definite answers to research questions.
Similarly, .
defines research design as all processes required in the planning and implementation of research.
3 Population Population refers to the entire group of individuals, events, or objects that form the focus of research and are to be investigated .
In this study, the population consists of all respondents who use AI-based technologies, with an unknown .
4 Sample According to .
, a sample is a subset of the population that possesses the characteristics being studied.
The respondents in this study were determined using an infinite population Jurnal Humaniora A Aida Fitri.
Muhammad Jaka Wiratama.
Daffa Azka.
Harbiyah G ycu= "($%&)! Where:
= Normal distribution level at a 5% significance level = 1.
= Sample size Moe = Margin of error, with a maximum error rate of 10% ((.
* )!
ycu = " (-,(-)! 0"( ycu = -.
ycu = 96.
ycu = 96 Based on the formula, the sample size for this study is 96 respondents, consisting of individuals who use chatbots and social media marketing in Banda Aceh.
The sampling technique applied is Accidental Sampling.
defines accidental sampling as selecting respondents by coincidence, i.
, anyone who happens to meet the researcher and meets the criteria as a data source can be used as a respondent.
This technique is often chosen due to time, resource, and budget constraints.
Its strength lies in the researcherAos ability to select appropriate data sources that align with the research variables.
5 Data analysis techniques According to .
, data analysis in this study was conducted using the SPSS program (Statistical Package for the Social Science.
The techniques included validity testing, reliability testing, descriptive analysis, normality testing, linearity testing, and classical assumption tests.
To assess relationships among variables, multiple regression analysis was employed, including the calculation of the coefficient of determination, t-tests, and F-tests.
The regression model used in this study is formulated as follows:
Y= a b1X1 b2X2 ea.
Where:
Y = Loyalty a = Constant X1 = Chatbot Users X2 = Social Media Marketing M = User Satisfaction B = Regression coefficients of X1.
X2 e = Error term 6 Moderated regression analysis (MRA) Moderated Regression Analysis (MRA) is employed to maintain the integrity of the sample and to control the effect of moderating variables .
A moderating variable is an independent variable that strengthens or weakens the relationship between an independent and a dependent In this study, the moderating variable is user satisfaction.
The independent variables are chatbot usage and social media marketing, while the dependent variable is customer loyalty.
Jurnal Humaniora A Aida Fitri.
Muhammad Jaka Wiratama.
Daffa Azka.
Harbiyah G Thus, the study examines the interaction between chatbot usage, social media marketing, and user satisfaction in relation to loyalty.
The regression equation can be expressed as Y = 1ZX1 2ZX2 3ZM .
ZX1-ZM| .
ZX2Ae ZM| ea.
ZX2
OZX1OeZMO OZX2OeZMO \alpha \beta = Loyalty = Standardized value of Chatbot Usage = Standardized value of Social Media Marketing = User Satisfactio = Interaction measured by the absolute difference between ZX1 and ZM = Interaction measured by the absolute difference between ZX2 and ZM = Constant = Regression Coefficient = Error Term 7 Data testing Validity testing was conducted to ensure that the instrument accurately measured the intended variables under study.
The validity of each item was assessed using the Pearson Product-Moment Correlation at a 95% confidence level .
Reliability testing was performed to assess the internal consistency of the instrument by examining item-total correlations.
Items with correlation values above 0.
20 were considered reliable and retained for further analysis .
8 Classical assumption tests The normality test was conducted to determine whether the data followed a normal distribution, with data considered normally distributed if the probability value exceeded 0.
The multicollinearity test was performed to detect correlations among independent variables, as a good regression model should be free from multicollinearity to ensure that independent variables are not highly correlated with one another.
Additionally, the heteroscedasticity test was conducted to examine whether residual variance remained constant across observations, where homoscedasticity indicates equal variance across observations, while heteroscedasticity suggests unequal variance that can affect the reliability of regression estimates .
9 Hypothesis testing The t-test was used to evaluate the partial significance of each independent variable, specifically chatbot usage and social media marketing, on customer loyalty.
Meanwhile, the Ftest was employed to assess the simultaneous significance of all independent variables on the dependent variable, determining whether the model as a whole had a significant effect on customer loyalty .
Result 1 Validity test results The validity of the data was tested statistically using the Pearson Product-Moment Correlation through SPSS.
All items obtained correlation values above the critical value of 0.
( = 5%), which indicates that the items are significant and valid.
Statistically, this demonstrates internal consistency, meaning that the items measure the same construct.
As shown in Table 1 Jurnal Humaniora A Aida Fitri.
Muhammad Jaka Wiratama.
Daffa Azka.
Harbiyah G Variable Loyality (Y) Chatbot users (X.
Social (X.
User
(M)
Table 1.
Validity tes result Loyalty: chatbots and social media marketing users and satisfaction as mediating variables Item .
-hitun.
Value r-tabel .
= .
Description Valid Valid Valid Valid 2 Reliability test results Reliability testing was performed using internal consistency (CronbachAos Alph.
Create a Discussion A CronbachAos Alpha value greater than 0.
60 is considered acceptable, while values 80 indicate high reliability.
All research variables exceeded 0.
80, confirming that the instruments were reliable.
Reliability testing was performed using internal consistency (CronbachAos Alph.
The results are presented in Table 2:
No.
Table 2.
Reliability test results Name Alpha value Loyalty (Y) Pengguna chatbots (X.
Social media marketing (X.
User satisfaction (M) Category Reliable Reliable Reliable Reliable 3 Normality test results Normality testing was conducted using regression analysis with SPSS.
As illustrated in Figure 1, the scatterplot results indicated that data points were distributed around the diagonal line, suggesting that the data were normally distributed.
Jurnal Humaniora A Aida Fitri.
Muhammad Jaka Wiratama.
Daffa Azka.
Harbiyah G Figure 1.
Normal P-P plot of regression standardized residual 4 Multicollinearity test results The multicollinearity test aimed to determine whether high correlations existed among independent variables.
Tolerance values greater than 0.
10 and VIF values below 10 indicate the absence of multicollinearity.
The results indicate that no independent variable had a tolerance value below 0.
10 or a VIF value above 10, confirming that multicollinearity was absent.
No.
Table 3.
Multicollinearity test results Name Toleran VIF Chatbot users (X.
0,216 Social media marketing (X.
0,402 User satisfaction (M) 0,270 Category Non multikolinearitas Non multikolinearitas Non multikolinearitas As presented in Table 3, none of the independent variables exhibits a Tolerance value below 10, indicating that there is no significant correlation among the independent variables.
Similarly, the results of the Variance Inflation Factor (VIF) analysis confirm that all independent variables have VIF values below the threshold of 10.
Accordingly, it can be concluded that the regression model employed in this study does not suffer from multicollinearity.
5 Hypothesis testing The hypothesis states that Chatbot Usage (X.
Social Media Marketing (X.
, and User Satisfaction (X.
as a mediating variable (M) have an influence on loyalty.
The model employed to estimate this effect is as follows:
No.
Table 4.
The influence of independent variable on the dependent variable Name Standar eror Thiting Thiting Constanta Chatbots Media sosial marketing User satisfaction Sig 6 Partial test .
-Tes.
Partial testing was conducted to determine the effect of each independent variable on loyalty in Banda Aceh.
The results show that all three independent variablesAiChatbot Usage.
Social Media Marketing, and User SatisfactionAisignificantly influence customer loyalty in Banda Aceh.
Jurnal Humaniora A Aida Fitri.
Muhammad Jaka Wiratama.
Daffa Azka.
Harbiyah G 7 Simultaneous test (F-Tes.
To examine the influence of chatbot usage, social media marketing, and user satisfaction on loyalty in Banda Aceh, the F-test .
tatistical F-tes.
was employed.
If the calculated F-value (F<sub>calculated</sub>) is greater than the critical F-value (F<sub>table</sub>), the null hypothesis (HCA) is rejected and the alternative hypothesis (HC.
is accepted.
Conversely, if F<sub>calculated</sub> is less than F<sub>table</sub>, the null hypothesis is accepted and the alternative hypothesis is rejected.
The partial test results are presented in the following table:
No.
Model Regression Residual Total Table 5.
Anova Sum of Squeres Fhiting Fhiting Sig Discussion 1 The effect of chatbot usage on customer loyalty The findings indicate that chatbot usage significantly influences customer loyalty.
The regression coefficient for chatbot usage was 0.
587 with a significance level of 0.
000, suggesting a strong effect.
Furthermore, the t-value .
exceeded the critical t-table value .
supporting the hypothesis.
This implies that chatbots play a crucial role in enhancing customer interactions through rapid and responsive communication, which directly increases loyalty.
Moreover, user satisfaction strengthens the relationship, highlighting its mediating role.
These results align with .
, who found that chatbots improve customer satisfaction and trust, which ultimately foster loyalty.
2 The effect of social media marketing on customer loyalty Social media marketing was also found to have a significant positive effect on loyalty.
The regression coefficient was 0.
433, with a significance level of 0.
002 and a t-value of 2.
673, exceeding the t-table value.
This demonstrates that effective social media marketing strategies enhance customer loyalty by fostering interaction, personalization, and emotional engagement with These findings are consistent with .
, who emphasized that interactive, consistent, and relevant social media content boosts customer trust and loyalty.
3 The effect of user satisfaction on customer loyalty User satisfaction strongly influenced customer loyalty, with a regression coefficient of 0.
and a significance level of 0.
The t-value of 3.
913 confirmed the significance of the This finding suggests that when customer expectations are met or exceeded, they are more likely to remain loyal.
The results support .
, who argued that satisfaction increases customer retention, positive word-of-mouth, and repeat purchases.
4 The moderating effect of user satisfaction User satisfaction was found to moderate the relationships between both chatbot usage and social media marketing with loyalty.
The findings suggest that satisfaction enhances the positive impact of chatbot usage and social media marketing on customer loyalty.
This result is in line with .
, who reported that satisfaction derived from accurate, interactive, and responsive digital services reinforces loyalty.
Similarly, .
found that creative and informative social media campaigns improve satisfaction, thereby strengthening loyalty.
Jurnal Humaniora A Aida Fitri.
Muhammad Jaka Wiratama.
Daffa Azka.
Harbiyah G Conclusion Based on the results of testing, processing, and data analysis, it can be concluded that chatbot usage, social media marketing, and user satisfaction each have a significant partial effect on loyalty in Banda Aceh.
Furthermore, the study reveals that user satisfaction serves as a mediating variable that strengthens the effect of chatbots on loyalty and mediates the relationship between social media marketing and loyalty.
Thus, both directly and indirectly through user satisfaction, chatbots and social media marketing play a crucial role in enhancing consumer loyalty in Banda Aceh.
Acknowledgement The authors would like to express their deepest gratitude to the Faculty of Economics.
Universitas Muhammadiyah Aceh (Unmuh.
, for providing support and funding for this research through LP4M (Lembaga Pengembangan.
Penelitian.
Pengabdian, dan Penjaminan Mut.
Unmuha.
The financial assistance has been essential in ensuring the successful completion of this study.
The authors also extend their appreciation to the reviewers and proofreaders for their valuable feedback and suggestions, which greatly improved the quality of this manuscript.
Special thanks are also conveyed to the technical staff who assisted in preparing the necessary research tools, as well as to the students who contributed during the data collection and survey AuthorsAo contributions and responsibilities Aida Fitri: conceptualization, methodology, writing Ae original draft, supervision.
Muhammad Jaka Wiratama: investigation, formal analysis, visualization.
Daffa Azka: supervision, writing Ae review & editing.
Harbiyah G: resources, original draft, writing Ae review & editing.
Funding This research was funded by LP4M (Lembaga Pengembangan.
Penelitian.
Pengabdian, dan Penjaminan Mut.
Universitas Muhammadiyah Aceh through the internal research grant program of the Faculty of Economics.
Availability of data and materials All data are available from the authors.
Competing interests The authors declare no competing interest.
Additional information No additional information from the authors.
References