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The Effect of Chatbot Services on Online Shop Customer Satisfaction
Cecep M Kappi1*, Lina Marlina2
1,2Triguna Tasikmlaya Polytechnic, Indonesia
1cepkapi@poltektriguna.co.id, 2marlinatsm@poltektriguna.ac.id
*Corresponding Author
ABSTRACT
The increasing trend of e-commerce users has not been matched by customer
satisfaction in the shopping process. Indonesia has the highest level of
dissatisfaction compared to other ASEAN countries. Although chatbot
technology has been used as an aid to optimize services, dissatisfaction still
occurs with regard to agility, service assurance, reliability, scalability and
security. The purpose of this study is to determine chatbot services in providing
customer satisfaction. The research approach uses quantitative with explantory
survey method. The research population is online shop users using rondom
sampling, 175 respondents were collected. Assisted by PLS SEM analysis tool.
The results show that chatbot social orientation services contribute to online
shop customer satisfaction. Likewise, chatbot personification makes a positive
contribution to online shop customer satisfaction.
Article History:
Submitted: 07-11-2023
Accepted: 09-11-2023
Published: 17-11-2023
Keywords:
chatbot, customer satisfaction,
service, online shop
Brilliance: Research of
Artificial Intelligence is licensed
under a Creative Commons
Attribution-NonCommercial 4.0
International (CC BY-NC 4.0).
INTRODUCTION
Customer satisfaction is not just about keeping customers happy, but also about building a strong and sustainable
business (Ansary et al., 2023). Businesses that focus on customer satisfaction tend to be more successful in the long run,
and this is why customer satisfaction is so important in business (Magotra et al., 2018). Customer satisfaction in the
digital era has some unique characteristics and is influenced by technological developments and digital trends.
Digitalization allows companies to get closer to customers and provide a better experience. Companies must invest in
technology to ensure that companies can maximize the potential of digitalization in increasing customer satisfaction
(Adam et al., 2021).
Convenience is one of the characteristics that reflect changes in customer behavior in the era of globalization
(Magotra et al., 2018). One of the most striking aspects is the rise of e- commerce visitors and users in the online
marketplace. This has spurred the development of online marketplaces and forced companies to continue to innovate in
an effort to meet increasingly high customer expectations. The development trend of e- commerce users in 2017-2023
continues to increase, with an average increase of 15% per year (rumahmedia, 2022). Then 2024 it is predicted that there
will be at least a 5% increase of 189.6 million people. This development is inseparable from consumer dissatisfaction
with the digital experience. The following presents dissatisfaction by country in ASEAN.
Figure 1. Customer dissatisfaction by country Source: (Arbianto, 2020)
The graph above shows e-commerce consumer dissatisfaction by country in ASEAN. Indonesia has the highest
dissatisfaction compared to other countries. Thailand has the lowest dissatisfied consumers at only 30%. In Indonesia,
about half of consumers 45 percent expressed dissatisfaction with the digital commerce experience (Arbianto, 2020).
Shipping costs, trustworthiness of reviews, and product price are three concerns of consumers.
Customer dissatisfaction in marketplaces is an important issue that needs to be considered by e-commerce
companies. Although marketplaces provide easy shopping and a large selection of products. There are several issues that
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253
can cause customer dissatisfaction to occur. To overcome customer dissatisfaction, e-commerce companies should focus
on good customer service, and maintain transparency in communication. The company should understand customer
complaints and take concrete steps to fix the problem and build a good reputation in the marketplace.
Companies use technology to increase customer satisfaction (Rashid et al., 2023) but the reality remains that
dissatisfaction arises. For example, dissatisfaction arises if the service is not responsive enough, the technology is
difficult to use, the transaction process is complicated, or the waiting time is long. All these aspects of complaints can
have a negative impact on overall customer satisfaction, can lead customers to look for alternatives or even express
dissatisfaction to the company (Filip, 2013). Therefore, it is important to understand and address these dissatisfaction
issues in order to increase customer satisfaction and maintain a loyal customer base (Agarwal & Dhingra, 2023).
The era of globalization has brought major changes in the way businesses operate (Varriale et al., 2023),
especially in the e-commerce or marketplace sector. One of the key challenges in maintaining customer satisfaction is
handling customer complaints quickly and effectively. This is where artificial intelligence (AI) technology, specifically
chatbots, play an important role in business. A chatbot is a computer program designed to interact with humans through
chat or text messages (Wang et al., 2023). Chatbots are used to automate responses to customer queries and complaints
(Balderas et al., 2023). One of the main benefits of chatbots is the ability to provide non-stop service. This is especially
important in an era of globalization where customers can access marketplaces at any time, without being bound by time
zones or holidays. When customers visit the platform. In marketplaces, customers often have questions, need guidance,
or want to get information about products. Chatbots are here to provide instant responses, provide information, and
assist users in navigating the platform.
Research on chatbots has been conducted by (Eun et al., 2023) found the influence of anthropomorphic
conversation style and time orientation on chatbot effectiveness. In addition, social presence mediates the influence of
anthropomorphic design on user compliance (Adam et al., 2021). Greater anthropomorphism was seen in chatbots with
human names and informal language styles. Emotional connections are stronger towards the company through human
chatbots (Araujo, 2018). Dialogue between people is more diverse and lengthy. More messages are used in
conversations between humans and chatbots (Hill et al., 2015). The influence of communication style has an effect on
trust and intention to visit a website (Keeling et al., 2010). Communication embodiment positively affects social
presence, perceived enjoyment, and intention to use agent recommendations (Qiu & Benbasat, 2014). Chatbots with
higher conversational skills are more humanized and more engaging. Conversational skills require conversational
variety as well as skillful responses (Schuetzler et al., 2020).
Customer satisfaction empirically found the application of the Bayesian meta-frontier method with consistent and
efficient meta-technology, providing detailed information about business models, products, and services to improve
efficiency and customer satisfaction (Rashid et al., 2023). Likewise, the marketing mix in the supply chain (Tiganis et
al., 2023). Customer satisfaction can integrate sustainable supply chain management, technology orientation,
organizational culture, and delivery product quality based on natural resources and value perception theory (Ansary et
al., 2023). Convenience, reliability, ease of use, fulfillment, and security/privacy influence customer satisfaction
(Getachew et al., 2023). Optimizing a company's digital brand name by considering customer behavior data and web
analytics (Sakas et al., 2023).
Based on the explanation above about the many complaints of satisfaction provided by marketplace services.
Formulated a research problem to analyze how chatbots affect customer satisfaction. This research aims to determine
customer satisfaction from the services provided by the chatbot. This is important to evaluate and continuously improve
the quality of services provided to realize a stable company. Many previous studies have revealed customer satisfaction
from human services. While the current research evaluates customer satisfaction from robot services and at the same
time becomes a differentiator from previous research.
LITERATURE REVIEW
Customer Satisfaction
The Value-Percept Disparity theory introduced by Westbrook and Reilly in 1983 presents the concept of
customer satisfaction as an emotional state. Customer satisfaction is interpreted as a subjective judgment formed as a
result of the cognitive process following the purchase of a product or service (Brown et al., 2016). Customer satisfaction
develops after the purchase, use, and exploitation stages of the product or service. Factors such as customers' subjective
views, evaluations, and responses expressed by customers play a role in shaping the level of satisfaction (Darko &
Liang, 2022). The determinants of customer satisfaction include various theories. Most compare the actual attributes of
the product or service with consumer expectations. In addition, perceived value comes from individual attitudes, past
experiences, or recommendations from others. Customer satisfaction occurs when product or service performance
exceeds expectations. Customer satisfaction in the context of restaurant services is the result of a post-consumption
judgment involving the dining experience (Pizam & Ellis, 2016). It involves a feeling of pleasure or disappointment,
which is the result of a cognitive appraisal of the experience, an emotional response, and an overall evaluation of the
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benefits received compared to the costs incurred. Customer satisfaction is strongly influenced by various factors,
including individual characteristics, attitudes, expectations, moods, current needs, health conditions, and other factors.
This suggests that different customers may achieve different levels of satisfaction despite receiving the same service
(Agarwal & Dhingra, 2023).
When talking about customer satisfaction in the context of cloud services, factors such as agility, service
assurance, reliability, scalability, security, service responsiveness, and usability all have a significant influence.
Therefore, in this study, we chose to use agility, service assurance, reliability, and scalability as key indicators of
customer satisfaction, as these factors all relate to the use of the same digitization technology, namely cloud services
(Agarwal & Dhingra, 2023).
Chatbot
One of the key aspects of chatbot design is the application of anthropomorphic design elements. Two significant
aspects of anthropomorphic chatbot design are personification (referring to human-like appearance) and social
orientation in communication style (describing more responsive and comprehensive communication) (Janson, 2023).
Chatbots operate as text-based dialog systems without physical and face-to-face interaction, facing challenges in
creating the perception of effective social presence. Social presence can be strengthened through socially-oriented
communication strategies, defined as attempts to incorporate elements of informal dialog involving social interaction
such as greetings, light conversation, emotional support, and positive expressions, to achieve socioemotional goals
(Chattaraman et al., 2012).
A stronger level of perceived social presence has a positive impact on chatbot user satisfaction. By creating a
more holistic perception of customer service interactions through the level of perceived social presence, customers can
experience a more satisfying experience (Fabio et al., 2023). The positive effects of social presence were found to be
more dominant than the negative effects (Huang & Dootson, 2022). In addition, promoting social presence has a
positive effect on satisfaction, as seen in the context of online learning environments, computer conferencing, and
online customer service (Verhagen et al., 2014). Therefore, our first hypothesis is as follows:
H1: The social presence of a chatbot affects customer satisfaction.
Personification refers to attributes that make a chatbot appear more human, such as the use of names or a more
humanized physical appearance (Verhagen et al., 2014). This aims to increase the perception of social presence in the
interaction with the chatbot. The reasoning behind this is that individuals tend to attribute personality to the chatbot,
even when the information available is very limited (Kim & Sundar, 2012). Providing additional information, such as a
name or picture, assists users in forming a better judgment of their conversation partner and, if necessary, establishing a
closer relationship (Kim & Sundar, 2012). For example, when the chatbot's name is displayed during a conversation
(Balderas et al., 2023), users perceive the conversation partner as more human, thus improving well-being (Paluch &
Wirtz, 2020) and helping to overcome the impression of less personal communication from the chatbot (Kim & Sundar,
2012). Findings from (Araujo, 2018) suggest that an emotional bond is formed between customers and companies when
interacting with a chatbot that has a human name. Facial appearance, which specifically reflects human identity, has
been designed with human-like elements (Sproull et al., 1996).
Research by (Koda, 2014) investigated individuals' responses to diverse facial displays of software agents and
found that agents with faces were perceived as more sympathetic and pleasant than those without faces. Therefore, this
element of pleasantness may also contribute positively to the level of satisfaction in interactions with chatbots (Fabio et
al., 2023). Satisfaction with a chatbot is defined as the attitude of customers and users towards the service interaction
experience experienced (Hobert & Law, 2023). Interacting with a software agent represented by an anthropomorphic
face in a similar way as when interacting with a real person. (Barik et al., 2023) found evidence indicating greater levels
of disclosure with chatbots that have been personified, therefore, a second hypothesis can be formulated.
H2: Chatbot personification affects customer satisfaction.
METHOD
The research approach uses quantitative methods, with explanatory survey methods. The population determined
is marketplace users who receive services from chatbots on the grounds that consumers can feel positive or negative
experiences after being served by chatbots. This study uses a questionnaire data collection tool with a Likert scale of 7.
The questionnaire was distributed online using google form to marketplace customers. The sample used rondom
sampling and managed to collect 175 respondents. This sample determination is sufficient to be processed using PLS
SEM. This is based on the provisions that (Hair Jr et al., 2023) regarding the determination of the minimum sample, the
following is presented in the table below.
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Table 1. Minimum sample sizes
Pmin
Significance Level
1%
5%
10%
0.05-0.1
1004
619
451
0.11-0.2
251
155
113
0.21-0.3
112
69
51
0.31-0.4
63
39
29
0.41-0.5
41
39
19
Source: (Hair Jr et al., 2023)
The table above shows that the sample collected is in accordance with the provisions for processing using PLS
SEM. RESULT
Demographic profile of respondents
Respondent characteristics/demographic profiles for this study, there are three characteristics set gender,
education and age. The characteristics of the respondents obtained can be seen from the following summary table.
Based on the table of respondent characteristics, it is known that online customers are more dominated by
women, it is possible that women prefer to shop than men. Respondents' education is more high school graduates, and
the average age of employees is in the gen Z category which is very familiar with technology. This data shows that
young respondents are more likely to shop and surf in the marketplace.
Statistical analysis using PLS
This research applies Partial Least Squares (PLS) Analysis. PLS is a method commonly used to analyze cause-
and-effect relationships based on variation and applies an estimation approach based on principal components, as
described by (Hair et al., 2017).There are various reasons that support the use of PLS in this study, but one of the
strongest reasons is because PLS-SEM is a very effective multivariate approach to analyzing data. (Sarstedt et al., 2019)
explained that PLS-SEM has the ability to simultaneously estimate multiple associations, focusing on the overall picture
of the entire model as well as its level of explanation. Although Structural Equation Modeling (SEM) has been used in
various contexts, PLS-SEM has become a very popular and widely adopted technique.
The PLS model evaluation procedure consists of two main steps. The first step involves applying measurement
model analysis to verify and assess the reliability of each individual item. The second step involves validating the
structural model using data to test the consistency of the causal relationships described in the model. Below, the results
of the model outputs generated through the use of the PLS approach are presented.
Measurement model analysis
Evaluation of the measurement model begins by first looking at the outer loading which is determined by the
criteria above> 0.70 (Hair Jr et al., 2023) or above> 0.60 (Chin, 1998). The results of the measurement model analysis
in this study resulted in outer loading above> 0.70 after removing X1.8, Y1.7, Y1.8, and Y1.10. Second, analyze
composite reliability with criteria above> 0.70. Third, determine convergent validity by analyzing Cronbach's alpha
above> 0.70. Fourth, analyze the AVE root above> 0.50 and fifth, discriminant validity by analyzing (cross loading,
Fornell Lacker and HTMT).
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Reporting validity and reliability Tabel 2. Reporting Validity and Reliability
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As a result of this study, Cronbach's alpha values ranged from 0.918-0.959, composite reliability values ranged
from 0.918-0.959, while AVE values ranged from 0.707-0.763. Thus, all these values confirm the reliability of the
measurements. In addition, the factor loading values were higher than 0.70, ranging from 0.718-0.884, which confirmed
the reliability of the indicators. Thus, all the validity and reliability values reported confirm the reliability of the
measurement. Table 4.2 illustrates that the research constructs passed the convergent validity test.
Discriminant validity was proved using Fornell and Larcker's (1981) conditions (AVE ≥ 0.5), with the square
root of the AVE exceeding the correlation between latent variables.
Table 3. Fornell-Larcker Criterion
Chatbot
Personificati on
Social
Chatbot
Customer
Satisfaction
Chatbot Personification
0,881
Social Chatbot
0,879
0,859
Customer Satisfaction
0,878
0,802
0,814
The results are summarized in Table 3 which indicates that all latent variables have an AVE of more than 0.50,
and the square root of the AVE is greater than the correlation between latent variables, indicating that the research
measurements have an adequate level of discriminant validity.
Structural model analysis
The structural model evaluation check is carried out in three stages, namely first checking for multicollinearity
with the Inner VIF measure (Sarstedt et al., 2019). Second, hypothesis testing between variables by looking at the t-
statistic or p-value.
Multicollinearity
Inner VIF is done to analyze the presence or absence of multicollinearity. So that the data collected is not biased.
Table 4. Inner VIF Values
Chatbot
Personificatio n
Social
Chatbot
Customer
Satisfaction
Chatbot Personification
4,396
Social Chatbot
4,396
Customer Satisfaction
The estimation results of the inner VIF value below <5 indicate that there is no multicollinearity between
variables. This result implies that parameter estimation in SEM PLS is unbiased.
Hypothesis testing
This stage looks at the influence between the variables studied and can be seen from the summary table below.
Table 5. Hypothesis Results
(O)
(M)
(STDEV)
T
Statistics
P
Values
Chatbot Personification Customer
Satisfaction
0,375
0,373
0,078
4,819
0,000
Social Chatbot Customer
Satisfaction
0,572
0,573
0,072
7,903
0,000
Based on the results of hypothesis testing above, it is known that.
1. The first hypothesis is accepted, namely that there is a significant influence between chatbot personification on
customer satisfaction with path coeficience (0.37) and V value (0.000 <0.05).
2. The first hypothesis is accepted, namely that there is a significant influence between chatbot social orientation on
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customer satisfaction with path coeficience (0.57) and V value (0.000 <0.05).
Based on verification data processing, the research results model is found as follows.
Figure 2. Research findings Source: Researcher 2023
DISCUSS ION
Chatbot personification and customer satisfaction
Chatbot personification, is giving a chatbot a human-like appearance or representation. This chatbot
personification has a significant impact on customer satisfaction in various aspects (Verhagen et al., 2014). It not only
changes the way customers interact with the chatbot, but also affects customer perceptions of service quality and trust in
the information provided. In addition, human-like chatbots can improve the overall customer experience (Janson, 2023;
Kim & Sundar, 2012).
Chatbot personification related to customer satisfaction from the element of agility. Chatbots that have an
appearance are easier for customers to use (Schuetzler et al., 2020). With a more humanized appearance, customers
feel more comfortable interacting with the chatbot. This reduces barriers to communication and increases agility,
making it easier for customers to get the answers or help they need. Human-like chatbot representation can also
improve service assurance. Customers feel more confident with chatbots that appear to be managed by humans, and
customers feel that the service received is more reliable (Lee & Li, 2023). Human-looking chatbot personifications
tend to provide more consistent and precise responses (Getachew et al., 2023). Humanized representation allows the
chatbot to understand and respond better to customer queries, reducing the risk of errors or mismatches in responses.
This helps improve the reliability of the service provided.
Scalability in online business is an important factor. Service coverage is easily increased without requiring a
significant increase in human staff. This allows companies to serve a larger number of customers. When a chatbot has a
human appearance, it can build customer trust in the information provided (Keeling et al., 2010). Customers tend to
feel more secure and trust in chatbots that appear to be staffed by competent individuals. This is especially important
in situations where sensitive information such as personal data or financial transactions are shared.
A positive and comfortable experience in interaction with a chatbot can increase overall customer satisfaction
customer satisfaction including agility, service assurance, reliability, scalability, security, and experience. This can
influence customers to return to the service and recommend it to others (Sakas et al., 2023). Such factors are
important in maintaining and improving customer satisfaction in a competitive online business environment.
Social orientation of chatbots and customer satisfaction
The social orientation of chatbots, which emphasizes friendly and warm communication, has a role in
improving customer satisfaction in various aspects (Balderas et al., 2023). When a chatbot communicates in a friendly
and warm manner, users tend to feel comfortable and easy to interact with. This makes the process of interacting with
the chatbot smoother and more efficient. When users feel well treated and valued during the conversation, customers
will be more satisfied with the experience. The interactivity of a chatbot can also help users navigate through a website
or e-commerce application more easily. In addition, the social orientation of the chatbot can provide assurance to the
customer that the customer is talking to a caring and trustworthy entity (Keeling et al., 2010). This creates trust in the
services provided by the company. When the chatbot asks additional questions or says hello during the
Chatbot
Customer Satisfaction
Personification
Social Orientation
Scurity
Agility
Service Guarantee
Reliability
Scalability
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conversation, it reflects the company's commitment to providing quality support. Thus, customers feel more confident
that the company will properly address customer needs and concerns.
A communicative and responsive chatbot is likely to provide relevant answers (Lee & Li, 2023).
Companies can serve a large number of customers quickly and efficiently. In other words, the social orientation of
chatbots helps increase the capacity of companies to respond well to customer requests, which in turn increases
customer satisfaction. The communication style of chatbots can build trust, and have a positive impact on security
aspects (Agarwal & Dhingra, 2023). Customers tend to feel that personal data and financial transactions can be
managed properly and safely. The social orientation of the chatbot helps to overcome any security-related discomfort
or doubts. When customers feel well treated, provided with reliable service, and feel warmth in interactions with
chatbots, customers are more likely to feel satisfied with the shopping experience, ultimately increasing customer
loyalty and business growth.
CONCLUSION
Summary
Based on the formulation of the problem, objectives and discussion, this research can be concluded.
1) Chatbot personification contributes to customer satisfaction. The higher the chatbot personification, the higher the
customer satisfaction.
2) Chatbot social orientation contributes to customer satisfaction. The higher the social orientation of the chatbot, the
higher the customer satisfaction.
The results of this study provide valuable direction for marketplaces to improve customer satisfaction, develop chatbot
personification technology and chatbot social orientation.
Implications
1) The main implication of the results of this study is the improvement of customer satisfaction made by chatbots. By
strengthening chatbot technology with personification and social orientation can improve customer experience,
retaining existing customers.
2) Further development of chatbots by improving technology that supports the optimization of anthropomorphic
chatbots.
3) Online shop business is highly competitive customer satisfaction can be a key factor in achieving competitive
advantage. marketplace that is able to provide high-quality customer satisfaction has a greater chance of success in a
fiercely competitive market.
Advice
1) For decision makers.
Marketplace management can continue to invest in the development of anthropomorphic chatbots in improving quality.
2) For future researchers
The research results that show a low indicator is the capacity for self- development associated with chatbot reliability
has not provided the right answer. Researchers can relate self-development to other variables in human resource theory.
Developing reliability variables in customer satisfaction combined with other theories to develop a deeper
understanding.
REFERENCES
Adam, M., Wessel, M., & Benlian, A. (2021). AI-based chatbots in customer service and their effects on user
compliance. Electronic Markets, 31, 427–445. https://doi.org/https://doi.org/10.1007/s12525-020-00414-7
Agarwal, R., & Dhingra, S. (2023). Factors influencing cloud service quality and their relationship with customer
satisfaction and loyalty. Heliyon, 9(4), e15177. https://doi.org/10.1016/j.heliyon.2023.e15177
Ansary, A., Dulal, M., & Habib, A. (2023). Cleaner Logistics and Supply Chain The influence of sustainable supply
chain management, technology orientation, and organizational culture on the delivery product quality-customer
satisfaction nexus. Cleaner Logistics and Supply Chain, 7(November 2022), 100107.
https://doi.org/10.1016/j.clscn.2023.100107
Araujo, T. (2018). Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative
agency framing on conversational agent and company perceptions. Computers in Human Behavior.
https://doi.org/10.1016/j.chb.2018.03.051
Arbianto, Y. (2020). 45 Persen Konsumen Indonesia Kecewa Situs E-Comemerce.
https://www.batukita.com/2020/09/45-persen-konsumen-indonesia-kecewa-situs-ecommerce.html
Balderas, A., García-mena, R. F., Huerta, M., Mora, N., & Manuel, J. (2023). Chatbot for communicating with
university students in emergency situation. Heliyon, 9(9), e19517. https://doi.org/10.1016/j.heliyon.2023.e19517
Barik, K., Misra, S., Kumar, A., & Shukla, A. (2023). A blockchain-based evaluation approach to analyse customer
E-ISSN : 2807-9035
Volume 3, Number 2, November 2023
https://doi.org/10.47709/brilliance.v3i2.3133
This is an Creative Commons License This work is licensed under a Creative
Commons Attribution-NonCommercial 4.0 International License.
260
satisfaction using AI techniques. Heliyon, 9(6), e16766. https://doi.org/10.1016/j.heliyon.2023.e16766
Brown, G., Smith, A., & Assaker, G. (2016). Revisiting the host city : An empirical examination of sport involvement,
place attachment, event satisfaction and spectator intentions at the London Olympics. Tourism Management, 55,
160–172. https://doi.org/10.1016/j.tourman.2016.02.010
Chattaraman, V., Kwon, W., & Gilbert, J. E. (2012). Virtual agents in retail web sites: Benefits of simulated social
interaction for older users. Computers in Human Behavior, 28(6), 2055–2066.
https://doi.org/10.1016/j.chb.2012.06.009
Darko, A. P., & Liang, D. (2022). Modeling customer satisfaction through online reviews: a FlowSort group decision
model under probabilistic linguistic settings. Expert Systems with A, 195(June).
https://doi.org/https://doi.org/10.1016/j.eswa.2022.116649
Eun, S., Ju, N., & Lee, K. (2023). Service chatbot: Co-citation and big data analysis toward a review and research
agenda. Technological Forecasting & Social Change, 194(June), 122722.
https://doi.org/10.1016/j.techfore.2023.122722
Fabio, M., Ho, M. H. C., & Tsai, T. (2023). Drivers of customer satisfaction with restaurants during COVID-19 . A
survey of young adults in Taiwan and Indonesia. Asia Pacific Management Review, xxxx.
https://doi.org/10.1016/j.apmrv.2023.08.001
Filip, A. (2013). Complaint management : A customer satisfaction learning process. Procedia - Social and Behavioral
Sciences, 93, 271–275. https://doi.org/10.1016/j.sbspro.2013.09.188
Getachew, A., Assefa, A., & Mamo, G. (2023). Effects of automated teller machine service quality on customer
satisfaction : Evidence from commercial bank of Ethiopia. Heliyon, 9(8), e19132.
https://doi.org/10.1016/j.heliyon.2023.e19132
Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2023). Partial Least Squares
Structural Equation Modeling (PLS-SEM) Using R: A Workbook. In Structural Equation Modeling: A
Multidisciplinary Journal (Vol. 30, Issue 1). https://doi.org/10.1080/10705511.2022.2108813
Hill, J., Ford, W. R., & Farreras, I. G. (2015). Real conversations with artificial intelligence : A comparison between
human – human online conversations and human – chatbot conversations. COMPUTERS IN HUMAN
BEHAVIOR, 49, 245–250. https://doi.org/10.1016/j.chb.2015.02.026
Hobert, S., & Law, E. L. (2023). Chatbots for active learning : A case of phishing email identification. International
Journal of Human - Computer Studies, 179(June). https://doi.org/10.1016/j.ijhcs.2023.103108
Huang, Y.-S. (Sandy), & Dootson, P. (2022). Chatbots and service failure: When does it lead to customer aggression.
Journal of Retailing and Consumer Services, 68(September 2022), 103044.
https://doi.org/https://doi.org/10.1016/j.jretconser.2022.103044.
Janson, A. (2023). How to leverage anthropomorphism for chatbot service interfaces : The interplay of communication
style and personification. Computers in Human Behavior, 149(August), 107954.
https://doi.org/10.1016/j.chb.2023.107954
Keeling, K., Mcgoldrick, P., & Beatty, S. (2010). Avatars as salespeople : Communication style , trust , and intentions.
Journal of Business Research, 63(8), 793–800. https://doi.org/10.1016/j.jbusres.2008.12.015
Kim, Y., & Sundar, S. S. (2012). Anthropomorphism of computers: Is it mindful or mindless? Computers in Human
Behavior, 28(1), 241–250. https://doi.org/10.1016/j.chb.2011.09.006
Koda, T. (2014). Agents with Faces : the effects of personification of software agents. Proceedings of the Fifth IEEE
International Workshop on Robot and Human Communication., January 1996, 189–194.
Lee, K., & Li, C. (2023). It is not merely a chat : Transforming chatbot affordances into dual identification and loyalty.
Journal of Retailing and Consumer Services, 74(129), 103447. https://doi.org/10.1016/j.jretconser.2023.103447
Magotra, I., Sharma, J., & Kumar, S. (2018). Investigating linkage between customer value and technology adoption
behaviour : A study of banking sector in India. European Research on Management and Business Economics,
24(1), 17–26. https://doi.org/10.1016/j.iedeen.2017.11.001
Paluch, S., & Wirtz, J. (2020). Artificial Intelligence and Robots in the Service Encounter – The Guest Editors’
Perspective. Journal of Service Management Research, 1(February), 3–8. https://doi.org/10.15358/2511-8676-
2020-1-3
Pizam, A., & Ellis, V. S. (2016). Customer satisfaction and its measurement in hospitality enterprises: a revisit and
update. International Journal of Contemporary Hospitality Management, May. https://doi.org/10.1108/IJCHM-
04-2015-0167
Qiu, L., & Benbasat, I. (2014). Evaluating Anthropomorphic Product Recommendation Agents: A Social Relationship
Perspective to Designing Information Systems. Journal of Management Information Systems, 25(August 2015),
145–181. https://doi.org/10.2753/MIS0742-1222250405
Rashid, A., Taliah, B., & Zervopoulos, P. D. (2023). Using a Bayesian meta-frontier approach to evaluate airline
performance: The nexus between airline efficiency and customer satisfaction. Heliyon, 9(10), e20391.
E-ISSN : 2807-9035
Volume 3, Number 2, November 2023
https://doi.org/10.47709/brilliance.v3i2.3133
This is an Creative Commons License This work is licensed under a Creative
Commons Attribution-NonCommercial 4.0 International License.
261
https://doi.org/10.1016/j.heliyon.2023.e20391
rumahmedia. (2022). Predikasi Anggka Pengguna E-commerce di Indonesia 2024.
https://www.rumahmedia.com/insights/prediksi-angka-pengguna-e-commerce-di-indonesia-2024
Sakas, D. P., Reklitis, D. P., Terzi, M. C., & Glaveli, N. (2023). Growth of digital brand name through customer
satisfaction with big data analytics in the hospitality sector after the COVID-19 crisis. International Journal of
Information Management Data Insights, 3(2), 100190. https://doi.org/10.1016/j.jjimei.2023.100190
Sarstedt, M., Hair, J. F., Cheah, J., Becker, J., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-
order constructs in. Australasian Marketing Journal (AMJ), 27(3), 197–211.
https://doi.org/10.1016/j.ausmj.2019.05.003
Schuetzler, R. M., Grimes, G. M., & Scott, J. (2020). Impact of Chatbot Conversational Skill on Engagement and
Perceived Humanness ABSTRACT. Journal of Management Information Systems, 37(3), 875–900.
https://doi.org/10.1080/07421222.2020.1790204
Sproull, L., Kiesler, S., Subramani, M. R., & Walker, J. (1996). When the Interface Is a Face. HUMAN-COMPUTER
INTERACTION, 11(June), 97–124. https://doi.org/10.1207/s15327051hci1102
Tiganis, A., Grigoroudis, E., & Chrysochou, P. (2023). Customer satisfaction in short food supply chains : A multiple
criteria decision analysis approach. Food Quality and Preference, 104(October 2022), 104750.
https://doi.org/10.1016/j.foodqual.2022.104750
Varriale, V., Cammarano, A., Michelino, F., & Caputo, M. (2023). Knowledge management in high-tech products and
customer satisfaction: The smartphone industry. Journal of Open Innovation: Technology, Market, and
Complexity, 9(1), 100012. https://doi.org/10.1016/j.joitmc.2023.100012
Verhagen, T., van Nes, J., & Feldberg, F. (2014). Virtual Customer Service Agents: Using Social Presence and
Personalization to Shape Online Service Encounters. Journal of Computer-Mediated Communication Virtual, 19,
529–545. https://doi.org/10.1111/jcc4.12066
Wang, X., Lin, X., & Shao, B. (2023). How does artificial intelligence create business agility ? Evidence from chatbots.
International Journal of Information Management, 66(May 2022), 102535.
https://doi.org/10.1016/j.ijinfomgt.2022.102535
