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MARKETING | RESEARCH ARTICLE
COGENT BUSINESS & MANAGEMENT
2024, VOL. 11, NO. 1 , 2326107
The power of AI: enhancing customer loyalty through satisfaction
and eciency
Pragya Singha and Vandana Singhb
aSymbiosis Centre for Management Studies (NOIDA), Symbiosis International University, Pune, India; bDepartment of
Computer Science, Birla Institute of Technology, Mesra, India
ABSTRACT
In the rapidly evolving landscape of customer service, integrating AI-powered solutions
has emerged as a game-changer. This study delves into the intricate dynamics of
AI-Powered Customer Service and its profound impact on customer loyalty, specifically
focusing on the mediating roles played by customer satisfaction and perceived
efficiency. Data were collected from 373 respondents in a cross-sectional study
conducted in 2023. A structured questionnaire was administered electronically to
individuals with recent experiences with AI-powered customer service within the last
six months. The findings provide compelling evidence of the significant influence of
AI-Powered Customer Service on customer satisfaction and perceived efficiency, as
indicated by path coefficients of 0.91 and 0.95, respectively. Moreover, a strong
relationship between customer satisfaction and loyalty (path coefficient = 1.05) and
perceived efficiency and customer loyalty (path coefficient = 0.22) underscores their
pivotal roles in driving customer loyalty. Organizations should strategically embrace
AI-powered customer service, emphasizing efficiency and customer satisfaction. They
prioritize customer-centric design in AI solutions to align technology with customer
preferences and needs.
Introduction
Artificial Intelligence (AI) combines cutting-edge technology, such as chatbots, machine learning (ML),
and natural language processing (NLP) to automate and customize client interactions, resulting in faster,
and more effective service (Mehrotra, 2019). The process involves the examination of consumer data in
order to get insights into their preferences, behaviors, and purchasing history. This data assists in cus-
tomizing interactions, suggestions, and promotions, resulting in a more individualized and captivating
client experience (Dwivedi et al., 2021). AI-driven chatbots and virtual assistants facilitate continuous
accessibility for client inquiries and support. This practice guarantees that consumers are able to obtain
support at any given time, so augmenting their level of satisfaction and fostering a sense of confidence
(George & George, 2023). The system has the capability to address common client concerns and com-
monly encountered inquiries, so allowing human agents to allocate their attention toward more intricate
situations. These factors contribute to the expeditious settlement of issues, the reduction of waiting peri-
ods and the enhancement of overall customer satisfaction (Zerilli et al., 2019). It uses past data and
behavioral patterns to forecast the demands of its customers. To effectively satisfy client expectations,
businesses can take proactive measures to resolve complaints, provide pertinent products or services and
optimize their strategies (Rygielski et al., 2002). By automating repetitive operations, implementing
AI-powered customer services can drastically save operating expenses. Businesses are able to carefully
allocate resources and invest in improving overall customer happiness because to this cost-effectiveness
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
CONTACT Pragya Singh pragya.singh@scmsnoida.ac.in Symbiosis Centre for Management Studies NOIDA, Sector 62, Uttar Pradesh;
A constituent of Symbiosis International University, Pune, India
https://doi.org/10.1080/23311975.2024.2326107
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been
published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
ARTICLE HISTORY
Received 4 December 2023
Revised 5 February 2024
Accepted 26 February 2024
KEYWORDS
Customer Service;
Customer Loyalty;
Customer Satisfaction;
Perceived Eciency;
Customer Experience; AI
Powered customer service
REVIEWING EDITOR
Ediz Akcay, Bournemouth
University Talbot Campus:
Bournemouth University,
United Kingdom of Great
Britain and Northern Ireland
SUBJECTS
Consumer Psychology;
Articial Intelligence;
Management of IT
2 P. SINGH AND V. SINGH
(Al-Mekhlal et al., 2023). Thus, by providing individualized experiences, increasing service effectiveness
and promoting a deeper understanding of client wants and preferences, AI-powered customer services
play a critical role in building customer loyalty. Companies are better positioned to create enduring and
lucrative relationships with their clientele when they include AI into their customer service operations
(Trawnih et al., 2022).
The interconnection between perceived efficiency and customer happiness within the realm of
AI-powered customer service is evident. The pleasure of customers is influenced by their perception of
the AI system’s efficiency in resolving difficulties and offering relevant support (Yeo etal., 2022). On the
other hand, a gratifying engagement with the AI system amplifies the perception of its efficacy. Perceived
efficiency and customer satisfaction are both significant factors in cultivating customer loyalty through
the establishment of trust, the fulfillment of expectations and the cultivation of a favorable brand per-
ception (Floridi et al., 2021). In conclusion, the implementation of a meticulously crafted and optimally
performing AI-driven customer service system holds the potential to exert a substantial impact on cus-
tomer allegiance and the establishment of enduring associations with the organization (Gupta, 2021;
Fernando et al., 2023).
The purpose of this study is to investigate how perceived effectiveness and customer satisfaction
function as mediators in the relationship between customer loyalty and AI-powered customer service.
AI-powered customer service should be strategically implemented by organizations, with an emphasis on
both customer satisfaction and efficiency. In order to match technology with client preferences and
demands, they give priority to customer-centric design in AI solutions.
Review of literature
In contemporary times, there is a notable trend of substituting human chat service operators with
conversational software agents or chatbots. These agents are specifically developed to engage in nat-
ural language interactions with human users, often employing artificial intelligence (AI) techniques
(Adam et al., 2021). The utilization of chatbots has proven to be advantageous for companies, partic-
ularly when it is linked to a favorable customer experience. The primary determinants of chatbot effec-
tiveness in customer service are the relevancy of responses provided and the capacity to resolve client
issues. These variables typically lead to favorable outcomes, such as customer satisfaction, greater like-
lihood of continued chatbot usage, product purchases and product recommendations (Nicolescu &
Tudorache, 2022). In instances involving tasks of low complexity, consumers perceive AI to possess a
higher problem-solving capacity compared to human customer service. Consequently, they exhibit a
greater inclination toward utilizing AI. Conversely, for tasks characterized by high complexity, consum-
ers regard human customer service as superior and are more inclined to opt for its utilization over AI
(Song et al., 2022; Xu et al., 2020). Nevertheless, the widespread adoption of AI-based chatbots is
hindered by a deficiency in user trust. However, there remains a deficiency in the availability of sys-
tematically obtained design knowledge pertaining to user confidence in these agents (Yang et al.,
2023; Zierau et al., 2020). Although there is a lot of potential for AI to improve competitiveness, there
are also a number of issues that could arise. The use of features like convenience, customization and
humanization has the benefit of clarifying the value co-creation process. Technology’s drawbacks
include worry, privacy worries, a loss of control, and a reduction in personal connection, all of which
lower the value of shared value. In order to create a service ecosystem that puts the requirements of
its customers first, AI-driven value co-creation methods must be carefully integrated while taking care
of any problems relating to value co-destruction (Chaturvedi & Verma, 2023). In their 2019 study,
Al-Adwan and Al-Horani (2019) emphasized that trust is essential for customers and that customer
satisfaction influences their intention to repurchase.
By integrating AI, ML and NLP, enterprises have the ability to examine data in order to discover new
insights that can be used to automate operations and propel corporate initiatives. Consequently, compa-
nies that want to maintain competitiveness and enhance consumer loyalty have to embrace these prac-
tices (Patel & Trivedi, 2020). Both AI and staff service quality have been shown to significantly contribute
to the evaluation of overall service quality, as well as customer happiness and loyalty (Prentice et al.,
2020). However, it is observed that customers tend to have a more pronounced unfavorable attitude
COGENT BUSINESS & MANAGEMENT 3
toward customer service provided by AI as compared to their human counterparts. The primary concerns
expressed by consumers with AI customer service are to its limited problem-solving capabilities.
Additionally, customers are dissatisfied with the late response and absence of a human touch. The like-
lihood of customers providing favorable feedback is mostly contingent upon voice features and service
attitudes (Zhao et al., 2022). The satisfaction of users is highly influenced by the quality of service recov-
ery and conversational capabilities of AI chatbot systems. In contrast, the quality and pleasure of the
core AI chatbot service shown a substantial impact on the loyalty of users using the chatbot (Hsu & Lin,
2023). Customer loyalty is favorably impacted by the quality of AI chatbot services in terms of perceived
value, cognitive trust, emotional trust, and satisfaction (Qian et al., 2023). Al-Adwan et al. (2022) exam-
ined the role of environmental cues and stimuli in online settings in Jordan and in their study high-
lighted the importance of policy service quality dimensions in establishing customer trust. Al-Adwan
et al. (2020) explored the factors affecting online trust, satisfaction and loyalty in Jordan and provided
relevant and beneficial insights for e-commerce businesses in Jordan. Their study also compares relation-
ships between online trust, loyalty and satisfaction in Jordan and internationally.
Upon conducting an extensive analysis of the available scholarly works pertaining to the intermediary
function of perceived efficiency and customer satisfaction in the correlation between AI-driven customer
service and customer loyalty, it is evident that a significant research void exists in the examination of
proactive communication by AI systems. The current body of research has not yet thoroughly investi-
gated the effects of AI-initiated communication on perceived efficiency, customer satisfaction and even-
tual customer loyalty. AI-initiated communication refers to the proactive approach of AI systems in
anticipating consumer requirements and reaching out to give assistance or solutions before customers
actively seek aid. Gaining a comprehensive understanding of the ramifications associated with proactive
communication within the realm of AI-driven customer service has the potential to provide valuable
insights for the optimization of client interactions and the augmentation of customer loyalty.
Methodology
Sampling methodology
We employed a purposive sampling approach to select participants for this cross-sectional study. The
target population consisted of individuals who had recent experiences with AI-powered customer service
within the last six months in various industries.
Number of respondents
Initially, a total of 478 respondents participated in this study. However, 105 respondents who indicated
that they had not had recent experiences with AI-powered customer service within the last six months
were eliminated from the study. This resulted in a final sample size of 373 respondents.
Nature of sampling
The nature of sampling was non-probabilistic purposive sampling, with the selection of respondents
based on their specific experience with AI-powered customer service. The respondents are customers
who were purchasing online and have experienced AI-powered services in various domains, such as a
Chatbot service and other personalized services ensuring diversity in the dataset. Purposive sampling
was utilized in this research to identify AI applications and services for users and non-user. This method
works well for this study because it is challenging to compile a list of people who use AI services to
share their opinions and experiences.
Time period of the study
Data collection for this study took place in the year 2023.
4 P. SINGH AND V. SINGH
Variables
We examined a set of key variables to investigate the relationship between AI-powered customer service and
customer loyalty. The independent variable (IV), AI-powered customer service, was assessed based on respon-
dents’ experiences and perceptions of interactions with AI-driven customer service solutions. Our research also
incorporated two mediating variables (MVs), namely Perceived Efficiency and Customer Satisfaction. Perceived
Efficiency was gauged by assessing respondents’ perceptions of the efficiency of AI-powered customer service
in handling queries and tasks. Customer Satisfaction was measured by evaluating respondents’ overall satis-
faction with their interactions with AI-powered customer service. Finally, the dependent variable (DV), Customer
Loyalty, was assessed by examining respondents’ intentions, behaviors and loyalty-related attitudes toward
companies or brands that employ AI-powered customer service solutions. These variables collectively formed
the foundation of our analysis, allowing us to explore the mediating role of Perceived Efficiency and Customer
Satisfaction in the relationship between AI-powered customer service and Customer Loyalty.
Data collection
The collection of data involved the use of an electronic questionnaire distributed to participants, considering
the sensitive nature of the information provided by the respondents. The questionnaire consisted of 5-point
Likert-scale items and was administered electronically through Google Forms (Appendix). This method was
chosen to uphold strict ethical considerations regarding the confidentiality of participant information.
Hypothesis
Following are the hypothesis formulated for the study:
H01 There is no signicant relationship between AI-Powered Customer Service and Customer Satisfaction.
H02 There is no signicant relationship between AI-Powered Customer Service and Perceived Eciency.
H03 There is no signicant relationship between Customer Satisfaction and Customer Loyalty.
H04 There is no signicant relationship between Perceived Eciency and Customer Loyalty.
H05 The path from AI-Powered Customer Service to Customer Satisfaction to Customer Loyalty does not have
a signicant eect.
H06 The path from AI-Powered Customer Service to Perceived Eciency to Customer Loyalty does not have a
signicant eect.
Data analysis
Data analysis was conducted using the PLS-SEM technique, allowing for the assessment of both the
direct and indirect effects in the proposed mediation model. The analysis aimed to explore the relation-
ships among AI-powered customer service, perceived efficiency, customer satisfaction and customer loy-
alty. The mediation analysis specifically examined whether perceived efficiency and customer satisfaction
mediate the relationship between AI-powered customer service and customer loyalty.
Data analysis and interpretation
Respondent summary
Table 1 provides a summary of respondents categorized by gender and age groups. The table illustrates
the distribution of respondents across different age categories, namely ‘Under 18,’ ‘18-24,’ ‘25-34’ and
Table 1. Respondent summary.
Gender Under 18 18–24 25–34 35 years and above
Female 61 32 45 11
Male 84 59 63 18
Source: Author calculation.
COGENT BUSINESS & MANAGEMENT 5
‘35 years and above,’ further broken down by their gender, either ‘Female’ or ‘Male.’ The data reveals that
among the female respondents, there are 61 individuals under the age of 18, 32 individuals aged 18–24,
45 individuals aged 25–34 and 11 individuals aged 35 years and above. On the other hand, among the
male respondents, there are 84 individuals under 18, 59 individuals aged 18–24, 63 individuals aged
25–34, and 18 individuals aged 35 years and above (Figure 1).
Structural equation modeling
Table 2 provides an overview of the outer loadings within the structural equation model, which seeks to
examine the relationships among latent variables, including Cross-Cultural Adaptation (CCA), Cultural
Intelligence (CI), Key Competencies (KC) and Managerial Success (MS). These outer loadings represent the
strength and direction of the relationship between each indicator (item) and its corresponding latent
variable. To evaluate the adequacy of these outer loadings, a benchmark value of 0.6, as suggested by
Nunnally (1994), serves as a guideline. Indicators with loadings exceeding 0.6 are considered robust mea-
sures of their latent variables, effectively capturing the underlying constructs.
Within CCA, items CCA1 through CCA6 exhibit relatively strong outer loadings, ranging from 0.619 to
0.807, suggesting their effectiveness in measuring CCA. Similarly, CI indicators, represented by CI1 through
CI5, display substantial outer loadings, ranging from 0.723 to 0.888, indicating their strength as measures
of CI. KC indicators, denoted as KC1 through KC4, also demonstrate meaningful outer loadings, ranging
from 0.664 to 0.838, signifying their ability to effectively capture KC as a latent variable. MS indicators,
encompassing MS1 through MS5, display strong outer loadings ranging from 0.598 to 0.821, establishing
their role as robust measures of MS.
In summary, Table 2 provides valuable insights into the strength of the relationships between individ-
ual indicators and their respective latent variables within the structural equation model. Higher outer
loadings, surpassing the benchmark of 0.6, indicate the effectiveness of these indicators in measuring
and contributing to the understanding of the underlying constructs.
Assessment of the measurement model
Table 3 presents important metrics related to the reliability and validity of the constructs in the study,
including Cronbach’s Alpha, Composite Reliability and Average Variance Extracted (AVE). These metrics
provide insights into the internal consistency, convergent validity and discriminant validity of the con-
structs. The table shows that for all constructs, including AI-Powered Customer Service, Customer
Loyalty, Customer Satisfaction and Perceived Efficiency, the Cronbach’s Alpha values exceed the recom-
mended threshold of 0.7. This indicates strong internal consistency within each construct, suggesting
that the items measuring these constructs are reliable and consistently measure the same underlying
concept. Specifically, AI-Powered Customer Service, Customer Loyalty, Customer Satisfaction and
Perceived Efficiency exhibit high Cronbach’s Alpha values of 0.95, 0.93, 0.92 and 0.94, respectively.
Additionally, the Composite Reliability values are consistently high, further confirming the reliability of
Figure 1. Respondent summary. Source: MS Excel.
6 P. SINGH AND V. SINGH
the constructs. The AVE values calculated for each construct in the study are shown in Table 3.
Convergent validity assesses whether the items within a construct converge to measure the same
underlying concept. All AVE values in the table exceed the recommended threshold of 0.5 (Fornell &
Larcker, 1981), indicating that a substantial proportion of the variance in the observed variables can be
attributed to the underlying constructs. Specifically, AI-Powered Customer Service, Customer Loyalty,
Customer Satisfaction and Perceived Efficiency demonstrate AVE values of 0.73, 0.63, 0.59 and 0.68,
respectively. These results confirm that the items effectively measure their respective constructs and
provide evidence of convergent validity.
Discriminant validity assesses whether the constructs are distinct from each other. It ensures that each
construct measures a unique and separate underlying concept. In this context, discriminant validity was
evaluated using heterotrait-monotrait (HTMT) values, with a threshold of 0.85 (Henseler etal., 2015). The
key criterion is that HTMT values between constructs should not exceed 0.85 to confirm discriminant
validity (Table 4).
Structural model and hypothesis testing
The structural model’s integrity and the relationships among key variables were rigorously examined by
analyzing standardized path coefficients (β values), t statistics and associated p values. These path
Table 2. Outer loadings of latent variables.
AI-Powered customer service Customer loyalty Customer satisfaction Perceived eciency
AI1 0.857
AI2 0.824
AI3 0.9
AI4 0.866
AI5 0.872
AI6 0.83
AI7 0.862
AI8 0.824
CL1 0.872
CL2 0.838
CL3 0.854
CL4 0.872
CL5 0.752
CL6 0.714
CL7 0.72
CL8 0.71
CS1 0.799
CS2 0.669
CS3 0.83
CS4 0.705
CS5 0.793
CS6 0.803
CS7 0.839
CS8 0.722
PE1 0.843
PE2 0.809
PE3 0.879
PE4 0.865
PE5 0.84
PE6 0.854
PE7 0.838
PE8 0.658
Source: SMART-PLS calculation.
Table 3. Descriptive statistics, reliability and validity assessment.
Latent variables Mean Std dev Cronbach’s alpha Composite reliability AV E
AI-Powered customer
service
29.02 9.47 0.95 0.95 0.73
Customer loyalty 27.98 9.28 0.93 0.93 0.63
Customer satisfaction 27.49 9.16 0.92 0.92 0.59
Perceived eciency 26.28 8.91 0.94 0.94 0.68
Source: SMART-PLS calculation.
COGENT BUSINESS & MANAGEMENT 7
coefficients serve as standardized regression coefficients and offer critical insights into the strength and
significance of relationships between independent and DVs (Hair et al., 2021). The results, as displayed
in Table 5, decisively confirm the validity of all hypothesized relationships. The path from AI-Powered
Customer Service to Customer Satisfaction is marked by a substantial path coefficient of 0.91. This rela-
tionship is supported by a remarkably high t statistic of 59.13 and an associated p value of 0.00, strongly
validating the hypothesized link between AI-Powered Customer Service and Customer Satisfaction.
Likewise, the path from AI-Powered Customer Service to Perceived Efficiency reveals a robust path coef-
ficient of 0.95, supported by an exceedingly high t statistic of 85.61 and an associated p value of 0.00.
This outcome underscores the hypothesized relationship’s statistical significance, affirming the positive
influence of AI-Powered Customer Service on Perceived Efficiency. Moving to the subsequent relation-
ship, Customer Satisfaction to Customer Loyalty, the path coefficient stands at 1.05. This is coupled with
a notable t statistic of 9.45 and an associated p value of 0.00, providing compelling evidence that
Customer Satisfaction has a significant positive effect on Customer Loyalty. Lastly, the path from Perceived
Efficiency to Customer Loyalty is characterized by a path coefficient of 0.22. This relationship is sup-
ported by a t statistic of 3.00 and an associated p value of 0.00, clearly demonstrating the statistical
significance of the influence of Perceived Efficiency on Customer Loyalty. Hence, the null hypothesis
formulated H01, H02, H03 and H04 are rejected (Figure 2).
Table 4. Heterotrait-monotrait ratio.
AI-Powered customer service Customer loyalty Customer satisfaction
AI-Powered Customer Service
Customer loyalty 0.78
Customer satisfaction 0.65 0.72
Perceived eciency 0.80 0.82 0.79
Source: SMART-PLS calculation.
Table 5. Hypothesis testing.
Hypothesis Path coecients t Statistics p Values
AI-Powered customer service -> Customer satisfaction 0.91 59.13 0.00
AI-Powered customer service -> Perceived eciency 0.95 85.61 0.00
Customer satisfaction -> Customer loyalty 1.05 9.45 0.00
Perceived eciency -> Customer loyalty 0.22 3.00 0.00
Source: SMART-PLS calculation.
Figure 2. Structured model. Source: SMART-PLS calculation.
8 P. SINGH AND V. SINGH
Mediation indirect eect
The mediation effect representing the path from AI-Powered Customer Service to Customer Satisfaction
to Customer Loyalty is particularly notable. It is accompanied by a remarkably high T statistic of 9.42 and
an associated p-value of 0.00. This outcome unequivocally confirms the presence of a significant media-
tion effect. It indicates that the influence of AI-Powered Customer Service on Customer Loyalty is medi-
ated, at least in part, by the intermediary role of Customer Satisfaction. In simpler terms, AI-Powered
Customer Service has a substantial indirect impact on Customer Loyalty through its positive influence on
Customer Satisfaction. Similarly, the mediation effect corresponding to the path from AI-Powered
Customer Service to Perceived Efficiency to Customer Loyalty also demonstrates statistical significance.
This mediation effect is supported by a t statistic of 3.00 and an associated p value of 0.00. These find-
ings provide robust evidence that AI-Powered Customer Service Influences Customer Loyalty through its
impact on Perceived Efficiency. In essence, AI-Powered Customer Service exerts an indirect positive effect
on Customer Loyalty by enhancing Perceived Efficiency.
In summary, the results indicate that both proposed mediation effects are statistically significant
within the structural model. The high t statistics and associated p values affirm the presence of these
mediating pathways, demonstrating the importance of Customer Satisfaction and Perceived Efficiency in
transmitting the influence of AI-Powered Customer Service to Customer Loyalty. Hence the null hypoth-
esis formulated H5 and H6 are rejected (Table 6).
Coecient of determination
The R-squared (R2) values provided for the DVs in the analysis offer valuable insights into the explanatory
power of the structural model. For Customer Loyalty, the R2 value stands impressively at 0.845, indicating that
approximately 84.5% of the variability in Customer Loyalty can be attributed to the collective influence of the
IVs within the model. This high R2 value underscores the effectiveness of the model in elucidating and pre-
dicting variations in customer loyalty, emphasizing the significance of AI-Powered Customer Service, Customer
Satisfaction and Perceived Efficiency as key contributors to this crucial outcome. Similarly, for Customer
Satisfaction, the R2 value of 0.837 signifies that roughly 83.7% of the variance in Customer Satisfaction is
accounted for by the explanatory variables, highlighting the model’s ability to capture the drivers of customer
satisfaction. Additionally, the R2 value of 0.898 for Perceived Efficiency underscores its substantial impact, with
nearly 89.8% of its variability elucidated by the model. In essence, these R2 values collectively affirm the
robustness of the structural model and its capacity to shed light on the complex relationships and influential
factors at play in the realm of customer loyalty, satisfaction and perceived efficiency (Table 7).
Discussion
To investigate the impact of AI-powered customer service on customer loyalty, with a particular focus on
the mediating roles of customer satisfaction and perceived efficiency. Our findings offer valuable insights
into the complex dynamics between these key variables. The first two hypotheses proposed a significant
Table 6. Mediation eect hypothesis testing.
Mediation eect t Statistics p Values
AI-Powered customer service -> Customer satisfaction -> Customer loyalty 9.42 0.00
AI-Powered customer service -> Perceived eciency -> Customer loyalty 3.00 0.00
Source: SMART-PLS calculation.
Table 7. Coecient of determination.
Dependent variables R2
Customer loyalty 0.845
Customer satisfaction 0.837
Perceived eciency 0.898
Source: SMART-PLS calculation.
COGENT BUSINESS & MANAGEMENT 9
positive relationship between AI-Powered Customer Service and Customer Satisfaction and between
AI-Powered Customer Service and Perceived Efficiency. The results strongly support both hypotheses. The
path coefficients of 0.91 and 0.95, along with remarkably high t statistics of 59.13 and 85.61, indicate that
AI-Powered Customer Service indeed has a substantial influence on both Customer Satisfaction and
Perceived Efficiency. This implies that businesses investing in AI-powered customer service can expect to
enhance customer satisfaction and efficiency significantly. The third and fourth hypotheses postulated a
significant positive relationship between Customer Satisfaction and Customer Loyalty and between Perceived
Efficiency and Customer Loyalty. The results provide robust support for both hypotheses. A path coefficient
of 1.05 for the relationship between Customer Satisfaction and Customer Loyalty, coupled with a notable t
statistic of 9.45, highlights the strong impact of Customer Satisfaction on customer loyalty. Similarly, the
path coefficient of 0.22 for the relationship between Perceived Efficiency and Customer Loyalty, with a t
statistic of 3.00, signifies the importance of Perceived Efficiency in driving customer loyalty. These findings
underscore that satisfied and efficiently served customers are more likely to exhibit loyalty to a brand.
Furthermore, our analysis revealed compelling mediation effects in the model. The mediation path-
ways from AI-Powered Customer Service to Customer Satisfaction to Customer Loyalty and from
AI-Powered Customer Service to Perceived Efficiency to Customer Loyalty both exhibited statistically sig-
nificant relationships. These results confirm that AI-Powered Customer Service not only directly impacts
Customer Loyalty but also does so indirectly through its influence on Customer Satisfaction and Perceived
Efficiency. This highlights the multifaceted role of AI-powered customer service in shaping customer loy-
alty. The R2 values for the DVs are notably high, with Customer Loyalty at 0.845, Customer Satisfaction
at 0.837, and Perceived Efficiency at 0.898. These values demonstrate that a substantial proportion of the
variance in these variables is explained by the IVs in our model. This emphasizes the model’s effective-
ness in elucidating and predicting customer loyalty, satisfaction, and perceived efficiency based on the
interplay of AI-Powered Customer Service, Customer Satisfaction, and Perceived Efficiency.
Conclusion
This research has provided insight into the significant intermediary function of perceived efficiency and
customer pleasure in the correlation between AI-driven customer service and customer loyalty. After
doing a thorough analysis of these interconnected variables, it becomes apparent that the effective
implementation of an AI-driven customer service system can have a substantial impact on consumer
perceptions and behaviors.
The first driver of perceived efficiency plays a crucial function in molding customers’ impressions of
the AI system’s capacity to promptly and accurately meet their requirements. The apparent velocity, effi-
cacy, and availability of the AI system collectively contribute to an augmented overall perception of
efficiency. The notion of efficiency subsequently has a cascading effect on customer satisfaction, so influ-
encing customers’ perceptions of the service, the brand and their whole experience. The cultivation of
consumer loyalty is contingent upon customer satisfaction, thereby emphasizing its significance. When
customers express contentment with the contacts, services, and customized experiences offered by the
AI system, there is a higher probability of them establishing robust emotional affiliations with the busi-
ness. The establishment of an emotional bond between the consumer and the brand results in height-
ened levels of loyalty, which in turn leads to a greater likelihood of repeat patronage, favorable
word-of-mouth promotion and an increased inclination to endorse the brand to others. Gaining insight
into the intermediary function of perceived efficiency and customer happiness holds significant impor-
tance for enterprises aiming to enhance the effectiveness of their customer service strategies driven by
AI. Organizations may cultivate deeper relationships with their consumers, develop loyalty, and ultimately
generate sustainable growth and success in today’s competitive market scenario by prioritizing the
improvement of perceived efficiency and the delivery of pleasant customer experiences.
As the evolution of AI progresses and its integration into customer service strategies becomes more
prominent, it is imperative to conduct additional research and explore the underlying mechanisms involved.
This will yield valuable insights that can be utilized by businesses to enhance their approaches and formu-
late strategies that effectively leverage AI’s capabilities in fostering long-lasting customer loyalty.
10 P. SINGH AND V. SINGH
Limitations and future scope
This study has yielded significant findings regarding the intermediary function of perceived efficiency
and customer satisfaction in the association between AI-driven customer service and customer loyalty.
However, it is crucial to recognize certain constraints that could potentially affect the understanding and
applicability of these results. First, the study’s sample size may not adequately represent the myriad of
demographic and market subgroups. The generalizability of the study is constrained by its narrow focus
on a single environment, industry, or geographical area. It is important to use caution when extrapolat-
ing the findings to other industries or countries, as there may be significant variances in customer
behavior and attitudes that need to be taken into account. Subjective measures are used to assess per-
ceived efficiency and customer satisfaction, employing self-reported surveys. The precision of these mea-
surements was contingent upon the participants’ perceptions and interpretations, which have the
potential to induce response bias or subjective judgment. The study has failed to consider the potential
disparities in the maturity and integration levels of AI systems among various enterprises. The efficacy
and influence of customer service powered by AI might exhibit variability contingent upon the level of
advancement and implementation of AI technologies within individual organizations. The study did not
take into account the potential impact of external factors of a broader nature, such as economic condi-
tions and industry competition, on consumer loyalty.
This study provides a robust basis for future investigations and progress in the domains of cus-
tomer service, AI technology, and consumer behavior. Longitudinal research can be conducted to
examine the correlation between customer service utilizing AI, perceived efficiency, customer happi-
ness and customer loyalty over an extended duration. This analysis would yield valuable insights into
the temporal evolution and mutual influence of these variables. The perception and impact of AI on
customer loyalty might be influenced by the distinct customer needs and expectations observed in
various industries. Hence, this study can be carried out in context of specific industries. In order to
assess variations in perceived effectiveness, customer happiness and the ensuing customer loyalty,
compare the customer service provided by AI-powered firms. Examine the effects of different AI imple-
mentation levels on these factors in various organizational contexts. Examine how customers’ impres-
sions of AI-powered customer service are shaped by providing them with information about the
potential and limitations of AI. Strategies for customer education can be more effectively guided by
an understanding of how customer knowledge affects customer happiness and loyalty. Investigate
how AI-powered customer support functions in cutting-edge fields including Internet of Things (IoT),
virtual reality (VR) and augmented reality (AR). This study aims to examine the impact of various tech-
nologies on customer experience and its subsequent influence on customer loyalty. By embarking on
these prospective avenues of inquiry, one may augment our comprehension of the subtle dynamics
that will steer firms in maximizing their AI strategies to cultivate enduring consumer interactions and
attain sustainable success.
Policy implications
The policy implications can provide guidance to firms and governments in effectively utilizing AI tech-
nologies to augment consumer pleasure and cultivate customer loyalty. AI Integration Strategy is imper-
ative to advocate for businesses to formulate a comprehensive strategy aimed at facilitating the smooth
integration of customer service powered by AI into their operational framework. The Standards for
Perceived Efficiency Management establishes industry benchmarks for gauging the perceived effective-
ness of AI-powered customer support. Also, establishing the key performance indicators (KPIs) that com-
panies should use to gauge the effectiveness of their AI systems in order to provide a uniform and
standardized evaluation process. AI technologies can also be used for various Customer Education
Initiatives. They propose and execute strategies aimed at enlightening consumers regarding the intrica-
cies of AI technology and its many implementations within the realm of customer service. This commu-
nication aims to raise knowledge on the advantages and constraints associated with AI-enabled
interactions in the context of customer management, with the objective of improving customer
COGENT BUSINESS & MANAGEMENT 11
expectations and augmenting their comprehension. Data Privacy and Security Regulations are also
imperative to enhance legislation pertaining to data privacy and security in order to guarantee respon-
sible handling of customer data employed in AI-driven customer service, while also ensuring compliance
with privacy laws. To ensure the preservation of customer trust, it is imperative to institute consequences
for instances of non-compliance. Incentives for customer-centric AI solutions provide incentives, subsi-
dies, or tax advantages to enterprises that engage in the development and execution of AI solutions
with a specific emphasis on enhancing customer happiness and fostering loyalty. Promote the advance-
ment of research and innovation in AI technologies with the aim of augmenting customer experiences.
Customer Feedback Integration mechanisms into AI-powered customer service platforms for enterprises
should be encouraged. The significance of leveraging consumer insights to optimize system efficiency,
personalization and overall customer satisfaction should be underscored.
By applying the aforementioned policy implications, firms and policymakers can effectively utilize
AI-powered customer service to augment perceived operational effectiveness, customer contentment and
eventually foster customer allegiance. The establishment of durable and meaningful relationships between
organizations and their consumers will depend on the careful equilibrium between technical improve-
ments, ethical considerations and customer-centric initiatives.
Author contribution details
Dr Pragya Singh – conceptualized the article. She also contributed in writing the introduction and the literature
review of the article. Upon completion revised the article for intellectual content.
Vandana Singh – Helped in designing the questionnaire. She took active participation in the research methodol-
ogy part and also did the interpretation of the analysis.
Both the authors agree to be accountable for all aspects of the work.
Disclosure statement
No interests to declare.
Funding
No funding was received for this research.
About the authors
Dr Pragya Singh is associated with Symbiosis Centre for Management Studies NOIDA, Symbiosis International
University Pune India since 2018. Her research areas include Digital Marketing, Entrepreneurship and Leadership
Studies. She has published research papers, case studies and books with international publishers.
Vandana Singh is a research scholar in the department of Computer Science, Birla Institute of Technology Mesra
Ranchi. Her research area includes Trac Signal Management, AI Driven customer service. She has published papers
with international publishers of repute.
Data availability statement
My study involved the collection of data through a self-prepared questionnaire stored in an Excel sheet format.
Considering the sensitive nature of the participant information and ethical considerations surrounding condential-
ity, we are unable to share the raw dataset publicly. In light of this, we are seeking your valuable guidance on
potential alternatives or acceptable practices that would comply with the Taylor & Francis Open Data Policy while
maintaining the condentiality of our participants. My aim to uphold the principles of transparency and reproduc-
ibility while respecting ethical boundaries. If there are specic protocols or methods that would better align with
the policy while safeguarding participant condentiality, I would greatly appreciate your expertise and suggestions
in this regard.
Your insights and guidance on potential solutions or acceptable practices for sharing data while ensuring partic-
ipant condentiality would be immensely helpful for me to adhere to the journal’s policies eectively.
12 P. SINGH AND V. SINGH
References
Adam, M., Wessel, M., & Benlian, A. (2021). AI-based chatbots in customer service and their eects on user compli-
ance. Electronic Markets, 31(2), 1–15. https://doi.org/10.1007/s12525-020-00414-7
Al-Adwan, A. S., Al-Debei, M. M., & Dwivedi, Y. K. (2022). E-commerce in high uncertainty avoidance cultures: The
driving forces of repurchase and word-of-mouth intentions. Technology in Society, 71, 102083. https://doi.
org/10.1016/j.techsoc.2022.102083
Al-Adwan, A. S., & Al-Horani, M. A. (2019). Boosting customer e-loyalty: An extended scale of online service quality.
Information, 10(12), 380. https://doi.org/10.3390/info10120380
Al-Adwan, A. S., Kokash, H., Adwan, A. A., Alhorani, A., & Yaseen, H. (2020). Building customer loyalty in online shop-
ping: The role of online trust, online satisfaction and electronic word of mouth. International Journal of Electronic
Marketing and Retailing, 11(3), 278–306. https://doi.org/10.1504/IJEMR.2020.108132
Al-Mekhlal, M., Al-Buraik, M., & Al-Lubli, M. (2023 Digital transformation: AI-powered bot solutions and automation for
customer services [Paper presentation]. 2023 International Conference on Digital Applications, Transformation &
Economy (ICDATE), Malaysia (pp. 1–7). IEEE. https://doi.org/10.1109/ICDATE58146.2023.10248458
Chaturvedi, R., & Verma, S. (2023). Opportunities and Challenges of AI-Driven Customer Service. In J. N. Sheth, V. Jain,
E. Mogaji, & A. Ambika. (Eds.), Articial Intelligence in Customer Service. Palgrave Macmillan. https://doi.org/10.1007/
978-3-031-33898-4_3
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A.,
Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams,
M. D. (2021). Articial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and
agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.
org/10.1016/j.ijinfomgt.2019.08.002
Fernando, E., Sutomo, R., Prabowo, Y. D., Gatc, J., & Winanti, W. (2023). Exploring customer relationship management:
Trends, challenges, and innovations. Journal of Information Systems and Informatics, 5(3), 984–1001. https://doi.
org/10.51519/journalisi.v5i3.541
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F.,
Schafer, B., Valcke, P., & Vayena, E. (2021). An ethical framework for a good AI society: Opportunities, risks, princi-
ples, and recommendations. Philosophical studies series (pp. 19–39). Springer International Publishing.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measure-
ment error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
George, A. S., & George, A. H. (2023). A review of ChatGPT AI’s impact on several business sectors. Partners Universal
International Innovation Journal, 1(1), 9–23. https://doi.org/10.5281/zenodo.7644359
Gupta, S. (2021). Impact of articial intelligence on nancial decision making: A qualitative study. Journal of
Cardiovascular Disease Research, 12(6), 2130–2137.
Hair, J., Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation
modeling (PLS-SEM). Sage publications.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structur-
al equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Hsu, C. L., & Lin, J. C. C. (2023). Understanding the user satisfaction and loyalty of customer service chatbots. Journal
of Retailing and Consumer Services, 71, 103211. https://doi.org/10.1016/j.jretconser.2022.103211
Mehrotra, A. (2019 Articial intelligence in nancial services-need to blend automation with human touch [Paper presen-
tation]. 2019 International Conference on Automation, Computational and Technology Management (ICACTM), (pp.
342–347). IEEE. https://doi.org/10.1109/ICACTM.2019.8776741
Nicolescu, L., & Tudorache, M. T. (2022). Human-computer interaction in customer service: The experience with AI
chatbots—A systematic literature review. Electronics, 11(10), 1579. https://doi.org/10.3390/electronics11101579
Nunnally, J. (1994). Psychometric theory.
Patel, N., & Trivedi, S. (2020). Leveraging predictive modeling, machine learning personalization and NLP customer
support and chatbots to increase customer loyalty. EQM ResearchBerg, 3(3), 1–24.
Prentice, C., Dominique Lopes, S., & Wang, X. (2020). The impact of articial intelligence and employee service qual-
ity on customer satisfaction and loyalty. Journal of Hospitality Marketing & Management, 29(7), 739–756. https://doi.
org/10.1080/19368623.2020.1722304
Qian, C., Lu, Y., Gong, Y., & Xiong, J. (2023). Can AI chatbots help retain customers? Impact of AI service quality on
customer loyalty. Internet Research, 33. https://doi.org/10.1108/INTR-09-2021-0686
Rygielski, C., Wang, J. C., & Yen, D. C. (2002). Data mining techniques for customer relationship management.
Technology in Society, 24(4), 483–502. https://doi.org/10.1016/s0160-791x(02)00038-6
Song, M., Xing, X., Duan, Y., Cohen, J., & Mou, J. (2022). Will articial intelligence replace human customer service?
The impact of communication quality and privacy risks on adoption intention. Journal of Retailing and Consumer
Services, 66, 102900. https://doi.org/10.1016/j.jretconser.2021.102900
Trawnih, A., Al-Masaeed, S., Alsoud, M., & Alkufahy, A. (2022). Understanding articial intelligence experience: A cus-
tomer perspective. International Journal of Data and Network Science, 6(4), 1471–1484. https://doi.org/10.5267/j.
ijdns.2022.5.004
COGENT BUSINESS & MANAGEMENT 13
Xu, Y., Shieh, C. H., van Esch, P., & Ling, I. L. (2020). AI customer service: Task complexity, problem-solving ability, and
usage intention. Australasian Marketing Journal, 28(4), 189–199. https://doi.org/10.1016/j.ausmj.2020.03.005
Yang, B., Sun, Y., & Shen, X. L. (2023). Understanding AI-based customer service resistance: A perspective of defective
AI features and tri-dimensional distrusting beliefs. Information Processing & Management, 60(3), 103257. https://doi.
org/10.1016/j.ipm.2022.103257
Yeo, S. F., Tan, C. L., Kumar, A., Tan, K. H., & Wong, J. K. (2022). Investigating the impact of AI-powered technologies
on Instagrammers’ purchase decisions in digitalization era–A study of the fashion and apparel industry. Technological
Forecasting and Social Change, 177, 121551. https://doi.org/10.1016/j.techfore.2022.121551
Zerilli, J., Knott, A., Maclaurin, J., & Gavaghan, C. (2019). Algorithmic decision-making and the control problem. Minds
and Machines, 29(4), 555–578. https://doi.org/10.1007/s11023-019-09513-7
Zhao, T., Cui, J., Hu, J., Dai, Y., & Zhou, Y. (2022). Is articial intelligence customer service satisfactory? Insights based
on microblog data and user interviews. Cyberpsychology, Behavior and Social Networking, 25(2), 110–117. https://
doi.org/10.1089/cyber.2021.0155
Zierau, N., Hausch, M., Bruhin, O., & Söllner, M. (2020 Towards developing trust-dupporting design features for AI-based
vhatbots in vustomer service [Paper presentation]. Forty-First International Conference on Information Systems,
India.
Appendix
Questionnaire
Demographic Information
1. Age:
2. Under 18
3. 18–24
4. 25–34
5. 35 or older
6. Gender:
7. Male
8. Female
Please rate your agreement with the following statements on a scale from 1 (Strongly Disagree) to 7 (Strongly Agree):
Section 1: AI-Powered Customer Service
i. AI-powered customer service provides quick and efficient responses to my queries.
ii. I find AI-powered customer service interactions to be helpful in resolving my issues.
iii. Using AI-powered customer service makes my overall experience with a company more convenient.
iv. AI-powered customer service is reliable and consistent in its performance.
v. I prefer using AI-powered customer service over traditional customer service methods.
vi. AI-powered customer service has a positive impact on my perception of the company’s technological
capabilities.
vii. AI-powered customer service is an essential part of my interactions with companies.
viii. Overall, I am satisfied with the AI-powered customer service I have experienced.
Section 2: Perceived Eciency
i. AI-powered customer service interactions save me time compared to traditional customer service.
ii. AI-powered customer service responses are timely and meet my expectations.
iii. I perceive AI-powered customer service as efficient in handling routine inquiries.
iv. AI-powered customer service streamlines the process of resolving issues or getting information.
v. The speed and accuracy of AI-powered customer service contribute to my perception of its efficiency.
vi. AI-powered customer service effectively manages high volumes of customer queries.
vii. I believe that AI-powered customer service has the potential to improve its efficiency further.
viii. Overall, I find AI-powered customer service to be efficient in its operations.
14 P. SINGH AND V. SINGH
Section 3: Customer Satisfaction
i. I am satisfied with the level of service provided by AI-powered customer service.
ii. AI-powered customer service meets my expectations for quality customer service.
iii. Using AI-powered customer service enhances my overall satisfaction with the company.
iv. AI-powered customer service interactions leave me with a positive impression of the company.
v. I am likely to recommend a company that offers effective AI-powered customer service.
vi. AI-powered customer service contributes to my overall satisfaction as a customer.
vii. I feel valued as a customer when interacting with AI-powered customer service.
viii. Overall, I am satisfied with my experiences with AI-powered customer service.
Section 4: Customer Loyalty
i. I am loyal to companies that provide efficient AI-powered customer service.
ii. My positive experiences with AI-powered customer service make me more loyal to a company.
iii. AI-powered customer service plays a significant role in my decision to continue using a company’s
products or services.
iv. I am more likely to remain a customer of a company that offers AI-powered customer service.
v. Efficient AI-powered customer service enhances my loyalty to a brand.
vi. I would choose a company that uses AI-powered customer service over competitors that do not.
vii. AI-powered customer service positively influences my long-term commitment to a company.
viii. Overall, I am loyal to companies that excel in AI-powered customer service.
