The Effect of Artificial Intelligence on Service Quality and Customer Satisfaction in

Jordanian Banking Sector

REEM AL-ARAJ

Financial and Accounting Sciences Department, Faculty of Business,

Middle East University, Amman,

JORDAN

HOSSAM HADDAD

Department of Accounting, Faculty of Business,

Zarqa University, Zarqa,

JORDAN

MAHA SHEHADEH

Finance and Banking Sciences Department, Faculty of Business,

Applied Science Private University, Amman

JORDAN

ELINA HASAN

Financial and Accounting Sciences Department, Faculty of Business,

Middle East University, Amman,

JORDAN

MOHAMMAD YOUSEF NAWAISEH

Financial and Accounting Sciences Department, Faculty of Business,

Middle East University, Amman

JORDAN

Abstract:- The study emphasizes the importance of Artificial Intelligence (AI) and its applications on the service

quality provided by Jordanian banks for their customer satisfaction. This research paper thoroughly reviews the

literature on the numerous emergent applications of artificial intelligence and its impact on the banking sector. A

rigorous study of the available literature is conducted to examine AI's uses in banking. Artificial intelligence

improves the banking experience for millions of clients and employees by providing credit score checking, system

failure prediction, emergency alarm systems, fraud detection, phishing website detection, liquidity risk assessment,

customer loyalty evaluation and intelligence systems by reducing the employee workload. A questionnaire gathered

data from 270 consumers in Jordan's banking sector. The SPSS program used exploratory factor analysis to

statistically evaluate the sample data to determine service quality and customer satisfaction. The results show that

the updated SERVQUAL Model extracted five subscales instead of the eight in the original model. The extracted

subscales were tangibility, assurance, reliability, responsiveness, and empathy. According to this study, artificial

intelligence is statistically relevant to service quality and customer satisfaction. The updated SERVQUAL model,

according to the authors, helps address customer satisfaction in the banking sector. The research findings suggest

that the demand for artificial intelligence in the Jordanian banking sector is equally essential for the customers;

thus, there should be an optimal balance between virtual and human agents based on the customers' requirements

and preferences. Further, this study found practical implications of using AI in banking, particularly those related

to Jordanian customer perception.

Keywords: - The Banking Sector, artificial intelligence, customer experience, service quality

Received: June 20, 2022. Revised: October 15, 2022. Accepted: November 11, 2022. Published: December 13, 2022.

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DOI: 10.37394/23207.2022.19.173

Reem Al-Araj, Hossam Haddad,

Maha Shehadeh, Elina Hasan,

Mohammad Yousef Nawaiseh

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1 Introduction

Due to globalization and increased transparency in

the economy, the banking environment has been

more volatile and competitive as of late, [1]. When

interacting with a company's goods or services,

clients expect superior treatment, or in other words,

there has been more emphasis on customer

happiness. The banking sector has been thriving, and

client loyalty will keep rising due to cutting-edge

technologies like artificial intelligence, which have

been more common in businesses over the last

several decades.

Robots showcase cutting-edge automation and

artificial intelligence. The term “machine learning”

describes a computer's capacity to learn and utilize

information independently of a programmer,

allowing it to be used in various contexts. In recent

years, a contemporary company transformation has

tended to make activities quicker and more efficient

at the expense of some of their original complexity,

[3]. Artificial intelligence (AI) offers a superior,

cutting-edge solution that can improve problem-

solving, task automation and customer service for

organizations. AI may be used to automate data

administration operations, improve credit rating and

identify potentially fraudulent transactions, [2].

Artificial intelligence applications, like data, are

crucial to almost every industry – from deposit-

taking and lending to investment banking and asset

management – due to the nature of the modern

corporate environment. As a result, banks may

greatly benefit from autonomous data management

without human intervention to enhance speed,

accuracy and efficiency, [4]. The several possible

uses of AI in the banking sector can be grouped into

four. First, there are front-office apps geared toward

customers and back-office programs geared toward

operations. The second concern concerns the rules

and laws governing trading and portfolio

management. While some banks have completely

integrated new technology into their operations, most

are still in the testing phase. Further, there seems to

be a greater emphasis on researching AI technologies

with an eye on improving customer service and

streamlining business operations.

Third, online banking fraud is studied as a potential

area for artificial intelligence use; with the rise of

online and mobile payments, credit card fraud has

quickly become one of the most common types of

cybercrime. Thus, many businesses have begun using

artificial intelligence (AI) algorithms to verify the

legitimacy of their customers’ credit card

transactions in real time, comparing them to the

previous ones in terms of amount and location, [5],

[6].

Lastly, chatbots are another area where financial

institutions are experimenting with AI technology.

Chatbots are virtual assistants that may communicate

with customers at a bank through text or voice and

attempt to fulfil their needs without involving a

human worker. Financial institutions are also

experimenting with AI to display data from reports

and legal documents, such as annual reports, to

extract the necessary provisions, [7]. AI software

may build models by analyzing data and using

backtesting to learn from past errors.

As time has progressed, several pre-existing

financial technology tools have also developed into

precise AI solutions. For example, we may look to

robot advisors that allow for complete automation in

some asset management services and online financial

planning tools, which assist consumers in making

better consumption and savings choices. Moreover,

as financial technology solutions become more

sophisticated, they increasingly use methods that

scan data and detect patterns automatically. 13

domestic Jordanian banks, four Islamic banks, and

seven international banks make up Jordan's banking

sector, [8].

The banking industry's reputation is in jeopardy

due to customers' legitimate fears for the safety of

their data and financial transactions, which stem from

everything – from credit card fraud and unauthorized

data sharing to stolen data and fraudulent purchase

orders resulting from human error or hasty

investment decisions. This study will raise awareness

about the usefulness of AI among the decision-

makers in Jordan's financial services industry by

adding a case of a new use for the technology.

Despite the abundant research on client-perceived

banking service quality, no study has examined the

impact of AI on service quality and customer

satisfaction in the Jordanian banking industry. This

study argues that the banking industry should

anticipate an impact on its service quality and

customer happiness due to the increased use of AI

applications. Further, clients evaluate a service's

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quality based on their satisfaction. As

aforementioned, raising the bar on service quality

may thus increase customer retention and revenue.

The study puts forth the following research

questions: (1) how has introduced AI into the

banking industry affected its service quality in

Jordan; and (2) how satisfied are the customers with

their interactions with the financial institutions?

This study elucidates the most important quality

criteria in the Jordanian banking industry – AI, which

helps financial institutions formulate plans to

enhance the quality of their services. This will

strengthen the bank's position in the banking sector

and increase the likelihood of the banks in Jordan and

the surrounding area surviving in this highly

competitive environment. The primary purpose of

this research is to illustrate the considerable impact

that AI and its applications have on service quality

and customer happiness by assessing how they

influence the standard of service provided by the

banking sector.

The two hypotheses were developed based on the

research topic and associated questions. This study's

primary hypothesis (H1) is that AI has no appreciable

impact on the quality of banking services provided to

customers in Jordan. At the 5% significance level,

the relationship between AI and service quality in

Jordan's financial industry is insignificant (H1.1).

The second Hypothesis (H1.2) is that no statistically

significant relationship exists between AI and the

monetary institution regarding client satisfaction in

Jordan.

AI (Applications) is the independent variable in

the Jordanian banking industry. The quality of

service provided by banks in Jordan and the level of

client satisfaction are the dependent variables.

Computer systems that can perceive the world around

them, understand what they hear and say, make

decisions and translate languages are the focus of

Artificial Intelligence research and development.

Customer satisfaction can be defined as “a person's

emotion of joy or disappointment that comes from

evaluating the perceived performance or outcome of

a product versus their expectations”, [9], [10]. A

customer's opinion of the service quality, along with

the aspects of dependability, responsiveness,

assurance, empathy and tangibles, constitutes a

“targeted evaluation” of service quality. Businesses

can swiftly fix issues, gauge how they are doing in

the eyes of their clients and make adjustments based

on the feedback about the quality of services

supplied.

2 Literature Review

2.1 Artificial Intelligence

Artificial intelligence (AI) is a set of theories and

algorithms that enables computers to perform tasks

that typically require human intelligence (such as

visual perception, voice recognition or the

interpretation of a text, taking its context into

account) and that, in some cases, augments these

abilities. Machine learning is a subfield of AI that has

recently been widely used.

Financial service providers currently use AI

technology, such as predictive analytics and speech

recognition, to give banks the advantages of

digitalization and to help them compete with FinTech

companies, [10]. Artificial intelligence (AI) may help

banks improve their customers' experiences by

facilitating seamless, around-the-clock interactions

with customer support representatives. However, the

use of AI in banking applications goes well beyond

traditional retail banking. The back and middle

offices of investment banking and any other financial

help might indeed profit from AI, [11].

With AI's potential to detect and prevent fraud

while enhancing compliance measures, the banking

industry has a bright future, [13]. When combating

money laundering, an artificial intelligence program

may do what would usually take hours or days within

seconds. Banks may also benefit from the AI's ability

to swiftly glean actionable information from

enormous data sets. Artificial intelligence bots,

online payment counsellors and biometric fraud

detection methods contribute to higher-quality

service for a wider audience, [12]. As a result, the

bank revenues rise, expenses fall, and profits soar.

2.1.1 The Scope of AI Applications in the Banking

Sector

1- Credit Score

Banks must acquire cash after carefully assessing the

credit ratings of loan-seeking consumers. Further, the

banking industry produces a profit regardless of the

danger as it monitors and manages risks, [13]. Credit

risk is one of the most severe hazards since it focuses

on preventing complete system collapse, which could

be challenging to adjust, [12]. This classification

differentiates between a positive score with a low

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likelihood of defaulting and a negative score with a

high likelihood of default. Classification and

regression tree models are created using the decision

tree approach, one of the AI methods for classifying

situations. This approach surpasses other tactics for

examining credit ratings, such as logistic regression

and discriminant analysis, in terms of profit and

marketing for the bank. Loan approval is crucial as

well.

2- Mobile Banking

Mobile wallets are prevalent nowadays; 65 percent of

member organizations and most consumers use them,

[14]. Most consumers regard online payment services

favourably since they steer people away from

conventional card transactions and enhance banking

services by maximizing revenue generation. This

user experience transformation enables the collection

and analysis of user-generated data to improve

service for each client, depending on the trends or

insights gleaned from the data. Mobile payments

support mobile banking services that clients choose

because of their comfort and convenience, and

financial institutions wish to preserve positive

relationships with them.

3- Customer Loyalty

The relationship between bankers and customers is

crucial for maintaining and expanding consumer

loyalty. The client connection is crucial if banks are

to satisfy their consumers' fluctuating needs and

expectations. Customers' loyalty may be increased if

reasonably priced, high-quality services attract them.

In the banking industry, client loyalty may be

predicted using an artificial neural network

previously used for the same purpose in other sectors.

After data collection, multiple regression should

isolate the most critical factors from the available

variables, thus preparing the data for future

modelling, [15]. This prediction model employs the

feedforward deep residual approach and the artificial

neural network, [34].

4- Tracing Scams and Frauds

In this age of digital technology, there are innovative

and efficient methods for identifying fraud, [16]. Due

to the volume of data offered by electronic

documents, contracts, emails, text messages and bank

transactions, regulators must develop more

sophisticated fraud detection techniques. AI and

machine learning are perfect for fraud detection due

to the large quantity of digital data and the simplicity

with which language and data can be evaluated. The

aim is to incorporate AI into operational

configurations and strategic goals, which may aid

administrators in completing their tasks more

effectively, [17]. AI must thus be incorporated into

authority instead of considered a technological toy

distinct from the organization's most critical

functions. Its incorporation into the everyday

operations of the service aids personnel in

understanding core strategies and deciding how to

implement them in their particular domains.

5- Aggregating Cybersecurity Data

Applications that include capabilities like Pattern

Scout and Threat Match may aid banks in boosting

network visibility and monitoring internal systems

for network issues in real-time, [18]. According to

reports, the software solutions may assist banks in

detecting and identifying cybersecurity risks in their

networks, reducing long-term security expenditures

and preventing data breaches, [35]. The platforms

can use recognition patterns based on machine

learning on businesses to assist enterprise-wide

security and operational tasks. Older technologies

used by the banks meant that human security

professionals spent 15–60 minutes on average

working on very particular events, [36]. After

integrating cybersecurity systems, the personnel

could evaluate the breadth of an occurrence within

one to five minutes to decide whether the event

required escalation. Some other relevant studies can

be found in [31], [32], [33].

2.2 Service Quality

Service quality refers to how firms fulfil or exceed

client expectations. It can coordinate with, meet or

override customer preferences. Customer happiness

rises as service quality increases, and profit increases

as cost management improves. Service quality

measures how an organization delivers its services

compared to the expectations of its customers.

Customers purchase services as a response to their

specific needs. They consciously or unconsciously

have certain standards and expectations for how a

company's delivery of services fulfill those needs.

Therefore, measuring and improving service quality

can increase an organization's profits and reputation.

Regardless of the industry, service quality can

directly impact a company's ability to satisfy

customer needs while remaining competitive and

earning customer satisfaction.

To perform a complete analysis of a bank's

performance, the management must compare its

performance with its customers' expectations. With

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the implementation of other banks in the same

industry, a bank with high service quality offers

services that match or exceed its customers'

expectations and has better customer loyalty and

higher profits. The five Dimensions of Service

Quality refer to the SERVQUAL Model of five key

service dimensions, implemented by Parsu

Parasuraman, including Reliability, Assurance,

Tangibles, Empathy and Responsiveness. It is a

model for measuring the gap between what a

customer wants and their judgment of the service

quality. Many researchers have adopted the

SERVQUAL model to examine banks' service

quality and customer satisfaction. Satisfaction in the

banking sector, [19], has read service quality

dimensions delivered by banks to meet the needs of

their customers to achieve sustainable development

(through tangibles, responsiveness, empathy,

assurance, reliability, access, financial aspect and

employee competencies).

2.2.1 Qualitative Dimensions of Service

This research examines five factors of service quality

that influence customer satisfaction (reliability,

assurance, tangibles, empathy and responsiveness) to

determine the possible effect of each component on

the Jordanian banking industry.

2.2.2 Trustworthiness

Trustworthiness is the capacity to deliver services

securely and dependably to meet consumer

requirements. Reliability considerations include

consistently delivering the stated job or service,

demonstrating an interest in resolving customer

issues, implementing service improvements for the

first time and offering and delivering service at the

promised time.

2.2.3 Assurance

Assurance comprises competency and the capacity of

workers to instil clients with a feeling of the

organization's credibility, [20]. The assurance is high

if consumers feel safe interacting with the business,

the staff are always courteous while interacting with

the customers and the employees possess sufficient

expertise to answer the customers' questions, [21].

2.2.4 Tangibility

This refers to facilities, equipment, employees and

communicable commodities that are examples of

physical dimensions, [22]. In other words, these

criteria include sophisticated equipment, physical

facilities, well-dressed employees and well-organized

papers (such as booklets, ledgers, billing material,

etc.).

2.2.5 Empathy

It entails engaging with consumers according to their

spirit to feel like the company understands them and

that they are vital to the business. The empathy

aspects include the following: personal attention to

customers, good business hours for all customers,

workers demonstrating individual attention to

customers, employees' desire for the customers' best

interests and employees recognizing the specific

consumer demands.

According to [37], this instrument may be used

in various fields, including financial institutions,

libraries, hotels, restaurants, medical centres, banks,

the tourist sector, hospitals, libraries, transportation

services, postal services and the insurance business.

That is why factors of the SERVQUAL model were

used in this study to determine the effects of the

characteristics of service quality on brand personality

and identification.

2.2.6 Responsiveness

Being responsive involves a willingness to

collaborate and assist the consumer. The service

quality component stresses responsiveness and

vigilance towards client requests, inquiries and

complaints. This includes instances such as

employees communicating to customers about what

they will do, providing immediate services to

customers (in the shortest time possible), always

being willing to assist customers, and always being

prepared to answer customers' questions.

2.3 Customer Satisfaction

This refers to customer satisfaction with a company's

goods, services and capabilities. It is one of the most

critical determinants of future purchases and client

loyalty. Consequently, it facilitates growth and

revenue forecasting. The banking sector has reached

a state of standardization where one bank may have a

competitive edge over another based on its

customers' experiences, even though all banks

provide the same goods and services and have

minimal potential for price competition. There are

two ways in which banks may distinguish themselves

via excellent customer service. The connection

between a bank and its clients significantly affects

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customer satisfaction. People want to be treated with

respect, and they want their bank to try to get to

know them rather than just promote a product. There

are several methods for clients to connect with a bank

in contemporary banking, including online and

mobile banking, by using an ATM and over the

phone. One of the most significant findings is that

consumers seek a consistent experience across

channels. Whether conveying information swiftly

across channels or guaranteeing uniform deposit

timings regardless of how a deposit is made, these

factors are significant. To create a superior customer

experience, banks must meet the expectations of their

consumers across all channels.

Moreover, customer satisfaction is a statistic that

measures a company's capacity to satisfy customer

requirements. It also includes enabling the evaluation

of service quality. Customers may evaluate items and

services by providing input on service attributes, and

businesses that fail to produce high-quality goods and

services will lose clients to their rivals in today's

environment. As customers are becoming more

demanding, their quality expectations are also

increasing; thus, organizations must focus on the

client, provide more value, cultivate connections and

prioritize market innovation. Many modern

businesses monitor their consumers' expectations,

productivity, satisfaction and even the performance

of their rivals. The authors in [50] confirmed that

those clients had requested a better experience. As

technology has progressed over the years, industries

have started to embrace cutting-edge technologies

such as artificial intelligence to give higher-quality

service to their customers. The significance of the

banking sector and its influence on the nation's

growth are discussed. The contact between the

bankers and customers is essential for maintaining

the existing clients and fostering consumer loyalty.

The client’s connection to the bank is crucial if

financial institutions are to satisfy their clients'

fluctuating needs and expectations [15]. Also,

customer loyalty may be increased if reasonably

priced, high-quality services attract them. The

banking business may anticipate customer loyalty

using an artificial neural network used by other

industries for the same reason. Using factor analysis,

key variables should be extracted from all accessible

variables after data collection, preparing the data for

future modelling. In this prediction model, the

technique employs feedforward backpropagation and

an artificial neural network. K-fold cross-validation

is used, where K subsets are derived from the

classification of the training dataset. After evaluating

the dataset, the method's performance may are

determined using the efficiency coefficient and root

means square error. The outcome of the artificial

neural network's prediction of customer loyalty has

shown that high accuracy is attainable.

2.4 Banks

Banking is essential to the contemporary economy.

However, the characteristics and functions of

contemporary banks have altered with time. The

notion of banking has evolved with the concept of

money.

The bank is an institution that deals with money

by accepting deposits from customers, honouring

client withdrawals against such deposits on demand,

collecting customer checks and by lending or

investing excess deposits until they are due for

repayment. A bank is a financial institution that

handles deposits, advances and other services

associated with banking. It accepts deposits from

individuals who want to save and loans money to

those in need. It is a financial institution and financial

intermediary that receives deposits and directs them

into lending operations directly via lending or

indirectly through capital markets. Customers with

capital shortfalls and customers with capital

surpluses are connected via banks. Due to their

impact on the financial system and the economy,

banks are heavily regulated in the majority of

nations. Most banks use fractional reserve banking,

which maintains a tiny reserve of deposited cash and

lends out the remainder for profit. They are often

subject to minimum capital requirements established

on the Basel Accords, an international set of capital

regulations.

2.5 Jordanian Banking Sector

Banking is a unique sector that utilizes capital to

multiply wealth regardless of the risk [12]. The

Jordanian financial system comprises the Jordanian

Central Bank and all regulated banks operating in the

Hashemite Kingdom of Jordan. All commercial,

Islamic, and foreign banks are licensed to do business

in Jordan. Moreover, all Jordanian banks and

branches of foreign banks operating in Jordan must

be licensed.

Jordanian banks provide various conventional

and non-traditional services, including retail banking,

personal bank loans, business financing and e-

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services. In Jordanian banks, there is an immediate

need to explore the relationship between service

quality and client happiness.

The Jordanian Central Bank was established as a

distinct legal entity whose capital was wholly

controlled by the Jordanian government. The Central

Bank of Jordan is responsible for regulating and

overseeing all banks, manufacturing Jordanian

banknotes and coins, providing needed liquidity to

licensed banks, maintaining bank reserves and

keeping monetary stability, among other things.

3 Methodology

This study was implemented in the Jordanian

banking industry. It examined the significance of

artificial intelligence in banking service quality and

its impact on customer satisfaction. The study

employed books, annual reports, journals, and the

internet as secondary data sources to obtain

information for the model and analysis. A key source

of information for research on the effect of artificial

intelligence on service quality and customer

satisfaction in Jordanian banks was the survey.

Respondents' preferences were matched with the data

using traditional paper polls and an online Google

Survey. Bank employees and customers assisted in

the distribution and caching of questionnaires. From

December 2021 to March 2022, 270 clients of

Jordanian commercial banks were administered and

returned questionnaires. The proper sample size was

determined to represent the respondents' opinions

accurately. The questionnaire consisted of three

sections: a cover letter, demographic questions and

measurement of independent and dependent

variables. On a five-point Likert scale, the following

answers were given for each variable: strongly agree

(5 points), agree (4 points), neutral (3 points),

disagree (2 points) and severely disagree (1 point).

In the demographic analysis of data, the

questionnaire offers the distribution of respondents

by gender, age, occupation and educational level,

among other things. The organization then does a

statistical study of the research subjects. By

examining the collection of factors, we

comprehensively assessed the potential for further

study. Then, the reliability of the scales selected from

the literature was proved using the Reliability

coefficient indicator and a series of questions to

evaluate service quality and customer satisfaction.

Since the primary objective of our study was to

determine the impact of Artificial Intelligence on the

selected five dimensions of service quality and

overall customer satisfaction, we first examined the

correlation between Artificial Intelligence and the

target variables as items that do not correlate with the

target variables are irrelevant to our investigation.

Following this, the internal structure of the five

scales selected for the dependent variables and

customer satisfaction was analyzed. Based on the

literature analysis, the following operational

definition illustrates the link between artificial

intelligence, service quality and customer satisfaction

in the Jordanian banking industry. This section

explores research hypotheses and offers a conceptual

framework.

A descriptive research design was used to

summarise the data and describe the characteristics of

the variables. The strength of the examined link

between variables was assessed using a correlation

model. Secondary sources such as books, annual

bank reports, magazines, journals, references and the

internet were used to collect the necessary data. The

study served as a vital source of information for

investigating the impact of artificial intelligence on

service quality and customer satisfaction in Jordanian

banks.

The surveys were distributed and gathered from

Jordanian commercial banks in 2022. Jordan has

three kinds of banks: commercial, Islamic and

international. Accepted for data analysis were 270

replies from Jordanian bank customers, who were

from varied areas. The proper sample size was

chosen to represent the perspectives of the

respondents in order to construct a qualitative and

quantitative research strategy.

The developed questionnaire consisted of three

pieces; the first was a cover letter describing the

study's aims. In the cover letter, it was assured that

their feedback would be handled with discretion. The

second segment had questions regarding the

demographic data. All statements that assessed

research using independent and dependent variables

were included in the last portion. On a five-point

scale, strongly agree=5, agree=4, neutral=3,

disagree=2 and severely disagree=1 were assigned to

the variables. Two surveys, one for bank managers

and the other for customers, were prepared in Arabic

and English and were distributed in person and

through the Gmail accounts.

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The information of the participants was classified

and recorded in an Excel database. All of the data

was analyzed using the SPSS program. There were

two components to the examination of the data: a) an

examination of the demographic data and b) data

analysis for each research issue and the evaluation of

the study's premise. Due to the interviewees' ease of

comprehension, the five-point Likert Scale was

historically used by the majority of the studies. In the

pilot project, questionnaires were completed by 41

financial managers. The respondents were then

requested to provide their impressions and opinions

about the impact of artificial intelligence on

Jordanian banks' service quality and customer

satisfaction. Similarly, surveys were provided to

bank clients and were examined afterwards. Complex

topics were explored in conversations with small

groups of managers and consumers, and the

questionnaires were finished based on those

discussions. Validity describes how closely the

acquired data correspond to the research area.

Measure what is supposed to be measured is the

definition of validity. Reliability refers to the degree

to which the measurement of a phenomenon gives

consistency and stability in its outcome. Testing for

dependability is vital since it pertains to the

uniformity of the components of a measuring

instrument, [24].

The validity and reliability of the questionnaire, a

key research instrument, are crucial for determining

the dependability of the research results and

conclusions. Thus, it also determines the precision

and consistency of the variables' measurements and

their selection. Although validity and reliability are

closely connected, they represent distinct

characteristics of a measuring instrument. In general,

a measuring device may be accurate without being

valid, but it is also likely to be accurate if it is

precise. Nevertheless, dependability alone is

insufficient to guarantee legitimacy. Even though a

test is trustworthy, it may not correctly represent the

intended behaviour or quality. Thus, the content

validity of a measuring instrument is a validity study

that exposes how well each item in the measuring

instrument performs its intended function. A targeted

study of the literature, definitions and ideas was

utilized to guarantee the content validity of the

questionnaire to increase the quality of the

expressions in the measuring instrument and further

the research objectives. This section will discuss the

reliability and validity of this study. When measuring

scales are used in research, the measurement scale's

dependability must be established [29]. The word

“reliability” refers to the capability of the measuring

scale to represent the measured construct

consistently. Therefore, a reliable scale should

produce consistent results throughout time and

geography. A certain level of dependability is

necessary for a trustworthy measuring scale.

Data analysis uses a measuring scale that is both

reliable and accurate. Methods for measuring the

dependability of a scale include the test-retest,

different forms and internal consistency techniques.

Numerous research strategies have also been used to

establish dependability based on internal consistency.

Cronbach's Alpha Coefficient is employed in this

study since it is the most popular and extensively

used approach for measuring internal consistency.

Cronbach's alpha assumes values ranging between

zero and one (0–1). Higher values suggest more scale

dependability and vice versa. Generally, Cronbach's

alpha values should be at least 0.70 to ensure

dependability. However, even though a value of 0.70

or higher is often desirable, a value of 0.60 will

suffice for work utilizing freshly established

measures, such as those used in the present research.

As demonstrated in Table 3, all alpha values were

more than 0.70, indicating the dependability of

artificial intelligence applications and service quality.

Development of Hypothesis

Artificial Intelligence on Service Quality

Every day, millions of consumers make repeated

purchases. Customers thus create data, which is kept

and managed as an expansive database. Additionally,

most banking business operations need much human

labour; however, AI has made it simpler for them to

eliminate staff and customer manual labour. Due to

the machine learning approach, this formerly

complex process has been reduced to unprecedented

simplicity. Moreover, the banking industry has

enhanced the quality of its services by offering

various practical solutions to assure safety and

comfort. Technology improves every day, and it is

preferable to apply these technologies to the many

domains of a firm, [51]. Modern technology is

required to preserve and increase the financial

system's security, and banking industry sectors are

also prepared to adopt it. As people want their banks

to stay current in this age of digitalization, the

upgradability of the technology will enhance the

banks’ service and security, as well as their

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reputation. Internet banking and mobile banking

appeal to users because of their efficiency and

usability.

Table 1. Reliability of the Scale's Variables

Independent variable

Number of questions

Cronbach's alpha

Artificial Intelligence

6

0.822

Dependent variable

Service quality

25

0.950

Tangibility

5

0.888

Reliability

4

0.845

Responsiveness

4

0.842

Assurance

4

0.788

Empathy

4

0.764

Customer satisfaction

4

0.862

Source: Authors' analysis, 2022.

Numerous studies demonstrate that various

approaches strengthen customer-banking interactions

and produce a win-win scenario for both parties [15].

Due to the competition from non-banking industries,

banks must embrace the most cutting-edge digital

technology to enhance their service quality, [23].

Nonetheless, the banking sector is more positively

affected by technology. The banking sector should

thus use artificial intelligence tools to make client

banking transactions seamless and spontaneous.

Several AI applications have enabled banks to attain

their maximum efficiency, opening doors for a new

dimension in financial services.

To maintain a high level of service and create a

better-integrated system, it is necessary to

comprehend client attitudes. Creating a method to

gauge client satisfaction is crucial for providing bank

services. The SERVQUAL model generally

measures customer satisfaction and comprises five

dimensions: tangibles, responsiveness, empathy,

assurance and dependability. The SERVQUAL

model may be used to develop a superior instrument

for measuring customer satisfaction, [29].

The word “reliability” refers to the capability of

the measuring scale to represent the measured

construct consistently. Therefore, a reliable scale

should produce consistent results throughout time

and geography. Moreover, a certain level of

dependability is necessary for a trustworthy

measuring scale.

(H1.1): There is no statistically significant effect at

the significance level (0.05≥α) between artificial

intelligence and the service quality in the

Jordanian banking sector.

Artificial Intelligence on Customer Satisfaction

Undoubtedly, Artificial Intelligence improves the

banking experience for millions of clients and bank

personnel. AI enables many procedures that

minimize staff efforts, such as providing credit score

verification, system failure prediction, emergency

alert systems, fraud detection, phishing website

detection, liquidity risk evaluation, customer loyalty

evaluation and intelligence systems. Likewise, the

consumer experience is enhanced by various apps,

like mobile banking, chatbots and augmented reality.

Customers are captivated by the availability of

freshly introduced goods and services designed by

banks to expedite banking procedures, [25]. The

success of a Bank is assessed by the quality of

services provided to clients, which establishes its

competitive advantage.

Client happiness determines the survival and

success of a company in a competitive market. It is a

crucial performance indicator, particularly for retail

banking, which relies on customer loyalty to generate

profits by attracting new customers and retaining the

current ones. Despite the banks' increased efforts,

most clients are dissatisfied with their financial

services. In response to the increasing competition in

the banking industry, banks have made initiatives to

improve their service quality following client

demand and strengthening their service reliability,

[30]. Today, organizations should segment their

clients to provide them with the finest service based

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on their diverse demands, enabling them to serve

each customer properly.

Moreover, it is necessary to monitor customer

behaviour to fully understand consumers, [26] and to

serve them more effectively. The customer

relationship management technique combines the

marketing strategy with procedures; tasks conducted

inside the organization and external network

connections are built to retain current customers in a

highly competitive market by determining and

comprehending their demands. Banking

organizations may effectively use customer

relationship management to improve customer

service provided that they concentrate on four crucial

factors: 1) retention of the current clients, 2)

attracting new clients, 3) encouraging the clients to

work closely with the bank and 4) keeping the clients

informed about the bank's new offerings, [25].

Additionally, if the banking sector treats retail

depositors respectfully, it may attract more

outstanding deposits [34]. [51] examined customer

experiences in the age of Artificial Intelligence; the

research aimed to analyze the role of AI in the

shopping experience, specifically how the integration

of AI improves the customer experience. In response,

a model was proposed drawing on the trust-

commitment theory (Morgan & Hunt, 1994) and the

service quality model, [37].

This proposed model integrates trust and

perceived sacrifice as mediating factors between an

AI-enabled customer experience based on four

elements: (a) personalization, (b) convenience, (c)

AI-enabled service quality, and (d) relationship

commitment. Previous studies have also highlighted

the importance of trust and the sacrifices users may

have to make while using AI-enabled services, [52].

However, to our knowledge, both factors are yet to

be empirically tested as part of a holistic theoretical

model. The study by [52] is a novel theoretical model

that integrates trust and perceived sacrifice as factors

mediating the effects of personalization, convenience

and AI-enabled service quality on AI-enabled

customer experience. It focused on the AI-enabled

customer experience offered by a beauty brand, and

the findings provided new insights into the

customers’ view of trust and perceived sacrifice.

Furthermore, the results highlighted the significant

role that commitment toward the relationship with

the brand plays in evaluating an AI-enabled customer

experience when the customers have had an initial

experience with the brand.

The second hypothesis (H1.2): There is no

statistically significant effect at the significance

level (0.05≥α) between artificial intelligence and

customer satisfaction in the Jordanian banking

sector.

4 Further Developments

The analysis of the data acquired via the self-

administered questionnaire of the responding Sample

indicated the following in terms of the Sample:

gender, age, academic level, professional position,

employment experience and the experience

distribution for the Sample by gender. The data

indicated that 51.1% of the 138 respondents in the

sample research were male. In contrast, 48.9% of the

second stage's 132 responders were female. The

following describes the distribution of the study

sample by age: 11% of the research group was

between the ages of 18 and 24, 17.5% between the

ages of 25 and 29 and 27.8% of respondents were

between the ages of 30 and 34. Further, 19.6% of the

population was between the ages of 35 and 39, 9.2%

was between the ages of 40 and 44, 5.2% was

between the ages of 45 and 49, and 9.6% of the

respondents were between the ages 40 and 49. It was

discovered that the bulk of the Sample, or 27.8% of

the total respondents, were in their middle years. The

academic level also determined the distribution of the

research sample: 61.9% possessed a bachelor's

degree, 15.9% hold a master's degree, 14.1% have

received a diploma, 4.8% are in high school, and

2.8% hold a PhD.

Additionally, 0.40 percent possess a professional

qualification. The findings revealed that the majority

of the Sample subjects had a bachelor's degree,

indicating that most Jordanian bank customers are

well-educated. The distribution of the research

sample by experience revealed that for 3.3% of the

research sample, the number of years of experience

was less than one year, 9.6% had 1–4 years of

experience, 31.9% had 5–9 years, 31.5% had 10–15

years, and 23.7% had more than 15 years of

experience. A significant number of responders had

5–9 years of experience (31.9%), indicating that

consumers are dedicated to the banks for the long

haul. According to the table, 14.9% of the Sample

consisted of top management with 40 respondents,

18.1% of middle management with 49 respondents,

27.8% of supervisors with 75 respondents, and

39.2% of non-managers with 106 respondents. This

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conclusion reveals that Jordanian commercial banks

have a multi-tiered organizational structure.

The authors in [27] describe the correlation

coefficient analysis as a quantitative index indicating

the degree and direction of the relationship between

two variables. According to [52], it estimates all

regression relationships using the correlation

coefficient. The strength of the linear connection

between the dependent and independent variables

may be determined (r). A correlation coefficient is a

numerical measure or indicator of the degree of

relationship between two sets of scores. It reaches a

maximum of +1 and a low of -1.00. The plus sign

represents a positive correlation, which indicates that

as the scores of one variable increase, so do the

scores of the other variable. A "-" sign shows a

negative correlation, indicating that while one

variable score rises, the scores on the other variable

decrease, [27]. A correlation of 1.00 indicates an

optimum relationship between the two variables. In

other words, a scattergram of the two variables will

demonstrate that each point corresponds precisely to

a straight line. A correlation of -.5 shows a

significantly unfavourable relationship between the

two variables.

This research employed correlation analysis to

examine the correlations between artificial

intelligence (independent variable) and service

quality and customer satisfaction (dependent

variables). Analyses of the relationship between

artificial intelligence (independent variable) and

service quality and customer satisfaction (dependent

variables) were conducted. These experiments were

also used to evaluate various regression assumptions.

According to research, different models are

developed to strengthen the customer-banking

connection and produce a win-win scenario for both

parties, [15]. Due to the competition from non-

banking industries, banks must embrace the most

cutting-edge digital technology to enhance their

service quality, [23]. As the banking sector is more

positively affected by technology, they should use

artificial intelligence tools to make client banking

transactions seamless and spontaneous. Several AI

applications have enabled banks to attain their

maximum efficiency, opening doors for new

dimensions in financial services.

To maintain a high level of service and create a

better-integrated system, it is necessary to

comprehend client attitudes. Creating a method to

gauge client satisfaction is crucial for banking

services. The SERVQUAL model generally

measures customer satisfaction, comprising five

dimensions: tangibles, responsiveness, empathy,

assurance and dependability. The SERVQUAL

model may be used to develop a superior instrument

for measuring customer satisfaction.

Affirmed, [29]. The word “reliability” refers to

the capability of the measuring scale to represent the

measured construct consistently. Therefore, a reliable

scale should produce consistent results regardless of

time and place. A certain level of dependability is

necessary for a trustworthy measuring scale.

According to the descriptive analysis presented

in Table 2, it can be noted that the standard deviation

of the EPS is (0.40 due to the existence of a

difference. It can be noted that the standard deviation

of the AAI is (0.71266) and the Standard deviation

error mean reached (0.04337), with that considered

as the lowest ratio as shown in Table 4, while the

highest ratio was for the Standard deviation ACS,

which reached (1.00108) for standard error mean

(0.06092).

According to Table 3, the T-test result shows the

highest ratio of 95% Confidence Interval.

The difference for both the Lower and Upper is

ARES; the ratio was for the lower (3.9085) and for

the upper (4.0896), while for the lower both lower

and Upper 95% Confidence Interval, the Difference

is AEMP; for the lower, the ratio was (3.3470) and

the Upper(3.5289). The AI correlated strongly with

all the independent variables. Tangibility at r= (.753)

at p<0.01) also correlated at r= (.753) at (p<0.01 and

p<0.05) with artificial intelligence. Tangibility

correlated at r= (.753) p<0.01, reliability correlated at

r= (.710) p<0.01, responsiveness correlated at r=

(.722), p<0.01, assurance correlated at r= (.607)

p<0.01, and empathy correlated at r= (.594), p<0.01.

It can be seen from the above table that all

independent variables have significant and positive

relationships with artificial intelligence

Customer satisfaction correlated strongly with

artificial intelligence at r= (.538) at p<0.01. Service

quality also correlated at r= (.753) at (p<0.01 and

p<0.05) with artificial intelligence; tangibility

correlated at r= (.753) p<0.01, reliability correlated at

r= (.710) p<0.01, responsiveness correlated at r=

(.722) p<0.01, assurance correlated at r= (.607)

p<0.01, and empathy correlated at r= (.594), p<0.01.

It can be seen from the above table that all

independent variable factors have a significant and

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positive relationship with artificial intelligence. The

table below proves this statement.

Table 2. Relevant study variables and calculated measures

N

Mean

Std. Deviation

Std. Error Mean

AAI

270

3.8988

.71266

.04337

ATAN

270

3.8207

.86888

.05288

AREL

270

3.8259

.83197

.05063

ARES

270

3.9991

.75586

.04600

AASSU

270

3.7731

.74342

.04524

AEMP

270

3.4380

.75914

.04620

ASERVQUAL

270

3.7714

.69302

.04218

ACS

270

3.4870

1.00108

.06092

Table 3. Direct relationship among the different variables

Variables

T-Test

t

df

Sig. (2-tailed)

Mean Difference

95% Confidence Interval of the Difference

Lower

Upper

AAI

89.893

269

.000

3.89877

3.8134

3.9842

ATAN

72.255

269

.000

3.82074

3.7166

3.9248

AREL

75.564

269

.000

3.82593

3.7262

3.9256

ARES

86.936

269

.000

3.99907

3.9085

4.0896

AASSU

83.398

269

.000

3.77315

3.6841

3.8622

AEMP

74.415

269

.000

3.43796

3.3470

3.5289

ASERVQUAL

89.420

269

.000

3.77137

3.6883

3.8544

ACS

57.236

269

.000

3.48704

3.3671

3.6070

Table 4. The correlation between Average Artificial Intelligence and Service Quality Dimensions

Correlations

AAI

ATAN

AREL

ARES

AASSU

AEMP

AAI

1

ATAN

.753**

1

AREL

.710**

.822**

1

ARES

.722**

.805**

.770**

1

NASSAU

.607**

.672**

.670**

.785**

1

AMP

.594**

.641**

.655**

.623**

.600**

1

Table 5. The correlation between Average Artificial Intelligence and Customer satisfaction

Correlations

AAI

CS1

CS2

CS3

CS4

AAI

1

CS1

.433**

1

CS2

.439**

.674**

1

CS3

.508**

.660**

.752**

1

CS4

.440**

.471**

.535**

.570**

1

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N

Mean

Std. Deviation

Std. Error Mean

AAI

270

3.8988

.71266

.04337

ATAN

270

3.8207

.86888

.05288

AREL

270

3.8259

.83197

.05063

ARES

270

3.9991

.75586

.04600

AASSU

270

3.7731

.74342

.04524

AEMP

270

3.4380

.75914

.04620

ASERVQUAL

270

3.7714

.69302

.04218

ACS

270

3.4870

1.00108

.06092

Variables

T-Test

t

df

Sig. (2-tailed)

Mean Difference

95% Confidence Interval of the Difference

Lower

Upper

AAI

89.893

269

.000

3.89877

3.8134

3.9842

ATAN

72.255

269

.000

3.82074

3.7166

3.9248

AREL

75.564

269

.000

3.82593

3.7262

3.9256

ARES

86.936

269

.000

3.99907

3.9085

4.0896

AASSU

83.398

269

.000

3.77315

3.6841

3.8622

AEMP

74.415

269

.000

3.43796

3.3470

3.5289

ASERVQUAL

89.420

269

.000

3.77137

3.6883

3.8544

ACS

57.236

269

.000

3.48704

3.3671

3.6070

Table 6. The correlation between Average Artificial Intelligence with Service Quality and Customer satisfaction

Correlations

AAI

ATAN

AREL

ARES

AASSU

AEMP

AAI

1

ATAN

.753**

1

AREL

.710**

.822**

1

ARES

.722**

.805**

.770**

1

NASSAU

.607**

.672**

.670**

.785**

1

AMP

.594**

.641**

.655**

.623**

.600**

1

ACS

.538**

.579**

.577**

.540**

.532**

.677**

Table 7. Correlation between Average Artificial Intelligence with Service Quality and Customer satisfaction

Correlations

AAI

ASERVQUAL

ACS

AAI

1

ASERVQUAL

.777**

1

ACS

.538**

.664**

1

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Fig. 1: Artificial Intelligence on service quality and customer satisfaction with correlation values.

Source: Authors' analysis, 2022.

Additionally, it is evident from the above results

that the highest correlation was between Artificial

Intelligence and service quality and customer

satisfaction (r=0.777). Furthermore, the results reveal

no perfect correlation among the independent

variables higher than (0.90), which is a good initial

indicator that there will be no collinearity diagnostic

among the independent variables. The correlation

matrix for the variables from Artificial Intelligence

on service quality and customer satisfaction is

initially analyzed for possible inclusion in Factor

Analysis.

Figure 1 indicates a positive and significant

relationship between AI and SQ with (r= 0.777).

Moreover, there is a significant correlation value of

service quality dimensions (tangibility with (r=

0.753), empathy with (r=0.594), assurance with

(r=0.607), reliability with (r=0.710) and

responsiveness with (r=0.722). Lastly, customer

satisfaction has been correlated positively with

(r=0.538). Moreover, two tests are applied to the

resultant correlation matrix to test whether the

relationship between the variables is significant.

First, Bartlett's test of sphericity is used to test

whether the correlation matrix was an identity matrix

(Table 18), i.e., all the diagonal terms in the matrix

are one, and the off-diagonal terms in the matrix are

zero. The calculated test value is 1494.379

(approximate chi-square). It shows that the

correlation matrix is not an identity matrix, i.e., a

correlation exists between the variables. Another test

of the Kaiser-Meyer Olkin (KMO) measure was used

to experiment with sampling adequacy. This test is

based on the correlation and partial correlation of the

variables. If the KMO measure is closer to 1, it is

superior to use factor analysis. If the KMO measure

is closer to 0, the factor analysis is not ideal for the

variables and the data. The value of the test statistics

is 0.910, meaning that the factor analysis for the

selected variables is appropriate for the data. Table 6

shows that all the hypotheses are accepted.

Table 8. Regression model summary for all hypothesis

Dimension

Beta

t

sig

Decision

Service Quality

0.756

20.210

0.000

Accept

Tangibility

0.918

18.724

0.000

Accept

Responsiveness

0.766

17.090

0.000

Accept

Empathy

0.633

12.084

0.000

Accept

Assurance

0.633

12.504

0.000

Accept

Reliability

0.829

16.494

0.000

Accept

Customer Satisfaction

0.755

10.440

0.000

Accept

Artificial

Intelligence (AI)

Service Quality (r=. .777a)

r=.753a Tangability

r=.710arelaibility

r=.722aresponsivness

r-.607aassurance

r=.594aempathy

Customer

Satisfaction r=.538a

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5 Discussion

The primary objective of this study is to examine the

effect of artificial intelligence on service quality in

the Jordanian banking sector. The results from testing

hypotheses demonstrated that artificial Intelligence

has a statistically significant influence on service

quality; this also corroborated the findings of past

research concerning the statistically significant

impact of Artificial Intelligence on service quality

[39], [40], [38], [41], [42], [43], [44], [45], [46], [47],

[48], [49].

The conclusion indicated a significant

association between Artificial Intelligence and the

tangibility of the service quality factor. Thus, there is

a correlation between banks in Jordan using AI in

their processes and the fact that their services are

current, as evidenced by the existence of physical

facilities, an interactive website and an application,

as well as the need for bank employees to be

proficient in IT.

The results also demonstrated a favourable

association between artificial intelligence and the

dependability of the service quality component about

the banks' capacity to satisfy customer expectations

and maintain openness and timeliness with their

consumers.

The findings indicate a statistically significant

association between Artificial Intelligence and the

responsiveness of the service quality dimension

based on the workers' willingness to assist and

inform consumers about when their services will be

completed.

The assurance component has a favourable

correlation with artificial intelligence, and bank

workers must provide courteous, discreet and

professional service to consumers.

When it comes to the connection between AI and

empathy, the findings demonstrate that banks can

provide personalized services to consumers

throughout their business hours by recognizing each

customer’s unique requirements.

Last but not least, there is a favourable

association between AI and customer satisfaction,

with the findings indicating that consumers are

satisfied with their banks when banks almost meet

their expectations.

The findings indicate that Artificial Intelligence

has a favourable but little impact on consumer

satisfaction. The outcome suggests that Jordanian

banks have endeavoured to foster a solid connection

with their clients to increase and cultivate their

loyalty. The findings also show that Jordanian banks

have provided clients with more information, yet

customer satisfaction is not yet complete.

In terms of the explanations behind the

outcomes, this study’s conclusion varies from past

empirical investigations in some ways. Starting with

service quality, there is a positive relationship

between AI and service quality, which will allow

banks to identify customer needs and desires and

enable the banking sector to identify market gaps and

changes in the external environment. As a result,

banks can generate new, contemporary strategies to

meet these changes, empower customers and

ultimately contribute to achieving organizational

goals. In addition, the impact of Artificial

Intelligence on customer satisfaction will impact

Jordanian banks' corporate culture and profitability.

6 Conclusions & Recommendations

The primary objective of this study was to examine

the influence of artificial intelligence on service

quality in the Jordanian banking sector. The testing

hypotheses showed that Artificial Intelligence has a

statistically significant influence on service quality;

this result corroborated the findings of past research

about the substantial impacts of Artificial

Intelligence on service quality, [27]. The conclusion

indicated that there is a significant association

between Artificial intelligence and the tangibility of

the service quality factor; the commercial banks in

Jordan that prioritize the use of AI in their processes

have demonstrated a correlation between the bank

services being current and the existence of physical

facilities, an interactive website and applications, as

well as the need for bank employees to be familiar

with IT applications. The research examined the

positive association between Artificial Intelligence

and service quality dimension dependability in terms

of whether or not banks fulfil customer expectations.

It assures consumers that banks delivering services

on time would maintain customer transparency. The

findings demonstrate a statistically significant

association between AI and service quality

dimension responsiveness as measured by the staff's

willingness to assist clients and notify them when

their services will be completed. The assurance

component correlates well with AI since the bank's

staff provides courteous, confidential and

professional client service. To demonstrate the link

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between Artificial Intelligence and empathy, the

findings show that banks provide personalized

service to consumers during business hours by

recognizing their unique requirements.

In conclusion, there is a favourable association

between Artificial Intelligence and customer

satisfaction; the findings suggested that customers

were satisfied with their banks when they almost met

their expectations. The findings indicate that

Artificial Intelligence has a favourable but not

statistically significant impact on consumer

satisfaction. Moreover, they suggest that Jordanian

banks have endeavoured to foster a solid connection

with their clients to increase and cultivate their

loyalty. The data also reflect that Jordanian banks

have shared more information with their clients, yet

consumer satisfaction is still not optimal. In terms of

the explanations behind the outcomes, this study's

conclusion varies from past empirical investigations

in some manner. Starting with service quality, there

is a positive relationship between artificial

intelligence and service quality that enables banks to

identify customer the needs and wants of their

customers, enabling the banking sector to recognize

market gaps and changes in the external

environment; this will help banks generate new

contemporary strategies to meet these changes,

empower customers and ultimately contribute to the

achievement of organizational objectives.

Moreover, the impact of Artificial Intelligence on

customer satisfaction will affect the corporate culture

and profitability of Jordanian banks. AI has made it

simpler for them to eliminate staff and customer

manual labour. Due to the machine learning

approach, formerly complex processes have been

reduced to unprecedented simplicity. Numerous

studies demonstrate that various approaches

strengthen customer-banking interactions and

generate a win-win scenario. The banking sector

should thus use artificial intelligence tools to make

client banking transactions seamless and

spontaneous. Several AI applications have enabled

banks to attain their maximum efficiency, opening

doors for new dimensions in financial services. As a

result of the research conducted in the Jordanian

banking industry, we recommend evaluating the

relationship between artificial intelligence and

assurance; Jordanian banks should be aware of the

needs of their customers in order to make them feel

more valued and make it possible for them to conduct

transactions at any time and place. The Jordanian

banking industry must thus enhance its service

quality by offering various solutions to assure safety

and comfort. In this digital age, clients want their

banks to be current and have their demands and best

interests at heart in exchange for their loyalty and

acceptance. Further, the upgradability of technology

will enhance service and security and enhance the

banks’ reputation for sustainability. Consequently,

Jordanian banks should pay greater attention to

customer satisfaction by introducing artificial

intelligence applications and by increasing the

customers’ awareness of banking services through

publications and advertising campaigns to reach the

largest possible customer segment and achieve

general satisfaction.

Analysis of correlation coefficients is a

numerical measure that represents the degree and

direction of the relationship between the two

variables, according to [28]. According to [52], it is

used to estimate all regression relationships. Using

the correlation coefficient, the strength of the linear

connection between the dependent and independent

variables may be determined (r). A correlation

coefficient is a numerical measure or indicator of the

degree of relationship between two sets of scores. It

reaches a maximum of +1 and a low of -1.00. The

plus sign represents a positive correlation, which

indicates that as the scores of one variable increase,

so do the scores of the other variable. A "-" sign

indicates a negative correlation, which indicates that

while the scores on one variable rise, the scores on

the other variable decrease, [28]. A correlation of

1.00 indicates an optimum relationship between the

two variables. In other words, a scattergram of the

two variables will demonstrate that each point

corresponds precisely to a straight line. The

scattergram's data points are arranged in a curve if

the value exceeds zero. If the value is less than zero,

the scattergram's data points are structured arbitrarily

along any straight line drawn over the data. A

correlation of -.5 between the two variables suggests

a significantly negative relationship.

This research employed correlation analysis to

examine the correlations between artificial

intelligence (independent variable) and service

quality and customer satisfaction (dependent

variable). Analyses of the relationships between

artificial intelligence (independent variable) and

service quality and customer satisfaction (dependent

variables) were conducted. These experiments were

also used to evaluate various regression assumptions.

WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2022.19.173

Reem Al-Araj, Hossam Haddad,

Maha Shehadeh, Elina Hasan,

Mohammad Yousef Nawaiseh

E-ISSN: 2224-2899

1944

Volume 19, 2022

According to research, different models were

developed to strengthen the customer-banking

connection and to produce a win-win scenario for

both parties, [15]. The SERVQUAL model generally

measures customer satisfaction, comprising five

dimensions: tangibles, responsiveness, empathy,

assurance and dependability. The SERVQUAL

model may be used to develop a superior instrument

for measuring customer satisfaction, [29]. The word

“reliability” refers to the capability of the measuring

scale to represent the measured construct

consistently. Therefore, a reliable scale should

produce consistent results throughout time and

geography. A certain level of dependability is

necessary for a trustworthy measuring scale. In the

current business environment, communication and

technology have been enhanced, and the tools of

Industry 4.0 have been implemented, such as

Blockchain, the Internet of Things, cryptocurrencies,

the cloud and big data, which can be investigated in.

Further research will determine their impact on the

quality of service in the Jordanian banking sector.

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WSEAS TRANSACTIONS on BUSINESS and ECONOMICS

DOI: 10.37394/23207.2022.19.173

Reem Al-Araj, Hossam Haddad,

Maha Shehadeh, Elina Hasan,

Mohammad Yousef Nawaiseh

E-ISSN: 2224-2899

1947

Volume 19, 2022