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International Review of Management and
Marketing
ISSN: 2146-4405
available at http: www.econjournals.com
International Review of Management and Marketing, 2025, 15(2), 180-188.
International Review of Management and Marketing | ol 1 ssue
180
Big Data Backed Business Intelligence to Upthrust Commercial
Banks Decision-Making Processes
Abeer Sultan Altarawneh1, Hasan Khaled AlAwamleh2, Fawwaz Tawq Awamleh1, Ala Nihad Bustami3*
1Department of Business Administration, Faculty of Business, Amman Arab University, Jordan, 2Department of Business
Administration, Al-Balqa Applied University, Amman University College, Jordan, 3University of Glasgow, Glasgow, UK.
*Email: alabustami@outlook.com
Accepted: 16 September 2024 Received: 04 January 2025 DOI: https://doi.org/10.32479/irmm.17709
ABSTRACT
Using business intelligence-backed big data leverages the decision-making processes in Jordanian commercial banks. The current study aimed to look
into the consequence of using big data to upthrust the link between business intelligence and decision-making processes within Jordanian commercial
banks. The study sample comprises 1600 employees from all levels in the 12 Commercial Banks in Jordan. Based on a random sampling of 371
questionnaires that were spread, (94.8%) of the total questionnaires were found ¿t for the analysis. This study utilized AMOS as a tool to examine the
collected data. This study’s results revealed that big data, business intelligence and decision-making processes are interrelated and signi¿cantly affect
each other in Jordanian commercial banks. The current study results recommend banks in Jordan to devote more investment in big data infrastructure
and also take measures to boost the abilities of personnel in analyzing and utilizing innovative data technology. Also, this study recommends researchers
investigate other related factors that affect the relationship between the current study’s concepts.
Keywords: Business Intelligence, Decision-Making Processes, Big Data, Data Quality and Integrity, Real-time
Analysis, Flexibility and Scalability
JEL Classications: G21, C53, O33
1. INTRODUCTION
In the big data era where a massive amount of data is available
for ¿nancial institutions to handle, effective decision-making is
what steers the wheel towards success. Business intelligence is
a central piece in leveraging decision-making processes. Using
business intelligence tools and techniques, bank managers can
analyze and interpret vast data from innumerable sources such
as client transactions, market-related trends, and risk-factors
assessments. Hence, allows them to make informed decisions
such as product offerings, pricing strategies, risk management,
and customer retention. Furthermore, business intelligence enables
bank managers to spot patterns and associations in customer
behaviour, therefore allow them to personalize their services and
offerings based on individual customer preferences. Ultimately,
the use of business intelligence in banking helps drive pro¿tability,
mitigate risks, improve operational ef¿ciency, and enhance overall
customer satisfaction.
Big data analysis tool, within the banking industry, is at the
center of decision-making processes.(Aliu, 2019; Soltani
Delgosha et al., 2021). It enables banks to get the best out of
vast amounts of information in real-time and leads to accurate
predictions and assessments (Soltani Delgosha et al., 2021).
Various alarming challenges are facing ¿nancial institutions such
as process automation, heightened customer expectations, ¿erce
competition, mergers and acquisitions, new developments, and
market segmentation. Financial institutions hoard vast amounts of
data daily, recording information for each customer individually,
encompassing personal details, property and ¿nancial decisions,
as well as their accounts, transactions per account, debit, and
credit liabilities. Therefore, effective management that is built on
This Journal is licensed under a Creative Commons Attribution 4.0 International License
Altarawneh, et al.: Big Data Backed Business Intelligence to Upthrust Commercial Banks Decision-Making Processes
International Review of Management and Marketing | Vol 15 Issue 2 2025 181
effective decision-making is at the heart of the survival of ¿nancial
institutions. Consequently, poses a need for a cost-effective, data-
driven, and reliable information decision-making process. This is
the means for the banks to adopt robust analysis tools to enhance
their performance, primarily aligned with the attainment of their
objectives (Alshehadeh et al., 2023).
The integration of BI into the systems of organizations has
evolved into a crucial scienti¿c and organizational innovation
for contemporary ¿rms, endorsing information dissemination and
forming the foundation of business decision-making processes
(Borissova et al., 2020). Therefore, business intelligence empowers
a company to comprehend its characteristics, and operational
ef¿ciency, and aids in designing a framework suitable for its
organizational environment. It ensures that the implementation
facilitates accurate decision-making, thereby enhancing overall
¿rm performance (Olszak, 2022).
The Jordan Banking System is on a steady upward trend as it
evolves to adapt to the challenges and changes of the global
market, in the framework of implementing the best banking
practices. It has always fulfilled its mission in economic
productivity, monetary and ¿nancial stability, and supported
development projects, and has begun to take innovative initiatives
driven by new technologies, such as big data (Al-Khatib, 2022;
Al-Okaily et al., 2023). In dealing with vast and complex
datasets, adopting a business intelligence framework proves
advantageous for any modern business entity (Borissova et al.,
2020). This approach aligns employees’ intellectual resources
with the ef¿ciency of computer-supported support systems,
enhancing the quality of decision-making (Gołbiowska et al.,
2021). Banks can use business intelligence, considering factors
such as data quality, analytical capabilities, and usage levels, to
enhance the transparency and intelligent visibility of their data
and core operations (Nithya and Kiruthika, 2020), improving
decision-making processes Awan et al.,(2021), integrates data
across the value chain allowing for even more informed decision
making (Singu, 2021). It equips managers with tools to report and
analyze business information enabling them to comprehensively
understand both internal and external organizational environments
(Kašparová, 2023). As a result, managers have access, to data that
signi¿cantly inuences their decision-making and guides their
endeavors (Maaitah, 2023).
In recent years, big data and business intelligence have
revolutionized numerous industries, yet the banking sector still
faces significant research gaps in effectively utilizing these
technologies to support decision-making (Nithya and Kiruthika,
2020; Ranjan and Foropon, 2021). Despite the strides of
interpreting and analyzing the dimensions of business intelligence,
many banks still lag in the proper utilization of this valuable
tools in the decision-making process. However, there is still an
incomplete understanding of how business intelligence can be best
used to enhance the effectiveness of banking operations, which
is especially important in light of the intense competition within
the sector. Therefore, this study aims to pave the way to a holistic
approach of using intelligent systems to boost decision-making
in the Jordanian banking sector by investigating the mediating
role of big data in decision-making processes based on business
intelligence in Jordanian commercial banks.
2. THEORETICAL FRAMEWORK
2.1. Fact-Based Decision-Making Culture
Factual-driven decision-making is critical in banking, especially
in business intelligence. Within the banks’ context, this philosophy
is ingrained in smart tools in order to analyze vast ¿nancial data
(Bany Mohammad et al., 2022). Therefore, decision-making
based on factual data in banking uses Big Data, enabling banks
to organize massive data from different sources, such as ¿nancial
transactions, customer records and sometimes even economic
predictions (Li et al., 2022). Working on this data assists in
understanding trends and forecasts, which have a bene¿cial effect
while making decisions, for they are based on true facts and ¿g ures.
The relationship of FBDM culture to business intelligence lies in
the use of advanced analytics and smart technologies to make the
most of data. Business intelligence helps transform this data into
valuable information and strategic insights (Gad-Elrab, 2021).
Bharadiya, (2023) indicated that business intelligence systems can
predict market trends, identify potential opportunities and risks,
and provide guidance for improving the decision-making process.
According to (Othman, 2021), the culture of decision-making
based on facts and business intelligence contributes to enhancing
banks’ ef¿ciency and improving their risk management.
2.2. Business Intelligence and Decision-making Process
The term “Business intelligence” highlights the use of technologies
and strategic techniques to support management decisions
(Shao et al., 2022). Business Intelligence and analytics has
established literature in supporting the business in general and
speci¿cally strategic intelligence (Awamleh and Bustami, 2022),
entrepreneurship (Awamleh et al., 2024), and business model
innovation (Božič and Dimovski, 2019). Through business
intelligence (BI), insightful perspectives must be gained, and
without the analysis of intelligence, decisions regarding any part of
the information cannot be made (Niño et al., 2020). The goal is to
uncover the underlying processes or patterns that lead to a speci¿c
perspective or desired outcomes (Nithya and Kiruthika, 2020).
Business Intelligence (BI) encompasses the gathering, integrating,
analysing, and presenting business-related information,
technology, applications, and practices. Its primary purpose is to
facilitate informed and effective decision-making within a business
context (Bharadiya, 2023). BI strives to enhance organizational
ef¿ciency in internal matters and promote transparency in crucial
process trends (Ramakrishnan et al., 2020).
Niu et al., (2021) indicates that the core aim of BI is to transform
data into knowledge, thereby contributing to the improvement of
decision-making processes. Business Intelligence (BI) presents a
solution that can collect and process data ef¿ciently. Currently,
BI stands out as a rapidly advancing domain within information
technology (Skyrius, 2021). It encompasses a range of capabilities,
technologies, tools, and methodologies aimed at assisting managers
in comprehending business conditions (Olaniyi et al., 2023). BI
comprises tools and processes to transform data into actionable
knowledge, facilitating informed decision-making. Furthermore,
Altarawneh, et al.: Big Data Backed Business Intelligence to Upthrust Commercial Banks Decision-Making Processes
International Review of Management and Marketing | Vol 15 Issue 2 2025
182
professionals and analysts can enhance their workflows by
leveraging straightforward functionalities, leading to improved
outcomes (Skyrius, 2021). For information to genuinely bene¿t
the business, it is imperative to deliver precise data promptly,
exactly when required. Furthermore, this information must prove
vital for decision-making, strategic planning, and ultimately, the
triumph of the business. The primary advantage of employing BI
systems lies in the signi¿cant enhancement of decision-making
effectiveness (Skyrius, 2021). Chatzistefanou, (2023) indicates
that key tools within the BI-integrated framework encompass data
storage, extraction, transmission, loading (ETL), online analytical
processing, data mining (DM), and reporting.
The precise and trustworthy data leads to long-term success
(Duggineni, 2023). BI tools are useful for organizing and
transforming data into valuable insights, contributing to more
reliable decision-making (Khong et al., 2023). The concentration
on data quality and precision enhances the credibility of
organizational decisions making (Singu, 2021). In addition to
providing integrated, high-quality data, business intelligence
contributes to real-time data analysis.
The BI’s reporting and analyzing capabilities reduce the waiting
time for decision-makers. This shift enables organizations to
make structured decisions promptly, enhancing their ability
to respond quickly to changes and align with industry trends
(Ranjan and Foropon, 2021). Also, (El Ghalbzouri and El
Bouhdidi, 2022) highlight the capability of BI tools to handle
large-scale data requirements ef¿ciently (Nambiar and Mundra,
2022). Furthermore, BI tools allow organizations to scale up their
infrastructure without compromising ef¿ciency, ensuring they can
meet the growing data-demand. Ultimately, Borissova et al., (2020)
indicated that business intelligence systems essentially serve as
decision support systems based on the analyzed data. Therefore,
the researcher believes that:
H1: Business intelligence signi¿cantly impacts Decision-Making
Processes in Jordanian commercial banks.
2.3. Business Intelligence and Big Data
Business Intelligence (BI) systems enable the consolidation of
data from diverse environments, locations, operating systems, and
databases rapidly (Kašparová, 2023). Frequently, this voluminous
data is either unstructured or stored in databases with distinct
schemas, necessitating specialized handling before integration
(Negro and Mesia, 2020). Gad-Elrab, (2021) pointed out that
Big data has changed the way enterprises handle and pro¿t from
enormous amounts of data through BI. Companies get useful
information from big datasets analytics which are easily interactive
with BI tools for the decision-making process. Through structure,
analyses, and graphs of large sizes of data, BI tools have the power
to allow companies to follow patterns trends, and relationships
that may not have been followed otherwise (Al-Okaily et al.,
2023). The blend of business intelligence and big data empowers
organizations to improve strategic planning, optimize operations,
and get a competitive advantage in the data-driven world of today.
They can therefore view how business intelligence affects big
data in de¿ning a new era where ideas of innovation and success
are accessible in all sectors. Hence, the researcher believes that:
H2: Business intelligence Significantly Impacts Big Data in
Jordanian commercial banks.
2.4. Big Data and Decision-Making Process
Weerasinghe et al. (2022) indicated that the employment of big
data has allowed organizations to rethink how they make decisions
and re¿ne decision-making processes. Upper-ton Big Data refers
to the huge amount of the same, new, and different types of data
that swamps a business. One of the great things about big data
is that it provides a lot of information, which results in great
decision-making (Pattnaik and Shah, 2023). Big data plays an
important role in making the right decisions based on data rather
than a hunch using different shared analytical tools, managers
effectively sort/¿lter the data and then process it, thus drawing out
valuable insights in a much shorter time (Jabbar et al., 2020). This
results in organizations having autonomy and can make the most
informed decisions because the data is current, and the decision
is made instantly (Kauffmann et al., 2020). By using big data
the banks can ef¿ciently predict future outcomes and can make
sound decisions on current occurring issues. Predictive analysis
techniques help organizations identify potential future challenges
and upcoming opportunities. It helps them to manipulate their data
and plan accordingly (Balbin et al., 2020). Seyedan and Mafakheri,
(2020) believe that through predictive analysis using big data,
more reliable forecasting can be done, leading to better decisions
in different areas. In the opinion of Kahila, (2023), large-scale data
is going to be inevitable, thus companies need to ¿nd a strategy
through it now or later. Therefore, the researcher believes that:
H3: Big Data has a Significant Impact on Decision-Making
Processes in Jordanian commercial banks.
2.5. Business Intelligence, Big Data, and
Decision-Making Process
Business intelligence with the help of big data, enables banks
to get valuable insights and to make decisions such as how to
perform, modify strategies, and cut off any extra costs that can
be a barrier to achieving goals, without violating any compliance
standard (Al-Okaily et al., 2023). Business intelligence is a
strategic focus, in today’s world, to have an edge for informed
decision-making. Through business intelligence, the whole data
can be analyzed and then key decisions can be made ef¿ciently
(Duggineni, 2023). One of the most important features of business
intelligence tools is to gather data from various sources throughout
the organization. The data, after being structured into a repository,
can be retrieved faster by the relevant department (Skyrius, 2021).
However, (Olszak, 2022) found a direct link between Big Data
and Business Intelligence and organizational success. This is by
stressing the importance of decision making optimization. Big
data is an important tool to understand the business context and
monitor changes. With the assistance of big data, ¿rms can have
a system that will provide in-depth insights into this data, making
decision-making easier. Big data is a comprehensive tool to gather
data, from multiple sources such as social media platforms, and
different technologies, to derive insights from this data (Shahid
and Sheikh, 2021). Diversity in data sources ultimately helps
understand deep relations and all possible perspectives relevant to
a business (Li et al., 2022). Therefore, the researcher believes that:
H4: Big Data mediates the relationship between Business
Altarawneh, et al.: Big Data Backed Business Intelligence to Upthrust Commercial Banks Decision-Making Processes
International Review of Management and Marketing | Vol 15 Issue 2 2025 183
intelligence and Decision-Making Processes in Jordanian
commercial banks.
Consequently. The theoretical model outlined in (Figure 1) shows
interrelationships among the study’s aspects.
3. RESEARCH METHODOLOGY
The methodology section prescribes the methodology to achieve
the study’s objectives. Besides, details of the selected sample and
its characteristics. Furthermore, it explicates the preparation and
development of the data collection instrument. Then, the required
statistical methods utilized to process the collected data.
The researchers followed a descriptive-analytical approach to
answer this study’s question; identify the big data meditating
effect in the interaction of business intelligence capabilities and
decision-making processes. This method is based on an exhaustive
explanation of the subject problem. In other words, detecting
the research model variables and unfolding the interrelated links
within them (Sekaran and Bougie, 2016).
3.1. Population and Study Sample
This study addresses the Commercial Banks in Jordan as the
study population. Speci¿cally, it consists of 12 Banks that hold
administrative positions. In total, it targets (1600) employees.
The sampling method is a random sample of 371 questionnaires
which is suitable for the study (Sekaran and Bougie, 2016). Out
of all the distributed questionnaires, 359 were retrieved and
(7) questionnaires were excluded. Hence, (352) questionnaires
(94.8%) of the questionnaires were found ¿t for the analysis.
3.2. Study Tool
The researchers designed the study instrument based on a thorough
investigation of related previous studies. The questionnaire
highlighted (39) items organized as the following sections:
Firstly, (21) questions related to the independent variable which
is Business intelligence (Data Quality and Integration (7) items,
Real-time Analysis (7) items, Flexibility, and Scalability (7) items).
Secondly, (11) questions related to the dependent variable which
is Decision-Making Processes. Thirdly, (7) questions about the
intermediary variable which is Big Data. The questions formulated
as a five-point Likert scale. Namely, 1 as strongly agree to
5 strongly disagree. This is to give respondents greater exibility
in selecting the right response (Sekaran and Bougie, 2016).
3.3. Reliability Test
To test the reliability of the study’s instrument, the Cronbach
Alpha coef¿cient values were calculated for all items. The overall
reliability test result is 86.4% shown in Table 1. The Cronbach
Alpha score ranged from 87.5% to 91.2% which is higher than
the acceptable percentage of 70%. This agrees with the acceptable
scores within the ¿eld (Sekaran and Bougie, 2016).
4. RESULTS
According to Table 2, the descriptive statistics show that 62.8%
of the total sample were males, leaving 37.2% for females. In
terms of education, 65.1% of the total sample held a bachelor’s
degree. The highest percentage for years of experience was
6-10 years constituting 32.7% of the total sample. Finally, 27% of
the sample were Managers and unit heads whereas the remaining
were employees.
In order to check that the data is normally distributed, the result of
the normal distribution test is shown in Table 3. As the response
number is large, one sample Kolmogorov-Smirnov (K-S) was
used. The result indicates that K-S value is < 5 at a Sig value
> 0.05, and the skewness values < 1, collectively indicating a
normal distribution (Hair, et al., 2011).
The multicollinearity test and the VIF and tolerance scores
were recorded in Table 4. The results show the VIF values < 3,
while the tolerance values > 0.10. This con¿rms the absence of
multicollinearity amid the independent variables.
Table 1: The study instruments stability coefcients
Variable Alpha value Number of statements
Independent variable
Business intelligence 0.887 21
Independent variable 0.875 7
Real-time analysis 0.902 7
Flexibility and scalability0.887 7
Mediation variable
Big data 0.912 7
Dependent variable
Decision-Making Processes0.877 11
All 0.85 39
Table 2: Descriptive statistics
Variable Frequency Percentage
Gender
Female 131 37.2
Male 221 62.8
Academic quali¿cation
Diploma 61 17.3
Bachelor’s degree 229 65.1
Postgraduate studies 62 17.6
Years of experience
5 years or less 96 27.3
6-10 years 115 32.7
11-15 years 104 29.5
16 years and above 37 10.5
Job vacancy
Employee 257 73
Unit manager 36 10.2
Department head 59 16.8
Total 352 100
Table 3: The normal distribution test of data
Variable Mean SD Skewness k-s Sig
Business intelligence 3.73 0.64 –0.43 0.84 0.51
Data quality and
integration 3.83 0.42 –0.43 1.03 0.374
Real-time analysis 3.45 0.62 –0.33 1.12 0.087
Flexibility and scalability3.92 0.53 –0.42 1.25 0.261
Big data 3.64 0.60 –0.09 1.09 0.174
Decision-making
processes 3.84 0.57 –0.35 1.245 0.085
Altarawneh, et al.: Big Data Backed Business Intelligence to Upthrust Commercial Banks Decision-Making Processes
International Review of Management and Marketing | Vol 15 Issue 2 2025
184
As shown in Table 5, the details of the inter-correlation which
is represented by the Pearson Correlation test to validate the
relationship between the current study variables. The value of
the correlations between the variables ranged from (0.417 to
0.783). Hence, indicates the occurrence of a positively signi¿cant
relationship amongst the study variables at P = 0.000.
The researchers used multiple regression to examine the study’s
hypotheses. H1 test the link between business intelligence and the
decision-making process. Table 6 shows the testing results of H1.
The results in Table 6 indicate a statistically signi¿cant effect
at the signi¿cance level of 0.00. Furthermore, the coef¿cient of
determination is (R
2 = 0.766) hence, business intelligence explained
(76.6%) of the variance in Decision-Making Processes. According
to the results, all variables contributed to the impact of business
intelligence on decision-making processes. Therefore, this leads
to alternative hypothesis acceptance “Business intelligence has
a signi¿cant impact on Decision-Making Processes in Jordanian
commercial banks.”
Table 7 speci¿es the results of the multiple linear regression test
for H2. The coef¿cient (R
2 = 0.562), indicates that the (business
intelligence) explained (56.2%) of the variance in (big data), at
a signi¿cance level of 0.00. Consequently, business intelligence
signi¿cantly impacts Big Data with moderate variance explanation.
Therefore, this indicates the acceptance of the alternative
hypothesis “Business intelligence has a signi¿cant impact on Big
Data in Jordanian commercial banks.” is accepted.
Table 8 displays the result of the coefficient (R
2 = 0.579),
indicates that the Big Data explained (57.9%) of the variance
in Decision-Making Processes at (Sig = 0.000), the value of (F)
reached (176.111), which con¿rms the signi¿cance of the model
at (α ≤ 0.05). the coef¿cient value (B = 0.498) equivalent to
Decision-Making Processes at (Sig = 0.000). Collectively, the
alternative hypothesis is accepted “There is a positive relationship
between Big Data and the Decision-Making Processes in Jordanian
commercial banks”.
The ¿t model data analysis appears in Table 9, showing that
(χ2/df =4.01) is signi¿cant at (Sig = 0.000), (GFI = 0.976) is
above the recommended minimum. The (CFI = 0.982) exceeds
the recommended minimum limit (RAMSEA = 0.082) and meets
the recommended limit. Accordingly, all the mentioned results
indicate that the proposed model ¿ts the collected data.
Table 10 illustrates the values of the direct and indirect impact
of the relationship of the intermediate variable (Big Data) in
the bridge between business intelligence and decision-making
Processes. The direct effect of business intelligence BI-decision-
making Processes is (0.539), which indicates that business
Table 5: Pearson correlation
Variable Business
intelligence
Data quality
and integration
Real-time
analysis
Flexibility and
scalability
Decision-making
processes
Big
data
Business intelligence 1
Data quality and integration 0.523** 1
Real-time analysis 0.427** 0.783** 1
Flexibility and scalability0.418** 0.783** 0.765** 1
Decision-making processes 0.748** 0.663** 0.658** 0.752** 1
Big data 0.559** 0.506** 0.453** 0.594** 0.579** 1
**Correlation is signi¿cant at the 0.01 level (2-tailed).
Table 4: Tolerance and VIF
Variables Tolerance VIF
Data quality and integration 0.579 1.725
Real-time analysis 0.453 2.208
Flexibility and scalability 0.463 2.19
Business Intelligence
Data Quality
and
Integration
Real-Time
Analytics
Scalability
and
Flexibility
Big Data
Decision-
making
processes
H2
H3
H1
H4
Figure 1: Theoretical model
Altarawneh, et al.: Big Data Backed Business Intelligence to Upthrust Commercial Banks Decision-Making Processes
International Review of Management and Marketing | Vol 15 Issue 2 2025 185
intelligence affects Decision-Making Processes. As for the direct
impact of big data on the DMP is (0.5). This indicates that big
data moderately impacts the decision-making processes and thus
increased attention to big data will generate an impact on the
decision-making processes.
Business intelligence’ direct impact on big data equals (0.76) at
(P < 0.05), hence there is an indirect effect of the mediating variable
statistically accounting for (0.38). As for the business intelligence’s
total impact on the decision-making processes equals (0.91)
with big data presence. The effect of the mediating variable was
statistically signi¿cant, the mediation is partial due to the presence
of business intelligence in both direct (Business intelligence –
Decision-Making Processes) and indirect (Business intelligence –
Big Data – Decision-Making Processes) effects. However, business
intelligence and big data substantially and signi¿cantly enhance
the decision-making process in commercial banks.
Based on (Figure 2), this study concluded that integrating business
intelligence and big data improves the decision-making processes.
This conclusion relates to another study’s ¿ndings that using big
data analytics alongside business intelligence tools aids leaders in
making fast, data-driven, precise decisions for the most optimized
outcomes (Olaniyi et al., 2023). However, the dimensions of the
variables were different in both studies. Speci¿cally, the utilization
of internal benchmarks, banks can identify what is working well and
what needs improvement. This, in turn, ensures that the bank can
improve its ef¿ciency and productivity across different channels.
This study con¿rms that linking business intelligence with big
data is useful for commercial banks. Similarly, Shouman and
Table 6: The multiple regression test of H1
Variable B Standard
error
TSig T*
Constant 1.01 0.133 7.597 0.00
Data quality and integration 0.313 0.057 5.491 0.049
Real-time analysis 0.217 0.049 4.428 0.017
Flexibility and scalability0.492 0.054 9.11 0.00
R=0.766, R²=0.587, R² Adj.=0.583, F=164.68, Sig.=0.00
Table 7: The multiple regression test of the H2
Variable B Standard
error
TSig T*
Constant 0.943 0.283 3.33 0.001
Data quality and integration 0.229 0.069 3.318 0.001
Real-time analysis 0.249 0.075 3.324 0.001
Flexibility and scalability0.21 0.07 3.001 0.003
R=0.562, R²=0.316, R² Adj.=0.306, F=32.514, Sig.=0.00
Table 8: Testing the impact of big data on decision-making
processes
Variable B Standard error T Sig T*
Constant 1.78 0.157 11.353 0.00
Big data 0.498 0.037 13.271 0.00
R=0.579, R²=0.335, R² Adj.=0.333, F=176.111, Sig.=0.00
Figure 2: Test results of the model
Altarawneh, et al.: Big Data Backed Business Intelligence to Upthrust Commercial Banks Decision-Making Processes
International Review of Management and Marketing | Vol 15 Issue 2 2025
186
Chehade, (2020); Pillay and Van Der Merwe, (2021) mention that
business intelligence enables the analysis of vast amounts of big
data collected from multiple sources within a banking organization.
This comprehensive analysis allows a deeper understanding
of customer behaviour, market trends, and potential ¿nancial
risks. Banks can utilize their business intelligence technologies
to recognize where the investment opportunities lie. Therefore,
it can provide better ¿nancial services (Yu and Song, 2020). On
the other hand, integrated technologies offer a vital pathway
through which the leaders and strategic managers make important
operation decisions by analyzing essential insights that eventually
lead to success in the volatile ¿nancial environment (Awamleh
and Bustami, 2022; Al-Okaily et al., 2023). This supports the
current study ¿ndings which indicate big data’s signi¿cant role
in elevating the bridge between business intelligence and the
decision-making process.
5. CONCLUSIONS
The current study empirically proved that incorporating business
intelligence with big data quips Jordanian commercial banks
with the necessary tools to enhance the quality of data. This
is via systematically collecting and analyzing vast amounts of
information. In more depth, this rigid process leads to usable
data and therefore more reliable information. Consequently,
this contributes to increased accuracy in analysis and reduces
the likelihood of errors. Moreover, business intelligence and big
data facilitate comprehensive data integration within Jordanian
commercial Banks which aids in forming a comprehensive
understanding of the business investment. This holistic approach
enables commercial banks to form cohesive knowledge of
customer behaviors and market dynamics. Therefore, enabling
more informed and ef¿cient decision-making regarding resource
utilization.
This study also found that real-time big data analysis contributes
to enhancing decision-making processes in Jordanian commercial
banks. The primary bene¿t is embedded in the availability of real-
time data analysis, which leads to instant and immediate access to
information. This function enables the banking sector to keep pace
with ongoing developments, hence easing the process of making
rapid decisions based on the latest data. Therefore, Jordanian
commercial banks can cope with rapid uctuations in ¿nancial
markets and improve their responsiveness to customer needs.
Another conclusion of the current study is to highlight the big
data transformative impact on the decision-making process. The
vast volume, diversity, and speed of data generated in Jordanian
commercial banks provide them with unprecedented insights
and a better understanding of their business. Based on this study
practical implications, big data analysis and business intelligence
integration, benefit make a powerful tool that is able to re-
engineering of data processing in organizations which in turn
enhance the data generated by its daily activities and practices.
Finally, the study has demonstrated the impact of business
intelligence and big data analysis on decision-making processes,
highlighting the importance of understanding the utilization and
management of big data.
5.1. Practical Implications
The current study’s highlight several practical implications
for Jordanian commercial banks. Firstly, big data enables
commercial banks to leverage business intelligence in improving
the decision-making process ef¿ciency. This involves utilizing
business intelligence for informed decision-making regarding
operations, personnel, processes, and customer relationships.
Secondly, the study urges commercial banks to apply appropriate
measures that ensure the robustness and quality of real-time
big data analysis, by utilizing business intelligence. Thirdly, it
encourages commercial banks to utilize advanced analytics tools
such as predictive analysis and machine learning to maximize
understanding of big data. Fourthly, the study underscores
the need for commercial banks to employ a solid grasp of the
relationship between business intelligence, big data, and the
decision-making process. Finally, the study endorses commercial
banks in Jordan to invest heavily in infrastructure development
and human resource training to earn the full potential of this
data-driven approach.
5.2. Limitations and Future Research
This study inuences the current literature to take different
approaches to explore further the connection between business
intelligence and decision-making processes, the study recommends
that researchers take other related factors into account. However,
this current study was limited to the following:
• Jordanian commercial banks.
• The employees who hold administrative positions in the
Jordanian commercial banks.
• Fiscal year 2024.
Table 9: Fit model
Indicator AGFI χ2/df GFI RMSEA CFI NFI
Value recommended >0.8 <5 >0.90 ≤0.10 >0.9 >0.9
References (Miles and
Shevlin, 1998). (Tabachnick and
Fidell, 2007) (Miles and
Shevlin, 1998). (MacCallum
et al., 1996) (Hu and
Bentler, 1999). (Hu and
Bentler, 1999).
Value of model 0.9 4.01 0.976 0.082 0.982 0.979
Table 10: Direct and indirect effects H4
Variable Direct impact Indirect impact Total Impact C.R S.E Sig.
Business intelligence – decision making processes 0.539 0.38 0.919 9.98 0.054 ***
Business intelligence - big data 0.761 0.761 14.11 0.054 ***
Big data – decision making processes 0.5 0.5 12.19 0.041 0.007
Altarawneh, et al.: Big Data Backed Business Intelligence to Upthrust Commercial Banks Decision-Making Processes
International Review of Management and Marketing | Vol 15 Issue 2 2025 187
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