Iberoamerican Business Journal
Vol 6 N° 1 | Julio 2022 pp. 04 - 27 ISSN:2521-5817 DOI: http://dx.doi.org/10.22451/5817.ibj2022.vol6.1.11063
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1Case Western Reserve University, USA
ORCID: https://orcid.org/0000-0003-3489-9416
Email: mesbaul.sazu@case.edu
2Independent University, Bangladesh
ORCID: https://orcid.org/0000-0002-0285-0530
Email: 1720714@iub.edu.bd
Can big data analytics improve the quality of decision-making in
businesses?
Can big data analytics improve the quality of decision-making in businesses?
Mesbaul Haque Sazu 1, Sakila Akter Jahan 2
Recepción: 20/05/2022. Aceptación: 30/05/2022. Publicación: 31/07/2022
Can big data analytics improve the quality of decision-making in businesses?
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ABSTRAC
cv
INTRODUCTION
Big data analytics (BDA) initiatives
are crucial for changing conventional
firms' decision-making (DM) directly into a
data driven one that helps achieve the
firm's objectives. Nevertheless, prior
information methods analysis has not
given sufficient interest in the effect of
BDA use on DM merit. Using the
computational ability of information
analytics, this analysis examined the
effect of BD on DM merit and evaluated
the arbitrating impact of data analytics
abilities. We gathered information through
480 software companies in America. The
empirical methods demonstrated that
BDA implementation had a strong effect
on DM merit, where BDA had an
arbitrating role in the relationship between
BDA use and DM merit. Thus, companies
would not only increase BDA use in
business DM, but also take steps to boost
the data analytics abilities, which will
boost the DM merit in the direction of
acquiring competitive advantage.
Key Word: Strategic decision,
business performance, BD analysis, data
science
Big data analytics (BDA) solutions
are changing the way companies run and
forging how businesses can make
choices (Rukanova, et al., 2021). The
importance of it in creating firm
competitiveness remains well known.
Over eighty% of companies think big data
(BD) will alter the competitive landscape.
The acceptance and implementation of
BD is an important way to gain industry
share (Awan, et al., 2021). The use of
BDA equipment may significantly revamp
service precision, operation network, and
production standardization (Mikalef,
Pappas, Krogstie, & Giannakos, 2018).
Recently, many companies have sped up
the deployment of their BDA initiatives,
with the goal of acquiring awareness,
which can eventually supply them with a
competitive edge. Nevertheless, several
scientific studies have discovered that
only twenty% of companies say the use
of BDA has substantially enhanced their
firm's results, and many companies which
have used BDA have still to get your own
insights to boost the outcomes (Basyurt,
Marx, Stieglitz, & Mirbabaie, 2022). A
good reason behind the disappointment is
Mesbaul Haque Sazu, Sakila Akter Jahan
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that many companies still need
knowledge of BDA and do not understand
the required problems for producing
insights from BDA. Thus, knowing the
best way to efficiently utilize BDA to boost
DM merit is important to firms' competitive
upper hand.
Pre-existing investigation on BD
and DM merit has several limits. Earlier
research has provided sporadic
conclusions about the effect of BDA use
on DM merit. Several scholars have
found that BDA implementation has a
good effect on DM merit, while others
have claimed contrary results (LaBrie,
Steinke, Li, & Cazier, 2018). There's
limited understanding of how BDA
initiatives can help companies grow their
DM merit, and the outcome mechanisms
of BDA use on DM merit are unclear.
Thus, extra-in-depth scientific studies are
justified to explain the systems by that the
advantages of BDA use on DM merit
could be achieved (Rukanova, et al.,
2021).
During the BD atmosphere, BDA
features, which direct towards the ability
of a firm to properly deploy talent and
technology to shoot, shop, and evaluate
information to come up with awareness,
are crucial to a firm’s ability, which can
lead to competitive advantage. BDA
competencies can boost firms' DM
effectiveness and usefulness by
recording, analyzing, searching, sharing,
transmitting, storing, and imagining
information. Appropriately, companies
that cultivate better data analytics
features by fostering the pervasive
implementation of BDA must optimize DM
merit (Shamim, Zeng, Shariq, & Khan,
2019). Nevertheless, earlier research has
primarily focused on BDA features as the
antecedent of firms' choices and has
talked about the indirect and direct
negative effects of BDA features on DM
merit. It's still unclear whether BDA
competencies arbitrate the relationship
between BDA use and DM merit.
To fill up the mentioned gaps and
lead this type of investigation, this
analysis drew on computational principles
and then created a section which
investigated the effect of BDA use on DM
merit and gauged the arbitrating role of
BDA features within the linkages between
them (Van Rijmenam, Erekhinskaya,
Schweitzer, & Williams, 2019). The
analysis uses 3 crucial efforts: exploration
on the importance of BDA, evaluating the
Can big data analytics improve the quality of decision-making in businesses?
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LITERATURE REVIEW
consequences of BDA use on DM merit,
and delivering empirical proof which
companies can certainly use large data
analytics to facilitate DM merit; revealing
the mechanism whereby BDA use
favorably influences the firms' DM merit,
identifies the mediation role of BDA
features, and offers a novel lens for
companies using large data analytics to
get competitive advantages; and
supplementing the applicability of the
computational electrical capacity principle
and locating which BDA implementation
is able to promote BDA competencies.
BDA application
BDA has revolutionized data
processing engineering and evaluation
strategies, enhanced information
processing competencies, and is
commonly used in several areas of
business (Koot, Mes, & Iacob, 2021). To
exemplify, BDA has now discovered
numerous uses within software industries,
like water forecasting, checking for crops'
insects, and customer personal
preference. Companies use BDA
equipment to approach and evaluate
information of online resources, get data-
backed choices, and develop a
competitive edge (LaBrie, Steinke, Li, &
Cazier, 2018). Companies can acquire
serious insights to generate choices by
using various kinds and levels of
information (Mikalef P. B., 2019). Based
on prior investigation, the use of BDA can
help companies gather and examine
information and generate predictions and
choices, which can offer helpful
assistance for more DM (Chon & Kim,
2022). Nevertheless, based on the IT
complexity, BDA use might not have a
good effect on DM merit. BDA
implementation requires corresponding
BDA storage space engineering, analytics
skills, and managing understanding,
which could present a specialized load on
companies, which might not draw out
valuable information (Awan, et al., 2021).
With this thought, we're aware that there's
simply no regular investigation over the
relationship between BDA use and firm
DM merit, and the consequences and
systems of BDA use on firm DM merit
remain unfamiliar. Thus, a lot more
analysis of the effect of BDA use on firm
DM merit is justified.
Mesbaul Haque Sazu, Sakila Akter Jahan
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Dynamic ability theory
The computational ability of BDA
posits that the firm's capability to mix,
craft, and configure outer and inner
competencies to react too quickly.
Changing locations can maintain a firm's
competitive upper hand (Chon & Kim,
2022). The concept additionally suggests
abilities: low and high-order firms'
competencies which produce competitive
advantage. Businesses can create
unusual high order competencies with the
amalgamation and segregation of low
order competencies (Mikalef P. B., 2019).
Within the current literature, BDA use
refers to the range and frequency of
utilizing BDA mining and evaluation
methods in just businesses (Koot, Mes, &
Iacob, 2021). It primarily mirrors the
functional ability of BD division, which is
regarded as a low order computational
ability. BDA competencies direct towards
the capability to mobilize and deploy BDA
based strengths by pairing competencies
and resources to enhance DM merit and
develop competitive advantage. It
accomplishes specific goals via cross-
departmental collaboration and cross-
level, and it is regarded as a high order
ability (Kim, Choi, & Byun, 2019). As a
result, BDA implementation stands out as
the ability of the low order firm, which is
important to attain BDA competencies. As
a result, the computational principle is a
suitable lens to recognize the effect of
BDA use on BDA competencies and DM
merit.
Data analytics competencies
BDA has grown to be a firm's
competency to process information.
Because of the increased volume, types
of, and speed of information change in a
company, the BDA can be a significant
tool (Rukanova, et al., 2021). Firms' BDA
competencies direct to the ability to use
methods to get insights, which may lead
to competitive advantage. Creating BDA
competencies requires the integration of
strategic resources, which include
physical, and non-tangible resources
(Chon & Kim, 2022). Manpower is the
most essential component for performing
& building BDA tools as competitive
advantages. Human competencies,
however, cannot be copied, and it helps
build BDA tools, but also play a crucial
role in obtaining the maximum opportunity
(Basyurt, Marx, Stieglitz, & Mirbabaie,
2022). Previous studies have highlighted
the benefits of manpower and
Can big data analytics improve the quality of decision-making in businesses?
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demonstrated that companies switching
to BDA can make absolutely no
distinction if adoption does not use
relevant manpower. Particularly, technical
competencies direct towards the
technology necessary to employ new
types of BDA to draw out insights out of
BD, which includes information cleansing,
data wrangling and statistical evaluation.
Specialized expertise not only carries out
BDA, but also records industry sentiment
(Novak, Bennett, & Kliestik, 2021).
Therefore, it helps companies locate
potentials, rationally use the firm's
resources, and achieve maximum
profitability (Rozario & Issa, 2020).
Managerial competencies could be
referred to as the ability of workers to
arrange and configure BDA to do daily
work and generate decisions, for example
strategic insights for BD deployments and
use of the extracted insights (Mazzei &
Noble, 2017). Effective managerial
expertise helps personnel create real time
decisions by using BDA, which helps
companies effectively gather and assess
industry intelligence, quickly mobilize
resources to answer modifications, and
realize a firm's changes in dynamic
conditions. To sum up, companies should
incorporate manpower to develop
computational competencies to enhance
DM and gain competitive advantage
(Choi, Wallace, & Wang, 2018).
Together with the considerable
implementation of BDA, researchers
focus on BDA within companies. They
have suggested that utilizing BDA can
help companies determine, discuss, and
evaluate information materials, and
motivate them to cultivate corresponding
BDA competencies (Van Rijmenam,
Erekhinskaya, Schweitzer, & Williams,
2019). Additionally, information evaluation
competencies might completely take
advantage of the importance of
information and provide companies with
insights useful for optimizing allotment,
item traceability, functioning preparation,
and decision making. Thus, BDA
competencies can't be dismissed within
research of BDA implementation (Niu,
Ying, Yang, Bao, & Sivaparthipan, 2021).
DM merit
DM merit refers to the accuracy
and precision of choices, which is
examined by decision usefulness and
decision effectiveness in the system of
DM. As (Rukanova, et al., 2021)
mentions, decision usefulness
concentrates on reliability, precision, and
Mesbaul Haque Sazu, Sakila Akter Jahan
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RESEARCH STYLE AND
THEORY DEVELOPMENT
accuracy of decision benefits, while
decision effectiveness considers time,
price, and other facets of the materials
concerned. BD is happening whatsoever
phases of the manufacturing chain, with
the way companies can make choices.
This allows companies to quickly
recognize problems and opportunities, cut
short the process of DM, and enhance
DM merit (Monino, 2021). For example,
BDA can offer software companies with
correct manufacturing information and
enhance DM merit by smart prediction.
Furthermore, the use of BD to run a
business procedure can help software
companies use rapidly moving customer
and market information and do real time
evaluation and insights. As outlined by
this conception, effective choices can
help companies manage expenses,
guarantee merchandise merit, and boost
client satisfaction. Thus, this analysis
targeted to look at the arbitrating
functions of BDA features in BDA use and
DM merit.
BDA use and DM merit
BDA use can recognize the
importance of information created by
companies and alter the traditional DM
processes (Koot, Mes, & Iacob, 2021).
For starters, BDA implementation
encourages the group of information
within the manufacturing chain, such as
manufacturing, processing, and product
sales information, and the development of
a data source. Sufficient and
comprehensive data can offer concealed
benefits for companies and enable them
to boost DM merit. Next, utilizing
innovative data analytics equipment can
help companies offer systematic
evaluation outcomes, altering the way
experiential decision improving is
achieved and making DM capable (Kim,
Choi, & Byun, 2019). Thirdly, companies
use BDA equipment to mine information,
community conduct, and psychological
semantic analyses, each of which will
help companies comprehend
modifications within client need; enhance
DM effectiveness. Finally, via BD design
prediction, which includes machine
Can big data analytics improve the quality of decision-making in businesses?
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learning, designing, and then running
various simulation, companies can get
forecasting reports and decision support,
making regular choices. Earlier scientific
studies have also discovered that the
veracity, volume, and velocity of BD
supply the promise of DM merit. BDA
implementation plays a vital role in
improving information analysis and solid
DM. Consequently, we hypothesize the
following:
H1: BDA implementation is favorably
linked to DM merit.
BDA use and BDA competencies
BDA implementation has
encouraged the digitalization of
companies and brought brand new vigor
to the development. It can recognize
smart production, reorganize supply
chains, and allow the digital
transformation of companies (Chon &
Kim, 2022). Nevertheless, BDA
additionally creates problems for
companies. One will find numerous types
of BD, including semi structured,
organized, and unstructured data.
Intricate data requires companies to have
the corresponding BDA competencies to
offer BD architectures to keep huge
amounts of information, and a nested
computer system to evaluate various data
types (Dremel, Wulf, Herterich,
Waizmann, & Brenner, 2017). BDA
competencies can help companies shoot
and evaluate each data type quickly,
skipping the importance of merit
information. Additionally, when
companies use BDA, they sensibly allot
methods in relation to information
processing, for example skills,
infrastructure, managing, and various
other online resources, to push the
development of BDA competencies and
ensure the successful use of BDA
equipment. Consequently, we propose
the following hypotheses:
H2: BDA implementation is favorably
linked with BDA competencies.
Data analytics competencies and DM
merit
BDA competencies are essential to
a firm's competencies, which can
certainly help companies use information
methods, mine concealed information
within the supply chain, search for threats
and opportunities, and enhance DM merit
(Al-Sai & Abualigah, 2017). For example,
BDA competencies can improve the
evaluation necessary for merchandise
production and produce manufacturing
Mesbaul Haque Sazu, Sakila Akter Jahan
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METHODOLOGY
systems for each product, optimizing
manufacturing functions. As a result, BDA
competencies can change raw data from
several IT uses into client insights, and
help companies hunt for concealed
implementation patterns, determine
customer tastes, and style and innovate
advertising techniques. In addition, BDA
competencies may also earn proper
choices for potential preparation and
resource allotment by getting insights into
the main tasks of companies (Dremel,
Wulf, Herterich, Waizmann, & Brenner,
2017). In a nutshell, companies with a lot
of BDA competencies can also benefit
from the data analytics to confirm DM
merit. Particularly, with the evaluation and
processing of BD, BDA competencies can
acquire farsighted and comprehensive
insights, assisting companies to boost
decision efficiency and decision success
(LaBrie, Steinke, Li, & Cazier, 2018).
Consequently, we hypothesize the
following:
H3: Data analytics competencies are
favorably linked with DM merit.
Utilizing the computational ability
concept, this analysis proposes a
research design provided within Fig. one
and shows the hypothesized interactions
between BDA implementation, BDA
features, and DM merit.
Scale preparation
The analysis utilized questions to
gather information from software
companies. The questionnaire
incorporated essential information
regarding the measurements and
software firms of every adjustable within
the model. DM merit was assessed
through the methods discussed by (Choi,
Wallace, & Wang, 2018). We validated
the questionnaire to ensure the validity of
the weighing mechanism. Because the
specific responders were American
workers, the questionnaire was translated
into Spanish, Portuguese, and English.
Any kind of inconsistency was talked
about until a good understanding was
achieved. We pretested the questionnaire
with twenty companies. Based on the
feedback, the method was improved and
enhanced, making the information much
more precise.
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Figure 1: Relation between BD and decision merit
Data collection
The target responders of this
analysis were senior middle or top
professionals through the provincial
degree of software companies in the
Americas, because of the detailed
knowledge of their firm's BD initiatives,
business strategies, and resource
allotment. We obtained the
communication listing of 2600 software
companies in North and South Americas.
The companies delivered an online link to
the survey questionnaire to the target
responders. We sent reminder email
messages to individuals who had not
even responded 2 weeks after distributing
the questionnaire. When 3 weeks, 526
questions were finished and returned.
Excluding forty-six questions with lacking
written content, 480 legitimate questions
have been gathered for the empirical
evaluation. In terms of firm sizing, 61.8%
of companies had under 1000 personnel,
and 38.2% had more than 500 workers.
Companies have been mentioned to
connect value to IT, with 62.9% of
companies owning more than twenty IT
workers. Inadequate workers accounted
for 35.0% of responding companies
regarding the use of BDA equipment.
People who had used BDA evaluation
resources for over two years have been
62.7%.
The distributions of responders:
45.3% had been males, 54.7% had been
females, 78.7% had been between twenty
and forty years of age, and 71.9% had an
undergraduate training.
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EMPIRICAL EVALUATION
AND RESULTS
Table 1: Attributes of the data
Traits
Choice
Frequenc
y
(%)
Gender
Male-gender
217
45.3%
Female-gender
263
54.7%
Age
20–30
225
46.9%
31–40
153
31.8%
41–50
78
16.3%
> 50
24
5.0%
Educatio
n
High school and below
4
1.7%
Junior college
18
7.5%
Bachelor’s degree
143
59.4%
Master degree and
above
30
12.5%
Common technique bias and
nonresponse bias assessment
We supervised the marker
adjustable method suggested by (Chon &
Kim, 2022) to deal with the typical
technique bias. The marker variable
experienced absolutely no relation with 1
or maybe more variables, therefore any
correlation between it and other variables
could be due to the typical technique
bias. With this research, we utilized
gender, a construct in theory not related,
as the marker variable. The correlation
between other variables and gender was
denoted by the common correlations (rM),
and it was viewed as a sign of
widespread approach bias. The end
result demonstrated that rM was 0.015,
and the differences between the adjusted
and basic correlations were fairly little ((∆r
<0.001), implying that the typical way bias
wasn't a problem in the data. The
discussed variations of endogenous
variables during the two designs were
additionally similar, so the path estimates
of the 2 designs were not statistically
distinct (χ2, p<0.101), suggesting
widespread approach bias was not an
issue within this research.
This analysis also examined
nonresponse bias by evaluating
premature responders with late
responders. For starters, the test was
divided into 285 initial responders and
Can big data analytics improve the quality of decision-making in businesses?
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Escuela de Posgrado Newman
fifty-five late responders. Next, a T-test
was carried out on the primary key
constructs, and the result exposed that
there was clearly simply no substantial
distinction within the ways on the
constructs between the two subgroups.
Chi-square assessments evaluating
earlier & late responders within
terminology of firm size (p<0.35)
disclosed no considerable response bias.
Consequently, we believe that
nonresponse bias was not a problem
within the model.
Table 2: Estimating bias
Base model
Model
Correlations
rM = 0.015
r (DAU, AC)
0.50**
0.50***
r (DAU, DMQ)
0.39**
0.38***
r (AC, DMQ)
0.44**
0.43***
Path Structure
β (DAU→ DMQ)
0.20***
0.19***
β (DAU → AC)
0.52***
0.51***
β (AC → DMQ)
0.31***
0.31***
SMC (AC)
0.33
0.32
SMC (DMQ)
0.27
0.26
DAU, BD analysis application; AC, analysis implementation; DMQ, decision- making
standard, ***p<0.001, **p< 0.01
Measurement model
The outcomes of element loading
demonstrated that 3 signs were below the
threshold of 0.700, such as DAU4,
DMQ1, DMQ4, and DMQ8, which
decreased the complete dependability
and validity. When the signs were
excluded, most signs were competent.
As Table 3 below shows, most signal
loadings ranged through 0.50 to 0.70, and
AVE1 scores ranged through 0.41 to
0.57, hinting the weighing mechanism
has good enough convergent validity.
Most VIF1 values were below ten and
between 0.98 and 1.45, suggesting that
multicollinearity was not an issue in the
constructs. The α ranged through 0.61 to
0.64, below the threshold of 0.70.
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Table 3: Validity and reliability
Factor
Item
Loading
VIF1
AVE1
CR1
α
BD analysis
application
DAU1
0.67
1.27
DAU2
0.67
1.32
0.49
0.67
0.63
DAU3
0.70
1.45
Data analysis
implementation
AC1
0.64
1.12
AC2
0.60
0.98
0.57
0.71
0.64
AC3
0.66
1.16
Decision-taking
standard
DMQ2
0.60
1.23
DMQ3
0.58
1.11
DMQ5
0.61
1.27
0.41
0.68
0.61
DMQ6
0.60
1.21
DMQ7
0.50
1.04
We additionally applied the
variable-single-trait ratio of correlations to
determine discriminant validity. The VST
must be less than 0.87 or even 0.92 or
even considerably lesser than one. The
end result is provided with Table. The
VSTs of variables were below the
suggested threshold of 0.92. Thus, the
weighing mechanism had excellent
discriminant validity.
Table 4: VST ratio of correlation
Factors
1. BD analysis application
2. Analysis implementation
0.69
0.00
3. Decision-taking standard
0.54
0.56
Structural model
We applied PLS 3.5 to check the
theory, and this method was also
selected for 3 reasons: The PLS method
continues to be popular around IS
investigation, which may evaluate the
accurate model fit and be used for DM
merit. Nor did the IT division sizing
influence the exploratory concept of
creating for merit of DM. We used the
emerging investigation design to check
out the consequences of BDA
implementation on BDA competencies
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and DM merit. PLS have advantages in
small to medium sample size evaluation.
With this analysis, the sample size of 480
was not big, and that is adequate for the
use on the PLS method.
PLS does not need the exact same
division of residuals. To analyze the
coefficients of kurtosis and skewness, we
discovered that the sample data did not
completely stick to the typical division.
Thus, we believe PLS was well suited for
this specific research. All the outcomes
are revealed with Table. Most hypotheses
have been supported. BDA
implementation had positive impacts on
DM merit (β = 0.23, p < 0.010),
supporting H1. It similarly had a good
effect on BDA competencies (β = 0.63, p
< 0.001), supporting H2. Data analytics
competencies had positive impacts on
DM merit (β 0.39, p < 0.001), supporting
H3. The variance interpretation rates of
BDA competencies and DM merit were
39.8% and 32.2%, respectively.
Additionally, we found firm size and IT
division as control variables to evaluate
the impact on decision marking merit (ß =
-0.011). We discovered the firm size did
not affect.
Table 5: Model evaluation
Relationship
Beta coefficient
BDA implementation→DM merit (H1)
0.23; (2.96), p<0.01
BDA implementation→Data analytics competencies (H2)
0.63; (16.00), p<0.001
Data analytics competencies→DM merit (H3)
0.34; (4.61), p<0.001
Arbitrating impact testing
We utilized the Sobel and
bootstrap testing to look at the arbitrating
impact of BDA features, and the
outcomes are revealed with Table. The
Sobel test demonstrated that BDA
competencies had significant arbitrating
consequences on the interactions
between BDA use and DM merit.
Bootstrap test benefits demonstrated that
under the ninety-five% confidence
interval, the confidence interval of the
effect of BDA use on DM merit did not
Mesbaul Haque Sazu, Sakila Akter Jahan
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feature zero, and the arbitrating outcome
size was 0.26. Thus, BDA competencies
have a partly arbitrating outcome
between BDA use and DM merit.
Table 6: Test of arbitrating effect
Path
Test
Standard
error
Confidence
interval (95%)
T-value
Standar
d error
P-
value
Conclusion
DAU→AC→
DMQ
0.28
0.03
[0.13, 0.39]
4.14
0.06
0.00
Supported
Robustness test
We supervised a robustness test to
confirm the results and acquire extra
insights. We embraced identical
strategies as did previous exploration by
talking about BDA implementation, data
analytics competencies, and DM merit as
composite constructs and reran the
model. An evaluation on the
measurement design revealed that
almost all VIF values were below 1.45,
hinting that multicollinearity was not an
issue. Additionally, all loadings have been
significant, indicating that just about all
composite signs must be kept. After that,
we carried out the confirmatory composite
evaluation to evaluate the goodness of fit
of saturated design. As a result, we
evaluated the standardized root squared
method, unweighted least squares,
inconsistency, and geodesic
inconsistency. The end result within Table
demonstrated that the SRMR was lower
compared to the threshold of 0.10; SRMR
and dULS had been in the ninety-five% of
bootstrap discrepancies, and dG was in
the ninety-nine% quantile of bootstrap
discrepancies. Generally, all the
outcomes exhibited good qualities for the
measures.
Table 7: Confirmatory composite analysis
Discrepancy
Value
HI
95
HI
99
Conclusion
SRMR
0.04
0.09
0.02
Supported
dULS
0.17
0.19
0.26
Supported
d
G
0.07
0.09
0.02
Supported
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DISCUSSION
We additionally approximated the
beta coefficients and significance degree
of hypothetical interactions. As revealed
in Table, all the hypotheses were backed,
and the end result wasn't significantly
different from the earlier discussed major
results. We additionally evaluated the
effect size (f2) and R2 values of
hypothetical interactions. The R2 values
of the endogenous variables have been
0.43 and 0.31, indicating excellent
explanatory strength. F2 values ranged
through 0.08 to 0.65, indicating weak to
large impact measurements within the
hypothesized important interactions. We
also examined the goodness of model fit
for the structural design. SRMR values for
each unit were below the threshold of
0.80, and SRMR, while dULS and dG
were under the ninety-five% quantile of
bootstrap inconsistencies, implying a
good fit between the product and
information. To sum up, all the results
suggest the structured model was
suitable and supported the former
outcomes.
Additionally, we estimated just one
extra study design to look for the
robustness of the suggested analysis
model. Within the model, we assumed
that BDA competencies favorably affect
BDA use. The empirical outcomes
demonstrated that the values of DULS
and SRMR were most unqualified. As a
result, the alternative design wasn't
statistically of higher merit compared with
the complete believed unit fit on the
suggested analysis version, indicating a
gain which the proposed research model
was proper.
The objective of this analysis was
to explore the impact of BDA use on DM
merit. From a computational perspective,
this analysis examined the effect of BDA
use on DM merit by using BDA
competencies. The results showed that
BDA implementation had positive impacts
on DM merit, and BDA competencies had
positive impacts on DM merit, partly
arbitrating impact on the relationship
between BDA use and DM merit. Table
displays a summary of empirical test
benefits.
Mesbaul Haque Sazu, Sakila Akter Jahan
20
Escuela de Posgrado Newman
Table 8: Hypothesis test result
Path
Path coefficient
T value
P-value
Conclusion
H1: DAU→DQ
0.23
2.96
p < 0.01
Supported
H2: DAU→AC
0.63
16.00
p < 0.001
Supported
H3: AC→DMQ
0.34
4.61
p < 0.001
Supported
For starters, we discovered that
BDA implementation had considerable
impacts on DM merit. BDA
implementation was significantly and
favorably associated with DM merit
(β=0.23, p < 0.01), therefore supporting
H1. This finding might be attributable to
the point where BD implementation can
help companies acquire the confidence
that they can easily boost firm choices by
improving data analysis and changing
from experiential DM. BDA
implementation allows companies to
create complete implementation of BD,
like production data, processing data,
blood circulation data, and product sales
data, to offer insights for DM, improving
DM merit. Next, we discovered that BDA
implementation had a huge impact on
BDA competencies. BDA implementation
was positively and significantly
associated with BDA competencies (β
0.63, p < 0.001), supporting H2. This
suggests that BDA implementation plays
a crucial role in the development of BDA
competencies. BDA implementation
allows companies to approach and sense
the information and changes them into
understanding for staff members. In
addition, advanced data analytics
equipment allows companies to get
comprehensive information and in-depth
information about customers,
competitors, and their partners. In
accordance with the resource-based
perspective, the use of BDA applications
in companies will be viewed as a valuable
learning resource, which is usually used
to create and boost BDA features within
the companies. Thus, the results confirm
the idea that BDA could be used to
operate a vehicle BDA capability.
Finally, we learned that BDA
competencies had considerable impacts
on DM merit. BDA had been positively
Can big data analytics improve the quality of decision-making in businesses?
21
Escuela de Posgrado Newman
CONCLUSION
and significantly associated with DM merit
(ß 0.34, p < 0.001), therefore supporting
H3. This finding is consistent with the
normally believed opinions regarding the
results of BD, specifically that BDA
proficiency improves the correctness and
precision of choices. This end result
suggests that BDA competencies can
help companies create and implement
proper choices fast and look for methods
to change and innovate rapidly. In
addition, this analysis discovered that the
use of BDA impacted DM merit by
developing BDA features, revealing the
key arbitrating impact of BDA
competencies. This suggests that if BDA
matches competencies and resources, it
will provide great results to companies.
Theoretical contributions
For starters, from a theoretical
viewpoint, looking at the effect of BDA
use on DM merit is a crucial investigation
subject for extant IS investigation.
Nevertheless, there is simply no opinion
on whether BDA implementation
advances or perhaps hampers the DM
merit of businesses. Most prior
investigation claims that BDA
implementation has a good impact on DM
merit (Chon & Kim, 2022). Nevertheless,
several researchers have discovered the
presence associated with a huge
information paradox that improved BDA
use in several companies is related to no
or even decreased DM merit.
Researchers claim that the usage of BDA
will lead to know-how hiding, which
includes evasive hiding and dumb, which
adversely affects DM merit, (Koot, Mes, &
Iacob, 2021). In a nutshell, even though
most earlier analyses discover that the
important data decision relation is usually
good across scientific studies, they also
reveal that BDA use may decrease DM
merit within certain circumstances (Kim,
Choi, & Byun, 2019). Consequently, there
is still a small knowledge of BDA use and
how it pertains towards the merit of firm
choices. To deal with the gap, this
analysis examined the effect of BDA use
on the DM merit of businesses. The end
result suggests that BDA use not only
had a beneficial impact on DM merit, but
also favorably impacted BDA
competencies and consequently
enhanced DM merit. This locating
improves scientific studies of BDA use
and DM merit, in case a novel lens for
Mesbaul Haque Sazu, Sakila Akter Jahan
22
Escuela de Posgrado Newman
companies using large data analytics to
enhance DM merit.
Next, there's proof that utilizing
BDA to enhance firms' DM merit is not
necessarily uncomplicated. Not to
mention, there might be a concealed
process that interprets BDA use directly
into DM merit. Nevertheless, the internal
mechanism remains unclear. This
analysis contributes to the increasing
expertise in BDA by conceptualizing BDA
competencies as responsible for firms'
responsiveness to abrupt alterations and
industry volatility. Particularly, each
managerial capability and specialized
competencies are game-changing,
including information analytical
competencies to companies (LaBrie,
Steinke, Li, & Cazier, 2018). Companies
can improve the data analytics
competencies by enhancing the technical
and managerial competencies of staff
members, allowing a firm to sustainably
do enterprise level realizing, seizing, and
reconfiguring external and internal tasks.
This perspective can explain just one
probable reason behind the disagreeing
results related to the company's
importance of BDA in companies. This
analysis uncovers the mechanism that
influences the effect of BDA use on DM
merit and plays a role in understanding
how BDA use can boost firms' DM merit
by using information analytical
competencies.
Finally, this supplements the
computational ability concept by using it
to elaborate on how BDA implementation
impacts BDA features and consequently
DM merit. Although studies have
analyzed the link between BDA
competencies and DM merit, there is still
a small knowledge of BDA use and the
way they connect with BDA
competencies. Our results suggest which
BDA implementation had a good effect on
BDA features, indicating which BDA use
can promote BDA competencies. This
locating improves managing scientific
studies of BDA use and BDA features by
providing a clear knowledge of how BDA
can allow BDA features via improving
large data analytics use (Rukanova, et
al., 2021).
Managerial contributions
Coming from a useful viewpoint,
our results suggest that to better achieve
decision efficiency and decision
effectiveness, companies must
Can big data analytics improve the quality of decision-making in businesses?
23
Escuela de Posgrado Newman
completely use BD analytics instruments
to fasten the transformation from
standard to data driven DM. Companies
can use BD analytic resources to
incorporate directories, identify
modifications, determine competitive
advantages, reorganize the firm's
resources, and finally make high merit
choices. This has been confirmed around
training, and there are lots of profitable
instances to bring on. (Choi, Wallace, &
Wang, 2018) found for example, Pagoda,
a top berry firm for Americas, signed the
"Pagoda BD project" in 2018 to create a
list information facility. Pagoda and then
strengthened the promotion of it of the
positive aspects of utilizing BDA to
improve the understanding of workers,
offer opportunities for personnel to
discover how to use BD analytics, and
acquire and enhance technical
competencies and managerial skills to
come down with BDA. In the pandemic,
Pagoda staff members carried out need
forecasting, development layout,
inventory management, merit
management, and public viewpoint,
overseeing various information evaluation
methods. Product sales increased by
twenty% season above season due to
these careful choices. Therefore,
companies must acquire strategic
blueprints for BDA implementation, offer
specialized awareness education, and
motivate personnel to use BD analytics
resources for everyday labor (Chon &
Kim, 2022).
Next, as BDA competencies have
positive effects on DM merit, the
advancement of BDA competencies
cannot be ignored. BDA competencies
can help companies create educated
conclusions, recognize threats and
opportunities, and fine tune functions
(Rozario & Issa, 2020). When the biggest
software firm of Shanghai province,
Weiming group has put in a lot in the
improvement of BDA features and
exercise workers in company and
information evaluation technologies. The
BDA competencies help Wens
comprehend and understand the
modifications in deep market competition,
market capacity, and market demand,
maximizing profits. To enhance BDA
features, companies must strengthen
their IT infrastructure by boosting
methods, introducing superior evaluation
programs, and following large capacity
information storage space equipment to
ensure BDA use (LaBrie, Steinke, Li, &
Cazier, 2018). Likewise, companies must
Mesbaul Haque Sazu, Sakila Akter Jahan
24
Escuela de Posgrado Newman
REFERENCES
get the specialized skills required for BDA
use by recruiting cutting edge IT skills
and enhancing training courses for
existing specialists. Therefore, they must
offer a skill assurance just for the
effective implementation of BD and
reaction to specialized crises.
Future research and research
limitations
This analysis has many
unavoidable limits. The variance analysis
speed of DM merit was 31.5%, implying
which other factors might impact DM
merit, a problem that will require
additional exploration down the road
(Chon & Kim, 2022). Although this
analysis was based on a fixed style and
cross-sectional data, utilizing BDA to
boost firms' DM merit is a long-range
objective. Therefore, it is important to
carry out longitudinal empirical evaluation
within potential studies. The results
propose that succeeding scientific studies
must investigate not only the effect of
BDA use on DM merit, but also likely
elements that could moderate this
influence. Potential study could construct
on the findings by looking at the possible
role of BDA features in arbitrating the
effect of BDA implementation on various
other firm results and firm resilience.
Acknowledgement
None
Conflict of Interest
None
Author Contribution
Both authors contributed equally to the
research paper
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