Big Data Analytics Capability and Firm Performance in the Hotel
Industry: The Mediating Role of Organizational Agility
MUHAMAD LUQMAN KHALIL1, NORZALITA ABD AZIZ1, AHMAD AZMI M. ARIFFIN1,
ABDUL HAFAZ NGAH2
1Graduate School of Business, Universiti Kebangsaan Malaysia,
43600 UKM Bangi, Selangor,
MALAYSIA
2Faculty of Business, Economics and Social Development, Universiti Malaysia Terengganu,
Kuala Terengganu,
MALAYSIA
Abstract: - The emergence of the Covid-19 pandemic and restrictions on international mobility have negatively
impacted the tourism market. Tourism players, particularly the hotel industry, have turned to big data analytics
to mitigate uncertainties and offer better products and services. Nonetheless, the central question for researchers
and practitioners is how the usage of big data analytics can help the hotel industry improve firm performance.
Drawing on the resource-based view and dynamic capability theories, this study analyses the relationship
between big data analytics capability and firm performance in the hotel industry. This study expands the current
research by examining the role of organizational agility in mediating the relationship between big data analytics
capability and firm performance. To empirically test the research model, the author used survey data from 115
star-rated hotels throughout Malaysia. Through partial least square equation modeling, the findings revealed
that big data analytics capability positively affects organizational agility and firm performance. The result also
demonstrated that organizational agility mediates the relationship between big data analytics capability and
firm performance. This study can also guide hoteliers to identify resources required to build big data analytics
capability and further highlight the significance of organizational agility in improving firm performance in the
hotel industry.
Key-Words: Big data analytics capability, Firm performance, Organizational agility, Hotel industry
Received: May 25, 2022. Revised: December 16, 2022. Accepted: January 17, 2023. Published: February 17, 2023.
1 Introduction
The growing digitalization of business processes has
led to the abundance and viability of a large volume
of data for analysis. A large volume of data is one of
big data's key characteristics, including other
characteristics such as variety, velocity, veracity,
and value, [1]. Big data can be analyzed through
statistical techniques and analytical tools to generate
valuable business insight and predict future patterns,
[2]. Consequently, the generated insight and
information can eliminate the guesswork in
decision-making and assist company executives in
improving business operations, [3]. The usage of big
data analytics also fosters innovation and efficiency
in business operations that ensure an organization's
long-term economic sustainability, [4], [5]. By
adopting big data analytics, firms can enhance
business decision-making, ultimately boosting their
competitive advantage, [3]. According to IDC, [6],
the global revenue for big data and analytics
solutions was projected to surge around US215.7
billion in 2021, an increase of 10.1% from 2020.
Hence, the reliance on data-driven decisions is
essential for firms to maintain business resiliency
during uncertain periods.
Specifically, the emergence of the Covid-19
pandemic and limitations on international mobility
has negatively affected the tourism sector, [7]. The
travel and tourism sector reported a loss of US$ 4.5
trillion globally in 2020, [8]. It is concerning given
that the tourism industry is a significant contributor
to the growth of the national economy and GDP.
The economic benefits of the tourism industry
include the generation of export revenue, local
employment providers, and a source of foreign
exchange, [9]. Big data analytics can assist firms in
mitigating uncertainties, particularly during the
Covid-19 pandemic, [10]. According to a report by
UWNTO & ADB, [11], the usage of big data can
aid in the recovery of tourism players by assisting in
improving their products and services. One of the
key tourism players is the hotel business, which is
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DOI: 10.37394/23207.2023.20.40
Muhamad Luqman Khalil, Norzalita Abd Aziz,
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considered as one of the most significant drivers of
employment and economic revenue in the tourism
industry, [12].
Even though big data analytics benefits the
tourism industry, there are limited studies
examining firms' capability in its application and its
impact on firm performance in the tourism industry.
The majority of previous research that examines the
relationship between big data analytic capability
(BDAC) and firm performance is primarily
conducted in the IT, technology, and manufacturing
industries, [1], [2], [4], [13]. According to [14], the
gap in firms' capability to use big data analytics
warrants further research to assess the value of big
data investment against firm performance. Hence,
this study will examine the relationship between
BDAC and firm performance centered on the
tourism context. This study’s findings could guide
hoteliers on the importance of leveraging BDAC in
daily hotel operations. It is important as the hotel
industry is still reeling from past losses due to the
COVID-19 pandemic.
The tourism industry is susceptible to changes
in the economic and environmental landscape. Prior
studies have reported a growing interest in studying
organizational agility in unstable periods,
particularly in the tourism landscape, [15], [16].
Agility relates to the adaptability of organizations to
prosper in challenging periods or environments to
improve firm performance, [17]. Consequently, it is
important to examine how agile the hotel sector is in
applying big data analytics to improve firm
performance. In addition, several studies have
indicated that BDAC indirectly affects firm
performance through intermediate variables, [1],
[18]. The authors in [3] further argue that BDAC is
not an adequate prerequisite to influence firm
performance, and this relationship must be subject
to other factors. Hence, this study intends to analyze
the mediating effect of organizational agility in the
relationship between BDAC and firm performance
in the tourism context. To conclude, this study will
address the following two research questions based
on the stated research gap:
RQ1.What is the impact of BDAC on firm
performance in the tourism context?
RQ2. Is organizational agility a mediator in the
relationship between BDAC and firm performance?
Based on the resource-based view and dynamic
capability theories, this study will address the
research questions highlighted above and further
enrich big data studies in tourism and hospitality
literature. Furthermore, [19] reported that big data
literature in tourism is still lacking in theory-based
research, and further explanations on the impact of
big data on hotel performance based on theoretical
perspectives are warranted.
2 Theoretical Background
2.1 Resource-based View
The resource-based view (RBV) theory measures
the strategic value of organizational resources and
explains why some firms exhibit superior
performance compared to other firms, [20]. This
theory states that firms managing their resources
and capabilities would gain a competitive advantage
based on two fundamental assumptions. The first
assumption is based on resource heterogeneity,
which assumes firms own diverse resources
contributing to their competitive advantage, even
though they may compete in the same industry, [21].
The second assumption relates to the firms' unique
and long-lasting resources, which competitors find
difficult to obtain and develop, [22]. The second
assumption is coined as resource immobility. These
resources include tangible and intangible assets,
which could provide a competitive advantage for
firms if these resources are rare, valuable, imperfect,
imitable, and non-substitutable, [22].
Furthermore, firm resources can be combined to
conduct a set of coordinated tasks to achieve
specific purposes, [23]. This action is referred to as
organizational capability, and it could become a
point of competitive advantage as it is difficult to
trade, copy and substitute. Competitive advantage
can be achieved when organizations manage and
exploit their capabilities and resources.
2.2 Dynamic Capability
The dynamic capability (DC) theory has been used
extensively to understand the differential
performance of an organization in a dynamic
setting. This theory extends the RBV theory, which
emphasizes the organization’s ability to adapt and
adjust its resources to attain a competitive advantage
in a dynamic business environment, [24]. The DC
theory is different from the RBV theory as the latter
emphasizes utilizing resources and capabilities to
achieve a competitive advantage based on a static
market only, [2]. In addition, RBV theory does not
sufficiently explain how competitive advantages and
disadvantages can evolve. However, the DC theory
covers the limitations of the RBV theory by
considering the development of resources and
capabilities over time, [24]. Based on the seminal
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paper in [25] the DC theory is further defined as the
firm's capacity to combine, develop, and rearrange
firm resources to respond to opportunities and
threats in a fast-changing environment.
2.3 Big Data Analytic Capability (BDAC)
There are various definitions of big data analytics
provided in the literature. Big data analytics has
been described as the application of analytical tools,
data mining, statistics, artificial intelligence, and
machine learning to generate significant patterns
from the data analyzed, [26]. According to [4], big
data analytics is a multidisciplinary field that
employs computer science, data science, statistics,
and mathematical models to collect and evaluate
data methodically. The latest definition by IBM
states that big data analytics is the "use of advanced
analytic techniques against very large, diverse big
data sets that include structured, semi-structured and
unstructured data from different sources, and in
different sizes from terabytes to zettabytes”, [27].
Despite the differences in meaning, the purpose of
its use remains the same. Big data analytics is
applied to determine meaningful insight and
patterns, which can improve decision-making in a
firm, [28], [29].
The current research extends the knowledge by
examining big data analytics as an organizational
capability. Scholars have provided different ranges
of BDAC in the literature. According to Mikalef,
BDAC is defined as "the ability of the firm to
capture and analyze data towards the generation of
insights by effectively deploying its data,
technology and talent through firm-wide processes,
roles and structure", [29]. Meanwhile [13] defined
BDAC as the capability to gather, incorporate and
utilize organizations' big data-specific resources.
The majority of the previous studies have
conceptualized BDAC as a multidimensional and
hierarchical construct. Several studies
demonstrated that BDAC is measured through
dimensions such as big data analytics
infrastructure flexibility, big data analytics
management capability, and big data analytics
personal expertise, [17], [30]. In other studies,
BDAC comprises several organizational resources
that include tangible (data, technology, basic
resources), intangible (data-driven culture and
organizational learning), and human skills (technical
skills, managerial skills), [3], [28], [31]. The
conceptualization of BDAC is grounded on RBV
theory, whereby the usage of firm resources and
capabilities will result in a competitive advantage.
The following subsections explain the dimensions of
BDAC.
2.3.1 Data
Data in the term 'big data' encompass structured,
unstructured, and semi-structured data, which are
large in volume and fast-moving, [13].
Organizations seek to boost their competitive
advantage by effectively handling internal and
external data so that they can make effective
business decisions, [32].
2.3.2 Technology
Firms need technological resources that can
compile, distribute, and analyze big data to generate
insight, [18]. Technological resources such as non-
relational databases, middleware, and data
warehousing are able to extract, incorporate and
analyze big data so that actionable insight can be
formulated to assist in decision-making, [33]. In
addition, firms are now moving away from
relational databases to open-source software
frameworks such as Apache Hadoop. This software
provides distributed storage and allows parallel
processing of big data based on a Java-based
framework, [34].
2.3.3 Basic Resources
Firms need basic resources such as financial funding
so that they can invest in technology and
infrastructure to support big data initiatives, [35].
This investment requires time to provide the desired
result, [13]. Thus, firms need to allocate appropriate
time and funds for ventures into big data analytics.
2.3.4 Technical Skills
There are an increasing number of firms employing
staff with technical skills in big data. This technical
skill relates to the competency and know-how of
employees in utilizing data and technology to obtain
insight for company decision-making, [13]. A case
in point is skills related to data extraction, data
cleaning, and statistical analysis, [29].
2.3.5 Managerial Skills
Managers need to understand the insight extracted
from big data to predict upcoming business growth
and effectively apply the insight generated in
making business decisions, [2], [13]. Managers also
need to work and coordinate with other managers in
the firm, suppliers, and customers in implementing
big data-related initiatives, [36].
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2.3.6 Data-Driven Culture
Employees from upper management, middle
management, and lower levels should make any
business action based on information gleaned from
big data analytics and rely less on their experience,
[13]. Employees also can obtain necessary
information when data-driven culture is embedded
in the internal process of the firm's decision-making,
[29].
2.3.7 Organizational Learning
Organizational learning describes how organizations
search, retain, distribute, and utilize knowledge,
[13]. According to [4], the organizational learning
encourages employees to upgrade their knowledge
and enhance competitive advantage in firms.
2.4 Organizational Agility
Organizational agility is generally defined as the
capability of firms to detect and respond to changes
in the market with ease and speed, [37]. Similarly,
the article in [38] relates organizational agility as the
capacity of the organization to respond quickly to
changes and opportunities in the market.
Researchers have argued that organizational agility
is the new management paradigm in which
organizations are subjected to fluctuations in
technology, customers, competitors, and climate,
[16]. Due to changes in the environment, firms
cultivate the ability to be agile in responsiveness,
speed, and flexibility to maintain competitive
advantage. According to [39], agile firms will
survive and thrive in a globalized business setting as
they are more aligned to increase their revenue and
profit margin. Agile firms also can react rapidly to
client demand, unexpected changes, and
opportunities in the market, [40]. Consequently,
firms can improve their business performance, [41].
Several studies have claimed that
organizational agility is a part of dynamic
capability, [18], [42], [43]. Organizational agility is
viewed as a specific dynamic capability that assists
firms in thriving in challenging environments,
which competitors cannot easily replicate, [42].
Numerous previous studies have conceptualized
organizational agility into several dimensions. These
dimensions include market responsiveness agility
and operational adjustment agility, [30], customer
responsiveness, operational flexibility, and strategic
flexibility, [38]. Additionally, other studies have
conceptualized organizational agility as a
unidimensional construct, [18], [44].
2.5 Firm Performance
Across the literature, there appears to be a
consensus on the definition of firm performance
among researchers. Firm performance is assessed
based on a series of performance criteria in
comparison with fellow rival firms. According to
[45], the firm performance is defined as the extent to
which a company performs better than its rivals.
Similarly, [46] point out that the examination of
inter-company comparison is vital in measuring firm
performance. Many studies have studied firm
performance as a multidimensional construct
consisting of financial and non-financial indicators,
[47], [48]. For example, firm performance is
measured based on financial returns, customer
perspective, and operational excellence, [49]. Also,
several studies on big data have categorized firm
performance into two separate and distinct
dimensions, namely financial performance and
market performance, [2], [13]. In these studies,
market performance is measured by market shares,
entrants to new markets, the introduction of product
services, and its success rate. In contrast, operational
performance relates to the firm's productivity, profit
rate, financial goal, and return on investment. The
research on the relationship between BDAC and
firm performance in the tourism literature is still
limited and warrants further study to improve
understanding and generalizability.
3 Research Model & Hypotheses
The research model is developed by integrating both
RBV and DC theories (Fig.1). Neither theory can
theoretically support this study’s empirical result on
its own. Both theories are required to explain the
direct and mediating relationships in the research
model. These include the direct relationship
between BDAC and firm performance and between
BDAC and organizational agility. This study also
examines the role of organizational agility in
mediating the relationship between BDAC and firm
performance. The DC theory complements the RBV
theory as the former examines firms’ use of
resources to achieve competitiveness in a highly
volatile market.
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Fig. 1: Research Model
3.1 BDAC and Firm Performance
Every firm aims to maximize shareholder wealth by
being competitive and profitable. Thus, firms seek
to use every advantage, including using big data, to
remain competitive, [28]. The authors in [50] stated
that companies that applied big data in their daily
operation could experience an increase of 5% in
productivity and 6% in profitability in comparison
to their rivals. Using big data analytics can assist
companies in reducing operating costs and
improving their product and services, [4].
Additionally, the market insight generated by big
data analytics allows firms to focus on higher profit
investment, [51]. Nevertheless, spending on big data
does not necessarily lead to successful business
outcomes, [35]. Several studies have pointed out
that big data investment does not yield the intended
results due to a lack of data-driven culture, [13],
[52].
Companies that efficiently manage their
resources to build BDAC would be able to improve
firm performance, [2]. Based on RBV theory, firms'
resources and capabilities, which might be valuable,
rare, imperfectly imitable, and not substitutable, can
create a competitive advantage. Significantly, most
previous studies demonstrate that BDAC has a
direct positive relationship with firm performance,
[1], [2], [31]. Other studies also reported that BDAC
significantly affects firm performance, where the
latter construct is signified by market and
operational performances, [2], [13]. Hence, the
following hypotheses are proposed:
H1: BDAC is positively related to market
performance.
H2: BDAC is positively related to operational
performance.
3.2 BDAC and Organizational Agility
Previous empirical studies have established that IT
capabilities are the enablers of organizational
agility, [37], [39]. These studies show the
importance of firms' capability to leverage IT-based
resources, which positively impact organizational
agility. Nonetheless, there is a shift toward
examining BDAC as the antecedent of
organizational agility in the literature. Big data
analytics can generate market insight that would
assist firms in identifying and reacting quickly to
any market changes in terms of challenges and
opportunities, [18]. The information generated
would also assist firms in decision-making and
managing risk during uncertain periods, [30].
Grounded on the DC theory, organizations operating
in a dynamic market would continuously
reconfigure and reshape their resources to achieve a
competitive advantage.
A study, [53], highlighted that marketing-
enabled data analytics capability has a significant
positive relationship with organizational agility. In
this study, the insight generated from data analytics
would enable firms to react to emerging customer
demand and potentially capitalize on new business
opportunities. Several studies also reported that
BDAC positively affects organizational agility, [17],
[54]. Thus, the following hypothesis is developed in
response to these studies.
H3: BDAC is positively related to organizational
agility.
3.3 Organizational Agility and Firm
Performance
Several studies have reported that organizational
agility positively influences firm performance, [39],
[55], [56]. Agility can boost performance by
optimizing the firm's range of reactions toward
market changes and reducing risk and uncertainty.
This range of responses includes expansion into new
territories, multiplying the rate of innovation, and
making changes in products and services based on
customer demand, [37]. As a result, agile firms can
increase market share, reduce cost, and exhibit
higher revenue and profitability. Similarly, [38]
found that organizational agility positively affects
firm performance. Firms that are agile and
responsive to market changes would be able to
formulate an effective business strategy to increase
their competitive advantage in the market.
Underlining the DC theory, organizational agility is
recognized as a specific dynamic capability that can
explain why firms reconfigure and reshape their
resources to achieve competitive advantage in a
H2
H1
H5
H6 H7
Big Data Analytics
Capability
Operational
Performance
Market
Performance
Organizational
Learning
TechnologyTec
Firm Performance
ddddddddPerfor
H4
Data-Driven
Culture
TechnologyTec
Managerial
Skills
Technical
Skills
Basic
Resources
Data
Technology
H3
Organizational
Agility
Fig.1 Research Model
H3
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dynamic environment, [17], [57]. The next
postulated hypotheses are:
H4: Organizational agility is positively related to
market performance.
H5: Organizational agility is positively related to
operational performance.
3.4 Mediating Effect of Organizational
Agility
Mediation relates to the concept that the effect of
BDAC on firm performance is conveyed by
organizational agility, [58]. The authors in [49]
called for further research on the mediating effect of
firm agility on the relationship between big data and
firm performance. Firms can improve decision-
making based on the insight generated through big
data and rapidly react to opportunities and threats in
the market. Consequently, agile companies can
improve their bottom-line performance, [47]. In a
dynamic business environment, the DC theory
supports the mediating role of organizational agility
as it emphasizes the company’s capability to adapt
and adjust its resources to achieve a competitive
advantage, [24]. Following the prior discussion,
there is evidence in the literature supporting the
positive effect of BDAC organizational agility.
Likewise, organizational agility significantly affects
firm performance, according to prior studies.
Therefore, it is proposed that organizational agility
is the mediational pathway through which BDAC
affects firm performance. Our next postulated
hypotheses are:
H6: Organizational agility mediates the relationship
between BDAC and market performance.
H7: Organizational agility mediates the relationship
between BDAC and operational performance.
4 Research Methodology
4.1 Instrument Design
The data collection was conducted by distributing
self-administered questionnaires. The questionnaire
items of the variables from this research were
adapted from established articles from various
journal publications. Big data analytics capability
comprises seven dimensions: basic resources, data,
technology, technical skills, managerial skills, data-
driven culture, and organizational learning, adapted
from, [4], and, [29]. Organizational agility was
adapted from, [18], and, [37], while both market
performance and operational performance were
adapted from, [2], and, [13]. The dependent variable
was measured using a five-point Likert scale,
whereas the independent and mediating variables
were measured using a seven-point Likert scale.
Using two different Likert scales reduces the
common method bias as the data collection was
based on a single source and a single respondent.
Common method bias can be described as the
resultant bias due to independent and dependent
variables measured using the same source or
method, [59].
The developed questionnaire had gone through
pre-testing before the actual data collection. This
pre-test was conducted by four academic reviewers
and another four respondents from the hotel
industry. The pre-test was performed to review the
clarity of the measurement item and confirm
whether the target respondent could comprehend the
question, [60]. Based on the comments from the pre-
test, the survey was refined by amending the
questions' wording, layout, and format.
4.2 Sampling and Data Collection
This study examines the relationship between
BDAC and firm performance in the tourism context.
Hence, the fitting unit of analysis for this study is
hotel organizations. The respondents of this research
came from middle to upper management of star-
rated hotels in Malaysia. Malaysia is ranked second
behind Thailand for the highest international tourist
arrivals in Southeast Asia, with 26.1 million tourist
arrivals in 2019, [61]. Since this research is focused
on the hotel industry, applying the purposive
sampling method was suitable for this study. This
study focused on hotels rated 3-stars and above, as
hotels with a higher star rating are more likely to
have more professional and qualified staff and more
inclined to apply innovation activities in their daily
operation, [62]. Thus, it is more likely that higher-
rated hotels would utilize big data analytics in their
daily operation. Out of the 180 hotels that were
contacted, 115 gave consent to take the survey,
yielding a response rate of 64%. Among the hotels
that participated, 76 respondents (66%) came from
5-star hotels, followed by 33 respondents (29%)
from 4-star hotels, and six respondents from 3-star
hotels (5%).
The sample size of the study was determined
using the G*Power application. The G*Power is an
independent program frequently used in social and
behavioral studies to conduct statistical tests, [63].
Based on the application, the calculated minimum
sample size was 103. As recommended by [64] the
calculated value was based on seven predictors from
the research model at medium effect size and the
power of 80%. Since the number of responses
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collected was higher than the minimum sample size,
this study proceeded with the data analysis.
5 Data Analysis and Results
Since the data collection was based on a single
source, the collected data may be subject to
common method bias. Full collinearity testing was
performed to remedy the issue, [65]. In this test, all
the constructs were regressed against a common
construct, and it showed that there was no bias from
the data. The VIF values of all constructs were
below the recommended maximum limit of 5.
Furthermore, as recommended by the authors in [66]
this study examined the multivariate skewness and
kurtosis, and the results indicated that the data
collected was not multivariate normal with Mardia's
multivariate skewness (β = 3.184, p< 0.01) and
Mardia's multivariate kurtosis (β = 26.779, p< 0.01).
The data analysis was conducted using Smart partial
least squares (Smart PLS) software, which is a non-
parametric analysis software.
The application of PLS-SEM is also ideal for
analyzing formative constructs and hierarchical
models in the theoretical framework, [67]. There are
two main stages when applying PLS-SEM as the
method of data analysis. The first stage is the
measurement model analysis, which shows the
association between the latent variable and its
measurement items, [68]. After the measurement
model is verified, the second stage, the structural
model, is analyzed to test the hypotheses, [67].
5.1 Measurement Model
There is both reflective and formative construct in
the research model, which has different assessment
criteria. As for the reflective construct, two types of
validity need to be assessed: convergent and
discriminant. Convergent validity is confirmed
when a particular item measures the construct that it
is supposed to measure, [67]. Convergent validity
was achieved as all loadings exceeded the minimum
value of 0.5. Plus, all the average variance extracted
(AVE) was larger than the minimum value of 0.5,
and all composite reliability (CR) was larger than
the minimum value of 0.7. Table 1 depicts the value
of all the loadings, CR and AVE, which met the
minimum threshold value to proceed with the
discriminant validity. The discriminant validity
would test the degree to which latent variables are
exclusive and not represented by other variables
[68]. As depicted in Table 2, the values of the
Heterotrait-Monotrait ratio (HTMT) of all ten
constructs were lower than the ceiling value of 0.9,
which confirms the discriminant validity of this
study, [69].
Table 1. Convergent Validity
Constructs
Items
Loadings
CR
AVE
Basic
Resources
BR1
0.976
0.974
0.950
BR2
0.974
Data
D1
0.941
0.944
0.895
D2
0.951
Technology
T1
0.915
0.953
0.836
T2
0.936
T3
0.895
T4
0.912
Technical
Skills
TS1
0.855
0.968
0.885
TS2
0.974
TS3
0.970
TS4
0.958
Managerial
Skills
MS1
0.962
0.978
0.918
MS2
0.970
MS3
0.972
MS4
0.927
Data-Driven
Culture
DDC1
0.847
0.919
0.739
DDC2
0.841
DDC3
0.897
DDC4
0.851
Organizational
Learning
OL1
0.851
0.950
0.827
OL2
0.933
OL3
0.930
OL4
0.921
Organizational
Agility
OA1
0.791
0.916
0.687
OA2
0.843
OA3
0.862
OA4
0.902
OA5
0.736
Market
Performance
MP1
0.945
0.964
0.870
MP2
0.929
MP3
0.958
MP4
0.899
Operational
Performance
OP1
0.945
0.973
0.898
OP2
0.953
OP3
0.945
OP4
0.948
Note: To get better discriminant validity, item D3 was
dropped
BDAC was measured as a type II reflective-
formative higher-order construct and required
different criteria for examining the formative
measurements. Firstly, the convergent validity was
assessed using global single-item measurement
through redundancy analysis, which produced a path
coefficient higher than the minimum limit of 0.7, as
shown in Table 3. Convergent validity is used to
examine the extent to which the measurement items
correlate with other items measuring the same latent
variable, [70]. Subsequently, the formative
measurement must be assessed on collinearity issues
so that there would not be any two or more
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DOI: 10.37394/23207.2023.20.40
Muhamad Luqman Khalil, Norzalita Abd Aziz,
Ahmad Azmi M. Ariffin, Abdul Hafaz Ngah
E-ISSN: 2224-2899
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measurements that have high collinearity with each
other, [68]. The VIF values of all the formative
measurements were lower than the ceiling value of
5, [67]. Lastly, the formative measurements needed
to be assessed individually on their significance and
relevance through the bootstrapping technique, [70].
Based on Table 3, the lower-order constructs of
Data, Technology, Managerial Skills, Technical
Skills, and Organizational Learning were not
significant based on the outer weight. Nonetheless,
these constructs were kept in the model as all five
had significant outer loading.
Table 2. Discriminant Validity
Table 3. Measurement Properties for Higher-order
Construct
Higher
Order
Lower
Order
Convergent
Validity
VIF
Weight
Sig.
BDAC
BR
0.873
4.684
0.723
0.002
D
4.610
-0.246
0.179
T
4.023
0.190
0.166
TS
4.075
0.254
0.094
MS
3.744
-0.200
0.175
DDC
2.299
0.355
0.005
OL
1.797
0.063
0.166
Note: BR= Basic Resources; D= Data, T= Technology;
TS=Technical Skills;, MS=Managerial Skills; DDC=
Data-driven Culture; OL=Organizational Learning.
5.2 Structural Model
Following confirmation of the measurement model,
the next stage was to assess the structural model.
The hypotheses developed in the research model
were tested by looking into the path coefficient,
standard errors, t-values, p-values, and confidence
interval between the lower and upper levels. Before
performing the analysis, the collinearity issue was
checked in the structural model. Table 4 shows that
all VIF values were well below the threshold value
of 3.3, as recommended by the authors in [71]. This
result showed that the collinearity issue was not
significant in this study.
Subsequently, following the suggestion of [70]
this study analyzed the structural model using the
bootstrapping technique with a resampling of 5000.
For the t-test, all five direct relationships were found
to have t-values >2.33, thus significant at a 0.01
level of significance, as indicated in Table 4.
Specifically, BDAC Market Performance (β =
0.412, p< 0.01), BDAC Operational Performance
(β = 0.409, p< 0.01), BDAC Organizational
Agility (β = 0.629, p< 0.01), Organizational Agility
Market Performance (β = 0.443, p< 0.01) and
Organizational Agility Operational Performance
(β = 0.289, p< 0.01). Furthermore, the confidence
intervals bias-corrected 95% did not indicate any
intervals straddling a 0, which confirmed our result.
Hence, these findings support this study's H1, H2,
H3, H4, and H5. The findings also show that the
relationship between BDAC and market
performance (f2= 0.255) has a larger effect size than
the relationship between BDAC and operational
performance (f2= 0.169). Next, based on the
recommendation of [67] the mediating relationship
in this study was tested by bootstrapping the indirect
effect. As shown in Table 4, BDAC
Organizational Agility Market Performance (β =
0.279, p< 0.01) and BDAC Organizational
Agility Operational Performance (β = 0.182, p<
0.05) were all significant. In addition, the
confidence intervals bias-corrected 95% also did not
indicate any intervals straddling a 0, which
confirmed our result. Hence, these findings support
H6 and H7 of this study.
Table 4. Hypotheses testing
The predictive validity of the framework was
assessed using PLS Predict. According to [72] the
application of PLS Predict allows the examination
of a model's out-of-sample predictive power through
a holdout sample-based procedure. As illustrated in
Table 5, all the LM model errors were higher than
the PLS model, indicating that the research model
had a large predictive power.
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Muhamad Luqman Khalil, Norzalita Abd Aziz,
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Table 5. PLS Predict
Item
PLS
LM
PLS-LM
Q²_predict
RMSE
RMSE
RMSE
MP1
0.789
0.808
-0.019
0.367
MP2
0.829
0.843
-0.014
0.351
MP3
0.850
0.869
-0.019
0.333
MP4
0.864
0.892
-0.028
0.294
OP1
0.892
0.908
-0.016
0.305
OP2
1.031
1.061
-0.030
0.217
OP3
1.038
1.071
-0.033
0.195
OP4
1.085
1.134
-0.049
0.200
OA1
0.877
0.902
-0.025
0.152
OA2
0.841
0.877
-0.036
0.216
OA3
0.867
0.892
-0.025
0.232
OA4
0.829
0.877
-0.048
0.225
OA5
1.049
1.112
-0.063
0.251
Note: MP = Market Performance, OP = Operational
Performance, OA = Organizational Agility
6 Discussion
This paper aims to assess BDAC's impact on firm
performance in the tourism context. The empirical
result shows that BDAC positively affects firm
performance among star-rated hotels in Malaysia.
Given that, empirical research between BDAC and
firm performance has been mainly carried out in the
IT and manufacturing industries. It fills the research
gap by understanding the impact of big data on
tourism players, particularly the hotel industry. The
result of the study also implies that hotels with
stronger BDAC will lead to better firm performance.
A case in point is big data analytics have been used
to improve revenue management techniques, which
can boost hotel competitive advantage and improve
sales revenue. The revenue management system
analyzes internal and external data in order to
provide reliable revenue decision-making to the
hotel management, [73]. The findings are also
consistent with past studies by [74] and [75], which
examine the relationship between BDAC and firm
performance in the IT and manufacturing industries.
The results also show that BDAC has a larger effect
size on market performance than operational
performance. A possible explanation for this might
be due to the adverse effect of the Covid-19
pandemic, which shows that financial indicators are
considerably more affected than non-financial ones.
In this study, the financial indicators are represented
by operational performance, whereas non-financial
indicators are represented by market performance.
Additionally, this study identifies and examines
organizational agility as the mediating variable in
the relationship between BDAC and firm
performance. So far, research on the mediating
effect of organizational agility on the relationship
between BDAC and firm performance is limited.
This study empirically tested the mediation analysis
and found that the relationship between BDAC and
firm performance is mediated by organizational
agility. This finding indicates that hoteliers should
be agile in their business operations to maximize
BDAC's impact on firm performance. Given the
insight generated from big data, hotels can optimize
their reactions to market changes and uncertainty
and improve their competitive advantage. This
finding also corroborates with the past study by
[17]. In the IT literature, [39] and [76] also
demonstrated that organizational agility can act as a
mediator between IT capability and organizational
performance.
The empirical result demonstrates that BDAC is
positively related to organizational agility. Previous
studies by [30] and [54] supported this finding.
Likewise, organizational agility is found to have a
positive effect on firm performance. Prior studies
have also reported the positive impact of
organizational agility on firm performance, [37],
[55].
6.1 Theoretical Contributions
The research model used the resource-based view
and dynamic capability as the theoretical
underpinning. Integrating both theories in a single
framework broadened the scope of these theories
and highlighted their importance. Consequently, this
study contributes to theory-based studies of big data
in tourism and hospitality literature. Past literature
has shown that BDAC positively affects firm
performance. However, what is less understood is
the role organizational agility plays in this
relationship. This study’s results demonstrate the
importance of organizational agility in mediating the
relationship between BDAC and firm performance.
In addition, based on prior studies, this study
conceptualized BDAC as a multidimensional and
hierarchical construct. Grounded on the RBV
theory, this study identified that resources such as
data, technology, basic resources, technical skills,
management skills, organizational learning, and
data-driven culture are needed to build and measure
BDAC. Given that the studies of BDAC in tourism
literature are limited, the empirical study on the
conceptualization of BDAC based on a tourism
setting further enriches the literature.
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DOI: 10.37394/23207.2023.20.40
Muhamad Luqman Khalil, Norzalita Abd Aziz,
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6.2 Managerial Contributions
From a management point of view, this study can
create awareness among key management in the
hotel industry on the importance of leveraging
BDAC to improve hotel performance. The findings
from this research can support hoteliers in justifying
big data investment and initiatives. It is crucial as
the hotel industry is still recovering from the losses
due to the Covid-19 pandemic. This study could
also guide hoteliers in identifying resources needed
to build their BDAC. Hotel managers must drive
and build resources such as data, technology, basic
resources, technical skills, management skills,
organizational learning, and data-driven culture. The
combination of these resources can assist hotels in
shaping their BDAC and consequently improve their
bottom line. Having said that, hotels must be agile
to react quickly to the information extracted from
big data. They need to develop new strategies or
reap new opportunities based on the knowledge
gleaned from big data analytics. Hence, agility in
hotel organizations is important as it improves
competitive advantage and consequently boosts firm
performance.
This study can provide a blueprint for
policymakers on the way forward for the tourism
industry. Despite the adverse consequences of the
Covid-19 pandemic, tourism players should turn to
emerging technology, such as big data analytics, to
facilitate recovery. Thus, policymakers should
incentivize industry players to apply big data
analytics in their day-to-day business. Even the
policymakers themselves need to formulate policies
and directives based on big data so that it can
benefit the tourism industry as a whole.
7 Limitations and Future Research
This paper has several limitations. First, the study
samples were primarily drawn from managers in the
hotel industry. Hence, it is uncertain whether the
results of the study can be generalized to other
tourism sectors, such as retail and restaurant
businesses. Further research on the application of
big data analytics in other tourism sectors can shed
more light on its impact on the tourism industry.
Second, this study is based on a cross-sectional
design, and future research should focus on
longitudinal studies to compare and generalize the
results. The longitudinal design also has a stronger
basis for deriving causal inference in testing
mediation than cross-sectional data, [58]. Third, this
study examined the mediating role of organizational
agility in the relationship between BDAC and firm
performance. Other variables, such as marketing
capability, could be tested as a mediator in the
relationship between BDAC and performance, [36].
Finally, this study's dependent variables (market
performance and operational performance) are based
on perceptual measures. Future research should base
performance on objective measures to better
comprehend BDAC's influence on firm
performance.
8 Conclusion
This study further enriches the literature on big data
by examining how BDAC influences firm
performance in the tourism context. Based on the
resource-based view and dynamic capability
theories, these two theories are integrated to support
and explain the relationship in the research
framework. The study also conceptualized the
organizational resources needed to build and
measure BDAC and further tested the relationship
between BDAC and firm performance in a tourism
setting. The empirical results show that BDAC
positively affects firm performance and
organizational agility based on data from the hotel
industry in Malaysia. The results also indicate that
organizational agility mediates the relationship
between BDAC and firm performance. These
findings suggest that hotels must be agile in reacting
to potential insight from big data to enhance their
competitive advantage. Overall, the study results
signify that BDAC and organizational agility are
important drivers of hotel performance, particularly
against the backdrop of the Covid-19 pandemic.
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
Norzalita Abd Aziz and Ahmad Azmi M. Ariffin
carried out the research instrument development and
data collection.
Muhamad Luqman Khalil managed the writing and
editing.
Abdul Hafaz Ngah was responsible for Data
Analysis.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
The Ministry of Higher Education (Malaysia)
funded this study under the Fundamental Research
Grant Scheme (FRGS/1/2019/SS01/UKM/02/4)
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.40
Muhamad Luqman Khalil, Norzalita Abd Aziz,
Ahmad Azmi M. Ariffin, Abdul Hafaz Ngah
E-ISSN: 2224-2899
453
Volume 20, 2023
Conflict of Interest
The authors have no conflicts of interest to declare
that are relevant to the content of this article.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
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Creative Commons Attribution License 4.0
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