Impact of big data on the retail industry

Abstract

With the recent emergence of Big Data with its Volume, Variety and Velocity (3V’s), data analysis has emerged as a crucial area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be resolved in business organizations, including the retail industry. This study has methodically identified and analysed four factors, namely, data source, data analysis tools, financial and economic outcomes and data security and data privacy, to gauge their influence on the impact of Big Data in the retail industry. This research analyses the impact of big data analysis on retail firms that use data and business analytics to make decisions, termed a data-driven decision-making (DDD) approach. The new finding is arrived that financial and economic outcome showed a strong support and have direct relationship with data analysis tools of retail industry. Data for the study were collected using a survey of various business practices and investments in information technology by retail organizations. The data analysis showed that retail organizations which use DDD have higher output and productivity. Using SMART PLS data analysis methods with solid support of review from ISI Journals, the relationship between DDD and performance is also evident in aspects of organization such as the utilization of inventory, customer engagement and market value in the retail industry.

Full text

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IMPACT OF BIG DATA ON THE RETAIL
INDUSTRY
A. Seetharaman*, Indu Niranjan*, Varun Tandon*, A. S. Saravanan**
*S P Jain School of Global Management, Singapore-Sydney-Dubai-Mumbai
**Taylors University, Malaysia
Abstract
With the recent emergence of Big Data with its Volume, Variety and Velocity (3V’s), data analysis
has emerged as a crucial area of study for both practitioners and researchers, reflecting the
magnitude and impact of data-related problems to be resolved in business organizations,
including the retail industry. This study has methodically identified and analysed four factors,
namely, data source, data analysis tools, financial and economic outcomes and data security and
data privacy, to gauge their influence on the impact of Big Data in the retail industry. This
research analyses the impact of big data analysis on retail firms that use data and business
analytics to make decisions, termed a data-driven decision-making (DDD) approach. The new
finding is arrived that financial and economic outcome showed a strong support and have direct
relationship with data analysis tools of retail industry. Data for the study were collected using a
survey of various business practices and investments in information technology by retail
organizations. The data analysis showed that retail organizations which use DDD have higher
output and productivity. Using SMART PLS data analysis methods with solid support of review
from ISI Journals, the relationship between DDD and performance is also evident in aspects of
organization such as the utilization of inventory, customer engagement and market value in the
retail industry.
Keywords: Data Source, Data Analysis Tools, Data Security, Financial and Economic Outcomes, Retail
Industry
1. INTRODUCTION
The world is inundated with data and this is
increasing exponentially day by day. Computer
systems store vast amounts of data. Researchers at
the University of California, Berkeley, recently
estimated that approximately 1 Exabyte (1 million
terabytes) of data is generated annually worldwide,
99.97% of which is available only in digital form
(Keim, 2009). The marketing industry is teeming
with data captured by companies and the rise of
social media, multimedia and the Internet will add
exponential growth in the near future (Manyika et
al., 2011).
The retail industry is one of the largest sectors
in the world. This industry is expected to grow as
the middle classes are increasing substantially in
size and in buying power. Retail purchases via e-
commerce and m-commerce are growing at a high
rate due to the advent of high-speed internet
connections, advancements in Smartphone
technology and online-related technology,
improvements in the product lines of e-commerce
firms, a selection of delivery options and better
payment options (Keim, 2009; Wixom, B.H. and
Watson, H.J., 2001). It is estimated that consumers
and large organizations generate 2.5 billion GB of
data yearly and this is increasing at the rate of 40%
year on year (Manyika, J. et al., 2011).
This growth in data is possible with the advent
of high-speed Internet access and the availability of
new data types for data analysis. The introduction of
these data types has become possible with the
introduction of Smartphones, tablet computers and
other electronic devices. The data are collected
because retail companies – including those engaged
in some kinds of e-commerce – view them as a
source of potentially valuable information, which, as
a strategic asset, could provide competitive
advantage (Keim, 2001). These retail data from Big
Data are a powerful means of creating a way forward
for marketers to accomplish their objectives in an
effective manner.
Business intelligence and analytics (BI&A) and
the related field of big data analytics have become
increasingly important in both the academic and
business communities over the past two decades
(Chen et al., 2012; Watson, H.J. et al., 2007). In the
rising tide of retail business transaction data, these
tools help distinguish what are strategic assets and
what are not worth collecting in the first place
(Keim, 2009). The analysis of these new data types
can make the decision-making process more
effective in marketing. Until recent times,
appropriate software tools and algorithms were
scarce in marketing research (Baier, D. and Daniel, I.,
2012). However, with the advancement in
technology, software tools and algorithms coupled
with velocity of Big Data are now available to analyse
content uploaded at different locations in the form
of images/snaps, music, or video. For innovation,
growth and to excel in competition, analysis of these

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large data sets (also known as big data) plays an
important role (Manyika et al., 2011). Big data not
only have an impact on data-oriented managers, data
analysts, etc., but also on the entire retail sector
which will increasingly have to cope with the
Volume, Variety and Velocity (3V’s) of big data.
Business analytics was identified as a
technological trend in the 2010s by the IBM Tech
Trends Report (2011). In a survey of the state of
business analytics by Bloomberg Businessweek
(2011), 97% of companies with revenues exceeding
$100 million were found to use some form of
business analytics. Such analysis has been facilitated
by the advent of advanced tools for data analysis
and techniques such as visual data exploration,
which allows the visualization of data to gain
insights and come up with new hypotheses. In
addition to granting the user direct involvement,
visual data exploration has several key advantages
over automatic data-mining techniques in statistics
and machine learning (Keim, 2009). Other powerful
data analysis tools, such as IBM’s SPSS, SAS, Hadoop
and Big Data, can also enable users to undertake
analysis using a combination of statistical tools
(Russom, P., 2011), technology and a process of
strategic thinking in marketing.
2. LITERATURE REVIEW AND HYPOTHESIS
DEVELOPMENT
A literature review was undertaken encompassing
academic and research papers. This section reviews
Big Data as undertaken by organizations and then
addresses several key aspects identified from the
literature review, leading to the development of the
research hypotheses.
Data analysis in organizations employs a
qualitative approach that includes statistical
procedures. The analysis comprises an ongoing
iterative process in which data are continuously
collected and analysed in real time. To analyse the
data, patterns in the data are sought during the data
collection phase (Savenye W.C. and Robinson R.S.,
2004). The qualitative approach adopted determines
the form of the analysis to be performed. For
example, the types of data that can be analysed
using content analysis include field notes,
documents, audiotapes, videotapes, etc.
Maintaining data integrity is an import aspect
of Data analysis; similarly, accurate and appropriate
analysis of the data is also essential in maintaining
data integrity to derive the research findings. Data
integrity issues that include statistical and non-
statistical data are relevant in data analysis.
Incorrect data analysis will have a negative impact
on the scientific findings and the public perception
of research (Shepard, 2002).
The evolution of data analysis methods
employed in organizations are listed below (ISACA,
2011)
 Ad-hoc – This data analysis technique is
used in the initial investigation, mainly to support a
specific project. This type of analysis technique is
rarely applied directly to live systems or production
systems. The technique is highly dependent on the
skills of the individual.
 Repeatable – This data analysis technique is
predefined and scripted to perform the same tests
on similar data. Data access tools may be used to
import data directly from production systems. The
technique is less dependent on the individual and
the acquisition process is automated to improve the
output of data analysis.
 Centralized analytics – For development,
operation and data storage purposes, a centralized
approach should be developed. Standards for the
development of data analysis from Big Data are
documented. Batch jobs are created for the
applications to run the data analysis against the
centralized storage location. Data can either be
pushed or pulled from different sources.
 Continuous monitoring – Data analytics,
referenced to the centralized storage of Big Data, is a
continuous process using automated jobs. These
jobs are monitored and maintained by operations
teams with the help of technical teams.
Turning to the key aspects of Big Data analysis
in the retail context identified in the literature, four
focal areas are discussed in turn: i) data source; ii)
data analysis tools; iii) data security and data
privacy; iv) financial and economic outcomes.
2.1. Data source
For many years, data collection and analysis have
proven helpful for marketing purposes (Ashley, C.
and Noble, S.M., 2013). Marketing researchers and
practitioners collect data and analyse them (Baier et
al., 2012). Data are collected because companies,
including those engaged in some kind of e-
commerce, view them as a source of potentially
valuable information which, as a strategic asset,
could provide competitive advantage (Keim, 2009;
Wixom, B.H. and Watson, H.J., 2001). The data that
are collected by businesses about their customers
are one of the greatest assets of the business
(Ahmed, 2004).
The quality of data from a source largely
depends on the degree to which they are governed
by schema and on integrity constraints controlling
permissible data values (Van Till, S., 2013; Wu, J.,
2002). Vast quantities from Big Data are used as raw
material to enable searches using data analysis tools
and group the data according to desired criteria that
could be useful for future targeted marketing
(Ahmed, 2004).
New data types are becoming available for data
analysis and classification in marketing with
advancements in Smartphones, tablet computers
and other equipment (Baier et al., 2012; Chiang et al.,
2012). In today’s world of social media, customer
perceptions can very easily be accessed across the
Internet in the form of blogs and on-line forums.
These customer data are mostly sought by retail
organizations to gauge the extent of positive or
negative perceptions (Cerchiello and Giudici, 2012)
and also for quick decision support (Lohr, S., 2012;
Graen, M., 1999). The analysis of these data makes a
considerable difference to companies. The valuable
information obtained could make significant
difference for an organization in running its
business, the way it interacts with current and

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prospective customers and enabling it to gain a
competitive edge on their customers (Ahmed, 2004).
Based on the above, the following hypotheses
are proposed:
H1. The data source, used as the basis for
performing data analysis and comprising data from
various sources, has a positive impact on the use of
data analysis tools.
H2. The data source, used as the basis for
performing data analysis, has a positive impact on
data analysis.
2.2. Data analysis tools
Data analysis tools are used to extract buried or
previously unknown information from large
databases using different criteria, allowing the
discovery of patterns and relationships (Ahmed,
2004). Data analysis tools can be divided into data-
profiling and data-mining tools (Written, I. H. et al.,
2011). A large number of commercial tools support
the extraction, transformation and loading (ETL)
process for data warehouses in a comprehensive
way (Turban E. et al., 2008). In short they solidly
support Volume, Variety and Velocity (3V’s) of Big
Data.
In the 1980s, factor analysis became one of the
more widely used procedures in the arsenal of
analytic tools for market research (Stewart D.W.,
1981). Core variables were selected from the
collected data and then analysed. When the amount
of data is so large as to be beyond comprehension,
factor analysis can be used to search data for
qualitative and quantitative distinctions (Stewart
D.W., 1981).
Companies use multiple data sources to
undertake analyses. Drawing data from multiple
sources and employing various types of analysis can
provide robust findings and overcome the risk of
method bias (Davis et al., 2011). Advancements in
technology have helped researchers analyse and
group respondent data (Lohr, S., 2012) available in
the form of videos, images or audio files by using
different algorithms and software (Berry, M.J.A. and
Linoff, G. (1997); Baier et al., 2012). Text
categorization has become one of the key techniques
for handling and organizing data in textual format
with the rapid growth of online information
(Cerchiello and Giudici, 2012).
The information that is derived from such
analyses can be used for decision support,
prediction, forecasting and estimation to make
important business decisions (Sallam, R.L. et al.,
2011). Indeed, they can help a business to gain a
competitive edge (Ahmed, 2004). In recent years,
business intelligence tools and technology are
improving and all business are taking advantage of
this situation (Chaudhuri S. et al., 2011). Big data
analytics has come to be considered the most
advanced data analysis technology, it helps in.
Based on the above, the following hypothesis is
proposed:
H3. Data analysis tools, used to analyse data
collected from different sources, have a positive
impact on the data analysis performed.
2.3. Data security & data privacy
The data that the companies collect regarding their
customers are one of the greatest assets of the
company. The security and privacy of these data are
important concerns from the companies’ point of
view as well as from the customers’ point of view
(Van Till, S., 2013; Wu, J., 2002). Data security and
data privacy are also important aspects of electronic
commerce (Acquisti, 2004). A PWC study in 2000
stated that nearly two thirds of the consumers
surveyed would shop more online if they knew retail
sites would not do anything with their personal
information.
As the Internet develops and matures, its
success will depend in large part on gaining and
maintaining the trust of visitors. This will be of
paramount importance to sites that depend on
consumer commerce (McKnight D.H. et. al., 2002).
The development of trust not only affects the
intention to buy, but it also directly affects the
effective purchasing behaviour in terms of
preference, cost and frequency of visits and
therefore the level of profitability provided by each
consumer. In addition, analyses show that trust in
the Internet is particularly influenced by the level of
security perceived by consumers regarding the
handling of their private data (Wu, J., 2002; Flavián
and Guinalíu, 2006).
Thus, the following hypotheses are proposed:
H4. Ensuring data security and data privacy has
a positive impact on financial and economic
outcomes.
H5. Ensuring data security and data privacy has
a positive impact on the data source.
H6. Ensuring data security and data privacy has
a positive impact on the effect of data analysis in the
retail industry.
2.4. Financial & economic outcomes
The data derived from consumer transactions is
increasing by 40% per year (Johnsen, 2013). Making
sense of these data makes a considerable difference
for businesses. Such analysis will potentially give
companies a competitive advantage (Smith, 2014;
Lohr, S., 2012). To develop better decision making,
retail companies are using new technology, such as
big data analytics, not to build massive databases or
develop costly technological products, but to help in
identifying five to ten combinations of existing and
new data sources that can drive better decision
making when combined with sophisticated real-time
analytics (Johnsen, 2013; Sallam, R.L. et al., 2011). As
noted by Andrew Appeal, IRI’s new CEO, at the
company summit in March 2014 in Las Vegas, data
analysis technologies, including big data analytics
and other services, will reach $17 billion by 2015,
from a base of just $3 billion in 2010 (Johnsen,
2013). This is a modest estimate.
Companies leveraging average analytic
capabilities are 20% more likely to provide higher
returns for their stakeholders than their non-
analytic-orientated competitors; companies that use
advanced analytic capabilities, such as those using
big data, are 50% more likely to provide higher
returns (Johnsen, 2013; Savitz, E., 2012). According

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to IRI, the retailing industry could potentially see
more than $10 billion in annual value created as a
result of the improved application of advanced
analytics to support brands and channels.
Thus, the following hypotheses are proposed:
H7. Financial and economic outcomes have a
positive impact on the use of data analysis tools.
H8. Financial and economic outcomes have a
positive influence on the impact of Big data analysis
in the retail industry.
The findings from the literature review are
summarized in terms of the flow of related research
over the years as illustrated in Table 1 below. This
table captures how retail organizations have used
data analysis since advancements in technology for
data capture and data analysis and it shows the
impact of big data analysis on organizations’
growth. Moreover, it illustrates the advancements in
the technology and tools available for data analysis,
as well as the importance that organizations accord
data analysis in taking operational and financial
decisions. This table gives the chronological
contribution of various experts in the field with a
view to identify relevant independent variables
influencing the Data Analysis through Big data. At
the end the Table further illustrates that the
findings from this paper strengthen and enrich the
earlier findings further it also introduces new
variable on Financial & Economic Outcome, which is
distinctly different from earlier contributions.
Table 1. Comparison of the impact of big data analysis in the retail industry
Study
Data Source
Data Analysis Tools
Data Security &
Data Privacy
Financial &
Economic Outcome
Impact of Big Data
Analysis on Retail
Industry
Donthu
and Yoo
(1998)
Data from various
stores related to
customers,
collected and used
for analysis
Data envelopment
analysis (DEA)
No
Helps to measure
productivity in the
retail industry
Organizations
should use data
analysis techniques
to measure retail
productivity
Ahmed
(2004)
Businesses collect
data on their
customers
Data mining tools
Confidentiality and
individuality of the
common man
should be preserved
Data mining can
help businesses
plan for the peak
periods of
consumption,
irregular
transactions, etc.
Improvement in the
market.
Organizations
should be able to
react quickly to
changes
Brynjolfsso
n et al.
(2011)
Survey data on 179
firms
Instrumental variable
testing
No
Firms adopting
DDD have 5–6%
higher output and
productivity
Particularly for
large firms, DDD is
worth adopting
Baier et al.
(2012)
New data types
available in market
with the
advancement of
Smartphones,
tablet computers
and other
equipment
Analysis of image data
is possible with the
help of the SPSS-like
software package
IMADAC
No
Organizations
exploring advanced
data analysis
technology
Grouping of content
is possible with the
use of proper
analytic tools
Wyner
(2013)
N/A
Discussion of customer
relationship
management (CRM)
No
Data can help in
predictive analysis,
customer
segmentation and
also have
operational and
financial benefits
Used by companies
to improve
customer
experience and
increase
profitability
Falcioni
(2013)
N/A
Discussion of big data
No
Data analysis can
predict purchasing
trends, predictive
analysis and
operational benefits
Helps companies to
plan for production,
reduce inventory
and improve
margins
This
research
paper
With advancements
in technology, new
data sources are
available for
analysis
Advanced data analysis
tools like big data
analytics are helping in
the analysis of complex
data sources
Technology is
helping companies
to protect the
security and privacy
of data
Data analysis has a
positive impact on
the financial and
economic outcomes
of retail companies
Users have
demonstrated a
strong inclination
for research
variables for the
impact of the Data
Analysis on the
Retail industry
3. RESEARCH METHODOLOGY
The research methodology was developed after the
completion of the literature survey largely from ISI
Thomson listed journals. The research methodology
is centred on the variables identified that appeared
repeatedly during the literature review. A
meaningful direct and indirect relationship between
independent variable and dependent variables was
identified and a research model created to
understand their dominance, as illustrated in
Figure 1.

Corporate Ownership & Control / Volume 14, Issue 1, Fall 2016, Continued - 3
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Figure 1. Research framework
3.1. Data collection
Secondary data were obtained by following the
ground research (Glaser & Strauss, 1967) from the
literature survey, resulting in four independent
variable. Then, a survey questionnaire was
developed to gather primary data on the impact of
big data analysis in the retail sector from various
individuals working for global medium-sized and
large retail organizations. The questionnaire was
piloted through personal interviews with 20
respondents and selected experts from medium-
sized and large retail organizations (Departmental
stores, super markets and online retailing etc.) to
obtain feedback. Based on the responses from these
interviews, the questions were restructured and the
final survey questionnaires were sent.
Table 2. Demographic characteristics of respondents
Survey Participants (n = 238)
No. of employees in analysis team
Less than 10
68
28.6%
10–49
43
18.1%
50–99
28
11.8%
100–300
31
13.0%
More than 300
68
28.6%
Geographic region for data analysis
Asia
128
53.8%
Australia
19
8.0%
Europe
34
14.3%
US
45
18.9%
UAE
12
5.0%
Annual revenue
Less than USD 100,000
24
10.1%
USD 100,001 to USD 1 million
17
7.1%
USD 1 million to USD 10 million
26
10.9%
USD 10 million to USD 100 million
31
13.0%
USD 100 million to USD 1 billion
10
4.2%
More than USD 1 billion
130
54.6%
Market share of the organization
Less than 10%
69
29.0%
10–29%
90
37.8%
30–50%
41
17.2%
More than 50%
38
16.0%
Type of data analysis performed
Customer data analysis
28
11.8%
Report generation
93
39.1%
Financial report data analysis
12
5.0%
Transaction data analysis
97
40.8%
All of the above
8
3.4%
Frequency of data analysis
Daily
65
27.3%
Weekly
41
17.2%
Monthly
83
34.9%
Quarterly
44
18.5%
Half yearly
2
0.8%
Yearly
3
1.3%
Organizations’ spend on data analysis compared to marketing spend
Less than 5%
107
45.0%
5–19%
79
33.2%
20–40%
31
13.0%
More than 40%
21
8.8%
Organizational growth experienced with data analysis initiative
Less than 5%
77
32.4%
5–9%
71
29.8%
10–14%
26
10.9%
15–20%
37
15.5%
More than 20%
27
11.3%
Impact of data
analysis in the
retail industry
Data analysis tools
Data security & data
privacy
Financial & economic
outcomes
Data Source

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The survey was undertaken using a
questionnaire based on the four independent
variables identified from the literature survey,
covering both quantitative and qualitative aspects.
The final survey was sent only to those aware of
data analysis and within the retail industry. At the
start of the questionnaire a brief description was
provided, highlighting the purpose of the research
together with an assurance regarding the
confidentiality of the data collected. The
questionnaire was divided into various sections,
each containing questions to obtain information
related to the variables identified. For each variable,
three to five questions were formulated using a five-
point Likert scale to capture the use and adoption of
data analysis by the companies in taking decisions.
Each question is solidly supported by literature
mostly from ISI Journals.
The final online survey was then sent to a vast
number participants using personal contacts via
email. The profile of the respondents were people
who belong to retail industry particularly Super
Markets, Departmental Stores and online retailing (e-
Commerce). A total of 284 responses were received,
of which only 238 were found useful for analysis
due to missing data. All the Likert-scale questions
were mandatory and there were other optional items
that were optional, such as comments. Table 2
summarizes the demographic characteristics of the
238 respondents. The respondents in this research
were from developed countries around the globe.
3.2. Data analysis
SmartPLS (Wende et al., 2005; Ringle et al., 2005)
software was used for the following:
 To analyse the model
 To test the hypotheses developed
 For path modelling with latent variables
 To measure the validity and reliability of the
constructs
SmartPLS uses the partial least squares (PLS)
technique, a component-based approach for
examining and testing theory without imposing any
normality condition on the data (Hulland, 1999). PLS
is also useful for relatively small amounts of data
and when the data are skewed (Wong, 2011). PLS
makes no assumption about data distribution (Vinzi
et al., 2010) and removes the problem of undesirable
solutions (Löhmoller, 1989; Wold, 1989). Structural
equation modelling (SEM) is a component-based
estimation method (Tenenhaus, 2008) that allows
testing of both built theories and concepts (Rigdon,
1998; Haenlein & Kaplan, 2004). The structural
modes should be compatible with experimental
designs (Bagozzi, 1980). Hoyle (1995) suggested
using a sample size of 100–200 for path modelling
analysis. The sample size used for analysing this
model is 238.
The analysis was done in stages: during the
first stage the structural model was estimated to
assess the quality of the measures, followed by
hypothesis validation using the structural model
(Jöreskog and Sorbom, 1993). Browne et al. (2002)
suggested validating the model in the first stage
before examining the hypotheses. Reliability
(consistency of measures) and validity (measure of
concept) are the two criteria for testing the measures
(Sekaran and Bougie, 2010).
3.2.1. Reliability
To evaluate the reliability and consistency of the
model, composite reliability and Cronbach’s alpha
were used. Composite reliability is a comprehensive
estimate of reliability (Chin and Gopal, 1995). For
adequacy, the constraints are a Cronbach’s score of
0.6 and above (Hair J.F. et al., 2012) and a value
greater than 0.7 for composite reliability is
recommended (Gefen et al., 2000). Table 3 shows the
composite reliability values are greater than 0.7 and
the Cronbach’s alpha values are above 0.6. This
shows that the model is robust and reliable.
Table 3. Reliability validation for latent constructs
Overview
AVE
Cronbach's alpha
R2
Data analysis tools
0.5814
0.6399
0.6671
Data security & data privacy
0.7494
0.8326
0
Data source
0.6951
0.9505
0.6763
Financial & economic outcomes
0.591
0.9458
0.6115
Impact of Big data analysis
0.684
0.9575
0.7163
3.2.2. Convergent validity analysis
Convergent validity is the measure of variable
indicators. This measures the extent of conformity
between scores. Construct validity is also tested with
the help of convergent validity (Straub et al., 2004;
Fornell and Larcker, 1981). A value above 0.7 is
considered the ideal value for the convergent
validity of each item (Chin, Marcolin, & Newsted,
2003) and the average variance extracted (AVE) for
each construct should be above 0.5 (Barclay et al.,
1995).
As shown in Table 3, the minimum AVE value is
0.58, which is above 0.5 as required. Also, as shown
in Table 4 the loading reliability indicator is more
than 0.7 (Chin et al., 2003). Moreover, the loading
constructs for all items are above 0.5, so the
measurement model satisfies the requirements for
convergent validity.
Table 4. Results for reflective outer models
Construct
Loadings (indicator reliability) (min–max)
Data analysis tools
0.7304–0.7908
Data security & data privacy
0.8321–0.8913
Data source
0.7197–0.9052
Financial & economic outcomes
0.7104–0.8729
Impact of Big data analysis
0.7356–0.9364

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Table 5 compares the item-to-construct correlation
against the correlations with other constructs.
3.2.3. Discriminant validity
Discriminant validity is used to assess the degree of
discrimination between variables. Smart PLS was
employed to validate discriminant validity by
comparing the measured value for each variable with
other constructs; if a weak correlation is resulted,
discriminant validity is established (Hulland, 1999).
Also, following Compeau et al. (1999), the average
variance for an indicator should be greater than the
variance of other variables. To calculate discriminant
validity, the square root of the AVE value for each
indicator is calculated and then compared to the
AVE of other variables. The value of the square root
of the AVE should be greater than the AVE of other
variables (Fornell and Larcker, 1981). Table 6 shows
the results of the discriminant validity testing, with
the square root of AVE in bold.
Table 5. Comparison of item-to-construct correlation against correlations with other constructs
Construct
Item definition
Data
analysis
tools
Data security
& data
privacy
Data source
Financial &
economic
outcomes
Impact of
big data
analysis
Data
analysis
tools
Cost analysis
0.7908
0.4664
0.4865
0.7701
0.4863
Advanced
0.7304
0.5461
0.555
0.5261
0.7839
Specific
0.765
0.3506
0.6051
0.5695
0.4921
Data
security &
data
privacy
Data security important
0.4703
0.8321
0.7346
0.6162
0.5341
Data privacy important
0.5211
0.8727
0.6641
0.6274
0.5976
Latest technology
0.567
0.8913
0.7356
0.7772
0.587
Data source
Research
0.6649
0.6149
0.7535
0.6791
0.7981
Mobile devices
0.5178
0.5071
0.7197
0.546
0.5353
Internet
0.5375
0.8315
0.8671
0.6993
0.5898
Other internal & external sources
0.5821
0.67
0.8614
0.6647
0.7093
Promotional data
0.5488
0.6136
0.8487
0.7456
0.6047
Consumer buying patterns
0.6107
0.7491
0.9052
0.752
0.6863
Store video data
0.6199
0.8455
0.8634
0.7983
0.6204
Customer feedback
0.6244
0.5961
0.7571
0.6057
0.5835
Inputs from employees
0.6896
0.6331
0.8379
0.8111
0.6993
Financial &
economic
outcomes
Impact on cash flow
0.5847
0.7405
0.9005
0.7762
0.7158
Reduce human intervention to improve
accuracy
0.6138
0.4829
0.587
0.7062
0.7491
Ability in data analysis
0.7908
0.4664
0.4865
0.7701
0.4863
Elevate the quality of people
0.4523
0.5235
0.4212
0.7184
0.2131
Reducing human error
0.6972
0.5997
0.6597
0.8556
0.6348
Optimization of inventory
0.41
0.4061
0.3992
0.7154
0.2436
Prevent local and corporate losses
0.7086
0.6648
0.7312
0.8377
0.6432
Help in product mix
0.705
0.6498
0.7991
0.8561
0.6947
Customer purchase behaviour
0.6414
0.7394
0.7609
0.8729
0.6422
Impact on customer behaviour
0.3952
0.6494
0.6526
0.7244
0.4919
Impact of
big data
analysis
Understand customer needs
0.5488
0.6136
0.8487
0.7456
0.6047
Identify customer behaviour
0.641
0.8151
0.811
0.8256
0.6723
Retain customers
0.6481
0.6921
0.7456
0.7883
0.7871
Attract new customers
0.7908
0.4664
0.4865
0.7701
0.4863
Identify customer satisfaction levels
0.6139
0.5444
0.622
0.7104
0.691
Increase customer engagement
0.6665
0.4626
0.5557
0.5194
0.8216
Improve customer spending patterns
0.6
0.4879
0.583
0.5734
0.863
Improve demand
0.6918
0.5275
0.7091
0.7415
0.8908
Real-time customer purchase patterning
for better decision making
0.7315
0.5101
0.6596
0.6229
0.9055
Re-marketing
0.4586
0.3662
0.4829
0.5016
0.7466
Impact on the client relationship
0.5951
0.4356
0.5491
0.5394
0.8078
Note: Construct item loadings are highlighted in grey.
Table 6. Discriminant validity
Data analysis
tools
Data security & data
privacy
Data
source
Financial &
economic outcomes
Impact of Big
data analysis
Data analysis tools
0.7625
Data security & data privacy
0.6016
0.8657
Data source
0.7197
0.8224
0.8337
Financial & economic
outcomes
0.7255
0.752
0.7541
0.7688
Impact of big data analysis
0.759
0.6619
0.7894
0.7543
0.8270
As can be seen from Table 6, discriminant validity is
proven and supported as all the square root AVE
values are greater than the AVE of the other
variables.
3.2.4. Structural equation modelling
To test the hypotheses, SmartPLS software was used
for path analysis and the 238 samples were
bootstrapped using the re-sampling procedure to

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513
establish confidence intervals (Mooney & Duval,
1993; Manski, 1996). To model the unknown
population, bootstrapping results can be used
(Hesterberg et al., 2003). The t-statistic is used as the
basis for checking the level of significance. The
different significance levels (p-values) and
corresponding t-values are given in Table 7 (Cowles
& Davis, 1982; Neyman and Pearson, 1933).
Table 7. Significance levels
Significance
t-value
Significance
values
p < 0.1
1.650
p < 0.05
1.968
p < 0.01
2.592
4. RESULTS
Table 8 shows the results of the hypothesis
testing. A total of eight hypotheses were created and
of these, seven hypotheses are supported. As can be
observed, H1 (β = 0.0856, p > 0.1) is not supported
because the path from data source to data analysis
tools is not significant. This might be expected, as a
good and advanced data analysis tool can make
sense of a very basic data source. With the
advancements in Smartphone, tablet, computer and
other technology, an increasing number of data
types are available on the market for analysis (Baier
et al., 2012). This is contrary to our findings in the
sense that though more Data Sources are available
they has less impact on Data Analysis.
Table 8. Results of hypothesis testing
Path
coefficient (β)
Mean
St. Dev.
St. Error
t-value
Supported
H1
Data Source > Data Analysis
Tools
0.0856
0.0902
0.0804
0.0804
1.0647
No
H2
Data Source > Impact of Big
Data Analysis
0.4667**
0.4736
0.1837
0.1837
2.5404
Yes
H3
Data Analysis Tools > Impact
of Big Data Analysis
0.412***
0.407
0.083
0.083
4.9636
Yes
H4
Data Security & Data Privacy
> Financial & Economic
Outcome
0.782***
0.7854
0.0433
0.0433
18.055
Yes
H5
Data Security & Data Privacy
> Data Source
0.8224***
0.8256
0.0263
0.0263
31.2101
Yes
H6
Data Security & Data Privacy
> Impact of Big Data Analysis
0.6822***
0.6862
0.0478
0.0478
14.265
Yes
H7
Financial & Economic
Outcome > Data Analysis
Tools
0.7424***
0.7393
0.0814
0.0814
9.1212
Yes
H8
Financial & Economic
Outcome > Impact of Big Data
Analysis
0.3663*
0.3556
0.1982
0.1982
1.8486
Yes
Notes: *, ** and *** denote significance at the 10%, 5% and 1% levels respectively (one-tailed).
H2 is supported (β = 0.4667, p < 0.05) showing
a significant link between data source and the
impact of big data analysis. Using the wrong data
source will provide spurious information and this
will potentially have a negative impact on the
organization. The incorrect analysis of data has a
significant impact on the organization’s revenue
(Chartered Institute of Management Accountants,
2013). This is in conformity with our findings.
H3 is supported (β = 0.412, p < 0.01), with a
significant path from data analysis tools to impact
on data analysis. The use of different tools will
affect the data analysis as different tools use
different algorithms. Information in blogs and
forums, for example, can be analysed by combining
different analysis trees. Good results can be
obtained using the Kruskal–Wallis and Brunner–
Dette–Munk tests (Cerchiello and Giudici, 2012). This
is in conformity with our findings.
The paths from data security and data privacy
to financial & economic outcomes (β = 0.782, p <
0.01) and to data source (β = 0.8224, p < 0.01) are
significant, providing support for H4 and H5
respectively. This indicates that ensuring data
security and data privacy and using appropriate and
diverse data sources will have a positive impact on
financial and economic outcomes. This is in
conformity with earlier findings by Ahmad, 2004
that confidentiality and individuality of the common
man should be preserved.
H6 is also supported (β = 0.6822, p < 0.01),
demonstrating the link from data security and data
privacy to the impact of big data analysis. This is in
conformity with earlier findings that security issues
are a major concern for most organizations (Chen et
al., 2012) and will have an effect on data analysis.
H7 (β = 0.7424, p < 0.01) is strongly supported.
Financial and economic outcomes show a direct
relationship with data analysis tools of retail
industry. This is our new finding contributing to the
literature of Big Data.
There is also support for H8 (β = 0.3663, p <
0.1), showing that the path from financial &
economic outcomes to the impact of big data
analysis is significant. In a survey of the state of
business analytics by Bloomberg Businessweek
(2011), our findings are validated by the statement
that 97% of companies with revenues exceeding
$100 million were found to use some form of
business analytics (Chen et al., 2012).
The results of the PLS structural modelling are
shown in Figures 2 and 3 below.

Corporate Ownership & Control / Volume 14, Issue 1, Fall 2016, Continued - 3
514
Figure 2. Results of PLS analysis (extracted from SmartPLS) showing direction of path and beta coefficients
Note: The line in red represents the non-supported hypothesis.
Figure 3.Results of PLS structural model analysis showing paths and hypotheses
Note: Significant relationship (

), insignificant relationship (--->).
4.1. Goodness of Model Fit
ADANCO (Wende et al., 2005; Ringle et al., 2005)
software was used to analyse the overall goodness of
fit of the model. If model does not fit the data, then
it means that the data contain more information
than the model conveys. For frame of reference it is
needed to determine the model fit both for the
estimated model and for the saturated model.
(Hensler et al., 2014)
The SRMR value of a perfect model fit is 0, but a
value less than 0.05 indicates an acceptable fit
(Byrne, 2013). Hensler et al., 2014, suggest that
SRMR value above 0.06 is also acceptable. Hu and
Bentler (1999) proposed 0.08 as a cut-off value for
SRMR. Also a value between 0.05 and 0.08 suggest
reasonable error of approximation (Browne and
Cudeck, 1993).
As can be seen from Table 8 & 9, SRMR value is
less than 0.08 for saturated and estimated model.
Table 8. Results of Goodness-of-model-fit (saturated model)
Value
HI95
HI99
SRMR
0.0712
0.0452
0.0470
dULS
0.6793
0.1999
0.2167
dG
1.6020
0.5666
0.6393

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Table 9. Results of Goodness-of-model-fit (estimated model)
Value
HI95
HI99
SRMR
0.0785
0.0539
0.0599
dULS
0.7520
0.2849
0.3511
dG
1.6203
0.5552
0.6580
5. IMPLICATIONS FOR THE RETAIL INDUSTRY
The retail industry contributes to 6–7% of the world
economy and covers a large ecosystem. The retail
industry includes specialty groceries, consumer
products, goods, e-commerce, department stores,
apparel, discount drugstores, home improvement,
discount retailers, electronics, and specialty
retailers. Retail is increasing day by day with the
invention of new technology. The squeeze on
industry incumbents is coming from e-commerce
and new “point, scan and analyse” technologies that
give shoppers decision-making tools – powerful
pricing, promotion and product information, often
in real time. Applications in iPhones and Android,
such as Red Laser, can scan barcodes and provide
immediate price, product and cross-retailer
comparisons. They can even point customers to the
nearest retailer providing free shipping (total cost of
purchase optimization). This leads to further margin
erosion for retailers that compete based on price.
Thus, it is important for businesses, whether
large or small, to ensure they are competitive. The
competition intensifies as online retailers interact
with their customers in real time. Data analysis
through Big Data can help retailers in the following
ways coupled with Volume, Varity and Velocity (3V’s
of Big Data):
 Improved customer service – Data analysis
can help an organization to improve its customer
service to attract more customers and retain existing
ones. For example, when customers complain online
or through social media, data analysis can provide
background to the issue, helping customer care to
address it and provide a better service. This will
result in good customer handling, quicker resolution
of problems and the customer feeling privileged and
important.
 Greater customisation – Consumers shop with
the same retailer in different ways, for example
online, using mobile apps, etc. When data are
collected in real time for analysis from multiple
sources, companies can provide a customised
experience for customers. For example, data analysis
helps in segmenting customers, identifying those
who are loyal customers and those who are new.
This helps organizations to reward loyal customers,
whilst also appealing to and attracting new
customers.
 Product status and availability – With the
increase in technology, customers like to know the
real-time availability, status and location of their
orders. This is challenging when many parties are
involved in the product delivery. To keep customers
happy, it is important for them to be able to know
the exact status of the product. To communicate the
real-time status of the product, all the relevant
parties, including third parties involved in the
transaction, should communicate with each other.
To implement this functionality, companies should
start early and improve their services over time.
 Managing fraud – The availability of larger
data sets helps improve fraud detection rates, but to
achieve this, companies require huge infrastructure.
This infrastructure leads to a safer environment in
which to run businesses and also helps improve
profitability. For example, to detect online fraud,
companies need to process their transactions
against pre-defined fraud patterns in real time,
otherwise it is not possible to detect fraudulent
behaviour.
 Predictive analytics – Irrespective of a firm’s
size, analytics is crucial for all online retailers.
Without analytics it is difficult to sustain a business
in this competitive environment. Predictive analysis
helps organizations to identify events before they
occur. This can be achieved using data analysis and
many businesses are now using predictive analytics
to plan for the future. For example, retailers can use
this analysis to plan inventory, helping them to save
on inventory costs and avoid out-of-stock issues.
 Dynamic pricing – With the increase in
competition, dynamic pricing is very important to
compete on pricing with other sites. To achieve this,
companies collect data from multiple sources, such
as product sales, competitor pricing and customer
actions, to determine the right price for the sale of
products. Large online retail giants such as Amazon
already support this functionality. This analysis
gives large businesses a huge competitive advantage
over small and medium competitors.
6. LIMITATIONS AND SCOPE FOR FURTHER
RESEARCH
The variables considered in this study are based on
the current state of data analysis in the retail sector.
Future research could incorporate other critical
variables, such as geographic location and socio-
economic data, to examine social implications. This
will help provide a more social and global
perspective on data analysis in the retail industry.
The inclusion of companies and respondents from
other geographies and from SMEs would also help in
making comparisons of the impact of big data
analysis in the retail industry around the globe and
in retail organizations of different sizes.
With the increased adoption of Big Data,
another recommendation would be to conduct
further research into factors that influence the
choice of data analysis tools in the retail industry
(for example, revenue, macro and micro factors
influencing the success of data analysis, etc.).
The final recommendation is to research and
analyse the company’s performance in terms of the
financial and operating benefits that companies can
achieve with data analysis.
7. CONCLUSIONS
Data analysis through Big Data has been acclaimed
as a tool that can revolutionize the retail industry.
There are various advantages of data analysis

Corporate Ownership & Control / Volume 14, Issue 1, Fall 2016, Continued - 3
516
technology and an increasing number of firms are in
the process of implementing the data analysis
through Big Data to provide insights into and
improve their revenues. Research has considered
many businesses and the operational challenges
companies face in the adoption and implementation
of data analysis in the retail industry. Despite these
challenges, data analysis has become a significant
strategy for companies in gaining competitive
advantage and accelerating growth (Davenport, T.H.,
2006). James, L. (2010) on data analysis in the retail
industry suggests that it is an integral part of
business, revealing the customer data that should be
analysed and the benefits that retail organizations
can obtain.
This study has methodically analysed four
factors, namely, data source, data analysis tools,
financial and economic outcomes and data security
and data privacy, to gauge their influence on the
impact of Big Data in the retail industry. Based on
this analysis, there is a remarkable change in the
factors identified that affect data analysis in the
retail industry. The data source and data analysis
tools are now perceived as given factors or “must
haves” in terms of their impact and these are not
considered to be differentiators for companies.
Rather, it is observed that data security and data
privacy are major considerations for companies in
adopting data analysis, followed by the financial and
economic impact.
Although the data source and data analysis
tools are not the most significant influences for
companies to adopt data analysis, both still have a
positive impact on data analysis in the retail
industry. This observation is supported by Wixom
and Watson (2001) and Park and Kim (2003). The
research also discovered that the ability to perform
data analysis is one of the most important factors
driving an organization’s success in the retail
industry. This observation is supported by
Davenport, T.H. (2006).
Based on this research, it can be predicted that
data analysis will continue to exert an impact of the
retail industry and extended its use could also
potentially benefit other sectors, such as healthcare,
etc.
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