Appendix - A
Questionnaire
A Study of Big Data Retail Business Analytics and its Impact on Customer
Acquisition and Retention in Indian Retailing - An Empirical Analysis
Introduction the questionnaire
Dear Sir / Madam
You are invited to participate in a marketing research study conducted by
A.L.V. R Chowdary, Research scholar of Acharya Nagarjuna University, Guntur.
The purpose of this study is to examine the Impact of Big Data Retail Business
Analytics on Customer Acquisition and Retention Strategies in Indian Retailing.
As a retail manager and user of big data analytics from different retail sectors, we
trust that you have insightful information to share. The study also focuses on
adaption, implementing, uses, obstacles and perspectives of big data retail business
analytics and IOT (Internet of Things). We will be grateful if you could spare some
of your valuable time to full this questionnaire. Your views are very important for us.
The knowledge gained from this study will contribute to the growth and development
of retail industry using bid data retail analytics. The survey is designed to take a few
minutes of your time. It is very important that your answer every item on the
questionnaire.
If you have any quires or need for further clarification, feel free to contact Mr.
ALVR Chowdary, Research Scholar, ANU, Guntur at +971553992727. For e-mail
contact: chowdary2104@yahoo.com
Please note:
1)
There are no correct answers to the questions. We are only interested in
knowing your opinion
2)
Instructions and scales are provided at the top of each question. Please read
carefully before answering the question.
3)
Some items may appear to be similar, but they address different issues. Please
respond to all items.
4)
Lastly, I value your opinions and respect your privacy. I hereby promise that no
information about your name or identification will be directly used in the
research of for any other purpose.
239
Part-A
1. Please specify your Gender: (a) Male (
)
(b) Female (
)
2. Please mention your Age (in years):
(a) 25 – 35 Years (
)
( b ) 35 – 45 Years
(
)
(c) 45 – 55 Years (
)
( d) 55 – 65 Years
(
)
3. Please mention your Marital Status: (a) Married (
)
( b) Unmarried
(
)
4. Please mention your educational qualification:
(a) Degree (
)
(b ) Postgraduate (
)
5. Please specify your relevant organization in retailing:
(a) Apparel & Accessories (
)
(b) Food & Grocery
(
)
(c) Consumer Durables
)
(d) Entertainment
(
)
(
6. Please specify your designation/level in the organisation:
(a) Top level / Administrative level (
)
(b) Middle level / Executive (
(c) Low level / Supervisory
)
(d) Operative / First-line managers (
(
)
)
5. Please specify your expertise:(If relevant select more than one)
(a) Customer Service & CRM (
)
(b) Retail Logistic and Supply Chain ( )
(c) ICT
(
)
(d) Retail Merchandising
(e) Retail Communications
(
)
6. Please mention number of years of experience you have in above domain:
(a) Less than 5 years(
(d) 10 - 15 years (
)
)
(b) 5 - 10 years
(
)
(d) More than 15 years(
)
240
(
)
PART-B
)in the block for your responses
Please put a right mark (
1
Do you trust that your organization is working on big Data?
(a) YES ( )
2
(b) NO (
)
Do you think that using big data analytics are important for managerial decision
making?
(a) YES ( )
3
(b) NO (
Does your firm have a well-defined policy for analysing data
(a) YES ( )
4
)
(b) NO (
)
Which of the following big data analytics techniques (s) that your organisations is
currently using in managerial decision making?”?
5
(a) Predictive Modelling
(
)
(b) Optimisation methods
(
)
(c) Data mining
(
)
(d) Cluster Analysis
(
)
(e) Machine Learning
(
)
(f) Neural networks
(
)
How important, do you think, using big data analytics is, if at all, for retail
organization to stay competitive?
(b)Extremely Important
6
(
)
(b) Important
(
)
(c) Neither important nor unimportant (
)
(d) Unimportant
(
)
(e) Extremely Unimportant
)
(
How would you rate the access to relevant, accurate and timely big data in the retail
organizations?
(a) Minimal (
7
(b)< Adequate (
)(c) Adequate (
) (d) World Class (
)
How would you rate the business analytics capabilities in retail organizations?
(a) Minimal (
8.
)
)
(b)< Adequate (
)
(c) Adequate (
) (d) World Class (
Please indicate the number that best indicates your agreement or
disagreement with statements that as a retailer how would you define big
data analytics as
Strongly Agree
Agree
Neutral
Disagree
Strongly Disagree
5
4
3
2
1
241
)
a)
A tool for real-time market knowledge about the hottest trends
(
)
b) Identifying right customers
(
)
c)
(
)
d) Optimizing customer experiences
(
)
e)
Customer acquisition and retention strategies
(
)
f)
Customer satisfaction strategies
(
)
g) Forecasting local buying preferences
(
)
h) Predicting product affinities
(
)
i)
Forecasting demand for better inventory management
(
)
j)
Optimizing pricing
(
)
k) Product profitability
(
)
l)
(
)
(
)
What must be the objective of big data retail business analytics? (Please Rank
the following functions)
a. Customer Centric Outcomes
(
)
b. Operational Optimization
(
)
c. Risk Management
(
)
d. Financial Management
(
)
e. New Business Model
(
)
f. HR Analytics
(
)
Segmenting and targeting customers precisely
Operations and performance management
m) Supply chain and delivery channel strategy
Which Parameter of big data analytics is the most important as per you?
(a) Volume ( )
(b) Variety (
) (c) Velocity
(
)
(d) veracity (
)
How seriously is big data analytics taken by retail organizations in India in the
decision making?
(a) Not taken seriously (
)
(b) Little Seriously (
)
(c) Seriously
)
(d) Very Seriously (
)
(
What as per you is the most important element of big data retail business
analytics scenario in India?
(a) Need
(
)
(b) Data (
)
( c) Analytical model ( )
(d) Technology (
)
(e) Skills(
)
(f) Operationalization (
242
)
Please indicate the number that best indicates your agreement or
disagreement with statements that as a retailer what are the key
challenges in big data and analytics in retailing?
Strongly Agree
Agree
Neutral
Disagree
Strongly Disagree
5
4
3
2
1
(a) Understanding customers by establishing a single view across multiple (
sources of customer information (point-of-sale, loyalty program, social
media, etc.)
)
(b) Predict the consumer buying habits.
(
)
(c) Improving the accuracy of product data to support cross-channel
(
merchandising programs, discount pricing models and operations
management
(d)Enhancing the reliability of vendor information to support pricing (
negotiations, contract renewals, score carding and profitability analysis.
)
)
Please indicate the number that best indicates your agreement or
disagreement with statements concerning- What are the retailers'
obstacles in adopting big data retail business analytics in retail
organizations?
Strongly Agree
Agree
Neutral
Disagree
Strongly Disagree
5
4
3
2
1
a. Existing infrastructure is not sufficient
(
)
b. We do not know where to begin
(
)
c. Risk-averse corporate culture
(
)
d. Right tools are not available
(
)
e. Lack of right internal skills
(
)
f. Lack of understanding of data requirements
(
)
g. Difficult to justify from an ROI standpoint
(
)
h. Lack of visibility into information and processes
(
)
i. Lack of budget or resources
(
)
j. Security or compliance concerns
(
)
k. Organizational complexity
(
)
l. There are no major obstacles
(
)
243
16.
Please indicate the number that best indicates your agreement or
disagreement with statements concerning retailers' biggest obstacles in
getting big data analytics in order to make better data-driven business
decisions
Strongly Agree
Agree
Neutral
Disagree
Strongly Disagree
5
4
3
2
1
a. Different users and different departments have different ways of
measuring the business
b. Can't analyze data at a low enough level of detail (Ex:
Store/SKU/Day/Transaction/Customer)
c. Difficulty accessing and integrating the enterprise or 3rd party data users
need to analyze
d. Queries take too long to run
e. Reporting tools can't handle the level of sophistication of retailers'
business questions
f. Lack of self-service and long queues of reporting requests to IT
17
18
(
)
(
)
(
)
(
)
(
)
(
)
As per you, mention Business functions which are leveraged big data
analytics more strategically in retail organizations? (Please put a right
mark ( ) in the block for your responses, choose only one)
a. Merchandising
(
)
b.
Marketing
(
)
c.
Supply Chain
(
)
d.
Customer Insights
(
)
e.
Multi-channel
(
)
f.
Other, please specify _____________
(
)
Which business functions in the retail organizations stand to make the best
use of insights from big data retail business analytics? (Please Rank the
following functions)
a. Merchandising (including category management, buying planning, (
)
allocation)
(
)
b. Direct and Digital Marketing
(
)
c. Stores Operations
(
)
d. e-commerce, e-Business, Digital Operations
(
)
e. Supply chain
(
)
244
f. Finance
(
)
g. Fraud Management
(
)
h. Human Resource
(
)
i.
Risk management
(
)
j.
Product development/management
(
)
a. Targeted offers and promotions
(
)
b. Demand forecasting and supply chain modeling
(
)
c. Customer-centric merchandising
(
)
d. Loyalty program management
(
)
e. Store design
(
)
f. Loss prevention
(
)
k. Customer and market analysis
19
On which of these retail business processes do you think Big Data technology
can have the greatest impact? ( Please Rank the following functions)
20.
Please indicate the number that best indicates your agreement or
disagreement with statements related to “Why, if at all, do you think
retailer organizations are holding out on using big data solutions?”
Strongly Agree
Agree
Neutral
Disagree
Strongly Disagree
5
4
3
2
1
a. Retailers need to better understand how Big Data can solve their business (
)
problems
b. The cost and/or complexity of implementing of Big Data solutions needs to (
)
come down
c. Need simplified Big Data solutions that are intuitive to business users
(
)
d. Retailers are still challenged with basic business reporting and not ready for (
)
Big Data
e. Need Big Data solutions to better address the needs of retailers
(
)
f. Need better time to value for Big Data
(
)
g. Retailers aren't holding out on using Big Data
(
)
245
Please indicate the number that best indicates your agreement or
disagreement with statements related to “How can big data retail business
analytics help retailers do a better job of managing product availability for
consumers?”
Strongly Agree
Agree
Neutral
Disagree
Strongly Disagree
5
4
3
2
1
a. By reducing out-of-stock situations that lead to lost sales and dissatisfied (
)
customers
b. By predicting future demand to inform supply chain decisions
(
)
c. By reducing overstocks that negatively impact turns and could lead to (
)
margin erosion
d. By ensuring product assortments are finely turned to store and channel-based (
)
demand
e. By enabling alternative fulfillment means such as ship-to-store and ship- (
)
from-store.
What tangible business value / benefits do retail organizations hope to achieve
through big data retail business analytics to outperform competition? (Please put
a right mark ( ) in the block for your responses)
a. Improved customer experience
(
)
b. Increased sales
(
)
c. Higher quality products and services
(
)
d. New product innovations
(
)
e. More efficient operations
(
)
f. Better, fast-based decision making
(
)
g. Reduced risk
(
)
a. Digital Marketing and sales
(
)
b. Customer engagement / customer experience management
(
)
c. Operational processes
(
)
d. Inventory/stock management
(
)
e. Forecasting future trends
(
)
f. Staff Productivity
(
)
g. Building customer trust models
(
)
Which areas of retail business do you think benefit (or could benefit) the most
from IOT - Internet of Things technology? (Please put a right mark ( ) in the
block for your responses)
246
Please indicate the number that best indicates your agreement or
disagreement with statements “Which of the following the biggest
stumbling blocks in adopting IOT Technology?”
Strongly Agree
Agree
Neutral
Disagree
Strongly Disagree
5
4
3
2
1
a. Initial investment / Cost
(
)
b. Data Privacy and Security
(
)
c. Low consumer confidence over trust and security
(
)
d. Lack of a clear business model or business case
(
)
e. Technical issues with interoperability between different solutions
(
)
f. Fragmented eco-system, not enough successful partnerships being formed
(
)
g. Lack of legal clarity over standards and regulation
(
)
a. Improve operational transparency
(
)
b. Spot future business trends
(
)
c. Predict business performance
(
)
d. Increase business agility
(
)
e. Improve operational efficiency
(
)
f. Improve customer insight
(
)
a. Enterprise data warehouse
(
)
b. Olap + basic reporting& querying
(
)
How would you describe your organization’s use of big data retail business
analytics compared to your competitors? (Please put a right mark (✔) in the
block for your responses)
Better than our competitors ( ) At par with competitors (
Lagging our competitors (
)
)
What are the most important goals from big data retail business analytics in the
coming five years? (Please put a right mark ( ) in the block for your
responses)
What are the big data retail business analytics solutions that you are going to
invest and adopt over the next five years? (Please put a right mark ( ) in the
block for your responses)
247
c. Enterprise bi analytics tools
(
)
d. Web or social media analytics
(
)
e. Data visualization
(
)
f. Digital dashboards
(
)
g. Master data management
(
)
h. Big data analytics
(
)
i. Predictive analytics
(
)
j. Mobile business intelligence
(
)
PART-C
1.
Please indicate the number that best indicates your agreement or
disagreement with statements concerning the relationship between
customer process and customer acquisition in retailing.
Strongly Agree
Agree
Neutral
Disagree
Strongly Disagree
5
4
3
2
1
a
Your organization has a clear customer relationship management policy
(
)
b
Your organization views its customer relation as communication to describe
the objectives
Customer relation supports describing the current relationship between your
organization and the customers
Customer relation management is an important way to establish a successful
relationship with the customers
Your CRM system regularly and automatically updates the data contents
(
)
(
)
(
)
(
)
)
g
Company uses any basic information about the customers in order to attract (
them.
(
Company utilizes different analytical tools to attract the customers
h
Selecting a new customer is considered an important part of attraction
(
)
i
The quality of data existing has an impact on the attracted customer
(
)
j
Customer knowledge capture helps in understanding how to capture the
knowledge needed
Customer knowledge capture needs to determine the source of customer
knowledge
Customer knowledge capture is essential to test the reliability and
correctness of customer knowledge for further processing
Customer knowledge capture stage focuses on capturing customer
knowledge existing within the customers
(
)
(
)
(
)
(
)
c
d
e
f
k
l
m
248
)
n
The quality of data completeness has an impact on the analysis phase
(
)
o
)
p
Analysing customer’s data can help in predicting the behavior of the (
customers
(
Analysing data requires classification of the composed data
q
The analysis of customer data contributes to building knowledge of customer (
)
r
)
s
Your organization adopts certain analytical techniques for acquiring new (
customers
(
Marketing communication tools are used for acquiring new customers
t
Organization uses customer profiling
(
)
)
)
2. Is your retail organization more focused on acquisition or retention marketing?
(Please put a right mark ( ) in the block for your responses)
a
More focused on acquisition
(
)
b
more focused on retention
(
)
c
Equal focus on acquisition and retention.
(
)
PART-D
(Customer Acquisition Strategies)
1. Please indicate the number that best indicates your agreement or
disagreement with statements concerning customer acquisition strategies
driven by big data analytics.
a. By offering customized services
(
)
b. By offering wide-variety merchandize
(
)
(
)
e. By focusing on advertisement’s reliability
(
)
g. By offering the price discounts and other benefits
(
)
h. By creating and positioning well-known image
(
)
(
)
c. By establishing partnerships with other firms
d. By establishing stores at nearby locations
i. Contact by recommendations
j. Contact by emails and SMS
2. Please indicate the number that best indicates your agreement or
disagreement with statements “concerning the big data analytics versus
customer acquisition”
Strongly Agree
Agree
Neutral
Disagree
Strongly Disagree
5
4
3
2
1
249
a
b
Best retail analytics practices will define the framework of a customer (
acquisition strategy
It gathers customer information in real time over all distribution channels (
)
)
(telephone, sales, Internet...).
)
e
Companies use big data retail business analytics to decide to launch new, (
targeted products as an acquisition strategy.
It enables understand customer information like demographics, behavior or (
usage information and the average lifetime value.
(
It enables improvement of customer acquisition
g
Improvement in terms of regaining lost customers
(
)
h
Improvement in the terms of the expansion of customer relationships
(
)
i
It Increase understanding of unique consumer needs
(
)
j
It enables obtain 360º customers view to gain a deeper understanding of
customer sentiment from both internal and external sources
(
)
k
It enables gain buying pattern insights
(
)
l
It enables deliver valuable, personalized customer messages
(
)
c
d
)
)
3. What’s most effective practice for customer acquisition? (Please rank the
following functions)
a
Daily deals
(
)
b
Internet ads
(
)
c
Web listing sites (presumably directories)
(
)
d
Social media ads
(
)
e
Online coupons
(
)
4. What’s most effective for engaging existing customers (loyalty)? (Please Rank
the following practices)
a
Online survey tools
(
)
b
Digital loyalty/frequent shopper tracking systems
(
)
c
CRM systems
(
)
d
Email marketing
(
)
e
Contact management tools
(
)
250
5.
Which tools are effective at both attracting and engaging customers? (Please put
a right mark ( ) in the block for your responses, choose only one)
a Websites
(
)
b
Blogs
(
)
c
Social media
(
)
d
Video sites, like YouTube
(
)
e
Event management tools
(
)
f
Email marketing
(
)
PART-E
(Customer Retention Strategies)
1. Please indicate the number that best indicates your agreement or
disagreement with statements concerning the relationship between big data
analytics and customer retention strategies.
Strongly Agree
Agree
Neutral
Disagree
Strongly Disagree
5
4
3
2
1
a. Consistent quality
(
)
(
)
d. Good service quality at low price
(
)
e. Competitive prices
(
)
f. Switching cost
(
)
(
)
i. Advertisements as Reminder
(
)
j. Rational advertisement
(
)
k. Loyalty card programmes
(
)
(
)
n. Problem solving
(
)
o. Caring attitude
(
)
p. Skilled and experienced employees
(
)
(
)
b. Additional product/service categories
c. Uniqueness
g. Convenient location
h. Ease of parking facility
l. Surety of promotional offers
m. Reminder by emails and SMSs
q. Familiarity with service staff
251
(
)
Predict which consumers may be experiencing issues with a product or (
service
(
Reduction of customer migration
)
)
d
Make customized offers so that you can keep the customer satisfied and (
make a sale.
(
Improve customer experience through real-time data
e
Reduce product proliferation
(
)
f
Better identify needs of potential customers
(
)
g
Big data analysis offers companies a way to identify those shoppers (
who are the most valuable as returning customers.
(
It prevents customer churn and detect up selling opportunities
)
It creates successful customer loyalty and retention programs, and (
personalize consumer interactions in meaningful ways
)
r. Recognition as regular and special consumer
s. Familiarity with service surrounding
t. high level of emotional connect with the target consumers?
u. product/service prices are competitive
v. product/service prices are over overpriced as compared to others
w. Providing satisfactory customer service along with incentives to buy
again
2. Big data analytics versus Customer retention
a
b
c
h
i
3. Please pick the following customer retention strategies that you have adopted
(Please Rank the following strategies)
a. Blogs
(
)
)
)
)
b. CRM systems
(
)
c. Loyalty programs
(
)
d. Magic moments
(
)
e. Overcome buyer’s remorse
(
)
f. Personal touches
(
)
g. Premiums and Gifts
(
)
h. Questionnaires and Surveys
(
)
252
i. Regular reviews
(
)
j. Social media
(
)
k. Welcome book
(
)
Thanks for your participation in this study.
253
Appendix- B
Levene's Test of Equality of Error Variances (Q8-H1)
Dependent variables
A tool for real-time market knowledge about the hottest trends
F
10.470
df1
3
df2
576
Sig.
.000
Identifying right customers
8.945
3
576
.000
Segmenting and targeting customers precisely
5.413
3
576
.001
Optimizing customer experiences
27.958
3
576
.000
Customer acquisition and retention strategies
1.077
3
576
.358
Customer satisfaction strategies
13.420
3
576
.000
Forecasting local buying preferences
12.048
3
576
.000
Predicting product affinities
13.498
3
576
.000
Forecasting demand for better inventory management
9.138
3
576
.000
Optimizing pricing
15.646
3
576
.000
Product profitability
3.555
3
576
.014
Operations and performance management
10.830
3
576
.000
Supply chain and delivery channel strategy
29.114
3
576
.000
Tests the null hypothesis that the error variance of the dependent variable is equal across groups.
a. Design: Intercept + Type of retail organization
254
Univariate Analysis of Variance
Tests of Between-Subjects Effects
Source
Dependent Variable
Type III
Sum of
Squares
df
Mean
Square
F
Sig.
Partial Eta
Squared
Noncent.
Parameter
Observed
Powern
Corrected
Model
A tool for real-time market
knowledge about the hottest
trends
44.968a
3
14.989
7.576
.000
.038
22.727
.987
Identifying right customers
48.554b
3
16.185
6.736
.000
.034
20.208
.976
c
Segmenting and targeting
customers precisely
1.747
3
.582
.570
.635
.003
1.711
.168
Optimizing customer
experiences
10.778d
3
3.593
1.904
.128
.010
5.712
.493
Customer acquisition and
retention strategies
111.430e
3
37.143
27.694
.000
.126
83.082
1.000
Customer satisfaction
strategies
23.126f
3
7.709
5.733
.001
.029
17.200
.949
Forecasting local buying
preferences
43.021g
3
14.340
7.091
.000
.036
21.274
.981
Predicting product affinities
77.548h
3
25.849
14.571
.000
.071
43.712
1.000
Forecasting demand for
better inventory
management
41.474i
3
13.825
9.823
.000
.049
29.470
.998
Optimizing pricing
82.572j
3
27.524
14.125
.000
.069
42.375
1.000
k
3
.507
2.530
.056
.013
7.589
.625
l
3
1.195
2.944
.032
.015
8.831
.699
Product profitability
Operations and
performance management
1.522
3.586
255
7.658m
3
2.553
4.706
.003
.024
14.119
.897
A tool for real-time market
knowledge about the hottest
trends
5621.406
1
5621.40
2841.085
.000
.831
2841.085
1.000
Identifying right customers
5521.982
1
5521.98
2298.230
.000
.800
2298.230
1.000
Segmenting and targeting
customers precisely
9397.765
1
9397.76
9205.385
.000
.941
9205.385
1.000
Optimizing customer
experiences
6022.456
1
6022.45
3191.735
.000
.847
3191.735
1.000
Customer acquisition and
retention strategies
6985.413
1
6985.41
5208.258
.000
.900
5208.258
1.000
Customer satisfaction
strategies
8563.988
1
8563.98
8
6369.709
.000
.917
6369.709
1.000
Forecasting local buying
preferences
5113.073
1
5113.07
2528.462
.000
.814
2528.462
1.000
Predicting product affinities
3962.707
1
3962.70
2233.711
.000
.795
2233.711
1.000
Forecasting demand for
better inventory
management
6017.905
1
6017.90
4276.087
.000
.881
4276.087
1.000
Optimizing pricing
3316.419
1
3316.41
1701.940
.000
.747
1701.940
1.000
Product profitability
2138.990
1
2138.99
10668.52
7
.000
.949
10668.527
1.000
Operations and
performance management
1322.785
1
1322.78
3257.999
.000
.850
3257.999
1.000
Supply chain and delivery
channel strategy
1894.589
1
1894.58
3493.174
.000
.858
3493.174
1.000
44.968
3
14.989
7.576
.000
.038
22.727
.987
Supply chain and delivery
channel strategy
Intercept
Type of retail
organization
A tool for real-time market
knowledge about the hottest
trends
256
Error
Identifying right customers
48.554
3
16.185
6.736
.000
.034
20.208
.976
Segmenting and targeting
customers precisely
1.747
3
.582
.570
.635
.003
1.711
.168
Optimizing customer
experiences
10.778
3
3.593
1.904
.128
.010
5.712
.493
Customer acquisition and
retention strategies
111.430
3
37.143
27.694
.000
.126
83.082
1.000
Customer satisfaction
strategies
23.126
3
7.709
5.733
.001
.029
17.200
.949
Forecasting local buying
preferences
43.021
3
14.340
7.091
.000
.036
21.274
.981
Predicting product affinities
77.548
3
25.849
14.571
.000
.071
43.712
1.000
Forecasting demand for
better inventory
management
41.474
3
13.825
9.823
.000
.049
29.470
.998
Optimizing pricing
82.572
3
27.524
14.125
.000
.069
42.375
1.000
Product profitability
1.522
3
.507
2.530
.050
.013
7.589
.625
Operations and
performance management
3.586
3
1.195
2.944
.032
.015
8.831
.699
Supply chain and delivery
channel strategy
7.658
3
2.553
4.706
.003
.024
14.119
.897
A tool for real-time market
knowledge about the hottest
trends
1139.681
576
1.979
Identifying right customers
1383.962
576
2.403
Segmenting and targeting
customers precisely
588.038
576
1.021
Optimizing customer
experiences
1086.849
576
1.887
257
Total
Customer acquisition and
retention strategies
772.542
576
1.341
Customer satisfaction
strategies
774.424
576
1.344
Forecasting local buying
preferences
1164.791
576
2.022
Predicting product affinities
1021.851
576
1.774
Forecasting demand for
better inventory
management
810.627
576
1.407
Optimizing pricing
1122.400
576
1.949
Product profitability
115.485
576
.200
Operations and
performance management
233.863
576
.406
Supply chain and delivery
channel strategy
312.405
576
.542
A tool for real-time market
knowledge about the hottest
trends
6946.000
580
Identifying right customers
7327.000
580
Segmenting and targeting
customers precisely
10315.000
580
Optimizing customer
experiences
7348.000
580
Customer acquisition and
retention strategies
8372.000
580
Customer satisfaction
strategies
9847.000
580
Forecasting local buying
6689.000
580
258
preferences
Corrected
Total
Predicting product affinities
5333.000
580
Forecasting demand for
better inventory
management
7281.000
580
Optimizing pricing
4850.000
580
Product profitability
2342.000
580
Operations and
performance management
1634.000
580
Supply chain and delivery
channel strategy
2250.000
580
A tool for real-time market
knowledge about the hottest
trends
1184.648
579
Identifying right customers
1432.516
579
Segmenting and targeting
customers precisely
589.784
579
Optimizing customer
experiences
1097.628
579
Customer acquisition and
retention strategies
883.972
579
Customer satisfaction
strategies
797.550
579
Forecasting local buying
preferences
1207.812
579
Predicting product affinities
1099.398
579
Forecasting demand for
better inventory
management
852.102
579
259
Optimizing pricing
1204.972
579
Product profitability
117.007
579
Operations and
performance management
237.448
579
Supply chain and delivery
channel strategy
320.062
579
a. R Squared = .038 (Adjusted R Squared = .033)
b. R Squared = .034 (Adjusted R Squared = .029)
c. R Squared = .003 (Adjusted R Squared = -.002)
d. R Squared = .010 (Adjusted R Squared = .005)
e. R Squared = .126 (Adjusted R Squared = .122)
f. R Squared = .029 (Adjusted R Squared = .024)
g. R Squared = .036 (Adjusted R Squared = .031)
h. R Squared = .071 (Adjusted R Squared = .066)
i. R Squared = .049 (Adjusted R Squared = .044)
j. R Squared = .069 (Adjusted R Squared = .064)
k. R Squared = .013 (Adjusted R Squared = .008)
l. R Squared = .015 (Adjusted R Squared = .010)
m. R Squared = .024 (Adjusted R Squared = .019)
n. Computed using alpha = .05
260
Appendix-C
Significant ANOVAs with Tukey's HSD post-hoc tests
Multiple Comparisons
Tukey HSD
Dependent
Variable
A tool for real-time
market knowledge
about the hottest
trends
(I) Type of retail
organization
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment Retailing
Identifying the
right customer
Apparel retailing
Consumer Durable
retailing
(J) Type of retail
organization
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery
Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable
retailing
Entertainment Retailing
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery
Retailing
Entertainment Retailing
261
Mean
Difference (I-J)
Std.
Error
Sig.
95% Confidence Interval
Lower
Upper
Bound
Bound
-1.15
-.33
-.74*
.159
.000
-.46*
.154
.017
-.85
-.06
-.34
.74*
.28
.173
.159
.164
.191
.000
.310
-.79
.33
-.14
.10
1.15
.71
.40
.46*
-.28
.182
.154
.164
.131
.017
.310
-.07
.06
-.71
.86
.85
.14
.11
.34
-.40
.178
.173
.182
.923
.191
.131
-.35
-.10
-.86
.57
.79
.07
-.11
.178
.923
-.57
.35
.54*
.175
.011
.09
.99
-.11
.170
.922
-.54
.33
.52*
-.54*
-.65*
.190
.175
.181
.034
.011
.002
.03
-.99
-1.11
1.01
-.09
-.18
-.02
.200
.999
-.54
.49
Food & Grocery
Retailing
Entertainment Retailing
Segmenting
and
targeting customers
precisely
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment Retailing
Optimizing
customer
experiences
Apparel retailing
Consumer Durable
retailing
Apparel retailing
Consumer Durable
retailing
Entertainment Retailing
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery
Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable
retailing
Entertainment Retailing
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery
Retailing
262
.11
.65*
.170
.181
.922
.002
-.33
.18
.54
1.11
.62*
-.52*
.02
.196
.190
.200
.008
.034
.999
.12
-1.01
-.49
1.13
-.03
.54
-.62*
.196
.008
-1.13
-.12
-.07
.114
.926
-.36
.22
-.11
.111
.770
-.39
.18
-.15
.07
-.04
.124
.114
.118
.625
.926
.990
-.47
-.22
-.34
.17
.36
.27
-.08
.11
.04
.130
.111
.118
.931
.770
.990
-.41
-.18
-.27
.26
.39
.34
-.04
.15
.08
.128
.124
.130
.987
.625
.931
-.37
-.17
-.26
.29
.47
.41
.04
.128
.987
-.29
.37
.26
.155
.343
-.14
.66
.29
.151
.227
-.10
.67
.00
-.26
.03
.169
.155
.160
1.000
.343
.998
-.43
-.66
-.38
.44
.14
.44
Food & Grocery
Retailing
Entertainment Retailing
Customer
acquisition and
retention strategies
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment Retailing
Customer
Apparel retailing
satisfaction
strategies
Consumer Durable
retailing
Entertainment Retailing
Apparel retailing
Consumer Durable
retailing
Entertainment Retailing
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery
Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable
retailing
Entertainment Retailing
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery
263
-.25
-.29
-.03
.177
.151
.160
.477
.227
.998
-.71
-.67
-.44
.20
.10
.38
-.28
.00
.25
.174
.169
.177
.361
1.000
.477
-.73
-.44
-.20
.16
.43
.71
.28
.174
.361
-.16
.73
-.50*
.131
.001
-.83
-.16
.01
.127
1.000
-.31
.34
.86*
.50*
.51*
.142
.131
.135
.000
.001
.001
.50
.16
.16
1.23
.83
.86
1.36*
-.01
-.51*
.150
.127
.135
.000
1.000
.001
.97
-.34
-.86
1.74
.31
-.16
.85*
-.86*
-1.36*
.146
.142
.150
.000
.000
.000
.47
-1.23
-1.74
1.23
-.50
-.97
-.85*
.146
.000
-1.23
-.47
.21
.131
.367
-.12
.55
-.15
.127
.621
-.48
.17
.40*
-.21
-.37*
.142
.131
.135
.024
.367
.035
.04
-.55
-.71
.77
.12
-.02
Food & Grocery
Retailing
Entertainment Retailing
Forecasting
local
Apparel retailing
buying preferences
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment Retailing
Predicting
product
Apparel retailing
affinities
Consumer Durable
Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable
retailing
Entertainment Retailing
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery
Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable
retailing
Entertainment Retailing
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment Retailing
Apparel retailing
264
.19
.15
.37*
.150
.127
.135
.571
.621
.035
-.19
-.17
.02
.58
.48
.71
.56*
-.40*
-.19
.147
.142
.150
.001
.024
.571
.18
-.77
-.58
.94
-.04
.19
-.56*
.147
.001
-.94
-.18
.49*
.160
.012
.08
.90
.48*
.156
.011
.08
.88
.74*
-.49*
-.01
.175
.160
.166
.000
.012
1.000
.29
-.90
-.44
1.19
-.08
.42
.25
-.48*
.01
.184
.156
.166
.524
.011
1.000
-.22
-.88
-.42
.72
-.08
.44
.26
-.74*
-.25
.180
.175
.184
.471
.000
.524
-.20
-1.19
-.72
.72
-.29
.22
-.26
.180
.471
-.72
.20
.34
.150
.105
-.05
.73
.90*
.146
.000
.53
1.28
.72*
-.34
.164
.150
.000
.105
.30
-.73
1.14
.05
retailing
Food & Grocery
Retailing
Entertainment Retailing
Forecasting demand
Apparel retailing
for better inventory
management
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment Retailing
Optimizing pricing
Apparel retailing
Food & Grocery
Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable
retailing
Entertainment Retailing
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery
Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable
retailing
Entertainment Retailing
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment Retailing
265
.56*
.155
.002
.16
.96
.38
-.90*
-.56*
.172
.146
.155
.128
.000
.002
-.07
-1.28
-.96
.82
-.53
-.16
-.19
-.72*
-.38
.168
.164
.172
.685
.000
.128
-.62
-1.14
-.82
.25
-.30
.07
.19
.168
.685
-.25
.62
.59*
.134
.000
.25
.94
.37*
.130
.023
.04
.71
.68*
-.59*
-.22
.146
.134
.138
.000
.000
.374
.30
-.94
-.58
1.05
-.25
.13
.08
-.37*
.22
.153
.130
.138
.952
.023
.374
-.31
-.71
-.13
.48
-.04
.58
.30
-.68*
-.08
.150
.146
.153
.180
.000
.952
-.08
-1.05
-.48
.69
-.30
.31
-.30
.150
.180
-.69
.08
.11
.157
.908
-.30
.51
.30
.153
.198
-.09
.70
1.06*
.171
.000
.62
1.50
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment Retailing
Product profitability
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment Retailing
Operations and
performance
management
Apparel retailing
Apparel retailing
Food & Grocery
Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable
retailing
Entertainment Retailing
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery
Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable
retailing
Entertainment Retailing
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Consumer Durable
retailing
Food & Grocery
Retailing
266
-.11
.20
.157
.163
.908
.621
-.51
-.22
.30
.62
.96*
-.30
-.20
.180
.153
.163
.000
.198
.621
.49
-.70
-.62
1.42
.09
.22
.76*
-1.06*
-.96*
.176
.171
.180
.000
.000
.000
.31
-1.50
-1.42
1.21
-.62
-.49
-.76*
.176
.000
-1.21
-.31
-.12
.050
.093
-.25
.01
-.08
.049
.409
-.20
.05
.01
.12
.04
.055
.050
.052
.999
.093
.857
-.14
-.01
-.09
.15
.25
.18
.12
.08
-.04
.058
.049
.052
.141
.409
.857
-.03
-.05
-.18
.27
.20
.09
.08
-.01
-.12
.057
.055
.058
.466
.999
.141
-.06
-.15
-.27
.23
.14
.03
-.08
.057
.466
-.23
.06
.03
.072
.967
-.15
.22
.14
.070
.206
-.04
.32
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment Retailing
Supply chain and
delivery channel
strategy
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery
Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable
retailing
Entertainment Retailing
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery
Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable
retailing
Entertainment Retailing
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Based on observed means.
The error term is Mean Square(Error) = .542.
*. The mean difference is significant at the .05 level.
267
.20*
-.03
.10
.078
.072
.074
.045
.967
.506
.00
-.22
-.09
.41
.15
.29
.17
-.14
-.10
.082
.070
.074
.160
.206
.506
-.04
-.32
-.29
.38
.04
.09
.07
-.20*
-.17
.081
.078
.082
.832
.045
.160
-.14
-.41
-.38
.28
.00
.04
-.07
.081
.832
-.28
.14
-.26*
.083
.010
-.47
-.05
-.18
.081
.104
-.39
.02
-.28*
.26*
.08
.090
.083
.086
.010
.010
.811
-.51
.05
-.14
-.05
.47
.30
-.02
.18
-.08
.095
.081
.086
.996
.104
.811
-.27
-.02
-.30
.22
.39
.14
-.10
.28*
.02
.093
.090
.095
.720
.010
.996
-.34
.05
-.22
.14
.51
.27
.10
.093
.720
-.14
.34
Appendix-D
Levene's Test of Equality of Error Variancesa
Dependent variable
F
df1
df2
Sig.
5.616
3
576
.001
We do not know where to begin
3.610
3
576
.013
Risk-averse corporate culture
4.521
3
576
.004
Right tools are not available
7.956
3
576
.000
Lack of right internal skills
4.214
3
576
.006
data
5.957
3
576
.001
Difficult to justify from an ROI
8.126
3
576
.000
2.782
3
576
.040
Lack of budget or resources
23.754
3
576
.000
Security or compliance concerns
31.434
3
576
.000
Organizational complexity
13.062
3
576
.000
There are no major obstacles
11.645
3
576
.178
Existing
infrastructure
is
not
sufficient
Lack
of
understanding
of
requirements
standpoint
Lack of visibility into information and
processes
Tests the null hypothesis that the error variance of the dependent variable is equal across groups.
a. Design: Intercept + Type of retail Organization
268
Source
Corrected Model
Intercept
Tests of Between-Subjects Effects
Type III Sum of
df
Squares
Existing infrastructure is not
1.975a
3
sufficient
We do not know where to
22.218b
3
begin
Risk-averse corporate
9.419c
3
culture
Right tools are not available
27.387d
3
Lack of right internal skills
1.318e
3
Lack of understanding of
14.064f
3
data requirements
Difficult to justify from an
7.151g
3
ROI standpoint
Lack of visibility into
43.254h
3
information and processes
Lack of budget or resources
47.621i
3
Security or compliance
36.195j
3
concerns
Organizational complexity
6.648k
3
l
There are no major
16.467
3
obstacles
Existing infrastructure is not
2684.308
1
sufficient
We do not know where to
2630.810
1
begin
Risk-averse corporate
3043.136
1
culture
Right tools are not available
2594.319
1
Lack of right internal skills
2093.033
1
Lack of understanding of
2279.868
1
data requirements
Dependent Variable
269
Mean Square
F
Sig.
.658
.497
.684
7.406
5.330
.001
3.140
2.184
.089
9.129
.439
4.688
9.682
.746
4.902
.000
.525
.002
2.384
3.633
.013
14.418
13.130
.000
15.874
12.065
17.659
10.142
.000
.000
2.216
5.489
2.905
7.720
.034
.000
2684.308
2026.400
.000
2630.810
1893.414
.000
3043.136
2116.982
.000
2594.319
2093.033
2279.868
2751.463
3552.931
2383.953
.000
.000
.000
Type of retail
organization
Error
Difficult to justify from an
ROI standpoint
Lack of visibility into
information and processes
Lack of budget or resources
Security or compliance
concerns
Organizational complexity
There are no major
obstacles
Existing infrastructure is not
sufficient
We do not know where to
begin
Risk-averse corporate
culture
Right tools are not available
Lack of right internal skills
Lack of understanding of
data requirements
Difficult to justify from an
ROI standpoint
Lack of visibility into
information and processes
Lack of budget or resources
Security or compliance
concerns
Organizational complexity
There are no major
obstacles
Existing infrastructure is not
sufficient
We do not know where to
begin
1694.219
1
1694.219
2581.945
.000
2348.877
1
2348.877
2139.093
.000
3335.340
2802.548
1
1
3335.340
2802.548
3710.392
2355.848
.000
.000
1943.640
1872.280
1
1
1943.640
1872.280
2547.914
2633.103
.000
.000
1.975
3
.658
.497
.684
22.218
3
7.406
5.330
.001
9.419
3
3.140
2.184
.005
27.387
1.318
14.064
3
3
3
9.129
.439
4.688
9.682
.746
4.902
.000
.525
.002
7.151
3
2.384
3.633
.013
43.254
3
14.418
13.130
.000
47.621
36.195
3
3
15.874
12.065
17.659
10.142
.000
.000
6.648
16.467
3
3
2.216
5.489
2.905
7.720
.034
.000
763.009
576
1.325
800.325
576
1.389
270
Total
Risk-averse corporate
culture
Right tools are not available
Lack of right internal skills
Lack of understanding of
data requirements
Difficult to justify from an
ROI standpoint
Lack of visibility into
information and processes
Lack of budget or resources
Security or compliance
concerns
Organizational complexity
There are no major
obstacles
Existing infrastructure is not
sufficient
We do not know where to
begin
Risk-averse corporate
culture
Right tools are not available
Lack of right internal skills
Lack of understanding of
data requirements
Difficult to justify from an
ROI standpoint
Lack of visibility into
information and processes
Lack of budget or resources
Security or compliance
concerns
Organizational complexity
827.993
576
1.437
543.103
339.322
550.852
576
576
576
.943
.589
.956
377.959
576
.656
632.489
576
1.098
517.777
685.217
576
576
.899
1.190
439.393
409.567
576
576
.763
.711
3559.000
580
3495.000
580
3975.000
580
3162.000
2515.000
2913.000
580
580
580
2130.000
580
3105.000
580
3911.000
3595.000
580
580
2472.000
580
271
There are no major
obstacles
Corrected Total
Existing infrastructure is not
sufficient
We do not know where to
begin
Risk-averse corporate
culture
Right tools are not available
Lack of right internal skills
Lack of understanding of
data requirements
Difficult to justify from an
ROI standpoint
Lack of visibility into
information and processes
Lack of budget or resources
Security or compliance
concerns
Organizational complexity
There are no major
obstacles
a. R Squared = .003 (Adjusted R Squared = -.003)
b. R Squared = .027 (Adjusted R Squared = .022)
c. R Squared = .011 (Adjusted R Squared = .006)
d. R Squared = .048 (Adjusted R Squared = .043)
e. R Squared = .004 (Adjusted R Squared = -.001)
f. R Squared = .025 (Adjusted R Squared = .020)
g. R Squared = .019 (Adjusted R Squared = .013)
h. R Squared = .064 (Adjusted R Squared = .059)
i. R Squared = .084 (Adjusted R Squared = .079)
j. R Squared = .050 (Adjusted R Squared = .045)
k. R Squared = .015 (Adjusted R Squared = .010)
l. R Squared = .039 (Adjusted R Squared = .034)
2400.000
580
764.984
579
822.543
579
837.412
579
570.490
340.640
564.916
579
579
579
385.110
579
675.743
579
565.398
721.412
579
579
446.041
426.034
579
579
272
Appendix-E
Multiple Comparisons
Tukey HSD
Dependent
Variable
(I) Type of retail
Existing
infrastructure is
not sufficient
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment
Retailing
We do not know
where to begin
(J) Type of retail Organization
Mean
Difference
(I-J)
Std.
Error
Sig.
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
.15
.03
.01
-.15
-.12
-.14
-.03
.12
-.02
-.01
.14
.02
.09
-.26
-.43*
-.09
-.35
-.53*
.26
.35
-.18
.43*
.53*
.130
.126
.141
.130
.134
.149
.126
.134
.145
.141
.149
.145
.133
.129
.145
.133
.137
.152
.129
.137
.149
.145
.152
.674
.997
1.000
.674
.808
.798
.997
.808
1.000
1.000
.798
1.000
.895
.198
.015
.895
.055
.003
.198
.055
.623
.015
.003
Organization
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment
Retailing
273
95% Confidence
Interval
Lower
Upper
Bound
Bound
-.19
.48
-.30
.35
-.35
.37
-.48
.19
-.47
.23
-.52
.25
-.35
.30
-.23
.47
-.39
.36
-.37
.35
-.25
.52
-.36
.39
-.25
.44
-.59
.08
-.81
-.06
-.44
.25
-.70
.01
-.92
-.14
-.08
.59
-.01
.70
-.56
.20
.06
.81
.14
.92
Risk-averse
corporate
culture
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment
Retailing
Right tools are
not available
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment
Retailing
Lack of right
internal skills
Apparel retailing
Consumer Durable
retailing
Food & Grocery Retailing
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
.18
-.34
-.10
-.12
.34
.24
.22
.10
-.24
-.02
.12
-.22
.02
.149
.135
.131
.147
.135
.140
.155
.131
.140
.152
.147
.155
.152
.623
.057
.864
.848
.057
.320
.481
.864
.320
.999
.848
.481
.999
-.20
-.69
-.44
-.50
-.01
-.12
-.18
-.24
-.60
-.41
-.26
-.62
-.37
.56
.01
.24
.26
.69
.60
.62
.44
.12
.37
.50
.18
.41
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery Retailing
-.33*
-.13
-.61*
.33*
.20
-.27
.13
-.20
-.48*
.61*
.27
.48*
.01
.11
.02
-.01
.11
.109
.106
.119
.109
.113
.125
.106
.113
.123
.119
.125
.123
.086
.084
.094
.086
.089
.013
.610
.000
.013
.282
.129
.610
.282
.001
.000
.129
.001
1.000
.527
.997
1.000
.635
-.61
-.40
-.91
.05
-.09
-.60
-.14
-.49
-.79
.30
-.05
.16
-.21
-.10
-.22
-.23
-.12
-.05
.14
-.30
.61
.49
.05
.40
.09
-.16
.91
.60
.79
.23
.33
.26
.21
.34
274
Food & Grocery
Retailing
Entertainment
Retailing
Lack of
understanding
of data
requirements
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment
Retailing
Difficult to
justify from an
ROI standpoint
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment
Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
275
.01
-.11
-.11
-.09
-.02
-.01
.09
.23
.31*
-.09
-.23
.08
-.31
-.31*
-.08
-.39*
.09
.31
.39*
.01
.23*
-.06
-.01
.22
-.07
-.23*
-.22
-.29*
.06
.07
.29*
.099
.084
.089
.097
.094
.099
.097
.110
.107
.120
.110
.114
.126
.107
.114
.124
.120
.126
.124
.091
.089
.099
.091
.094
.105
.089
.094
.102
.099
.105
.102
.999
.527
.635
.765
.997
.999
.765
.170
.022
.892
.170
.892
.066
.022
.892
.008
.892
.066
.008
.999
.043
.935
.999
.084
.912
.043
.084
.023
.935
.912
.023
-.24
-.33
-.34
-.34
-.26
-.27
-.16
-.06
.03
-.40
-.51
-.21
-.64
-.58
-.38
-.71
-.22
-.01
.07
-.22
.01
-.32
-.25
-.02
-.34
-.46
-.47
-.56
-.20
-.20
.03
.27
.10
.12
.16
.22
.24
.34
.51
.58
.22
.06
.38
.01
-.03
.21
-.07
.40
.64
.71
.25
.46
.20
.22
.47
.20
-.01
.02
-.03
.32
.34
.56
Lack of
visibility into
information and
processes
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment
Retailing
Lack of budget
or resources
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment
Retailing
Security or
compliance
concerns
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
276
-.03
-.66*
-.23
.03
-.63*
-.21
.66*
.63*
.42*
.23
.21
-.42*
-.65*
-.52*
-.67*
.65*
.13
-.02
.52*
-.13
-.15
.67*
.02
.15
-.37*
-.65*
-.34
.37*
-.28
.03
.65*
.118
.115
.129
.118
.122
.135
.115
.122
.132
.129
.135
.132
.107
.104
.116
.107
.111
.122
.104
.111
.120
.116
.122
.120
.123
.120
.134
.123
.127
.141
.120
.995
.000
.262
.995
.000
.419
.000
.000
.009
.262
.419
.009
.000
.000
.000
.000
.628
.998
.000
.628
.573
.000
.998
.573
.014
.000
.057
.014
.117
.995
.000
-.33
-.95
-.57
-.28
-.94
-.56
.36
.31
.08
-.10
-.14
-.76
-.93
-.79
-.97
.38
-.15
-.34
.25
-.42
-.46
.37
-.29
-.15
-.69
-.96
-.68
.05
-.61
-.33
.35
.28
-.36
.10
.33
-.31
.14
.95
.94
.76
.57
.56
-.08
-.38
-.25
-.37
.93
.42
.29
.79
.15
.15
.97
.34
.46
-.05
-.35
.01
.69
.04
.40
.96
Retailing
Consumer Durable retailing
Entertainment Retailing
Entertainment
Apparel retailing
Retailing
Consumer Durable retailing
Food & Grocery Retailing
Organizational
Apparel retailing
Consumer Durable retailing
complexity
Food & Grocery Retailing
Entertainment Retailing
Consumer Durable
Apparel retailing
retailing
Food & Grocery Retailing
Entertainment Retailing
Food & Grocery
Apparel retailing
Retailing
Consumer Durable retailing
Entertainment Retailing
Entertainment
Apparel retailing
Retailing
Consumer Durable retailing
Food & Grocery Retailing
There are no
Apparel retailing
Consumer Durable retailing
major
Food & Grocery Retailing
obstacles
Entertainment Retailing
Consumer Durable
Apparel retailing
retailing
Food & Grocery Retailing
Entertainment Retailing
Food & Grocery
Apparel retailing
Retailing
Consumer Durable retailing
Entertainment Retailing
Entertainment
Apparel retailing
Retailing
Consumer Durable retailing
Food & Grocery Retailing
Based on observed means. The error term is Mean Square (Error) = .711.
*. The mean difference is significant at the .05 level.
277
.28
.32
.34
-.03
-.32
.24
.23
.07
-.24
.00
-.17
-.23
.00
-.17
-.07
.17
.17
-.09
-.35*
.11
.09
-.27*
.20
.35*
.27*
.47*
-.11
-.20
-.47*
.127
.138
.134
.141
.138
.098
.096
.107
.098
.102
.113
.096
.102
.110
.107
.113
.110
.095
.092
.104
.095
.098
.109
.092
.098
.107
.104
.109
.107
.117
.100
.057
.995
.100
.079
.073
.925
.079
1.000
.439
.073
1.000
.438
.925
.439
.438
.804
.001
.701
.804
.034
.267
.001
.034
.000
.701
.267
.000
-.04
-.04
-.01
-.40
-.67
-.02
-.01
-.21
-.49
-.27
-.46
-.48
-.26
-.45
-.34
-.12
-.12
-.33
-.59
-.15
-.16
-.52
-.08
.12
.01
.19
-.38
-.48
-.74
.61
.67
.68
.33
.04
.49
.48
.34
.02
.26
.12
.01
.27
.12
.21
.46
.45
.16
-.12
.38
.33
-.01
.48
.59
.52
.74
.15
.08
-.19
Appendix-F
Levene's Test of Equality of Error Variancesa
Dependent variable
Delivery of insights to the right resource at the right time
Lack of clearly articulated analytics strategy
Inadequate analytics resources
Poor data quality
Outdated software and tools
Difficulty in measuring analytics ROI
Management style restraining data-driven decisions
Previous failure in analytics investment
Tests the null hypothesis that the error variance of the dependent variable is equal across groups.
a. Design: Intercept + Retail organization
Source
Dependent Variable
Corrected
Model
Delivery of insights to the right
resource at the right time
Lack of clearly articulated analytics
strategy
Inadequate analytics resources
Poor data quality
Outdated software and tools
Difficulty in measuring analytics
ROI
Management style restraining datadriven decisions
Previous failure in analytics
investment
Delivery of insights to the right
resource at the right time
Intercept
Tests of Between-Subjects Effects
Type III
Df
Mean
Sum of
Square
Squares
27.213a
3
9.071
F
6.595
11.955
28.252
.451
6.171
8.679
7.837
45.727
F
Sig.
4.779
df1
3
3
3
3
3
3
3
3
df2
576
576
576
576
576
576
576
576
Sig.
.000
.000
.000
.717
.000
.000
.000
.000
Noncent.
Parameter
Observed
Poweri
.003
Partial
Eta
Squared
.024
14.336
.901
93.787b
3
31.262
15.278
.000
.074
45.835
1.000
105.579c
65.562d
60.284e
56.771f
3
3
3
3
35.193
21.854
20.095
18.924
19.636
23.469
12.588
8.672
.000
.000
.000
.000
.093
.109
.062
.043
58.907
70.408
37.763
26.015
1.000
1.000
1.000
.995
5.334g
3
1.778
.953
.415
.005
2.858
.261
46.162h
3
15.387
12.295
.000
.060
36.885
1.000
3936.766
1
3936.766
2073.915
.000
.783
2073.915
1.000
278
Retail
Organization
Error
Lack of clearly articulated analytics
strategy
Inadequate analytics resources
Poor data quality
Outdated software and tools
Difficulty in measuring analytics
ROI
Management style restraining datadriven decisions
Previous failure in analytics
investment
Delivery of insights to the right
resource at the right time
Lack of clearly articulated analytics
strategy
Inadequate analytics resources
Poor data quality
Outdated software and tools
Difficulty in measuring analytics
ROI
Management style restraining datadriven decisions
Previous failure in analytics
investment
Delivery of insights to the right
resource at the right time
Lack of clearly articulated analytics
strategy
Inadequate analytics resources
Poor data quality
Outdated software and tools
Difficulty in measuring analytics
ROI
Management style restraining datadriven decisions
3553.145
1
3553.145
1736.490
.000
.751
1736.490
1.000
3796.709
3322.604
3200.072
4157.232
1
1
1
1
3796.709
3322.604
3200.072
4157.232
2118.332
3568.161
2004.612
1905.006
.000
.000
.000
.000
.786
.861
.777
.768
2118.332
3568.161
2004.612
1905.006
1.000
1.000
1.000
1.000
3987.777
1
3987.777
2136.671
.000
.788
2136.671
1.000
2816.973
1
2816.973
2250.830
.000
.796
2250.830
1.000
27.213
3
9.071
4.779
.003
.024
14.336
.901
93.787
3
31.262
15.278
.000
.050
45.835
1.000
105.579
65.562
60.284
56.771
3
3
3
3
35.193
21.854
20.095
18.924
19.636
23.469
12.588
8.672
.000
.000
.000
.000
.093
.109
.062
.043
58.907
70.408
37.763
26.015
1.000
1.000
1.000
.995
5.334
3
1.778
.953
.415
.005
2.858
.261
46.162
3
15.387
12.295
.000
.050
36.885
1.000
1093.380
576
1.898
1178.591
576
2.046
1032.371
536.360
919.501
1256.986
576
576
576
576
1.792
.931
1.596
2.182
1075.018
576
1.866
279
Total
Corrected
Total
Previous failure in analytics
investment
Delivery of insights to the right
resource at the right time
Lack of clearly articulated analytics
strategy
Inadequate analytics resources
Poor data quality
Outdated software and tools
Difficulty in measuring analytics
ROI
Management style restraining datadriven decisions
Previous failure in analytics
investment
Delivery of insights to the right
resource at the right time
Lack of clearly articulated analytics
strategy
Inadequate analytics resources
Poor data quality
Outdated software and tools
Difficulty in measuring analytics
ROI
Management style restraining datadriven decisions
Previous failure in analytics
investment
720.879
576
5338.000
580
5095.000
580
5211.000
4103.000
4335.000
5515.000
580
580
580
580
5244.000
580
3654.000
580
1120.593
579
1272.378
579
1137.950
601.922
979.784
1313.757
579
579
579
579
1080.352
579
767.041
579
a. R Squared = .024 (Adjusted R Squared = .019)
b. R Squared = .074 (Adjusted R Squared = .069)
c. R Squared = .093 (Adjusted R Squared = .088)
d. R Squared = .109 (Adjusted R Squared = .104)
e. R Squared = .062 (Adjusted R Squared = .057)
f. R Squared = .043 (Adjusted R Squared = .038)
g. R Squared = .005 (Adjusted R Squared = .000)
h. R Squared = .060 (Adjusted R Squared = .055)
i. Computed using alpha = .05
280
1.252
Appendix-G
Multiple Comparisons
Tukey HSD
Dependent Variable
Delivery of insights to the
right resource at the right time
(I) Type of retail organization
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Lack of clearly articulated
analytics strategy
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Inadequate analytics
resources
Apparel retailing
Consumer Durable retailing
(J) Type of retail organization
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery Retailing
281
Mean
Difference
(I-J)
Std.
Error
Sig.
.06
.02
.58*
-.06
-.04
.53*
-.02
.04
.56*
-.58*
-.53*
-.56*
.89*
.91*
.75*
-.89*
.02
-.14
-.91*
-.02
-.16
-.75*
.14
.16
.57*
1.09*
.80*
-.57*
.52*
.155
.151
.169
.155
.161
.178
.151
.161
.174
.169
.178
.174
.161
.157
.176
.161
.167
.185
.157
.167
.181
.176
.185
.181
.151
.147
.164
.151
.156
.982
.999
.003
.982
.996
.017
.999
.996
.007
.003
.017
.007
.000
.000
.000
.000
.999
.878
.000
.999
.819
.000
.878
.819
.001
.000
.000
.001
.005
95% Confidence
Interval
Lower
Upper
Bound
Bound
-.34
.46
-.37
.41
.15
1.02
-.46
.34
-.45
.38
.07
.98
-.41
.37
-.38
.45
.11
1.01
-1.02
-.15
-.98
-.07
-1.01
-.11
.47
1.31
.51
1.31
.30
1.20
-1.31
-.47
-.41
.45
-.61
.34
-1.31
-.51
-.45
.41
-.62
.31
-1.20
-.30
-.34
.61
-.31
.62
.18
.95
.71
1.46
.38
1.23
-.95
-.18
.12
.92
Food & Grocery Retailing
Entertainment Retailing
Poor data quality
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Outdated software and tools
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Difficulty in measuring
analytics ROI
Apparel retailing
Consumer Durable retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery Retailing
Entertainment Retailing
282
.24
-1.09*
-.52*
-.28
-.80*
-.24
.28
.56*
.87*
.39*
-.56*
.31*
-.17
-.87*
-.31*
-.48*
-.39*
.17
.48*
.53*
.82*
.29
-.53*
.30
-.24
-.82*
-.30
-.53*
-.29
.24
.53*
-.82*
-.25
-.51*
.82*
.56*
.30
.173
.147
.156
.169
.164
.173
.169
.109
.106
.118
.109
.113
.125
.106
.113
.122
.118
.125
.122
.142
.138
.155
.142
.147
.163
.138
.147
.160
.155
.163
.160
.166
.162
.181
.166
.172
.191
.514
.000
.005
.337
.000
.514
.337
.000
.000
.006
.000
.031
.527
.000
.031
.001
.006
.527
.001
.001
.000
.239
.001
.181
.472
.000
.181
.005
.239
.472
.005
.000
.398
.024
.000
.006
.388
-.21
-1.46
-.92
-.72
-1.23
-.68
-.15
.28
.60
.08
-.84
.02
-.49
-1.14
-.60
-.79
-.69
-.15
.16
.16
.47
-.11
-.89
-.08
-.66
-1.18
-.68
-.95
-.69
-.18
.12
-1.25
-.67
-.98
.39
.12
-.19
.68
-.71
-.12
.15
-.38
.21
.72
.84
1.14
.69
-.28
.60
.15
-.60
-.02
-.16
-.08
.49
.79
.89
1.18
.69
-.16
.68
.18
-.47
.08
-.12
.11
.66
.95
-.39
.16
-.05
1.25
1.01
.79
Food & Grocery Retailing
Entertainment Retailing
Management style restraining
data-driven decisions
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Previous failure in analytics
investment
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
Based on observed means.
The error term is Mean Square (Error) = 1.252.
*. The mean difference is significant at the .05 level.
283
.25
-.56*
-.26
.51*
-.30
.26
.25
.04
.07
-.25
-.21
-.18
-.04
.21
.03
-.07
.18
-.03
-.42*
-.74*
-.36*
.42*
-.32
.06
.74*
.32
.38*
.36*
-.06
-.38*
.162
.172
.187
.181
.191
.187
.154
.150
.168
.154
.159
.176
.150
.159
.173
.168
.176
.173
.126
.123
.137
.126
.130
.144
.123
.130
.141
.137
.144
.141
.398
.006
.503
.024
.388
.503
.378
.994
.979
.378
.558
.734
.994
.558
.999
.979
.734
.999
.005
.000
.043
.005
.068
.979
.000
.068
.039
.043
.979
.039
-.16
-1.01
-.74
.05
-.79
-.22
-.15
-.35
-.37
-.64
-.62
-.64
-.42
-.20
-.42
-.50
-.27
-.47
-.74
-1.06
-.72
.09
-.66
-.31
.42
-.02
.01
.01
-.43
-.74
.67
-.12
.22
.98
.19
.74
.64
.42
.50
.15
.20
.27
.35
.62
.47
.37
.64
.42
-.09
-.42
-.01
.74
.02
.43
1.06
.66
.74
.72
.31
-.01
Appendix-H
Levene's Test of Equality of Error Variancesa
Dependent variable
F
Retailers need to better understand how Big Data can solve their business problems
df1
df2
Sig.
9.617
3
576
.000
The cost and/or complexity of implementing of Big Data solutions needs to come down
30.545
3
576
.000
Need simplified Big Data solutions that are intuitive to business users
70.338
3
576
.000
Retailers are still challenged with basic business reporting and not ready for Big Data
19.887
3
576
.000
Need Big Data solutions to better address the needs of retailers
26.625
3
576
.000
Need better time to value for Big Data
14.482
3
576
.000
7.027
3
576
.000
Retailers aren't holding out on using Big Data
Tests the null hypothesis that the error variance of the dependent variable is equal across groups.
a. Design: Intercept + Retail organization
284
Tests of Between-Subjects Effects
Source
Dependent Variable
Corrected
Model
Retailers need to better understand how Big Data can solve
their business problems
The cost and/or complexity of implementing of Big Data
solutions needs to come down
Need simplified Big Data solutions that are intuitive to
business users
Retailers are still challenged with basic business reporting and
not ready for Big Data
Need Big Data solutions to better address the needs of retailers
Need better time to value for Big Data
Retailers aren't holding out on using Big Data
Retailers need to better understand how Big Data can solve
their business problems
The cost and/or complexity of implementing of Big Data
solutions needs to come down
Need simplified Big Data solutions that are intuitive to
business users
Retailers are still challenged with basic business reporting and
not ready for Big Data
Need Big Data solutions to better address the needs of retailers
Need better time to value for Big Data
Retailers aren't holding out on using Big Data
Retailers need to better understand how Big Data can solve
Intercept
Retail
285
Type III
Sum of
Squares
37.662a
df
Mean
Square
F
Sig.
3
12.554
9.991
.000
437.204b
3
145.735
86.703
.000
248.167c
3
82.722
48.777
.000
80.658d
3
26.886
25.743
.000
61.327e
51.257f
21.655g
6075.931
3
3
3
1
20.442
17.086
7.218
6075.931
19.520
8.525
3.849
4835.650
.000
.000
.010
.000
4205.067
1
4205.067
2501.754
.000
3609.080
1
3609.080
2128.072
.000
3279.699
1
3279.699
3140.235
.000
2680.826
5195.215
3920.056
37.662
1
1
1
3
2680.826
5195.215
3920.056
12.554
2559.870
2592.135
2090.126
9.991
.000
.000
.000
.000
Organization
Error
Total
their business problems
The cost and/or complexity of implementing of Big Data
solutions needs to come down
Need simplified Big Data solutions that are intuitive to
business users
Retailers are still challenged with basic business reporting and
not ready for Big Data
Need Big Data solutions to better address the needs of retailers
Need better time to value for Big Data
Retailers aren't holding out on using Big Data
Retailers need to better understand how Big Data can solve
their business problems
The cost and/or complexity of implementing of Big Data
solutions needs to come down
Need simplified Big Data solutions that are intuitive to
business users
Retailers are still challenged with basic business reporting and
not ready for Big Data
Need Big Data solutions to better address the needs of retailers
Need better time to value for Big Data
Retailers aren't holding out on using Big Data
Retailers need to better understand how Big Data can solve
their business problems
The cost and/or complexity of implementing of Big Data
solutions needs to come down
Need simplified Big Data solutions that are intuitive to
business users
286
437.204
3
145.735
86.703
.000
248.167
3
82.722
48.777
.000
80.658
3
26.886
25.743
.000
61.327
51.257
21.655
723.736
3
3
3
576
20.442
17.086
7.218
1.256
19.520
8.525
3.849
.000
.000
.010
968.168
576
1.681
976.861
576
1.696
601.581
576
1.044
603.216
1154.432
1080.295
7071.000
576
576
576
580
1.047
2.004
1.876
5612.000
580
5094.000
580
Retailers are still challenged with basic business reporting and
not ready for Big Data
Need Big Data solutions to better address the needs of retailers
Need better time to value for Big Data
Retailers aren't holding out on using Big Data
Corrected Total Retailers need to better understand how Big Data can solve
their business problems
The cost and/or complexity of implementing of Big Data
solutions needs to come down
Need simplified Big Data solutions that are intuitive to
business users
Retailers are still challenged with basic business reporting and
not ready for Big Data
Need Big Data solutions to better address the needs of retailers
Need better time to value for Big Data
Retailers aren't holding out on using Big Data
a. R Squared = .049 (Adjusted R Squared = .045)
b. R Squared = .311 (Adjusted R Squared = .308)
c. R Squared = .203 (Adjusted R Squared = .198)
d. R Squared = .118 (Adjusted R Squared = .114)
e. R Squared = .092 (Adjusted R Squared = .088)
f. R Squared = .043 (Adjusted R Squared = .038)
g. R Squared = .020 (Adjusted R Squared = .015)
287
3923.000
580
3337.000
6730.000
5175.000
761.398
580
580
580
579
1405.372
579
1225.028
579
682.240
579
664.543
1205.690
1101.950
579
579
579
Appendix-I
Dependent Variable
(I) Retail
organization
Retailers need to better
understand how Big Data can
solve their business problems
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment
Retailing
The cost and/or complexity of
implementing of Big Data
solutions needs to come down
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment
Retailing
Need simplified Big Data
Apparel retailing
Pair wise Comparisons
(J) Retail organization
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
Consumer Durable retailing
288
Mean
Difference
(I-J)
-.088
-.516*
.246
.088
-.428*
.334*
.516*
.428*
.762*
-.246
-.334*
-.762*
1.584*
2.047*
.170
-1.584*
.463*
-1.414*
-2.047*
-.463*
-1.877*
-.170
1.414*
1.877*
-.438*
Std.
Error
.123
.133
.139
.123
.128
.134
.133
.128
.144
.139
.134
.144
.142
.154
.161
.142
.148
.156
.154
.148
.167
.161
.156
.167
.142
Sig.b
.471
.000
.078
.471
.001
.013
.000
.001
.000
.078
.013
.000
.000
.000
.290
.000
.002
.000
.000
.002
.000
.290
.000
.000
.002
95% Confidence
Interval for
Differenceb
Lower
Upper
Bound
Bound
-.329
.152
-.778
-.255
-.027
.519
-.152
.329
-.680
-.176
.070
.598
.255
.778
.176
.680
.479
1.046
-.519
.027
-.598
-.070
-1.046
-.479
1.306
1.863
1.744
2.350
-.146
.487
-1.863
-1.306
.171
.754
-1.720
-1.109
-2.350
-1.744
-.754
-.171
-2.204
-1.549
-.487
.146
1.109
1.720
1.549
2.204
-.718
-.159
solutions that are intuitive to
business users
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment
Retailing
Retailers are still challenged
with basic business reporting
and not ready for Big Data
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment
Retailing
Need Big Data solutions to
better address the needs of
retailers
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Entertainment Retailing
Apparel retailing
289
-1.159*
.802*
.438*
-.720*
1.240*
1.159*
.720*
1.960*
-.802*
-1.240*
-1.960*
.148
.134
-.837*
-.148
-.014
-.985*
-.134
.014
-.971*
.837*
.985*
.971*
.425*
.443*
-.391*
-.425*
.017
-.817*
-.443*
-.017
-.834*
.391*
.155
.162
.142
.149
.156
.155
.149
.168
.162
.156
.168
.112
.121
.127
.112
.117
.123
.121
.117
.132
.127
.123
.132
.112
.122
.127
.112
.117
.123
.122
.117
.132
.127
.000
.000
.002
.000
.000
.000
.000
.000
.000
.000
.000
.186
.270
.000
.186
.906
.000
.270
.906
.000
.000
.000
.000
.000
.000
.002
.000
.883
.000
.000
.883
.000
.002
-1.463
.484
.159
-1.013
.933
.855
.427
1.631
-1.120
-1.547
-2.290
-.071
-.104
-1.086
-.368
-.244
-1.226
-.373
-.216
-1.229
.588
.744
.713
.206
.204
-.641
-.645
-.213
-1.058
-.681
-.247
-1.093
.142
-.855
1.120
.718
-.427
1.547
1.463
1.013
2.290
-.484
-.933
-1.631
.368
.373
-.588
.071
.216
-.744
.104
.244
-.713
1.086
1.226
1.229
.645
.681
-.142
-.206
.247
-.576
-.204
.213
-.575
.641
Retailing
Need better time to value for
Big Data
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment
Retailing
Retailers aren't holding out
on using Big Data
Apparel retailing
Consumer Durable
retailing
Food & Grocery
Retailing
Entertainment
Retailing
Consumer Durable retailing
Food & Grocery Retailing
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
Consumer Durable retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Food & Grocery Retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Entertainment Retailing
Apparel retailing
Consumer Durable retailing
Food & Grocery Retailing
.817*
.834*
.269
.539*
.838*
-.269
.270
.570*
-.539*
-.270
.300
-.838*
-.570*
-.300
-.042
-.364*
.222
.042
-.322*
.264
.364*
.322*
.587*
-.222
-.264
-.587*
Based on estimated marginal means
*. The mean difference is significant at the .05 level.
b. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments).
290
.123
.132
.155
.168
.176
.155
.162
.170
.168
.162
.182
.176
.170
.182
.150
.163
.170
.150
.157
.164
.163
.157
.176
.170
.164
.176
.000
.000
.083
.001
.000
.083
.096
.001
.001
.096
.100
.000
.001
.100
.780
.026
.191
.780
.040
.108
.026
.040
.001
.191
.108
.001
.576
.575
-.035
.208
.493
-.573
-.048
.236
-.869
-.588
-.058
-1.184
-.903
-.658
-.336
-.684
-.112
-.252
-.630
-.058
.045
.015
.241
-.556
-.587
-.933
1.058
1.093
.573
.869
1.184
.035
.588
.903
-.208
.048
.658
-.493
-.236
.058
.252
-.045
.556
.336
-.015
.587
.684
.630
.933
.112
.058
-.241