1. 1
MSc
Marketing
&
Strategy
(MSMS)
2014-­‐2015
Student
Name:
Udit
Karan
Chandhok
ID
Number:
1464519
Supervisor:
Dr.
David
Arnott
Date
of
submission:
September
2015
Word
Count:
15021
Cross-Pollination via the online grocery channel in the UK
and exploring the need for virtual in-store experience

2.
2
Cross-Pollination via the online grocery channel in the UK and
exploring the need for virtual in-store experience
Submitted
by
Udit
Karan
Chandhok
Year
of
submission:
2015
Declaration
This
is
to
certify
that
the
work
I
am
submitting
is
my
own.
All
external
references
and
sources
are
clearly
acknowledged
and
identified
within
the
contents.
I
am
aware
of
the
University
of
Warwick
regulation
concerning
plagiarism
and
collusion.
No
substantial
part(s)
of
the
work
submitted
here
has
also
been
submitted
by
me
in
other
assessments
for
accredited
courses
of
study,
and
I
acknowledge
that
if
this
has
been
done
an
appropriate
reduction
in
the
mark
I
might
otherwise
have
received
will
be
made.
Copyright
This
dissertation
protected
by
original
copyright.

3. 3
Acknowledgement
First
and
foremost,
I
would
like
to
thank
Dr
David
Arnott,
my
dissertation
supervisor,
whose
patience,
advice
and
guidance
have
been
highly
valued
and
appreciated.
His
expertise
really
helped
me
to
shape
my
dissertation
overall.
I
owe
my
success
to
Dr
Arnott’s
thorough
and
always
helpful
feedback.
I
would
not
be
here
today
if
it
were
not
for
the
unconditional
help
and
support
I
always
received
from
my
family.
They
have
always
shown
immense
faith
in
me
and
have
driven
me
to
out
perform
myself.
I
also
would
like
to
thank
God
for
showing
me
the
right
path.
Finally,
I
would
also
like
to
thank
my
friends
for
always
being
there
to
motivate
me
and
be
there
for
me
whenever
I
needed
them.

4.
4
Table
of
Contents
Declaration
2
Acknowledgment
3
Chapter 1: Introduction
7
Chapter 2: Literature Review
9
2.1: Modern Consumption and Identity
9
2.2: Models of Consumer Behaviour
10
2.3: Theory of Reasoned Action and Theory of Planned Behaviour
13
2.3.1: Theory of Reasoned Action
14
2.3.2: Theory of Planned Behaviour
14
2.4: Online Shopping
16
2.4.1: Online Grocery Shopping
18
2.4.1.a: UK Online Grocery Market
21
2.4.1.b: Operational Strategy adopted for online grocery
22
2.4.1.c: Customer Segmentation
24
2.5: Future of the Grocery Industry
26
Chapter 3: Research Methodology
28
3.1: Research Strategy
28
3.2: Research Design
29
3.2.1: Primary Research
29
3.2.1.a: Semi-Structured Interviews
30
3.2.1.b: Self-Administered Questionnaires
30
3.2.1.c: Sampling
31
3.2.2: Secondary Research
32
3.3: Research Ethics
32
3.4: Reliability
33
3.5: Validity
33
3.6: Generalizability
34
3.7: Limitations
34
Chapter 4: Findings and Interpretations
36
4.1: Online Shopping
36
4.1.1: Frequency of online shopping and Average basket size
36
4.1.2: Delivery Cost influencing online shopping behaviour
38
4.1.3: Products purchased online
40
4.2: Behaviour of online shoppers
42
4.2.1: Risk factors in online shopping
46
4.3: Online Grocery Shopping
47
4.3.1: Cross-Pollination via online grocery shopping
51
4.4: Virtual in-store experience
55

5. 5
Chapter 5: Conclusions
57
5.1: Consumer behaviour being planned
57
5.2: Typology of Consumers
58
5.3: Cross-pollination via the online grocery channel
58
5.4: Virtual in-store experience
59
Chapter 6: Managerial Implications
60
Chapter 7: Future Research
61
References
62
Appendices
76

6.
6
Abstract
The research focuses on the food retailing industry in the UK and evaluates the online
grocery shopping industry and its value propositions for customers. Based on the
typology of consumers, the research aims to suggest possible cross-pollination avenues
for the online grocery shopping industry, which includes strategic alliances, brand
associations, service associations etc. The research first takes in the feedback of the
respondents via a semi-structured interviewing approach and then carries out a detailed
research using the questionnaires. Conclusions of the research have been discussed in
depth in the fifth section suggesting the impact of delivery cost on the frequency of
shopping and hence the average basket size of the consumers. The research also
identifies the factors that consumers would prefer to see in the cross-pollination strategy
along with their adoption percentage.

7. 7
Chapter 1: Introduction
The research has been conducted with the objectives of studying the online shopping
industry along with the online grocery shopping industry; to explore the possible cross-
pollination avenues, the online grocery industry could adopt to offer value propositions
to the consumers. The research lays its foundations on the theory of reasoned action and
the theory of planned behaviour as the defining factors of consumer behaviour in the
online retailing industry. Conducting the research primarily in the United Kingdom,
significant changes have been identified in the food-retailing industry and the varying
consumer needs, which is an enabling factor to study these developments and suggest
synergies that the industry could adopt to service the consumer better.
The relationship between modern consumerism and identity has been traced back to
identify the various consumer behaviour models. Social-psychological theories of
behaviour and change have been used in the study to analyse the varying needs of the
consumers. Strategic outcomes based on the social-psychological theories have been
mapped to the ‘Big Middle theory’ that is used to create shopping motivations in
consumers. The theory has been used to identify the similarities in the online shoppers
and the online grocery shoppers. Focus has been laid on testing the typology of
consumers in the industry using the conceptual framework developed by Huang &
Oppewal (2006). The findings have been used to analyse the possibilities of cross-
pollination via the online grocery-shopping channel. The cross-pollination objective of
the research focuses on identifying the common needs of the online shopping
consumers and suggest combined services, which can be offered to consumers in terms
of product line expansion using the ‘Big Middle Theory’. Supportive facts such as the
growing market size of the online shopping industry and the online grocery shopping
industry have been discussed in chapter 2.

8.
8
Customer segmentation techniques have been identified in the online shopping industry
and a need for delivery cost and time frame based segmentation is found. The research
has mapped delivery cost to the frequency of online shopping and also its possible
effects on the average basket size of the consumers. Understanding the influence of
delivery and the importance of convenience factors for the customers, the research also
focuses on cross-pollination via the online grocery channel. The product line extension
and strategic alliances between different online retail verticals are suggested and tested
to provide the customers with a more convenient and well priced service. The study also
highlights the parameters with which the online channel should look at possible
integration of services. This research is important to give more value propositions to the
customers and to co-create value in the longer run. The importance of the study lies
with the dependence of the industry on the seeking newer value propositions that can
help maintain differentiation of services provided as against their competitors.
The study adopts a semi-structured interviewing approach to frame the basic ideology
of the questionnaire and the objectives of the research. The study has focused on the
long-run strategy that could help the food retailing industry at large and suggests the
first movers advantage on such a platform.

9. 9
Chapter 2: Literature Review
In the last decade or so, food-retailing industry has seen significant changes with the
emergence of new store formats and the increased prevalence of large retail chains
(Dobson et al., 2003). Such developments have added to the changing consumer
behaviour towards shopping habits and increased convenience attribute of shopping
(Dobson et al., 2003). Enriching the shopping experience and making it a one-stop shop
for consumer needs, retailing giants have diversified retail channels with multiple
category stores and have been worked closely on improving the convenience factor with
the online retail channel. This pattern of development in the industry has been common
across Europe (Dobson et al., 2003).
The chapter focuses on the modern consumption and identity, which has directed
researchers to study the concept of consumer behaviour. The literature review aims at
analysing the varying behaviour of consumers with respect to online shopping and more
specifically the online grocery shopping industry and the strategies organizations adopt
to broad base their product offerings to the consumers. Studying the online grocery
industry in the UK, typology of consumers in the online shopping industry has been
identified to structurally approach the task of future value propositions that the online
grocery shopping industry can offer and at large the food retailing industry. The chapter
also identifies the need for improvements in the enjoyment factors of shopping online
with improvements in ease of use.
2.1: Modern Consumption and Identity
Consumption in the words of Miller, a social scientist has become the ‘vanguard of
history’ (Miller, 1995). Irrespective of tension between conspicuous and inconspicuous
consumption, modern society has struck a broader agreement on the fact that,
consumption is in some sense inextricably linked to the personal and collective identity
(Jackson, 2005). Over the past few years authors have debated and taken stands on the

10.
10
relationship between identity and consumerism, arguing for it to be a good or a bad
thing. Jackson (2005) complied these thoughts in his study and identified; an accessible
choice of consumer goods is an important aspect in defining the individualistic modern
consumer (Campbell, 1997). Some author’s views observed by Jackson (2005) also
referred to the ‘empty self’ of the modern consumer with a continuous need of ‘filling
up’ their desires (Cushman, 1990).
Even with differences in the various schools of thoughts on the modern consumption,
the link between the consumption of material goods and the construction and
maintenance of personal identity is one of the most profound elements in modern
understanding of consumer behaviour (Jackson, 2005).
2.2: Models of Consumer Behaviour
The field of consumer behaviour embraces a lot of ground by studying the processes involved
in the selection, purchasing, using or disposing of any goods; that individuals or groups engage
in to satisfy their needs and desires (Solomon et al., 2013). Consumers can be of diverse age
groups and may consume anything from tinned peas to a massage, democracy, pop music or a
celebrity and fulfil desires ranging from hunger and thirst to love, status or even spiritual
fulfilment (Solomon et al., 2013). Consumers get passionate about a broad range of products
whether it is Nike’s MAG shoe, the perfect coffee bean or even the latest tablet computers;
leading us to explore platforms that can give consumers a marketplace stage to co-create values
by enriching the online shopping experience with cross-pollination. Innovation in the field of
online shopping needs to be evaluated using the models of consumer behaviour to give an
understanding of how consumers behave when their convenience can be amplified. Table 1
describes some of the various developed models on the social-psychological theories of
behaviour and change highlighting the model the study would be adopting the theory of

11. 11
reasoned action and the theory of planned behaviour which have been discussed in depth below
(refer 2.3).
Table 1: Social-Psychological theories of Behaviour and Change
Social Psychological
Theory
Key References Description
Attitude-Behaviour-
Context (ABC) Theory
Stern and
Oskamp, 1987
A kind of field theory (Lewin, 1964),
which explores the environmental
significant of behaviour. The theory
suggests, behaviour (B) to be a
collaborative product of the ‘internal’
attitudinal variables (A) and the
‘external’ contextual factors (C) that
influence individuals (Stern & Oskamp,
1987).
Cultural Theory Thompson et al,
1990
The theory incorporates the hypothesis
of a four-fold typology of cultural
‘types’ with wide-ranging philosophies
about governance and the good life:
hierarchists, egalitarians, individualists
and fatalists (Thompson et al., 1990).
Elaboration-Likelihood
Model
Petty and
Cacioppo, 1981
A persuasion model, which predicts the
long-term success of a persuasive
message depending on the level of
mental processing or ‘elaboration’ of the
message undertaken by the subject
(target) (Petty & Cacioppo, 1986).
Expectancy-Value
Theory
Ajzen and
Fishbein, 1980
A broad class of theories based on the
idea that behaviour of an individual is
inspired by the expectations they have
about the results of their behaviours and
the values they attach to the outcomes of
their behaviour (Jackson, 2005).
Motivation-Ability-
Opportunity Model
Ölander and
Thøgersen, 1995
An integrated behavioural model that
combines both of the internal
motivational variables – usually based
on the theory of reasoned action
developed by Ajzen & Fishbein (1980) –
with external contextual variables of
opportunity and ability (Jackson, 2005).
Norm Activation
Theory
Schwartz, 1977 One of the better known attempts to
model pro-social or altruistic
behaviours: a personal norm (PN) to

12.
12
behaviour in a manner that is very pro-
social and is activated by awareness of
the consequences (AC) of one’s actions
and the ascription of personal
responsibility (AR) for them (Schwartz,
1977).
Persuasion Theory Hovland et al.,
1953; Petty et
al., 2002
A set of theoretical approaches to the
‘art of persuasion’ that identifies (1) the
credibility of the source, (2) the
message, and (3) the thoughts/feelings
of the receiver as the three critical
structural elements in the success of
persuasion strategies (Petty et al., 2002).
Rational-Choice
Theory
Elster 1986 The fundamental basis of most
economic theories of consumer
preference and several other social-
psychological theories of behaviour,
suggesting that behaviour is the outcome
of rational deliberations in which
individuals seek to maximise their own
expected ‘utility’ (Jackson, 2005).
Self-Discrepancy
Theory
Higgins 1987 Feelings aroused by the perceived gap
between their actual and ‘ideal’ selves
motivate people’s actions (Higgins,
1987).
Subjective Expected
Utility (SEU)
Ajzen and
Fishbein, 1980;
Eagly and
Chaiken, 1993
A form of expectancy value theory
(Ajzen & Fishbein, 1980) closely related
to the ration (Ajzen & Fishbein, 1980)al
choice (Elster, 1986) model, SEU theory
suggests that behaviour is a function of
the expected outcomes of the behaviour
and the value assigned to those
outcomes (Eagly & Chaiken, 1993).
Structuration Theory Giddens 1984 Attempts to provide a model of the
relationship between agency (how
people act) and structure (the social and
institutional context). Giddens
structuration theory relies on a
distinction between ‘practical’ and
‘discursive’ consciousness (Giddens,
1984).
Theory of Planned
Behaviour (TPA)
Ajzen, 1991 Adjusts the Theory of Reasoned Action
to encompass the actor’s apparent
control over the consequences of his or

13. 13
her behaviour (Ajzen, 1991).
Theory of Reasoned
Action (TRA)
Ajzen and
Fishbein, 1980
The best-known social-psychological
attitude-behaviour model, the Theory of
Reasoned Action adjusts expectancy
value theory to incorporate normative
social influences on behavioural
intention as an element of understanding
the influence of social intentions on the
consumers’ behaviour and also on
innovation (Ajzen & Fishbein, 1980)
(Jackson, 2005).
Value-Belief-Norm
Theory
Stern et al.,
1999; Stern,
2000
An attempt to modify Schwartz’s Norm
Activation theory by including a more
sophisticated association between
values, beliefs, attitudes and norms
(Stern, 2000).
Source: Adapted from (Jackson, 2005)
2.3: Theory of Reasoned Action and Theory of Planned Behaviour
In recent years, many theories and models have been developed and proposed aiming to
explain and predict consumer’s behaviour online. Klein (1998) in his study suggested,
“The Internet is particularly useful for seeking information in relation to search products
due to low perceived search costs”. Theory of diffusion of innovations approach was
adopted to devise the possible determinants of consumers’ adoption of electronic
grocery shopping by Verhoef and Langerak (2001). According to the online pre-
purchase intention model by Shim et al. (2001) which basis its findings on the theory on
planned behaviour (Ajzen, 1991) conclude an important aspect of predicting consumer
online buying intention. Hence it is important to have a detailed understanding of the
theory of reasoned action and the theory of planned behaviour, which has been used to
carry out the study.

14.
14
2.3.1: Theory of Reasoned Action
The theory of reasoned action by Fishbein and Ajzen (1975) regards a consumer’s
behaviour as determined by the consumer’s behavioural intention, where behavioural
intention is a function of ‘attitude towards the behaviour’ and ‘subjective norm (SN)’.
Chang (1998) described in his study on the theory, describes ‘attitude toward the
behaviour’ as the “general feeling of favourableness or unfavourableness for that
behaviour” and ‘subjective norm’ as the perceived opinion of other people in relation to
the behaviour that is subject to questioning. The theory makes a conjecture on the
intention of the consumer to perform a behaviour based on the attitude towards the
behaviour rather than the consumer’s attitude towards a product or service (Hansen et
al., 2004). The theory of reasoned action is concerned with the rational, volitional and
systematic behaviour (Fishbein & Ajzen, 1975), that is, behaviours which are under the
control of the individual (Thompson et al., 1994). This assumption has been widely
criticised by researchers such as Sheppard et al., which suggest that, “actions that are at
least in part determined by factors beyond individuals volitional control fall outside the
boundary conditions established for the model” (p. 326). Such considerations have been
incorporated into the theory of planned behaviour (Hansen et al., 2004).
2.3.2: Theory of Planned Behaviour
The theory of reasoned action extends to the theory of planned behaviour where an
addition of the ‘perceived behaviour control’ is made, which is a determinant of
behavioural intention (Hansen et al., 2004). Figure 1 is an illustration of the theory of
planned behaviour.

15. 15
Figure 1: The Theory of Planned Behaviour
Image credits: (Ramus & Nielsen, 2005)
Theory of planned behaviour frames the intention to perform an action on three
constructs: attitude towards the action, subjective norm and perceived behavioural
control (Ramus & Nielsen, 2005). Attitude towards the action and subjective norm have
been discussed in depth under theory of reasoned action (refer 2.3.1). Perceived
behavioural control refers to a person’s ability to perform a given behaviour (Ramus &
Nielsen, 2005). According to Ajzen (1991), perceived behavioural control is expected to
have an effect on the formation of intentions and on the behaviour itself (Ramus &
Nielsen, 2005). The determinants of perceived behavioural control are the beliefs about
factors that impede the performance of the behaviour (Ramus & Nielsen, 2005). In
relation to the Internet purchase behaviour various researchers have adopted this model.
Hansen (2008) suggests that the theory is well suited for the purpose of investigating
consumer online grocery shopping behaviour. His research indicates that consumers
may perceive obstacles and difficulties in performing online shopping which is based on
the study carried out by Shim & Eastlick (2001) which stated that, “ when studying
consumers, Internet purchasing behaviour, researchers should take perceived
behavioural control into consideration in that Internet shopping does require skills,
opportunities, and resources, and thus not occur merely because consumers decide to

16.
16
act” (Shim et al., 2001; Hansen et al., 2004). Hansen also identified that consumers may
perceive risk and difficulties when considering online shopping, as they can be expected
to use their cognitive resources to form their beliefs towards a related attitude, which in
turn would result in the development of an overall feeling towards the behaviour in
question (Zaichkowsky, 1985) (Rossiter & Percy, 1987). When trying to reduce
perceived risk, consumers may also seek normative guidance from relevant others
(Hansen, 2008).
Consumer values are a central aspect to the consumer decision-making process. Claeys
et al., (1995) claim, “values are the ultimate source of choice criteria that drive buying
behaviour” (p.193). Social values define the desired behaviour or the end result for a
society or the group, whereas personal values define desired behaviour or the end state
of an individual (Blackwell et al., 2001). Social values are indirectly inherent in the
theory of planned behaviour through the conceptualization of ‘social norm’, however
personal values are not explicitly dealt with in the theory. Thus, both social norms and
personal values are an important factor influencing Internet purchasing practices
(Hansen, 2008).
2.4. Online Shopping
Forecasts in the past few years have predicted that the value of goods and services
purchased over the Internet could increase rapidly (Burger, 1996). E-commerce is the
fastest growing retail market in Europe with sales in the UK, Germany, France, Sweden,
The Netherlands, Italy, Poland and Spain are expected to grow from £132.05bn
[€156.28bn] in 2014 to £156.67bn [(€185.39bn] in 2015 (+18.4%), reaching £185.44bn
(€219.44bn) in 2016 (Centre for Retail Research , 2015). Researchers have examined
the impact of online shopping environments on consumer choice (Swaminathan et al.,
1999), the role of Internet shopping as a channel of distribution (Alba et al., 1997),
factors influencing shopping online (Swaminathan et al., 1999), the impact of online

17. 17
shopping on price sensitivity (Shankar et al., 1999). Rohm & Swaminathan (2004)
carried out a study describing the typology of online shoppers basing it on shopping
motivations. They identified a need that was highly relevant in competitive online retail
markets.
Based on the Big Middle Theory, Ganesh et al. (2010) examined the online patronage
behaviour and a comparison of shopper typologies, which would help; reveal shopper
segments similar to those found in the traditional store formats. Levy et al. (2005)
define the Big Middle Theory as “the market space in which the largest retailers
compete in the long run, because this is where the largest number of potential customers
reside” (p.85). Short term success can be found outside the Big Middle Theory, but
many authors argue that over the long-term most successful niche or segment retail
players would migrate towards the largest market segment by expanding their
merchandising mix, increasing inventory turnover rates, lowering product margins and
eliminating certain customer service elements (Ganesh et al., 2010). In the retail
landscape, retailers adopt the Big Middle position – as either “Low-price” or “
Innovative” players (Ganesh et al., 2010). Innovative players target quality-conscious
customers seeking high-end products while the Low-price retailers target price-
conscious customers (Ganesh et al., 2010). Consumers from all segments gravitate
retailers, which excel at innovating, offering low prices or both. With increased
consumer acceptance for online purchasing and the continuous advancements in
technology, some online retailers have adopted to the Big Middle market space like
Amazon.com, Overstock.com and EBay Stores (Ganesh et al., 2010). Today these
online retailer have diverse product lines and have been successful in lowering margins
and expanding their concepts such as; affiliated marketing programs, blogs, cross-
selling push technology, diversification into high priced segment of products (Ganesh et
al., 2010). Retailers are always adapting to the ever-changing customer needs hence it is

18.
18
important to adapt the Big Middle theory which suggests “the existence of a core group
of shoppers seeking a relatively consistent and more demanding bundle of retail
attributes: broad and deep product mixes with consistently low prices” (Ganesh et al.,
2007).
Chang et al. (2005) describe the relative advantages of online shopping for customers as
time saving, product value (price and quality), ease to order and decreased transaction
costs. Their study also complied studies on online shopping experience, which
highlighted concerns on user-friendliness and aesthesis of online shopping websites
(Chang et al., 2005). Service quality is a factor of superiority or excellence in the online
shopping industry (Parasuraman et al., 1985) as it has a positive impact on the purchase
intention of the customer to shop online. Jeff Bezos of Amazon.com said “one secret of
the company’s success is thinking of ways to make online shopping experience more
fun” (Star Tribune , 1999).
2.4.1 Online Grocery Shopping
A study conducted by the University of Michigan concluded that among the 22
favourite household tasks, grocery shopping came next to last (Richards, 1996). The
founders of the online grocer Peapod.com suggested in their research that consumers
regarded grocery shopping as the chore they dislike the most next to dentist (Corral,
1999). These findings lead to hypermarkets and large food retailers to develop the
online channel of grocery shopping. Internet grocery shopping has faced serious
difficulties such as transaction obstacles, slow load times, inability to locate items,
incomplete information, lack of human interaction, missed or late deliveries; which has
affected the adoption percentage (Kaufman-Scarborugh & Lindquist, 2002). Research
has also highlighted issues such as ease of use and security (Elliot & Fowell, 2000).
Consumer adoption of online grocery buying has driven researchers to study the

19. 19
perceived characteristics of innovation that enable this adoption such as perceived
compatibility, perceived relative advantage, and perceived complexity (Rogers, 1983)
are some of the factors studied (Huang & Oppewal, 2006).
A conceptual model (refer figure 2) developed by Huand and Oppewal (2006) tests the
consumers’ choice of channel for grocery shopping. The study measures the effects that
are mediated by the perceived differences between online and in-store shopping
conditions considering factors such as costs, convenience, enjoyment and risk.
Cost factors in the study concerns with the difference in monetary cost perceived by
consumers when comparing online and in-store grocery shopping (Huang & Oppewal,
2006). Bell et al. (1998) identified ‘fixed costs as travel costs associated with going to a
store plus a shoppers’ inherent preference and historic loyalty for the store, while the
variable costs depend on the consumers’ shopping list.’
Convenience factors concerns with the psychological costs and other forms of non-
monetary costs such as time, effort and stress (Huang & Oppewal, 2006). Shopping
convenience can be defined as ‘a reduction of the opportunity costs of effort and time
involved in shopping activities’ (Berry et al., 2002). Compared to in-store shopping,
online shopping offers a great deal of comfort to the consumer possibly from anywhere
and anytime. Online shopping also has inconveniences such as Internet connectivity and
ease of use. However, time and effort are the some of the selling points in the online
grocery industry.
Enjoyment factors or shopping enjoyment as defined by Beatty and Ferrell (1998) is
‘the pleasure one obtains from the shopping process.’ The concept of shopping
enjoyment relates the differences between hedonic and utilitarian shoppers (Huang &
Oppewal, 2006). In the online setting and with the technological advancements, “virtual

20.
20
reality” and “interaction” with customers and providers has aided the process of online
shopping which was identified as one of the future implications in their study.
Figure 2: A conceptual model to test consumers’ choice of channel for grocery
shopping
Source: (Huang & Oppewal, 2006)
Risk factors are the most crucial aspect the industry as a whole has been trying to
counter for years. A wide array of research on perceived risk and its impact on
consumer behaviour has been carried out (Mitchell, 1999). It is of particular interest that
perceived financial risk and leakage of personal information is only one segment of the
perceived risk customers have when adopting online shopping (Forsythe & Shi, 2003).
Product performance risk was a key factor identified through this study (Forsythe &
Shi, 2003).
The conceptual model to test the consumers’ choice of channel for grocery shopping
and the Big Middle Theory (refer 2.4) has been integrated to examine the responses in
this study along with consumer behaviour model (refer 2.3).

21. 21
2.4.1.a: UK Online Grocery Market
In the UK market, key players in the online channel have been the traditional retailers,
including Tesco, Sainsbury, Asda and Waitrose (Mills, 2001; Stewart, 2000; Thomas,
2002; Wearden, 2002); with only Ocado being the exception to the market with only
online channel of servicing (Thomas, 2002). Various other players have entered in the
market ever since such as Aldi, Lidl, Marks & Spencer’s which have been doing well in
the store concept but not scoring so well in the online channel of retail. The online
grocery market in the UK is about £8.9bn in the year to March 31sr 2015 as shown in
figure 3 (The Institute of Grocery Distribution , 2015). As a whole, the UK grocery
market was worth £177.5 billion in the year to March 31st 2015, an inflation adjusted
increase of 1.7% on 2014 as shown in figure 4 and as forecasted by IGD in their survey
the UK grocery market value in absolute terms would be worth £200.6bn in 2020, a
13.0% increase in 2015 (The Institute of Grocery Distribution , 2015).

22.
22
Figure 3: What channels make up the UK grocery market?
Source: IGD UK Grocery: Market and channel forecasts 2015-2020 (The Institute of
Grocery Distribution , 2015)
Figure 4: Market Forecasts on previous trends
Source: IGD UK Grocery: Market and channel forecasts 2015-2020. (The Institute of
Grocery Distribution , 2015)
2.4.1.b: Operational Strategy adopted for Online Grocery
The operations strategy process is most often modeled as a hierarchical one under which
functional strategies such as operations, logistics, marketing and finance that are driven

23. 23
by the business level strategy of the organization (Boyer & Hult, 2005). A key element
of the strategic framework involves co-ordination of the functional level strategies to
work in concert to achieve the overall business strategy of the firm. Majority of studies
carried out on the operational strategy of e-commerce industry, implicitly assumes the
business methods to provide a new means of seamless integration in the functional level
strategies (Boyer & Hult, 2005). Figueiredo (2000) provides a conceptual mapping of
the marketing characteristics onto the operating characteristics to identify the promising
e-commerce strategies. Benefits of syndication were examined by Werbach (2000) to
identify the strategies for using the Internet to revise supply chain management.
Analyzing the operational strategy of Webvan in the US and Tesco in UK, Boyer &
Hult (2005) highlight completely different operational approaches adopted for online
grocery retailing. The study highlights first and foremost challenge for companies
which channels there sales through online grocery as, the delivery of groceries to the
customer being stricken with severe logistical difficulties (Boyer & Hult, 2005).
Webvan tried to build their market share by offering its customers door delivery in a 30-
minute time window. Webvan’s marketing and operations strategy were or well
matched (Boyer & Hult, 2005). Providing low cost groceries and providing timely
delivery could not be matched with the operational resources required such as
warehouses, logistics and supply chain management with a very difficult marketing
strategy to accomplish (Boyer & Hult, 2005).
On the other hand, Tesco adopted a completely rational and successful strategy. Tesco’s
marketing strategy for its online channel is convenience and not a low-price option.
Secondly, Tesco has kept the operations of grocery delivery simple by using their
existing assets to help integrate the channel operations (Boyer & Hult, 2005). Online
orders are filled by employees at the nearest Tesco store and are followed through by
the delivery team at each store (Boyer & Hult, 2005). Taking an alternative approach of

24.
24
marketing their services to customers, Tesco provides convenience as an added option
that cost’s customers more, but which can in turn be supported by operations at a little
extra financial cost (Boyer & Hult, 2005). In the UK, the major players in the online
grocery-retailing segment follow a similar model other than Ocado, which follows a
single warehouse practice to deliver groceries (Ocado Group plc, 2013).
2.4.1.c: Customer Segmentation
A variety of shopping behaviors have been identified by researchers such as; economic,
store-loyal, recreational, convenience, price-oriented, brand-loyal, name-conscious,
quality, brand conscious, and impulsive shoppers (Siu et al., 2001). Sin & Tse (2002) in
their research found that convenience oriented and impulse consumers were more
inclined to shop online, however time conscious consumers were not. Customers with more
experience purchasing approach tend to avoid online shopping and recreational shoppers had
mixed approaches towards online shopping (Sin & Tse, 2002). With today’s social structure
and corporate and business pressures, recreational shopping should be one of on-go activities
people engage in as expressed by the growing online shopping market (refer section 4).
However, the study also highlighted the fact that online shopping was not a preferred choice for
price-oriented and brand conscious customers (Sin & Tse, 2002).
Consumer demographics researches have incorporated mixed variables such as; gender,
age, education, and income, which have been found to have positive relationships with
online shopping in general (Chang et al., 2005). Researchers have often concluded that
probably, demographic variable do not generate the relationships, but they are caused
by the deeper structure variables as discussed above (Chang et al., 2005).
Innovativeness, which is regarded as a personality characteristic (Chang et al., 2005), is
another segmentation factor that separates online shoppers from the traditional in-store
shoppers in today’s times. Goldsmith (2001) suggested that the innovative behaviour is
not apparently consistent across domains and hence devised the domain specific

25. 25
innovativeness (DSI) index, to study online shopping behaviour and segmentation of
customers. Researchers have found positive relations of the DSI index and the intention
to purchase online (Limayem et al., 2000). Personality variables have also been the
determinant factors of consumers shopping online. Theory of planned behaviour (Ajzen,
1991) and the Traindis model (Triandis, 1980) have been used to guide psychosocial
researches in the field (Chang et al., 2005); supporting the study using the Theory of
planned behaviour to evaluate the typology of consumers shopping online.
Figure 5: View of the shopping typology literature
Source: (Rohm & Swaminathan, 2004)
As shown in Figure 5, Rohm & Swaminathan (2004) identified numerous motives to
research on the typology of online shoppers and their research focused on the online
grocery shopper segment. Their literary review identified convenience as a distinct
motive for store choice in the offline setting for the convenience shopper. Information
seeking in the retail setting is a shopping motive explicit in the offline context in the
early times (Bellenger & Korgaonkar, 1980). Rohm & Swaminathan (2004) also
highlighted the findings of Balasubramanian (1998), which suggested that direct
marketers could reduce consumer resistance to catalog or Internet purchases by fine

26.
26
scaling the delivery time frame. Social interaction as a source of shopping motivation
was another factor that Rohm & Swaminathan concluded in their literary research.
Conclusions of their study mapped the store-oriented shoppers to be motivated by
factors such as immediate possession and social contact (Rohm & Swaminathan, 2004).
Variety seekers were characterized as seeking in retail alternatives or products and
brands and the balanced buyers exhibited lower propensity to the planned purchases
suggesting more impulsive purchases online (Rohm & Swaminathan, 2004). Enriching
the online retail shopping experience was one of the crucial industry implications that
the study concluded along with indicative managerial suggestions to adopt strategic
alliances with a long run objective to address the rising needs of online shoppers of all
segments (Rohm & Swaminathan, 2004). Hence, delivery cost and time frame have
been identified to be critical factors influencing the consumer purchase behaviour of
online shopping resulting in better customer segmentation practices in terms of
minimum cost of delivery depending on the basket size of the shoppers.
2.5: Future of the Grocery Industry
Terbeek (1996) suggests, “The future of the retail food industry is less about the
incremental supply chain improvements and more about redistributing rewards and
profits along the consumer’s value chain according to the value created” (p. 93).
Retailers should aim at enabling consumers to increase the number of tasks they can
accomplish in one trip or reduce the time required to complete the shopping task by
adding product lines and thus improving convenience for shoppers (Kinsey & Senauer,
1996). Product assortment and addition of service variety are some of the key
dimensions highlighted by Kinsey & Senauer (1996) to counter the convenience factor
of food retailing shopping. Another dimension suggested by them was home shopping
(Kinsey & Senauer, 1996), which is central task in this study. Online grocery shopping

27. 27
services have the potential to fulfill the goals of both consumers and grocery store
operators.
The online shopping industry is almost a reflection of our culture today and the practice
is becoming widely acceptable. Barriers to entry and exit are relatively lower in the
industry as compared to the traditional supermarkets (Keh & Shieh, 2001). The online
grocery industry has vied its solid presence in the e-commerce derby. Bargaining power
of retailers and the buying power of consumers is really high in the industry that are big
supermarket players with more leverage on relationships on both ends (Keh & Shieh,
2001). The industry has also been growing rapidly as it today constitutes about 5% of
the overall industry revenue in the UK (refer 2.4.1.a) and is part of a £132.05bn online
retail shopping industry (refer 2.4).
The industry offers lucrative returns on expansion of the product lines or strategies
alliances using cross-pollination in the retailing segment. The chapter has helped
identify the research gap and empirically supports the exploration of the relationship
between online grocery shopping and online shopping habits of consumers. The chapter
has also identified the cost of delivery of goods being a major influencing factor in the
purchasing decision of consumers. With convenience being an important factor in the
online channel of retail, ease of use and enjoyment enrichment for consumers is a key
finding in the chapter. This has helped support the need for ‘Virtual in-store shopping’
which is one of the research objectives.

28.
28
Chapter 3: Research Methodology
This chapter discussed the research strategy and the research methodology that has been
used test and answer the research questions. The chapter begins with the explanation of
research strategy and the research design that are adopted to carry out the study. Data
collections and sampling techniques are discussed elaborately in the chapter as well.
The chapter is concluded with limitations of the research strategy chosen.
3.1: Research Strategy
The central objective of the research is to identify possible value propositions for the
food retailing industry through the online channel of retail. To test the ‘Big-Middle
theory’ and the conceptual model developed by Huand and Oppewal (2006), it is crucial
to carry out an in-depth analysis to conceptualise the future model of retailing that the
industry could adopt. As suggested by Bryman & Bell (2011), “qualitative research has
an inductive view of the relationship between theory and research” (p.286). The study
not only works on the generation of theory but also tests the theory in the deductive
process. The study also embraces the epistemological basis of the natural sciences to be
constructed referred to as realism (Bryman & Bell, 2011). Fleetwood (2005) suggests
that critical realism offers a more effective alternative to postmodernism for
organization and management studies as it shadows the ambiguity associated with the
postmodernism that curtails from ontological exaggeration.
Cognitive mapping is used to capture individual perspectives as the process is based on
the assumption that people interpret data differently and hence will have different ways
of comprehending a problem (Eden, 1992). The method of research draws inferences
from the personal construct theory (Kelly, 1955), which supports the use of repertory
grid technique and is based on the assumption that there is active engagement of the
respondents in construction of models, hypothesis, or representations on the real-life
scenarios (Bryman & Bell, 2011). The mapping process involves participants in

29. 29
identifying the factors that affect a particular decision making goal (Bryman & Bell,
2011). This method has been used in carrying out the study to analyse the factors that
influence the purchasing decisions of consumers via the online channel of retail. Hence,
in-depth interviewing is one of the approaches of data collection.
3.2: Research Design
Qualitative research tends to view social life in terms of processes and lays strong
footings on ‘the sequence of individual and collective events, actions and activities
unfolding over time in context’ (Perrigrew, 1997). As explained the section 3.1, the
research uses cognitive mapping techniques to gain feedback on the respondent
behaviors to frame the hypothesis and test them in real time. The research employs
open-ended questions as favour respondents to answer questions in their own terms and
also allows unusual responses to be derived (Bryman & Bell, 2011). The study uses the
survey methodology of research, which comprises of the cross-sectional design in
relation to which data are collected predominantly by questionnaire or structured
interview (Bryman & Bell, 2011). The research follows a cross-sectional design in the
second phase that helps entail the collection of data on more than one case at a single
point in time and collects both qualitative and quantitative data in connection with one
or more variables that are analyzed to detect patterns of association (Bryman & Bell,
2011).
3.2.1: Primary Research
Vidich & Shapiro (1955) suggested “Without the survey data, the observer could only
make reasonable guesses about his area of ignorance in the effort to reduce bias” (p.
31). Researchers using qualitative methodology should be encouraged to systematize
observations using sampling techniques and developing quantifiable schemes for
answering complex problems (Jick, 1979). Primary research is carried out in two
phases. Phase one focused on the in-depth interviewing of the respondents and phase

30.
30
two took a more quantifiable feedback using questionnaires, which was formulated on
the learning from phase one of the research.
3.2.1.a: Semi-Structured Interviews
Business research interviewers aim to elicit all manners of information from the
interviewees about their behaviour or that of others, attitudes, norms, beliefs and values
(Bryman & Bell, 2011). Many writers embrace qualitative interviewing as being both
the semi-structured and unstructured kind (Carol, 2002). The study adopts the semi
structured approach interviewing to collect data in a general frame of reference with
latitude to ask further questions in responses to find significant replies (Bryman & Bell,
2011). Under phase one of the data collection, 10 interviews were collected from online
shoppers and online grocery shoppers. The in-depth interviewing approach was adopted
to probe important aspects of the online shopping experience and the personal
characteristics and behaviors of shopping via the online channel. Probing is a highly
problematic area for researchers employing a structured interviewing technique
(Bryman & Bell, 2011). This directed the study to adopt the semi-structured approach in
order to help respondents understand the concepts being tested and also get adequate
answers and feedback on the same for analysis. The probing approach has directed the
study to unexplored knowledge and has given consumer attitude towards shopping
(Bryman & Bell, 2011). Also the interviewing technique has helped develop the
questionnaire for the second phase of data collection.
3.2.1.b: Self-Administered Questionnaires
In the second phase of primary research, questionnaires responses were collected from
the respondents. Self-completion questionnaire or self-administered questionnaire is the
approach where the respondents answer the questions by completing the questionnaire
themselves (Bryman & Bell, 2011). The questionnaire focused on the objectives of
shopping online for respondents and also measures the factors such as convenience,

31. 31
enjoyment, risk and price that influence their decision-making. The questionnaire also
tests the possibility of virtual in-store experience, which is one of the research
objectives. The questionnaire uses open-ended questions on the feedback users would
have on the concept of virtual in-store and would it enrich the factors of enjoyment for
the respondents. The questionnaire adapts its scale from the study conducted by Huand
and Oppewal (2006) on the typology of online grocery shoppers.
3.2.1.c: Sampling
Phase one of data collection used the snowball sampling technique to individuals and
groups of people for whom there is no sampling frame. Pettigrew and Mchulty (1995)
used the same technique to carry out in-depth interview for their research into part time
board members of top UK firms. When there is no accessible sampling frame for the
population from which the sample can be derived and the study has difficulty in
defining the sampling frame snowball sampling should be applied (Bryman & Bell,
2011). This approach has been helpful in getting respondent referral to widen the
consumer perspective on online shopping and its influencing purchasing factors. The
drawback of the sampling technique has been with the representation of the population,
which lead to the second phase of the research.
Stratified sampling was applied for the second phase of data collection. Under stratified
sampling, the population is stratified by a criterion and selecting either a simple random
sampling or a systematic sampling from each of the resulting strata (Bryman & Bell,
2011). The study creates strata on the basis of gender and the consumer shopping habits
via the online channel. A simple random sampling has been applied to connect to 100
respondents out of which 54 respondents replied to the questionnaire. Time and cost
considerations were relevant factors influencing the sample size. The response rate was
calculated to be 54% with 37 responses being unsuitable or uncontactable member of
the sample. The sampling frame is also adapted from the study conducted by Rohm &

32.
32
Swaminathan (2004), which segments the users as being online shoppers, online
grocery shoppers, online shoppers and online grocery shoppers, and people who do not
engage in online shopping.
3.2.2: Secondary Research
Operational theories for cross-pollination and the outsourcing strategy by various online
grocery retailers have been adapted to study the cross-pollination strategy for online
grocery stores. Secondary research has also been used to understand the ‘Big Middle
Theory’ of online shopping (Levy et al., 2005) and its possible linkages with the
conceptual online grocery-purchasing model (Huang & Oppewal, 2006). Advantages of
the cataloging system in the online shopping experience (Gehrt & Shim, 1998) as
compared to the proposed virtual in-store experience have been compared. Also the
diminishing risk factors via the online shopping channel have been evaluated using
literature and the data collected in the primary research as more and more organizations
have moved to a transparent returns policy and the e-commerce industry providing safer
monetary transactions.
3.3: Research Ethics
The research has been conducted ethically and the questions asked during both the
phases of data collection have been strictly under the boundaries of ethical code of
conduct and are concerned with the purpose of the study. A growing concern has been
expressed with the ethical ways of data collection and ensuring anonymity in the data
collection (Bryman & Bell, 2011). The research has been conducted keeping the privacy
of respondents in mind. The interviews were recorded with consent of the interviewees
and anonymity has been maintained. In the second phase of data collection, only the
required sign in to access the questionnaire and a time stamp of the response being
submitted have been recorded. The respondents have been given an option of not
answering the question if deemed inappropriate and irrelevant to the study.

33. 33
By taking the above stated measures, the researcher has aimed in creating a stress free
setting for interviewees and respondents to revert while maximizing the effectiveness of
the research.
3.4: Reliability
Reliability concerns with the fact whether the results of the study are repeatable
(Bryman & Bell, 2011). The interviewing phase of data collection may not be entirely
repeatable due to differing viewpoints of the interviewees, which is one of the
disadvantages of using the interviewing approach to collect data. The questionnaire
phase of data collection is repeatable as the scaling technique of the questionnaire has
been adapted from the study on online grocery shopping conducted by Huang &
Oppewal (2006). Using the cross-sectional study the problems with the reliability of the
research is primarily related to the quality of the measures that are employed to evaluate
the concepts (Bryman & Bell, 2011). Replicability is highly present in the study as the
study employs the process of selection of the respondents, designing of the measures of
the concepts, administrating the research instruments and analyzing the data collected
(Bryman & Bell, 2011). Hence, the replicability of the study is highly possible as the
survey research design has been used.
3.5: Validity
Validity is concerned with the integrity of the conclusions that are generated from the
research (Bryman & Bell, 2011). Measurement validity addresses the question whether
or not a measure that is devised of a concept reflect the concept it should denote
(Bryman & Bell, 2011). The causal impact of the independent variable on the dependent
variable defines the internal validity of the research. The study has embraced the
concepts of internal validity research and has measured the results, which are stable
leading to its reliability. The study is also ecologically valid as it credible, transferable,
dependable and confirmable as suggested by Lincoln & Guba (1985). Conclusions of

34.
34
the study develop a causal impact of the delivery costs on the frequency of purchase and
also on the average basket size of shopping and also affecting the consumers’
behaviour. Internal validity in the cross-sectional study is typically weak, however, the
external validity is quite strong (Bryman & Bell, 2011). The results are stable leading to
reliable and generalizable research as the research employs the stratified sampling
techniques.
3.6: Generalizability
Generalizability concerns with the question whether the results of the study can be
generalized beyond the specific research context (Bryman & Bell, 2011). The results of
the research can be applied to other industries seeking future value propositions and also
adapted by the food retailing industry in the other channels of retail. The study analyses
the typology of customers and test the theory of planned behaviour in the decision
making process. This aspect of the research can be validated in the external scenario
with industries such as the technological retailing and the gaming industry with
consumer needs on virtual reality.
3.7: Limitations
The study has been constrained with a time bound response rate and the sample size
taken into consideration is not adequate to suggest strong organizational or theoretical
changes. The spread of the strata in the sampling technique are not sufficient to give a
detailed analysis of the cross-pollination need in the food retailing industry. This has
bounded the research to present facts generalizable only to a certain extent. The
interviewing phase of data collection suffered the gender disparity in the sample and a
more influencing conclusion could not be drawn, as aspects of primary shopper in the
family could not be identified in many cases. The qualitative aspect of the research
could not be supported due to financial aspects involved in creating a virtual in-store
experience platform where the interviewees could give detailed feedback. The

35. 35
questionnaire approach was adopted for data collection with lesser open ended
questions to improve the effectiveness of the research and hence could not incorporate a
broader perspective of the respondents, as the questions were self-administered and no
probing could be done. 36 respondents could not be contacted again for further
feedback on the questionnaires that were incomplete.
The study only concerns with the consumer aspect of the online shopping experience
and rely on previous researches to help give a more operational and commercial
reasoning for the suggestions. There is empirical need to carry out research on the
operational aspect of cross-pollination strategy in the food retailing industry.

36.
36
Chapter 4: Findings and Interpretation
4.1: Online shopping
Phase one of the data collection shows the interviewees to be online shoppers with
engagement in purchasing at least once a week and come under the category of regular
online shoppers. In the second phase of data collection, out of the 54 completed
responses, 52 respondents engage in online shopping (refer appendix 1).
4.1.1: Frequency of online shopping and Average basket size of the shopping
Interviewing phase of the research suggested that, consumers generally shop once or
twice a week and some interviewees shopped once a month. Hence the scale was
adopted to understand the shopping patterns in the second phase of the research. Data
analysis in the second phase revealed that 8% of the respondents shop at least once a
week and 9% of the respondents shop twice a week. Both the categories of shoppers
come under the frequent category of online shoppers. 25% of the respondents shop at
least once in two weeks and 10% of the respondents shop at least once in three weeks.
Data analysis has revealed that 50% of the respondents shop at least once a month via
the online channel.
Figure 6: Average Frequency of shopping online
Once
a
Week
8%
Twice
a
Week
9%
Once
in
two
Weeks
23%
Once
in
three
Weeks
10%
Once
a
Month
50%
Average
online
shopping
cycle

37. 37
The interviewing phase of the research reflected upon the frequency of online shopping
as being associated with the average basket size while shopping online because of the
delivery costs involved. Most of the interviewees’ shopped in the price bracket of £20 to
£40 when they shopped online. Some of the interviewees’ were explicit about the
change in average basket when the delivery cost over a certain amount is free. The
stretch in budget often seemed to be a hurdle as stated by one of the interviewee (Refer
appendix 2). Generally the feedback in the interviewing phase about the frequency and
the average basket size ranged from once a week to once a month with varied time
frames, but the average basket size changed drastically in case of family interviewees
where their frequency was once in two weeks with an average basket over £100 pounds.
In one of the interviews, the respondent said that, “being a man I do like to planned
purchases, however my wife is an impulsive shopper.” The respondent also added, “my
average basket size varied between £100 to £200 in a week” (refer appendix 4). In the
second phase of the research, respondents were asked about their average basket size
and frequency of shopping and they have been plotted below in figure 6.
Table 2: Average basket size of shopping along with the frequency of shopping (52
respondents)
Average
basket
size
(52)
Once
a
week
(6)
Twice
a
week
(5)
Once
in
two
weeks
(12)
Once
in
three
weeks
(5)
Once
a
month
(26)
£10
-­‐
£20
(8)
0
1
1
0
6
£21
-­‐
£40
(28)
1
4
5
5
13
£41
-­‐
£50
(10)
1
0
5
0
4
More
than
£50
(6)
2
0
1
0
3
The data in figure 6 shows a lot of variation in the average basket size of shopping by
the respondents. The average basket size shows over 54% of respondents shop for £21
to £40 and 19% in the range of £41 to £50. Only 12% of the respondents shop over £50

38.
38
and 15% of the respondents are budgeted shoppers in the basket range of £10 to £20.
When drilling down the average basket size with the frequency of shopping, as shown
in figure 6, the data shows that out of the 8 respondents in the shopping basket range of
£10 to £20, 6 of the respondents shop once a month. The basket size of £21 to £40
shows the most variation in the respondent behaviour with only 3.5% of respondents
shopping once a week, 14.28% of respondents shopping twice a week, 17.85% of
respondents shopping once in two weeks, 17.85% of respondents shopping once in three
weeks and 46.42% of respondents shopping once a month in the price range. Data
collection shows more preference for this basket size with some variations in the upper
and lower brackets as well. An encouraging percentage of online shoppers, shop for
over £50 shop in a week with 33.33% shoppers in the segment shop at least once a week
and 50% of the shoppers in the segment shop at least once a month. 83.33% of shoppers
with a frequency of once in two weeks shop in the price range of £20 to £50.
4.1.2: Delivery Cost influencing online shopping behaviour
The in-depth interviewing phase highlighted the factor of delivery costs influencing the
frequency of shopping online and the average basket size. One of the interviewee’s
respondent to the impact of delivery cost by saying, “I don’t like paying for delivery
because I am from India and back home everything was free delivery.” Some of the
interviewees’ felt that the free delivery scheme did not benefit them because the
delivery time is very elongated (refer appendix 3). One of the respondents said that,
“the cost of delivery is okay the only thing I don’t feel good about is the delivery time”
explicitly mentioning the discomfort to the factor. One of the interviewee’s gave a
reference example of a friend who did not receive his order in over 15 days and had to
cancel on the later stage (refer appendix 7). The feedback from the interviewing phase
of data collection helped tabulate this factor in the second phase of the research and the
response spread is shown in table 3 and table 4. In the strata of 52 respondents who

39. 39
engage in online shopping, 86.53% find delivery cost to be an influencing factor in the
decision-making process of shopping online. Analysis of table 3 and table 4 show that
delivery costs influence the purchasing decision of online shoppers, which influences
their frequency of shopping online and also the average basket size. This indicates
towards the minimum order value to affect the decision making of customers of both the
genders and can be seen across age groups. Table 3 discusses the delivery cost
influencing the frequency of online shopping. 49% of the respondents shop online only
once a month because of the delivery costs being higher and their average basket size is
over £20 (refer table 2). The data also highlights another time frame that the consumers
generally shop online. 27% of the respondents projected their views about shopping
only once in two weeks and the time frame includes a combined average of 47% (refer
table 3)
Table 3: Delivery cost influencing the frequency of shopping online
Delivery
Cost
Once
a
week
(4)
Once
in
two
weeks
(5)
Once
in
two
weeks
(12)
Once
in
three
weeks
(5)
Once
a
month
(26)
No
(7)
1
1
0
1
4
Yes
(45)
3
4
12
4
22
Table 4: Delivery cost influencing the average basket size of shopping online
Delivery
Cost
£10
-­‐
£20
(8)
£21
-­‐
£40
(28)
£41
-­‐
£50
(10)
Over
£50
(6)
No
(7)
1
6
0
0
Yes
(45)
7
22
10
6
Table 3 and table 4 highlight the effects of delivery costs on the frequency of online
shopping and hence having a resultant effect on the average basket size of the shopping.
The data shows that the delivery costs have a strong affects on the frequency of
shopping and hence the average basket size. Shoppers aim at reducing effects of the
overall cost of shipment by purchasing products within a timeframe when they can

40.
40
create the basket of the amount to minimize delivery costs for the products they shop
online.
4.1.3: Products purchased online
In the first phase of the research, interviewees were asked to give feedback on the
products they usually buy online. All the respondents who shopped online engaged in
buying clothes online. The female interviewees were keener on purchasing clothes
online and described that apart from the clothing segment they preferred purchasing
shoes and fashion accessories online. One of the respondents also referred to a category
of electronic accessories, which they purchased because of a wide variety and the cost
factor (refer appendix 5). One of the female respondent showed keen interest in
purchasing branded bags online (refer appendix 6). On similar grounds, the second
phase of study asked the respondents to choose products that they usually shop for via
the online channel and the responses have been charted in figure 7 below.
Figure 7: Product respondents purchase online
The data in figure 7 shows that clothes are the most preferred products purchased online
with 75% of the respondents shopping for clothes online. Fashion accessories are the
39
24
26
21
25
2
Clothes
(75%)
Shoes
(46.2%)
Fashion
Accessories
(50%)
Groceries
(40.4%)
Electronics
(48.1%)
Others
(3.8%)
0
5
10
15
20
25
30
35
40
45
Products
purchased
by
respondents
online

41. 41
second most purchased product online with 50% respondents shopping them online.
48% of the respondents liked shopping for electronics online and 46% of them preferred
shoe shopping as well. Online grocery shopping is preferred by only 40.4% of the
respondents, which has been discussed in depth in the later sections of data analysis.
Respondents in the questionnaire suggested books and vehicular accessories to be some
other product categories they prefer to shop online. The data reflects on the product
categories that the consumers are generally interested in. Clothing being the most
preferred category of products purchased online. The analysis was done to get a
consumer perspective of what they want to buy when they shop online.
Table 6: Cross-tabulation analysis of Gender and Clothes Shopped online
Crosstab
Clothes shopped online
TotalYes No
Gender Male Count 12a 9b 21
% Within Gender 57.1% 42.9% 100.0%
Female Count 27a 4b 31
% Within Gender 87.1% 12.9% 100.0%
Total Count 39 13 52
% Within Gender 75.0% 25.0% 100.0%
Each subscript letter denotes a subset of Clothes shopped online categories
whose column proportions do not differ significantly from each other at the
.05 level.

42.
42
Table 6.a: Chi- Square Tests
Chi-Square Tests
Value df
Asymp. Sig.
(2-sided)
Exact Sig.
(2-sided)
Exact Sig.
(1-sided)
Pearson Chi-
Square
5.991a
1 .014
Continuity
Correctionb 4.500 1 .034
Likelihood
Ratio
5.959 1 .015
Fisher's Exact
Test
.022 .017
Linear-by-
Linear
Association
5.876 1 .015
N of Valid
Cases
52
a. 0 cells (0.0%) have expected count less than 5. The minimum expected
count is 5.25.
b. Computed only for a 2x2 table
The chi-square test reveals the Pearson Chi-square value to be 5.991 with a significance
of 95%. This shows that there is a valid association between the products those
consumers buy depending on their gender and hence a dependence on their average
basket size and the frequency of shopping can be established.
4.2: Behaviour of online shoppers
Big Middle refers to the market-space in which large retailers compete in the longer run
with economies of scale, increased revenues and incremental profits (Levy et al., 2005).
The model refers to the expansion of offerings into broader and deeper product lines
and also expansion of markets to reach a large potential audience (Levy et al., 2005).
The theory is well implemented in the online channel of retailing but doesn’t refer to
any typology of customers is the potential audience.
The interviewing phase of the study focused on identifying the important typology
factors that influence respondents’ engagement with online shopping. Most of the

43. 43
interviewees’ felt that online shopping was very convenient and shared their
convenience stories in the study. One of the interviewee stated that, ‘It just saves my
time and one gets more product variety with suitable ease of access and use as the
shopper can browse through the categories and click and buy it’ (refer appendix 6). “It
my gateway to know what’s out there” was another highlighting input by one of the
interviewee’s suggesting the cataloging system adopted by online shopping portals
provides convenience and ease of use to the shoppers. A reason that was seldom
preferred by one of a respondent in the in-store shopping was the queuing and space
constraints in the stores, which makes online shopping a much conducive and
convenient option (refer appendix 3). One of the interviewee’s categorically mentioned
about convenience being the biggest plus point of shopping online and added, “the
online shopping is bit cheaper in the overall terms as I don’t like to go far away to buy
clothes in London when I can just buy one or two things online” (refer appendix 8).
The interviewing phase also highlighted the cost factor as being an advantage when
shopping online. An interviewee shared an experience of prices at stores like H&M
being almost double of what is available online (refer appendix 3). Another respondent
also felt that the offers are a lot better on the online channel and it brings with it a
convenience factor, which makes online shopping a very enjoyable experience (refer
appendix 9). The respondent also added, “ unless its like a luxury market, like I would
love the Louis Vuitton experience, like you enter their showroom and the way they treat
you” and referred to it as being a shopping experience which can never be replicated or
replaced by online shopping (refer appendix 9). Hence the convenience, ease of use and
accessibility, and the price factors were given positive feedback by the interviewees
with an interesting feedback on the risk factors, which have been discussed in the
section 4.2.1. The questionnaire phase asked the respondents about their perception
about these factors affecting their online shopping behaviour.

44.
44
Figure 8: Graphical plot of the factor important for customers when shopping
online
Figure 8 is a summary of the responses highlighting the importance of the factors that
influence or add as a catalyst factors in helping them shop online. Convenience factors
were calculated to be the influencing factors driving the purchase decision with about
63.46% of respondents rating it very important during the survey and another 21.15%
respondents rating it an important factor. The cost factors show a divided plot between
being a very important and an important factor influencing the respondent’s purchase
decision. The study found that 34.61% of the respondents found cost to be a very
influencing and important factor in their purchasing decision and 36.53% of the
respondents considered it important. However 19.23% of the respondents seemed to
find the factor moderately important when shopping online. Ease of use factors deals
with the familiarity with the website along with being accessible and the attached factor
of browsing the products. According to Huang and Oppewal (2006), ease of use factors
add pleasure in the online shopping making it enjoyable. 48.07% of respondents
considered it to be a very important factor influencing their purchase decision or making
Very
Important
Important
Moderately
Important
Not
so
important
Not
at
all
important
Convenience
Factors
33
11
2
2
4
Cost
Factors
18
19
10
4
1
Ease
Of
Use'
Factors
25
16
6
2
3
0
5
10
15
20
25
30
35

45. 45
it easier for users to purchase goods online. The study also found 30.76% of the
respondents considering it to be an important factor influencing their decision-making
process.
Table 7: Correlations between the convenience factor, cost factor and ease of use
factor in online shopping
Table 5 reveals; the three factors have a positive correlation between the convenience
factor, cost factor, ease of use factor and decision of online shopping. The analysis also
reveals a 99% significance of the relationship between the variables tested. This
supports the fact that the consumer decision-making process involves the convenience,
cost and ease of use factors affecting the relationship. The analysis also emphasizes the
interrelation between these factors as being a collective force influencing the overall
decision-making process of the consumer. The online shopping industry deals with a
wide variety of products as it gives a large retail space with boundless accessibility and
price range to offer. A combination of these factors influences the consumers
purchasing behaviour. Findings in table 5 are indicative towards the concept of virtual
in-store experience or virtual reality.

46.
46
4.2.1: Risk factors in online shopping
Advancements in technology have made online transactions really secure with the
shoppers’ credentials and bank details kept secured. Many respondents in the
interviewing phase stressed on the fact that the returns policy and the process are really
conducive to online shopping. One of the interviewee’s emphasized the role of social
media platforms in helping fine tune the online shopping return policy and security of
the card details (refer appendix 9). The strengths of social media have been harnessed to
create negative word of mouth in case of poor servicing or being wrongly debited
(Weber, 2009). Customer services platforms have been strengthened to meet with
growing consumer requirements and concerns (Nidumolu et al., 2009). One of the
respondents stated, “credit card information is always kept confidential and the return
policy is so good that I just have to send the products back and I get the refund on its
own without follow ups or reminders” (refer appendix 6). This has not only help build
the trust factor in the online shopping industry but has also given the consumers an
overall experience enrichment. One interviewee was very upfront with the belief of
payments via PayPal because of the risk-free transactions and not being double debited
due to poor connectivity at times (refer appendix 3). The interviewee also highlighted
the factor of paying more or preferring to shop on website using PayPal as a payment
method (refer appendix 3). Consequently with risk-free transactions along with
transparent and convenient returns policies have been identified to be the minimalistic
influencing factor in the consumer decision-making process and was not tested in the
second phase.

47. 47
4.3: Online Grocery Shopping
The interviewing phase revealed consumers averagely shopped for groceries in the
timeline of once in two weeks or once a month depending on the size of the family and
consumption. Eight interviewees engaged in online grocery shopping and did so largely
for the convenience. Feedback from the consumers revealed the familiarity with the
products being one of the key factors in shopping for groceries online (refer appendix
5). The more profound reason for doing groceries online has been because of the
distance and the opportunity cost involved in shopping for groceries online. As revealed
in one of the studies by the founders of Peapod.com, grocery shopping was one of the
most disliked household chores (Corral, 1999); this has been an explicitly mentioned by
one of the respondents (refer appendix 4). One of the interviewee’s regarded the product
line being easily accessible via the online channel of retail and added, “there are so
many products that I actually like shopping for groceries online than in store, so unless
you know what you are looking for, you are going to have so many heads that you wont
have the patience or time for that” (refer appendix 9). The cataloging system for
groceries has been referred to by a lot of interviewees as being a convenient and an ease
of use factor contributing to their shopping for groceries online. An interviewee gave an
example about the shopping experience and said that, “So if I want to buy certain type
of meat or other things I cannot find them in the real store but I can get them online”
(refer appendix 8). One the interviewees referred to the shopping of online groceries as
being a planned activity rather than being an impulsive purchasing behaviour and
added, “ In offline you see products in front of you and you end up buying seven to
eight things instead of buying five things you had in your list” (refer appendix 9).
Interviewees have not regarded cost to be an important factor in the online grocery
shopping but rating convenience to be more important. A family respondent stated,

48.
48
“even when we are paying for delivering groceries, it saves time and we don’t have to
carry so much grocery from the store to our residence” (refer appendix 7).
The second phase of the study showed that out of the 54 completed responses, 51.85%
of the respondents do their grocery shopping online. The respondents were also asked
the reasons they like purchasing their groceries online and the responses have been
tabulated in figure 9.
Figure 9: Why customers engage in online grocery shopping? (28 respondents)
The second phase of data collection showed that 92.9% engage in online grocery
shopping for the convenience. 28.6% of the respondents’ find cost and ease of shopping
to be key benefit them when shopping for groceries online. On some of the
interviewees’ feedback on the products being of better quality when purchased online,
the study also tested this factor influencing the online grocery shoppers. Only 14.3% of
the respondents felt it was the factor pushing them to engage in online grocery
shopping.
26
8
4
8
0
5
10
15
20
25
30
Convenience
(92.9%)
Cost
(28.6%)
Products
(14.3%)
Ease
of
shopping
(28.6%)
Reasons
to
engage
in
online
grocery
shopping

49. 49
To analyze the relationship between the typology of consumers engaging in online
shopping and online grocery shopping a simple linear regression analysis has been
carried out to map the factors that influence online shopping.
Table 7: Regression analysis for online shopping
Variables Entered/Removeda
Model Variables Entered Variables Removed Method
1 Cost factor Online
Shopping,
Ease of use Online
Shopping,
Convenience factor
Online Shopping b
. Enter
a. Dependent Variable: Online shopping
b. All requested variables entered.
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
1 .637a
.405 .370 .151
a. Predictors: (Constant), Cost factor Online Shopping, Ease of
use Online Shopping, Convenience factor Online Shopping
ANOVAa
Model
Sum of
Squares df
Mean
Square F Sig.
1 Regressio
n
.781 3 .260 11.365 .000b
Residual 1.145 50 .023
Total 1.926 53
a. Dependent Variable: Online shopping
b. Predictors: (Constant), Cost factor Online Shopping, Ease of use Online
Shopping, Convenience factor Online Shopping

50.
50
Coefficientsa
Model
Unstandardized
Coefficients
Standardiz
ed
Coefficient
s
t Sig.B Std. Error Beta
1 (Constant) .573 .070 8.184 .000
Convenience
factor Online
Shopping
.002 .034 .013 .052 .959
Ease of use
Online
Shopping
.041 .032 .291 1.267 .211
Cost factor
Online
Shopping
.058 .027 .382 2.180 .034
a. Dependent Variable: Online shopping
The regression analysis accounts for the factors such as cost, ease of use and
convenience and establishes how online shopping is influenced by these factors. Model
Summary shows the percentage of variability in DV accounted for all the independent
variables together. ANOVA table gives the F value from the f-test stating the model to
be good and also gives the significance of the model at p value < 0.01. Hence the
resulting equation can be understood from the beta-coefficients calculating the online
shopping to be
Y = 0.573 + 0.002 (Convenience factors) + 0.041 (Ease of use factors) + 0.058 (Cost
factors)
This above analysis reveals that there is a high degree of correlation between these
terms and also that the typology of customers shopping online for products other that
groceries also have a similar typology.

51. 51
The relationship also indicates that cross-pollination via the online grocery channel or
vice versa could prove fruitful as value propositions to the consumers. In the
interviewing phase of data collection an interviewee suggested, “I feel like if there was
a company that said we will come to you doorstep and deliver you the goods and offers
a personalized service” and also added, “an organization that could provide a
personalized service with of delivering all my deliveries/shopping together it be more
convenient” (refer appendix 3). Convenience is a factor that encourages the consumers
of both the industries have been seeking and deriving out of the experience. The factor
analysis of the online shopping industry and the online grocery industry (refer appendix
10) has facilitated the study of cross-pollination via the online grocery channel.
4.3.1: Cross-Pollination via online grocery shopping
Cross-pollination via the online grocery refers to the concept of integrating services
from other online retailing firms to be delivered / serviced to the consumer together.
The idea is based on the improvement of factors like convenience and cost of delivery,
which could be maximized and minimized respectively using this platform. The
industry for such cross-pollination currently exists on a small scale with smaller
developers like ‘convivo’ aiming to maximize their services across platforms. The
research focused on the concepts of strategic alliances in the front end to have the
products delivered to the customer together at the desired time and place of
convenience. Phase one of the research evaluated the concept of cross-pollination via
the online grocery channel and received quite varied perspectives. Enthusiastic shoppers
believed that they would try the concept and were depending more to do with the entire
experience of shopping online. An interviewee was very pleased with the concept and
said that,

52.
52
“I think combining the services would be a very good option because people
these days tend to prefer to shop online and not in the store, and if we can have
everything under one roof and coming to your doorstep together rather than shopping
two things from here and three thing the other, and also pay separate delivery charges if
the basket size is not big enough.”
This viewpoint really highlights some very important factors that cross-pollination
could offer consumers. One of the interviews suggested if cross-pollination could
schedule weekly deliveries of groceries along with other products such as medicines,
which are need-based, products could be useful for the consumers and also felt that such
a service would add to the convenience and also consumers may not mind paying extra
for such a service because of the sheer convenience this would offer (refer appendix 5).
Interviewing phase also revealed a factor that there should be a differentiation in the
interface for the consumers’ ease of use and the backend could be connected. This
would let the consumers have different product categories and needs to meet but have
them delivered together or collected to a store closer to them (refer appendix 6). Some
of the interviewees mentioned the factor of the service cost or for that instance the
delivery cost of such a service as being a deciding factor, whether they would like to
adopt it or not. The concept was welcomed well by the interviewees and was tested at
the larger audience in the second phase of data collection.
The study asked the respondents about whether they would find it convenient if their
online shopping was delivered to them along with their grocery shopping or vice versa
and their responses were mapped in figure 10.

53. 53
Figure 10: Convenience map for cross-pollination via online grocery shopping
As depicted in figure 10, 35% of the respondents feel it would be convenient if they
could shop for other products and groceries online and have them delivered together.
22% of the respondents find the concept moderately convenient and 17% of the
respondents were neutral to the concept. However, 13% of the respondents felt it would
be moderately inconvenient to have their online shopping be delivered with their
groceries or vice versa. The study also revealed that 13% of the respondents found the
concept to be inconvenient and not conducive to their needs.
The study also incorporated the possible product categories that would interest
consumers and would meet their needs. Some product categories that came out in the
interviewing phase were off the shelf medicines, clothes (brands such as H&M, GAP,
Zara), books and electronic accessories. The questionnaire phase of data collection
received feedback on various product categories people would like to have along with
their grocery shopping. Clothing and electronic accessories were some of the most
preferred product categories people would like to be delivered along with their
groceries.
35%
22%
17%
13%
13%
A
total
of
54
responses
Convenient
Moderately
Convenient
Neurtal
Moderately
Inconvenient
Inconvenient

54.
54
The relationship between the typology of online shoppers and the online grocery
shoppers indicates that there is a strong potential for such a value proposition to be
rewarding the consumers with services they would prefer, if the experience of such a
service were kept up with. An interviewee suggested that if there is such a strategic
alliance, then she would be fascinated by the concept of going to her grocery store and
buying her favorite clothing brand as well if they can have an in-store kiosk as well
(refer appendix 9). Figure 10 also shows a large section of the respondents welcoming
the concept of cross-pollination via the online grocery channel.
Big Middle theory also supports the concept of cross-pollination by suggesting the
broad basing of product lines and addressing a larger range of segments of customers to
serve all there possible needs. The concept also adds value to the consumer servicing
chain at large. Growing need of the food retailing industry looking at past trends and the
financial trends elaborated in section 2.4.1.a supports the development of the cross-
pollination platform. Adapting the online grocery conceptual framework (refer figure 2)
that states; cost, convenience, enjoyment (ease of use) and perceived risk together
define online shopping preference and has been tested to define the typology of online
grocery shoppers. The model also holds true for the typology of the online shoppers as
well and can be supported by the regression analysis shown in table 7.

55. 55
4.4: Virtual in-store experience:
Ease of use and the overall experience of shopping online have been identified as being
some important factors influencing the purchase decision of the customers. Simulation
experience or having the real feel of the store would benefit the customers who engage
in shopping online as the feeling of the store is missing and there is no human contact.
The concept was receipted well but lacked the experience, as the study could not show
the interviewees or the respondents with how the virtual in-store concept would be in
reality. Reference points were used in the interviewing phase using the ASOS Catwalk
concept, which was the only virtual experience in the retail segment. Just as a concept,
the interviewees’ felt that it would add in more structuring to the cataloging system in
the online shopping websites. One of the interviewee’s referred to the problems of
Internet connectivity being a major issue in this case and the cost that it would add to
the consumers (refer appendix 3). The enjoyment factor in shopping would only be
enriched by such a platform stated on of the respondent in the interview and added, “if I
can see everything lined up and spaced out and if I can pick it up in the basket it would
be very convenient” (refer appendix 6). One of the interviewee’s related it to a concept
developed by ASOS and explained the virtual catwalk concept and said that, “the
customer can view a product in 360 degrees and one can see the model walking and
turning around helping judge how the garment flows, how thick is the fabric” (refer
appendix 9). “The experience and the enjoyment factor is maximized with such a
concept and makes purchasing the product easier for the consumer” added the
interviewee.
When the respondents in the second phase of data collection were probed with the
concept (refer figure 11), 24% of the respondents felt that it would be very helpful and
an enjoyable experience. 41% of the respondents were positive about the concept but
were sure about the experience and another 22% of the respondents were very neutral

56.
56
about the concept. A small percentage of the population felt it would hardly be helpful
and enjoyable on the similar grounds as one of the interviewee’s who believed, “It
would be difficult as I prefer the cataloging system which improves ease of use.” 9% of
the respondents felt it wouldn’t help them shop online at all.
Figure 11: Virtual in-store experience
When respondents in the second phase were asked how this would help them, responses
remained quite positive. Most of the responses ended it as being a convenient and an
alternate shopping experience. Despite execution and experience in reality not being
available as a sample to showcase, the respondents were optimistic about the concept
adding to there overall shopping experience and being helpful to them. The concept of
virtual in-store would improve the overall shopping experience for the shoppers and add
to the enjoyment factors of shopping online. The ease of use factors for the online
shopping and the online grocery shopping are relatively a higher factor influencing the
purchase decision of the customers. The concept when implemented would add to the
convenience for the shoppers in both the categories and could be one of the early value
propositions that can support cross-pollination via online grocery shopping.
Helpful
and
Enjoyable
24%
Helpful
41%
Neutral
22%
Hardly
Helpful
4%
Wouldn't
Help
9%
Virtual
in-­‐store
Experience

57. 57
Chapter 5: Conclusions
The chapter concentrates in drawing the conclusions from the finding and
interpretations done on the data collected. It also summarizes the relationships proved
using the data analysis. Models discussed in chapter 2 have been used to support the
theoretical framework of the conclusions drawn.
5.1: Consumer Behaviour being planned
The study has been able to conclude the factors that classifies the typology of
consumers in the online shopping channel and also suggests modifiers in them. The
theory of planned behaviour has been identified the behavioral backing for Hunag &
Oppewal (2006) conceptual model of typology of customers. This has helped map the
perceived behaviour control to the behavioral intention of the consumers. The intention
to perform an action is the defined factor developed by the theory of planned behaviour.
Hansen (2008) in his study suggested that the theory of planned behaviour could be
used to understand the behaviour of online grocery shoppers.
The model has also been used to analyze the effects of the delivery cost on the average
basket size of the consumers. Price range of £20 to £50 has been identified to be an
appropriate basket given the delivery costs involved in attaining the products or
services. Delivery cost has also been an influencing in the frequency of online shopping
by the consumers and a resultant effect on the average basket size. The average industry
pricing for free delivery is £50, but the research identifies that consumers tend to shop
for the price where the relative effect of the delivery cost is minimized as opposed to the
products attained. Clothes are the most preferred of the products purchased online.
5.2: Typology of Consumers
The study had adopted the online grocery shopper typology developed by Huang &
Oppewal (2006) to study the typology of online shoppers and to find common factors
that influence the purchasing behaviour of both the industries. Pearson’s correlation

58.
58
coefficient helped establish a positive correlation between adoption of online shopping
and the convenience factor, cost factor and ease of use factor in the research with a
significance of 99%. The relationship has helped analyze the factor of ease of use to an
important factor in the purchasing behaviour. Similarities in the typology of consumers
in both the industries has helped map the possibility of cross-pollination via the online
grocery channel, hence creating a value proposition the food retailing industry can offer.
Studying the typology of online shoppers using the model developed by Huang &
Oppewal (2006) has lead to fine tune the model in terms of the risk factors that have
been reduced over the years due to technological advancements in the banking and e-
commerce sector. Consumer information being kept confidential and the transactions
being tracked by the consumer at all points have inculcated a sense of trust with online
shopping experience. Platforms such as PayPal, Paypoint, 3-D secure by Visa etc. have
been some of the financial securities that have influenced the consumer perception over
the last years. An important factor with online shopping that has helped build trust with
the consumers is the returns policy being transparent and hassle-free.
5.3: Online Grocery shopping and the factors leading to cross-pollination via the
online grocery channel
Convenience has been identified to be one of the most influencing factor leading
consumers to engage in online grocery shopping. Ease of use and the cataloging system
have added to the overall shopping experience. A market need for a personalized
servicing was identified in the first phase of the study using interviews and recorded
that delivering of the groceries and shopping together would be more convenient. The
study then identified the cross-pollination feasibility via the online grocery channel.
Using the Big Middle theory, a theoretical support was sourced for cross-pollination
being a value proposition for the food retailing industry. The study acknowledged the
need in the market for a combined and a personalized serviceability in the online

59. 59
grocery channel and the online shopping industry. The study was able to identify
possible strategic alliance parameters that would enable cross-pollination via the online
grocery channel. Some of the parameters were similar pricing strategy and brand appeal
like the grocery store. Collection of the products purchased online from a more
accessible and convenient store was identified in the study. Developing a larger product
line and convenience parameters for the consumers using the model can meet with the
growing consumer needs of personalized service.
5.4: Virtual in-store experience
The study also explored the need for virtual in-store experience and the enhancement of
the shopping experience and the ease of use factor. Ease of use was identified to be the
second most influencing factor in the consumer behaviour of shopping online (refer
appendix 10). Evaluating the concept results showed that ease of use and convenience
factors were very important in the online shopping and the online grocery shopping
experience. Hence, developing a platform of such would add to the enjoyment factors
that a consumer can experience when engaging in shopping online and also minimize
the lacking in-store charm that consumers feel (Forsythe & Shi, 2003).

60.
60
Chapter 6: Managerial Implications
The study brings out the effects of delivery costs that influence consumer behaviour and
their purchasing of products online in terms of average basket size of shopping and the
frequency of shopping. Understanding the financial factors involved in setting up the
delivery costs to make convenience feasible for the consumers in the longer run,
platform of cross-pollination could prove beneficial for the consumer in meeting their
needs and also for organizations to meet their financial aspects involved in the
delivering of goods. Hence a strategic review is needed to reduce the overall cost that
the consumers incur in attaining the product at their doorstep along with its financial
feasibility for the service providers.
Analysis of the data collected also highlighted a large gap between the adoption
percentages of online shopping as compared to that of online grocery shopping. This
has implicated the need for more value propositions in the online grocery shopping
industry. The research also helped implicate some suggestive parameters like pricing
strategies, brand image and goals, and consumer-centric goals for the cross-pollination
alliances with the online grocery industry.
Store layout was identified as one of the concerns that consumers have been moving to
online shopping. Hence having a more interactive virtual in-store experience would
enrich the overall shopping excitement on the online channel. With the advancements
in the technology and the identification of the growing need of the consumers for an
online trail room or experience of the product in the clothes retail industry has given a
challenge for managers to implement a platform where the consumers can experience
the garment. Some of the suggestions made for such a platform were to the ASOS
Catwalk, which enables the consumer to look at the flow of the garment and the texture
of the cloth and also view it in 360 degrees with a model wearing it to showcase how