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1. Introduction
Online shopping is a relatively recent
phenomenon, giving consumers the opportunity to perform at least one
aspect of the buying process online. It is important for businesses to
understand the motivations of consumers to enable them to target those
motives in order to expand and maximise their share of this market.
Since the late 1990s, many
companies in a range of countries, have attempted to establish online
grocery stores, however a large percentage experienced only very short
lifespans. Despite this, the potential market for online replenishment
of grocery supplies lures both startup and traditional grocers.
A number of studies have analysed
electronic commerce by looking at business models, delivery methods and
availability of services. From a consumer perspective, aspects covered
have included privacy, risk, usability and accessibility. These
technologically driven frameworks are in place for widespread consumer
adoption, however past adoption case studies have shown that
technological superiority alone will not ensure the successful adoption
of a new service.
This study further develops our
view on the adoption of electronic commerce by looking at the role that
social factors play in a consumer's decision to adopt an online shopping
service. In order to provide a more meaningful analysis, the focus of
this study is on the Australian electronic grocery shopping (EGS)
industry.
Online shopping is a relatively
recent phenomenon, giving consumers the opportunity to perform at least
one aspect of the buying process online. The business-to-consumer (B2C)
sector of the online market was worth $1.98bn in 2001 (Ahn et al 2001)
and continues to grow. It is important for businesses to understand the
motivations of consumers to enable them to target those motives in order
to expand and maximise their share of this market.
Electronic grocery shopping has
been defined as shopping when at least part of the transaction is
started electronically via third party services or the Internet, but
paying and logistics are not necessarily performed digitally (Anderson &
Henrikson, 2000). This definition has been used in the study to identify
EGS services.
2. Current state of EGS in Australia
To date, the success of EGS has
been fairly limited. In Australia, a number of companies have attempted
to implement a range of business models online. These companies include
those with a presence in the traditional grocery marketplace, such as
Woolworths and Coles, for whom the online option is an additional
service, and also Internet start-ups, such as GreenGrocer.com and
ShopFast, who operate solely online.
Despite enthusiasm from these
businesses, there has been unwillingness from the consumer to purchase
groceries online, particularly when compared with other areas of
electronic commerce. A survey from 1999, conducted by Fast Company,
revealed "significant attitudinal barriers" (Kirsner, 1999) to buying
groceries online.
By obtaining the social
demographics that define each group, the target market and most
potentially profitable group may be identified, and decisions made about
the future markets for electronic grocery shopping. This study is taken
entirely from the point of view of the consumer. Advantages and
disadvantages of EGS for the retailer are beyond the scope of this
research.
3. Research focus
There are a number of streams of
research that are relevant to this study. These include those addressing
the patterns of consumers with respect to traditional grocery shopping
formats, as well as innovation diffusion research (Rogers, 1983,
Antonides et al 1999, Vrechopoulos et al 2000, Bain 1999, Plouffe et al
2000, Tepper-Tian et al 2001, Henderson et al.1998, Deutsch et al.
2001). The importance of diffusion research to this study is that it can
be used to look at the consumer's perception of the social factors in
the adoption of an innovation, and the affect that this will have on
their adoption inclination, specifically addressing factors that
influence consumer adoption of online shopping.
There is also a body of knowledge
that has examined different consumer motivations for adoption of an EGS
system (Mathwick et al 2001, Puhakainen 2001, Ahola et al 2000, Ahn et
al 2001, Raijas 2002, Geuens et al 2001).
This study also attempts to
identify the importance of social factors (Tauber 1972, Dembeck 1999,
Puhakainen 2001, Kahn & McAlister 1997, Dahl at al 2001, Burke 1997,
Bunker & MacGregor 2000) in such a decision and therefore incorporates
aspects of each of these different areas.
Merchants must create a 'marketspace'
in which customers can interact with the company and each other,
exchange information and purchase products or services to succeed in an
online environment.
As the demographics of contemporary
Australian society change, so do the consumer patterns. The mounting
average age of consumers increases the need for more convenience in
grocery shopping. EGS provides this convenience by enabling consumers to
order groceries from home and to have the groceries subsequently
delivered at home.
3.1 Differing priorities
for consumer groups
Studies have shown that individual
differences are associated with the acceptance of new information
technology and also new forms of retail marketing and shopping (Crisp et
al. 1997, Deutsch et al 2001). Consumer lifestyle relates to how people
live, how they spend their money, and how they allocate their time.
These factors will all affect the consumer's consumption of groceries,
and the relative importance of social factors to their adoption
decision.
As identified before, there are
several different recognized criteria for defining groups of consumers.
Traditional shopping literature defines two groups: goal-oriented and
experiential customers. Innovation literature distinguishes consumers
based on a timeline of adoption (Rogers, 1995), and other studies
related to electronic commerce have developed methods based on socio
demographic details and familiarity with technology (Deutsch et al
2001).
Social demographics have been
identified as affecting intentions towards Internet shopping (Crisp et
al 1997, Bain 1999). Factors include prior Internet experience,
attitudes towards computers, age, household size, and frequency of
shopping via direct marketing channel, gender and income, factors
influencing household food inventories and time availability and the
number of household’s employed members. The study by Citrin et al.
(2000) indicated that Internet usage (for reasons other than shopping)
and domain-specific innovativeness, have a direct influence on
consumers' adoption of Internet shopping.
4. Research methodology
The first stage of this project was
an interview with the Managing Director of a major online retail
shopping division of a company that also has a major “bricks & mortar”
presence in the marketplace. This interview was conducted to gain
insight into the factors that current members of the online shopping
industry see as important to their customers, and to use these factors
in the construction of the next stage of the data collection process.
The second stage was the pilot of a
questionnaire to a small number of prospective EGS customers and the
third stage was the administration of the questionnaire to a
representative sample of prospective and actual users of online shopping
services.
4.1 Interview
The main focus of the interview was
to determine the type of consumers that were attracted to online
shopping, and the type of products that these consumers are prepared to
buy. Results from the interview confirmed that a number of items that
had been identified by past studies as potentially influential, were
significant factors in the online shopping industry.
The findings included confirmation
that the online shopping market is no longer the exclusive domain of
young, affluent males. The Managing Director who was interviewed works
for an organization that markets its service at essentially the same
market as its the traditional “bricks and mortar” stores. This has
proved to be a successful strategy, both for the online and traditional
business.
The organization also identified
that distance to a shop will not necessarily correlate with inclination
to shop online. Many of the online customers have been shown to live in
close physical proximity to one of their retail stores.
The organization has found that
some of the most successful goods sold online have included food items
packaged as gifts. Other popular items include replenishment goods, and
bulky purchases that may require extensive effort to transport. The
number of different product lines available online is increasing to
include items that do not fall into these categories. However, this
criteria fits closely with the attributes of groceries, excluding fresh
produce items which generally require a greater degree of involvement
from the consumer.
This online store has considered
the idea of selling fresh food and groceries through their site, but
have declined to put it into practice at this stage due to back-end
systems requirements (optimal levels of inventory and appropriate
conditions for storage and transport).
Other topics covered included
confirmation that return customers are extremely important for online
stores, especially in the long term. One issue that had not previously
been considered in depth was that of customer service facilities. The
interview revealed that customers of the online store seemed to be quite
happy to use the email facility on the site for customer service.
4.2 Research questionnaire
4.2.1 The pilot study
The pilot study took place in late
2002, and was made up of two main sequential stages. The first was a
preliminary analysis where confusing or difficult questions within the
questionnaire were identified and modified. The second was the
administration of the questionnaire to a small sample of participants.
The sample used in the pilot was
composed of ten individuals, with a number of common demographics. All
respondents were female, employed and had Internet access from their
workplace. By selecting this group, the pilot was easily controlled and
emphasis could be placed on the frame of the questionnaire and
interpretation of results, rather than on the results themselves. The
aim of this pilot was to assess and refine the construction of the
questionnaire, and at the same time trial methods of analysing the
responses.
The data collected was made up of
three types; demographic characteristics, orientation to the
environment, and reported behaviour. The demographic information allowed
the classification of respondents into the sociological categories
previously identified. Orientation refers to individual's perceptions,
knowledge and attitudes towards the world. Orientation items made up the
body of the questionnaire and assessed the relative weightings consumers
gave to the issues being addressed. These findings were analysed against
the reported behaviour. The reported behaviour questions were those
relating to objective measures of individual's previous experiences,
such as whether or not they had purchased goods or services online.
The majority of items are presented
in the form of seven point Likert scales. This format for input allows
respondents to state the relative strength of their opinions on a number
of issues, allowing easy comparison between respondents. For a similar
reason, almost all items are closed in design, with a limited
variability of responses.
4.2.2 The survey
Variables measured
The items in the final
questionnaire measured a number of variables identified through the
background literature as potentially influential to consumers with
respect to their inclination to adopt EGS (Table 1). The questionnaire
has been presented in full in Appendix A.
The operationalisation of the
concepts brought up in the literature review into these variables was
based on a number of criteria. Each item in the questionnaire was
selected as being part of one of two groups. The first group consisted
of items that had been shown to be influential in adoption of online
shopping in general, and in this case were assessed as to their
applicability to EGS. The second group consisted of factors which had
been shown to be influential in the purchasing behaviour of consumers in
supermarkets and other grocery store formats, and in this study were
assessed as to their applicability to the online format. These items
were then grouped with a number of other items in the same area. This
grouping helped to provide a more meaningful analysis of the data
collected.
Control Variables
Items that were found to be
influential in the innovation adoption decision by a previous adoption
study, were incorporated into the questionnaire as control variables.
These items were positively correlated with electronic grocery shopping
and are highlighted in a study from the Netherlands by Verhoef &
Langerak (2001). The purpose of these variables was to assess the
influence on adoption inclination for the respondents in this sample and
provide a guide as to the generalisability of the results.
Sample Description
The sampling strategy used in this
study was based on two main criteria, relevance and convenience.
Relevance relates to the methodology being used, and convenience relates
to the time restrictions of the study. It was desired that potential EGS
consumers would complete the questionnaires. Participants for this study
were solicited from two main sources. The first of these were students
at the University of New South Wales. The second was the general
population. Students at the University were easily accessible and by
using both undergraduate and postgraduate classes, the diversity of
demographics was maximized as much as possible. The sampling of
consumers from the general population, in this case the suburbs of
Sydney, was intended to expand the demographics and was located to
target grocery consumers.
A total of one hundred and thirty
(130) responses to the questionnaire were received, however four of
these were unusable due to significant item omissions, leaving 126
usable responses, of these 73 were UNSW students.
Data Collection Procedure
The procedure for the collection of
this data took two main forms. The first of these, for the participants
from UNSW, involved approaching School of Information Systems,
Technology and Management lecturing staff and explaining to them the
study that was being carried out. They were asked if it would be
possible to give a brief questionnaire to their students as part of this
study.
Upon approval, students were
informed that the study was taking place within the SISTM faculty, and
were given the questionnaire to complete immediately. The questionnaire
was then collected, once completed, within several minutes.
The other method of soliciting
participants was to approach consumers outside their local shopping
centre and explain the study, the fact that it was endorsed by UNSW, and
to ask whether they would mind completing a brief questionnaire. Again
the questionnaires were completed and collected immediately. This method
of sampling proved to be less successful in terms of response rate, as
the acceptance rate was only approximately 20% (15 out of 70
approached). Due to time constraints with the study, two extra
postgraduate classes from UNSW were included in the sample. It was hoped
that the postgraduate students would represent a wider ranging
demographic than the undergraduate classes. As well as this,
acquaintances of the researcher were included in the sample, once again,
to bring greater diversity to the demographics included in the sample.
No incentives were offered to any
of the groups. The questionnaires were anonymous and the items designed
to be impersonal, so confidentiality did not present any problems.
Because the questionnaires were all completed on the spot, some issues
that would normally arise out of survey methods, such as response rate
to distributed questionnaires, are not relevant to this study.
5. Data analysis
Due to the exploratory nature of
this study, the aim of this analysis was not to confirm or eliminate
hypotheses, but rather to identify potential relationships between
variables that have been suggested by background research to this study.
A number of methods were used to
analyse the data that was collected from these questionnaires. The
social factors under examination were looked at in terms of the priority
they were given by the consumers, and also by the different consumers
allocating the priorities. The factors were looked at not only as a
collective group, but also as individual aspects of the same issue.
Reliability was assessed through
calculation of Cronbach’s alpha, and items in the questionnaire were
subjected to correlation analysis to determine consistency. The items
that were shown to have a significant relationship with intention to
adopt then underwent regression analysis to determine the relative
influence of each of the factors.
5.1 Categorisation of
respondents – Previous adoption behaviour
Data was analysed to show 3 obvious
groups of adopters. The first consumer group was made up of individuals
who have neither researched, nor purchased goods nor services online.
The second consumer group is made up of individuals who have researched,
but not made purchases online. The third group consists of those
respondents who have purchased online. The breakdown of these groups is
shown in Table 2.
Table 2: Percentage of respondents
who visit online stores
| Online experience
variable |
Group 1 |
Group 2 |
Group 3 |
All |
|
Researched purchases online |
0% |
100% |
95.00% |
80.20% |
|
Purchased online |
0% |
0% |
100.00% |
52.40% |
| N |
22 |
38 |
66 |
126 |
| Percent |
19% |
31% |
50% |
100% |
By comparing the overall adoption
intention ratings with the adopter group for each respondent, it can be
seen that the adopter categories are relatively good predictors for
adoption intention.
Figure 1 shows the levels of
adoption intention for each adopter group. It can be seen that the peaks
of each distribution are relative to the predicted innovativeness of
each group. The respondents rated their intention on a scale from 1
(will definitely adopt) to 7 (will definitely not adopt). Group 1 peaks
at the value for those least inclined (strongly disagree that they will
use an EGS in the future), whereas Group 3 peaks on the positive side of
the scale, and Group 2 at the neutral value.
Figure 1: Group vs. Inclination
While this is an intuitive
analysis, an assessment of the correlation between the two factors
provides a more technical confirmation of this finding, and is detailed
in Table 3
Table 3: Correlation between Item
31 and Group
The strong negative correlation
(r=-0.343) shows that the higher the number of the group to which the
individual belongs (the greater the individual’s online experience), the
more likely they are to have a favorable adoption intention rating.
The significance level in this
analysis is representative of the likelihood that the two items measured
are independent of each other. The lower the significance value, the
less likely it is that the two items are independent. The figures show
that past adoption behaviour, in this case, is a fairly good indicator
of future intention to adopt EGS.
The next step in this process was
to identify the demographics that make up each of these adopter groups,
and then determine the relative strength of the relationship between the
groups and the priority given to social factors in adoption inclination,
by consumers.
Table 4 details the
socio-demographics of each adopter group. A number of interesting
observations can be made from this table. The first of these is that
neither age nor gender, appear to have influence over the classification
of respondents into adopter groups. While age varies very slightly over
the different groups, the percentage of males and females is almost
exactly equal in all three groups (and is equal in two out of the
three).
Differentiation based on employment
showed that Group 1 was under represented in the Part-time and Full-time
categories. Those in Group 2, and even more noticeably, Group 3, were
more likely to be employed on a Part-time or Full-time basis. Group 1
has a disproportionate amount of the highest spending consumers ($200 or
more per week), with Group 2 containing the largest proportion of lowest
spending consumers (less than $50 per week).
Despite these small fluctuations,
it would appear that the demographic variables are not strongly linked
to the classification of adopter groups.
Table 4: Demographic
characteristics by adopter category
* Totals do not add up to 100.00%
due to rounding of components
5.2 Sense of community
(Items 13, 14, 15, 16)
Four items in the questionnaire
were used to measure the priority that consumers gave to the sense of
community, and personal interaction, in the grocery shopping experience,
and the relative importance of these factors in overall EGS adoption
inclination. Table 5 presents the correlation between these four items.
Table 5: Correlations between Items
13, 14, 15 and 16
By calculating the correlations
between the community aspect items, it can be seen that each item has a
significant relationship with at least one of the other items. This is a
good indication of the internal consistency of this factor.
As well as comparing the
relationship between these variables, the relationship with overall
intention to adopt EGS (Item 31) was examined. Only items 14 and 15 had
significant correlations with adoption intention, showing that both a
consumer’s willingness to use self check-out facilities in a
supermarket, and their awareness of other consumers looking at their
purchases, were possible predictors of EGS adoption intention. Items 15
and 16 are related in terms of the content that they are examining. It
is shown by the correlation between 15 and 31, that those individuals
who are aware of others perceptions are more likely to express intention
to adopt an online service, however by the responses to Item 16, they do
not believe that this is a direct cause of intention to adopt. This may
be because Item 16 is not the direct link with intention to adopt, but
rather is linked to another factor which is more significant in the
decision. Another reason may be that individuals are unwilling to admit
that other people’s perception of their purchases would induce them to
make their purchases online.
The items that were deemed not to
have a significant correlation with overall adoption intention were not
used in the regression analysis. The correlation between items 14, 15
and 31 is detailed in Table 6. These were the significant influences in
the community factor.
Table 6: Correlations between Items
14, 15 and 31
The responses to Item 15 reveal an
interesting statistic. The correlation between Item 15 and Item 31
(overall adoption intention) gives r=.209. This correlation comes
largely from one end of the response scale. 100% of the individuals who
strongly agree with the statement ‘I am aware of other people looking at
what is in my trolley when I am shopping’, will have either a neutral or
positive intention to adopt EGS. Of those who do not show agreement with
the statement, there is a wide range of adoption ratings, indicating
that the relationship does not work both ways. This may be an indication
that awareness of others is a factor in the adoption decision, but
different factors will be important for different consumers.
5.3 Perception of
innovators (Items 22, 23, 24, 25 and 26)
The correlations in this factor are
detailed in Table 7.
Table 7: Correlations between Items
22, 23, 24, 25 and 26
A large degree of ambivalence was
shown towards inclination to be the first to adopt a new service (Item
22), with 27% (34 out of 126) of individuals choosing the neutral value
on the response scale. The responses are detailed in Table 8. It is not
surprising that those who have indicated responses towards the higher
end of the scale for this question (disagreement with the importance of
early adoption), have indicated low levels of adoption intention towards
EGS, and is confirmed by the correlation between the two factors
(r=0.253, see Table 12).
Table 8: Cross tabulations between
Items 22 and 31
Item 23 deals with the likelihood
of consumers sharing their positive experiences of online shopping with
others. The following table (9) shows a high percentage indication of
intention to share such experiences.
Table 9: Cross tabulation between
Items 23 and 31
The cross tabulation with overall
adoption intention shows that those who are more likely to tell others
of their experiences are more inclined to adopt the service in the first
place. This confirms some previous innovation studies research, which
identified that some consumers would rush to be the initial adopters of
an innovation, largely so that they could tell others about it and be
seen to be leaders in technology adoption.
Item 24 (Table 10) looks at the
same issue from the other side of the communication process, whether or
not consumers would adopt or trial a service, on the recommendation of
someone they know. Unsurprisingly, those who indicate that they would
not, are also shown to have low adoption intentions anyway. There is a
high level of correlation between a positive response to this question
and a positive adoption intention.
Table 10: Cross tabulation between
Items 24 and 31
The responses to Item 25 (Table 11)
imply that the majority of individuals show that they do not perceive
negative peer opinion concerning the adoption of an online grocery
service, or they do not worry about a negative opinion. This perception
does not seem to have an affect on intention to adopt.
Table 11: Cross tabulation between
Items 25 and 31
Item 26 which directly asked
respondents whether peer opinion would influence their decision to adopt
EGS was answered overwhelmingly in the negative, and was not shown to
significantly affect adoption inclination, and was not included in Table
12 for this reason.
By looking at the patterns in the
adoption variable responses in the following graph (Figure 2), a number
of issues emerge.
Figure 2: Adoption variable
correlations
Items 25 and 26 have remarkably
similar distributions. Since these items are measuring different aspects
of the same issue, some consistency can be seen in the individual
responses. The responses to questions 22, 23, and 24 have approximately
similar distributions. These items relate to inclination to try a new
service, and the impact of peer encouragement.
Table 12 details the correlation
between items related to perception of innovators that have a
significant relationship with adoption inclination.
Table 12: Correlations between
Items 22, 23, 24 and 31
5.4 Shopping enjoyment
(Items 17, 18, 19, 27, 28, 29 and 30)
Six items combined to measure the
relationship between consumer’s enjoyment from shopping, and their
inclination to adopt EGS. A range of strengths was found amongst these
items that measured different aspects of this issue.
Difficulty with transportation of
bought groceries (Item 19) positively but weakly, correlates with
inclination to adopt EGS, however it remains that the majority of
consumers sampled do not consider the transportation of their goods a
major issue. This may be due to the sample being overly represented in
the younger age categories. It may be found that the importance of this
factor would increase with an aging population. The reason that this
item does not correlate strongly with inclination to adopt is that there
is not an overall pattern in the responses. It is only a small group of
respondents for whom there is a relationship between these variables.
The correlation values are included in Table 13. Item 19 does not appear
to have a strong relationship with any of the other items being
measured.
All the inter correlations between
items in the shopping enjoyment group are presented as some interesting
relationships become apparent, outside of the significant relationships
with EGS adoption inclination. The strongest correlation is between
consumers perception of advantageous customer service online (r=0.414),
measured by Item 29, and their overall adoption inclination, and
consumers perception of the influence of customer service online
(r=0.535, Item 30) and overall adoption inclination.
The other item with a significant
correlation to adoption inclination was Item 27 that assessed consumer’s
trust in online grocer’s to provide them with quality fresh foods. This
relationship produced a strong positive correlation (r=0.378),
indicating that consumer’s who perceive online grocers as delivering
quality fresh foods are more likely to have a favorable adoption
intention than those consumers who do not.
Those consumers that do not enjoy
the social aspects of the traditional grocery shopping experience, or
those that have difficulty completing such a task have been shown to
have a higher probability of inclination to adopt EGS.
Table 13: Correlation between Items
17, 18, 19, 27, 28, 29 and 31
5.5 Control variables
(Items 20 and 21)
The control variables, items
adopted from those used by Verhoef & Langerak (2001) in their study
concerning consumer intentions in the Netherlands, were used to assess
the consistency of this study’s results with other studies in the same
area, using different samples (Table 14). These items were not
standalone, in that they were not only included for the purpose of
control. They were also items seen to be potential factors in a
consumer’s EGS adoption decision.
Table 14: Correlation between Items
20, 21 and 31
The majority of respondents
indicated that they usually found themselves “pressed for time” (Item
21). Therefore, amongst those who felt time management is an issue,
there was a large spread of adoption intention towards EGS. For those
however, whom did not indicate that they had trouble with time, there
was a large swing towards non-adoption of an online grocery service.
This may indicate that time efficiency is one factor that would
influence a consumer to adopt an EGS, but that there are a number of
other factors that would also need to be taken into consideration.
Item 22, which questioned the
individual’s willingness to pay extra to use an EGS was shown to be a
strong indicator of future intention to adopt (r=0.547). This suggests
that many of those who are looking at adopting such a system are those
who dislike, or are inconvenienced by, grocery shopping, in ways other
than price.
5.6 Regression analysis
Regression analysis was used to
determine the relative influence of factors that were shown to have
significant correlations with adoption intention. Overall, the factors
examined in the questionnaire were calculated to account for 58% of the
variance in adoption intention, however this variance was largely the
result of a small core of factors.
The following table (15)
demonstrates that nine of the eighteen measured items were shown to
account for 56% of the variance. The items that were responsible for
this influence represent a range of the issues being addressed, with a
common theme throughout. The items were those numbered 14, 15, 20, 21,
22, 23, 24, 27 and 29, and the relative influence of each of these items
is shown in Table 15. These items relate largely to the perception of
other users, lifestyle, and perception of services currently available.
The items that were found to not have a significant influence included
most of those related to shopping enjoyment and reported influence of
peer opinion.
Table 15: Regression analysis of
significant items
The ANOVA Table (16) from this
analysis shows the figures that were used to calculate the percentage
influence that this group of factors had on the overall adoption
intention (regression sum of squares value as a percentage of the
total). Again, the significance value of the F statistic demonstrates
that the variation explained by these items is not chance.
Table 16: ANOVA Table from
regression analysis in Table 4-14
5.7 Overall adoption of
EGS by respondents
The quantitative results from this
study show that overall consumer intention to adopt EGS in the future is
mixed. 31% of consumers envisage themselves as using EGS, 46% do not,
while 23% remain unsure. This shows that EGS has moved from the initial
stages of the adoption cycle, however, while the respondents showed that
they saw themselves using such a service in the future, 76% had not used
one up to this point. Almost half the sample responded negatively to the
suggestion that EGS would become a part of their life. The respondents
revealed that social factors have a role to play in their reluctance to
adopt EGS.
5.7.1 Influence of Social
Factors on EGS Adoption Inclination
The items in the questionnaire
related to social factors combine to account for 58% of the variance in
adoption intention as indicated by Table 15. This finding is consistent
with the theory that the integration into the social system is equally,
or more, important than technological superiority in the successful
adoption strategy of an innovation. The significance value of F (0.00)
shows that the variation explained by these items is not chance.
The three primary factors that were
being measured by the questionnaire were sense of community, perception
of innovators, and shopping enjoyment (as outlined in table 1). All were
shown to have different priorities for the respondents. Unsurprisingly,
sense of community was shown to exert relatively little influence in the
online grocery domain, accounting for only 12% of the overall variance
in adoption inclination. This may be related to the fact that groceries
are a low involvement product (Verhoef & Langerak, 2001), and therefore,
consumers are unlikely to require assistance from others in the buying
process. Through the exploration of this factor, it was seen that,
though many people do shop with members of their immediate social
circle, this is not necessarily an important factor in their adoption
decision. A small proportion of the sample showed an awareness of other
shoppers looking at their purchases, and a significant relationship with
positive inclination towards EGS, was shown by these consumers.
A significant relationship was
found between a consumer’s willingness to use a self checkout facility
and their inclination to adopt EGS. Each of these services represents a
future direction for grocery shopping, both without interaction with a
cashier. It would appear that for the consumers that favour these
services, efficiency has a higher priority than social interaction.
Perception of innovators was shown
to account for 28% of the variance. This included not only the
respondent’s perception of other adopters, but also the reaction they
expected towards themselves from their immediate social circle. The
results showed that not only were consumers likely to share their online
experiences with others, but that they would also listen to other
experiences, and take them into consideration when making their own
adoption decision. These findings are supported by the experiences of
ShopFast in the Sydney area. ShopFast did no formal advertising for the
first two years of its existence, relying solely on word of mouth for
growth. From these results it would appear that respondents are not
afraid that their peers would disapprove of EGS adoption.
The third factor, shopping
enjoyment, accounted for 31.2% of the variance. This is in line with the
studies that have shown grocery shopping to be perceived as a chore, and
stronger responses were received in relation to the negative aspects of
a traditional grocery-shopping trip, than the positive aspects. A
particularly strong relationship was found between a consumer’s
perception of online customer service, and their intention to use EGS in
the future. This indicates that consumer service is an area that online
retailers should focus on as, by ensuring the quality of consumer
service, the patronage of the online shopping community is encouraged.
The benefit of a strong customer service section was supported by the
experiences of the Managing Director of the online store that found that
many shoppers from the physical stores were using the online format (a
more cost effective method) for customer service enquiries. Another
significant indicator of adoption intention was the perceived difficulty
of transporting bought goods for consumers. While the correlation did
not show a significant value, an examination of the responses revealed
that though the numbers were small, for those for whom transporting
their groceries was perceived as difficult, there was a strong trend
towards favourable adoption intention.
The control variables, were found
to correlate very strongly with EGS adoption intention (r=0.473 and
r=0.547 respectively), increasing the comparability of this study with
others in the same area. Since the results in the control area are
consistent, it could be reasonably expected that the responses from this
sample are consistent with those in this research area.
The results from this study suggest
that social factors will play a part, and in some areas, an important
part, in a consumer’s decision to adopt an EGS service. This is
evidenced by the overall relationship between the variables and adoption
intention, and also the relationship between individual items, and
adoption intention.
Unexpectedly, socio demographic
details were not found to play a large part in this decision process. In
particular, the attributes that have in the past been strongly linked to
adoption intention, such as age and gender, were shown to have only a
weak relationship with overall intention, despite showing a fluctuating
influence on individual factors. This may be different with a different
sample of respondents, given that this sample was largely based around
the university community. Recent literature has suggested that the
online shopping community demographic is changing to become more
diverse, and these findings appear to support this theory. Past studies,
which have found that larger households were more likely to have
favourable intentions towards shopping online (Crisp et al. 1997), were
not consistent with the results from this study, which found household
size not to be significantly related to adoption intention.
Favourable adoption intentions were
found for those individuals who dislike grocery shopping and those who
experience difficulty with transportation to the grocery store.
Previous online and technological
experience was found to be a significant indicator of future online
shopping intentions. The sample in this study had a high proportion of
technologically literate individuals, in relation to the general
community. Close to 100% of the sample indicated that they had a
computer at home, and all had Internet access of some kind.
6. Conclusion and research contribution
This study has evidenced the
presence of a number of online shopping trends in the online grocery
market. Social factors that have been shown to be factors in the
adoption of online shopping in general have been found to have a
significant influence of adoption inclination of EGS. By adding to this
research area, implications become apparent for both the academic and
commercial bodies of information concerning this topic.
The relatively small relationship
between sense of community and adoption inclination for consumers
suggests that production efforts should be concentrated in other areas
when constructing an online grocery store at this stage of the adoption
cycle. In the future, as adoption becomes more widespread and the target
market expands, these features will need to be added to increase market
share. For the moment, however, it appears that the issues of perception
of innovators and shopping enjoyment are the aspects of the social
experience that will attract consumers to EGS.
It is important to note that while
social factors will play a part in a consumer’s decision to adopt EGS,
there are other factors that may be equally or more important, but that
are not addressed by this study.
The implications of these findings
for the retail community, are that consumers are in a position to adopt
EGS, and can see it in their future, but require a greater incentive at
present, to change their lifestyle, regardless of whether or not it will
be beneficial in the longer term. Raijas (2002) found that consumers are
set in their grocery shopping routine, and do not calculate the cost of
time spent shopping or transportation when using their own cars. It
would seem that at this stage there is not enough motivation for the
majority of consumers to make the switch.
The conclusion from these findings
is that social factors play a part in the EGS adoption intentions of
consumers. The importance of these social factors will vary across
consumer groups based on technological experience, and this is
consistent with findings in past studies concerning similar online
spaces. These findings contribute to the online shopping research area
by addressing the issue of social factors in the adoption of EGS
systems. Previously in this area the adoption intentions of consumers
have been addressed through issues such as delivery, security and cost.
These issues may have been the most important in the initial stages of
the technology cycle, but as access becomes widespread and adoption
increases, looking at factors outside of these initial adoption hurdles
is becoming increasingly important.
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