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The International Review of Retail, Distribution and
Consumer Research
ISSN: 0959-3969 (Print) 1466-4402 (Online) Journal homepage: http://www.tandfonline.com/loi/rirr20
Online grocery shopping: the impact of shopping
frequency on perceived risk
Gary Mortimer, Syed Fazal e Hasan, Lynda Andrews & Jillian Martin
To cite this article: Gary Mortimer, Syed Fazal e Hasan, Lynda Andrews & Jillian Martin
(2016) Online grocery shopping: the impact of shopping frequency on perceived risk, The
International Review of Retail, Distribution and Consumer Research, 26:2, 202-223, DOI:
10.1080/09593969.2015.1130737
To link to this article: http://dx.doi.org/10.1080/09593969.2015.1130737
Published online: 10 Jan 2016.
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Online grocery shopping: the impact of shopping frequency on
perceived risk
Gary Mortimer*, Syed Fazal e Hasan, Lynda Andrews and Jillian Martin
Business School, Queensland University of Technology, Brisbane, Australia
(Received 4 May 2015; accepted 1 December 2015)
Online grocery shopping has enjoyed strong growth and it is predicted this channel
will continue to grow exponentially in the coming years. While online shopping has
attracted an abundance of research interest, examinations of online grocery shopping
behaviour are only now emerging. Shopping online for groceries differs consider-
ably from general online shopping due to the perishability and variability of the pro-
duct, and frequency of the shopping activity. Two salient gaps underpin this
research into online grocery shopping. This study responds to calls to investigate
the online shoppers’experience in the context of online purchasing frequency. Sec-
ond, this study examines the mediating effect of perceived risk between trust and
online repurchase intention of groceries. An online survey was employed to collect
data from shoppers who were recruited from a multi-channel grocery e-retailer’s
database. The online survey, comprising 16 reflective validated scale items, was sent
to 555 frequent and infrequent online grocery shoppers. Results find that while cus-
tomer satisfaction predicts trust for both infrequent and frequent online grocery
shoppers, perceived risk fully mediates the effect of trust on repurchase intentions
for infrequent online grocery shoppers. Furthermore, path analysis reveals that the
developed behavioural model is variant across both groups of shoppers. Theoreti-
cally, we provide a deeper understanding of the online customer experience, while
gaining insight into two shopper segments identified as being important to grocery
e-retailers. For managers, this study tests an online customer behavioural model with
actual purchasing behaviour and identifies the continued presence of perceived risk
in grocery e-retailing, regardless of purchase frequency or experience.
Keywords: online grocery shopping; online customer experience; perceived risk;
trust; satisfaction; purchasing frequency; e-retailing
Introduction
With total online retail sales estimated to reach €191 billion in Europe and $370 billion
in the USA by 2017 (Mulpuru 2013), many retailers have moved to capitalise on the
advantages this channel has to offer (Christodoulides, Michaelidou, and Argyriou 2012;
Euromonitor 2012). Equally, online grocery shopping has also enjoyed strong growth
and it is argued this channel will continue to thrive in the coming years (Shukri 2014).
In the UK, it is estimated that some 20% of adults now do all or most of their grocery
shopping online and sales are projected to increase in value to £9.5 bn by 2015 (Shukri
2014). Similarly, in the USA, online grocery sales are projected to grow from $23 bil-
lion in 2014 to nearly $100 billion by 2019, capturing 12% of total grocery spending
*Corresponding author. Email: gary.mortimer@qut.edu.au
© 2016 Taylor & Francis
The International Review of Retail, Distribution and Consumer Research, 2016
Vol. 26, No. 2, 202–223, http://dx.doi.org/10.1080/09593969.2015.1130737
(Kumar 2014). As a result, the segment is swiftly becoming crowded with multi-
channel, multi-format and pure-play grocery retailers (Jayasankaraprasad and
Kathyayani 2014; Nilsson et al. 2015). Existing grocery retailers face increasing chal-
lenges, like maintaining online customer loyalty, improving profitability and under-
standing how to progress occasional, sceptical or non-online grocery shoppers to
become more established, trusting, frequent shoppers (Hansen 2006,2008). Accord-
ingly, there have been calls for more research concerning consumer online grocery
shopping experience (Hansen 2006; Soopramanien 2011).
Knowledge in the area of the online customer experience (OCE) in relation to
online grocery shopping remains emergent and provides a fertile ground for ongoing
research (Chiagouris and Ray 2010; Rose et al. 2012; Trevinal and Stenger 2014).
While initial work has resulted in the development of a holistic model (Rose, Hair, and
Clark 2011; Rose, Clark et al. 2012), there are still limitations in understanding this
experience in an applied online grocery setting. The experience of purchasing groceries
online is unlike other forms of online shopping due to product perishability and vari-
ability. The perceived risks associated with receiving perishable food products pur-
chased online present a significant barrier for online grocers (Citrin et al. 2003; Huang
and Oppewal 2006). In order to overcome these barriers, online grocers need to ensure
shoppers are satisfied with the quality of their products ordered. Accordingly, shoppers
who are satisfied with their past purchases will develop higher levels of trust for the
online grocer and be more likely to engage in online repurchase behaviour (Ha and
Perks 2005; Ha, Janda, and Muthaly 2010). It is further argued that the increasing
frequency of purchase will additionally reduce perceived risk and improve the probabil-
ity of repetitive purchasing (Anschuetz 1997; Min, Overby, and Im 2012). Hence,
examining the constructs of shopping satisfaction, trust, perceived risk and frequency
of online grocery shopping will provide academia with a deeper understanding of this
unique online shopping experience. For practitioners, we are able to demonstrate the
application of an empirical model in an applied online grocery context, which should
encourage online grocers to implement satisfaction and trust-building strategies
(Newholm et al. 2004), as well as risk mitigation strategies (Cases 2002).
This research addresses two important gaps in online grocery shopping knowledge.
First, we respond to calls to investigate online purchasing frequency in order to draw
closer links between online shopping satisfaction and trust and the actual behaviour of
shoppers (Rose et al. 2012). Second, although the importance of perceived risk in the
online domain remains an important factor (Penz and Hogg 2011; Soopramanien 2011;
Faqih 2013), it has not been fully examined in the context of online grocery shopping
knowledge. Perceived risk, particularly in relation to purchasing food from grocery
e-retailers, is of vital importance (Dholakia 2012;Xiao2015); as such, we examine the
role of perceived risk and how it mediates the relationship between the outcome vari-
ables, trust and repurchase intention. The objectives of this study are to first, examine
the specific relationships between online shopping satisfaction,trust and repurchase
intention, across two groups, frequent and infrequent online grocery shoppers, and sec-
ond, to investigate the impact of perceived risk. An online survey was employed to col-
lect data from shoppers who were recruited from an Australian multi-channel grocery
e-retailer’s database. The online survey, comprising 16 reflective validated scale items,
was sent to 555 frequent and infrequent online grocery shoppers. In addressing these
objectives, this study contributes to the ongoing development and understanding of the
OCE in the context of online grocery shopping while providing practical insights for
grocery e-retailers. We begin with a brief description of the customers’online shopping
The International Review of Retail, Distribution and Consumer Research 203
experience, before detailing the context of online grocery shopping. Constructs are then
described, shopping frequency determined before justifying the methods, presenting
results and discussion.
Literature review
Online customer experience
The Rose, Hair, and Clark (2011) seminal work conceptualised OCE. In contrast to the
in-store customer experience, a number of unique factors have been shown to affect
consumers’attitudes and behaviours when shopping online (Scarpi, Pizzi, and Visentin
2014), such as the tangibility of products and the spatial and temporal separation
between the retailer and the customer. Online shoppers can perceive greater unreliabil-
ity of infrastructure and systems (Pavlou 2003; McCole, Ramsey, and Williams 2010),
as well as lowered trust and higher perceived risk (Laroche et al. 2005). In a second
study, Rose et al. (2012) empirically tested the antecedents, components and outcomes
of the OCE model. Although perceived risk has been recognised as an important con-
struct in studies of online behaviour (Forsythe et al. 2006; Moore and Mathews 2006),
neither study examined the impact specifically, although the authors acknowledged per-
ceived risk may be significant (Rose, Hair, and Clark 2011). In concluding, Rose et al.
(2012) called for research in the context of purchasing frequency. In a broad managerial
sense, it is important to understand market segments based on purchasing frequency
because frequent shoppers contribute a far higher volume of sales than infrequent shop-
pers and cost less to service (Anschuetz 1997; Kotler 1999). The proposed model is
depicted in Figure 1.
Online grocery shopping
The experience of shopping online for food and groceries is fundamentally different
from other forms of online shopping due to the perishability and variability of the pro-
duct, and frequency of shopping. Hansen (2006) found that some shoppers attached
lower relative advantage and higher complexity specifically to online grocery shopping.
This differs from general online shopping where shoppers often report convenience and
ease of use as positive drivers of adoption (Sin and Tse 2002). Further, where online
shoppers will visit multiple e-retailers, making sporadic purchases often linked to their
disposable incomes, online grocery shopping accounts for a much larger proportion and
Figure 1. Proposed model.
204 G.S. Mortimer et al.
regular outlay of consumer income (Ramus and Asger Nielsen 2005). Products such as
fresh produce, baked goods and meat tend to fall into the see/touch/smell category
(Huang and Oppewal 2006), which presents a challenge in an online environment
(Citrin et al. 2003). Even though superior freshness and quality can be claimed online,
a shopper must contend with the risk that the product purchased may deteriorate prior
to delivery (Tsiros and Heilman 2005). The repetitiveness of grocery shopping
(Blaylock 1989) and similarly, online grocery shopping (Chiagouris and Ray 2010)
tends to be more frequent than general online shopping (Opreana 2013), again due to
the habitual nature of grocery shopping (Mortimer and Weeks 2011). Finally, the very
nature of general online shopping conjures up notions of excitement, flow and enjoy-
ment (Wolfinbarger and Gilly 2001), as shoppers search sites for exclusive and novel
products. In contrast, the activity of online grocery shopping is mostly considered a
mundane, routine task (Dawes and Nenycz-Thiel 2014; Brengman and Geuens 2002).
Hypotheses development
Online grocery shopping satisfaction
Satisfaction has previously been described as an ‘affective condition’(Belanche,
Casaló, and Guinalíu 2012) where the consumer derives a pleasurable state of con-
sumption-related fulfilment from emotions such as happiness, surprise or delight during
the shopping experience (Ha and Perks 2005). Contrasting views posit that expectancy
disconfirmation, attribution and inequity judgements inform a cognitive evaluation of
satisfaction based on attribute evaluation (Oliver and Swan 1989). Oliver (1997) pro-
posed a framework whereby consumer satisfaction is a product of both affective and
cognitive experiences (O’Guinn and Faber 1989). Several studies have argued that sat-
isfaction (Shim et al. 2001; Nesset, Nervik, and Helgesen 2011) and trust (McCole,
Ramsey, and Williams 2010; Toufaily, Souiden, and Ladhari 2013) are the most impor-
tant antecedents of customers’repurchase intentions in online shopping. The relation-
ship between satisfaction and trust is well established (Cronin, Brady, and Hult 2002;
Ha and Perks 2005; Ha, Janda, and Muthaly 2010). In the context of online grocery
shopping, shoppers order perishable products, such as fruit, vegetables and meat, trust-
ing that the e-retailer will select quality products and have them delivered in a timely
manner. It is therefore argued that shoppers, who experience satisfying transactional
outcomes from their online grocery purchases, will develop higher levels of trust.
Accordingly, the following hypothesis is presented;
H1: Online shopping satisfaction has a positive impact on customer trust in the online grocer.
Trust and online repurchase intention
Online trust is defined as the conviction that allows consumers to willingly become
exposed to online retailers after having taken the retailers’characteristics into consider-
ation (Newholm et al. 2004; Toufaily, Souiden, and Ladhari 2013). The importance of
trust is further emphasised in an online transaction context, particularly involving con-
sumables like food and groceries (Citrin et al. 2003) and is a critical condition for the
success of an online grocer (Pavlou and Fygenson 2006; Toufaily, Souiden, and
Ladhari 2013). Trust may take the form of subjective beliefs about trust in the online
retailer (McCole, Ramsey, and Williams 2010; Bianchi and Andrews 2012; Toufaily,
The International Review of Retail, Distribution and Consumer Research 205
Souiden, and Ladhari 2013) or aspects of the grocery retailers’website that enhances
consumer trust during their online experiences (Ogonowski et al. 2014). Once trust is
established, repurchase intention is more likely. As such, we predict a positive relation-
ship between trust and online repurchase intention of groceries. Accordingly, we
hypothesise;
H2: Customer trust has a positive impact on the customers’repurchase intention from the
online grocer.
Trust and perceived risk
Trust and perceived risk continue to be important constructs in studies of online pur-
chasing behaviour because of the spatial and temporal separation between the retailer
and the customer (Aghekyan-Simonian et al. 2012; Belanche, Casaló, and Guinalíu
2012; Nepomuceno, Laroche, and Richard 2014). It is argued that a shopper will weigh
their levels of trust against their levels of perceived risk during an online grocery pur-
chase decision; therefore, to measure trust alone is not sufficient because its influence
is relative to, and determined in some part, by that of perceived risk (Soopramanien
2011; Bianchi and Andrews 2012). Simply, a customer who trusts the online grocery
retailer will perceive less risk during online shopping, whereas a less trusting customer
will perceive higher risk. Therefore, it is hypothesised;
H3: Customer trust in the online grocer has a negative impact on perceived risk.
Perceived risk and online repurchase intention
Perceived risk is a particularly relevant construct because of its close ties to intention
to repurchase (Hansen 2006; Soopramanien 2011). Given the centrality of perceived
risk to online retailing (Pechtl 2003), the customer experience and actual buying beha-
viour, it is surprising that this construct was not investigated in the context of OCE.
While Rose, Hair and Clark (2011) acknowledged the potential impact of perceived
risk, they did not include it in their subsequent model (Rose et al. 2012). Perceived risk
has consistently been identified as an inhibitor to online purchasing, regardless of
advances in technology and the increasing skill and competence of consumers on the
Internet (Belanche, Casaló, and Guinalíu 2012; Bianchi and Andrews 2012). It is pro-
posed that during the online shopping process for food and groceries, the customer
may develop feelings of negative affect such as displeasure, disappointment, sadness,
anxiety, anger or frustration over the transaction, which in turn increases their percep-
tions of risk with the experience and accordingly reduces their intentions to repurchase
from the grocery e-retailer. This it is hypothesised;
H4: Perceived risk has a negative impact on the customers’repurchase intention from the
online grocer.
Purchasing frequency
Online repurchase intention is a key outcome of the customers’online shopping experi-
ence, recognising that past purchasing behaviour often leads to continued purchasing
behaviour (Hansen 2006; Rose et al. 2012). We argue that it is important to examine
206 G.S. Mortimer et al.
customer groups based on online shopping frequency because frequent shoppers may
be more loyal to a grocery e-retailer and accordingly provide higher revenue and profit
than infrequent shoppers (Anschuetz 1997; Min, Overby, and Im 2012). Frequency of
shopping is also specifically relevant for grocery e-retailers as transactions tend to be
more regular and consistent than those found in other online retail channels, like cloth-
ing or consumer electronics (Chiagouris and Ray 2010). Online retailing as a channel
experiences a high amount of customer churn, so understanding the different needs of
these two groups is particularly important to customer retention (Joia and Sanz 2006).
Additionally, research in other shopping contexts highlights variances between frequent
and infrequent shopping behaviours (Chen and Dubinsky 2003; Bridges and Florsheim
2008).
It is argued that frequent and infrequent online grocery shoppers, given their vary-
ing exposure to, and experience with, the grocery e-retailer, may be at different stages
in the satisfaction–loyalty development process. Using our model to explain variations
in levels of satisfaction, it is reasonable to assume that infrequent customers, who have
not necessarily engaged in repeat purchasing behaviour, will experience less developed
levels of experience and satisfaction. Conversely, frequent customers of a grocery
e-retailer will have begun to transition more towards higher levels of satisfaction, after
many transactional experiences. We further argue, that although perceptions of risk will
be present in both groups, the indirect effect will be greater for infrequent than frequent
shoppers because infrequent shoppers may have less familiarity with the retailers’web-
site (Citrin et al. 2003; Huang and Oppewal 2006). Following this logic, infrequent
shoppers may accordingly be less trusting of a grocery e-retailer because of lower
exposure and experience with the website, or past unsatisfying transactions, whereas
frequent shoppers would have attained higher levels of trust (Chiagouris and Ray
2010). Based on this above discussion, it is predicted that frequent and infrequent
online grocery shoppers will exhibit different degrees of satisfaction, trust and
perceived risk. As such, it is hypothesised;
H5: The model will be variant across frequent and infrequent online grocery shoppers.
Method
Participants
The sampling frame used was a database of online shoppers held by a large multi-
channel grocery e-retailer. The stratification of ‘frequent’and ‘infrequent’was defined
by the e-retailer’s metrics, where frequent purchasers had made 4–6 transactions in the
12 weeks prior to the survey and infrequent purchasers had purchased only once during
this period. These metrics also determined that frequent purchasers attained higher
aggregate spending in comparison to infrequent purchasers. Those who completed the
survey were offered the chance to enter a prize draw. Response bias testing between
early vs. late respondents (Armstrong and Overton 1977) showed no evidence of
differences.
Questionnaire and procedure
Respondents were recruited from a multi-channel grocery e-retailer’s database. The gro-
cery e-retailer forwarded an email invitation to respondents explaining the nature of the
The International Review of Retail, Distribution and Consumer Research 207
study and the ethical considerations together with an embedded URL link to the online
survey. As we wanted to capture data from frequent and infrequent online grocery
shoppers, respondents received an explicit URL depending on their purchasing fre-
quency as identified above. In order to reflect the context of the study, respondents
were asked to answer questions in relations to their online grocery shopping experi-
ence. Respondents first answered demographic questions, followed with 16 reflective
scale items, anchored from 1 (Strongly disagree) to 7 (Strongly agree), online shopping
satisfaction, trust, perceived risk and repurchase intention. Scales for all constructs in
the model, except perceived risk, were adapted from the validated scales used in Rose
et al. (2012) by adding the words, ‘… this supermarket’s website …’. Measures for
perceived risk came from Bianchi and Andrews (2012) (See Appendix 1).
Analysis
The data were analysed using structural equation modelling in AMOS 21 (Arbuckle
2005). Following the deletion of outliers, there were 381 valid responses from frequent
online grocery shoppers and 174 responses from the infrequent group (see Table 1),
which is consistent with the requirements of AMOS analysis (Arbuckle 2005). Consid-
ering the guidelines of Marsh, Balla and McDonald (1988) and Westland (2010), our
sample (n= 555) meets the requirement of lower bound sample size. Tests for
non-response bias were carried out (Armstrong and Overton 1977), revealing no poten-
tial threat of non-response bias in either data-set. Harman’s single factor test (Podsakoff
et al. 2003) also revealed no common methods bias in either group data-set. While the
sample was significantly skewed towards women, previous research has suggested,
women are more often responsible for grocery shopping (Beynon, Moutinho, and
Veloutsou 2010).
Results
Confirmatory factor analyses (CFA) and path analyses
Psychometric properties of the constructs were evaluated by conducting a CFA using
AMOS 21 on the data-set. We employed the covariance-based SEM approach (Jöreskog
and Sörbom 1993) which is usually used with an objective of model validation and
Table 1. Sample characteristics for frequent and infrequent purchaser groups.
Demographic features
Frequent (N= 381)
Infrequent
(N= 174) All (N= 555)
Number Percent Number Percent Number Percent
Age
18–24 years 9 2.4% 10 5.7% 19 3.42%
25–35 years 119 31.2% 50 28.7% 169 30.45%
36–45 years 113 29.7% 52 29.9% 165 29.73%
46–55 years 70 18.4% 35 20.1% 105 18.92%
56–65 years 35 9.2% 13 7.5% 48 8.65%
65 + years 35 9.2% 14 8.0% 49 8.83%
Gender
Female 345 90.6% 157 90.2% 502 90.45%
Male 36 9.4% 17 9.8% 53 9.55%
208 G.S. Mortimer et al.
needs a moderately large sample. As our primary aim was theory development and
model testing across two groups of consumers, the covariance-based SEM approach
was a more appropriate choice in comparison to components-based approach which is
mainly used for score computation and can be carried out on very small samples
(Henseler 2012).
Although chi-Square (χ
2
) remains significant with χ
2
= 416.171, df = 114, χ
2
/
df = 3.6651, (p< .01), the fit of the CFA for the study conducted is deemed acceptable
with other indices such as comparative fit index (CFI) = .962, incremental fit index
(IFI) = .962, standard root mean square residual (SRMR) = .0406 and root mean square
error of approximation (RMSEA) = .040. Considering all these goodness of fit mea-
sures, the model is adequately suitable to fit the data from the sample. Items having
cross (<.3) or poor (<.5) factor loadings were deleted (Chin 1998). Perceived risk was
the only construct that had two items. Following Gardner et al. (1998) and Wanous and
Hudy’s(
2001) recommendations, reliability and convergent validity scores of two-item
construct of risks were deemed appropriate to for further analysis. Table 2shows that
the values of Composite Reliability and Cronbach αscores of all constructs were above
than the recommended cut-off, i.e. .70, demonstrating good reliability (Nunnally and
Bernstein 1994).
Table 2further demonstrates that all item loadings are significant (p< .01), in sup-
port of convergent validity (Gerbing and Anderson 1988). Inspection of inter-factor cor-
relation matrix revealed (see Table 3) slightly high correlations between trust and
satisfaction and trust and perceived risk constructs. While these slightly high correla-
tions are understandable due to their uniqueness (identification of perceived risk as a
possible moderator between trust and repurchase intentions and predictor of repurchase
intentions and constructs’close nature in an online environment), we could expect
respondents to identify the theorised constructs as nearly indistinct (Hair et al. 2006;
Ping 2007). Chi-square difference test (Bagozzi and Phillips 1982) was used to assess
discriminant validity between each pair of constructs. In this method, the first model
analysed through CFA will be a model where the two constructs are not correlated,
while the second will be the one where we will allow for correlation. Each model will
present a value for chi-square and degrees of freedom (df ). After doing the difference
between the values of the two models, we can see if the test is significant or not
(Segars 1997; Bertea and Zait 2011). Significant value of chi-square difference test
represents the discriminant validity between each pair of constructs in the model. The
chi-square difference test is significant for satisfaction and trust (Δχ
2
(1) = 977.958
(27) −22.003(24) = 955.955, p< .01), exhibiting discriminant validity between the two
constructs. As the non-correlated model between trust and perceived risk returned a
negative eigenvalue, a correlation regression weight was constrained by 1 before analy-
sis was conducted (Ping 2007). The chi-square difference test is significant for trust
and perceived risk (Δχ
2
(1) = 649.697 (5) −21.701(4) = 627.989, p< .01) confirming
discriminant validity between the two constructs.
Path analysis
In order to test the hypotheses, relationships were modelled and tested using AMOS
21. Although chi-square difference remained significant χ
2
(120) = 516.356 (p< .01),
other indices demonstrate that fit of the structural model is acceptable, with CFI = .924,
IFI = .925, and SRMR = .070 and RMSEA = .055.
The International Review of Retail, Distribution and Consumer Research 209
Table 2. Scale items, sources and CFA results.
Construct
Item
number Source Items description
Item
loadings
Z-
value CR
Cronbach
alpha (α)AVE
Satisfaction Satisfaction
1
Rose et al.
(2012)
I am satisfied with the purchase experience of this supermarket’s website (e.g.
ordering, payment procedure)
.750 1 .797 .798 .567
Satisfaction
2
I am satisfied with the experience I have after I purchase from this
supermarket’s website (e.g. customer support and after sales support, handling
of returns/refunds, delivery care)
.739 16.491
Satisfaction
3
I am satisfied with my overall experiences of this supermarket’s website .770 17.165
Trust Trust 1 Rose et al.
(2012)
This supermarket’s website is reliable .687 1 .764 .769 .521
Trust 2 In general, I can rely on this supermarket’s website to keep the promises that
they make
.684 14.363
Trust 3 Internet shopping on this supermarket’s website is a trustworthy experience .789 16.224
Risk Risk 1 Bianchi and
Andrews
(2012)
I feel safe making purchases on this supermarket’s website using my credit
card
.787 1 .769 .768 .624
Risk 2 I feel safe giving my personal details to this supermarket’s website if requested .793 16.691
Repurchase
intention
Repurchase
intention 1
Rose et al.
(2012)
I anticipate shopping again at this supermarket’s website in the near future .714 1 .775 .763 .538
Repurchase
intention 2
I regularly repurchase from this supermarket’s website .633 13.061
Repurchase
intention 3
I expect to repurchase from this supermarket’s website in the near future .838 15.389
(N= 555), All items were measured using seven-point scales anchored by 1 = ‘strongly disagree’and 7 = ‘strongly agree’, unless otherwise stated. All item loadings are significant
at p< .01 level, where AVE = average variance extracted and CR = composite reliability.
210 G.S. Mortimer et al.
Path analysis for frequent and infrequent online grocery shoppers
Table 4shows that direct positive impact of satisfaction on trust was significant for
both frequent (β= .880, p< .01) and infrequent groups (β= .824, p< .01), hence
hypothesis (H1) is accepted. The effect of trust on repurchase intentions was positive,
but non-significant for frequent online grocery shoppers (β= .250, p= .367) but
achieved significance for the infrequent group (β= .861, p< .01), accordingly hypothe-
sis (H2) is accepted for the infrequent group, but rejected for the frequent group. The
relationship between trust and perceived risk was negative and significant for both fre-
quent (β=−.896, p< .01) and infrequent (β=−.793, p< .01) groups; therefore,
hypothesis (H3) is accepted. Perceived risk to repurchase intentions relationship was
found to be non-significant for frequent shoppers (β=−.267, p= .333), but significant
for infrequent shoppers (β= .282, p< .05); as such, hypothesis (H4) is rejected for the
frequent group, but accepted for the infrequent group. Overall, variance explained for
frequent group ranged from 25.3% (repurchase intentions) to 80.3% ( perceived risk ).
For infrequent, the variance explained ranged from 43.5% (repurchase intentions) to
68.0% (trust).
Table 3. Inter-factor correlations.
Constructs
Mean/standard
deviation Satisfaction Trust
Perceived
risk
Repurchase
intentions
Satisfaction 5.824/.833 1
Trust 5.080/1.085 .840 1
Perceived risk 3.279/1.371 −.615 −.878 1
Repurchase
intention
6.360/.736 .719 .493 −.433 1
Notes: (N= 555), All values are significant at p< .01 level.
Table 4. Path analysis for frequent and infrequent groups.
Hypotheses
Frequent Infrequent
Estimate
Z-
value
Accepted/
rejected Estimate Z-value
Accepted/
rejected
(H1) Online shopping satisfaction has
a positive impact on customer trust
.880** 7.187 Accepted .824** 10.779 Accepted
(H2) Customer trust has a positive
impact on online repurchase
intention
.250 (ns) .903 Rejected .861** 5.777 Accepted
(H3) Customer trust has a negative
impact on perceived risk
−.896** −8.185 Accepted −.793** −10.226 Accepted
(H4) Perceived risk has a negative
impact on online repurchase
intention
−.267 (ns) −.967 Rejected −.282* 2.034 Accepted
Variance explained (%) for (trust) 77.4 68.0
Variance explained (%) for (risk) 80.3 62.8
Variance explained (%) for
(repurchase intentions)
25.3 43.5
*
p< .05;
**
p< .01.
Two-tailed tests.
The International Review of Retail, Distribution and Consumer Research 211
Path invariance
As the sample was collected from two groups, frequent and infrequent online grocery
shoppers, path invariance across the two groups was tested. A multi-sample analysis
for measurement invariance was conducted to establish invariance across two groups.
The non-significant value from the chi-square difference (Δχ²) between the uncon-
strained model (χ²/df = 516.356/120) and constrained model (χ²/df = 516.356/120) is
Δχ²/df = 32.75/22; p= .065 indicated that there were non-equivalent parameters across
the infrequent and frequent samples. The structural invariance was subsequently used
to test for the equality of structural covariances and factor variances. The results
demonstrated that the difference in chi-square was significant between the constrained
and unconstrained models for the structural models (Δχ² = 26.511, df = 20; p= .150),
thus indicating that the structural model was equivalent across two groups. A further
assessment of path invariance was conducted with comparison of Z-score differences.
The results (Table 5) indicate that for frequent and infrequent online grocery shoppers,
frequency of purchase moderates the path from satisfaction to trust (Z-value = 2.458,
p< .05) and from trust to perceived risk (Z-value = 2.677, p< .01); therefore,
Hypothesis (H5) is accepted in that our behavioural model is variant across frequent
and infrequent online grocery shoppers.
Mediation analysis
Based on the approach employed by Baron and Kenny (1986), Hayes (2009) and Vaske
and Kobrin (2001), we tested direct and indirect effects for a mediation effect for
frequent and infrequent groups: (1) the relationship between the independent variable
(IV) and dependent variable (DV) is represented by relationship ‘c’in Table 6; (2) the
relationship between IV and mediator variable (MV) is represented by relationship ‘a’
in Table 6; (3) the relationship between mediator and the DV is represented by relation-
ship ‘b’in Tables 4and 5; and (4) the original relationship between the IV and the DV,
when the mediator is added, is represented by relationship c* in Table 6. In line with
the recommendation of Shrout and Bolger (2002) and Delcourt et al. (2013), once
mediation is detected, we can examine its significance by bootstrapping the product of
the IV →MV and MV →DV effects. If the direct effect between the IV and the DV is
non-significant, there is full mediation. If all effects remain significant, there is partial
mediation. By applying a non-parametric bootstrapping procedure, we test the
mediating role of perceived risk on the relationships between trust and repurchase
intentions.
Table 6shows that risk does not mediate the relationship between trust and repur-
chase intentions for frequent online grocery shoppers. However, results demonstrate the
Table 5. Results of Z-score differences.
Relationship Frequent group Infrequent group
Dependent variable Estimate pEstimate pZ-score
Trust ←Satisfaction .865 .000 1.291 .000 2.458*
Risk ←Trust −1.717 .000 −1.100 .000 2.677**
Repurchase intention ←Risk −.236 .000 −.327 .000 −1.503
*p-value < .05;
**p-value < .01.
212 G.S. Mortimer et al.
Table 6. Mediated role of perceived risk.
Group Hypotheses
Dependent
variable
(DV)
Ab c c*
Confidence
interval (CI)
(LLCI) −(ULCI)
Sobel’s
Z-value
Type of
mediation
Trust →
perceived
risk
Risk →
repurchase
intentions
(DV)
Trust →
repurchase
intentions
(DV)
Trust →repurchase
intentions (DV)
(Mediator controlled)
Frequent Risk mediates the
relationship between trust
and repurchase intentions
Repurchase
intentions
−.896** −.267 (ns) .506** .250* (−.621) −(1.081) –No
mediation
Infrequent Risk mediates the
relationship between trust
and repurchase intentions
Repurchase
intentions
−.793** −.282* .665** .127 (ns) (−.608) −(−.018) −2.831** Full
mediation
*
p< .05;
**
p< .01.
Two-tailed tests.
LLCI = lower level confidence interval.
ULCI = upper level confidence interval.
The International Review of Retail, Distribution and Consumer Research 213
full mediation of risk between its predictor, i.e. (trust) and outcome variable (i.e.
repurchase intentions) for infrequent online grocery shoppers. In order to further test
the mediation effect of the mediator for the infrequent group, we used Sobel test (Sobel
1986) and confidence interval (CI) for the mediation and report significant Sobel’s
Z-values and values of lower level CI and upper level CI in Table 6. Sobel’s test and
CI statistics support our mediation results.
Discussion
As online grocery shopping continues to grow exponentially around the world,
researchers are beginning to examine the attitudes, behaviours and experiences of shop-
pers in this e-retailing domain (Picot-Coupey et al. 2009; Kumar 2014; Shukri 2014).
The aim of this study was to examine the role of perceived risk and how it mediates
the relationship between trust and the repurchase intention of frequent and infrequent
online grocery shoppers. Our results show, that for infrequent or occasional online gro-
cery shoppers, perceived risk fully mediates the relationship between trust and the
online grocery shoppers’intentions to repurchase. For frequent online grocery shoppers,
who experience less perceived risk and higher levels of trust due to their regular online
transactions and experience with the e-retailer, no mediation was evident. This is an
important finding as these perceptions of risk in dealing with a grocery retailer’s
website may prevent infrequent shoppers from becoming regular, loyal and profitable
shoppers. This finding indicates the need to quickly transition infrequent shoppers with
limited experience or exposure, into frequent, experienced online grocery shoppers, as
such shoppers offer great economic value to grocery e-retailers.
We tested the relationship between online shopping satisfaction and trust, which
was significant for both frequent and infrequent online grocery shopper groups. The
results confirm that customers, who use a grocery e-retailers’website and experience
satisfactory transactional exchanges, will develop trust in this channel. This is not
unsurprising as research has previously shown if expectations are met or exceeded,
shoppers will be satisfied (Oliver 1981,1997), and under certain conditions, these feel-
ings of satisfaction lead to increased trust and repurchase intentions. In situations where
shopper expectations are high and are consistently met, retention becomes less elastic
over time, meaning that e-retailers who maintain consistently high levels of shopper
satisfaction will be less sensitive to changes in satisfaction evaluations where purchas-
ing behaviour is concerned (Anderson and Sullivan 1993). The effect of trust on repur-
chase intentions was non-significant for frequent online grocery shoppers but achieved
significance for the infrequent group. We assert that as the frequent online grocery
shopper has already established high levels of trust in the grocery e-retailer, trust no
longer acts as a barrier or driver to repurchase intentions. In contrast, as infrequent
shoppers are still in the process of establishing trust and experiencing transactional out-
comes, trust remains a significant attribute.
The relationship between trust and perceived risk was negative and significant for
both frequent and infrequent groups. While this relationship could be intuitively linked
to infrequent online grocery shoppers, our findings demonstrate that degrees of
perceived risk are still present in even the most frequent and regular online grocery
shoppers. Risk occurs when shoppers perceive an element of uncertainty to a potential
outcome (Chang and Tseng 2013). Trust, on the other hand, is a mechanism which
shoppers induce to reduce the complexity of decisions which involves risk, such as
online shopping (Riegelsberger, Sasse, and McCarthy 2003; Harridge-March 2006).
214 G.S. Mortimer et al.
Simply put, the more a shopper trusts the grocery e-retailer, the less likely they will be
to experience perceived risk and the less effort they need to put into evaluating other
criteria, such as price, quality or service. The findings of this study are consistent with
those of prior studies which tend to find that consumer anxiety and other forms of
negative effects, such as loss of control (Novak, Hoffman, and Yung 2000; Novak,
Hoffman, and Duhachek 2003), can lead to higher perceptions of risk (Weber,
Malhotra, and Murnighan 2004), but the presence of trust can help lessen risk percep-
tions (Jarvenpaa, Tractinsky, and Vitale 2000; Chadwick 2001; Harridge-March 2006).
The perceived risk to repurchase intentions relationship was found to be non-signif-
icant for frequent online grocery shoppers. It is claimed, like above, these regular
online grocery shoppers have attained high levels of trust through multiple and regular
transactions; therefore, although perceived risk to some extent is still present, the
impact has been mitigated (Hansen 2006). Perceived risk was however significant for
infrequent online grocery shoppers. As trust has not yet fully developed, perceived risk
still plays a role; hence, infrequent shoppers who perceive high risks will have lower
repurchase intentions (Pires, Stanton, and Eckford 2004; Wu and Chang 2007). It is
postulated that this is the case only for infrequent online grocery shoppers because they
rely on prior satisfaction evaluations in lieu of extensive experience with grocery
e-retailer (Pires, Stanton, and Eckford 2004). In contrast, perceived risk does not influ-
ence repurchase intentions for frequent shoppers, so there is no need for them to draw
on satisfaction evaluations to overcome this barrier.
Contributions
Theoretical contributions
Our study makes several theoretical contributions to the area of OCE research. Some
have argued that OCE knowledge is limited, yet emerging (Rose, Hair, and Clark 2011;
Rose, Clark et al. 2012); as such, our work contributes to the literature in this area by
testing a behavioural model in an online grocery context, drawing on actual shopping
data. We extend this theoretical work by examining two important shopper segments,
high and low frequency online grocery shoppers (Liu and Forsythe 2010; Liu, Forsythe,
and Black 2011; Min, Overby, and Im 2012). Finally, we include the variable, per-
ceived risk, to extend our understanding of its moderating impact on online grocery
shoppers’repurchase intentions. This addition of perceived risk extends the explanatory
scope of our model, accounting for inferences that a consumer will weigh their levels
of trust against their levels of perceived risk during online purchasing. Our findings
suggest that perceived risk with grocery online shopping continues to be a factor that
needs attention, regardless of consumers’online shopping experience.
Managerial contributions
Grocery online retailing remains both an area of opportunity and of significant manage-
rial challenge to multi-channel and pure-play grocery e-retailers (Chen and Chang
2003; Chen and Dubinsky 2003). Our study makes a number of managerial contribu-
tions. First, our work study investigates the effect of purchasing frequency, which is a
more managerially relevant outcome than repurchase intentions alone (Mittal and
Kamakura 2001). This should encourage grocery e-retailers to apply greater time and
energy interrogating the purchasing data of these two groups in order to identify
The International Review of Retail, Distribution and Consumer Research 215
specific aspects of their behaviour that may lead to profitable outcomes. Second, our
study shows that both frequent and infrequent online grocery shoppers develop trust as
a result of long-term satisfying experiences with the e-retailer. Accordingly, satisfaction
and trust-building strategies (Newholm et al. 2004), as well as risk mitigation strategies
(Cases 2002) should be considered, such as making it easier for shoppers to customise
the grocery e-retailers’website to suit their own needs or more clearly articulating the
benefits of grocery shopping online. In addition, grocery e-retailers may also engage in
trust-building exercises to minimise feelings of anxiety associated with risks inherent in
online shopping (Nepomuceno, Laroche, and Richard 2014). Finally, we find perceived
risk continues to be relevant and is a potential barrier to repurchase intentions (Bianchi
and Andrews 2012). Therefore, as more supermarkets and grocers move to capitalise
on the advantages the online channel has to offer, they should remain conscious of the
potential obstacle, perceived risk, may have on organic growth.
Limitations and future research
The potential limitations of this work create opportunities for future research. We
acknowledge the heavy skew towards female participants. While such a skew can be
common in grocery shopping studies (Chang and Nicholas 2004; Beynon, Moutinho,
and Veloutsou 2010), it is suggested future research may consider specifically examin-
ing male supermarket shoppers in this online context. Conducting gender comparison
studies relating to online grocery shopping behaviours would offer additional dimen-
sions to researchers and retailers. While our research extends the understanding of
online grocery shopping, as data were captured in only one country, we would caution
the generalisation of findings. Future studies might attempt a cross-cultural analysis to
determine its relevancy in diverse national cultural settings. Our research reports the
findings of online shopping behaviour of a single e-retailer in a specific product
category, groceries. Future researchers may choose to examine the influence of other
product categories, such as apparel or consumer electronics (Wang, Hernandez, and
Minor 2010). Finally, our determination of frequent and infrequent online grocery shop-
pers was based on our industry partners’transaction metrics. It might be interesting to
examine factors such as age, gender, education, length of relationship with the retailer
or extent of experience with online grocery shopping to identify what other variables
influence frequency.
In conclusion, our study has contributed to advancing methodological and theoreti-
cal knowledge in the field of OCE by examining the role of perceived risk and how
risk mediates the relationship between trust and the repurchase intention of online
grocery shoppers. Further, our behavioural model demonstrates that in relation to shop-
pers’satisfaction, trust, perceived risk and repurchase intentions, differences exist across
frequent and infrequent online grocery shoppers. Moreover, its practical relevance to
e-retail grocery managers is evidenced through its strong links to actual e-retailing per-
formance outcomes, that is actual shopping data, rather than self-reported shopping
data. It is anticipated that this study will provide researchers with the required
motivation to continue empirical work in the area of OCE in order to aid managers in
developing future strategic directions.
Disclosure statement
No potential conflict of interest was reported by the author.
216 G.S. Mortimer et al.
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Appendix 1. Scale items
Please answer the following 16 questions thinking about the last time you purchased groceries
online from this supermarket.
Construct Source Code Original item Adapted item
Online shopping
satisfaction
Rose
et al.
(2012)
SATN1 I am satisfied with the
experience I have before I
purchase on Internet
shopping websites (e.g.
good information about
products, product
comparisons and search
functions)
I am satisfied with the
experience I have before I
purchase from this
supermarket’s website (e.g.
good information about
products, product
comparisons and search
functions)
SATN2 I am satisfied with the
purchase experience of
Internet shopping websites
(e.g. ordering, payment
procedure).
I am satisfied with the
purchase experience of this
supermarket’s website (e.g.
ordering, payment
procedure)
SATN3 I am satisfied with the
experience I have after I
purchase from Internet
shopping websites (e.g.
customer support and after
sales support, handling of
returns/refunds, delivery
care).
I am satisfied with the
experience I have after I
purchase from this
supermarket’s website (e.g.
customer support and after
sales support, handling of
returns/refunds, delivery
care)
SATN4 I am satisfied with my
overall experiences of
Internet shopping.
I am satisfied with my
overall experiences of this
supermarket’s website
Trust Rose
et al.
(2012)
TRUS1 Internet shopping can be
trusted, there are no
uncertainties.
This supermarket’s website
can be trusted, there are no
uncertainties
TRUS2 In general, I can rely on
Internet shopping websites
to keep the promises that
they make.
In general, I can rely on this
supermarket’s website to
keep the promises that they
make
TRUS3 Internet shopping is reliable. This supermarket’s website
is reliable
TRUS4 Internet shopping is a
trustworthy experience.
Internet shopping on this
supermarket’s website is a
trustworthy experience
Perceived risk Bianchi
and
Andrews
(2012)
RISK1 There is too much
uncertainty associated with
using the internet to make
purchases
There is too much
uncertainty associated with
using this supermarket’s
website to make purchases
RISK2 Compared with other ways
of making purchases, I think
that using the internet is
more risky
Compared with other ways
of making purchases, I think
that using this supermarket’s
website is more risky
RISK3* I feel safe giving my
personal details to an
Internet shopping website if
requested
I feel safe giving my
personal details to this
supermarket’s website if
requested
(Continued)
222 G.S. Mortimer et al.
Table1. (Continued).
Construct Source Code Original item Adapted item
RISK4* I feel safe making purchases
on the internet using my
credit card
I feel safe making purchases
on this supermarket’s
website using my credit card
Repurchase
intention
Rose
et al.
(2012)
RINT1 It is likely that I will
repurchase from Internet
shopping websites in the
near future.
It is likely that I will
repurchase from this
supermarket’s website in the
near future
RINT2 I anticipate shopping again
at Internet shopping
websites in the near future.
I anticipate shopping again
at this supermarket’s website
in the near future
RINT3 I regularly repurchase from
the same websites.
I regularly repurchase from
this supermarket’s website
RINT4 I expect to repurchase from
Internet shopping websites
in the near future.
I expect to repurchase from
this supermarket’s website in
the near future
*Reversed items (All items anchored from 1 –Strongly disagree to 7 –Strongly agree)
The International Review of Retail, Distribution and Consumer Research 223
