Antecedents
of Online Shopping Behavioural Intention in Asia's Second Largest Economy
Dr Deepak
Halan
Associate Professor
Apeejay Stya University,
School of Management Sciences,
Palwal - Sohna Rd, Haryana, India
ABSTRACT
As
e-commerce booms in India, e-tailers compete fiercely and focus more on
enhancing customer satisfaction. Moreover, due to the deadly and highly
infectious COVID-19 disease, online shopping has
gained higher importance, in India as well as globally. This paper
explores the influence of time convenience, value for money, return process and
trust, on online shopping behavioural intention. These aspects have not been
empirically tested concurrently in a model in the Indian context. The study
builds on existing literature, by testing certain critical variables which are
rare in literature on online consumer behaviour in India, but important in
context of online shopping in the country. A structured questionnaire was used
to conduct a web-based survey. Exploratory factor analysis (EFA), confirmatory
factor analysis (CFA) and structural equation modelling (SEM) were used to
analyse a total of 500 usable responses. The results show that perceived
usefulness in terms of time saved and ease of use emerged as a strong predictor
of attitude, while perceived usefulness in terms of money saved and the
return process emerged as a moderate predictor. Trust has a considerable impact
on behavioural intention but subjective norm do not have significant effect on
it. While a number of studies have been conducted to comprehend the
accelerators of online customer loyalty, this empirical study looks at
improving the
relationship between e-tailers and customers, based on key customer experience
factors in an
emerging economy. The
study is expected to yield valuable insights on building B2C online shopping
loyalty and advocacy and these are likely to be useful to e-tailers intending
to enter emerging global markets like India.
Keywords - emerging economies, online
shopping, return process, discounts, trust, COVID
INTRODUCTION
While
a number of researches have been carried out in the developed countries to
understand the accelerators of online customer loyalty, this empirical study
looks at improving relationship between e-tailers and customers based on key
customer experience factors in an emerging economy like India. As opined by Vijay, Prashar & Sahay (2019), there are few
studies that asses how the factors related to online consumer behaviour are
associated with each other in case of emerging global economies such as India. The subject has attracted considerable
investigation via several studies in the Western world;
however, Asian literature on this aspect remains relatively sparse. As
per studies conducted by Tamimi & Sebastianelli (2015) and Brashear,
Kashyap, Musante, & Donthu (2009), changes across countries in online customer behaviour can
be attributed to a dissimilar social, economic and technological eco-system.
Internet penetration in India is much lower, at about
41%, vis-a-vis developed countries. On
the other hand, Internet penetration in
the U.K, the U.S and Germany lie in the range of 95% to 96% (https://www.internetworldstats.com/stats.htm). A Retail Industry Outlook Survey based report by KPMG
(2014) asserts that there are almost 15 million retail outlets in
India. The market is highly fragmented with about as many as 13.8 million
conventional family run neighbourhood stores that fall in the unorganised sector.
Organised retail’s share in the Indian retail market is less than 10%, however
in the developed countries it is much higher. E-commerce in India is likely to
reach $100bn by 2020 driven mainly by online retail, is expected to touch
$69bn, as per a Goldman Sachs report (Malviya &
Mukherjee, 2015). While there is a boom in B2C e-commerce sector in India,
majority of Indians still trust their neighbourhood brick & mortar stores
for shopping. The shoppers believe more in touching and feeling the products
and negotiating discounts over-the-counter, before buying. However from March
2020 onwards, online shopping gained significant importance, across the world.
The deadly and highly infectious coronavirus disease, commonly known as
COVID-19, infected lakhs of people worldwide, including India. Since it spreads
mainly via contact with an infected person or by touching a surface that has
the virus on it, the best protection is to stay at home. This led to increased
online shopping in terms of existing customers shopping more, consumers who did
not shop online using it and a number of initiatives from both online and
offline retailers facilitating it, globally.
Online
shoppers in India face various problems such as, those related to product
delivery timelines and customer support services. Online retailers have been known
to concentrate their efforts more on
creating a customer base, enhancing the website quality and building up
price-based competition. The global forecast, projects China and India as
the most rapidly developing economies and hence research on online shoppers in
these countries would significantly help global e-tailers. There have been
only few empirical studies which have focused on B2C e-commerce in India (Prashar, Vijay & Parsad, 2016). This paper
explores the influence of time convenience, value for money, return process and
trust, on online shopping behavioural intention, after testing a large number
of variables. A comprehensive literature review revealed there was no empirical
evidence that these aspects have been tested concurrently in a model in the
Indian context. This study has been constructed to build on existing literature
by testing certain critical variables which are rare in literature on online
consumer behaviour in India, but important in context of online shopping in the
country, in particular.
Ecommerce
companies in India have been notorious for predatory pricing; however, the
e-commerce policy guidelines issued by the Government in December 2018 intend
to put a halt on the high discounts. This forces e-commerce retailers to
concentrate more on enhancing customer satisfaction rather than just offering
high discounts, to increase loyalty. It is therefore, critical to unravel the
antecedents which impact behavioural intention and subsequently customer
loyalty in the online retailing environment in India. To test the hypotheses,
the study depended on a qualitative exploratory research, followed by an
extensive quantitative web survey wherein a structured questionnaire was used.
Factor analysis (exploratory and confirmatory) and structural equation modelling
(SEM) were used to analyse a total of 500 usable responses. SEM was deemed
appropriate as it enables testing of models that include latent constructs and
estimation of multiple and crossed relationships between dependent and
independent variables (Hair, Hult, Ringle &
Sarstedt, 2013).
LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK
During the
literature review, “the Theory of Planned Behaviour (TPB)” emerged as the
underpinning theory. As per TPB, a person's intent to conduct a specific
behaviour governs whether the person will perform that specific behaviour.
Attitude toward the specific behaviour and subjective norms associated with
performing the behaviour
are believed to impact intention (George, 2004).
The Theory of
Planned Behaviour remains the most often cited and the most widely recognised
model for predicting behavioural intention. However, the TPB model has been
found to be deficient in terms of number of variables to explain consumer
behaviour in a given situation. Some studies have questioned its predictive
potential on intention and hence added external factors (Rivis & Sheeran, 2003). Ajzen (1991), concluded “…the theory of planned behaviour is, in principle, open to
the inclusion of additional predictors if it can be shown that they capture a
significant proportion of the variance in intention or behaviour after the
theory’s current variables have been taken into account”
Content analysis
revealed several controllable elements influence the online buying behaviour and
this study highlights certain key variables through the conceptual augmentation
of TPB. Some classical variables have been chosen from this theory while
certain critical variables which are important in context of online shopping in
India have been added.
Influence
of Return Process on Attitude
The post-purchase
experience is one of the most critical components of the entire customer
experience journey as it adds vastly in building the repurchase intention (Park, Cho & Rao, 2012). Post-purchase experience
assumes greater significance in an online shopping environment since the
customer is actually able to touch, feel and use the product only after it has been bought. Returning
goods that customers are highly dissatisfied with, is a key aspect of the
post-purchase experience. Moreover, return process has greater importance in
case of emerging economies where the Internet penetration is relatively lower and consumers depend more on shopping from
brick and mortar stores, which enable them to see, touch and feel the products
before buying. Almost one-fourth of the items purchased online in India are
returned back to the online shopping sites and this raises the average cost of
delivery by about 50% (Sarkar, 2014). There are
research studies which explore online post-purchase customer experience only as
a small component of their measurement of overall customer experience. However,
many of these studies miss out important aspects such as “return and exchange,
refund options, etc.”(Kumar & Anjaly, 2017). Literature
review revealed few prior researches that have investigated the influence of
returns on various aspects. Wood (2001) concluded
that a lenient return policy, positively influences the quality of online
products both before and after purchase. Mukhopadhyay
and Setaputra (2007) opined that a liberal return policy boosts sales
income, however it also adds to expenses due to higher probability of return. Pei, Paswan and Yan (2014) analysed that a “fair return
policy” has a positive impact on purchase intention. A return process is
considered effective if it is fast and easy. This provides more confidence to
the shoppers, makes their attitude more positive, which in turn increases their likelihood of
buying products online. Literature review revealed that the return process as an antecedent of attitude has been included
only in few studies. Hence it is important to study the influence of the return
process on attitude, which in turn impacts behavioural intention.
Accordingly: H1:
The return process affects attitude in terms of online shopping.
Influence of Perceived Usefulness (in
terms of Time Saved) On Attitude
One of key drivers behind the steep growth in
B2C ecommerce has been the greater significance associated with more efficient
use of time (Singh, Kumar & Dash, 2016). Khalifa
& Liu (2007) identified quicker and enhanced search and buying, better shopping productivity
and superior shopping performance, as e-commerce usefulness aspects. Hence a
shopping site which is more time efficient vis-a-vis other sites, is expected
to be perceived as more useful. Mikalef, Giannakos &
Pateli (2013) opined that the Internet has become the biggest and most
effective source of obtaining information. This is also relevant to online
shopping via which consumers can collect information on products that they are
involved with (Lee & Ngoc, 2010). Karahanna (1999) opined that perceived usefulness by
way of getting helpful information, to a great extent impacts the online
shoppers’ attitude to buy groceries on the Internet. If all the required and
in-depth product information is available on a site, it will lead to saving in
time as the shopper will then not need to visit multiple sites. Ibrahim, Suki & Harun (2014) clarified that in
terms of returns, the time related perceived usefulness while shopping online
was not impacted as "the perceived risk of time lost in ordering, receiving
and returning an unsatisfactory product did not have a significant relationship
with unwillingness to buy". According to Khare
& Sadachar (2014), simple guidelines and comprehensive information
about the products to be purchased online, is likely to progress the attitude
of the youth in India, towards online shopping.
Accordingly:H2: Perceived usefulness w.r.t
time saved, impacts attitude
Influence of Perceived Usefulness (in
terms of Money Saved) On Attitude
Promotions offered by online shopping sites have
a direct positive impact on the number of purchase transactions (Sonia & Carmen, 2009). Prashar,
Vijay & Parsad (2016) opined that for the typical shopper in India,
“the marginal value of the rupee is very high” and hence it is important that
e-tailers provide the confidence that payment transactions are secure and also
offer shopper-friendly return and exchange policies. In the same light,
investing on deals and offers would attract the discount-sensitive
buyer toward an online shopping site.
and price conscious" vis-à-vis those in
a developed economy. In fact, the deals and promotions run by most online
shopping sites is one of the key drivers for shopping online in an emerging
economy (Khare & Rakesh, 2011). Most B2C
ecommerce sites in India, attract consumers to shop online via offers such as
free delivery, discounts, redeemable vouchers, sweepstakes, buy-one-get-one-free offers and exchange schemes. However Indian shoppers known to
be mostly parsimonious as a part of their value system, may not be lured into making impulsive
decisions based on promotions and advertisements (Pandey
& Chawla, 2018). Sarkar & Khare (2017) concluded
after extensive literature review that there is scope to study the influence of
“price perception on online shopping attitude”
Thus, the following is expected:
H3: Perceived
usefulness w.r.t money saved, impacts attitude.
Impact of “Ease of Use” on
Attitude
A well-designed user interface system can
lower online shoppers’ search costs and the time invested for information
processing. Company and product related information is essential for shoppers
during the purchase process (Kaur & Quareshi, 2015).
Close & Kinney (2010) opined that presence
of sufficient information on an online shopping site could help facilitate conversion
of a browser into a buyer. Hence it is important
that the information required for shopping is sufficient, as well as easy to
locate. Aren, Güzel, Kabadayi & Alpkan (2013) found
that “faster navigation” and “effectiveness of search process” had considerable
influence in choosing an online shopping site for making a purchase. In India,
more consumers find shopping from brick & mortar stores easier since they
can touch and feel the products and bargain
over-the-counter. Moreover, a study conducted in India showed that since
customers lacked confidence in the existing shopping websites, their online
shopping behaviour was affected by website characteristics such as layout, design,
interactivity, user-friendliness and ease of access (Gupta,
Handa & Gupta, 2008). Therefore, it becomes important to understand the
impact of “ease of use” on attitude. Thus, the following is hypothesised:
H4: “Perceived ease of use” affects attitude
in terms of online shopping.
Effects of Attitude on Behavioural Intention
Hassanein
& Head (2007) opined that attitude is associated with behavioural intention during
intentional technology adoption. Li & Zhang (2002) argued
that consumer attitudes influence online shopping intention and decide if an
e-commerce purchase will take place or not. Javadi,
Dolatabadi, Nourbakhsh, Poursaeedi, & Asadollahi (2012) suggested that
attitude of consumers significantly affects their behaviour while shopping
online.
Thus the following is hypothesised:
H5: Attitude has an
impact on behavioural intention in terms of online shopping.
Effects of Subjective Norm on Behavioural Intention
Javadi et
al.,(2012) examined
that “family members, friends and peers' online experience and suggestions
positively influence online buying behaviour.” Certain studies point out that
while subjective norm influence behavioural intention, the relationship is
weak. Ranadive (2015)
analysed that “Subjective Norm factor reached a level of significance, which indicates that the influence of social
relationships (family, friends and colleagues) will weakly but positively
affect the consumer’s intention to purchase groceries online.“ Hence the
following is hypothesised:
H6: Subjective norm
influence behavioural intention in terms of online shopping.
Effects of Trust on Behavioural
Intention
A number of studies
have reported that trust influences behaviour. Thamizhvanan and
Xavier (2013) concluded that
trust is an antecedent of online purchase intention, in context of online
shopping in India. Verma, Sharma and Sheth (2016) concluded that “trust
is most important in maintaining relationship continuity in online retailing”.
A study conducted by Alam and Yasin (2010) suggested
that "perceived security or privacy" has a significant effect on
online brand trust. The inclination towards cash-on-delivery over other payment
modes such as credit/debit card, net banking, mobile wallet etc by online
shoppers in India, proves there are trust related concerns associated with
online shopping in India (Thakur & Srivastava,
2015). Khare, Khare and Singh (2012) opined that
customers in India seem to be at ease with conventional shopping formats
because these are not impersonal and allow easy interaction with service staff.
Most consumers are not familiar with online technology and they see websites as
complex and hard to comprehend. Inferior IT set-up and absence of government
support aggravate consumers’ view of risk toward online web sites. There are
many sites selling fake products and also several sites do not provide accurate,
updated and complete information, in India. Moreover, inferior IT
infrastructure exaggerates security fears and users are scared of Internet
cheating and hacking. Online product reviews are also of significance and a
large number of online buyers communicate their feelings via social networking
sites. Wobker, Eberhardt and Kenning (2015) established
that trust brings down the complication associated with online retailing. Shoppers can engage with
customer care via chat, learn from others experience through product reviews
and gain information to decide whether to buy online or not.
Hence the following is hypothesised:
H7: Trust is a
determinant of behavioural intention in terms of online shopping.
This study intends to test
seven hypotheses discussed above, and the research framework is depicted in Figure1.
Figure1: The proposed conceptual model
TRUST BEHAVIOURAL
INTENTION TIME SAVED ATTITUDE EASY & FAST
RETURNS EASE OF USE MONEY SAVED SUBJECTIVE NORM H6 H5 H7 H2 H4 H3 H1
METHODOLOGY
Sampling Procedure
To test the hypotheses, an online pan
India survey was conducted. The study depended mainly on the
quantitative data collected from consumers, however an extensive qualitative
exploratory research was also conducted. Depth interviews were conducted with
10 respondents who qualified as relevant target audience and were spread across
age, income and occupational backgrounds. Depth interviews were also conducted
with 4 experts who had substantial experience in the area of B2C ecommerce. The respondents
were selected through acquaintance and snowballing. The final set of variables
to measure online shopping behaviour were finalised based on the literature review and
the insights generated from the exploratory research. The instrument was
developed on basis of the exploratory and secondary research. It was closely
examined by experts as well as piloted amongst diverse target respondents. The
instrument was finalised after variations in items as well as wording, thus
ensuring face validity. The pilot study was carried out to check the measurement
items for lucidity and comprehension. It is essential to detect any issues with
the research instrument and to determine the content and face validity of the
measures adopted in the instrument.
Judgmental sampling was adopted for the quantitative study
due to unavailability of a sampling frame to draw out random sampling. Online
shoppers were mostly selected through social media contacts on WhatsApp, Facebook and Linkedin and
snowballing was also used. The target respondents were specified as those who were 18 years or above, living
in India and shopped online, now and then or frequently. Also, they should have
purchased a tangible product online, in at least last 6 months. An empirical study was conducted on a sample
size of 500. More than a 1000 questionnaires were distributed, a total of 578
questionnaires were received back and out of these, 500 were usable valid
responses.
Analysis
of demographic data showed that both genders were almost in equal proportion
and the unmarried were in majority at 57%. Age composition of the sampled
respondents indicated that majority i.e. nearly 58% of the respondents were of
18 to 29 years of age. Occupational outline of the respondents showed that half
of them were students with the next highest segment being of those in service
at about 27%. Amazon led as the most visited site in last 6 months, for almost
half the respondents, followed by Flipkart for one-fifth of the respondents. As
per industry reports, currently, Amazon is the B2C e-commerce market leader in India,
in terms of gross merchandise value (GMV), followed by Flipkart. Amongst the
product types bought most in the last 6 months from various shopping websites,
clothes and apparels emerged on top (34%) followed by computers, mobile and
accessories at 15.8%. This data (see Appendix A for further details) is in line with e-commerce market
trends in India.
Measurement
Scale items for ‘trust’ and ‘perceived ease of use’ were
adapted from Chang and Chen (2009); Park and Kim
(2003) and subjective norm and behavioural intention from Lin (2007). The adaptation was based on the Indian
environment and the influencers of behaviour in it. The constructs for ‘easy
& fast returns’, ‘perceived usefulness (money saved)’ and ‘perceived
usefulness (time saved)’ were based on desk research, exploratory qualitative
research and expert opinion.
Except for demographic variables, all items in the instrument
were measured on a seven-point scale ranging from “strongly disagree” to
“strongly agree.”
Since the literature was quite varied, it was considered
necessary to thoroughly pre-test the instrument to ensure clarity, validity and
proper wording of all questions. Before launching the survey instrument in the
field, it was piloted amongst few experts and experienced online shoppers,
spread across age, income and occupational backgrounds.
RESULTS
Exploratory Factor Analysis
EFA with varimax rotation was conducted to explore the
underlying dimensions. The Kaiser-Meyer-Olkin (KMO) Test – resulted in a value
greater than 0.5 indicating that EFA was appropriate in this case. The
Bartlett’s test of Sphericity produced a value of 0, indicating that a
substantial correlation existed. The EFA resulted in 7 factors based on 33 items.
Most factor loadings were higher than 0.5, indicating healthy correlations
between the items and their factors. The total variance explained was 72.45%.
The internal consistency or reliability of each factor was computed by Cronbach
alpha and was mostly found to be good or acceptable based on the following
measure: 0.7–0.9: good; 0.6–0.7: acceptable; (Kline, 2013). The
factor loadings and the Cronbach α values have been shown in Table 1.
Table -1: Initial
model Factor loadings (after conducting EFA)
|
Factors |
Items |
Factor loadings |
Cronbach α |
|
TRUST |
This site has a
good reputation in the market |
0.78 |
0.69 |
|
This site cares
for and invests on its customers |
0.75 |
||
|
I have found
this site to be secure for transmitting sensitive information such as
debit/credit card details |
0.68 |
||
|
I feel
comfortable sharing my personal details required for shopping on this
site |
0.62 |
||
|
This site offers
useful customer reviews (i.e. they constitute feedback from genuine
customers......) |
0.59 |
||
|
This site is
free from errors and provides accurate, updated and complete information |
0.54 |
||
|
Generally, this
site offers lower prices as compared to other sites |
0.41 |
||
|
This site does
not deliver fake products |
0.32 |
||
|
|
|
|
|
|
EASE of USE |
This site has
high interaction speed |
0.37 |
0.69 |
|
It has
interactive features (such as online chat and easy phone access) which help
in quick problem resolution... |
0.76 |
||
|
The information
required for shopping on this site is sufficient |
0.72 |
||
|
This site offers
complete information on all its deals. Hence, I do not need to refer to other
sources |
0.70 |
||
|
Information required for shopping on this site is easy
to locate |
0.55 |
||
|
Easy to search
and find way while shopping on this site |
0.35 |
||
|
|
|
|
|
|
EASY & FAST RETURNS |
I am sure that
this site has a fast process for returns |
0.87 |
0.61 |
|
I am convinced
that this site has an easy process for returns |
0.78 |
||
|
I am certain
that this site does not have any hidden terms & conditions in its return
policy |
0.68 |
||
|
I am confident
that this site offers good after sales service such as product installation
and demo |
0.40 |
||
|
|
|
|
|
|
TIME SAVING |
I feel assured
that all items ordered will be delivered on time |
0.42 |
0.61 |
|
Shopping on this
site saves me time as compared to other sites. |
0.74 |
||
|
This site
provides access to all required or necessary product information in-depth |
0.66 |
||
|
|
|
|
|
|
VALUE FOR MONEY |
This site
provides attractive offers and deals from time to time |
0.79 |
0.62 |
|
This site offers
prices which are cheaper than those offered by a physical store |
0.72 |
||
|
Generally, the
items that I am looking for, are available and in-stock on this site |
0.32 |
||
|
|
|
|
|
|
SUBJECTIVE NORM |
I have read news
reports which say this is a good site for online shopping |
0.83 |
0.50 |
|
I am influenced
by mass media reports to try this site |
0.82 |
||
|
After watching
the advertisements on various media, I was tempted to try this site |
0.43 |
||
|
My relatives
feel this is the right site for online shopping |
0.42 |
||
|
My classmates
and friends feel this is the right site for online shopping |
0.38 |
||
|
My family feels
this is the right site for online shopping |
0.78 |
||
|
|
|
|
|
|
BEHAVIOURAL
INTENTION |
I plan to shop
on this site again |
0.92 |
0.89 |
|
I intend to use
this site within the near future |
0.91 |
||
|
I will recommend
this site to others |
0.90 |
Measurement Model: Confirmatory Factor Analysis (CFA)
Byrne
(2010) suggested
that the structured equation modelling (SEM) consists of two sub-models – i.e.:
measurement model and structural model. Confirmatory factor analysis (CFA) was
conducted using AMOS 21.0 software to estimate the measurement model. The
quality of the measurement model was assessed through CFA and its estimation
generated a good fit: CMIN=345.8; p<0.001; CMIN/DF =3.144; GFI=0.924;
NFI=0.918; TLI =0.929; CFI =0.942; RMSEA =0.0471; SRMR =0.0471. The Measurement
model (CFA) results are summarised in Table 2. The statistical significance of
variables was assessed via Critical Ratio (CR). Factor loadings for all items
(related with each of the seven constructs) were found to be greater than 0.60.
All factor loadings emerged significant at the 0.05 level. This indicates key
confirmation for convergent and discriminant validity of the measurement model.
Once the model strength was established via Confirmatory factor analysis (CFA),
the structural analysis was conducted.
Table - 2: Measurement model (CFA)
|
Factors |
Items |
Factor Loading |
Critical Ratio (CR) |
Cronbach α |
|
TRUST |
I have found
this site to be secure for transmitting sensitive information such as
debit/credit card details |
0.760 |
Fixed |
0.80 |
|
This site offers
useful customer reviews (i.e. they constitute feedback from genuine
customers......) |
0.714 |
14.68 |
||
|
This site is
free from errors and provides accurate, updated and complete information |
0.791 |
15.92 |
||
|
|
|
|
|
|
|
EASE of USE |
This site has
high interaction speed |
0.687 |
Fixed |
0.77 |
|
The information
required for shopping on this site is sufficient |
0.726 |
13.74 |
||
|
Easy to search
and find way while shopping on this site |
0.771 |
14.35 |
||
|
|
|
|
|
|
|
EASY & FAST RETURNS |
I am sure that
this site has a fast process for returns |
0.805 |
Fixed |
0.87 |
|
I am convinced
that this site has an easy process for returns |
0.961 |
12.62 |
||
|
|
|
|
|
|
|
TIME SAVING |
Shopping on this
site saves me time as compared to other sites. |
0.655 |
Fixed |
0.64 |
|
This site
provides access to all required or necessary product information in-depth |
0.718 |
12.23 |
||
|
|
|
|
|
|
|
VALUE FOR MONEY |
This site
provides attractive offers and deals from time to time |
0.640 |
11.02 |
N.A |
|
|
|
|
|
|
|
SUBJECTIVE NORM |
I have read news
reports which say this is a good site for online shopping |
0.758 |
1.60 |
0.82 |
|
My relatives
feel this is the right site for online shopping |
0.890 |
Fixed |
||
|
My classmates
and friends feel this is the right site for online shopping |
0.890 |
16.60 |
||
|
|
|
|
|
|
|
BEHAVIOURAL INTENTION |
I plan to shop
on this site again |
0.888 |
Fixed |
0.89 |
|
I intend to use
this site within the near future |
0.864 |
24.75 |
||
|
I will recommend
this site to others |
0.826 |
23.18 |
The alpha values indicate that scale items
for the seven latent constructs were largely reliable.
Structural Model
and Hypotheses Testing
The structural model was developed to test
the effect of ease of use, easy & fast returns, time saving and value for
money on attitude and the effect of trust, attitude and subjective norm on
behavioural intention. SEM with maximum likelihood estimation (MLE) was used to
assess the research model. The structural path coefficients and the results of
hypotheses testing are summarised in Table 3.
Table - 3: Results of the structured equation
model
|
Hypothesis |
Structural path |
Standardized estimate |
p |
Result |
||
|
H1 |
Easy & fast returns |
|
Attitude |
0.493 |
*** |
Accepted |
|
H2 |
Perceived usefulness in terms of Time saved |
|
Attitude |
0.929 |
*** |
Accepted |
|
H3 |
Perceived usefulness in terms of Money saved |
|
Attitude |
0.640 |
*** |
Accepted |
|
H4 |
Perceived Ease of use |
|
Attitude |
0.917 |
*** |
Accepted |
|
H5 |
Attitude |
|
Behavioural Intention |
0.187 |
** |
Accepted |
|
H6 |
Subjective Norm |
|
Behavioural Intention |
0.082 |
.065 |
Rejected |
|
H7 |
Trust |
|
Behavioural Intention |
0.542 |
*** |
Accepted |
Note: In p-value column, (***) indicate significance smaller than 0.001,
(**) indicate significance smaller than 0.01
H1 tests whether the return process of a
site, affects attitude in terms of shopping from the site. The results show
that this path coefficient is positive and significant (0.49, p < 0.001),
demonstrating the return process influences attitude. H2 proposes that
perceived usefulness in terms of time saved while shopping on a site, impacts
attitude towards the site. This is supported by a significant path coefficient
(0.929, p < 0.001), indicating that time saved impacts attitude. H3 suggests
that perceived usefulness w.r.t money saved, impacts attitude. This is affirmed
with a significant and positive path coefficient (0.64, p < 0.001). H4 tests
if perceived ease of use affects attitude in terms of online shopping. Results
indicate a significant and positive path coefficient (0.917, p < 0.001). H5
tests if attitude has an impact on behavioural intention in terms of online
shopping. Empirical analysis reveals a significant and positive path
coefficient (0.187, p < 0.01). H6 tests if subjective norm influences behavioural
intention towards online shopping. This hypothesis was not accepted as the path
coefficient was not significant in the hypothesised direction. H7 tests if
trust is a determinant of behavioural intention in terms of online shopping.
Results indicate a significant and positive path coefficient (0.542, p <
0.001). The final model along with the structural path coefficients (in
brackets) and results of hypotheses testing are depicted in Figure 2.
Figure 2. Structural model
TRUST BEHAVIOURAL
INTENTION TIME SAVED ATTITUDE EASY & FAST
RETURNS EASE OF USE MONEY SAVED SUBJECTIVE NORM (0.082) H6 Rejected (0.187) H5 Accepted (0.542) H7 Accepted (0.929) H2 Accepted (0.917) H4 Accepted (0.640) H3 Accepted (0.493) H1 Accepted
DISCUSSION
The purpose of this
study is to understand which factors impact online shopping behavioural
intention. The findings add to existing literature on online buying behaviour
in emerging markets. The study investigates aspects which are rare in
literature on online consumer behaviour in India, but important in context of
online shopping in the country. Post-purchase experience assumes greater
significance in an online shopping environment since the customer is actually
able to physically interact with the product, only once it has been bought.
More so, in case of emerging
economies, where the Internet penetration is relatively lower and consumers
depend more on shopping from conventional stores which enable them to closely
assess the products before buying.
The Relationship between Perceived Ease of Use and Attitude
Ease of use refers to aspects such as high
interaction speed and sufficiency of information required for a good online
shopping experience. It emerged as a strong predictor of attitude in this
study. Hence, ease in using a particular online shopping site is an important
aspect that directly shapes consumer attitude which then impacts loyalty and
advocacy towards that site. This is supported by studies conducted by Lohse
and Spiller (1998); Gupta, Handa and Gupta (2008). If using the online
shopping site is simple and trouble free, the shoppers do not end up expending
time and energy struggling with complex systems. Rather, they end up enjoying
the shopping experience.
The Relationship between the Return Process and Attitude
A return process is considered effective, if
it is fast and easy. This provides more confidence to the shoppers, makes their
attitude more positive which in turn increases their likelihood of buying
products online. The results show that an easy and fast return process has
considerable influence on attitude. This finding is in confirmation with the conclusion
of Xu and Paulins (2005). The return process becomes
more significant in emerging economies like India where most consumers shop
from brick & mortar stores. Their comfort with online shopping is
relatively lower and hence having a buyer friendly return process increases the
confidence to shop online.
The Relationship between the Perceived Usefulness in Terms of Time Saved and Attitude
The perceived usefulness offered by a
shopping site in terms of time saved emerged as a strong predictor of attitude.
It was found to be chiefly associated with
availability of in-depth product information and the time saved on that site
vis-a-vis other shopping sites. This finding is consistent with previous
studies findings that, perceived usefulness, by way of getting useful
information, impacts online shoppers’ attitude (Lee & Ngoc,
2010).
The Relationship between the Perceived Usefulness In Terms Of Money
Saved and Attitude
The results show that perceived usefulness in
terms of money saved has considerable influence on attitude. The saving on a
given e-commerce site is made via the various offers and bargains that are
active from time to time and the price differences vis-a-vis offline stores.
Perceived usefulness in terms of money saved, is more important in price
sensitive emerging markets like India (Sharma, 2011). In India, large
discounts by e-tailers are prevalent, to achieve higher loyalty levels. Only
few studies have covered content elements such as discounts and offers, as the
focus has been more on aspects such as - price dispersion and price
expectations.
The above two discussions, echo research
conducted by Bhattacherjee (2001) which suggests -
“interaction between perceived usefulness and loyalty incentives” is important,
as continuance intention motivation is possible not by incentives alone, but
the service must also be perceived as useful.
The Relationship between Trust and Behavioural Intention
The results show that trust has a
considerable impact on behavioural intention. The higher the security for
transmitting sensitive information, greater is the freedom from errors,
inaccuracies and incomplete on-site information. More the number of authentic
customer reviews, higher are the trust levels. Online shopping in the
developing countries is a more novel trend and given that there are only a
handful of major shopping sites, such concerns are widespread (Khare, Khare & Singh, 2012). Trust in the online
situation is of high importance and is a factor that influences customer
experience across all interactions - before, during, and even after the
purchase. “Trust is most important in maintaining relationship continuity in
online retailing” (Verma, Sharma & Sheth, 2016).
The Relationship between Attitude and Behavioural Intention
As per the work of Li and Zhang (2002); Lee and
Ngoc (2010) and Javadi et al.,(2012), attitude influences behavioural intention.
According to this study results, attitude
added moderately to the explanatory power of behavioural intention. “Behaviours
are not fully under volitional control, even though a person may be highly
motivated by her own attitudes....she may not actually perform the behaviour
due to intervening environmental conditions” (Hasbullahet
al.,2016). This paper has delved on the “intervening environmental
conditions” that can deter online shopping in emerging markets such as India.
The Relationship between Subjective Norm and Behavioural Intention
Due to the absence of personal contact,
influences such as a persisting classmate walking you into a particular shop,
are absent in case of online shopping and the level of anonymity is higher
vis-a-vis brick-and-mortar stores. Though subjective norm may elucidate
behavioural intention to some extent, it was found that subjective norm does
not have significant effect on behavioural intention and is a weak predictor.
This outcome can also be attributed to the fact that respondents for this study
were those who had shopped online, now and then, or regularly, at least once
during the last 6 months to buy a tangible product i.e. they had online
shopping experience. Hence, they depended less on the online experience of
relatives and classmates. Also, these findings are consistent with some past
studies wherein subjective norm was found to be insignificant in predicting
intentions to purchase online (Lin, 2007); (Helmig, Huber & Leeflang, 2007)
CONCLUSION
Kumar and Anjaly (2017) argue that convergence of a youthful
population with rising internet penetration has brought online retail in the
limelight, in some developing markets. This research makes diverse
contributions to the online retail literature as it empirically validates and
extends dimensions culled out from studies conducted in the past, in developed
markets, to developing markets. The study tests aspects that have not been
empirically tested concurrently in a model in the Indian context. Moreover, it
builds on existing literature, by testing certain critical variables - uncommon
in literature on online consumer behaviour, in India, but important in
perspective of online shopping in India.
The online shopping sector is booming in
India and there is fierce competition amongst e-tailers. This study helps in
understanding the antecedents of B2C e-commerce behavioural intention. The
return process of a site affects attitude, in terms of shopping from the site.
Perceived usefulness (in terms of time saved) and ease of use emerge as strong
predictors of attitude while perceived usefulness (in terms of money saved) and
the return process, emerge as moderate predictors. Furthermore, attitude has an
impact on behavioural intention in terms of online shopping. Trust emerges as a
significant determinant of behavioural intention in terms of online shopping.
On the other hand, subjective norm does not influence behavioural intention
towards online shopping. Insights from this study are expected to enhance
online shopping relationship marketing leading to higher profitability. This
study is also expected to yield valuable insights on antecedents of B2C online
shopping loyalty and advocacy which are likely to be useful to e-tailers
intending to enter emerging markets like India.
MANAGERIAL IMPLICATIONS
Since
March 2020 onwards, online shopping gained significant importance, across the
world, like never before. The deadly and highly infectious coronavirus disease,
commonly known as COVID-19, infected lakhs of people worldwide, including
India. Since it spreads mainly via contact with an infected person or by
touching a surface that has the virus on it, the best protection is to stay at
home. This led to increased online shopping and a number of initiatives from
online and even offline retailers, in facilitating it, globally. However with
fierce competition in today’s B2C e commerce market, an increasing number of
e-tailers are currently facing issues in operating profitability. Insights from
this study are expected to enhance online shopping relationship marketing
leading to higher profitability, by improving the customer experience. This
study aspires to help e-tailers to fine-tune market communications and
reposition themselves to maintain the current customers as well as draw new
ones. In emerging markets such as India, a considerable amount of money that is
being burnt in providing discounts can be saved and invested in adding value to
the customers. Online retailers need to
ensure that the processing of returns is both easy and fast. This
provides more confidence to the shoppers, makes their attitude more positive
which in turn increases their likelihood of buying products online. Time saving
was found to be chiefly associated with availability of all required product
information in an in-depth manner, on the shopping site. The time saved in
relative terms, i.e. the time saved as compared to other shopping sites also
matters. Hence online retailers should
ensure that not only is all necessary product information available
on-site, in-depth, but also that their site has certain unique features which
enable shoppers to save more time vis-a-vis competition. Along with improving
customer experience, e-tailers need to provide attractive offers and bargains
from time to time. This will create a more positive attitude and incentivise
online shoppers to visit the site again as well as recommend it to others.
Higher
security for transmitting sensitive information, greater level of freedom from
errors, inaccuracies and incomplete on-site information and a higher
number of authentic customer reviews are likely to lead to
higher trust levels. Therefore e-tailers must ensure that they use the latest
cyber security technologies, keep information on their site updated 24*7 and
that the customer reviews are not forged.
The
study is expected to yield valuable insights on building B2C online shopping
loyalty and advocacy and these are likely to be useful to e-tailers intending
to enter emerging markets like India.
LIMITATIONS AND FUTURE RESEARCH DIRECTIONS
This study was conducted in India. A similar
study can be conducted in other emerging and developing economies after
adapting the research methodology as per the environmental factors. This would
yield valuable insights on antecedents of B2C online shopping loyalty and
advocacy which are likely to be useful to e-tailers intending to expand into
emerging markets.
Longitudinal studies are known to
provide more perfect information of the real behaviour. Such a study
could be conducted, given the dynamic nature of e-commerce markets and portals
and the changing patterns of online consumer behaviour as well.
REFERENCES
Ajzen, I. (1991). The theory
of planned behaviour. Organizational Behaviour and Human
Decision
Processes, 50 (2), 179-211.
Shah Alam, S. and Mohd Yasin, N. (2010).
What factors influence online brand trust:
evidence from online tickets buyers in
Malaysia. Journal of theoretical and applied electronic commerce
research, 5(3), 78-89.
Aren, S.,
Güzel, M., Kabadayi, E. and Alpkan, L. (2013). Factors affecting repurchase
intention
to shop at the same website. Procedia-Social and Behavioral Sciences,
(99), 536-544.
Bhattacherjee, A. (2001). An empirical
analysis of the antecedents of electronic
commerce service
continuance. Decision support systems, 32(2), 201-214.
Brashear, T.G., Kashyap, V., Musante, M.D.
and Donthu, N. (2009). A profile of the
internet shopper: evidence from six
countries. The Journal of Marketing Theory and Practice, 17 (3),
267-282.
Byrne, B. M. (2010). Structural equation modelling with AMOS: Basic concepts,
applications, and programming. New York: Routledge.
Chang, H. H. and Chen, S. W. (2009).
Consumer perception of interface quality, security,
and loyalty in electronic
commerce. Information & management, 46(7), 411-417.
Close, A.G. and Kinney, M.K. (2010). Beyond
buying: motivations behind consumer’s
online shopping cart use. Journal of
Business Research, 63 (9/10), 986-992.
George, J. F. (2004). The theory of
planned behaviour and Internet purchasing. Internet
research, 14(3), 198-212.
Gupta, N., Handa, M. and Gupta, B.
(2008). Young adults of India-online surfers or
online shoppers. Journal
of Internet Commerce, 7(4), 425-444.
Hair,
J. F., Hult, G. T. M., Ringle, C. M. and Sarstedt, M. (2013), A primer on
partial least
squares structural equation modelling (PLS-SEM). CA: Thousand Oaks.
Hasbullah, N. A., Osman, A., Abdullah,
S., Salahuddin, S. N., Ramlee, N. F. and Soha,
H. M. (2016). The relationship of
attitude, subjective norm and website usability on consumer intention to
purchase online: An evidence of Malaysian youth. Procedia Economics and
Finance, (35), 493-502.
Hassanein, K. and Head, M. (2007).
Manipulating perceived social presence through the
web interface and its impact
on attitude towards online shopping. International Journal of
Human-Computer Studies, 65(8), 689-708.
Helmig, B., Huber, J. A. and Leeflang,
P. (2007). Explaining behavioural intentions
toward co-branded products. Journal
of Marketing Management, 23(3-4), 285-304.
Ibrahim, S., Suki, N.
M. and Harun, A. (2014). Structural relationships between perceived
risk and consumers’
unwillingness to buy home appliances online with moderation of online consumer
reviews. Asian Academy of Management Journal, 19(1), 73.
Javadi, M. H. M., Dolatabadi, H. R.,
Nourbakhsh, M., Poursaeedi, A. and Asadollahi, A.
R. (2012). An analysis of factors
affecting on online shopping behaviour of consumers. International
Journal of Marketing Studies, 4(5), 81.
Karahanna, E. and Straub, D. W. (1999).
The psychological origins of perceived
usefulness and
ease-of-use. Information & management, 35(4), 237-250.
Kaur, G. and Quareshi, T.K. (2015), Factors
obstructing intentions to trust and purchase
products online. Asia Pacific Journal of
Marketing and Logistics, 27 (5), 758-783.
Khalifa, M. and Liu, V. (2007). Online
consumer retention: contingent effects of online
shopping habit and online shopping
experience. European Journal of Information Systems, 16(6),
780-792.
Khare, A. and Sadachar, A. (2014).
Collective self-esteem and online shopping attitudes
among college students: Comparison
between the US and India. Journal of International Consumer Marketing, 26(2),
106-121.
Khare, A., Khare, A. and Singh, S.
(2012). Attracting shoppers to shop online—
Challenges and opportunities for the
Indian retail sector. Journal of Internet Commerce, 11(2),
161-185.
Khare, A. and Rakesh, S. (2011). Antecedents
of online shopping behaviour in India: An
examination. Journal of Internet Commerce,
10 (4), 227–244.
Kline, P. (2013). Handbook
of psychological testing. MA: Routledge.
Kumar, A. and Anjaly, B.
(2017). How to measure post-purchase customer experience in
online retailing? A scale
development study. International Journal of Retail & Distribution
Management, 45(12), 1277-1297.
Lee, S. H. and Ngoc, H. T. B. (2010).
Investigating the on-line shopping intentions of
Vietnamese students: an extension of the
theory of planned behaviour. World Transactions on Engineering and
Technology Education, 8(4), 471-476.
Li, N. and Zhang, P. (2002). Consumer
online shopping attitudes and behavior: An
assessment of
research. AMCIS 2002 Proceedings, 74.
Lin, H. F. (2007). Predicting consumer
intentions to shop online: An empirical test of
competing theories. Electronic
Commerce Research and Applications, 6(4), 433-442.
Lohse, G. L. and Spiller, P. (1998).
Electronic shopping. Communications of the
ACM, 41(7), 81-87.
Malviya, S. and Mukherjee, W. (2015). Indian
e-commerce market to breach $100 billion
mark by FY20: Goldman Sachs. The Economic
Times.
Retrievedfrom:https://economictimes.indiatimes.com/industry/services/retail/indias-ecommerce-market-to-breach-100-billion-mark-by-fy20-goldman-sachs/articleshow/49532128.cms
Mikalef, P., Giannakos, M. and Pateli,
A. (2013). Shopping and word-of-mouth intentions
on social media. Journal of
theoretical and applied electronic commerce research, 8(1), 17-34.
Mukhopadhyay, S. K.
and Setaputra, R. (2007). A dynamic model for optimal design
quality and return
policies. European Journal of Operational Research, 180(3), 1144–1154.
Pandey, S. and Chawla, D.
(2018). Online customer experience (OCE) in clothing e-
retail: exploring OCE
dimensions and their impact on satisfaction and loyalty–does gender
matter? International Journal of Retail & Distribution Management, 46(3),
323-346.
Park, C. H. and Kim, Y. G. (2003).
Identifying key factors affecting consumer purchase
behaviour in an online shopping context. International
journal of retail & distribution management, 31(1), 16-29.
Park, I., Cho, J. and Rao, H.R. (2012). The
effect of pre-and post-service performance
on consumer evaluation of online retailers. Decision
Support Systems, 52 (2), 415-426.
Pei, Z., Paswan, A. and Yan, R. (2014). E-tailer׳ s return
policy, consumer׳ s perception
of return policy fairness and purchase intention. Journal
of Retailing and Consumer Services, 21(3), 249-257.
Prashar, S., Sai Vijay, T. and Parsad, C. (2016). Segmenting
online shoppers: a study of
consumers' web portal selection motivations for
e-shopping. Asian Academy of Management Journal, 21(1).
Ranadive, A. (2015). An empirical study
on the online grocery shopping intentions of
consumers in Vadodara city. International
Journal of Management and Social Sciences Research, 4(3), 8-14.
Rivis, A. and Sheeran, P. (2003).
Descriptive norms as an additional predictor in the
theory of planned behaviour: A
meta-analysis. Current Psychology, 22(3), 218-233.
Sarkar,
J. (2014, January 10). 25 per cent of online orders returned, study finds. The
Times
of India. Retrieved from:
:https://m.timesofindia.com/business/india-business/25-of-online-orders-returned-study-finds/articleshow/28614022.cms
Sarkar, S. And Khare, A.
(2017). Moderating effect of price perception on factors
affecting attitude towards
online shopping. Journal of Marketing Analytics, 5(2), 68-80.
Sharma, A. (2011). Take-off of online
marketing: casting the next generation
strategies. Business
Strategy Series, 12(4), 202-208.
Singh, D. K., Kumar,
A. and Dash, M. K. (2016). Using analytic hierarchy process to
develop hierarchy
structural model of consumer decision making in digital market. Asian
Academy of Management Journal, 21(1), 111.
Sonia San, M. and Carmen, C. (2009). How perceived risk affects online
buying. Online
Information Review, 33(4), 629-654.
Tamimi, N. and Sebastianelli, R. (2015), The
relative importance of e-tailer website
attributes on likelihood of online purchase. Internet
Research, 25 (2), 69-183.
Thakur, R. and Srivastava, M. (2015), A study
of impact of consumer risk perception and
innovativeness on online shopping in India. International
Journal of Retail and Distribution Management, 43 (2), 148-166
Thamizhvanan, A. and Xavier, M.J. (2013),
Determinants of customers’ online purchase
intention: an empirical study in India. Journal
of Indian Business Research, 5 (1), 17-32.
Verma, V., Sharma, D. and Sheth, J. (2016). Does relationship
marketing matter in
online retailing? A meta-analytic approach. Journal
of the Academy of Marketing Science, 44(2), 206-217.
Vijay, T. S., Prashar, S. and Sahay, V.
(2019). The Influence of Online Shopping Values
and Web Atmospheric Cues on E-Loyalty:
Mediating Role of E-Satisfaction. Journal of theoretical and applied
electronic commerce research, 14(1), 1-15.
Wobker, I., Eberhardt, T. and Kenning, P.
(2015), Consumer confusion in German food
retailing: the moderating role of trust”, International
Journal of Retail & Distribution Management, 43(8), 752-774.
Wood, S. L. (2001).
Remote Purchase Environments: The Influence of Return Policy
Leniency on
Two-Stage Decision Processes. Journal of Marketing Research, 38(2),
157–169.
Xu, Y. and Paulins, V. A. (2005).
College students' attitudes toward shopping online for
apparel products: Exploring a rural
versus urban campus. Journal of Fashion Marketing and Management: An
International Journal, 9(4), 420-433.
-x-
|
Profile of Online Shoppers by Gender |
||
|
|
Sample |
% |
|
Male |
249 |
49.8 |
|
Female |
251 |
50.2 |
|
Total |
500 |
100 |
|
Profile of Online Shoppers by Age |
||
|
|
Sample |
% |
|
18-21 |
132 |
26.4 |
|
22-29 |
156 |
31.2 |
|
30-39 |
94 |
18.8 |
|
40 & above |
118 |
23.6 |
|
Total |
500 |
100 |
|
Profile of Online Shoppers by Marital
Status |
||
|
|
Sample |
% |
|
Married |
211 |
42.2 |
|
Unmarried |
286 |
57.2 |
|
Other |
3 |
0.6 |
|
Total |
500 |
100 |
|
Profile of Online Shoppers by Occupation |
||
|
|
Sample |
% |
|
Homemaker |
26 |
5.2 |
|
Self Employed
Professionals |
63 |
12.6 |
|
Service |
133 |
26.6 |
|
Business |
22 |
4.4 |
|
Students |
251 |
50.2 |
|
Retired |
5 |
1.0 |
|
Total |
500 |
100 |
|
|
||
APPENDIX A: PROFILE OF RESPONDENTS FOR SURVEY
|
Profile of Online Shoppers by Most Visited
Site in last 6 months |
||
|
|
Sample |
% |
|
Amazon |
246 |
49.2 |
|
Paytm |
40 |
8.0 |
|
Flipkart |
99 |
19.8 |
|
Myntra |
63 |
12.6 |
|
Others |
52 |
10.4 |
|
Total |
500 |
100 |
|
Profile of Online Shoppers by Age of Most
Visited Site |
||
|
|
Sample |
% |
|
Less than 6 months |
110 |
22.0 |
|
6 months to 1 yr |
155 |
31.0 |
|
1+
to 2 yrs |
101 |
20.2 |
|
More than 2 yrs |
134 |
26.8 |
|
Total |
500 |
100 |
Profile of Online Shoppers by Product Type bought most often, online
|
% |
|
|
Clothes and apparels |
34.0 |
|
Computers, Mobile and accessories |
15.8 |
|
Electronics, gadgets and appliances |
11.4 |
|
Books, Movies discs, Music discs &
Video Games |
11.4 |
|
Accessories such as – sports & fitness
related footwear, belts, bags etc |
7.2 |
|
Groceries |
7.2 |
|
Health & Beauty, Personal care |
3.8 |
|
Baby products |
3.2 |
|
Home decor, furniture and Kitchen |
2.0 |
|
Jewellery, Watches & Eye wear |
1.2 |
|
Others
|
2.8 |
