A-Study-of-the-Impact-of-Individual-Differences-on-Online-Shopping

PERCEPTION TOWARDS ONLINE SHOPPING: AN
EMPIRICAL STUDY OF INDIAN CONSUMERS
Zia Ul Haq
Assistant Professor, Central University of Kashmir, Jammu & Kashmir
Email: [e-mail]

ABSTRACT
Consumers are playing an important role in online shopping. The increasing use of Internet by the younger generation in India provides an emerging prospect for online retailers. If online retailers know the factors affecting
Indian consumers’ buying behaviour, and the associations between these factors and type of online buyers, then they can further develop their marketing strategies to convert potential customers into active ones. In this study four key dimensions of online shopping as perceived by consumers in
India are identified and the different demographic factors are also studied which are the primary basis of market segmentation for retailers. It was discovered that overall website quality, commitment factor, customer service and security are the four key factors which influence consumers’ perceptions of online shopping. the study revealed that the perception of online shoppers is independent of their age and gender but not independent of their education & gender and income & gender Finally, the recommendations presented in this research may help foster growth of Indian online retailing in future.

Keywords: Online Shopping, consumers, India, website, behaviour.
INTRODUCTION
Commerce via the Internet, or e-commerce, has experienced rapid growth since the early years. It is well known to most of the Internet researchers that, the volume of online business-to consumer (B2C) transactions is increasing annually at a very high rate.
According to ACNielsen (2007), more than 627 million people in the world have shopped online. Forrester (2006) research estimates e-commerce market will reach $228 billion in
2007, $258 billion in 2008 and $288 billion in 2009. By 2010 e-commerce will have accounted for $316 billion in sales, or 13 percent of overall retail sales. ACNielsen also reported that, across the globe, the most popular items purchased on the Internet are books
(34%), followed by videos/DVDs/games (22%), airline tickets/reservations (21%) and clothing/accessories/shoes (20%).Goecart forecasts that
US online population will increase nearly 50%, from 1471.5 million in 2001 to 210.8 million by 2006 (Cumulative Annual Growth Rate of 8.2%) and online retail sales will grow from
US$47.8 billion in 2002 to $130.3 billion in 2006. Similarly WIPO (2007) cited that about
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10% of the world’s population in 2002 was online, representing more than 605 million users.
Much research has been concentrated on the online shopping in the world. However, there is still a need for closer examination on the online shopping buying behaviour in developing countries like India. While both established and new, large and small scale businesses are now using the Internet as a medium of sales of their products and services (for example Dell computer, Amazon.com, in the world and jobstreet.com, rediff.com). Still there is a huge research gap that exists not only between countries, especially between developed and developing countries, which may differ significantly between countries (Stieglitz, 1998;
Shore, 1998; Spanos et al., 2002) that limit the generalization of research results from developed countries to developing country contexts (Dewan and Kraemer, 2000; Clarke,
2001). Shore (1998) and Stiglitz (1998) reported that implementation of information system depend on specific social, cultural, economic, legal and political context, which may differ significantly from one country to another country. Dewan and Kraemer (2000) and Clarke
(2001) argued in their study that findings from developed countries are not directly transferable to developing countries. Thus, this research is needed for non-transferability of findings from research in developed countries like India, china, Brazil etc and also for the improvement of understanding of the determinants of online shopping in developing countries. Online shopping holds a great potential for youth marketers. According to Vrechopoulos et al. (2001) youth are the main buyers who used to buy products through online. Dholakia and
Uusitalo (2002) study examined the relationship between age and Internet shopping; found that younger consumers reported more linen to the online shopping. Another study by Sorce et al. (2005) found that younger consumers searched for more products online and they were more likely to agree that online shopping was more convenient.
OBJECTIVE OF THE STUDY
1. To know the demographic profile of the customers and its impact if any on the online buying.
2. To know the factors affecting the perception of Indian online buyers.
FACTORS AFFECTING ONLINE SHOPPING
There are a number of streams of research that are relevant to this study. These include those addressing the factors that have significant effect on online shopping (Shergill and Chen,
2005;
Phau and Poon, 2002; Jarvenpaa and Todd, 1997; George, 2002a; George, 2004b; Ward and
Lee, 2000; Hellier et al., 2003). We identified the factors that were found t o be significant in previous research about online shopping. In this research we studied the four factors i.e. overall website quality, commitment, customer service and web security which is also defined by Shergill and Chen (2005) in their empirical study in New Zealand. Overall website quality of a web page is one of the most important factors that influence online shopping. Shergill and Chen, (2005) identified web site design characteristics as the dominant factor which influences consumer perceptions of online purchasing. By using a sample of 250 online shoppers, Ranganthan and Ganapathy (2002) found four key dimensions of online shopping namely web sites; information content, design, security and
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privacy. They concluded that, though all these dimensions have an impact on the purchase intention, security and privacy will have greater impact on the purchase intent of online buyers. Turban et al. (2002) argue that elegant design of web site will serve better to its intended audiences. According to Kin and Lee (2002) the web site design describes the appeal of the user interface design presented to customer and customers are willing to visit more often and stay longer with attractive web sites (Shaw et al., 2000). Following them, than and
Grandon’s (2002) study found that quality web site design is crucial for online shopping.
Commitment is one of the important factors that have the most influential effect on online shopping. Commitment is closely associated with risk since it is a measure of customers’ perceptions about whether or not merchants can be counted on to deliver on their promises
(Vijayasarathy and Jones, 2000). According to Jun et al. (2004) online consumers apparently want to receive the right quality and right quantity of items that they have ordered within the time frame, promised by the retailers, and they expect to be billed accurately. Accordingly, to be considered as reliable online service providers, must deliver the promised services within the promised time frame (van Riel et al., 2003).
Studies by Mayer et al., (1995) and Hoffman et al., (1999) reveal that trust and consumer motivation have significant relationships. Other studies found that a high level of trust by buyers stimulate favourable attitudes and behaviour (Anderson and Narus, 1990).
A consumer’s trust in an Internet store can be thought as the consumer’s trust directly in the store. Nevertheless, Hoffman et al (1999) argued that the effectiveness of third -party trust, certification bodies and the public key encryption infrastructure for ensuring financial security, are the central success factors for building consumer trust in Internet shopping. Kini and Choobineh (1998) suggested that trust in the Internet business is necessary, but not sufficient, for an Internet buying behaviour to take place. The consumer must also trust the transaction medium for online shopping. The review of empirical studies has embodied different factors which influence online purchasers’ behaviour. The antecedents of online purchase include many attitudinal components; for example, attitude towards online shopping and perceived risk of an online purchase. Consumers’ online shopping experiences, website and fulfilment of quality expectations are deemed as the major components to successful online transactions. In essence, our interest is in discovering the factors affecting consumers’ intent to buy online as well as in quantifying their relative importance.
Specifically, through surveying consumers based on the literature, we are interested in identifying and rank-ordering factors affecting intent to buy through online in India.
REVIEW OF LITERATURE
Benedict et al (2001) in his study on perceptions towards online shopping reveals that perceptions toward online shopping and intention to shop online are not only affected by ease of use, usefulness, and enjoyment, but also by exogenous factors like consumer traits, situational factors, product characteristics, previous online shopping experiences, and trust in online shopping.
GfK Group (2002) shows that the number of online shoppers in six key European markets has risen to 31.4 percent from 27.7 percent last year. This means that 59 million Europeans
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use the Internet regularly for shopping purposes. However, not only does the number of online shoppers grow, the volume of their purchases also increases over-proportionally.
Reinhardt and Passariello, (2002) In the US, says that online sales are forecasted to exceed
$36 billion in 2002, and grow annually by 20.9 percent to reach $81 billion in 2006.
Europeans are spending more money online as well. Whereas combined revenues for
Amazon.com’s European operations grew at more than 70 percent annually in each of the past three quarters, topping $218 million. While these figures show that a large number of consumers in the US and Europe frequently use the Internet for shopping purposes, it is not clear what drives them to shop online and whether these numbers could be even
Dabholkar and Bagozzi et al, (2002) O’Cass and Fenech, (2002); Childers et al., (2001);
Davis, (1993). Their study reveals that if more attractive online stores were developed. This raises the issue of examining what factors affect consumers to shop online. Therefore, a framework is needed to structure the complex system of effects of these different factors, and develop an in-depth understanding of consumers’ perceptions toward Internet shopping and their intentions to shop online.
Davis (1993) in his study reveals that we build up such a framework based on previous research on consumer adoption of new self-service technologies and Internet shopping systems. The research suggests that consumers’ perception toward Internet shopping first depends on the direct effects of relevant online shopping features.
Menon and Kahn, (2002); Childers et al., (2001); Mathwick et al., (2001) concluded that
Online shopping features can be either consumers’ perceptions of functional and utilitarian dimensions, like “ease of use” and “usefulness”, or their perceptions of emotional and hedonic dimensions like “enjoyment by including both utilitarian and hedonic dimensions, aspects from the information systems or technology literature, as well as the consumer behavior literature are integrated in our framework. Burke et al., (2002) In addition to these relevant online shopping features, also exogenous factors are considered that moderate the relationships between the core constructs of the framework.
Burke et al.,(2002); Relevant exogenous factors in this context are “consumer traits”
“situational factors” “product characteristics” “previous online shopping experiences” and
“trust in online shopping” By incorporating these exogenous factors next to the basic determinants of consumers’ perception and intention to use a technology, the framework is applicable in the online shopping context. Together, these effects and influences on consumers’ perception toward online shopping provide a framework for understanding consumers’ intentions to shop on the Internet.
Venkatesh (2000) online shopping “Computer playfulness” is the degree of cognitive spontaneity in computer interactions. Playful individuals may tend to underestimate the difficulty of the means or process of online shopping, because they quite simply enjoy the process and do not perceive it as being effortful compared to those who are less playful
“Computer anxiety” is defined as an individual’s apprehension or even fear when she/he is faced with the possibility of using computers. This influences consumers’ perceptions regarding the “ease of use” of the Internet as a shopping medium in a negative way, since using a computer is one of the necessary requirements for online shopping.
Zenithal et al., (2002) In addition to these four latent dimensions, “site characteristics” like
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search functions, download speed, and navigation, also play a role in shaping “ease of use.
But since these site characteristics merely influence the “ease of use” of a particular Web site or online store, and not the Internet as a shopping medium in general, we choose not to elaborate on these sites.
Hirschman and Holbrook, 1982; Babin et al., (1994),Next to the evidence for the critical role of extrinsic motivation for technology use, there is a significant body of theoretical and empirical evidence regarding the importance of the role of intrinsic motivation.
Holbrook, (1994). Intrinsic motivation for Internet shopping is captured by the “enjoyment” construct in our framework. Intrinsic value or “enjoyment” derives from the appreciation of an experience for its own sake, apart from any other consequence that may result.
Childers et al (2001) concluded that “enjoyment” results from the fun and playfulness of the online shopping experience, rather than from shopping task completion. The purchase of goods may be incidental to the experience of online shopping. Thus, “enjoyment” reflects consumers’ perceptions regarding the potential entertainment of Internet shopping found
“enjoyment” to be a consistent and strong predictor of attitude toward online shopping.
Menon and Kahn, 2002; Mathwick et al., (2001). Says that If consumers enjoy their online shopping experience, they have a more positive attitude toward online shopping, and are more likely to adopt the Internet as a shopping medium. In our framework, we identify three latent dimensions of “enjoyment” construct, including “escapism”, “pleasure”, and “arousal”
“Escapism” is reflected in the enjoyment that comes from engaging in activities that are absorbing, to the point of offering an escape from the demands of the day-to-day world.
“Pleasure” is the degree to which a person feels good, joyful, happy, or satisfied in online shopping. Menon and Kahn (2002). Whereas “arousal” is the degree to which a person feels stimulated, active or alert during the online shopping experience. A pleasant or arousing experience will have carry-over effects on the next experience encountered If consumers are exposed initially to pleasing and arousing stimuli during their Internet shopping experience, they are then more likely to engage in subsequent shopping behavior: they will browse more, engage in more unplanned purchasing, and seek out more stimulating products and categories. HYPOTHESES
On the basis of review of literature the following hypotheses has been set:H1a - Perception of online shoppers is independent of his Age and Gender.
H1b - Perception of online shoppers is independent of his Educational Qualifications &
Gender.
H1c - Perception of online shoppers is independent of his Income and Gender.
H2 - There is a significant relationship between overall website quality and online shoping.
H3 - There is a significant relationship between commitment and online shopping
H4 - There is a significant relationship between customer service and online shopping
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H5 - There is a significant relationship between website security and online shopping
RESEARCH METHODOLOGY
The data for the study was gathered through a structured questionnaire. All variables were operationalised using the literature on online shopping (Shergill and Chen, 2005; Jarvenpaa and Todd, 1997; Mayer et al., 1995; Hoffman et al., 1999; Kini and Choobineh, 1998; Kim and Lee, 2002; Than and Grandson, 2002; Jun et. al., 2004; van Riel et al., 2003). The first part of the questionnaire included questions about their demographic profile like age, education and income followed by Internet usage habits of the respondents such as where do they access the Internet, how frequent they browse Internet, how much time they spent, purposes for Internet use and how frequent the respondents buy products through online. The second part consisted of questions measuring all the variables including two questions which are used to measure the online shopping. All the questions were utilizing on a Likert scale ranging from 1= strongly disagree to 6 = strongly agree.
Measures
The commitment reported by Moore and Benbasat (1991) for the scale and Cronbach’s alpha for scale commitment obtained from our sample. The Cronbach alpha estimated for website security scale was 0.887, for website commitment scale it was 0.800, for overall we bsite quality scale it was 0.723, and for customer service scale it was 0.796. Commitment of our sample showed a reasonable level of commitment (a>0.70). Factor analysis also confirmed that the construct validity of scales could be performed adequately. The factor loading for al elements exceeded the minimum value of 0.4 considered for the study.
Factor Loading Value
Variables
Security
Security
Security
Security
commitment commitment commitment commitment commitment
Overall website quality
Overall website quality
Overall website quality
OWQ Overall website quality
Overall website quality
Customer service
Customer service
Customer service

Factor.1
.809
.776
.776
.601
.173
.207
.170
.131
.180
.212
.128
.202
.097
.046
.145
.167
.269

Factor.2
.146
.210
.210
.244
.596
.573
.570
.551
.507
.244
.093
.099
.273
.305
.218
.301
.245

Factor.3
.172
.137
.117
.223
.221
.141
.152
.216
.202
.623
.605
.601
.484
.443
.254
.288
.257

Factor.4
.107
.099
.120
.147
.068
.114
.236
.175
.097
.071
.142
.128
.103
.170
.751
.575
.490

Extraction method: Principal Axis Factoring. Rotation Method: varimax with Kaiser
Normalization.
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Data analysis
Demographic Profile of the Respondents
The following table reveals the Age, Gender, Education & Income details of respondents who uses internet.
Table 1. AGE
Particulars
Valid -40 years
Total

No. of respondents
16
148
28
8
200

Percentage
8.0
74.0
14.0
4.0
100.0

Table 2. Gender
Valid Male
Female
Total

Frequency
132
68
200

Percent
66.0
34.0
100.0

Table 3. Education Qualification
Valid Intermediate
Graduate
PG
Others
Total

Frequency
28
68
88
16
200

Percent
14.0
34.0
44.0
8.0
100.0

Table 4. Monthly Income
Valid < 5,000
5,001 to 10,000
10,001 to 15,000
15,001 to 20,000
20,001 and above
Total

Frequency
8
32
68
32
60
200

Percent
4.0
16.0
34.0
16.0
30.0
100.0

The above tables reveal that from the sample which we have collected, 66% are males and remaining 34% are females. As far as the age of the respondents are concerned 74% are between 18-29 years followed by 30-39 years with 14%. If we consider the educational qualifications 78% of respondents are postgraduates & graduates and only 14% are having intermediate. As far as their monthly income is concerned 34% are earning between 100 0115000 rupees followed by 30% with an income of 20000 plus per month.
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H1a- Perception of online shoppers is independent of his Age and Gender.
Table 5.
Gender
Male
Female
Total

Age
18-29
30-39
96
24
52
4
148
28

40
4
4
8

132
68
200

To test whether the age & gender have significant impact on internet usage for online shopping, chi-square test is conducted.
Table 6 . Age * Gender
Value
Df
Asymp.Sig.(2-sided)
Pearson Chi- Square
7.672
3
.053
The analysis reveals that the calculated value is 7.672. As the P-Value (Asymp. Sig 2 sided) is found to be 0.053. Hence hypothesis is accepted at 5% level of significance, so the perception of on-line shopping is independent to Age & Gender.
H1b- Perception of online shoppers is independent of his Educational Qualifications and
Gender.
Table 7.
Gender

Educational qualification