E-Retailing: An assessment of the Mauritian Consumers’ Behaviour towards On-line Shopping

Vanisha Oogarah-Hanuman and HOK YIN Michael Stephen

Abstract

This research is about the study of the Mauritian consumer behaviour and its influence on online-shopping adoption. It has been undertaken due to the hype and growing importance of E-retailing worldwide and in a view of ascertaining whether Mauritian consumers can meet the government vision to turn Mauritius into a Cyber-Island. A critical review of the literature has been conducted based on Constantinides‟ online consumer behaviour model. Data has been collected from journals, internet sources, books and research agencies findings. A questionnaire prepared based on the literature review has been administered to a sample of 150 respondents who have been chosen and segmented according to each district gender proportion. The analysis showed that most of the respondents were unaccustomed to E-shopping. The existing online-shoppers have a high intention of shopping again in the future. The non-shoppers were indifferent about E-shopping but they seemed strongly interested in its convenience benefits. Young, educated, modest income-earners and online-experienced males are the ideal target market. However, risk is a strong deterrent online while branding plays a critical role to counteract it. A good and simplified web atmosphere can ease new-shoppers online. However, the Mauritian population is not “online” ready due to the current offline-shopping culture, the lack of necessary infrastructure and connectivity and costs barriers. Recommendations have been made accordingly. The government has a major role to play in instilling this online-shopping culture, through investment in infrastructures and lowering of connectivity and costs barriers. It should also provide opportunities for the marketers to go online and promote E-commerce laws. The study results have been consistent with similar research on this domain but further analyses are needed to validate these findings and to explore this subject extensively.

Keywords: E-retailing, online shopping, E-consumer behaviour Field of Study: Marketing

1.0 Introduction The benefit of the internet as a strategic tool is so phenomenal that it has been a major catalyst to boost internet retailing in the 1990s. Amidst the hype of E-retailing, the dot-com bubble-burst in 2000, veered this mirage toward organisations‟ failures, such as Webvan (Ecommerce-Land, 2004). Yet only the fittest survived and since then, E-retailing has carried its legacy in the name of Amazon or Tesco; ______________________________ Vanisha Oogarah-Hanuman (Corresponding author), Lecturer in Strategic Management and Marketing, Faculty of Law and Management, University of Mauritius, v.hanuman@uom.ac.mu, Tel: 403-7524 HOK YIN Michael Stephen Fook Chong, Senior Auditor, Deloitte Ltd Mauritius, mikastefh@yahoo.com, Tel: 799-1107.

Hanuman & Stephen

2

and has evolved into a fruitful business opportunity worldwide. Nielsen‟s research (2008a) found that more than 85% of the world‟s on-line population has engaged in internet purchasing in 2008, boosting by 40% the usage rate during the past 2 years. Since the inception of the 24/7 culture in Mauritius and the government‟s aim to turn Mauritius into a Cyber-island (Goering, 2006), Information and Communication Technology (ICT) has become the fifth pillar of the economy (Allafrica.com, 2008) and E-retailing appears to hold some potential. A good definition of E-retailing is propounded by Harris and Dennis (2002, cited Dennis et al. 2004, p.2), as the selling of goods and services through the Internet or other electronic medium to consumers for personal or household use. Mauritius is not so technology-fusty nowadays. It bears some of the emerging local E-commerce examples in term of lepoint.mu or tantebazar.com. The promotion of myjob.mu might even sound familiar to any Mauritian. Online Business to Consumer (B2C) opportunities are increasing worldwide and Mauritian consumers might be engulfed in the E-retailing waves. Marketers must try to identify and anticipate this potential need through in-depth research on E-consumer behaviour. E-retailing will permit consumers to shop 24/7, with a considerable saving on time-consuming things (for example traffic jam). Shopping will be simple, accessible and quick to sustain customer‟s interest in this business (Hadjiphanis and Christou. 2006, p.4). The paper has been structured as follows: firstly we have a thorough literature review that critically examines the different factors affecting consumer behaviour as regards to on line shopping. Secondly, a methodology that describes the collection of data has been included together with highlights of limitations and thirdly key findings have been analysed and interpreted followed by some useful recommendations. Finally some future directions for research have been highlighted.

2.0 Literature Review Consumer behaviour is the successful marketers‟ passion and obsession. The consumer buying process forms the core of the consumer behaviour theory. It is composed of five stages (Kotler and Keller, 2006) (See Appendix-1). An insightful marketer knows that selling a product or service is not the final stage. With a satisfied post-purchase behaviour, the consumer may repeat purchase again and starts a fruitful relationship with the buyer in the long-term (Kotler and Keller, 2006). However, not all individuals engage themselves into such a technique systematically. It depends on the degree of the purchase involvement (Hibbard et al. 2008). Contrarily to habitual or low involvement purchase, a consumer will pass through a complex buying behaviour process for a higher involvement purchase, due to significant difference between brands, products, high risks and high financial requirement (Hibbard et al. 2008). High and low involvement purchases are inherent in online-shopping (Constantinides, 2004).

Hanuman & Stephen

3

Figure 1: The buying-process

Adapted from: Kotler and Keller, 2006. p.191.

Online-shopping has been on the lips of many researchers recently and has attracted numerous studies on online consumer behaviour. An outcome perhaps derived from the increasing popularity of internet usage which increased by 362.3% worldwide from 2000 to 2009 (Internet World Stats, 2009). These studies described important factors such as attitudes, perceptions and motivations, which can influence consumer behaviour and dictate the success or failure of internet marketing strategies (Goodwin, 1999). Therefore, understanding and predicting the customers‟ mindset is „not an option or a luxury‟ but it is „an absolute necessity for survival‟ (Chaffey and Smith, 2008, p.140). Contrary to traditional means, online customers are now empowered due to the information power shift to consumers (Pires et al. 2006, p.946). There has been a revolution from „information scarcity‟ to „information democracy‟ (Swahney and Kotler, 2001, cited Pires et al. 2006, p.939). Internet consumers have acquired a transcendental role to affect directly the outcome of any transaction and the firm‟s value creation (Hoffman and Novak, 1996; Weiber and Kollmann, 1998, cited Martínez-López et al. 2005 p.313). Indeed, online consumers have been empowered and their actions, if not monitored, can cause the collapse of an E-retailer. 2.1 Factors affecting E-consumer behaviour The stimulus-response model states that the marketing stimuli and the other stimuli will exert pressure on the consumer‟s psychology and characteristics, to affect the buying decision process and the purchase decision (Kotler and Keller, 2006). Constantinides (2004) further developed this model to incorporate the web experience element (figure 2). The latter comprises of functionality, psychological and content ingredients. The web experience is „the consumer‟s total impression about the online company‟ (Watchfire Whitepaper Series, 2000, cited Constantinides, 2004, p.113) and the marketer has direct control over it, to influence consumer behaviour (Constantinides, 2004). All the three factors mentioned in the model below will influence consumer behaviour and will need to be analysed separately.

Hanuman & Stephen

4

Figure 2: Online consumer behaviour

Source: CONSTANTINIDES., 2004.p113.

2.1.1 Other Stimuli: Psychological Factors Motivation, perception, learning, beliefs and attitudes are the four major psychological factors that can motivate a consumer to buy a product or service (Armstrong and Kotler, 2000). Motivation Motivation is the driving force to solve an unsatisfied need within an individual (Schiffman and Kanuk, 2000). Chaffey (2004, cited Chaffey and Smith, 2008) identified 6C‟s of online customer motivations:

Hanuman & Stephen

5

Figure 3 Motivation

Adapted from: Chaffey and Smith, 2008. p143.

Motivation to shop offline or online is mainly founded on hedonic (pleasure-oriented) and utilitarian (goal-oriented) factors (Babin et al. 1994.). Online-shopping confers less hedonic benefits (Dholakia and Uusitalo, 2002) and is thus a deterrent for individuals who requires high social interaction (Swaminathan et al. 1999). Conversely, goal-oriented individuals enjoy the freedom and control of the E-shopping (Bidgoli, 2003). However, hedonic consumers can be attracted online, especially to a specific class of products or hobby-related websites while goal-oriented consumers are attracted towards information, convenience, selection and control (Sorce et al. 2005). Perception Perception is the way someone perceives the world around him. It can be framed by the Technology of Acceptance model (Davis et al. 1989), which includes perceived ease of use and usefulness dimension, to determine information-technology adoption. Ease of use, which is based on hedonic factors, refers to the effortlessness of using a specific process - the internet - to produce an outcome (Monsuwé et al. 2004). Usefulness, which is based on utilitarian elements, refers to the benefits of using this process to arrive at the desired outcome (Monsuwé et al. 2004). Moreover, perception will be influenced by differences between costs, convenience, enjoyment and risks (Huang and Oppewal, 2006). However, perception can also vary among individuals, based on selective: attention, distortion or retention. (Kotler and Keller, 2006). For instance, uncertainty and risks can influence the perception of an individual and deter them online

Hanuman & Stephen

6

(Doolin et al. 2005). Perception will ultimately contribute to influence the attitude and intention to adopt E-shopping (Monsuwé et al. 2004). Attitudes and beliefs Attitudes and beliefs are acquired through experience and incidental learning (Westwood, 2004). There is increased intention to shop online with favourable attitudes for E-shopping (Hoffman et al. 1999). Li and Zhang (2002, p.512-513) summarised these attitude theories into six concepts:

1. Acceptability of the internet as a shopping channel (Jahng et al. 2001). 2. Attitudes of consumers toward an E-retailer. 3. Two kinds of consumers‟ perceived risk level (Lee et al. 2001):

a. Product or service specific (functional loss, financial loss, time loss, opportunity loss, and product risk).

b. Transaction context (privacy risk, security or non-repudiation). 4. Trust level toward the E-retailer. 5. Perceived behavioural control (Ajzen,1991). 6. Perceived real value-added from membership (Koufaris et al., 2002;

Cho et al., 2001, cited Li and Zhang, 2002, p.513). Experience Experience is acquired through learning and the latter is an ever-evolving process, spurred by motivation, response and reinforcement (Kotler and Keller, 2006). Adopting E-shopping depends on the consumer‟s online experience (Montoya-Weis et al. 2003, cited Martínez-López et al. 2005). Past positive online experiences reduce perceived risk level (Shim et al. 2001), reinforce behaviour (Dholakia and Uusitalo, 2002) and increase trust in the seller and its website (Urban et al., 2009). However, for illiterate consumers, learning to shop online demands lots of time and costs and as such, only those who are experienced will buy online (Ratchford et al. 2001, cited Monsuwé et al. 2004). 2.1.2 Personal Factors Age Hedonic and utilitarian motivations to shop online are higher in the young individuals than the older one (Dholakia and Uusitalo, 2002). Although young people are more optimistic in E-shopping, older ones buy more online (Donthu and Garcia, 1999). Conversely, Joines et al. (2003) argued that young individuals shop online more than older ones. Sorce et al. (2005) debated that previous studies have conflicting results about age differences determining E-shopping intention. However, their own studies showed a lower intention to shop online from the young people. Although old people buy more online, younger ones do more information search online. They proposed that people of different ages have different shopping tastes and product preferences. Nevertheless, Forrester Research (Modahl, 2000) stated that demographic factors like age are not as important as consumer‟s attitudes toward using a new technology. Gender Technology is believed to be a masculine world while shopping is considered a female job (Dholakia and Chiang, 2003). Therefore, males are more likely to adopt E-shopping than females do (Dholakia and Chiang, 2003). However, this online gender difference is decreasing considerably (Jayawardhena et al. 2007). For instance in 2009, 51.1% men to 48.9% women are internet-users in UK (Abrams, 2009). Even though men spend more per product purchase online, due to their product complexity (technological products), women have

Hanuman & Stephen

7

been found to spend on overall more than men do (Mastercard, 2008, cited ChinaView, 2008). As such, gender difference still exists as it influences online product preference (Dholakia and Chiang, 2003). Males exhibit a preference for new, technical and expensive products (for example electronic gadgets) while female prefer the contrary (for example clothing) (Dholakia and Chiang, 2003). Males are goal-oriented shoppers while females are social shoppers (Kurihara et al. 2007). The shift from offline to online shopping results into men‟s feeling changing from angst-ridden to feeling powerful and from prey to predator, while women feel empowered online and have a sentiment of conquering time, tasks and personal limitations (Rollins, 2005). Personality and Self-efficacy Monsuwé et al. (2004) stated that online personality is made up of expertise, self-efficacy and need for interaction. They attributed expertise to knowledge and skill of using a computer and the internet. Consumers with low online expertise will assess the costs and benefits before deciding to acquire more expertise or not, as its acquisition can be time consuming and costly. Although an individual have the necessary expertise, perception will influence E-shopping adoption as a low ease of use and usefulness of using the internet will deter them from using it (Monsuwé et al. 2004). Self-efficacy refers to one‟s confidence in his ability to achieve a specific goal successfully and as such, low self-efficacy consumers who lack confidence online, can only be convinced to shop online with a high ease of using an electronic medium (Monsuwé et al. 2004). Need for interaction refers to the importance of socialising when conducting a transaction (Dabholkar, 1996, cited Monsuwé et al. 2004). For instance, an individual with a low need for interaction can be tempted online. Lifestyle and values Lifestyle is a mixture of attitudes, interest and opinions forming part of a person‟s life (Kotler and Keller, 2006). Time constrained people, living a „wired lifestyle‟, have a better probability for E-shopping (Bellman et al. 1999). 2.1.3 Cultural influence and Social influence Cultural influence Culture is a way of thinking, behaving and feeling that is pervasive among members of a society (Thomas, 1997). It is also comprised of subculture like religions, nationalities, racial groups and geographical regions (Kotler et al., 1999, p.230). Social influence Social factors are composed of family, reference groups and social roles and statutes (Kotler and Keller, 2006, p.176). Reference group The reference group influence can take three forms:

Hanuman & Stephen

8

Figure 4 Reference-groups

Adapted from: Carmen, [no date].p.1137.

Virtual community allows the sharing of information across online consumers and it can act as a social motive to shop online, especially for socialisation seeking-shoppers (Allred et al. 2006). Family Family, friend, and colleagues are influential factors toward online-shopping, especially if these reference groups are E-shoppers themselves (Ramus and Nielsen, 2005). Offline-shopping is a woman‟s treasure as it gives her the opportunity for social interaction and reinforcement with her family (Dholakia, 1999). Therefore, this can deter women online. Shopping nurtures a loving relationship within a family (Miller, 1998) and the „internet edges out family time‟ (Mcgann, 2005). Conversely, time saved by E-shopping can be spent on social events with the family or friends (Ramus and Nielsen, 2005, p.351). Role and Status Parson (2002, cited Dennis et al. 2004) stated that individuals having a higher role, status and authority will be more likely to shop online. 2.1.4 Web experience factor Propounded by Constantinides (2004, p.113), web experience factor consists of three pillars: Functionality, Psychological and Content factors.

Hanuman & Stephen

9

Figure 5: Web Experience factors

Source: Constantinides, 2004, p.114.

Functionality Functionality is how efficiently the online environment serves the consumer and is sub-categorised further by usability and interactivity. Usability and Convenience Web-usability is the quality and effectiveness of the web in helping the user to reach his purpose with minimal efforts (Nah and Davis, 2002). Internet-shopping saves more time than brick and mortar stores (Alreck and Settle, 2002). According to Lohse and Spiller (1998), convenience is in term of:

Timely delivery: getting the product on time.

Ease and swiftness of ordering online.

Attractive online product display. Industry researches reveal that around 60-75% shopping carts are abandoned because of unclear and slow checking out procedures (Business Link, 2009), which defeat the convenience advantage of E-shopping. Moreover, convenience is a questionable point as generally, physical delivery is slow and prevents immediate receipt of certain type of goods and physical inspection is deficient (Bidgoli, 2003). Nielsen survey (2008b) showed that convenience, time saving, avoiding crowds and saving gas are among the top four reasons to shop online. Interactivity Interactivity is made-up of two dimensions (Constantinides, 2004):

Interactivity with the E-seller to customise products/services.

Interactivity with the web users is about availability of customer service. The latter is a highly sought element online (Ghose and Dou, 1998).

Effective after-sales services and lenient refund policies will help to reduce perceived risk level toward E-retailing (Singh, S. 2006). Forrester-Consulting (Internet Retailer, 2008) underlined how by constructing a flexible return policy, about 81% of shoppers would be tempted to buy online and would remain loyal to an E-retailer. However, E-retailers must also ensure that their interactivity is speedy (Novak et al. 2000) and that they provide customer reviews (Lim and Dubinsky, 2004). Nevertheless, some consumers regard absence of human interaction as imposing less pressure on them and less reliance on unhelpful or uninformed sale force (Bidgoli, 2003). Customisation

Hanuman & Stephen

10

can also be intruding and therefore marketers must „walk a fine line between personalisation and intrusion‟. (Bidgoli, 2003, p.275). Psychological Lee and Turban (2001, cited Monsuwé et al. 2004, p.114) stressed the powerlessness feeling of consumers online, as they are unable to inspect the goods physically or monitor financial and personal information. Vijayasarathy and Jones (2000) identified five risks dimension:

I. Economic risk – risk of making poor purchase. II. Social risk – social disapproval of shopping method.

III. Performance risk –performance lower than expected of product or service.

IV. Personal risk –risks like theft or fraud. V. Privacy risk – divulgation of private and personal data.

Trust is the confidence consumers have concerning future online store behaviour and despite online transaction vulnerability, they will continue purchasing from the E-retailer (Kimery and McCard, 2002). Reliability forms part of trust and integrates the reputation of the E-seller, online security provided and the effectiveness of the E-retailer in safeguarding privacy (Lim and Dubinsky, 2004). Reliable third party endorsement (for example Verisign) is a recent strategy used by firms to reduce perceived risk (Constantinides, 2004). Content factor Content factor includes aesthetics and marketing mix (Constantinides, 2004). Although individuals will regard website aesthetics differently, based on their different perceptions (Holbrook, 1999), a good website aesthetics, information design and information focus, can help to reinforce a website‟s credibility (Fogg et al., 2002). An attractive aesthetic will motivate consumers to stay beyond the average 10 minute basis, to hold positive feelings and increased spending (Bidgoli, 2003). 2.1.5 Marketing Mix Forming part of the online marketing strategy to attract consumers, are the four P‟s (Constantinides, 2004): product, price, fulfilment (place) and promotion. Product Familiarity and confidence, customer attributes and product characteristics are three factors in the Electronic-shopping test (E.S test), to assess the likelihood of success of selling a product online (Kare-Silver, 2001). Promotion Although there is limited research on the influence of online promotion on consumer behaviour (Constantinides, 2004), some of the E-promotional strategies which can help in building brand awareness and brand positioning, can be advertising, sales promotion, or social networking (Stokes, 2009). Nielsen‟s study (2008b) observed that E-promotion is the fifth reason that influences consumers to shop online. When people exhibit a positive attitude toward a retailer, they will be more inclined to welcome its advertisement (MacKenzie et al. 1986). Price Nielsen‟s study (2008b) recognised price as the sixth top reason to shop online. Motives to go online are often due to lower price or easier comparison (Constantinides and Geurts, 2005). Search speed and costs are reduced

Hanuman & Stephen

11

while product information and price can be compared with competitors in the cyber-world (Lim and Dubinsky, 2004). To reduce risks, some consumers can even accept a higher price (Kervenoael et al. 2006). However, some E-shoppers are less price-sensitive (Donthu and Garcia, 1999). In fact, service quality has usurped the importance of low price and web presence in the E-retailing world (Parasuraman et al. 2005). Numerous researches on internet-shopping have been done in developed countries. They have led to a common thinking about who is the ideal online-shopper, based on studies on their psychological, personal and cultural elements. Possible strategies by the marketers to attract this “ideal” shopper are known as the controllable factors, such as the four P‟s or the web experience determinants. However, fragile areas such as risks and trust have also been detected and marketers must juggle cautiously with them. This research has studied online consumer behaviour in the Mauritian context to demystify the Mauritian consumers‟ propensity to shop online.

3.0 Methodology E-retailers business opportunities are so vast that traditional retailer cannot ignore this anymore (Enders and Jelassi, 2000, cited Kennedy and Coughlan, 2006). This research will help answer the questions whether Mauritius is ready to catch this bandwagon of innovation and welcomes the Cyber-island vision. This study will definitely shed light on E-retailing opportunity in the local context and will ascertain the consumer behaviour towards innovative way of shopping. After all, “The way we choose and buy goods will, of course, evolve, as it always has...” (Anon, 2006, p.21), but is this „evolution‟ inherent in the Mauritian consumers? The research objectives of the study are:

To identify the readiness of Mauritians toward E-shopping

To identify the percentage of consumers familiar with online shopping

Determine consumer‟s behaviour towards E-shopping

Determine the security and risks implications of online shopping. Hypotheses: 1: Is there a relationship between psychological factors and the intention to shop online? 2: Is there a relationship between gender and perceived risks? 3: is there a relationship between gender and products preference 4: Is there a relationship between gender and social influence 5: is there a relationship between gender and the intention to shop online 6: Is there a relationship between risk and intention to shop online. This study was based on a cross-sectional descriptive research from a survey technique. The target population is the population of Mauritius. A mixture of sampling method had been chosen. Convenience-sampling (none-probability sampling) was used for the pre-test phase of the questionnaire construction. While a quota-sampling, which combines both subjectivity and probability (Singh, Y.K. 2006), was used in the main survey to distribute the sample according to each district gender proportion; due to cost, timing and practicality, a sample size of 150 individuals was deemed appropriate.

Hanuman & Stephen

12

After the data collection, the information had been edited, coded and tabulated to facilitate data input and data analysis. The use of Excel 2007 and SPSS 16 analytical tool had proven to be an excellent fusion to present the information clearly, understandably and accurately. Hypotheses had been tested through the statistical software.

4.0 Discussions and Findings 4.1 Demographics/ determine the readiness of Mauritian towards E-

shopping Internet Access About 90% of the respondents have an internet connection at home. 50% of them have been interviewed through an online-mail survey. Internet access has increased from 13% to 15%, after the recent decrease in internet connection cost and the rising availability of Internet Service Providers (ISP), which amount to nine in 2008 (CSO, 2009c). The average growth rate of internet access amounted to approximately 2% per year (CSO, 2009c). There was about 20% of households in Mauritius having internet access while 29% have a computer at home (CSO, 2009c), Computer and internet are only available to middle-income earners and above, as the lower-income-earners still find it expensive to buy or rent them respectively. Others stated they were unnecessary, as they see television as a better means of entertainment (CSO, 2009c). Internet growth rate is still low and to increase it, further reduction in internet connection cost and computer price are desired. Broadband (rapid) internet connection in Mauritius is about 78.9% among internet subscribers in 2008. However, the percentage of people having access to it is only 12% (CSO, 2009c). E-retailing normally needs a swift internet connection. The latter is more expensive than a dialup connection and this explains why people are more reticent to rent it. Purpose of using the internet The most common purpose of using the internet is checking email, as it is fast, has a worldwide reach and global popularity. Many respondents searched a lot of information online as the web holds lots of easily available information. Searching information involves lots of “surfing”, hence explaining the latter third position. E-banking and other purposes are low as few opportunities are available in Mauritius.

Hanuman & Stephen

13

Figure 6: Purpose of internet

The study came up with the figures below, for the percentage of respondents per purpose. It has been contrasted with a CSO study (2009c). The two studies showed relatively similar figures about the most popular internet purpose.

Table 1: Study v/s CSO

The CSO study involved people greater than 12 years old while the current study included those greater than 18 years old. This may explain why CSO having a larger denominator, sometimes had a more diluted percentage. The high entertainment purpose is because the internet itself is an entertainment tool and offers other opportunities as well (music, surfing), for each according to his/her taste. 4.2 Percentage of consumers familiar with E-shopping About 27% of the respondents have shopped online. The few internet access percentage among Mauritians hinders E-shopping. Moreover, the latter is still in an introduction stage of the product-life-cycle, as we still do not have an internet-shopping culture yet entrenched compared to developed countries. However, their shopping frequency revealed that the few people shopping online, do it regularly, as 40% of them have shopped more than 4 times. They are repeated and hence, experienced shoppers in this niche market as sometimes, E-shopping satisfies them better than offline-shopping, due to the benefits it provides.

Hanuman & Stephen

14

Figure 7: Shopped-online

Age Younger individuals (80% in the 18-35 age-bands) shopped online more than the older people. The internet and E-shopping were “born” recently; therefore, younger customers are earlier adopters and are more accustomed to using them.

Table 2: Age

Intention The buyers on average agreed to the idea of shopping again in the future. No one was less than indifferent about it. This indicates that E-shopping can satisfy customers with the benefits it procures and is thus sustainable.

Table 3: Intention

Income & Job status

Income range of “none” was excluded in the analysis. It is because the respondents have shopped through their families‟ earning and including them will distort the mean. Most of the shoppers‟ income-level fell around the Rs20,001–Rs30,000 range (mean=4). E-shopping demands an adequate amount of income status due to its cost requirements (for example hardware). Job occupation‟s average was “near” middle level, due to job position and income range relationship.

Hanuman & Stephen

15

Table 4: Income

Gender

The buyers were 70% male and 30% female, highlighting the strong prevalence of male in this cyber-field. Research has shown that males are earlier online-shopping adopters than women are, as they are more accustomed to new technology (Bidgoli, 2003, p.14).

Location

Most of the shoppers were from the districts of Plaines Wilhems and Port Louis, two Mauritian urban areas that are advanced in term of internet connectivity and economic development. There seemed to be a possibility of E-shopping in these kinds of areas. It should be noted that “Tantebazar.com” has been implemented in Plaines Wilhems.

Figure 8: District

4.3 Determine consumers’ behaviour toward E-shopping. Physiological

(a) Motivation Most respondents were motivated to all of the six stimuli of E-shopping, with the mean of each stimulus being greater than “indifferent” (more than 3). People were relatively motivated to shop online because of the different benefits it provides compared to offline-shopping.

Hanuman & Stephen

16

Table 0-5 Motivation

Convenience and the large number of products or services available, were among the strongest stimuli. Saving time and 24/7 accessibility, had motivated many of the respondents. Consistent with other studies, convenience was the highest motive to shop online, especially for goal-oriented shoppers (Kare-Silver, 2001). Individuals were motivated by larger panoply of offering as online-shopping is one of the main methods to acquire products/services not available in Mauritius and the internet enables online products comparison and better evaluation as well.

(b) Experience Experience is also linked to those people who had shopped online before (R.O-2). About 66% of the people use internet everyday or almost every day. The total percentage of people using the internet at least once per week was 82%. This is consistent with CSO (2009c) which had an 86% figure. The respondents seemed to be having a “wired lifestyle” and are experienced “surfer” (Bellman et al. 1999). They connect everyday for the purposes highlighted in R.O-1: information search or surfing.

Table 0-6 Internet-usage

Compatible to Montoya-Weis et al. (2003, cited Martínez-López et al. 2005), SPSS cross-tabulation revealed that the greater the internet usage, the greater the intention to shop online. It is due to the increased familiarity and ease of using the internet.

Hanuman & Stephen

17

Figure 9: Experience

(c) Attitude, beliefs and Perception The answerers considered online-shopping as not difficult to learn or to use (hedonic perception) (mean=2.52). However, although the mean of “E-shopping is value for money” (utilitarian perception) bended towards indifference (2.76), a significant percentage of the respondents, disagreed to this idea (43%).

Figure 10: Physiological

People generally did not think online-shopping is difficult as most of them are experienced internet-users. However, they were pessimistic about whether it could hold more value for money than offline-shopping. A pessimistic perception about E-shopping can be caused by a high-perceived additional cost or high-perceived risk, as can be seen by the high mean perception (4/5) that E-shopping is risky. Consistent with Dowling and Staelin (1994), this perceived risk appears to deter people online.

Hanuman & Stephen

18

Personal (a) Gender

Both genders did not stereotype the words “technology” and “shopping” to a specific sex. Therefore, contrary to Dholakia and Chiang (2003), men can sometimes be involved in shopping and female can be attracted toward technological innovation.

Figure 11: Gender-1

Conversely to Rollins (2005), men did not feel more powerful online than women did. Moreover, women did not have a higher view of E-shopping helping to conquer time limitation than men did. Both were indifferent about feeling powerful online. They either did not realise the consumer empowerment benefits that the internet offers (Pires et al. 2006) or they were not attracted to it. They were rather relishing mostly the convenience benefit as they unanimously agreed that E-shopping helps to conquer time limitation.

Hanuman & Stephen

19

Figure 12: Gender-2

While both sexes agreed that offline-shopping leads to better social interaction, a significant percentage of females strongly agreed about it. Females value social interaction more than men do. Men have a higher intention to shop online while females were mostly indifferent about it, because the latter valued social interaction and shopping as a family ritual, more highly than men did. These statements had been proved in Hypothesis-5 and Hypothesis-4. In contrast, Mastercard (2008, cited China View, 2008) found women are most likely to adopt E-shopping nowadays while men will spend more from it. The two studies difference lay with the divergence of demographics or cultural factors. 4.4 Determine the security and risks implication of E-Shopping of

existing & new shoppers. As can be seen in part “Attitude, beliefs and perception”, individuals agreed that E-shopping is risky. Risk itself is composed of many factors. By breaking it down, this study had pinpointed what are the most feared common risks and why individuals think it is risky. The ranking was done through the Friedman test, which is similar to one-way ANOVA. Its purpose is to measure subjects under different conditions (Pallant, 2005, p.296) or in this study, different risks conditions. Therefore, the ranking of the common fears of online-shopping, showed that individuals categorised them as follows:

Hanuman & Stephen

20

Table 7 Online-fears

People were considerably perturbed about the riskiness of E-payments, as they were very sensitive to financial risk and personal risk. They feared of losing their money or be involved in an online fraud. The new and unfamiliar environment amplifies this fear. Performance and economic risks were second, as respondents were concerned about the inability to inspect the product and thus the risk of making a poor purchase. These two fears are increased with a pricier purchase. Delivery risk was in term of personal risk involved of not receiving the products after being ordered. Privacy risk came fourth, as they feared their personal information could be exposed to other person, who can usurp their identity or defraud them. No after-sale service was fifth because respondents were more concerned about the risks that may occur by ordering online than the remedy to take after a risk has occurred. As for social risk, section “Error! Reference source not found.” proved that the interviewees were indifferent by their peers‟ online-shopping customs. Tackling risks Among the most prevalent means of tackling risk of E-shopping are after-sale service and law. This study has similar results to Internet Retailer (2008), as people were agreeable that this would encourage them to shop online. After-sale service will decrease post-purchase dissonance and will diminish perceived risk of a product being damaged. However, law has a greater influence in tackling risks (mean 3.93/5). A decent legislation with an online regulatory body protecting E-consumers, can reduce part of the risks although not completely, as perceived risks level depend on each individual perception. E-retailers will be afraid to cheat on consumers for fear of legal actions and the latter will have a greater sentiment of security.

Hanuman & Stephen

21

Figure 13 Tackling risk

Familiarity and reputation of the seller have a direct relationship with perceived risks. The more familiar and reputable is the E-seller, the less perceived risks of online-shopping. Similar to Park and Stoel (2005); as the customer gains positive experience and familiarity when trading with a trustful E-retailer, he/she will repeat purchase.

Figure 14: Brand and Risk

The respondents were indifferent about having a higher price in return for an increased online security. Better online security is a right that E-customers demand rather than to pay for it. Moreover, consumers can buy the same product offline without the increased price.

5.0 Recommendations The recommendation has been made for E-Marketers and for the government. For E-Marketers

Hanuman & Stephen

22

Rather than going full swing online, complementing traditional offline-selling with online-retailing, provides an opportunity to be the first-mover. Multi-channel is believed to be the future of retailing (Dennis et al. 2004). Multi-channel is useful for prudent marketers to test the market first before investing fully. Companies wishing to adopt E-commerce must first identify their needs, resources, objectives and opportunities before considering at what level they will operate. Full E-retailing is not a necessity. Amazon was not built in a day. There are four levels of online playing field (Sweeney, 2007). Companies can either enter directly a specific level or adopt a systematic approach to implementing E-retailing.

Figure 15: Start-up

Adapted from: SWEENEY, 2007. p.12-17.

The target consumers must be the young population (18-35 age-ranges) as they are more receptive to innovation. Older consumers will require more convincing. Males have a higher shopping potential than females and thus, can be easier to target. By promoting convenience and saving time benefits, females can discover that E-shopping can allow them more time to spend with their family. E-shopping can be useful for some types of products while social shopping can be left for other products. As such, females may be attracted by products that they do not like to “social-shop” for, or by selling appealing, but unavailable products in Mauritius, at better cost than offline. Marketers can also offer virtual communities, websites and products customised for each gender to attract them. Risk & cooperation with other institutions Product and delivery risk can be decreased by providing an efficient after-sale service, perhaps at no extra cost. E-vendors should provide solid security and display these certificates of securities on their websites to reassure consumers (Allred et al. 2006). They should register themselves to reliable third-party endorsement, such as E-payment security system (for example “Verified by Visa”) and work with banks to ensure a more efficient and safer financial transaction. Marketers should provide online guarantees, attractive insurance package, customer reviews and fraud protection programme to reduce financial risks and purchase dissonance (Allred et al. 2006). Escrow-

Hanuman & Stephen

23

service, a regulated body acting as a financial intermediary between buyer and seller, can also help to reduce risk (eBay, 2009). Web Experience The website design and ordering process should be attractive, user-friendly, simplified and can be navigated swiftly. Availability of customer service and customer rating system can help to reduce risk. To reduce abandoned shopping carts syndromes and increase web experience, the following steps are required:

Figure 16 Web Experience

Adapted from: Chaffey, 2008. p.270.

The government can: Increase connectivity Government must reduce internet connection cost (for example removing VAT). Allowing more competitors like multinationals to enter in the telecommunication market and preventing price collusion, can urge firms to be competitive and offer quality or best-cost services. The government must also support these companies through subsidies and other methods as well. Few percentage of the population have accessed to a broadband connection. The latter is important for E-retailing. The government should increase the quality of internet connectivity by building appropriate infrastructures such as: more quality and efficient connection wires, ensures swifter connection, adequate internet coverage or promotes the use of wireless connection. If necessary, bank financing and public-private collaboration to construct these infrastructures can be another option. Decrease the cost of buying a computer Nowadays, education and computers are intertwined. For instance, obtaining a university degree is impossible without the use of computers. Government should help decrease the price of computers by removing tax from it, by

Hanuman & Stephen

24

providing subsidies to family wishing to acquire it or allowing more competitors in the market. Second-hand computer sales can also be promoted at cheaper price. Government can reap two results from this ambition: increase education level and enhance the chance for the population to have a medium through which to connect to the internet. Develop an internet-culture The Mauritius computer and internet literacy rate should be improved. Training should be given to the population and the students about how to use the internet and the security issues involved online. There must be encouragement of more parastatal body to trade online, like MRA E-Filing (MRA, 2009) and frequent trade-shows such as InfoTech, can be mobilised. Promulgate E-commerce law The government must provide adequate protection to consumers through E-commerce laws to boost their confidence to go online. Consumers will feel protected, while E-retailers would think twice before abusing of consumers‟ trust. Creating an online regulatory body is essential to ensure that the laws are enforced. Moreover, laws should be amended to enable companies to trade online easily and to protect and regulate them.

6.0 Conclusion We can conclude that all of the study‟s research questions were answered and its results were mostly consistent with other researches. However, more analysis of the Mauritian market is necessary to validate these findings. According to this study, the internet culture is still an infant in Mauritius and not all consumers might be “online” ready. The future of E-commerce and the Cyber-island vision lie mostly in the hand of the government. Only if it provides the necessary ingredients, can marketers follow suit. Presently, there are some possibilities of online niche marketing especially among the young, educated, online-experienced and male shoppers. The living local examples in the likes of tantebazar.com, lecygne.com or the emerging one like lexpressproperty.com, prove that E-retailing can be sustainable in Mauritius. In a world of intense competition, “Blue-Ocean” opportunities (Chan Kim and Mauborgne, 2005) exist. However, not everything that shines is gold and companies should not be bewitched by E-retailing‟s hype. Rushing online blindly can lead to another dot-com bubble burst. Mauritius is a small island but Singapore is smaller than it is. Yet, the latter is more developed and has many B2B and B2C E-commerce opportunities. The internet is not a revolution but an evolution of our traditional way of shopping. With the advancement of the 24/7 culture; more and more demanding working life; long end of month queues at hypermarkets; endless traffic jam and the government intention to implement “E-government Gateway”, it is surely not the last of what we will hear about E-retailing.

Limitation & future avenues for this research This study has been conducted under limited time, human and financial resources and therefore, generalisation of these results can be erroneous. Therefore, this research can be improved in terms of better sampling techniques.

Hanuman & Stephen

25

Potential future avenues for research:

Figure 17: Future Direction

References ABRAMS, K.V., 2009. UK men Online [Online], Available from: http://www.emarketer.com/Report.aspx?code=emarketer_2000571 [Accessed on 26 April 2009]

AJZEN, I., 1991. The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.

AJZEN, I., AND FISHBEIN, M., 1980. Understanding attitudes and predicting social behavior. Facsimile ed. NJ: Prentice-Hall.

ALLAFRICA.COM, 2008. Mauritius Cyber Island Strategy [online]. Available from: http://allafrica.com/stories/200806250953.html [Accessed 05 May 2009].

ALLRED, C.R., ET AL. 2006. E-Shopping lovers and fearful conservatives: a market segmentation analysis. International Journal of Retail & Distribution Management, 34 (4/5), 308-333.

ALRECK, P., AND SETTLE, R., 2002. The hurried consumer: time-saving perceptions of internet and catalogue shopping. Journal of Database Marketing, 10(1), 25-35.

ANON, 2006. Tesco drives e-retail message home: But “Bricks and Mortar” stores continue to thrive. Strategic Direction, 22(3). 21-24.

ARMSTRONG, G. AND KOTLER, P., 2000. Marketing. 5th ed. New Jersey: Prentice-

Hall.

BABIN, B. ET AL. 1994. Work and/or fun: measuring hedonic and utilitarian shopping value. Journal of Consumer Research, 20, 644-656.

BANKRATE.COM, 2007. Watch out for debit card pitfalls [Online]. Available from: http://articles.moneycentral.msn.com/Banking/CreditCardSmarts/DebitCardsTooRiskyForBigPurchases.aspx [Accessed 24 October 2009].

BELLMAN, S., ET AL. 1999. Predictors of Online buying behavior. Communications of the ACM, 42(12), 32-38.

BIDGOLI, H., 2003. The internet Encyclopedia. 1st volume. 1st ed. John Wiley & Son.

BIGNE, E., ET AL. 2005. The impact of internet user shopping patterns and demographics on consumer mobile buying behavior, Journal of Electronic Commerce Research, 6(3), 193-209.

BIZLED, [ca.2009]. Strategic Planning: Tesco – Activity [Online]. Available from: http://www.bized.co.uk/educators/16-19/business/strategy/activity/strategic1.htm [Accessed 17 October 2009]

Hanuman & Stephen

26

BURKE, R.R., 2002. Technology and the customer interface: what consumers want in the physical and virtual store. Journal of the Academy of Marketing Science, 30(4), 411-432.

BUSINESS LINK, 2009. Common E-commerce pitfalls [Online]. Available from: http://www.businesslink.gov.uk/bdotg/action/detail?r.l1=1073861197&r.l3=1075387751&r.lc=en&r.t=RESOURCES&type=RESOURCES&itemId=1075390287&r.i=1075390268&r.l2=1073866263&r.s=m [Accessed 23 April 2009].

CARMEN, P., [no date]. Considerations about group influences on Consumer Behavior [Online]. Available from: http://steconomice.uoradea.ro/anale/volume/2008/v4-management-marketing/209.pdf [Accessed on 29 April 2009].

CENTRAL STATISTIC OFFICE., 2009a. National Accounts Estimates (2006-2009): September 2009 Issue [Online]. Available from: http://www.gov.mu/portal/goc/cso/ei794/toc.htm [Accessed 10 October 2009].

CENTRAL STATISTIC OFFICE., 2009b. Digest of Demographic Statistics 2008 [Online]. Available from: www.gov.mu/portal/goc/cso/report/natacc/demo08/demo08.pdf [Accessed August 2009].

CENTRAL STATISTIC OFFICE., 2009c. Economic and Social Indicators Information and Communication Technologies (ICT) statistics – 2008 [Online]. Available from: http://www.gov.mu/portal/site/cso/menuitem.9b334e8917f863ce965c062ca0208a0c/?content_id=1468ee3a2fc03210VgnVCM1000000a04a8c0RCRD [Accessed 24 September 2009].

CENTRAL STATISTIC OFFICE., 2009d. Mauritius in figures 2008 [Online]. Available from: http://www.gov.mu/portal/site/cso/menuitem.19621772f6bc90fe965c062ca0208a0c/?content_id=96a5cdb1eaa91210VgnVCM1000000a04a8c0RCRD [Accessed 16 July 2009].

CHAFFEY, D., AND SMITH, P.R., 2008. eMarketing eXcellence: Planning and optimizing your digital marketing. 3

rd ed. Slovenia: Butterworth-Heinemann.

CHAN KIM, W., AND MAUBORGNE, R., 2005. Blue Ocean Strategy: How to Create Uncontested Market Space and Make Competition Irrelevant. 1st ed. Boston: Harvard Business Press.

CHEUNG, M.K.C., ET AL. 2003. Online Consumer Behavior: A review and Agenda for Future Research. 16

th Bled eCommerce Conference eTransformation.194-218.

CHINAVIEW, 2008. Survey: More women than men shopping online [Online]. Available from: http://news.xinhuanet.com/english/2008-09/22/content_10093155.htm [Accessed on 27 April 2009].

CONSTANTINIDES, E., 2002. The 4S Web-Marketing Mix model, Electronic Commerce Research Applications, 1(1),57-76.

CONSTANTINIDES, E., 2004. Influencing the online Consumer‟s behavior: The web experience. Internet Research, 14(2), 111-126.

CONSTANTINIDES, E., AND GEURTS, P., 2005. The impact of web experience on virtual buying behaviour: An empirical study. Journal of Customer behaviour, 4, 307-336.

DAVIS, F.D, ET AL. 1989. User Acceptance of Computer Technology: A Comparison of two theoretical models. Management Science, 35(8), 982-1003.

DENNIS, C. ET AL. 2004. E-retailing. 1st ed. Abingdon: Routledge.

DHOLAKIA, R.R., 1999. Going Shopping: Key determinants of Shopping behaviours and motivations, International Journal of Retail & Distribution Management, 27 (4). 154-165.

DHOLAKIA, R.R, AND CHIANG, K.P, 2003. Shoppers in Cyberspace: are they from Venus or Mars and does it matter?. Journal of Consumer Psychology [Online], 13(1/2), Available from: http://ritim.cba.uri.edu/working%20papers/Shopping%20in%20Cyberspace-Paper%20-final_10_171.pdf [Accessed on 27 April 2009].

DHOLAKIA, R.R, AND UUSITALO, O., 2002. Switching to electronic stores: consumer characteristics and the perception of shopping benefits. International Journal of Retail & Distribution Management, 30(10), 459-469.

DILORIO, C.K., 2005. Measurement in health behavior: methods for research and education. 1st ed. San Francisco: John Wiley and Sons.

DONTHU, N., AND GARCIA, A., 1999. The internet shopper. Journal of Advertising Research, 39 (3), 52-58.

DOOLIN, B., ET AL. 2005. Perceived Risk, the internet shopping experience and online purchasing behavior: A New Zealand Perspective. Journal of Global Information Management, 13(2). 66-88.

DOWLING, G.R, AND STAELIN, R., 1994. A model of Perceived Risk and intended risk-handling activity. Journal of Consumer Research, 21(1), 119-134.

Hanuman & Stephen

27

EBAY, 2009. Using escrow services [Online]. Available from: http://pages.ebay.com/help/pay/escrow.html [Accessed 21 October 2009]

ECOMMERCE-LAND, 2004. History of Ecommerce [online]. Available from: http://www.ecommerce-land.com/history_ecommerce.html [Accessed 05 May 2009].

EL SAID, G.R., AND GALAL-EDEEN, G.H., 2009. The role of culture in E-commerce use for the Egyptian consumers. Business Process Management Journal, 15(1), 34-47.

FOGG, B.J., ET AL. 2002. How do people evaluate a web site‟s credibility? Results from a large study. Consumer Report WebWatch Research Report [Online]. Available from: http://www.consumerwebwatch.org/dynamic/web-credibility-reports-evaluate-abstract.cfm [Accessed on 29 April 2009].

GAY, R. ET AL. 2007. Online Marketing: A Customer-led Approach. 1st ed. USA:

Oxford University Press.

GHOSE, S., AND DOU, W., 1998. Interactive functions and their impacts on the appeal of internet presence sites. Journal of Advertising research, 38(2), 29-43.

GOERING, L., 2006. Mauritius set to become First „Cyber Island‟ [online]. Available from: http://www.govtech.com/gt/articles/98096 [Accessed 05 May 2009].

GOODWIN, T., 1999. Measuring the effectiveness of online marketing”. Journal of the Market Research Society, 41 (4). 403-407.

GRADIRELAND.COM., [No date]. Types of Retailer [Online]. Available from: http://gradireland.com/retail/articleview-29a_596s_27.aspx [Accessed 13 October 2009].

HADJIPHANIS, L. AND CHRISTOU, L., 2006. The role of E-Commerce on Consumer Behaviour. Journal of Business Administration Online, 5 (1),1-8.

HARTMAN, P., AND DESJARDINS, J., 2008. Business Ethics: Decision Making for Personal integrity and Social responsibility. International ed. Singapore: McGrawHill.

HIBBARD, J.D. ET AL. 2008. Marketing, Encyclopedia Britannica 2008 Ultimate Reference Suite.

HOFFMAN, D.L, ET AL. 1999. Building Consumer Trust Online. Communications of the ACM. 42(4), 80-85.

HOFSTEDE, G., 2001. Culture‟s Consequences. 2nd

ed. Thousand Oaks: Sage Publications.

HOLBROOK, M.B., 1999. Consumer Value: A framework for analysis and research, 1

st ed, Routledge.

HUANG, Y., AND OPPEWAL, H., 2006. Why consumers hesitate to shop online: An experimental choice analysis of grocery shopping and the role of delivery fees. International Journal of Retail & Distribution Management, 34 (4/5),334-353.

INTERNET RETAILER, 2008. 81% of shoppers are more likely to buy online with easy returns, study says [Online]. Available from: http://www.internetretailer.com/dailyNews.asp?id=28349 [Accessed on 29 April 2009].

INTERNET WORLD STATS, 2009. World Internet Users and Population Stats [Online]. Available from: http://www.internetworldstats.com/stats.htm [Accessed 22 October 2009].

JAHNG, J., ET AL. 2001. The Impact of electronic commerce environment on user behavior. E-Service Journal, 1 (1), 41-53.

JARVENPAA, S.L., ET AL. 2000. Consumer trust in an Internet Store. Information Technology and Management, 1, 45-71.

JAYAWARDHENA, C., ET AL. 2007. Consumers online: intentions, orientations and segmentation. International Journal of Retail & Distribution Management, 35(6), 515-526.

JOINES, J., ET AL. 2003. Exploring motivations for consumer web use and their implications for e-Commerce. Journal of Consumer Marketing, 20(2). 90-108.

KANTOR, A., 2007. Internet suffering from information overload [Online]. Available from: http://www.usatoday.com/tech/columnist/andrewkantor/2007-06-14-internet-organization_N.htm [Accessed 22 October 2009].

KARE-SILVER, M., 2001. E-Shock The new rules: E-Strategies for retailers and manufacturers. 3

rd ed. London: Palgrave.

KENNEDY, A., AND COUGHLAN, J., 2006. Online Shopping Portals: An option for traditional retailers?. International Journal of Retail & Distribution Management, 34(7),516-528.

KERVENOAEL, R.D., ET AL. 2006. Exploring value through integrated service solutions: The Case of E-grocery Shopping. Managing Service Quality, 16(2),185-202.

Hanuman & Stephen

28

KIM, J. AND PARK, J., 2005. A consumer shopping channel extension model: attitude shift toward the online store. Journal of Fashion Marketing and Management, 9(1),106-121.

KIMERY, K.M., AND MCCARD,M., 2002. Third-party Assurances: mapping the road to trust in E-retailing. Journal of Information Technology Theory and Application, 4(2), 63-82.

KLEIN, L.R., 1998. Evaluating the potential of interactive media through a new lens: search versus experience goods. Journal of Business Research, 41(3), 195-204.

KORNUM, N., AND BJERRE, M., 2005. Grocery E-commerce: consumer behaviour and business strategies, illustrated ed. Edward Elgar Publishing.

KOTLER, P. AND KELLER, K., 2006. Marketing Management, 12th ed. New Jersey:

Pearson.

KOTLER, P., ET AL. 1999. Principles of Marketing. 2nd

ed. New Jersey: Prentice Hall.

KURIHARA, Y. ET AL. 2007. Information technology and economic development. illustrated ed. Hershey: Idea Group Inc.

LARDINOIS, F., 2009. Young families are the real early adopters [Online]. Available from: http://www.readwriteweb.com/archives/young_families_are_now_the_early_adopters.php [Accessed 17 October 2009].

LEE, D., ET AL. 2001. On the Explanation of factors affecting E-commerce adoption. Proceedings of the 22

nd International Conference on Information System, 109-120.

LEGGATT, H., 2007. Online business not geared towards the social shopper [Online]. Available from: http://www.bizreport.com/2007/06/online_business_not_geared_towards_the_social_shopper_3.html [Accessed 14 October 2009].

LI,N., AND ZHANG, P., 2002. Consumer online Shopping Attitudes and Behavior: An Assessment of Research. Eight Americas Conference on Information System, 508-517.

LIM, H., AND DUBINSKY, A.J., 2004. Consumers‟ perceptions of E-shopping characteristics: an expectancy-value approach. Journal of Services Marketing, 18(7), 500-513.

LOHSE, G.L., AND SPILLER, P., 1998. Electronic shopping: how do customer interfaces produce sales on the internet?. Communications of the ACM, 41(7), 81-87.

MACKENZIE, S.B., ET AL. 1986. The role of attitude toward the ad as an mediator of advertising effectiveness: a test of competing explanations. Journals of Marketing Research, 23(2), 130-144.

MARCZYK, G., ET AL. 2005. Essentials of Research Design and Methodology. 1st

ed. New Jersey: John Wiley & Sons Inc.

MARTíNEZ-LÓPEZ, F.J. ET AL. 2005. Online Shopping, the standard learning hierarchy, and consumers‟ internet expertise: An American-Spanish Comparison. Internet Research, 15(3), 312-334.

MAURITIUS CHAMBER OF COMMERCE AND INDUSTRY, 2009. Economy in figures [Online]. Available from: http://www.mcci.org/economy_figures.aspx#employment [Accessed 10 October 2009].

MAURITIUS REVENUE AUTHORITY (MRA), 2009. E-filing centres [Online], Available from: http://www.gov.mu/portal/sites/mra/efiling.htm [Accessed 21 October 2009]

MCGANN, R., 2005. Internet edges out family time more than TV time [Online]. Available from: http://www.clickz.com/3455061 [Accessed on 29 April 2009].

McGOLDRICK, P.J., 2003. Retailing. In: BAKER, M.J., 5th ed. The Marketing Book.

Burlington, MA : Butterworth-Heinemann, 776-804.

MILLER, D., 1998. A theory of Shopping. London: Polity Press.

MIN, S., AND WOLFINBARGER, M., 2005. Market share, profit margin and marketing efficiency of early movers, bricks and clicks, and specialists in e-commerce. Journal of Business Research, 58, 1030-1039.

MISHRA, S., AND OLSHAVSKY, R.W., 2005. Rationality unbounded: the internet and its effect on consumer decision making. In: HAUGTVEDT, C.P. ET AL. 1

st ed. Online

consumer psychology: Understanding and influencing consumer behavior in the virtual world. New Jersey: Lawrence Erlbaum Associates. 361-378.

MODAHL,M., 2000. Now or Never: How Companies must change to win the battle for the internet Consumer. 1

st ed. New York: Harper Business.

MONSUWÉ, T.P, ET AL. 2004. What drives consumers to shop online? A literature review. International Journal of Service Industry Management, 15(1), 102-121.

Hanuman & Stephen

29

NAH, F.F.H, AND DAVIS. S., 2002. HCI Internet research issues in E-Commerce. Journal of Electronic Commerce Research, 3(3).98-113.

NAOUM, S.G, 2007. Dissertation Research and Writing for Construction Students. 2nd

ed. UK: Butterworth-Heinemann.

NIELSEN., 2008a. Nielsen Reports 875 Million Consumers have shopped online [online]. Available from: http://nz.nielsen.com/news/OnlineShopping.shtml [Accessed 15 March 2009].

NIELSEN., 2008b. Holiday Shoppers continue to shift purchases online for convenience [Online]. Available from: http://blog.nielsen.com/nielsenwire/consumer/holiday-shoppers-continue-to-shift-purchases-online-for-convenience/ [Accessed 15 March 2009].

NIELSEN., 2008c. Trends in Online shopping: a global Nielsen Consumer report [Online]. Available from: http://th.nielsen.com/site/documents/GlobalOnlineShoppingReportFeb08.pdf [Accessed 20 October 2009].

NOVAK, T., ET AL. 2000. Measuring the customer experience in online environments: A structural modeling approach. Marketing Science, 19(1), 22-42.

PALLANT, J., 2005. Survival Manual: A step by step guide to data analysis using SPSS for Windows (Version 12). 2nd ed. Sydney: Allen & Unwin.

PARASURAMAN, A., ET AL. 2005. A multiple-Item Scale for assessing electronic service quality. Journal of Service Research, 213-233.

PARK,J., AND STOEL, L., 2005. Effect of a brand familiarity, experience and information on online apparel purchase. International Journal of Retail & Distribution Management, 33 (2),148-160.

PIRES, G.D. ET AL. 2006. The internet, consumer empowerment and marketing strategies. European Journal of Marketing, 40 (9-10). 936-949.

POH-KAM, W., AND YUEN-PING, H., 2004. E-Commerce in Singapore: Impetus and Impact of Globalization. Center for Research on Information Technology and Organizations [Online]. 342. Available from: http://repositories.cdlib.org/crito/globalization/342/ [Accessed 09 October 2009].

RAMUS, K., AND NIELSEN, N.A., 2005. Online Grocery retailing: What do consumers think?. Internet Research, 15(3).335-352.

RICKER, F.R., AND KALAKOTA, R., 1999. Order Fulfillment: the hidden key to E-commerce success. Supply Chain Management Review, 60-70.

ROLLINS, N., 2005. Gender Matters: She clicks differently from how he clicks-but most e-retailers haven‟t figured it out yet [Online]. Available from: http://www.internetretailer.com/internet/marketing-conference/35722-gender-matters.html [Accessed on 27 April 2009].

SCHIFFMAN, L., AND KANUK, L.L, 2000. Consumer Behaviour. 7th ed. India:

Prentice-hall.

SHARED SKILLS, 2002. Electronic Shopping [Online]. Available from: http://www.sharedskills.com/electronic_shopping.html [Accessed on 24 April 2009].

SHIM, S., ET AL. 2001. An online pre-purchase intentions model: the role of intention to search. Journal of Retailing, 77, 397-416.

SINGH, S., 2006. Cultural differences in, and influences on, consumer‟s propensity to adopt innovations. International Marketing Review, 23(2), 173-191.

SINGH, Y.K., 2006. Fundamental of Research Methodology and Statistics. 1st ed.

New Delhi: New Age International.

SORCE, P., ET AL. 2005. Attitude and age differences in online buying. International Journal of Retail & Distribution Management, 33(2).122-133

STOKES, R., 2009. eMarketing: The Essential Guide to Online Marketing. 2nd ed. South Africa: Quirk eMarketing (Pty) Ltd.

SWAMINATHAN, V., ET AL. 1999. Browsers or buyers in cyberspace? An investigation of factors influencing electronic exchange. Journal of Computer-Mediated Communication, 5(2). Available at: http://jcmc.indiana.edu/vol5/issue2/swaminathan.htm [Accessed on 25 March 2009].

SWEENEY, S., 2007. 101 Internet Businesses you can start from home: How to choose and build your own successful e-Business. 2nd ed. Florida: Maximum Press.

THOMAS, S., 1997. Migrations and cultures: A world view. 1st ed. New York: Basic

Books.

Hanuman & Stephen

30

URBAN, G.L., ET AL. 2009. Online trust: State of the art, new frontiers, and research potential. Journal of Interactive Marketing,179-190.

VENKATESH, V. ET AL. 2003. User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425-478.

VIJAYASARATHY, L.R., AND JONES, J.M., 2000. Print and internet catalog shopping: assessing attitudes and intentions. Internet Research, 10(3), 191-202.

WESTWOOD,P., 2004. Learning and learning difficulties: a handbook for teachers. 1

st ed. Acer Press.

YAHOO RESEARCH, 2007. Research Online, Buy offline: The Impact of Online Pre-Shopping on Consumer Shopping Behavior [Online]. Available from: http://us.i1.yimg.com/us.yimg.com/i/us/ayc/pdf/aa_insights_robo.pdf [Accessed on 28 April 2009].

ZIKMUND, W.G., 2000. Business Research Methods. 6th ed. Chicago: The Dryden

Press.