To what extent will age, gender and income of consumers influence online purchase behavior of clothing

(1)

0 | P a g e

MASTER THESIS

To What Extent Will Age, Gender And Income of Consumers Influence Online Purchase Behavior of Clothing.

Faculty of Economics and Business MSC Business Studies

Track: Marketing

Supervisor: Antoon Meulemans

By:

Yvette Groskamp 5623049

(2)

(3)

2 | P a g e

Abstract

Online consumer behavior can depend on many different variables, some of which have often been researched, but rarely together. For this research a selection of variables was chosen to build a framework to analyse the attitudes towards online shopping. Age, income and gender were selected because they have often been researched separately in relation to online behav-ior, but rarely together.

A literature review was conducted to ascertain what was researched before and how a quanti-tative study should be designed. The quantiquanti-tative study examined online shopping as a pro-cess that involves searching for information search, recognising problem, the purchase deci-sion, as well as the post-purchase factors that may affect the entire process.

Samples were selected with random sampling to increase the sample reliability by minimizing subjective bias. 250 survey respondents, consisting of primarily online and primarily offline shoppers were selected. The variables under investigation meant that the sample needed to be very well distributed among at least those three dimensions. All variables showed significant differences in behavior associated with them, with gender being particularly salient.

(4)

(5)

4 | P a g e

1.0 INTRODUCTION

Online shopping is made possible through the internet. It allows people to seek information, which can lead to comparison of the attributes and prices and products and can therefore af-fect decision-making by customers. It has become standard for customers to seek out infor-mation on the internet first, before even considering buying something, even if they don’t do this online. Worldwide internet use is on the rise, and it is expected to be used by the entire population of the world within decades.

A raft of recent developments, termed web 2.0, have increased interactivity and have allowed people to do more on the internet in a more interactive way than ever before. Web 2.0 allows people to give their opinions about products, read reviews from other consumers, and share experiences on different brands, products or services. Hernandez et al., (2011) addressed dif-ferent aspects of online shopping, including the consumer characteristics that affect both online and offline shopping. This research showed that researchers have investigated online topics such as security, emotions and experience of users before.

For this research, three topics in the online shopping field were selected as variables: age, gender, and income. But the personal perspective of consumers is also considered, and what advantages and disadvantages online shopping holds for them. The three variables are all sus-pected to have a different effect on the online shopping experience. Hernandez et al. (2011) researched the specific benefits of online shopping, and showed that the convenience makes consumers prone to pick it over “brick-and-mortar” retail experiences, and this was found in all culture and shoppers of many different backgrounds. Bellman et al. (1999) found that online shopping increases in recent years are positively related with the rise in Internet use in the general population, showing in their view that online shopping is becoming more accepted as a behavior. This means that, unlike in the past, it is no longer just technology- focussed customers that are shopping online, but that this is now a common behavior in many different groups. Allowing for this assumption, research can and should be done in how certain person-al variables may affect the online shopping experience.

The scope of this research is a limited set of personal information in the purchase decision-making process, being: age, income and gender. The research will further specifically investi-gate the online purchases of clothing products as well as how they are affected by these varia-bles.

(8)

7 | P a g e Age is the first variable under investigation in this research. However, online shopping behav-ior and age have been exhibiting inconsistent trends. Monsuwé et. al.(2004) showed that younger people shopped online much more often than older people. But Researchers such as Bellman et. al.(1999) state that the age of online shoppers has increased and that therefore older cohorts are catching up to younger cohorts, who have exhibited higher levels of adop-tion in the past. The variable is has not been researched as much as others, and has probably changed since these 11 and 16-year old papers were written. Current research into the variable is not sparse, but it is valuable to consider for this paper.

Education and employment determine an individual’s level of income. Income is evidently a factor in online shopping decisions, and as such is expected to play a significant role. Bellman et al. (1999) found a correlation between high levels of education and the choice of online or offline shopping. They posit that this due to the higher propensity for educated people to be employed and have the available income, and lack the time to shop in real life. These people are also likely to be less easily satisfied with a product’s quality or other features. The process of online comparison is particularly applicable to people with higher than average incomes. They know what they want and are looking for something specific. They require exact infor-mation from the shopping experience on price, quantity and they want the option to compare products. In short, education correlates to a higher income that, in turn, positively correlates with online shopping behavior. Hernandez et al. (2011) state that information and money are the biggest contributing factors to online shopping and that the level of education is the de-terminant for this.

Gender is one of the variables that affects online shopping and is part of the variables under investigation in this research. Bea & Lee (2010) state that men are less afraid to purchases products online. Differences between the preferences of men and women can have a signifi-cant impact. Bea & Lee’s (2010) research has shown that men purchase mainly electronics, cars and entertainment gadgets and women mainly purchase clothing. This difference in products alone justifies gender as a vital variable to investigate further. Hernandez et al. (2011) state that women are less likely to shop online, because they are affected by the immediate environment in their purchasing choice. Social construct of femininity and associated behav-iors, such as symbolic consumption, restrict women’s behavior online.

(9)

8 | P a g e The different preferences in products are therefore just one of the dimensions of this variable. Hansen and Jensen’s (2009) research has shown that men follow very specific gender roles when shopping offline, but that these are not followed by men shopping online. Men do not shop for the same stereotypically male products online, and in fact are more likely to buy more female oriented products there, such as cosmetics and fashion. Hansen and Jensen sug-gest that this because men are able to do this because of the anonymity provided, and the freedom from judgement that the internet provides. They also highlight the ability for men to form relationships with other men that focus on vulnerability and not on competition. Howev-er, in real life, men could still maintain a more stereotypical male profile.

The motives of consumers for online or offline purchases are the general area in which these variables are expected to make a difference. How are their decisions affected by these multi-dimensional issues?

1.1 Background

The rise of online shopping has recently begun to make headlines, as the numbers are beginning to rival existing old-fashioned retail. But the research has lagged behind this new reality. The research that has been conducted so far is fragmented, in the sense that there have been no clear explanations of what influences the consumers’ attitudes towards online or of-fline shopping and what drives them to use the internet (Shergill & Chen 2005). An approach within a new context is required and should take at least some of their motives into account, especially if these

affect decision-making and the attitude or even the preference for one over the other.

Convenience and the ease of use of the internet are obvious motives for shopping online, but exogenous factors may be equally important, at least according to Hernandez et al. (2011), they tried to map the effects of: income, consumer traits, gender, and age etc. In Europe, the increase in online shopping was from 27.7% to 31.4% between 2001 and 2002 and U.S. sales were 36 billion USD in 2002 (Hernandez et al., 2011). Considering a growth rate of 20.9%, this means an effective doubling in size every 5 years.

There is, to date, no satisfying explanation for these vast increases. What drives consumers to shop online instead of offline? Seeing as their motivations are unclear, retailers cannot adapt

(10)

9 | P a g e strategies to benefit from this explosion of interest. The motivations must be understood first, and then new strategies may be crafted.

It is the guiding principle of this research to find a context in which to relate variables that may lie at the heart of these questions of motive. How do the different variables effect the online shopping experience and what lessons can be drawn from this? To achieve this, three variables were selected and their interactions with online shopping will be investigated. It is expected that they will act as moderators to the experience itself.

1.2 Age and online/offline shopping

It would seem evident that younger people are more likely to engage in online shop-ping because of their greater familiarity with technology in general. Older shoppers are ex-pected to feel uncomfortable with online shopping compared to offline shopping. A higher percentage of young people are expected to partake , compared to earlier generations. The research quoted above suggests that this is not the case.

Recent studies have shown that older consumers have the same rates of internet use as young-er consumyoung-ers. A report by Pew Research categorised oldyoung-er consumyoung-er’s intyoung-ernet use as fo-cussed on seeking and comparing information from online stores, as opposed to social media use or entertainment purposes. But they also have a much higher disposable income on aver-age. Wan et al. (2012) even go so far as to suggest that their behavior is as vigorously pursued as social interaction by younger generations and only leads to more online shopping. A study by the University of Southern California, which focussed on enthusiasm about Web 2.0, found that people over the age of 30 where fare more interested in the concept than teenagers and young adults. The latter group was more interested in messaging and other social parts of web 2.0, whereas the former were interested in the presentation of more and more useful in-formation.

Wan et al. (2012) also found disposable time to be a major factor. While older generations did not spend as much time on social activities as younger generations, they do seem to spending more time than before on these aspects, but maintaining their information seeking and com-paring habits. A survey from the UK found no difference in the time spent on the internet be-tween generations at all. The Pew survey’s numbers indicate a higher interest in the median

(11)

10 | P a g e age brackets compared to the others. their bracket of 33-44 had a rate of 80%, and 19-32 a slightly lower 71%. By comparison 56% of those aged 64-72 years shopped online and of those 73 or older only 47% did so (Wan et al., 2012).

Hernandez et al. (2011) specifically highlight age in relation to online shopping, but they seem to believe that it is primarily for the young and not for the 33-44 year olds, as in the pre-vious research. Monsuwé et al., (2004) also seems to contradict this view. Research done in Germany shows a 15% increase in 2008 to 2009 in the 50-59 age group and a similar rise (10%) in the 60-69 age group in the same year. These were outliers in a general trend of more online shoppers in general.

Taking these results in to account would help explain what exactly is changing in online shopping. These results are from different studies in different countries done at different times, and things may have changed. Is only shopping still affected by age, and if so how? Or has it simply become mainstream and is there no real difference left? If there are differences are they significant?

1.3 Income and online/offline shopping

The relationship of income with online shopping is underexplored. Is it a moderating factor, as suggested by some studies? Income is itself a result of levels of education and of course whether or not one is employed. Shergill & Chen researched this in 2005 and found that a higher education meant more comfort with using “non store channels for purchases”. Being aware of their existence, the internet itself and how it works is responsible for this com-fort level and allows consumers to make purchases.

Education and income are positively correlated and can be taken as having the same effects on online shopping. Li & Zhang (2002) found that consumers with higher incomes were more likely to shop online and their explanation for this in 2002 was that high income homes are more likely to already own a computer. This reasoning may still apply, but is unlikely to have remained a defining factor. Disposable income seems more likely to still have an effect, though.

(12)

11 | P a g e

1.4 Gender and online/offline shopping

Gender influences online shopping as much as the other two variables. Male shoppers are interested mainly in electronics, cars and other entertainment gadgets. Female shoppers focus on clothing and jewellery. According to Alam et al. (2008), Male shoppers are more confident in using the internet and shop online more than women do. Women generally prefer catalogues to using the internet. But the research also found that those women who do use the internet use it much more than their male counterparts.

But there must be other differences than just the difference in products. Other research has found that there are differences even at the level of decision making and the process of online shopping itself. Hansen and Jensen (2009) might be a key to discovering more about this vari-able.

1.5 Objective of this research

The objective of this research is to create a framework of moderating factors on atti-tudes towards online shopping. To achieve that end, how the age, income and gender of the consumer influences their attitude towards online shopping must be investigated.

To narrow the scope, the online purchase of clothes was chosen as a domain in which to in-vestigate these questions.

1.6 Hypothesis

The following hypothesis were formulated to allow the objectives to be statistically tested:

H0 = The online purchase of clothing is not significantly effected by income, age, and gender.

H1 = The online purchase of clothing is significantly effected by income, age, and gender.

1.7 Research question

To what extent will the age, income, and gender of consumers influence their purchasing of clothes online?

(13)

12 | P a g e

2.0 THEORETICAL FRAMEWORK

2.1 The effects of age on online purchasing of clothes

Kim & Park (2005) state that Malaysia has well integrated internet shopping because of the spectacular satisfaction rate from the customers. Clothes shopping first expanded among young people and then spread to those wishing to save time (Alam et al., 2008). The under thirties were the first customers of many online clothes retailers and is still the fastest growing segment. They spent 10 hours a week online in 2008, and the figure has only risen since then, when they outstripped over-thirties by 30%. They are expected to make up a higher percent-age of the total sales in clothes online (Alam et al., 2008).

Teenagers and young adults aged 16-22 are more likely to visit clothing websites than adults, and are therefore also more likely to purchase. Adults tend to be more purposeful in their vis-its to websites in general and more likely to do research. They tend to focus on aspects like service and product quality that are of lesser concern to 16-22 year olds. They are more sus-ceptible to impulse buying and tend to enjoy the experience of online shopping more than adults do. A lack of purpose, ignorance of budgeting and a lack of financial responsibilities all contribute to this behavior (Monsuwé et al., 2004).

Bellman et al. (1999) also found that young people were more likely to shop online for clothes than older people were. 24% of young people shopped online for clothes compared to just 3% for older people, which is significantly more than in other sectors. Suri et al. (2003) found that young people that shop online will spend a significant part of their disposable income on clothes, namely 13%, older people tended to spend less than 4% of their disposable income. This means that more young people spend more of their money on clothes online. They are a desirable demographic for online clothing stores.

Kotler’s (1965) renowned theories of social marketing make studying the target market a pri-ority for marketers that want to have an impact. The theories are alive and well in the internet age and websites are well-versed in marketing to the young specifically. 45% of all teenagers and young adults spend an average of 250 dollars a year buying books online and 50% bought music for an average of 208 dollars. The clothing industry is third with 29% of everyone aged 16-22 buying clothes. (Bellman et al., 1999). Hernandez et al. (2011) found that a lot of the features that persist in online shopping are specifically there to attract the young, who are

(14)

13 | P a g e more prone to enjoying the experience. Older generations are more focussed on convenience and the ease of use of the internet when online shopping.

Adults, i.e. everyone of 22 or older tend to have jobs and this reduces the time they can spend shopping. Online shopping is convenient for them because of the time saved and sometimes the money spent (Liang & Lai, 2000). The young could be focussed on saving money, but research hasn’t shown this, they spend more of their money in fact, and this may be due to a lack of other financial responsibilities. It can be concluded that the young were successfully studied, using Kotler’s (1965) theories and that clothing websites already segment carefully.

The statement that people in particular age groups tend to have similar norms and values, which brings out a homogenous behavior, expressing itself in the purchasing of many of the same goods. Age groups are themselves closely related to the concept of a subculture, which requires conspicuous consumption of particular goods to signify membership in the subculture (Li & Zhang, 2002). Which subcultures there are in different age groups and what marketers must do to appeal to them lies at the heart of what they do. The young are likely to be very involved in particular subcultures that influence everything they consume, but particularly what clothes they wear (Li & Zhang, 2002).

Essentially, Teenagers and young adults are a very different market from the adult market, which is less fashionable in general. Teenagers take their fashion cues from their peer group and are more likely to desire trendy clothes that fit an identity they want to project and mar-keters have already adapted to this.

The young are an important segment because of their high adoption rate and the amount they spend, though this less reflected in the actual profits (Monsuwé et al., 2004). Their behavior is focused more on trying out different styles of clothing and the may change their wardrobes often.

In the recent past, tangible goods could be inspected and prices and other terms could be ne-gotiated directly with the sellers, which was particularly necessary in the clothing industry, as trying on clothing and seeing it displayed were the only ways in which consumers could have any experience of the material outside of catalogue shopping. The internet is essentially the same as the catalogue model.

(15)

14 | P a g e Goods are assessed based on what other goods are available. Wan et. al (2012) classify goods available on the internet into three types: experience goods, credence goods and search goods. Experience goods are goods that must be consumed before they can be evaluated by consum-ers, for instance foods. Credence goods are goods that consumers can not evaluate beforehand, the opposite of experience goods, for instance the new smartphone from the company that makes your old smartphone. Credence goods rely on brand names to spark interest and confer an idea of the quality. Finally, search goods are goods that can be researched and compared to other products before purchasing.

But Wan et al. (2012) also point to the relationship that exists between products and consum-ers as a factor in classification. By this they mean that based on the consumer, the same prod-ucts can take on different roles. They can be search goods for those who know a lot about the product already and experience goods for those who know nothing. Search goods are most often purchased online, followed by credence goods. Experience goods are least often bought online, because the risk is the greatest for the consumer. There is no way of knowing if the experience goods are going to be good, so the consumer is risking money. Credence goods have the security of a brand that may be familiar or at least highly regarded by other, and search goods are safe because they can be selected to do exactly what the consumer wants out of all available options.

Age and the types of goods are of course, also related. The lack of disposable income means that 16-22 year olds purchase search goods more than any other, even more than the rest of the population. they have to be careful with their resources and will carefully research before buying. The 40-60 age group will buy more credence goods, believing in existing brands, preferably one that they have bought more products from in the past. It is the median age group that buys all three types of product equally. They can risk experience goods, buy search goods when they need a specific set of requirements filled or splurge on a high quality brand if they want.

2.2 The effects of income on online purchasing of clothes

The internet is not only a platform for networking, but also a medium for most busi-nesses to bond with its customers according to Kim & Park (2005). Online shopping is a timesaver for the gainfully employed, who are at work during business hours. It reduces the

(16)

15 | P a g e time spent ravelling to and from stores (Li & Zhang, 2002). Online shopping is even preferred by some for more than just travel time savings, other activities that are associated with tradi-tional shopping are for instance crowded parking lots and waiting in queues to pay. These can also be stressful aside from the time they take (Chiang & Dholakia, 2003).

Kim & Park (2005) also point to saving money as well as time as a reason for a preference for online shopping. Saving money is closely associated with shopping online as opposed to brick-and-mortar stores. Not everyone realizes that transport costs can be significant when buying offline. It also reduces costs for the retailer, who does not need much staff or pay rent for more than a place to stock items. Online purchasing therefore usually has lower prices in general. The only sunk costs for consumers are internet connections and a phone or computer to access these. With no travel costs and lower prices, a preference for online shopping be-comes logical, even. however, clothing is somewhat of an exception, because sizes are not universal and people often want to try items to see if they actually fit. online stores need more generous return policies as a result.

Monsuwé et al. (2004) researched income as a variable. Consumers with a high income tend to live in high income areas. By this is meant places where the cost of living is high, starting with housing costs, but also extending to other costs, particularly goods. The opposite is true for low income consumers. Despite their higher costs of living in general it is these high in-come consumers that have the most disposable inin-come that can be spent on online purchases. And this is reflect in the amount of money they spend on online purchases, but also in the variety and different categories of goods they spend this money on.

With the above observations, Monsuwé, et al., (2004) attempts to elucidate what different experiences are correlated with the income level of consumers. Higher income consumers are usually better educated, better informed and have more experience with buying products online, aside from the disposable income they have at their discretion. Also, high-income are-as tend to be geographically close to centres of production, meaning the costs are less for pro-ducers to ship to them. The opposite is true for low-income consumers, who tend to live far from production and are also less likely to have access to money or information about online shopping at all (Monsuwé et al., 2004).

(17)

16 | P a g e

2.3 The effects of gender on online purchasing of clothes

Gender effects online purchasing clothes and is considered relevant for the strategies of companies. There are differences in Men and women’s behavior and companies must un-derstand these to be able to fulfil their desires (Weiser, 2000).Dittmar et al. (2004) explained the differences in behavior while online shopping between men and women with the “selec-tivity theory”. The theory states that women are ”comprehensive processors” and men are “selective processors”. By this, they mean that male shoppers are more motivated by func-tional factors, whereas women are more motivated by emofunc-tional and social factors. In other words, females tend to focus on the process of buying, whereas men focus on the outcome to obtain actual goods with the least fuss.

This is seen in Men’s negative attitudes towards buying clothes in general. They tend to see it as work that they want to accomplish it, with a minimum input of time and effort. Males are direct and goal oriented when conducting online purchasing. (Dittmar et al., 2004). Male shoppers are also less likely to seek social interaction than female shoppers are. Women shop communally in offline situations, and online communities are filled with the same behavior, information is exchanged about product and particular experiences. Women exchange infor-mation about online purchase and men do not.

Another dimension of gender differences is a different attitude towards risk. Male shoppers take more risks than female shoppers do, giving out their personal information more often and quicker in pursuit of a deal (Garbarino & Strahilevitz, 2004). Female shoppers spend more time researching online than actually buying. Women consequently spend more time online and more time interacting with others and asking for advice. This behavior often leads them to explore more alternatives than their male counterparts.

The trend of men being more willing to give out personal information compared to women may affect the purchase behavior of both gender groups. One possible explanation is that women tend to be more concerned with the risk of buying online then men. Garbarino & Stra-hilevitz (2004) explain women are afraid of credit card misuse, personal information becom-ing public, purchasbecom-ing from fraudulent sites and shippbecom-ing problems. This may lead them to purchase less clothes online.

Women consider online purchases to be risky and try to avoid this risk (Suri et al., 2003). If an activity is considered as risky, it is less likely to be performed. Shergill & Chen (2005)

(18)

17 | P a g e posit that men’s main motive for online shopping is necessity. The perceived risk level corre-lates with the amount of money spent on online purchases. More women still spend less money than the relatively fewer men, because of their perception of less risk. Shergill and Chen (2005) also state that women report that they have to experience a physical object before purchase, to diminish their doubts about the clothes they buy. This a significant factor for women in offline clothes shopping as well, where testing the quality is a part of the process, as is trying on clothes and feeling the material and fit of the clothes and how they look. Wom-en spWom-end more time than mWom-en on this offline as well. MWom-en perceive less risks about purchasing in general and have fewer doubts. Their motive of necessity makes them more likely to buy quickly and usually spend more money (Case et al., 2001).

This behavior would be of particular interest to marketers of online clothing stores because it would help them make shopping experiences less risky for women. For example, women tend to both share their experiences with others and respond to recommendations from others. Fo-cussing on both reducing the negative perceptions and allowing women the opportunity to communicate positive experiences could be the key to fixing this problem (Garbarino & Stra-hilevitz, 2004).

Bakewell & Mitchell (2003) state in their research that women tend to enjoy shopping more than men do. They enjoy the process itself and are happy to spend considerable time and men-tal energy on it. Studies have confirmed that shopping is leisure for women and that women shop for longer and are more involved than men. Women put and exert extra effort in order to get quality products. Hansen & Jensen (2009) agree with this statement, according to their article women are more fun shoppers, while men tend to be quick shoppers. Men are focussed on completing the transaction as quickly as possible. Women will spend more time searching comparing and asking for advice. This reflects itself in different sales figures. Men with single purchases in a short timeframe, women with many different purchases over a much longer period of time. This is especially the case for clothes shopping. Men tend to buy clothes only when they need them immediately, women will spend a longer time planning an entire ward-robe for many seasons. Liang & Lai (2000) also found that women enjoy offline shopping as much as online shopping and that it was also a gendered activity for many women, i.e. it is considered “women’s work”. Women already had a tendency to shop for many different things offline and generally spend more than men. Monsuwé et al. (2004) posit that men have always shopped less than women, but that the ease of online shopping is shifting the paradigm.

(19)

18 | P a g e Men are getting closer to women in the amount of time and money spent. Men’s interest in clothing is beginning to match that of women and they are putting their money where their mouth is (Hernandez et al., 2011). Women have slightly decreased the amount spent in the same period Hernandez et al. (2011) attribute this to women’s perceptions of risk and uncer-tainty.

Men and women’s different approaches are not always reflected in different designs for web-sites, which should be divided by gender. Website design attracts online consumers. Accord-ing to Cyr & Bonanni (2005) a website has to be designed for a targeted customer segment. There are gender differences in preferences for website design. For example, women tend to trust webpages less easily then men do. This suggests that women require a lot of information in order to trust a webpage. A website specifically for women must include as much infor-mation as possible. On the other hand, websites specific for men could be designed to contain specific and minimum information that would reduce the tendency of male purchasers to feel like they are wasting time. Differences in design preferences for women and men indicate that this area offers various opportunities to expand the understanding as to what is

gender-relevant when shopping online (Cyr & Bonanni, 2005).

Hernandez et al. (2011) researched the way gender influences reactions to products and also the advertising of products that appear online. They found huge differences in which adver-tisements were targeted towards men and women. Male consumers are regarded as utilitarian and motivated by ease of use. Men are said to experience stress in clothing stores and online shopping provides an alternative (Alam et al., 2008). But British research on online shopping found that men considered online shopping a form of socialization and perceive it as targeted at women, not men. Bellman et al. (1999)’s research thus runs counter to other findings, but this may be due to its age. In 1999, men’s attitudes may have averaged out differently than today.

The experience with using the internet is another factor of online shopping that differs among men and women (Hernandez et al., 2011). According to Kim et al. (2006) females spend more time on the internet and have a stronger positive attitude towards online information sources. However males start using the internet at a younger ages, that’s why they have more experi-ence and search the internet in a more specific way. This results in spending less time on buy-ing clothes online. Men have less knowledge about clothbuy-ing than women, which may affect

(20)

19 | P a g e their need for advice from the store personnel and their perceived difficulty in finding suitable items online (Hansen & Jensen 2009). Male shoppers are primarily motivated by convenience, necessity and speed and women would be more focussed on the journey itself according to Hernandez et al. (2011). They also state that men would blame themselves for not getting what they want, if they search for a product online, whereas women would blame the website itself for not providing her with enough information, and would also share this negative expe-rience with other women. According to Bellman et. al. (1999) women desire reassurance and interaction and men want convenience from online shopping.

Hernandez, Jimenez & Martin (2011) report high levels of “web apprehensiveness” in female consumers. They mean that women are more likely to fear the internet in some way, which lines up nicely with most of the other research on gender. Women’s traditional way of shop-ping offline, which required a lot of interaction had been compromised by the internet. But men suddenly where able to use skills they had already honed before online shopping to re-duce the difficulties in a task they did not enjoy and simultaneously using technology that they do appreciate (Alam et al., 2008). Women still shop more online than men do, but more men shop online than they do offline.

2.4 Theories about online shopping

2.4.1 Reasoned action

The theory of reasoned action is focussed on finding the factors that determine behav-ior through the psychological processes that underlie human thoughts. It states that people are not only affected by what the intend to do, but also whether or not this behavior is in line with what is expected of them. People will defer their desired behavior if it is deemed unacceptable in that particular situation. The intent is really only a measure of willingness combined with the amount of required effort. But the attitude towards the behavior, both of the person and of its surroundings are equally important to whether or not the behavior is actually performed. Behaviors are seen to have costs and benefits for those who perform them, and this leads peo-ple to form beliefs that can be positive or negative about behaviors. It is these beliefs that in-form whether or not someone actually perin-forms a behavior. These factors are more important than a person’s objective characteristics (Hansen et al., 2004).

(21)

20 | P a g e beliefs; customer risk, customer service, shopping experience and product perception. These beliefs are based on the opinions of friends and family members.

It offers an explanation for the age difference sin online shopping. Different beliefs are domi-nant in different age groups. Older groups may have a higher belief in the customer risk sec-tion of the general attitude and are therefore less likely to shop online. The rise in certain old-er groups of online shopping can be attributed to an aging population, but also to the change of that particular section, through more exposure. If for instance, children help with online purchase, older generations will feel confident that they are not risking for instance credit card theft when making online purchases (Madden et al., 1992).

2.4.2 Planned Behavior

The theory of Planned Behavior extends this idea of reasoned action by adding limits other than beliefs and intentions. These Behavioral controls can be many different things, from straightforward practical concerns to complex motivations. What opportunities there are to engage in the behavior and if the required resources are available are some of the most im-portant factors. Having a computer or a smartphone would be such a resource. Intention can be effected by controls directly. The intent is influenced by the effort that has to be expended and a perceived control increases the perception that effort and subsequently effects the per-formance of the behavior. The theory of planned action better explains how online purchases occur in reality, by introducing a theory for obstacles to purchasing itself (Madden et al., 1992).

Whereas the beliefs are composed of the opinions of friends and family, behavioral controls are composed of site accessibility, navigation ability, product description, transaction speed, and efficiency of transaction. The theory of planned behavior is especially useful when dis-cussing the variable income. It is the most obvious limiting factor for online shopping. It ex-plains another age issue, specifically, the correlation between the higher percentage of online sales in the 33-44 age group. The higher average salaries of this group are based on their age, the previous group would perhaps like to buy more online, but is hampered by lack of funds (Wan et al., 2012).

2.4.3 Transaction Costs

(22)

21 | P a g e “move” a good or service from one party to another. To achieve this, a market is required where the parties can exchange the products and services. To do this successfully, information needs to be symmetrical, i.e. both parties know everything about the product and the market works only through supply and demand. This model does not reflect reality, though. In the actual inefficient marketplace, consumers must first find information about product before they can make choices, discuss terms and price, delivery, etc. This requires time and money, the transaction cost.

Transaction cost theory can describe how consumers and companies try to minimize these costs as much as possible, to create a more efficient market. It is in the company’s interest to close the deal, but not necessarily to inform the customer fully. It is in the customer’s interest to spend as little time as possible to gain the most information. Information is asymmetrical, the company selling the product or service will likely have much more than the customer, and this can make the customer doubt the intentions of the company, leading to lost sales. Compa-nies and customers try to decrease doubt in order to achieve transactions, but customers have less of a motivation to do so (Frauendorf 2006).

2.4.4 Acceptance of Technology

Theories of how accepting people are of technology could play a role in describing the rise of online purchase. The model by Davis (1986) has as its main constituents “perceived ease of use” (i.e. the “subjective valuation of the benefits of the technology”) and “perceived usefulness” (i.e., the degree to which a prospective user anticipates the target system to be effortless). According to Legris et al.(2003), the intent to use is directly related to the per-ceived ease of use, by which they mean, the easier a technology seems to be to use, the more likely the subject will actually use it.

Wan et al. (2012)applied the model to online shopping behavior, with the caveat that the model has been criticised for being overly reductive and may require more features , such as social influences and cognitive process. The research on how the model actually operates have left its actual results in doubt, and it is suggested the model is amended to better reflect social and human dimensions of technological change.

(23)

22 | P a g e

2.5 Other possible effects on online purchase behavior of clothes

2.5.1 The effect of blogging on online clothes shopping

According to Singer (2009) blogging has become on the main sources for information online. On average, in 2009 people posted 900.000 blog posts every day, two-thirds are male. On the other hand Schler. et al (2006) found that teenage bloggers are predominantly female, while older bloggers are predominantly male. Also he noticed that female bloggers write more about fashion (clothes) than their male components. Similarly Hodkinson (2006) observed that the group of bloggers have a majority of female and teenage bloggers. The majority of the blog authors share their knowledge, skills and life experiences. Often bloggers will express their thoughts about products. The study has highlighted that the lack of face-to-face communication leads customers to seek alternative methods of quality determination. Corporate and individual blogs offer information on various products and their alternatives. blogging is viewed by many people as the online version of word of mouth (Osman, et al., 2009). Bloggers mostly give experience or reviews based on information produced by other people online (Bellman et al., 1999). Bloggers establish their credibility online by giving information on a certain topic based on their experiences. Also Cheong & Morrison (2008) have noticed the influence of online recommendations on consumer decision making. Blog’s are growing rapidly and frequently features comments about brands and products, they call this user-generated content (UGC). Customers take blog reviews the same way as word of mouth experiences of friends or family. This makes blogs one of the best ways to influence online shopping experiences. Most online companies provide information to independent bloggers or free samples to these bloggers in order to use them as

marketing avenues to customers who seek to discover how an online apparel business conducts itself. This provides direct access to customers while increasing the level of trust in the store and the various different avenues required (Hsu. C. et al., 2013).

2.5.2 Failure of services and customer defection

Online stores are similar to any other services based business. The main difference is that instead of vendors and attendants the main avenue of services is a website or the ERP system used by the system. Most web based shopping experiences fail when the sales offline also fail. looking at electronic service failure and concluded it was one of the main reasons for the collapse of online shopping stores (Hernandez et al., 2011). When the underlying software used by the electronic

business starts having problems, services lag that customers detest is created. Slow order purchasing as well as rebooting is considered the most annoying factors by customers when they go online to shop. Any electronic business that suffers from service failure risks losing customers no matter what quality their goods possess. E-commerce also has one of the highest customer defection rates in the business. Many alternative shopping stores provide similar quality of service and customers online are looking for convenience. When systems fail, there is a drop in the quality of service and this pushes many

(24)

23 | P a g e customers to look for alternative online stores that provide better quality of service. The combination of impatience and available alternative store provides an avenue stiff competition and a higher

likelihood of failure. A large number of online apparel shopping businesses go bankrupt due to failure of service. The online community also interacts a lot. When a few customers have bad service

experience, a ripple effect occurs when they tell their friends and others to avoid using the said e-business. This increases the likelihood of failure with many customers opting to avoid bad service. Most online shopping companies are advised to place some form of service recovery mechanism that will kick in when the main system fails. There is also a need to have skilled technicians available to respond to any service failures that occur. This reduced customer defection that could lead to business failure.

2.5.3 Online shopping customer satisfaction and repurchase intention

Most businesses have measures that seek to ensure there is a high rate of customer retentions. This is one of the key reasons why some businesses succeed while others fail. Customer retention in the online shopping business is based on ensuring customers are satisfied by the services provided. The better the services provided by an online store, the better the customer retention. Studies by Kim & Park (2005) identified experience as one of the main factors that influence online shopping. Experience is based on past quality of services and ease of use by online customers. Customers who were satisfied by the services provided by the customers are more likely to repurchase from the same online store. The study findings indicated that online customer retention was as important as

marketing in ensuring that e-shopping businesses achieve steady growth. Customers repurchasing provides any online business with a loyal customer base that can be relied on. Online shopping stores are encouraged to try to create customer retention incentives in order to promote repurchase among customers. Provision of free shipping as well as free advice on ways to ensure that they purchase these items is a definite method of customer retention (Monsuwé et al., 2004). Customer retention

techniques enable organization to continue improving their financial stability until when they are mature enough to retain a larger portion of the market.

3.0 RESEARCH METHODOLOGY

3.1 research design

The research techniques that was used was a quantitative research. A survey is productive here because it is a tool that enables to collect large amounts of data in highly economical way (Saunders et al., 2007). The hypothesis is tested by determining the strength of several variables by means of statistical analyzes.

(25)

24 | P a g e

3.2 sample and data collection procedure

The survey was designed with the Qualtrics survey software, this software is used at many universities all over the world including Yale, Harvard and Columbia. This is a self-administered and web-based survey. The survey can be divided into three parts. The first part contains 5 multiple choice questions, the second part are 3 ranking questions and the third and final part contain personal

questions (age, gender, income). The survey was translated and distributed in Dutch.

The data collection took place from the 15 October till 26 October 2013. Social media networks like Facebook, LinkedIn and Twitter were the most used channels to reach respondents, via posting in various groups, on the wall and sending private messages. Approximately 1400 people were asked to fill in the questionnaire Overall the survey was started 276 times and completed by 208 respondents (73%).

4.0 DATA ANALYSIS.

4.1 Results and data analysis

The results of an exhaustive primary research on the various factors that influence online shopping of clothes bring out the distinct variation of the preferences among different people basing on their gender, their level of income, and age. The fundamental questionnaire questions were designed to lead the participants into giving the information that would be useful in answering the research question. Preliminarily, the questions were analyzed at face value, for example, the behavior of the participants on the basis of gender, when it comes to purchasing of clothes online. The data in the results was mostly consistent with the literature, however, some of the variables in the findings of this research contradicted the findings of the various researchers as highlighted in the literature review.

Fundamentally, the researcher focused on the main variables that would lead to answering of the research question and the conclusion of the hypothesis. These variables included the age of the participants, where age was used to gauge whether the youth or the old are likely to purchase clothes online, or make any purchases online for that matter. Another factor is the level of income of the participants, where the researcher was investigating how the different levels of income could influence the online purchase behaviors of the participants. Gender, basically, is a factor that intrinsically involves the general behaviors of men and women, and these behaviors, in most cases, influence the purchase behaviors. The focus would be on how many men purchased clothing online, as well as women, and the differences in the numbers would show which group would rather buy their clothes online. There are other variables such as fashion changes, since as the questions in the questionnaires clearly point out; there is a comparison of the year 2010 and 2013. The questionnaire contained 11 questions in total, 3 ranking questions and the rest multiple choice. The questionnaire was designed to finish within 10 minutes, so all the respondents would finish it.

(26)

25 | P a g e The data analysis process utilized SPSS statistical software, where the preliminary steps were to code the data before inputting it in an SPSS interface as shown in the SAV files. Various tests were performed in an effort to conclude the hypothesis, as highlighted below.

4.2 Research findings

Approximately 1400 people were asked to fill in the questionnaire. Overall the survey was started 276 times and completed by 208 respondents in total. About 15% of the people who were asked to fill in the questionnaire actually did. 73% of the people who started the questionnaire completed it. 40% of the respondents were men and 60% of them women, 59% of all respondents were between the age of 20 – 29. Probably because of the age and gender of the researcher and the way the questionnaire was spread (Facebook, Twitter etc.). Almost 50% of the people have an income between 0 – 24.000 euro, this is because most of the people are students.

22% of the people sad that they did not shop online at all in 2010 compared to only 7 % in 2013. Also not buying clothes online went from 31% to 21% corollary the amount spend went up. The fifth question was to check to information given on the first 4 questions, the question was if they have spent less, the same or more online in 2013 compared to 2010. Only 4% answered the question with less, while 61% answered that is was increased.

The main reason people gave for the increase of online shopping in 2013 compared to 2010 is that they have less time for offline shopping, secondly they think they have more choice online and another important reason is the trust in the internet. In question 7 the respondents had to rank the reasons for online shopping. They had 10 options, but the next reasons where mostly placed in the top 3. Most importantly ‘to save time’, secondly ‘not depending on open-ing hours’ and on the third place ‘weather conditions’ and with the same score ‘easy to com-pare price’. The least favourable answer was ‘possibility to pay later or with creditcard’. In question 8 the respondents had to rank the reasons for not shopping online. Again they could choose between 10 options. The following reasons where mostly placed in the top 3. The most important reason is ‘not able to try on the clothes’, the next one is ‘photo online not clear’ and on the third place they answered ‘hard/expensive to return’.

4.3 Regression analysis

As pointed out, this research constituted a number of variables, and in order to be in position to answer the research questions effectively, as well as meet the objectives of the research, it was imperative that the relationships between these variables are investigated.

(27)

26 | P a g e

Model Summary

Model R R Square Adjusted R Square

Std. Error of the Estimate

1 .620a .384 .032 1.84466

a. Predictors: (Constant), how much did you spend on online clothe purchases, what did you spend on offline clothing in 2010, what did you spend on average in 2013 on online clothing, what did you spend on offline clothing purchases in 2013

The major objective of this research is to establish the relationship between the

aforementioned variables and the online shopping behavior of clothes among the participants, and coming up with specific relationships between individual variables to the overall research question. The analysis of variance table (ANOVA table) shown below is the SPSS output with the age of the participants as the depended variable. This analysis was carried out at 99% confidence interval, meaning that it was carried out at a 0.01 level of significance, or simply α = 0.01.

ANOVAa

Model Sum of

Squares

df Mean Square F Sig.

1

Regression 14.847 4 3.712 1.091 .430b

Residual 23.819 7 3.403

Total 38.667 11

a. Dependent Variable: what is your age

b. Predictors: (Constant), how much did you spend on online clothe purchases, what did you spend on offline clothing in 2010, what did you spend on average in 2013 on online clothing, what did you spend on offline clothing purchases in 2013

According to the above output, the sig. value = .430. The sig value plays an important role in concluding the research hypothesis, as well as leads to answering of the main research questions. In

(28)

27 | P a g e this case, sig. value = .430 > α = 0.01. This means that the researcher would either accept the null hypothesis of non-significance, or reject the null hypothesis of significance. From this point, all the variables have been taken into consideration as the ANOVA table illustrates.

Coefficientsa

Model Unstandardized Coefficients Standardized

Coefficients

t Sig.

B Std. Error Beta

1

(Constant) 4.295 1.740 2.468 .043

What did you spend on average in 2013 on online clothing

-.547 .299 -.776 -1.831 .110

What did you spend on

offline clothing in 2010 .325 .314 .498 1.037 .334

What did you spend on offline clothing purchases in 2013

-.419 .313 -.640 -1.336 .223

How much did you spend on online clothe

purchases

.296 .322 .377 .920 .388

(29)

28 | P a g e

4.4 Correlations

Correlations

What did you spend on offline clothing

purchases in 2013

offline clothing in

2010

What did you spend on average in 2013 on online clothing What is your age How much did you spend on online clothes purchases What did you spend on offline clothing purchases in 2013 Pearson Correlation 1 .730 ** -.125 -.254 -.199 Sig. (2-tailed) .007 .698 .426 .534 N 12 12 12 12 12 What did you spend on offline clothing in 2010 Pearson Correlation .730 ** 1 .199 -.096 .071 Sig. (2-tailed) .007 .536 .767 .827 N 12 12 12 12 12 What did you spend on average in 2013 on online clothing Pearson Correlation -.125 .199 1 -.341 .678 * Sig. (2-tailed) .698 .536 .279 .015 N 12 12 12 12 12 What is your age Pearson Correlation -.254 -.096 -.341 1 .014 Sig. (2-tailed) .426 .767 .279 .967 N 12 12 12 12 12 How much did you Pearson Correlation -.199 .071 .678 * .014 1

(30)

29 | P a g e spend on online clothe purchases Sig. (2-tailed) .534 .827 .015 .967 N 12 12 12 12 12

**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

The SPSS output below is an illustration of the various correlations of the variables in this research. Correlations show any statistical relationship between two variables or set of variables. The relationship between the variables is an indication of the likelihood of one variable influencing another in one way or the other. They would, therefore, be used to come up with meaningful relationships that would play a major role in answering the research question, as well as concluding the hypothesis. The correlation coefficient of various variables is shown in the matrix and variables that show stronger correlations influence each other more than those with weak correlations.

4.5 Two-stage Least Squares Analysis

For further accuracy in determining the relationships between the variables or factors that influence online shopping by the participants of this research, the researcher used standard linear regression models. These regression models assume that all the errors in the dependent variable are uncorrelated/not correlated with the independent variables. The fact the following computed values are based on values that are not correlated/uncorrelated, the results of the two-stage model would always be optimal.

Model Description

Type of Variable

Equation 1

What is your age dependent What did you spend on

average in 2013 on online clothing

predictor

(31)

30 | P a g e What did you spend on

offline clothing purchases in 2013

predictor

Is the online purchasing of clothing less or more in 2013 compared to 2010

instrumental

Rank reasons for increment of online clothing purchases

instrumental

What is your gross salary instrumental MOD_1 Model Summary Equation 1 Multiple R .015 R Square .000 Adjusted R Square -.375

Std. Error of the Estimate 87.899

ANOVA

Sum of Squares

df Mean Square F Sig.

Equation 1

Regression 13.323 3 4.441 .001 1.000

Residual 61810.571 8 7726.321

(32)

31 | P a g e The ANOVA table also illustrates the aforementioned situation by pointing out important values that are used in the determination of the research question, as well as concluding the hypothesis. According to the ANOVA, the sig. value = 1.00. The sig value plays an important role in concluding the research hypothesis, as well as leads to answering of the main research questions. In this case, sig. value = 1.00 > α = 0.01. This means that the researcher would either accept the null hypothesis of non-significance, or reject the null hypothesis of significance. The level of significance α = 0.01 since the analysis was carried out at 95% confidence interval.

Coefficients

Unstandardized Coefficients Beta t Sig. B Std. Error Equation 1 (Constant) -56.409 1618.169 -.035 .973 whatdidyouspendonavera gein2013ononlineclothing 11.320 313.224 16.043 .036 .972 whatdidyouspendonofflin eclthingin2010 27.029 730.766 41.390 .037 .971 whatdidyouspendonofflin eclothingpurchasesin2013 -26.720 729.504 -40.822 -.037 .972 Coefficient Correlations whatdidyousp endonaveragei n2013ononline clothing whatdidyouspe ndonofflineclt hingin2010 whatdidyousp endonofflinecl othingpurchas esin2013

(33)

32 | P a g e Equation 1 Correlations whatdidyouspendonavera gein2013ononlineclothing 1.000 .998 -.997 whatdidyouspendonofflin eclthingin2010 .998 1.000 -1.000 whatdidyouspendonofflin eclothingpurchasesin2013 -.997 -1.000 1.000

Numerous factors influence online shopping behavior of people, with regard to who is more likely to purchase more clothes online or who will have fewer tendencies to shop for clothes from online shopping websites. This is regardless of the region, number of hours spent online, whether looking for clothes to shop, or simply browsing and accidentally coming across an online clothing store. Such factors that influence the buying abilities of clothes from online stores by different people include age, gender, income, the occupation, technology among many other factors.

Age as a factor is dependent on the consciousness of the buyer. It is of course not possible to expect certain age of people to purchase clothes online due to lack of comprehension, an example being the extremely young or old. The age groups under discussion are the youth between fifteen and twenty-five years of age, who understand and appreciate the internet, in comparison with the older population comprised of people aged above twenty-five years, but less than sixty.

According to the literature review, young people are more likely to shop for clothes online compared to older people. The reasons given included the fact that youth spent more time on the internet, and are more likely to come across such sites, compared to older people. Results concur with the findings of the literature review, in showing that older people would shop less for clothes

compared to the youth in an online setting. Besides the amount of time spent on the internet by each age group, the other reason why the youth are more likely to be online shoppers is that they have a higher tendency to be trendy and fashionable. Youths would spend a designated amount of time to search through the latest trends and fashions available online, in order to purchase them, while older people are more likely to shop for clothes online for convenience rather for trends.

Gender is the other factor affecting the behavior of online shoppers, in this case for clothes. The literature review revealed that there are differences in the way men and women shop online for clothes. Men would be more likely to shop for clothes anywhere for the sake of convenience, while women would shop leisurely, as a duty to the family or due to tradition of women adoring the act of shopping. In an online setting, the literature reviewed indicated that women are more likely to shop online, because they spend more time on the internet searching for clothes to shop, and they designate

(34)

33 | P a g e a time for the act. This showed that men spent less on online shopping because they were less likely to spend time browsing for clothes. In short, men are very objective, and they go online for specific reason, and not multiple work. The ability to browse for other things as well as find online stores to shop is, therefore, diminished for men.

Men tend to be less interested in products such as clothing and jewelry but are rather more interested in cars, electronics, and other entertainment gadgets. Women, on the hand, are interested in shopping for clothing and jewelry. Men express more interest in using different types of technology as compared to women. Men are, however, comparatively more likely to use the internet as a shopping channel or medium.

However, according to the results, a controversy arises as it deviates from the literature review. Men would likely spend more on shopping online for clothes than would women. This can be

explained with the tendencies and objectivity of both genders. As seen in the literature review, men are more objective and would more than likely concentrate on a single task they undertake, rather than women who would tend to multitask. Despite the fact that women spend more time online, they face a high probability of being distracted as well as attending to various things at once, compared to men who would just undertake a single task at a time.

The logic is in the objectivity not the amount of time spent online. As women spend time searching for fashion and trends while browsing other things, men find it objective to go online to a specific site and purchase what they need. The other reason is convenience of the shopping experience. Since men do not have time to go around malls physically shopping, ordering clothes from an online store from where they are saves men time and the hustle of looking for clothes. Women, on the other hand, find it more enjoyable to shop physically or browse through many trends online resulting into shopping fewer clothes online for that matter.

Income is also a factor affecting the sale and buying of clothes online. The disposable income of a person comprises of the actual available amount of money that is a net cash flow of the total gross income, which is capable of being spent on other issues that are not immediate, after subtracting all the other expenditures. The literature indicated that people with a higher disposable income would be more likely to shop for clothes in an online setting, compared to those with less income. The results of the research concur with the explanation of the literature that there is a tendency of people with a high disposable income to use more money to buy clothes online. This is because they have the affluence and resources to search for clothes online.

The higher income allows them freedom to exercise the ability to be flexible. On the other hand, those with less income shop for fewer clothes online. This is because they spend less time on the internet due to constraints of resources, as well as the conflict of solving other issues first before

(35)

34 | P a g e settling on the ability to focus on online purchase. People with less income would more lily have the mentality that shopping for clothes physically, although tedious and time consuming, is less expensive and would allow one to save more, than online shopping.

4.6 Concluding the hypothesis

In regression analysis, sig. value = .430 > α = 0.01, and in two-stage least squares analysis, sig. value = 1.00 > α = 0.01, meaning that we accept the null hypothesis and conclude that income, age, and gender have a significant effect on the online purchase of clothing.

5.0 CONCLUSION AND RECOMMENDATION

The study revealed that there was an increase in the amount of money spent on purchases in 2013 as compared to 2010. The increased amount of money showed the willingness of customers to purchase clothes online. Several reasons led to an increase in customer purchases online. The first main reason was convenience with most people claiming that they had less time available to go for clothes shopping in offline stores while there was an increased confidence in the use of the internet. Despite these factors causing an increase in the number of people purchasing online, middle-aged people spent the most amount of time online. This is attributed to the higher disposable income and the reduced time available for offline shopping. The convenience of online shopping was perceived by most participants to be the reason why there was a decrease in offline shopping and an increase in online shopping. Online clothes shopping stores also provide convenience by helping customers avoid shopping malls that are overcrowded. This convenience is important in ensuring providing a better customer experience for online clothes shoppers.

Despite the many advantages highlighted for online shopping, there are several fundamental reasons that push some people away from online clothes shopping. The main reason given by the study is the inability to ascertain the quality of the products provided. Furthermore, respondents claimed the inability to try on clothes was another turn off for online clothes shopping. Shopping has always entailed the trying out of new clothes. Online shopping lacks that ability at that is one of the main reasons why it was not very well accepted.

The study revealed important aspects about the demographic that is most likely to participate in online shopping. Age is an important variable why people purchase online. The middle aged are most likely to purchase clothes online due to having a higher disposable income while having less available to engage in offline clothes shopping. The younger generation may be more tech savvy that the older generation, but they have less money to participate in online shopping. The older generation is skeptical of online shopping since they already got used to shopping offline.

Gender is also another motivating factor in online clothes shopping. Women are well known for their love of shopping while men prefer convenience. Women are more likely to want to try out

(36)

35 | P a g e clothes that make them less likely to purchase clothes online. However, when they do purchase clothes online they refer more to product reviews as well as blogs in order to ascertain the best deals, as well as the cheapest prices. On the other hand, men are driven by convenience and are more likely to purchase items online. Men are also more willing to take risks with online clothes shopping as compared to their female counterparts. However, men purchase clothes less frequently than girls and this makes women the large market of the online clothing business.

The amount of income that people have also determined whether one is likely to engage in online clothes shopping. People with small incomes are not willing to risk in online clothes shopping while those with too much income prefer buying from expensive offline stores for prestige. This leaves those who earn a median income as the most likely to purchase clothes online.

Age, income and gender all have different effects on online shopping. Consumer’s attitude towards online shopping and their intent to buy are not just related to convenience, but also to variables such as their specific consumer characteristics, age, income and gender. A deeper understanding of these drives is essential for retailers to revise their strategies in online shop-ping. Taking the three variables discussed in this paper under advisement is suggested.

The higher levels of internet use among the young suggested that they would also be more likely to be online shoppers, however, this research has shown that older shoppers are just as numerous, despite their different use of the internet in other areas, such as social media. They do shop and in more or less the same number as the young do. News and current affairs are their websites of choice, and they use them enough to make for almost the same levels of use in general. their higher disposable income also contributes to higher profits for companies on fewer sales, and they are more likely than the young to seek out and compare information.

Gender’s effect on internet shopping is also different than previously imagined. male shoppers are focussed on goals when purchasing, both online and offline. Men have often made the choice of product and focus on it and aim to get the best deal for that product, at all costs.