Analysis Of The Characteristics Of Online Fashion Shoppers Marketing Essay

Published Date: 23 Mar 2015

Online fashion shopping is a segment of e-commerce that has both a large consumer marketbase and an ever-progressing curve of technology information development. The elements that go into market research for shopping online involve two main aspects: the consumer and technology, as discrete yet related elements. There has been much research conducted which explores both facets, and research which has attempted to view the impact of one area on the other. Consumer behavior in the online marketplace has typically been reported as influenced mainly by the factor of convenience. If it is convenient, then the consumer will favor online shopping. Technology has been most often cited to influence consumer shopping behavior if it is easy to use. Both the consumer and the technology element have undergone an evolution that is not yet finished in terms of defining market elements. As technology develops, so too that influence consumer behavior, which in turn influences technology, and so on. Various consumer models have evolved to explain online shopping behavior, and for the purposes of the following research thesis, both the utilitarian and hedonistic motivation models are referenced and applied. Additionally, various technology acceptance models have been posited as antecedents in defining consumer behavior for online shopping. A description of some commonly utilized models is provided in this thesis, though no one model is utilized here. However, elements of the technology acceptance models that are fairly ubiquitous across the models are utilized, those chiefly being the perceived ease of use (of technology) and the perceived benefit to the consumer of utilizing the technology (here, online fashion shopping). This research thesis looks at personal characteristics as they influence various behaviours and attitudes toward online fashion shopping. A survey questionnaire is used as the data gathering instrument. Responses are reported as frequencies and cross-tabulations. A discussion on the findings is offered in both the section on the analysis and in the conclusion of this thesis. CHAPTER 1: Introduction: Concepts, Issues, and Definitions

Statement of Purpose

The purpose of this research paper is to examine the attitudes and characteristics of people who use the internet to engage in online-shopping to determine if there are personal or demographic factors that influence online fashion shopping. Various technology acceptance models are examined and utilized to elucidate those factors of the consumer that drive their online fashion shopping behavior (Zhou, Dai, & Zhang, 2007).

The importance of this issue has its foundations in the growing prevalence in web-based consumerism. Research into online consumer shopping behavior tends to focus on either the technology aspect or the consumer aspect. These two approaches are but mirrors of each other, with data from each aspect used to tweak marketing and technology elements.

While a great deal of online shopping research concerns the web interface and how it is perceived by users and how that influence their buying behavior, there is also a great deal of research on consumer behavior, including demographics. The relevance of this particular research project approaches the topic from the consumer end, through examining their actual personal attributes and characteristics, with influencing factors as found in the technology aspect.

Consumers, in this case online fashion shoppers, have choices in their shopping preferences, which is of primary interest to retailers and marketers. The factors which influence their buying decisions from front-end identification of a need or desire to back-end fulfillment through market selection and procurement can be elucidated through examining the attributes and characteristics of these consumers (Cho & Fiorito, 2008). This data may inform not only the identification of trends in consumerism, but also practical applications for web-based marketing applications.

The first decade of the 21st century shows a domination of internet activity in many aspects of modernized cultures, where business to consumer marketing has proliferated (Dennis, Harris, & Sandju, 2002). As knowledge increases regarding the factors that influence online fashion consumers, business can incorporate both the consumer characteristics and the technology aspects required to meet those consumer characteristic-driven choices (Kim & Forsythe, Factors Affecting Adoption of Product Virtualization Technology for Online Consumer Electronics Shopping, 2009).

The outcome is an optimal online fashion shopping environment that recognizes and adjusts to the influencing elements of its customer base. This research paper will explore those characteristics of the online fashion shopper, in order to better inform both business and technology requirements in this market segment. A comprehensive literature review on the topic is offered to illuminate the topic. A research strategy for examining the topic is given, with relevant design elements in the data-gathering instrument chosen for data acquisition. A conclusion is provided to highlight the main points of this research paper and to synthesize the topics.

Research Question

The research question for this thesis project is posed thusly:

What are the attributes and characteristics of online fashion shoppers that influence their utilization of online shopping sites?

1.3 Objectives of Research

The objectives of the research focus on the following areas:

To examine the characteristic of online clothing customer.

To explore the attitude of people toward online clothing purchasing. 

To identify the factors for the use of online fashion shopping of computer-skilled people.

To suggest areas of future research for online shopping that are informed by factors of technology acceptance models.

The first two objectives of this research focus on consumer attributes. The third objective will be elucidated through exploring technology acceptance models. The final objective synthesizes the information gained from this research project.

1.4 Statement of General Topic Area: Online Fashion Shoppers, Defined

Online shoppers are those consumers which have the following general characteristics which can inform the conceptualization of the particular consumer:

They are computer-saavy (Dennis, Harris, & Sandju, 2002).

They have access to utilize a spending account that works with internal payment portals, such as a credit card, bank account, or paypal (Goldsmith & Goldsmith, 2002).

They seek expanded choice (Jayawardhena, Wright, & Dennis, 2007).

They seek convenience (Dixon & Marsden, 2005).

Psychologically, they seek immediate gratification (Zhou, Dai, & Zhang, 2007).

They shop for fashion items on the internet as a major shopping venue (Kim & Kim, 2004).

These generalities can be informed through further analysis in any one characteristic area. However, for conceptual purposes of this research paper, the online fashion shopper is a consumer of the modern age, encompassing the areas of technology in the internet age (Jayawardhena, Wright, & Dennis, 2007).

*BLANK PAGE BETWEEN CHAPTERS*

CHAPTER 2: Literature Review

2.1 Fashion is a Gendered Practice

Psychological antecedents exist in the research literature for online consumer behavior, yet fundamentally there is the issue of how 'fashion' is perceived as a concept and how that conceptualisation affects cultural and societal perceptions. This is important to understand for its influences upon consumer behavior and the identification of consumer attributes.

Fashion: An Overview

In the modernized world of first world countries, asking the typical person on the street what is meant by 'fashion' may likely get a response that has something to do with the way people dress. Others may see fashion as a trend in an of itself, as things pass in and out 'fashion.' Yet commonly held ideas about fashion do indeed refer to the individual outward trappings that humans utilize to communicate their presence in the social sphere. These may be clothing, accessories like jewelry, or even items like tattoos or other types of body art. In essence, the making of a modern identity for the individual involves blending fashion with current social mores and attitudes (Entwhistle, 2000).

Mears (2008) describes the concept of fashion in terms of how society chooses to communicate popular ideas of fashion through the use of fashion models on the catwalk. Here, posits Mears, the fashion model is constructed based on the floating norms of society, which are subject to the instability in gender ideations of society, and the whims of the cultural production markets. The instability between the rigid structures of the marketplace and an impossible idealized femininity are displayed in modern culture through the idea of the fashion model (Mears, 2008). Fashion, then, is not separate from the body. The body (self) and the items that 'dress' the body are one; they are a reflection of the idea of gender in society (which is variable) and the product of cultural variances in the marketplace. Fashion, in essence, is the product of cultural ideas and practices about gender, place, social norms, and institutions.

Concept of Gender

Gender does not merely refer to the male/female dyad, in which stereotyped ways of acting are considered to be socially acceptable at any given point in social time. It is not only role ideals, though that is part of it the concept. Gender also encompasses issues of race, sexuality, geographic and spatial time and place, and social class as well (Healey, 2006). Gender also refers to sexual orientation, which may differ from one's biological reality.

Buss (2010) reports that gender refers to mate selection, encompassing ideas of physical attractiveness, parental investment, reproductive capacity, and asymmetries between the maternal and paternal role. Those characteristics that were valued more by males had to do with reproductive capacity, while those characteristics valued by women dealt with resource acquisition. The cross cultural references can illuminate the ways in which the gender phenomena causes people to behave; the role of fashion, as one aspect of gender relations demonstrates the various cultural ways in which people may behave in order to choose suitable mates (Buss, 2010).

Gender, as recognized as encompassing social ideas about male/female relationships and roles, as well as referring to the individual selection of mates based on male/female desirable characteristics, can inform the manner in which fashion, as a modern notion, is influenced by role stereotypes and evolutionary reproductive tendencies.

Consider the role of the female model on the catwalk. Based on a construction of floating norms of society and marketplace vagaries, this idea of the female is highly sexualized, appealing to the male value of reproductive capacity in mate selection. The female idealization of the model is a communication not only to men, but also of women, stating that the ideal mate of the woman is one that can provide resources. It, the 'model of the catwalk fashion model', encompasses the evolutionary, social, and cultural communication desired by males, and recognized as such by females, through the manipulation of the fashion construct as a media tool of communication between the sexes (Entwhistle, 2000).

Fashion, therefore, is essentially a gendered practice. Indeed, this is the practice of communication; the communication of ideas, values, desires, social ideas, and cultural norms is offered through the gender-infused evolutionary and anthropological elements of roles, mates, reproduction, and species survival (Buss, 2010). The example of the fashion model is one easy way to see the transfer of gender ideas. Male-valued ideas and female-valued conceptualizations are quickly viewed through sampling the fashion of a culture. Mate selection (sexuality, reproduction) as one aspect of the gender phenomenon, is provided through the media of fashion communication, which differs depending on time, place, and cultural influences.

2.2 Online Consumers' Attributes

Sorce, Perotti and Widrick (2005) studied age differences in online buying behavior. They studies 300 college students and staff from a United States university and looked for trends in attitudes based on age differences toward online shopping. Their results indicate that older and younger shoppers buy equally, though younger shoppers and especially first time online shoppers tend to be more varied in the products they buy, while older shoppers tend toward specificity in their buying behavior. Additionally, age differences in buying behavior can help retailers predict age-related product purchase variance, with older shoppers tending to purchase family-related items such as toys and sporting goods at a greater rate than younger shoppers, while younger shoppers tended to purchase items like music, technology, and health-related services as a greater rate than older shoppers. This age difference in product segmentation reflects a broader trend in generational internet use. Jones and Fox (2009) report that the 18-32 year old age group are more tech-savvy 'digital natives' engaging in a wide range of internet activities, while age groups above 32 years tend to use the internet for banking, shopping, and research (Jones & Fox, 2009).

Gender differences have also been researched as indicators of online shopping preferences (Kim & Forsythe, Factors Affecting Adoption of Product Virtualization Technology for Online Consumer Electronics Shopping, 2009). The use of internet related activities has traditionally been apportioned to a male-dominated sphere (Jayawardhena, Wright, & Dennis, 2007). Women perceive a higher level of risk in online shopping than men (Garbarino & Strahilevitz, 2004). The trend from a male-dominated, pro-risk approach to internet use is changing, and women are fast closing the perceived gender gap in online utilization (Kim & Forsythe, 2009). The male/female difference may be different depending on culture and sexual orientation.

Weiser (2000) studied the factors that influence the use of the internet by women. A large survey sample of males and females was used to assess user preferences for internet use. Men preferred to use the internet for education and leisure, while women preferred to use it for communication and assistance with academics (Weiser, 2000). Wolf (2004) suggests that women tend to use emotion in their utilization of the internet, reflecting a stance that gender-based market psychology may inform retailers on how to construct their online portals. Rodgers and Harris (2003) report that three areas affect both men and womens use of e-commerce, those being emotion, trust, and convenience. Men report satisfaction with using the internet in these three area, while women report dissatisfaction in these same areas (Rodgers & Harris, 2003).

Income level is also positively correlated with online shopping utilization, regardless of gender or age differences (Zhou, Dai, & Zhang, 2007). Forsythe and Shi (2003) posit that high internet users tend to have high incomes. They note that perceptions of risk in internet shopping tend to decrease as income level increases. Internet users tend to have more disposable income, which is a reflection on the fact that they use the internet at all. Bucy (2003) reports that internet use is lowest among single mothers, those in lower socioeconomic groups, and older low-income groups.

Education has been reported to have an effect on online activity, though these reports have been mixed, ranging from no effect to increased effect of education on online activity (Zhou, Dai, & Zhang, 2007). Oftentimes college users simply have greater access to internet resources than other groups, suggesting that simple access as a fundamental resource may impact the utilization of online e-commerce (Seock & Norton, 2007). Mahmood, Bagchi, and Ford (2004), however, suggest that education is not an influencing factor in internet use, while the factors of trust and economic conditions are.

Convenience is often cited in existing studies as a factor involved in online shopping (Demangeot & Broderick, 2007). Park and Kim (2003) discuss the depersonalization of the shopping experience by the utilization of e-commerce. They note that users report a high utility in ease of gathering information and finding good quality through well-designed retail web interfaces, suggesting an overall satisfaction with the e-commerce as found in the factor of convenience. This view is disputed by Jayawardhena, Wright, and Dennis (2007), who find that convenience is not a principally motivating factor for online shopping; indeed, they report that the chief factors that influence online shopping are prior purchase and gender.

Trust is another factor involved in online shopping behaviour and speaks to the attributes of the consumer. Women tend to be less trusting of internet shopping than men, and younger people tend to be more trusting of utilizing the internet than older people (Dennis, Harris, & Sandju, 2002). Connolly and Bannister (2008) state that the consumers' trust in online shopping relates to the perceived integrity of the retailer, and the perceived competence of the retailer. Integrity is based upon a social perception while competence is based upon a technological perception.

Internet retailers who seek to engender trust in their customers must incorporate the e-commerce store front with the latest information technology combined with the latest consumer marketing research. How people choose to embrace technology is also an important consideration in internet marketing to online shoppers (Forsythe & Shi, 2003). This element can be explained through the examination of various technology acceptance theories.

2.3 Technology Acceptance Theories

Technology acceptance theories attempt to explain how and why people come to adopt and use new technology. While there are several models that exist to explain this phenomenon, this paper will report on four theories to place online fashion shoppers attributes within, to help illuminate the factors involved in explaining the utilization of online shopping. The factors of technology that influence online shoppers in how they utilize the internet will be analyzed through the survey instrument of this research project.

Online Shopping Acceptance Model

Zhou, Dai, and Zhang (2007) constructed an Online Shopping Acceptance Model, illustrating elements of the internet (technology) and attributes of the consumer, to show a flow process model incorporating the elements of motivation, innovation, perception, shopping orientation, normative beliefs, attitude, online experience, shopping intention, and satisfaction, resulting in the event of online shopping. This is an inclusive model, and draws together the salient marketing elements regarding online shopping. In the Zhou et al. (2007) study, the resulting construction of the OSAM offer future researchers a holistic way to approach the area of online shopping.

Theory of Reasoned Action

The theory of reasoned actions (TRA) was presented by Fishbein and Ajzen in 1980. The origins of the theory stem from the study of social psychology. This field attempts to explain why attitude may affect behavior. TRA seeks to explain and even forecast behaviour based on the beliefs, attitudes and intentions of people. An individual's behavior is a result of these three factors, according to the theory of reasoned actions model. According to Fishbein and Ajzen (1980), behaviour is driven by behavioural intention. A person's intentions stem from the attitude toward the behavior. Moreover, the behavior in addition to the subjective norms, are also affected. During one's lifetime, various beliefs can impact attitudes. Descriptive beliefs can be formed by personal experience, or gained by obtaining outside information. More generally, the more 'likable' an object/concept is, the better the feeling regarding it, and the more unlikeable an object is, the more negative the feeling is regarding it. As a consequence, an individual makes an assessment about the outcomes of various behaviors. Indeed, the person will evaluate the desirability of these outcomes and associate either a positive or negative association with it.

The TRA model. This model reports behavior as a consequence of intention to behave, which is prompted by the attitude toward the subjective norm. (adapted from Ajzen and Fishbein, 1980).

Technology Acceptance Model

The theory of reasoned action was modified and adapted into a new model, called the technology acceptance model (TAM). Whereas the TRA model uses behaviour and subjective norm, the TAM uses measures of technology acceptance. These measures are how people perceive the ease of use of the technology, and how useful they perceive it to be. These two acceptance measures are meant to inform on the intention of people to actually use the new technology.  Ease of use refers to the amount of effort the person perceives must be spent in using the technology, and usefulness refers to the perception of the person that the technology will benefit them in some way (Vijayasarathy, 2004). The following diagram illustrates the TAM:

http://www8.org/w8-papers/1b-multimedia/integrated/im_03.gifSource: Sodergard et al. (1999).

Diffusion of Innovations Model

Diffusion of Innovations theory is the process by which new ideas and technology spread throughout society. DOI tells us how fast and why new ideas spread in the manner in which they do (Rogers, 1962; 2003, pp. 5-7). The communication process through which a new idea or technology is accepted by consumers is the diffusion rubric.  The rate of diffusion is the speed by which that innovation spreads from one consumer to another.  Consumers, by definition, become so through learning about new products, trying them out, and either accepting them or not.  Previous methods of getting market knowledge to consumers was through utilizing the tools of mass marketing, which is essentially a catch-all process, and is both time-intensive and expensive.  Following mass marketing came the use of market strategies that identified and targeted heavy users of a product (Bass, 2004).  This has since evolved into consumer-specific, early adopter model of consumer behavior marketing. The Diffusion of Innovations theory tells us how this happens (Im, Mason, & Houston, 2007, pp. 63-66).

The following diagram provides a snapshot of DOI theory:

http://upload.wikimedia.org/wikipedia/commons/thumb/0/0f/Diffusionofideas.PNG/330px-Diffusionofideas.PNG

As the diagram shows, an innovation has adopter categories. These categories are based upon a consumers 'innovativeness', or likelihood to use the innovation, which can be plotted on an S-curve (Rogers E. , 1962; 2003). Early adopters have more 'innovativeness' than 'laggards' or late adopters. As more consumers adopt the technology (shown in blue), the market share (shown in yellow) will reach market saturation; diffusion of innovation in consumer behaviors thus tells us that once an innovation spreads to a certain point in the consumer base, the market becomes saturated (Rogers, 1962; 2003) (Wikipedia, 2010).

Four Key Elements of Diffusion of Innovations Theory

DOI theory harnesses the concepts of innovation, communication, time, and social system (the target arena), to explain how and at what rate new ideas move through society and from person to person (or more appropriately, consumer to consumer) (Rogers, 1962; 2003). One can imagine the spread of information about a new idea; one person learns of it, tells another person, that person tells another person, and so on until the innovation idea is spread throughout society. In marketing, this concept works on consumer behavior at a micro-level; someone tries out a new product, tells another person about it, that person tries it and tells another person about it, and so on. Often we here about these ideas, as found in common phrases such as "word of mouth is the best advertising", or "put your money where your mouth is", and "one good turn deserves another"; these types market-related processes where the consumer is the biggest advertiser is one example of product/idea diffusion as found in DOI theory (Solomon, 2004, p. 174).

To reiterate, the key elements of Diffusion of Innovations Theory are:

Innovation: the change, the idea, the product that is being launched.

Communication: the methods through which the information about the idea are being disseminated.

Time: the process by which a person (consumer) makes a decision about the innovation, and the speed by which the innovation is adopted by others.

Social System: the cohesive arena of people engaged in seeking a common goal.

(Rogers, 1962; 2003, pp. 17-24).

Decision Making in Diffusion of Innovation Theory

A consumer (this report is focusing on consumer behavior) is exposed to an innovation, whether it be a new product or service, and must make a decision about it; will they use it, like it, reject it? The process of decision making in innovations follows a three step model:

Optional Innovation Decision: the decision about the innovation is made by a consumer who is somehow different from other consumers in the same social system.

Collective Innovation Decision: the decision about the innovation is made by all the consumers within the particular social system.

Authority-Innovation Decision: the decision about the innovation is made by one consumer, a leader, an authority figure or figures who are in a position of power.

(Rogers, 1962; 2003, p. 372).

Some types of decisions are dependent upon decisions made about other innovations. For example, a housewife will not be able to trial a new cleaning product until her local store makes the decision to stock the product. That is called a contingent decision innovation (Persichitte, Tharpe, & Caffarella, 1999).

Adoption Process in Innovation Decision Making

Within DOI theory, there is a decision making process as generally outlined above, as well as a finer decision making process consisting of five stages, known as the Adoption process. The following model details the Adoption process:

http://www.fidis.net/typo3temp/tx_rlmpofficelib_8cb7cf257e.png

(Royer, 2010).

Knowledge: the consumer does not have enough information to make a decision, and may not yet be inspired to seek the information.

Persuasion: the consumer is actively engaged in seeking information about an innovation.

Decision: the consumer is weighing the pro's and con's of the innovation, and may have trouble making a decision.

Implementation: the consumer is trialing the innovation, and determining its usefulness.

Confirmation: the consumer is finalizing their decision about the innovation.

(Rogers, 1962; 2003, p. 83).

This is a highly dynamic process, and while the consumer is moved through the stages, this process may rely as much on psychological inputs and variances as it does on hard empirical facts (Kleijnena, Leeb, & Wetzels, 2009).

Factors Affecting Adoption of Innovations

The speed at which an innovation is accepted/adopted depends upon the category of the adopter. A consumer can be a respected person and thus with a vast social network to draw upon, the adopter(s) can be a group of consumers who will heavily use the innovation, or the adopter can be one of the early variety who is influenced by positive messages about the innovation. Through influencing the adoption process, consumer behavior can be directed toward moving the innovation's acceptance toward a place on the DOI curve known as critical mass, where the innovation's use and adoption are self-sustaining (Rogers E. , 2004).

Innovations have inherent characteristics that act upon a consumer's choice of adoption or rejection.  First the innovation has some type of relative advantage over its predecessors; second is that the innovation has the quality of compatibility to be utilized in the consumer's life; the innovation must be sufficiently complex but not too complex for the consumer to use; the innovation must be able to be trialed with relative ease; and finally the innovation that has high visibility to others is more likely to be adopted by peer consumers (Tornatsky & Klein, 1982, pp. 28-30).  Applicable to online fashion shopping, one can meet the conceptual requirements of innovations, in that online shopping is more convenient than visiting brick and mortar stores, online fashion shopping for those that prefer to do their fashion shopping over the internet is highly compatible with the consumer's preferences, online shopping for the skilled computer user is very ease to use, and the prevalence and spread of e-commerce makes online fashion shopping an attractive venue for the savvy fashion shopping population who use computers.

Consequences of Decisions

When a consumer makes a decision, there will be some type of consequence or outcome (Burt, 1987). Three categories characterize the nature of these consequences:

Desirable vs. Undesirable

Direct vs. Indirect

Anticipated vs. Unanticipated

(Hubner, 1996) (Rogers E. , 1962; 2003).

A consumer's choice to use one product over another may have undesirable, indirect, and unanticipated consequences, such as the choice to buy fashion apparel online rather than at the locally based brick and mortar store. Where the brick and mortar store supports the local economy in which it exists, providing jobs and taxes, the internet based retailer may be not locally based, thus taking away monies from the local shops that may suffer economically.

2.4 Summary of the Literature

The literature thus presented has focused on the concepts of fashion and gender, attributes of online consumers, and technology acceptance models. While e-commerce is a product of the internet age, the field of research regarding online fashion shopping behavior and attributes of online fashion shoppers clearly leaves room for discovery. At best, the antecedents in the research literature offer a way to forward in the research through exploring the new face of consumerism in the age of technology.

The idea that is emerging in research into online shopping is that new models are coming to the forefront which offers a holistic picture of the factors that influence the final outcome of product/service purchase. On one hand there is the consumer, and on the other hand there is technology. In between the two facets are many influencing factors, which through the survey questionnaire designed for this research, some of those issues will be examined.

CHAPTER 3: Research Strategy

Conceptualization of the Issue

The purpose of this chapter is to define the methodology used to evaluate issues associated with online fashion shoppers. Given the growing prevalence of e-commerce and the spread of available internet to populations across the world, the nature of the consumer as a field of study is of high relevance. The attributes and characteristics of the consumer may change with time, place, and influencing social climates. While various technology acceptance models exist to explain how people come to adopt/accept the use of technology, the particular factors within these models, as previously described in Chapter 2, all have the element of 'personal attributes'. The Online Shopping Acceptance Model, the Theory of Reasoned Action, the Technology Acceptance Model, and the Diffusion of Innovations model all have the characteristic of the individual and their attributes as part of the model. This research project does not seek to construct a new technology acceptance model; rather, this project seeks to add to the research literature on the attributes and characteristics of a particular type of consumer, that of the online fashion shopper.

Description of Critical Variables

The independent/response variables for this research are identified as the personal characteristics and attributes of the survey respondents, such as age, income, gender, level of education, sexual orientation, and cultural identification. The dependent/explanatory variables are the survey responses on attitudes toward online shopping.

Hypotheses

The following hypotheses are made regarding the potential findings of the survey questionnaire and in keeping with the research question.

H1: Attitudes about online fashion shopping are associated with a person's age, gender, income, and education level.

H2: Attitudes about online fashion shopping are associated with the type of culture a person identifies with (individualistic or collectivist).

H3: Attitudes about online fashion shopping are related to gendered-fashion issues, such as sexual orientation.

Methodology

The research for this thesis project utilizes a survey questionnaire to gather information. The measurement technique is the survey instrument. Generally, surveys are exploratory in nature. Attitudes, beliefs, and behavioral intentions are hypothetical constructs. They cannot be measured directly, and therefore must be inferred from a person's responses. Hence, attitudes are measured by way of inference from an observed behavioral response (Fishbein & Ajzen, 1980).

The survey presents a series of personal characteristic and attitudinal type questions, designed to reflect the respondent's beliefs about their intentions and behaviors toward online fashion shopping. A Likert rating scale and single answer multiple choice questions are used for this survey as the main measurement technique. The Likert scale is a summated rating scale that provides a simple and economic means of obtaining data on a person's attitudes, beliefs, and behavioral intentions (Fowler, 2009).

Sampling Procedures and Questionnaire Design

This research project utilized an online survey dissemination. The survey itself consists of four pages, mailed through an online link through a Facebook page, potentially linking to 200 people. People on the Facebook group were sent a link to the survey interface, and of the 200 people that it was sent to, 30 completed responses were gathered in a 7 day period. This represents a 15% response rate. As all respondents were, a priori, computer users, the integrity of the responses is somewhat strengthened.. Additionally, most people in this online group regularly post links to shopping sites, political sites, and general news around the world, so the data administrator utilized this closed sample to gather attitudes about those in this discrete group of consumers: online fashion shoppers.

The questionnaire includes questions aimed at assessing the respondents attitudes and beliefs regarding online fashion shopping. The first part of the questionnaire deals with the independent variables, such as age, income, education, gender, sexual orientation, and cultural identification. The second part of the survey focuses the dependent variables, those being aimed at eliciting the attitudes of the respondents toward online shopping based upon a set of questions regarding technology, online shopping, and issues related to those elements.

The survey analysis is given through converting raw observed frequencies into percentages. This allows patterns in the data to be more readily apparent. On the basis of these percentages, inferences can be drawn about the sample, that is, what is in the data. The frequencies help to identify characteristics or attributes that may be indicative of common traits and properties of the survey sample, and perhaps in future studies can be applied to wider samples for data comparisons.

Data Limitations

Data limitations of this research project pertain to the small survey sample, and the closed nature of the survey. Data was essentially limited to a small online group that is implicitly skilled in computer use. This survey questionnaire did not capture responses for other categories of online shopping; only 'fashion' shopping was indicated on the questionnaire. Additionally, as the user group was randomly chosen, it was unknown if the user group would capture a wide variety of income levels, education levels, or age groups. However, the results do indicate a surprisingly highly diversified group. Additionally, the data limitations pertain to the time limitations in conducting the survey. With more time and a broader sample, a more robust data set would likely be gathered.

CHAPTER 4: Data Presentation, Analysis, and Discussion

The purpose of this chapter is to present the results of the survey questionnaire and to analyse the data. This is accomplished through a frequency analysis with corresponding visuals. In keeping with the hypotheses presented in Chapter 3, conclusions are reached which either reject or fail to reject the null hypotheses.

Results of the Frequency Analysis

A frequency analysis was performed to summarise the responses and allow patterns in the data to be more immediately apparent. The following presentation and discussion highlights frequencies of responses as they assist in understanding attitudes and behaviours toward online fashion shopping, as well as the personal characteristics of the respondents that may influence such perceptions.

Survey Response and Error Rate

A total of 30 responses were returned, which were usable and thus drawn upon for the analysis. This is a 15% response rate. The sampling design for this research attempts to achieve a 95/5 confidence level and error range. This was not achieved due to the low response rate of the surveys. In survey research, time and money are often factors limiting the sample size. Other factors, as discussed in the section on data limitations, also restricted this particular research project. In order to achieve the 95/5 confidence interval, this research survey rate of return would have needed to reach the order of 132 returned surveys out of the sample population of 200. Therefore, the error range for this research is 7%, meaning that for the percentages we view within the survey, we can be 95% certain that any given percentage does not err higher or lower by more than 7 percentage points.

Granted, this is larger than the desired range of error and has some drawbacks in the analysis and conclusion of the data. The accuracy of the results is sacrificed when the error rate is larger rather than smaller. The conclusions reached are subject to this question of accuracy given the error rate, and must be taken into account with the analysis (Fowler, 2009). Nonetheless, the results are useful and some conclusions can be made.

Personal Characteristics of the Respondents

This section is a summary of the personal characteristics of the respondents. These factors represent the independent variables, which the hypotheses of this research thesis hold that these characteristics do influence attitudes and behaviours of the respondents toward online fashion shopping. The information obtained in this research focuses on a survey sample reflective of the variability within the computer-using e-commerce community. Understanding personal characteristics may help to identify areas of interest as well as areas of potential in relation to attitudes and behaviours in online shopping.

Question #1 of the survey asks if the person has ever shopped online, represented thusly:

In the survey sample, 93.3% of respondents indicated that they do shop online, with 6.7% indicating a null answer. This question was a control question, identifying those participants who had not yet used e-commerce in their online activities.

Question #2 delineated the issue further, asking if the respondents had ever shopped for fashion items online. As in the previous question, the responses indicate that 93.3% have shopped for fashion items online, while 6.7% have not.

Questions #3 asks the respondents to indicate their biological gender. The responses are illustrated in the following chart:

The responses indicate that 70% of the survey sample were female, while 30% were male. Alreck and Settle (2002) note that in previous studies on gender and online shopping, men tend to be more heavy shoppers than women for online shopping. However, Hansen and Jensen (2009) note that buying fashion items like clothing is less fun for men than it is for women, with women indicating a preference for clothing shopping. This may demonstrate a trend reflecting less on the role of gender in online shopping, and more emphasis on a gender role for shopping preferences.

Question #4 asks the respondents to indicate their age group. The results are as follows:

The results indicate the largest percentage of respondents falling within the 18-30 age group, at 50%, followed by 26.7% in the 31-44 age group, 20% in the 45-60 age group, and 3.3% in the 60-70 age group. Given the nature of the survey sample, this is surprisingly diversified and seen as a strength of this research project. Sorce, Perotti, and Widrick (2005) report that as far as actual purchasing there is no difference between older and younger buyers, though older shopper tend to be more specific in their searches and buying of items.

Question #5 asks respondents to indicate their sexual orientation. In the Chapter 2 literature review for this thesis, the issue of fashion being genderized helped to inform the concept of what the public considers as fashion, and how that is influenced by ideas on sexuality. Therefore, including this question here can help inform the discussion on whether a online shoppers, in shopping for fashion, are influenced by their perceptions of sexuality as seen from their sexual orientation. The results are thus:

Interestingly, 20% of the respondents indicate a same-sex preference orientation, while the remaining 80% indicate an opposite-sex preference orientation. As notions of sexuality and gender roles subtly influence the fashion industry, it may be well worth the marketer's time to examine the niche market of online fashion shopping not only for closing the biological gender gap, but also closing the perceptual gender gap. Reilly, Rudd, and Hillary (2008) report that in a study of gay men's online shopping preferences, body image had an affect on their shopping choices. Gay and lesbian online shoppers tend to make their shopping choices to reflect their cultural identity, as well as to reinforce their individual identity and novelty-seeking behavior.

Question #6 asks respondents to indicate their income level:

The results indicate a fairly even division among income groups earning less than $71,000 U.S. Dollars per year, with a collapsed data value of n=21. A noted drop in the frequency of reporting occurs for those earning more than $71,000 per year, with a collapsed date value of n=9. It may be that those earning more income have more disposable income, yet it also could be fair to say that disposable income depends upon lifestyle. In a 2008 Pew Internet & American Life Project Survey, 44% of respondents with incomes lower than $25,000 per year suggest that sending credit card information over the internet is risky, compared to 25% of high income respondents saying that they did not like sending credit card information over the internet. The suggestion is that lower income groups feel less safe about sending their financial information over the internet, perhaps reflecting an income bias toward e-commerce regardless of shopping preference or other factors (Pew Internet & American Life Project Survey, 2008).

Question #7 asks respondents to indicate their education level, with results illustrated in the following chart: