The current chapter previous literatures reviews and studies related to the online behavior and intention and customers attitude toward online shopping. The chapter will explain various concepts related to online behavior such as attitude toward online shopping, consumer intention, perceived easy of use, perceived usefulness, and trust. After that, Technology Acceptance Model (TAM) theory will be explained and discussed. The purpose of the literature presented in this chapter is to look at past work in order to develop significant insights into the research area that is being examined (Henning et al., 2004). To provide necessary background for investigate, the chapter will provide recent studies and empirically finding of customers attitude toward online retail shopping.
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The internet populace has grown terrifically since its opening to commercial traffic in 1991, and it has reached more than 176 million having Internet access from the home or office (International Herald Tribune, 2002). In Asia, it increased to about 738 million by the end of 2000 (http://en.vivanews.com). Growing numbers online retail shopping shop online, to purchase goods and services, gather product information or even explore online retail shopping for enjoyment. This phenomenal driven by underpin consumers efficient use of time, as well as an increasing number of computer-trained consumers (Han and Maclaurin, 2002).
Literatures explain that online retailing relay largely on the characteristics of the products and services that would be presented. Various studies indicated that the suitability of online retailing to people varied by the type of products, due to either the exclusivity of the product itself, the nature of the Internet, or the newness of the distribution channel (e.g. Hui and Chau, 2002; Li and Gery, 2000). Furthermore, studies explain that to be fast-selling products on the Internet are clothes, books, computer products, travel, health and beauty products, in which marketer have sufficient information about their products (Reda, 2001; Schaeffer, 2000). Furthermore, in his study, Schaeffer (2000) explained that the recent integration of apparel manufacturers into direct Web selling, as well as the continuing invasion of traditional retailers into the online channel, has fueled the clothing surge.
Studies suggested that clothing retailing is more suffering from competition with other channels of distribution; this fact has challenges clothing retailers to adopt a multichannel strategy (Myers, 2002). Therefore, to expand their market share retailers can merge three different retail channels as a way to reach customers and channel brands internationally (Tiernan, 2000; Welling, 2000).
Moreover, the environments of online retail shopping founded to be an increasing importance for the overall relationship between e-retailer and their consumers. The impact of these online retail-shopping environments on consumer attitude and behavior is critical to understand for both for information technology and marketing planning. Literatures suggested that the web sites provide various benefits for both marketer and consumer (Hoffman et al., 1996). While a website can enlarge the image of online, retail shopping and create loyalty to its leadership (Sullivan, 1996). In his study, Kim (2003) uttered that a website can improve other communication channels with different organization.
In explaining, the major objective of a website, Simeon (1999) suggested that the objective of website is to attract a number of interested parties to visit the company’s online presence. Online retail clothes for example use web page as a mean to introduce themselves, services and products to a wide people all over the world, which create global corporate and product awareness in the market (Kim et al., 2003). In his study, Thelwall (2000) explained that because of the great role that website play to enhance business image and activities, its paramount important that webmasters should be aware of Internet technologies and be able to better design a good quality website and in the meantime they should try to provide an effective quality (Thelwall, 2000).
Numerous literatures considered how individuals interact with the computer interfaces, however relatively few studies have specifically underpin how individuals interact with the online shopping (Coyle et al, 2001; Li et al., 2002; Palmer and Griffith, 1998). Primary, literatures have suggested that environments influence consumer behavior (Bitner, 1992; Turley and Milliman, 2000). Online environments are different from offline environments in term of virtual vs. real small vs. large and sensory representation (Coyle and Thorson, 2001).
In his study, Bitner (1992) explained that online environments are specific types of interfaces, in which people access for the explicit purpose of shopping. Holbrook and Hirschman (1982) defined Shopping as “a consumption activity approached with a specific set of possible motivations which often go beyond the pure acquisition of products, and may include information acquisition and the search for escapism, fantasy or fun”. Literatures suggested that when people use their computers to visit retail web sites, shoppers deemed as consumers as well as other computer users. (Steuer, 1992).
Furthermore, literatures capturing the real meaning of the online interface that may be useful starting points, the focus of consumer research needs to remain on consumer perceptions of shopping environments, as opposed to user perceptions or technical properties of a generic interface (Draper et al., 1998; Huang, 2003; Davis et al., 1989). Voluminous studies of online shopping have considered the level of the individual attribute that influences behavior however, few attempts have considered the differential impact of holistic attributes of the environment on consumer response (Dailey, 2004; Fortin and Dholakia, 2005; Koufaris, 2002).
According to (Kim et al., 2003) many study has explained and determined the effectiveness of the websites and thus effort to enhance the quality of websites should been made. Furthermore, Kim et al., (2003) suggested that the quality of websites depends on three principle; presentation quality, content and function adequacy and navigability. Kim et al., (2003) explained that the performance principle consists of reliability of the website and the response time. Finally, they explained that development elements include flexibility, page coupling code readability, and modifiability.
Literatures have defined Usability as “a tool in which a system can be learned or used” (Kim, 2003), while Ease of use or usability defined as “a significant issue for a website” (Thelwall, 2000). Literatures suggested that used the requisite time, the error rate of the website and users prejudiced satisfaction are the basic measure of usability (Oppenheim and Ward, 2006). However, this type of measure for usability may not efficient to give solutions but assists the retailer to recognize problems and some hints for improvements (Oppenheim and Ward, 2006).
Literatures suggested that the design of web site on an online retail shopping (such as clothes) has a major impact on online purchase (Yang et al, 2003). Studies assist that when customers are unable to navigate around the site to find the accessories or clothes they require or are unable to carry out transactions securely on the retail web page, they are improbable to buy from the site or consider repeat purchases (Oppenheim and Ward, 2006). It is very important that the web site design of an online retail shopping is aimed at the retailer target market and that people are provided with an appropriate amount of information about the product they want to purchase (Oppenheim and Ward, 2006).
2.1 FACTORS AFFECTING ONLINE RETAIL SHOPPING
There are various factors that determinants of attitude toward online retail shopping, intention to shop online and online shopping behavior. these factors are;
2.1.1 ATTITUDE TOWARD ONLINE SHOPPING
Attitude is an individual evaluation about the consequences of performing a behavior (Athiyaman, 2002). Consistent with the findings of most IT adoption research, Attitude founded to be is a significant antecedent of intention to shop online” (e.g., Athiyaman , 2002; Chen et al., 2002; George 2002).
Reviews literatures, there are many studies of online shopping attitudes and behavior. Most of these studies attempted to explore factors influencing to online shopping attitudes and behavior. For example, Case et. at, (2001) has conducted expletory study among university students, they survey include 425 of undergraduate and MBA students. They found that income, internet knowledge, and the level of education found to be powerful predictors of students attitude toward online retail purchases.
Furthermore, Ho and Wu (1999) found that there are positive relationships between online shopping attitude and five categories of factors, these factors are:
E-stores logistical support,
Product characteristics and specification,
Web page technological characteristics,
information availability and characteristics, and
Schubert and Selz (1999) investigated the quality factors of online commerce sites in terms of information, agreement, and settlement phases. They also review the five factors related to online shopping that suggested by Ho and Wu (1999).
2.1.2 INTENTION TO SHOP ONLINE
Intention to shop online is the probability that an individual actually buys online (Chen et al., 2002). While many studies as well as the current study treats this variable as dependent variable, several studies found it to be an important determinant of online shopping behavior (e.g., Chen et al., 2002; George, 2002; Goldsmith and Goldsmith 2002; Limayem et al., 2000). Intention to shop online confirmed to be influenced by consumers’ Internet shopping history (Shim et al., 2001).
In their study, Eastlick and Lotz (1999) explain that prior online shopping experiences have a direct impact on Internet shopping intentions, the finding of their research confirmed by other researchers (e.g. Weber and Roehl, 1999). Helson (1964) has investigated individual response to a judgmental task, he explain that individual response is based on three aspects:
The past experiences
The context or background
Related to the online shopping climate, individual evaluate their Internet shopping experiences in terms of expectations and perceptions toward product information, the mean of payment, delivery terms, extra service offered, navigation and visual appeal, privacy issue, security, risk concerned personalization, , presentation, entertainment and enjoyment (Burke, 2002; Parasuraman and Zinkhan, 2002; Mathwick et al., 2002).
Furthermore, literatures explain that previous online shopping experiences resulted in satisfactory outcomes have positively leads consumers to keep on shopping on the Internet in the future (Shim et al., 2001). The positive attitude to experiences found to decrease people perceived risk levels associated with online shopping (Shim et al., 2001). Conversely, if these experiences were negatively, people attitude toward future online shopping would be reluctant to engage. In short, this explains to us the significance of turning existing Internet shoppers to repeat their shopping by enhancing their satisfying on the online shopping experiences (Weber and Roehl, 1999).
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2.1.3 PERCEIVED USEFULNESS (PU)
Perceived usefulness is the degree to which an individual believes that using a specific system would better enhance his job performance (Davis 1989). To online retail shopping (e.g. clothing), usefulness would refers to an individual perceptions that using the internet as online shopping medium to enhances their shopping experience. Such perceptions influence their attitude toward online shopping and their intention to shop on the Internet. Usefulness found to be linked with perceived ease of use to determine an individuals attitude toward online shopping.
Related to online consumer behavior, Childers et al., (2001), and Heijden et al., (2001) explain that perceived usefulness affects individual attitude toward online shopping. In the same way, literatures found perceived usefulness to be a considerable factor affecting intention to shop online (Chen et al., 2002; Gefen and Straub, 2000; Heijden et al., 2001; and Pavlou, 2001).
Furthermore, literatures confirm that there was a positive relationship between perceived control, perceived usefulness, and perceived ease of use of a web site (Chen et al., 2002). For example, if an individual find the web page of a retail clothing to be easy to use, useful, and safe to use, he or she would be more likely to make more purchases from this e-retailer. In the same bases, if an individual experience firm transactions online overtime and he or she feel confident about online transactions and shopping at online retailers, they are more expected to have higher purchase intention for the online retailer web site (Heijden et al., 2001).
3.1.4 PERCEIVED EASE OF USE (PEOU)
Literatures defined perceived ease of use as “the degree to which a person believes that using a particular system would be free of effort” (Davis, 1989). Perceived ease of use has received enormous attention in the IT adoption studies, these studies found that perceived ease of use influences attitudes toward online shopping (e.g. Chen et al., 2002; Childers et al., 2001; Heijden et al., 2001). To online retail shopping, perceived ease of use is an individual perception that shopping on the internet will involve a minimum of effort.
Whereas perceived usefulness referred to individual perceptions toward the outcome of the online shopping experience, the perceived ease of use refers to individual perceptions toward the process leading to the final online shopping transaction and outcome. Simply we can say that perceived usefulness is related to the effective online shopping to do a task, and perceived ease of use is related to how Internet as a shopping medium is easy to use.
Trust can be defined as “the confidence a person has in his or her favorable expectations of what other people will do, based, in many cases, on previous interactions: (Gefen, 2000). Therefore, underpinning the concept of trust is paramount important in the context of online retail shopping and consumer behavior. Momentous studies confirmed that trust is an outstanding determinant of online shopping attitude (George, 2002; Heijden et al., 2001; Pavlou and Chai 2002). Moreover, trust significantly affects a potential consumer’s intention to shop online. (Lynch et al., 2001)
The Internet shopping context regarded as a new form of commercial business that may involve a higher degree of uncertainty and risk when compared with offline shopping. The absent of trust is one of the most frequently cited reasons for individuals not shopping on the Internet (Lee and Turban, 2001). Individuals seems not fully comprehend the internet stores appear, as they cannot physically examine the products they before making a purchase. Furthermore, Lee and Turban (2001) explain that individuals cannot fully check the safety and security of sending sensitive personal and their financial information through the Internet.
Furthermore, many studies investigate how consumers perceived store size and reputation influence their trust in the store, risk perception, attitudes, and willingness to buy at the specific store (e.g. Jarvenpaa, et. at, 2000). The finding of these studies suggested a positive relationship between consumer trust in online shopping context and the stores perceived reputation and size. Jahng et. at, (2000) suggested that consumer trust reduces perceived risks associated with Internet shopping and improve a favorable attitudes towards shopping at a particular store, which in turn increases willingness to purchase from that store.
2.2 ADOPTION THEORIES
The literatures provide various theory and framework that we can used to investigate online shopping behavior and attitude, such as:
The Theory of Teasoned Action (TRA) proposed by Fishbein and Ajzen (1975),
Triandis’ model proposed by Triandis (1980)
The Technology Acceptance Model (TAM) proposed by Davis (1986),
The Theory of Planned Behavior (TPB) proposed by Ajzen (1991)
The Diffusion of Innovation Theory (DOI) proposed by Rogers ( 1995)
Reviewing literatures, we can easy note that Since TRA, TPB and TAM are the most popular theories. However, among the theories mentioned, the third theory (TAM) has been used primary by IT adaption filed. The current study thus, incorporates TAM theory to explain the customer’s attitude toward online shopping. TAM is very popular the most popular theories that employed to explain online consumer behavior, in following section of this chapter will review Technology Acceptance Model theory; based upon this theory, the researcher will we propose in chapter three a model of online shopping attitude toward retail clothes.
2.2.1 TECHNOLOGY ACCEPTANCE MODEL
In the seventies of the last century, studies have concerned to identifying the conditions or factors that facilitate the integration of information systems into business. Various factors have been indentified to influence the use of technology (Bailey and Pearson, 1983). In the mid-eighties, Information System (IS) studies have underpinned investigating models that could help in predicting system use. One of these models is Technology Acceptance Model (TAM), which proposed by Davis in 1989 in his doctoral thesis.
Technology Acceptance Model (TAM) is an adaptation of the theory of reasoned action. Attitude towards using (AT) and behavioral intention to use (BI) are common to TRA and TAM, when he proposed this mode, Davis used Fishbein and Ajzen’s method to measure them (Venkatesh, and Davis, 2000). By using TAM, Davis explained the determinants of user acceptance of a wide range of end-user computing technologies. TAM explain that perceived ease of use and perceived usefulness affect the intention to use.
Few years later after Davis proposed the model of Technology Acceptance Model; it has become one of the most widely used models of IT adoption. The model has received considerable attention in the IT community, and recently, studies suggested that this framework is best applies also to e-commerce and to the adoption of internet technology (Gefen and Straub, 2000). TAM become used as such or modified to study user acceptance of consumer services such as Internet services or e-commerce (Kaasinen, 2005).
Related to TAM, IT embrace is influenced by two variables: perceived Usefulness and perceived ease- of- Use. Perceived usefulness is defined as “the degree to which a person believes that using a particular system would increase his or her performance”. Perceive ease of use, in contrast, refers to “the degree to which a person believes that using a particular system would be free of effort” (Davis 1998). Two other constructs in TAM are attitude towards use and behavioral intention to use. Attitude towards Use is the user’s evaluation of the desirability of employing a particular information systems application. Behavioral intention to use is a measure of the likelihood a person will employ the application (Davis, 1989).
TAM assumed that the external variables interfere indirectly by influencing perceived ease of use usefulness. These external variables include factors such as situational involvement, argument of change, prior use, intrinsic involvement, Internal computing support, management support, external computing, internal computing training, external computing training, Role with regard to technology, tenure in workforce, level of education, prior similar experiences, Participation in training, Tool functionality, tool experience, task technology fit, task characteristics and etceteras. (Legris et al., 2003). Figure 1 show the original TAM model based on Davis et al., 1989).
Figure 1: Technology Acceptances Model
Source: (Davis et al., 1989)
In his PhD dissertation, Davis (1089) suggested that perceived ease of use as a factor, has a positive effect on system usage through perceived usefulness. Empirical studies of TAM have shown that usage of Information System (IS) is determined by user behavioral intentions, which themselves are jointly determined by user perceived usefulness and attitudes toward using the IS (information system), the last of which are jointly determined by user perceived usefulness and perceived ease of use as a factor. This also has a positive but indirect effect on attitude through perceived usefulness (Davis et al., 1989).
TAM model assume that a weak direct link between perceived usefulness and attitude, and a strong direct link between usefulness and intention (Davis et al., 1989). This was explained as originating from consumers intending to use a technology because it was useful, even though they did not have a positive affect toward using. According to TAM model, perceived usefulness is influenced by perceived ease of use because the easier a technology is to use, the more useful it can be (Venkatesh, 2000; Dabholkar, 1996; Davis et al., 1989).
Many Information System (IS) studies have been conducted based on the TAM, since perceived usefulness and perceived ease of use are two general beliefs suited to predicting information systems usage. Reviewing literatures, many empirical studies user acceptance of IT and the self-reported usage of IS have confirmed the hypothesis of TAM that perceived usefulness (PU) is directly related to IT/IS usage (Adams et al., 1992; Szajna, 1996). Furthermore, perceived ease of use has a positive confirmed to be direct effect on user acceptance of IT (Chau, 1996; Gefen et. at, 1998). However, no consistent conclusions have yet been reached about the effect of perceived ease of use has on IS/IT usage.
Voluminous studies have contributed to expand TAM framework in multiple directions. For example, TAM2 investigate the antecedents of perceived usefulness and incorporates the subjective norm (i.e., social pressures related to adoption (Venkatesh, 2000). Furthermore, objective Usability, computer self-efficacy, and experience with a system on perceived ease of use is examined (Venkatesh, 2000). Finally, the antecedents of perceived ease of use in terms of anchors (for example, general beliefs about IT and computer usage) and in term of adjustments (for example beliefs shaped by direct experience with the target system) have examined by researchers (e.g. Venkatesh and Davis, 1996).
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