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One important issue that needs clarification is whether a model based on the hierarchy of effects (such as the adoption of innovations model) is appropriate to the study of consumer adoption of electronic commerce. According to Gatignon and Robertson (1985), this type of model is an appropriate representation of the adoption process if the amount of cognitive processing involved is high. In their view, four variables determine the amount

of cognitive processing involved: consumer learning requirements, innovation or switching costs, social relevance and multiperson adoption unit.

Consumer learning requirements – For products requiring high consumer learning, a hierarchy of effects model should be expected. Purchasing using electronic commerce is likely to involve, to a large extent, learning, not only at the technological level (learning how to use the technology) but also at the purchasing level (learning how to use the technology for purchasing purposes). In the case of travel products the complexity associated with purchasing over the Internet is thought to be enhanced by the complexity of the travel product. This complexity is due to the existence of many and differentiated types of provider, not only at the highest level (airlines, accommodation, rent-a-car, tour operators, travel agencies, and so on), but also within each of these categories (independent hotels, chain hotels, consortium hotels; low-cost and full service airlines). Additionally, not only can the tourism product be presented in many different forms (packages or individual products, different types of food plans), but the rules that govern the purchasing and consumption of the product may be complex (for example, pricing often involves lengthy and complex restrictions).

Innovation or switching costs – When the adoption of the innovation involves high costs or it has consequences or costs for the consumption system in which they are placed, the hierarchy of effects model is likely to take place. This is certainly the case of electronic commerce, since the individual is likely to incur costs associated with the purchase and use of the technology, including a computer, the necessary software and the link to the Internet. Additionally, there may be indirect costs associated with misusing the innovation, such as those arising from purchasing the wrong type of product.

Social relevance – The greater the social relevance the more likely a hierarchy of effects adoption process. There is some suggestion that using electronic commerce is becoming more and more a socially relevant behaviour. For example, the degree to which ICT and e-commerce usage is often used as a measure of a nation’s development and many governments have specific plans for their promotion among citizens. Social relevance need not arise only from the desirability of adoption, but also from undesirability. For example, there may be some groups which associate using technology (computers, the Internet) and purchasing over the Internet as a signal of breaking traditional social relations within the

community. These individuals may not find acceptable purchasing methods that do not involve some degree of personalisation.

Multiperson adoption unit – Adoption decisions that involve other members of the social system are likely to follow a hierarchy of effects adoption pattern. This is likely to be the case when purchasing leisure travel. A recent study (Wang et al, 2004) found that 10 of the 13 sub-decisions associated with a family journey were joint decisions. More specifically, the study found that the choice of purchasing channel (i.e. the consideration and decision of what travel agency to use) was a shared decision for 85 percent of the families interviewed.

In addition, there is some agreement among researchers that both the use of ICT and of e-commerce entails a high degree of discontinuity (Rogers, 1995; Ram and Sheth, 1989;

Gatignon and Robertson, 1985). Therefore, it seems appropriate to have a hierarchy of effects models as frame of reference for this research.

4.4.3. Attitude

Attitude is one the areas that has received attention from researchers attempting to understand what determines the adoption of e-commerce. As Dickey et al. (2000) stated, gauging consumer attitudes toward online purchasing is crucial because not only does it provide insight into the short-term viability of e-commerce, but, even more importantly, it provides valuable information about consumers’ concerns and fears that must be addressed before implementing a successful strategy. The importance of attitudes in explaining the adoption of e-commerce has also been highlighted elsewhere (e.g. Goldsmith and Goldsmith, 2002; Shim et al., 2001).

4.4.3.1. Attitude models

Different models have been used to measure attitude towards purchasing over the Internet.

One such model is the expectancy-value model, notably the expectancy-importance approach. This model can be found in studies by Liao and Cheung (2001), Shim et al.

(2001) and more recently by Worthy et al. (2004). However, the majority of the studies on attitude towards purchasing over the Internet were developed using the composite model.

Studies involving TAM (e.g. Gefen and Straub, 2000; Childers, 2001; Chen et al., 2002;

Henderson and Divett, 2003; Chen and Tan, 2004; Vijayasarathy, 2004) and DAI (e.g.

Eastlick and Lotz, 1999; Verhoef and Langerak, 2001; Eastin, 2002; Pechtl, 2003) are examples of composite models of attitude. Other examples can be found in Teo and Yeong’s (2003) and Goldsmith and Goldsmith’s (2002) studies. The literature review did not reveal any studies explicitly undertaken based on the multicomponent and two-component models.

4.4.3.2. Perceived innovation attributes

There is significant evidence that potential adopters’ perceptions of an innovation influences their adoption decision (Rogers, 1995; Moore and Benbasat, 1991). Thus, understanding the perceptions of purchasing over the Internet may provide insights into why individuals use or do not use e-commerce.

Relative advantage

One assumption pervading research on e-commerce is that its use encompasses many benefits, that is, relative advantage. This is likely to be the result of the pro-innovation bias highlighted by Rogers (1995). However, it is important to differentiate between potential advantages and those actually perceived by consumers. For example, Subramanian et al (2000) argued that “the Internet interface, at the heart of the new process, provides a natural, use friendly and platform independent environment for the consumer to enhance the purchase experience” (p. 165).

However, research suggests that consumers in general do not perceive e-commerce as portrayed by these authors.

In broad terms, e-commerce provides buyers with an additional purchasing channel from which they can buy their leisure travel components. Not surprisingly, researchers have attempted to provide a more detailed description of the benefits that the e-commerce encompasses. However, there has been lack of a theoretically-based classification for the

benefits or advantages of e-commerce. Therefore, this section provides a classification framework of potential benefits of e-commerce in the purchasing of leisure travel. The proposed classification (Figure 4.1) is an adaptation of the framework proposed by Eason (1988) to classify the benefits of information technology in the office and presented in section 4.3.2.2.

Figure 4. 1: Consumer’s benefits from using e-commerce Source: adapted from Eason (1988)

Reduction of resources involves the accomplishment of the same outcome (e.g. purchase a flight ticket) using less resources. Resources involved in the purchasing though e-commerce refers to the effort, money and time needed to complete the purchase and these have been extensively studied (Hoffman and Novak, 1997; Strader and Shaw, 2000; Elliot and Fowell, 2000; Childers et al., 2001; Verhoef and Langerak, 2001; Chen et al., 2002;

Eastin, 2002; Elliot, 2002; Turban et al., 2002; Chang et al., 2005; Efendioglu and Yip, 2004; Worthy et al., 2004).

Effort refers to the physical resources necessary to complete the purchase. Examples of effort benefits include the ability to shop from different locations (notably work and home), eliminating the effort associated with travelling to the stores, and the access to all related providers for completing the purchase process through a single interface.

Since the Internet facilitates shopping in many places, individuals can compare prices among the different suppliers and potentially find the lowest price on the market, thus saving money. Moreover, some authors (e.g. Hoffman and Novak, 1997; Shaw, 2000; Turban et al., 2002; Lee et al., 2003) suggest that because of the greater competition and increased

Saving of resources

power of consumers, firms operating in the electronic marketplace are led to reduce prices in order to remain competitive. E-commerce not only enables the ‘direct’ saving of financial resources (i.e. those associated with the price of the product), but indirectly there are other costs that the individual does not incur, such as those associated with transportation to the store (for example, petrol and parking fees).

Time savings are gained from, for example, the ability to locate information quickly and not travelling to a store. Additionally, if the product can be digitised, such as the case of travel, the deliver of the product can be immediate (Elliot and Fowell, 2000; Turbal et al., 2002).

Many researchers have outlined the ‘convenience’ benefits of e-commerce, which includes both the elements of when a consumer can shop and where a consumer can shop (Childers et al., 2001). More specifically, these include the ability to shop from different locations, eliminating the inconvenience associated to travelling to the stores. Thus, convenience can be viewed as a combination of some of the previous resources, together with the ability to purchase at any time.

Improved productivity refers to the optimisation of resources. Generally speaking, this type of benefit involves maximising the purchase outcomes using the same resources.

Purchasing through e-commerce provides access to more options, access to information and booking at any time and facilitates comparison among alternatives.

Whereas the former two categories of benefits relate to the management of resources, and thus refer to more ‘quantitative’ aspects of purchasing, improved support and personal enhancement are associated with more intangible or ‘qualitative’ benefits. Improved support refers to the seeking of new ways of achieving the personal objectives. E-commerce facilitates access to a wide range of information, notably information that otherwise would be hard to obtain, such as the experiences of fellow consumers. For certain products, such as travel, e-commerce also enables a certain degree of ‘pre-testing’ of the product, by the means of pictures and videos. Moreover, e-commerce provides consumers with more choices, both in terms of suppliers and products. For example, the growth of low cost airlines was facilitated by the emergence of e-commerce. These airlines opened routes to many new destinations that were not covered by other airlines, thus extending the range of destinations available to the tourist. In addition, consumers are now able to purchase from many smaller tourism businesses whose existence they would not

have been aware of if it was not for the Internet. Thus, because consumers can get more information about the products and have access to a wider range of products, they are more likely to be in a position to make more accurate purchase decisions.

Finally, personal enhancement benefits are related to achieving the more important and abstract personal objectives in life. They can be regarded as the ‘end-states’ or values that an individual pursues. Two examples of such goal can be the enhancement of the quality of life and the enhancement of personal image.

Whatever the type of advantages, the “benefits of using e-commerce as compared to traditional channels are important in delineating whether consumers will have a positive attitude toward e-commerce”

(Childers et al., 2001; p. 515). Existing research seems to support this proposition, with results showing that advantages have a positive impact on online shopping (Chang et al., 2005). However, Chang and colleagues also noted that some inconsistent results can be found, notably in terms of price and transaction cost.

Compatibility

Several researchers have examined compatibility within the context of e-commerce (e.g.

Verhoef and Langerak, 2001; Chen et al., 2002; Oh et al., 2003; Chen and Tan, 2004;

Vijayasarathy, 2004). Compatibility in the context of e-commerce has been defined as “the extent to which a consumer believes that shopping online fits/matches his/her lifestyle, needs and shopping preference” (Vijayasarathy, 2004, p. 750). Compatibility addresses the social context in which online retail takes place (Chen and Tan, 2004) and is partially determined by the norms of the social system (Chen et al., 2002).

The results suggests that compatibility is a determinant of both attitude (e.g. Chen et al., 2002; Chen and Tan, 2004; Vijayasarathy, 2004) and intention (e.g. Verhoef and Langerak, 2001) to adopt e-commerce. As expected, the higher the compatibility, the higher the probability of adoption. In addition, it has been found that compatibility has a significant impact of perceived usefulness (Chen et al., 2002; Chen and Tan, 2004). Compatibility is likely to be influenced by time starvation (Vijayasarathy, 2004; Verhoef and Langerak, 2001), liking for in-home shopping (Vijayasarathy, 2004) and demographics, such as age and education (Verhoef and Langerak, 2001).

Chen et al. (2002), Chen and Tan (2004) and Vijayasarathy (2004) adapted the Moore and Benbasat (1991) scale to measure compatibility. This scale asks respondents to indicate the extent to which the innovation fits their lifestyle, the way they like to shop and seek product information and is compatible with their shopping preferences. Verhoef and Langerak (2001), on the other hand, measured the extent to which e-commerce suits the respondent, requires few adaptations in personal life and yields problems.

Complexity

Past research suggests that complexity is also an important determinant of e-commerce adoption (Gefen and Straub, 2000; Childers et al., 2001; Verhoef and Langerak, 2001; Chen et al., 2002; Vijayasarathy, 2004). Consumers who consider e-commerce as simple, easy to use or easy to learn have a more positive attitude towards e-commerce and demonstrate a greater intention to use e-commerce than those who perceive otherwise. The complexity associated with e-commerce refers to the extent to which purchasing over the Internet is perceived as relatively difficult to understand and use (Rogers, 1995). Studies have either focused on measuring complexity by the use of general statements, such as ‘complex’ or

‘easy’ (e.g. Gefen and Straub, 2000; Childers et al., 2001; Verhoef and Langerak, 2001;

Chen et al., 2002) or more specific areas of purchasing online. For example, Verhoef and Langerak (2001) measured the complexity of some steps in the purchasing process, such as how hard it is to find the needed products, how difficult is to order products and how problematic it is to compare products. Chen et al. (2002) included an item that evaluated whether the respondents thought that it was easy to find what he/she wanted. In addition to complexity of use, several researchers have studied the easiness/difficulty of learning to use e-commerce (e.g. Gefen and Straub, 2000; Chen et al., 2002; Vijayasarathy, 2004).

Finally, self-efficacy as also been used as a measure of complexity (e.g. Eastin, 2002).

Visibility

Visibility of e-commerce refers to the extent to which the individual has the opportunity to obtain information about the innovation without actually using it. Research has tended to operationalise this variable as the opportunity to see the innovation being used, that is,

‘sight visibility’. An additional way to operationalise visibility might be exploring the extent to which opportunities for discussion are available within the social system: the ‘verbal’

visibility. The adoption of innovations theory supports this type of visibility since it postulates that the extent to which an individual is able to communicate with opinion leaders or other adopters about the innovation may affect the adoption of that innovation.

For example, Katz and Aspden (1997) argued that social and work networks appear to be important for stimulating interest and providing users with support.

Trialability

Pechtl (2003) argued that trialability is assumed to establish no central characteristic [meaning] in online shopping but did not explain why. There are several possible reasons why trialability has not been regarded as a relevant characteristic to the adoption of e-commerce. First, Tornatzky and Klein (1982) reported inconsistent results on the effects of this attribute on adoption and this may have deterred researchers from using it. Second, trialability refers to the perception about the extent to which the innovation can be experimented with on a limited basis. Hence, assessing the perceptions of trialability only makes sense before adoption takes place. Eastlick and Lotz (1999), for example, only assessed trialability in non-adopters. Third, and perhaps most importantly, trialability in e-commerce means that the individual has to complete a purchase and hence e-e-commerce cannot be tried without the full commitment of the consumer. Thus, not surprisingly, a recent literature review on studies addressing the adoption of e-commerce found no references to this innovation attribute (Chang et al., 2005). However, one way of interpreting the concept of trialability within the adoption of e-commerce might be the purchasing of small value items before going into more expensive purchases. It can be argued that those who prefer to follow this pattern are experimenting with the innovation to check whether it is a viable option for future purchases.

Perceived risk

Many researchers have pointed out that the perceived risks associated with purchasing over the Internet are an important determinant of the adoption of e-commerce (Eastlick and

Lotz, 1999; Tan, 1999; Kim et al., 2000; Strader & Shaw, 2000; Cheung and Lee, 2001;

Eastin, 2002; George, 2002; Featherman and Pavlou, 2003; Forsythe and Shi, 2003; Lim, 2003; Liu and Wei, 2003; Pavlou, 2003; Teo and Yeong, 2003; Chang et al., 2005). In broad terms, perceived risk refers to the probability of any loss that occurs. In the field of electronic commerce, perceived risk can be defined as “the subjectively determined expectation of loss by an Internet shopper in contemplating a particular online purchase” (Forsythe and Shi, 2003, p.

869). When consumers perceive the likelihood of the outcomes of purchasing over the Internet not approximating the expected outcomes for that purchase, they may prefer to opt for an alternative method that entails less probability of expected losses.

Recently, Lim (2003) argued that it is important to differentiate sources from outcomes of perceived risk and stated that most of the research carried out in the past concentrates on outcomes. She identified seven outcomes of perceived risk that might be of interest in e-commerce adoption, with each of the dimensions referring to a type of loss consumers might perceive to suffer as a result of their actions. These outcomes of risk were financial, performance, social, physical, psychological, time-loss and privacy.

Consumers perceive risk because any transaction involves a certain degree of uncertainty.

According to Pavlou (2003), consumer uncertainty in electronic commerce is enhanced not only by the distant and impersonal nature of the online environment, but also due to the implicit uncertainty of using global open infrastructure for transactions. Pavlou (2003) further suggests that there are two types of uncertainty present in online transactions:

• Behavioural uncertainty exists due to the potential of Internet retailers to behave in an opportunistic manner and the inability of the government to monitor adequately all transactions;

• Environmental uncertainty results from the unpredictable nature of the Internet, which is beyond the full control of the Web retailer or the consumer.

Lim (2003) provided a similar, although more extended, scheme for classifying the sources of perceived risk. According to the author, perceived risk may be caused by one or more of the following factors:

o Technology sources: these relate to the lack of consumer control over how the technology (i.e. the Internet) handles personal information. It includes the issues of

security (e.g. danger of stealing credit card details) and privacy (e.g. use of cookies to collect personal information);

o Vendor sources: these include the dislike of dealing with unknown vendors who may not keep the promise of providing the service. In addition, potential misuse of credit card details by vendors and worries about the selling of customer information to third parties are important vendor sources of perceived risk;

o Product sources: these are related to the uncertainty about whether the products purchased meet expectations, in terms of fit and quality. Product sources are caused by the difficulty or impossibility to touch and feel the product;

o Product sources: these are related to the uncertainty about whether the products purchased meet expectations, in terms of fit and quality. Product sources are caused by the difficulty or impossibility to touch and feel the product;