3 Theoretical Underpinnings. 3.1 Theory of Reasoned Action (TRA)

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1 3 Theoretical Underpinnings Theories play a critical role in empirical researches. According to the Merriam- Webster dictionary, theory refers to a plausible or scientifically acceptable general principle or body of principles offered to explain phenomena. In this research, various theories have been explicitly or implicitly implemented to understand consumer tele-shopping perception, including the theory of reasoned action, the theory of planned behavior, the technology acceptance model, transaction cost theory, innovation diffusion theory etc. 3.1 Theory of Reasoned Action (TRA) TRA is a general well-researched intention model that has been applied extensively in predicting and explaining behaviour across many domains and virtually any human behaviour (Ajzen and Fishbein, 1980) in both online and offline context. Information System researchers often use this theory to study the determinants of IT innovation usage behaviour (Han 2003). Although current models of technology acceptance have their roots in many diverse theoretical perspectives, much literature related to technology acceptance begins studies with the Theory of Reasoned action (TRA). Figure 3.1 THEORY OF REASONED ACTION (TRA) 39

2 Consumers normally form perception that influence purchase intention to buy products through teleshopping. Therefore, television usage and perception towards teleshopping are strong predictors of the intention to purchase products through television (Salisbury et al., 2001; Eagly and Chaiken, 1993). These intentions and their influence on behaviors were first cited and developed in the Theory of Reasoned Action by Fishbein and Ajzen (1975). The theory suggests that behavioral intention leads to behavior and also that it determines consumer s attitudes toward purchasing or using a brand by influencing the normative value or subjective norm (Fishbein and Ajzen 1975). In this theory, socially relevant human behaviors are under the control of the individual and the most direct powerful predictor of a behavior is the intention to engage in that behavior. The Theory of Reasoned Action is based on the assumption that people make rational decisions based on the information available to them and their behavioral intention to perform or not perform a behavior is the immediate determinant of their actual behavior. This assumption has limitations in terms of generalization of results because it is difficult to exactly specify the expected behavior, target objective and time frame in each situation. According to researchers, it is not necessary to have a relationship between any given external variable and actual behavior because external variables often change over time (Ajzen and Fishbein, 1980). Hypothesizing that a given external variable is stable could harm the validity of the theory Nevertheless; the advantage of the Theory of Reasoned Action is the inclusion of subjective norms that can play an important role in certain situations. The Theory of Reasoned Action has been shown to have strong predictive power of consumer s behavioral intention formation for a variety of consumer products such as fashion, beer, toothpaste, dog food, mineral water and facial tissue (Chung and Pysarchik, 2000). Cho (2004) and Verhoef and Langerak (2001) adapted the TRA to study teleshopping intention. Specifically, Cho assumed that intention toward teleshopping is determined by perceived 40

3 consequences associated with e-shopping, past behavior, and attitudes toward other shopping channels, and that likelihood to abort an intended teleshopping transaction is jointly determined by these three dimensions as well as the attitude toward for e-shopping. Verhoef and Langerak (2001), who employed the TRA in a study, found that outcome beliefs had a significant influence on the intention toward teleshopping. The perceived benefits of teleshopping in relation to traditional store shopping are one of the attributes positively related to intention towards teleshopping. In these two studies, subjective norm was not considered as a determinant of behavioral intention, in keeping with its less well-understood status (Fishbein and Ajzen, 1975). 3.2 The Theory of Planned Behavior (TPB) Ajzen (1985) extended the Theory of Reasoned Action (TRA) to account for other conditions, where individuals do not have complete control over their behaviors. Similar findings are also evident in the research of Liska (1984) and Shappard, Hartwick and Warshaw (1988), who argued that TRA does not adequately deal with behaviors that require resources, cooperation, or skills (Chiou, 2000). In order to reduce these limitations, Ajzen (1985) incorporated an additional variable of perceived behavioral control into the model of reasoned action and called this new model, the Theory of Planned Behavior (TPB). The Theory of Planned Behavior suggests that intentions and facilitating conditions are the direct antecedents of behavior and at the same time, behavior is also affected by habitual arousal. This extended model has a strong ability to predict behaviour, even though it suffers empirically from multi-co linearity among independent variables employed in the model. TPB has been used in many different studies in the information systems literature (refer. Mathieson, 1991; Taylor and Todd, 1995, b; Harrison et al, 1997). TRA 41

4 and TPB have also been the basis for several studies of virtual store purchasing behavior (Battacherjee, 2000; George, 2002; Jarvenpaa and Todd, 1997, b; Limayem et al, 2000; Pavlou and Chai, 2002; Suh and Han, 2003; Song and Zahedi, 2001; Tan and Teo, 2000). According to TPB, an individual's performance of a certain behavior is determined by his or her intent to perform that behavior. Television is itself informed by intention toward the behavior, subjective norms about engaging in the behavior, and perceptions about whether the individual will be able to successfully engage in the target behavior. According to Ajzen (1985) an attitude toward a behavior is a positive or negative evaluation of performing that behavior. Attitudes are informed by beliefs, norms are informed by normative beliefs and motivation to comply, and perceived behavioral control is informed by beliefs about the individual's possession of the opportunities and resources needed to engage in the behavior (Ajzen, 1991). Figure 3.2 The Theory of Planned Behavior (TPB) The TPB is an extension of the TRA. The major difference of the TPB from the TRA is its inclusion of perceived behavioral control (Ajzen, 1991). The TRA 42

5 assumes that actual behavior is a motivational result of behavioral intention, and it does not consider the influence of behavioral constraints on the link between intention and behavior. In reality, most behavior is to some extent dependent on non-motivational factors such as availability of resources and opportunities. For example, an individual with a high intention to engage in e-shopping may not do so due to the lack of availability of the network. These factors can represent actual behavioral control; however, psychologists are more interested in the perception of behavioral control and its influence on behavioral intention and actual behavior. Perceived behavioral control refers to an individual s perception of how difficult it is for her to perform a behavior (Ajzen, 1991). As shown in Figure 2, the TPB postulates that an individual s behavioral performance jointly relies on and can be predicted by her behavioral intention and perceived behavioral control. Empirically, Hansen et al. (2004) applied both TRA and TPB. They found that TPB with an additional path from subjective norm to attitude explains a higher proportion of variation in online grocery purchasing intention than does TRA. Choi and Geistfeld (2004) used perceived risk and perceived self-efficacy to measure the individual s attitude and perceived behavioral control, respectively. Limayem et al. (2000) augmented the TPB with two additional constructs: perceived consequences and perceived innovativeness. These two constructs were assumed to influence both attitude and behavioral intention. In their models, subjective norms were evaluated by an individual s perception of the opinions of her family, friends, and media; and behavioral control consisted of site accessibility, product description, transaction efficiency, navigation ability, speed, and efficiency. Shim et al. (2001) adapted the TPB by incorporating the influence of past behavior and ignoring the attitude toward the behavior. Dan Su and Xu Huang (2011) found in his study of Online Shopping Intention of Undergraduate Consumer in China that online shopping intentions is greatly influenced by price of goods and the student s knowledge on computer and online currency clearing. 43

6 3.3 Technology Acceptance Model (TAM) The Technology Acceptance Model (TAM) was developed from TRA by Davis (1989). The Technology Acceptance Model (TAM) is an information systems theory that models how users come to accept and use a technology (Wikipedia). The model suggests that when users are presented with a new technology, a number of factors influence their decision about how and when they will use it, notably. Perceived usefulness (PU) - This was defined by Fred Davis (1989) as "the degree to which a person believes that using a particular system would enhance his or her job performance". Perceived ease-of-use (PEOU) - Davis defined this as "the degree to which a person believes that using a particular system would be free from effort" (Davis,1989). The TAM have been continuously studied and expanded, the two major upgrade being the TAM 2 (Venkatesh and Davis, 2000 and Venkatesh, 2000) and the Unified Theory of Acceptance and Use of Technology or UTAUT, Venkatesh and Davis, 2000). A TAM 3 has also been proposed (Venkatesh and Bala, 2008). 44

7 Figure 3.3 Technology Acceptance Model (TAM) This model used TRA as a theoretical basis for specifying the causal linkages between two key beliefs: perceived usefulness and perceived ease of use and users attitudes, intentions and actual computer usage behavior. Behavioral intention is jointly determined by attitude and perceived usefulness. Attitude is determined by perceived usefulness (PU) and perceived ease of use (PEOU) (see Figure 3.3 TAM replaces determinants of attitude of TRA by perceived ease of use. TAM specifies general determinants of individual technology acceptance and therefore can be and has been applied to explain or predict individual behaviors across a broad range of end user computing technologies and user groups (Bagozzi et al. 1989). The goal of TAM is to provide an explanation of the determinants of computer acceptance that is in general capable of explaining user behavior across a broad range of end-user computing technologies and user populations, while at the same time being both parsimonious and theoretically justified. But because it incorporates findings accumulated from over a decade of IS research, it may be especially well-suited for modeling computer acceptance (Bagozzi et al. 1989). Recently, Venkatesh and Davis (2000) proposed a second version of the TAM, which incorporates additional constructs regarding social influence (including 45

8 subjective norm, voluntariness, and image) and cognitive instrument process (including job relevance, output quality, and result demonstrability). Legris and his colleagues (2003) supported the usefulness of the TAM after reviewing a number of empirical studies, but they pointed out that results based on the TAM are not totally consistent or clear. They recommended the incorporation of factors related to human and social change processes, and the adoption of an innovation, into the model Transaction Cost Theory (TCT) Williamson (1985) defines a transaction as a process by which a good or service is transferred across a technologically separable interface. In classical economic theory, it is assumed that information is symmetric in the market. Since both buyers and sellers are assumed to have the same amount of information, the transaction can be executed without cost. In reality, however, markets are often inefficient. In order to proceed with a transaction, consumers must conduct activities such as searching for information, negotiating terms, and monitoring the on-going process to ensure a favorable deal. The costs involved with such transaction-related activities are called transaction costs (Liang and Huang, 1998, p. 31). TCT can explain various problems of economic organizations (Rindfleisch and Heide, 1997). Its basic principle is that individuals would like to conduct transactions in the most efficient way (Williamson, 1985). That is, the lower the transaction costs, the more likely individuals are to conduct the transaction. Transaction costs are determined by several constructs, including uncertainty and asset specificity as shown in Figure 3.4. Since assets with a high amount of specificity represent sunk costs that have little value outside of a particular exchange relationship (Rindfleisch and Heide, 1997, p. 41), higher asset specificity is associated with lower transaction costs for the exchange relationship to which the specificity applies, and higher transaction costs for other exchange 46

9 relationships. Since information in the market is always asymmetric, the outcomes of a transaction may not follow, or may even be contrary to expectations, leading to uncertainty. Transactions are encouraged through reducing uncertainty, as one form of lowering the transaction costs. Asset specificity refers to the lack of transferability of the assets from one transaction to the other. Figure 3.4 Transaction Cost Theory (TCT) Liang and Huang (1998) applied TCT to investigate consumers intention to teleshop. In addition to the modeling structure presented in Figure 3.4, they further assumed that e-shopping intention is also directly influenced by uncertainty and asset specificity. Teo and Yu (2005) proposed that buying frequency is also a (negatively-associated) predictor of transaction costs, with trust replacing asset specificity. TCT can explain various problems of economic organizations. Rindfleisch Liang and Huang (1998) applied TCT to investigate consumers intention to teleshop. In addition to the modeling structure presented in Figure 4, they further assumed that e-shopping intention is also directly influenced by uncertainty and asset specificity. Teo and Yu (2005) proposed that buying frequency is also a (negatively-associated) predictor of transaction costs, with trust replacing asset specificity. 47

10 3.5 Innovation Diffusion Theory (IDT) Compared to traditional shopping, teleshopping is an innovative application of information technology by retail industries. Therefore, IDT can be applied to explore consumers e-shopping behavior. According to Rogers, diffusion is characterized by four elements contained within the process; whereby (1) an innovation is (2) communicated through certain channels, (3) over time, and among members or a (4) social system. There are five primary characteristics of innovations that help illustrate the rate of individual adoption: (1) Relative Advantage. (2) Compatibility, (3) Complexity, (4) Trialability, and (5) Observability (Rogers, 1995). The diffusion theory suggests that one's adoption of an innovation depends on how one perceives the innovation as "better than the idea it supersedes" (relative advantage), "consistent with existing values, past experiences, and needs of potential adopters" (compatibility), "difficult to understand and use" (complexity), "experimented with on a limited basis" (trialability), and "the results of an innovation are visible to others" (observability). An idea that have the following characteristics will be adopted rapidly by the individual: greater perceived relative advantage, more compatible with the values and norms of a social system, easily understood by most members of the social system, can be tried on an "installment plan", and easier it is for an individual to see the results of an innovation. Among these five characteristics, four of them relate positively to the rate of adoption: relative advantage, compatibility, trial ability and observability. The fifth characteristic, complexity, is negatively related to the rate of adoption. These five characteristics of an innovation are good indicators in predicting if an innovation will be adopted or rejected in the final stage of the diffusion process. Dearing et al. (1994) further suggested that applicability and reliability are important for diffusion of risky innovations. Adapa S. (2008) considered adoption of internet shopping as an innovative method of shopping contrast to the traditional mall shopping. The adoption rate of an 48

11 innovation is further influenced by characteristics of the innovation itself; communication channels, time elapsed since the introduction of the innovation and the social system in which the diffusion of innovation takes place (Gong et al., 2007). Adapa S. (2008) also suggested that cultural situations should be considered for diffusion of the internet as well as for the development of e- commerce. 3.6 The Theory of Adoption of Innovations Some of the authors have proposed that teleshopping should be regarded as an innovation, which like other innovations takes time to spread through the social system (see Alba et al., 1997; Verhoef and Langerak, 2001; Chau and Hu, 2001; Sultan, 2002). The adoption of an innovation depends on various factors (including perceived compatibility, perceived relative advantage, perceived complexity, trialability and observability), that are related to the innovation itself and to the consumer (Rogers, 1983). The diffusion of innovations concerns the spread of a new service from its source of invention to its ultimate adopters (Verhoef and Langerak, 2001; Gatignon and Robertson, 1985), whereas the consumer adoption process focuses on the mental process through which an individual consumer passes from first hearing about teleshopping to final adoption. The theory of adoption of innovations shares some important characteristics with the TAM model. Although the TAM model does not include all the constructs proposed by Rogers (1983), it does include two constructs, perceived usefulness and perceived ease of use, which are quite similar to the constructs perceived relative advantage and perceived complexity (Davis, 1989); Al-Gahtani, 2001). In a study of Dutch households, Verhoef and Langerak (2001) found that consumers perception of relative advantage and compatibility positively influenced their intention to adopt online grocery shopping. Consumer s perception of the complexity of online grocery shopping negatively influenced 49

12 their intention to adopt online grocery shopping. Also, results obtained by Hansen (2005) suggested that perceived complexity, perceived compatibility, and perceived relative advantage highly influence consumers adoption of online grocery buying. Research concerning innovation characteristics and innovation adoption suggests that in general compatibility, relative advantage and complexity have the most consistent relationship to offline innovation adoption (e.g. LaBay and Kinnear, 1981; Tornatzky and Klein, 1982; Ram and Sheth, 1989). 3.7 Social Exchange Theory Social exchange involves the voluntary actions of individuals, which are motivated by the expectation that future returns received from others will be much larger than current costs input. In social exchange theory, a resource is defined as anything that provides pleasure and satisfaction (Bagozzi 1974; Holbrook 1999). Satisfaction is defined as "pleasurable fulfillment," a positive affective state (Oliver, 1997). It is a key concept in social exchange theory, and is wellestablished as an outcome of successful relationships in business-to-business and business-to-consumer marketing (Geyskens et al. 1996; Oliver 1997). To increase consumer satisfaction, organizations invest in resources. If consumers perceive that resources provided by a teleshopper meet their needs, satisfaction should result (Oliver 1997). If consumers perceive that needed resources are delivered on a predictable basis, they should develop trust in the organization. In the teleshopping customers typically perceive higher risks compared to conventional shopping environment as a result of long distances, virtual identities or lack of regulations (Tan, 1999). 50

13 3.8 Expectation-Confirmation Theory Expectation-confirmation theory (ECT) was proposed by Oliver (1980) to study consumer satisfaction, repurchase intention and behavior. The underlying logic of the ECT framework is: consumers firstly form an initial expectation prior to purchase, and then engender perceptions about its performance after a period of initial consumption. Satisfaction is the central notion of this model and it is formed by the gap between expectation and perceived performance (Oliver, 1980). The expectation-confirmation theory suggests that if the perceived performance meets one s expectation, confirmation is formed and consumers are satisfied. Bhattacherjee (2001b) stated that satisfied users are more likely to continue the information system use. Cheung et al., 2003 also pointed that adoption and continuance are connected to each other through several mediating and moderating factors such as trust and satisfaction. 3.9 Uses and Gratifications Theory Uses and gratifications (UandG) is a time-honored media use theory, helpful for understanding consumer motivations for media use, and has been applied to scenarios ranging from radio to television, cable television, direct to home, mobile television and now the Internet. The Television provides a wide range of networked telecommunications and media content delivery capabilities. The utility of the television as a powerful telecommunications medium is compelling, and the television is far more than just a conglomeration of selling goods. The UandG theory has been successfully applied in order to understand television uses and gratifications in the USA and in the European context (Stafford et al., 2004; Kargaonkar and Wolin, 1999). 51

14 In UandG theory the word gratification is defined as feeling of satisfaction. The theory has come a long way since its inception in early 1940s (Ruggiero, 2000). The theory considers not only the pleasure people search for in a media but also the attitudes of the audience towards the medium and its contents (Fagerlind et al., 2000). Severin and Tankard (1997) state that the uses and gratifications theory is a psychological communication perspective that focuses on individual use and choice by asserting that different people can use the same mass medium for very different purposes. The emphasis of this theory is on the audience and not on the effects of the media on the mass (Windahl, 1981). Researchers have tried to identify the psychological and behavioral aspect of the television users to identify the underlying motivations for television usage. Kaye and Johnson (2001) state that television users are more actively involved and engaged in using the television because of its interactivity. Since one of the major strengths of the television is its interactivity and since an active audience is the core concept of the uses and gratifications theory, gratifications theory is regarded as the most effective theoretical basis for studying this medium (Hangun, 2002). The immense opportunities for social interaction set the television viewing apart from conventional mass media; this has been well captured in studies on television uses and gratifications (Song et al., 2004). Researchers have applied the UandG theory to the case of television usage in order to understand the common underlying psychological and behavioral dimensions of television viewing (Lin, 1999; Larose et al,. 2001). Luo (2002) further extended the television uses and gratifications studies and explored the effects of television viewing motivations on attitudes towards an advertisement and satisfaction. Roy, S.K. (2008) identified that six gratifications factors for television use are self-development, wide exposure, relaxation, user friendliness, easily accessible and global exposure. Bhatnagar et al., 2000 demonstrated that the perceptions that television can meet need are positively correlated with increased teleshopping. 52

15 3.10. Social Influence Theory Rashotte, L. defines social influence as change in an individual s thoughts, feelings, attitudes, or behaviors that results from interaction with another individual or a group. Social influence comes in two forms: Normative social influence (more commonly referred to as subjective norms and informational social influence. Subjective norms refer to the perceived social pressure on individuals to perform or not to perform a behavior, regardless of their individual beliefs and attitudes toward the behavior (Lee et al., 2000) For example, some people may feel that by not adopting a particular technology, they may be perceived by others as old fashioned. This mindset creates a pressure for people to adopt the technology regardless of whether they have a positive or negative attitude toward the technology. On the other hand, information social group influence is a learning process in which people observe the successful experiences of their social groups with an innovation, before deciding whether to adopt it. In particular, research has indicated that people tend not to adopt an innovation until after they learn of social others (such as peers) successful experiences with the innovation. This theory has been tested in very small number of online shopping studies. Limayem et al., 2000 studied the effect of social influence on online shopping. They found that perceived norm plays a role in purchasing online specifically with regard to family influence although they did not find friend to be significant factor. The researcher explored that presence of an internet supportive environment including friends who shopped online did increase the likelihood of making an online purchase. (Limayem et al., 2000). There is also a significant link between perceived norms and intention to shop online. Kraut et al., (1996) found that people are far more likely to use the internet for shopping if they have a supportive social environment including friends and relatives who shop online. (Foucaultn and Sheufele, 2002) 53

16 applied this theory in case of online purchasing of textbooks and found that friends could potentially exert considerable influence on textbooks purchasing decisions Channel Theory Marketing activities occur through various channels. Kotler identified nine functions of marketing channels, including information, promotion, negotiation, ordering, financing, risk taking, physical possession, payment, and the actual transfer of product ownership. Peterson, et al., 1997 stated that all marketing functions are carried out through three distinctive types of marketing channels: communication channels, transaction channels, and distribution channels. By definition, communication channels enable the flow of various types of information between buyers and sellers. Transaction channels realize ordering and payment activities between buyers and sellers, and distribution channels facilitate the physical exchange of products and services between buyers and sellers. Stewart, Frazier, and Martin (1996) incorporated marketing functions into two types of channels: communication channels and distribution channels. The latter has a broader definition, meaning "a mechanism through which a product or service can be selected, purchased/ordered, and received by a segment of the firm's customers." (p.190). Although conceptually distinct, in the context of consumer decision making, these channels frequently overlap, and a given channel may be responsible for multiple functions. The multi-functionality can be best demonstrated in a telestore. For instance, a program can be advertised, paid for, and distributed to a consumer through the television. In this case, the television serves the functions of communication, transaction, and distribution channels. For non-digital products such as computer hardware, clothing, or wine, the web is not able to function as a distribution channel. (Li et al., 1999). 54

17 3.12 To Sum Up Various theories have been applied in previous research to explain and predict consumers tele-shopping behavior. Overall, more attention has been paid to theories in the social psychology area: TRA, TPB, and TAM. The empirical studies based on these theories highlight the importance of consumers attitudes, since attitudinal factors explain most of the variation in e-shopping behavior. However, the explanatory power of these theories varies from one study to another. The classic theory of reasoned action (TRA) (Ajzen and Fishbein, 1980), the theory of planned behavior (TPB) [Ajzen 1991], and Technology Acceptance Model have been extensively adopted for explaining and predicting user behavior in an online shopping environment (e.g., Pavlou 2001). TAM posits that actual system use is determined by users behavioral intention to use, which is in turn influenced by their attitude toward usage. Attitude is directly affected by users belief about a system, which consist of perceived usefulness and ease of use (Davis 1989). TAM has been extended to include subjective norms to explain perceived usefulness and usage intentions in terms of social influence and cognitive instrumental processes (Venkatesh and Morris, 2000). This belief-affect-intention-behavior causality has proven valid in the tele shopping environment (Chen et al. 2002; Limayem et al., 2000), although the TAM s goodness of fit varies across different studies. For example, behavioral intention was reported to have a very strong effect on users actual shopping behaviour with a path coefficient of 0.82 in (Chen et al. 2002), but was only 0.35 in (Limayem et al., 2000). Similarly, the effect of attitude on behavioural intention had a path coefficient of 0.77 in (Chen et al. 2002), but path coefficient was only 0.35 in (Limayem et al., 2000). Such a discrepancy may be due to different 55

18 definitions of constructs used in these studies. For example, attitude was treated as a cognitive evaluation (e.g., pros and cons of the behaviour) in (Chen et al. 2002), but as an affection (e.g., feelings toward the behaviour) in (Limayem et al., 2000). Actual behaviour was also measured differently. (Chen et al. 2002). Teleshopping purchase intention is a complicated decision process. First, consumers make a shopping decision based on their family needs, budget limitations, and other constraints impinging on them. Accordingly, they are likely to minimize transaction costs and maximize compatibility with needs. Second, tele-shopping intention is a social influence process and it is affected by social influence (e.g., social norms), vendor and consumer characteristics, and third parties (e.g., competitive offerings) (Bagozzi, 1974). Third, tele-shopping can be viewed as an innovation and its adoption is impacted by its intrinsic attributes as well as by mass media and word of mouth (Mahajan, et al., 1990). When multiple retail channels are present for the same transaction, the adoption of tele-shopping can become a substitution for traditional retail channels; alternatively, it may complement or supplement these channels. Therefore, no single theory appears capable, as is, of capturing the complexities of teleshopping behaviour: a comprehensive integration of several theories becomes necessary. For example, Konana and Balasubramanian (2005) incorporate elements of TRA, TPB, and TAM as well as economic factors (perceived financial gains) into their model of teleshopping