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Chapter- I REVIEW OF LITERATURE Internet shopping is still in evolutionary stage in India and very few studies have undertaken research exploring customer acceptance and diffusion of internet shopping in India. Although there has been a dearth of internet shopping related studies in Indian context, theoretical exploration can be based on various international studies carried out in other countries. As an initiative to explore the internet shopping acceptance and diffusion in India, this section discusses theories relevant to predicting and explaining actual behavior and behavioral intention and innovation diffusion within the context of internet shopping. It mainly focuses on Theory of Reasoned Action (TRA) (Fishbein and Ajzen 1975, 1980), Theory of Planned Behavior (TPB) (Ajzen, 1985,1989), Technology Acceptance Model (TAM) (Davis 1989) and Innovation Diffusion Theory (IDT) (Rogers, 1962, 1983, 1995). A literature based evaluation of these theories’ applicability to actual internet shopping behavior in India has been done and shopping orientations are predicted to have an impact on Perceived Ease of Use of internet shopping and Perceived Usefulness of internet shopping. Based on this, a modified Technology Acceptance Model has been proposed as the basis of this research. 1.1 Theories Relevant to Predicting and Explaining Actual Behavior 1.1.1 Theory of Reasoned Action (TRA) Before discussing Theory of Reasoned Action (TRA), following is quoted from Ajzen and Fishbein (1980) to be influential in the understanding of the relationship between 4 attitudes and behaviors, “In 1929 L.L. Thurston developed methods for measuring attitudes using interval scales. Following Thurston’s scale came the famous, more specific and easier to use Likert-scale. This scale is widely used today. In 1935, Gordon W. Allport theorized that the attitude-behavior relationship was not uni-dimensional as previously thought, but multi-dimensional. Attitudes were viewed as complex systems made up of the person’s beliefs about the object, his feelings toward the object, and his action tendencies with respect to the object. In 1944, Louis Guttman developed the scalogram analysis to measure beliefs about the object. Doob in 1947 adopted the idea of Thurstone that attitude is not directly related to behavior but it can tell us something about the overall pattern of behavior. In the 1950’s, this point of view that attitude is multi-dimensional became universal. Rosenberg and Hovland in 1960 theorized that a person’s attitude toward an object is filtered by their affect, cognition and actual behavior. In 1969, Wicker conducted an extensive survey and literature review on the subject and he determined that it is considerably more likely that attitudes will be unrelated or only slightly related to overt behaviors than that attitudes will be closely related to actions.“ As a result of these developments, Fishbein and Ajzen joined together to explore ways to predict behaviors and outcomes. They assumed, ”individuals are usually quite rational and make systematic use of information available to them. People consider the implications of their actual behaviors before they decide to engage or not engage in a given behavior” (Ajzen and Fishbein, 1980, p. 5). After reviewing all the studies they developed a theory that could predict and understand behavior and attitudes. Their framework, which has become known as the Theory of Reasoned Action takes into account behavioral intentions rather than attitudes as the main predictors of actual behaviors. 5 The Theory of Reasoned Action (TRA) was developed in 1967. During the early 1970s the theory was revised and expanded by Ajzen and Fishbein. By 1980 the theory was used to study human behavior and develop appropriate interventions. TRA is a widely studied model from social psychology, which is concerned with the determinants of consciously intended behaviors (Ajzen and Fishbein, 1980; Fishbein and Ajzen, 1975). Specific purposes of this theory are as follows: 1. To predict and understand motivational influences on actual behavior that is not under the individual's volitional control. 2. To identify how and where to target strategies for changing actual behavior. 3. To explain virtually any human behavior such as acceptance of internet shopping, why a person buys a new car, votes against a certain candidate, is absent from work or engages in premarital sexual intercourse. According to TRA, a person’s performance of a specified behavior is determined by his or her behavioral intention (BI) to perform the behavior, and BI is jointly determined by the person’s attitude towards using (A) and subjective norm (SN) concerning the behavior in question (Figure 1). With relative weights typically estimated by regression: BI = A +SN (1) Beliefs and Evaluations (Σ bi ei) Attitude Toward Behavior (A) Behavioral Intention (BI) Normative Beliefs and Motivation to comply (Σ nbi mci) Actual Behavior Subjective Norm (SN) FIGURE 1. Theory of Reasoned Action (TRA) (Ajzen and Fishbein, 1980) 6 BI is a measure of the strength of one’s intention to perform a specified behavior (e.g., Fishbein and Ajzen 1975, p. 288). A is defined as an individual’s positive or negative feelings (evaluative affect) about performing the target behavior (e.g. Fishbein and Ajzen 1975, p. 216). Subjective norm refers to “the person’s perception that most people who are important to him think he should or should not perform the behavior in question” (Fishbein and Ajzen 1975, p. 302). According to TRA, a person’s attitude toward a behavior is determined by his or her salient beliefs (bi) about consequences of performing the behavior multiplied by the evaluation (ei) of those consequences: A= Σ bi ei. (2) Beliefs (bi) are defined as the individual’s subjective probability that performing the target behavior will result in consequence i. The evaluation term (ei) refers to “an implicit evaluative response” to the consequence (Fishbein and Ajzen, 1975, p. 29). Equation (2) represents an information-processing view of attitude formation and change, which posits that external stimuli influence attitudes only indirectly through changes in the person’s belief structure (Ajzen and Feishbein 1980, pp. 82-86). TRA theorizes that an individual’s subjective norm (SN) is determined by a multiplicative function of his or her normative beliefs (nbi), i.e. perceived expectations of specific referent individuals or groups, and his or her motivation to comply (mci) with these expectations (Fishbein and Ajzen 1975, p. 302): SN = Σ nbi mci (3) TRA is a general model, and as such, it does not specify the beliefs that are operative for a particular behavior. Researchers using TRA must first identify the beliefs that are salient for subjects regarding the behavior under investigation. Fishbein and Ajzen 7 (1975, p. 218) and Ajzen and Fishbein (1980, p. 68) suggest eliciting five to nine salient beliefs using free response interviews with representative members of the subject population. They recommend using “modal” salient beliefs for a population, obtained by taking the beliefs most frequently elicited from a representative sample of the population. 1.1.2 Theory of Planned Behavior (TPB) The theory of planned behavior is an extension of the theory of reasoned action (Ajzen and Fishbein, 1980; Fishbein and Ajzen, 1975) made necessary by the original model’s limitations in dealing with actual behaviors over which people have incomplete volitional control. TRA works most successfully when applied to actual behaviors that are under a person's volitional control. If actual behaviors are not fully under volitional control, even though a person may be highly motivated by her own attitudes and subjective norm, he/she may not actually perform the actual behavior due to intervening environmental conditions. The Theory of Planned Behavior (TPB) was developed to predict behaviors in which individuals have incomplete volitional control. Figure 2 depicts the theory in the form of a structural diagram. As in the original theory of reasoned action, a central factor in the theory of planned behavior is the individual’s intention to perform a given behavior. Intentions are assumed to capture the motivational factors that influence actual behavior; they are indications of how hard people are willing to try, of how much of an effort they are planning to exert, in order to perform the actual behavior. As a general rule, the stronger the intention to engage in actual behavior, the more likely should be its performance. It should be clear, however, that a behavioral intention can find expression in actual behavior only if the behavior in question is under volitional control, i.e., if the person can decide at will to perform or not perform the actual 8 behavior. Although some behaviors may in fact meet this requirement quite well, the performance of most depends at least to some degree on such non-motivational factors such as availability of requisite opportunities and resources (e.g., time, money, skills, cooperation of others; see Ajzen, 1985, for a discussion). Collectively, these factors represent people’s actual control over the behavior. To the extent that he/she has required opportunities and resources, and intends to perform the actual behavior, he or she should succeed in doing so. Behavioral Beliefs (b) Attitude Toward the Behavior (A) Normative Beliefs (n) Subjective Norm (SN) Control Beliefs (c) Perceived Behavioral Control (PBC) Intention (BI) Behavior (B) Actual Behavioral Control (ABC) FIGURE 2. Theory of Planned Behavior (TPB) (Adapted from Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, p. 179-211.) According to Ajzen and Fishbein (1980) behavioral beliefs link the actual behavior of interest to expected outcomes. A behavioral belief is subjective probability that the behavior will produce a given outcome. Although a person may hold many behavioral beliefs with respect to any behavior, only a relatively small number are readily accessible at a given moment. It is assumed that these accessible beliefs determine the prevailing 9 attitude toward the behavior. Attitude toward a behavior is the degree to which performance of the behavior is positively or negatively valued. Attitude toward a behavior is determined by the total set of accessible behavioral beliefs linking the behavior to various outcomes and other attributes. It is also interesting to point out that how the attitude towards behavior is formed if there are no previous experiences and that way expectation. Attitude towards behavior consists of those beliefs and new experiences, which either strengthens or weakens beliefs. Thus it is reasonable to say that researching attitudes towards behavior have justification to find out intentions to behave in a particular manner. Normative beliefs refer to the perceived behavioral expectations of such important referent individuals or groups as the person's spouse, family and friends. It is assumed that these normative beliefs, in combination with the person's motivation to comply with the different referents, determine the prevailing subjective norm. Subjective norm is the perceived social pressure to engage or not to engage in actual behavior. It is assumed that subjective norm is determined by the total set of accessible normative beliefs concerning the expectations of important referents (Ajzen and Fishbein, 1980). Emphasis on social pressure is more accurate when it comes to customers doing something for the first time or doing something that is not their specialty. Also it is presumable that there are different effects on reference groups when it is the case of leisure services than if the individual is forced to use new services like in the workplace. Control beliefs have to do with the perceived presence of factors that may facilitate or impede performance of actual behavior. It is assumed that these control beliefs determine the prevailing perceived behavioral control. Actual behavioral control refers to the extent to which a person has the skills, resources, and other prerequisites needed to 10 perform actual behavior. Successful performance of the behavior depends not only on a favorable intention but also on a sufficient level of behavioral control. To the extent that perceived behavioral control is accurate, it can serve as a proxy of actual control and can be used for the prediction of the actual behavior. Perceived behavioral control refers to people's perceptions of their ability to perform a given behavior. Perceived Behavioral Control (PBC) factor reflects past experience as well as external factors, such as anticipated impediments, obstacles, resources and opportunities that may influence the performance of the actual behavior (Ajzen and Fishbein, 1980). It has two factors: the perceived likelihood of encountering factors that will facilitate or inhibit the successful performance of the actual behavior, weighted by their perceived power to facilitate or inhibit performance. Perceptions concerning ability may be different than actual control. Although the feeling of control, is especially important when it comes to adapting new things. In recent studies there have been corrections to a view that overarching concept of perceived behavioral control, is comprised of two components: self-efficacy (dealing largely with the ease or difficulty of performing actual behavior) and controllability (the extent to which performance is up to the actor) This is a hierarchical model of perceived behavioral control, which was introduced by Bandura, 1977 and Ajzen (2002). Intention is the cognitive representation of a person's readiness to perform a given behavior, and it is considered to be the immediate antecedent of behavior. The intention is based on attitude toward the behavior, subjective norm, and perceived behavioral control, with each predictor weighted for its importance in relation to the behavior and population of interest. Behavioral intention has long been recognized as an important mediator in the relationship between behavior and other factors such as attitude, subjective and perceived behavioral control (Ajzen and Fishbein, 1980). 11 According to the theory of planned behavior, perceived behavioral control, together with behavioral intention, can be used directly to predict behavioral achievement. At least two rationales can be offered for this hypothesis. First, holding intention constant, the effort expended to bring a course of behavior to a successful conclusion is likely to increase with perceived behavioral control. For instance, even if two individuals have equally strong intentions to learn to ski, and both try to do so, the person who is confident that he can master this activity is more likely to persevere than is the person who doubts his ability. The second reason for expecting a direct link between perceived behavioral control and behavioral achievement is that perceived behavioral control can often be used as a substitute for a measure of actual control. Whether a measure of perceived behavioral control can substitute for a measure of actual control depends, of course, on the accuracy of the perceptions. Perceived behavioral control may not be particularly realistic when a person has relatively little information about the behavior, when requirements or available resources have changed, or when new and unfamiliar elements have entered into the situation. Under those conditions, a measure of perceived behavioral control may add little to accuracy of behavioral prediction. However, to the extent that perceived control is realistic, it can be used to predict the probability of a successful behavioral attempt (Ajzen, 1985). However, behavior is weighted function of intention and perceived behavioral control; and intention is the weighted sum of the attitude, subjective norm and perceived behavioral control components. Thus, according to the TPB model: B = w1BI + w2PBC BI = w3A + w4SN + W 5PBC 12 A = Σ biei SN = Σ nimi PBC= Σ cipi Where, B Behavior BI Intention PBC Perceived Behavioral Control A Attitude toward the behavior SN Subjective Norm w1,w2,w3,w4,w5 are relative weights of BI, PBC, A, SN and PBC respectively bi Behavioral belief strength of ith belief ei Outcome evaluation of ith belief ni Normative belief strength of ith belief mi Motivation to comply with ith belief ci Control belief strength of ith belief pi control belief power of ith belief 1.1.3 Technology Acceptance Model (TAM) The Technology Acceptance Model (TAM) proposed by Davis (1989) was derived from the Theory of Reasoned Action (TRA). While TRA is a general theory to explain general human behavior, TAM is specific to information system usage. TAM was originally developed to understand the causal link between external variables and user acceptance of PC-based applications. TAM has been widely used as theoretical framework in the recent studies to explain technology acceptance, including the internet and World Wide Web (WWW) (Moon and Kim, 2001; Gillenson and Sherrell, 2002; Koufaris, 2002; McCloskey, 2004; Chen). 13 External Variables Perceived Usefulness (PU) Attitude Towards Using (A) Behavioral Intention to Use (BIU) Actual Use Perceived Ease of Use (PEOU) FIGURE 3. Technology Acceptance Model (TAM) (Davis, F. D. (1989)) The constructs of perceived usefulness (PU) and perceived ease of use (PEOU) are two salient beliefs that form the basis of TAM. According to Davis (1989), Perceived Usefulness (PU) is “the degree to which a person believes that using a particular system would improve his or her job performance” while Perceived Ease of Use (PEOU) is “the degree to which a person believes that using a particular system would be free of efforts”. PU and PEOU reflect the beliefs about the task-value and user-friendliness of new information systems respectively. As presented in Figure 3, the model posits that actual usage is determined by users’ behavioral intention to use (BIU), which in turn is influenced by their attitude (A) and the belief of perceived usefulness (PU). Users’ attitude, which reflects favorable or unfavorable feelings towards using the IS system, is determined jointly by perceived usefulness (PU) and perceived ease of use (PEOU). PU, in turn, is influenced by PEOU and external variables. The external variables may include system design features, training, documentation and user support, etc. The logic inherent in the TAM is that the 14 easier mastery of the technology, the more useful it is perceived to be, thus leading to more positive attitude and greater intention towards using the technology and consequently greater usage of the technology. However the above theories have certain limitations. Factors such as personality and demographic variables are not taken into consideration. There is much ambiguity regarding how to define perceived behavioral control and this creates measurement problems. Assumption is made that perceived behavioral control predicts actual behavioral control. This may not always be the case. The longer the time interval between behavioral intent and behavior, the less likely the behavior will occur. The theories are based on the assumption that human beings are rational and make systematic decisions based on available information. Unconscious motives are not considered. The theories would have questionable applicability in case of impulse buying behavior. 1.1.4 Innovation Diffusion Theory (IDT) Another well established theory for user adoption is IDT (Rogers, 1962, 1983, 1995). Innovation diffusion is achieved through users’ acceptance and use of new ideas or things (Zaltman and Stiff, 1973). The theory explains, among many things, the process of the innovation decision process, the determinants of rate of adoption, and various categories of adopters, and it helps predict the likelihood and the rate of an innovation being adopted. Rogers, (1995) stated that an innovation’s relative advantage, compatibility, complexity, trialability and observability were found to explain 49 to 87 per cent of the variance in the rate of its adoption. Other research projects including the meta-analysis of seventy-five diffusion articles conducted by Tornatzky and Klein, (1982) 15 found that only relative advantage, compatibility and complexity were consistently related to the rate of innovation adoption. 1.1.4.1 Key Variables in the Diffusion Model The paradigm for diffusion research can be traced to the rural sociology research tradition, which began in the 1940s. Rural sociology is a sub field of sociology that focuses on the social problems of rural life. One rural sociology study in particular influenced the methodology, theoretical framework, and interpretations of later students in the rural sociology tradition, and in other diffusion research traditions. Ryan and Gross (1943) investigated the diffusion of hybrid seed corn among Iowa farmers. Hybrid seed was made available to Iowa farmers in 1928. The hybrid vigor of the new seed increased corn yields on Iowa farms, hybrid corn varieties withstood drought better than the openpollinated seed they replaced, and hybrid corn was better suited to harvesting by mechanical corn pickers. By 1941, about thirteen years after its first release, the innovation was adopted by almost 100 per cent of Iowa farmers. Ryan and Gross studied the rapid diffusion of hybrid corn in order to obtain lessons learned that might be applied to the diffusion of other farm innovations. However, the intellectual influence of the hybrid corn study reached far beyond the study of agricultural innovations, and outside of the rural sociology tradition of diffusion research. Since the 1960s, the diffusion model has been applied in a wide variety of disciplines such as education, public health, communication, marketing, geography, general sociology, and economics. Diffusion studies in these various disciplines have ranged from the rapid diffusion of the internet to the nondiffusion of the Dvorak keyboard (in typewriters and computers). Diffusion is the process by which (1) an innovation (2) is communicated through certain channels (3) over time (4) among the members of a social system. Diffusion is a special 16 type of communication concerned with the spread of messages that are perceived as new ideas. The four main elements in the diffusion of new ideas are the innovation, communication channels, time, and the social system. An innovation is an idea, practice, or object that is perceived as new by an individual or other unit of adoption. The characteristics of an innovation, as perceived by the members of a social system, determine its rate of adoption. The characteristics, which determine an innovation’s rate of adoption, are relative advantage, compatibility, complexity, trialability, and observability. Relative advantage is the degree to which an innovation is perceived as better than the idea it supersedes. The degree of relative advantage may be measured in economic terms, but social prestige, convenience, and satisfaction are also important factors. It does not matter so much if an innovation has a great deal of objective advantage. What does matter is whether an individual perceives the innovation as advantageous. The greater the perceived relative advantage of an innovation, the more rapid its rate of adoption will be. Compatibility is the degree to which an innovation is perceived as being consistent with the existing values, past experiences, and needs of potential adopters. An idea that is incompatible with the values and norms of a social system will not be adopted as rapidly as an innovation that is compatible. The adoption of an incompatible innovation often requires the prior adoption of a new value system, which is a relatively slow process. Complexity is the degree to which an innovation is perceived as difficult to understand and use. Some innovations are readily understood by most members of a social system; 17 others are more complicated and will be adopted more slowly. New ideas that are simpler to understand are adopted more rapidly than innovations that require the adopter to develop new skills and understandings. Trialability is the degree to which an innovation may be experimented with on a limited basis. New ideas that can be tried on the installment plan will generally be adopted more quickly than innovations that are not divisible. An innovation that is trialable represents less uncertainty to the individual who is considering it for adoption, who can learn by doing. Observability is the degree to which the results of an innovation are visible to others. The easier it is for individuals to see the results of an innovation, the more likely they are to adopt it. Such visibility stimulates peer discussion of a new idea, as friends and neighbors of an adopter often request innovation-evaluation information about it. In summary, the innovations that are perceived by individuals as having greater relative advantage, compatibility, trialability, observability, and less complexity will be adopted more rapidly than other innovations. Communication Channels The second main element in the diffusion of new ideas is the communication channel. Communication is the process by which participants create and share information with one another in order to reach a mutual understanding. A communication channel is the means by which messages get from one individual to another. Mass media channels are more effective in creating knowledge of innovations, whereas interpersonal channels are more effective in forming and changing attitudes toward a new idea, and thus in 18 influencing the decision to adopt or reject a new idea. Most individuals evaluate an innovation, not on the basis of scientific research by experts, but through the subjective evaluations of near-peers who have adopted the innovation. Time The third main element in the diffusion of new ideas is time. The time dimension is involved in diffusion in three ways. First, time is involved in the innovation-decision process. The innovation-decision process is the mental process through which an individual (or other decision-making unit) passes from first knowledge of an innovation to forming an attitude toward the innovation, to a decision to adopt or reject, to implementation of the new idea, and to confirmation of this decision. An individual seeks information at various stages in the innovation-decision process in order to decrease uncertainty about an innovation's expected consequences. The second way in which time is involved in diffusion is in the innovativeness of an individual or other unit of adoption. Innovativeness is the degree to which an individual or other unit of adoption is relatively earlier in adopting new ideas than other members of a social system. There are five adopter categories, or classifications of the members of a social system on the basis on their innovativeness: innovators, early adopters, early majority, late majority, and laggards. Innovators are the first 2.5 per cent of the individuals in a system to adopt an innovation. Venturesomeness is almost an obsession with innovators. This interest in new ideas leads them out of a local circle of peer networks and into more cosmopolite social relationships. Communication patterns and friendships among a clique of innovators are common, even though the geographical distance between the innovators may be considerable. Being an innovator has several prerequisites. Control of substantial financial resources is helpful to absorb the possible 19 loss from an unprofitable innovation. The ability to understand and apply complex technical knowledge is also needed. The innovator must be able to cope with a high degree of uncertainty about an innovation at the time of adoption. While an innovator may not be respected by the other members of a social system, the innovator plays an important role in the diffusion process: That of launching the new idea in the system by importing the innovation from outside of the system's boundaries. Thus, the innovator plays a gatekeeping role in the flow of new ideas into a system. Early adopters are the next 13.5 per cent of the individuals in a system to adopt an innovation. They are a more integrated part of the local system than are innovators. Whereas innovators are cosmopolites, early adopters are localites. This adopter category, more than any other, has the greatest degree of opinion leadership in most systems. Potential adopters look to early adopters for advice and information about the innovation. This adopter category is generally sought by change agents as a local missionary for speeding the diffusion process. Because early adopters are not too far ahead of the average individual in innovativeness, they serve as a role model for many other members of a social system. The early adopter is respected by his or her peers, and is the embodiment of successful, discrete use of new ideas. Thus to maintain a central position in the communication networks of the system, he or she must make judicious innovation-decisions. The early adopter decreases uncertainty about a new idea by adopting it, and then conveying a subjective evaluation of the innovation to near-peers through interpersonal networks. Early majority is the next 34 per cent of the individuals in a system to adopt an innovation. The early majority adopts new ideas just before the average member of a system. They interact frequently with their peers, but seldom hold positions of opinion leadership in a system. The early majority's unique position between the very early and the relatively late to adopt makes 20 them an important link in the diffusion process. They provide interconnectedness in the system's interpersonal networks. They follow with deliberate willingness in adopting innovations, but seldom lead. Late majority is the next 34 per cent of the individuals in a system to adopt an innovation. The late majority adopts new ideas just after the average member of a system. Like the early majority, the late majority makes up one-third of the members of a system. Adoption may be the result of increasing network pressures from peers. Innovations are approached with a sceptical and cautious air, and the late majority do not adopt until most others in their system have done so. The weight of system norms must definitely favor an innovation before the late majority is convinced. The pressure of peers is necessary to motivate adoption. Their relatively scarce resources mean that most of the uncertainty about a new idea must be removed before the late majority feels that it is safe to adopt. Laggards are the last 16 per cent of the individuals in a system to adopt an innovation. They possess almost no opinion leadership. They are the most localite in their outlook of all adopter categories; many are near isolates in the social networks of their system. The point of reference for the laggard is the past and decisions are often made in terms of what has been done previously. As they are suspicious of innovations and change agents, resistance to innovations on the part of laggards may be entirely rational from the laggard's viewpoint, as their resources are limited and they feel certain that a new idea will not fail before they can adopt. The third way in which time is involved in diffusion is in rate of adoption. The rate of adoption is the relative speed with which an innovation is adopted by members of a social system. The rate of adoption is usually measured as the number of members of the system that adopt the innovation in a given time period. As shown previously, an 21 innovation's rate of adoption is influenced by the five perceived attributes of an innovation. The Social System The fourth main element in the diffusion of new ideas is the social system. A social system is defined as a set of interrelated units that are engaged in joint problem solving to accomplish a common goal. The members or units of a social system may be individuals, informal groups, organizations, and/or subsystems. The social system constitutes a boundary within which an innovation diffuses. A second area of research involve how norms affect diffusion. A third area of research focuses on how to do with opinion leadership, the degree to which an individual is able to influence informally other individuals' attitudes or overt behavior in a desired way with relative frequency. The fourth area of research involves the types of innovation-decisions (whether individual adoption decisions or organizational decisions, and whether they are made by an authority or by consensus). The last area of research has analyzed the consequences of innovation. A final crucial concept in understanding the nature of the diffusion process is the critical mass, which occurs at the point at which many individuals have adopted an innovation and the innovation further affects rate of adoption becomes self-sustaining. The concept of the critical mass implies that outreach activities should be concentrated on getting the use of the innovation to the point of critical mass. These efforts should be focused on the early adopters, the 13.5 per cent of the individuals in the system to adopt an innovation after the innovators have introduced the new idea into the system. Early adopters are often opinion leaders, and serve as role models for many other members of the social 22 system. Early adopters are instrumental in getting an innovation to the point of critical mass, and hence, in the successful diffusion of an innovation. Appendix A briefly sums up all the four theories in brief. 1.1.5 Technology Readiness (TR) The Technology Readiness (TR) refers to people’s propensity to embrace and use new technologies for accomplishing goals in home life and at work (Parasuraman, 2000). The construct of TR can be viewed as an overall state of mind resulting from a gestalt of mental enablers and inhibitors that collectively determine a person’s predisposition to use new technologies. In measurement level, the Technology Readiness Index (TRI) was developed to measure people’s general beliefs about technology. The construct of TR comprises four sub-dimensions: optimism, innovativeness, discomfort and insecurity. Optimism is defined as a positive view of technology and a belief that it offers people increased control, flexibility, and efficiency in their lives. Innovativeness refers to a tendency to be a technology pioneer and thought leader. Discomfort is a perception of lack of control over technology and a feeling of being overwhelmed by it. Insecurity is defined to be distrust of technology and scepticism about its ability to work properly. Optimism and innovativeness are drivers of TR, while discomfort and insecurity are inhibitors. Positive and negative beliefs about technology may coexist, and people can be arrayed along a technology beliefs continuum anchored by strongly positive at one end and strongly negative at the other. Theoretically and empirically, people’s TR correlates with their propensity to employ technology (Parasuraman, 2000). Besides, it has been proposed that consumers’ TR has positive impacts on their online service quality perceptions and online behaviors, but the empirical findings are limited (Zeithaml et al., 2002). 23 1.2 Analysis of Customer Research 1.2.1 Research in Indian Context Internet shopping is still in evolutionary stage in India and there has been very less systematic research undertaken exploring customer acceptance and diffusion of internet shopping in India. Indian e-tailing market was Rs 4000 million and was expected to be a market worth Rs 8000 million by the end of 2005. In 2006, the size was expected to increase to Rs 12,000 million, in 2007 to Rs 20,000 million. By 2008, the market is estimated to grow to Rs 50,000 million, while by 2010, the size would increase to as much as Rs 100,000+ million (Adesara, 2005). Taylor Nelson Sofres (TNS) Interactive's third annual global e-commerce report was part of TNS Interactive's Global E-commerce Report 2002, which was based on more than 42,000 interviews in 37 countries. In India the study was conducted in April 2002 among 1,029 internet users across SEC A and B groups representing the four metros of Delhi, Mumbai, Kolkata and Chennai. The industry's failure to allay fears about online payment security is a major factor preventing growth in addition to knowledge-based issues, which continue to deter Net users to shop online. Findings indicated that about 27 per cent of users in India have not purchased goods or services online because they think it is too difficult and lack of knowledge on such aggravates the situation and hence, it is safer buying goods or services in a store. This compares with a global average across all countries covered by the report, of 30 per cent abstainers and 28 per cent who are not willing to shop online due to security reasons. The other key findings of the research study include the fact that the most popular purchases online in India are clothes (46 per cent of shoppers) followed by music/CDs (29 per cent) and books (26 per cent). The 24 study conducted by Ramayah et al. (2005), published in E-Business (The ICFAI University Press), aimed at exploring the determinants of intention to use an internet bill payment system. Even if published in India, the study was carried out in Malaysia. Apart from this, there was no other published research found in Indian context. Parikh (2006) aimed at profiling online shoppers and the results of the study showed that long-term internet surfers, with heavy usage had the strongest affinity for internet shopping. In addition to this, prior experience of internet shopping had a multiplying impact on future intention to shop through internet. Contrary to expectations, there were no significant associations between the shopping segments and demographic characteristics. A research group, JuxtConsult, conducted an on-line survey of over 30,000 net users in India and found that 40 per cent of urban net users are also on-line buyers and as little as 5 per cent of the net consumers contribute to as much as 42 per cent of the total sales on the net (Techtree, 2005). Parikh (2006a) aimed at identifying various shopping orientations prevailing among the internet users and classified internet users into five shopping profiles: socializing, home, mall, economic and civil. Within accessible literature, only few systematic studies were found exploring diffusion of internet in India. These studies were aimed at diffusion of internet in India as a country rather than acceptance and diffusion of internet among Individual customers (eg. Dutta and Roy, 2003, 2004; Kshetri, 2002; Dholakia et al, 2003). Studies have prominently compared India and China for exploring internet diffusion patterns of both countries. Few studies comparing internet and e-commerce development in China and India arrived at seemingly inconsistent findings. Press et al. 25 (1999) analyzed internet diffusion in China and India in terms of six dimensionspervasiveness, geographic dispersion, sectoral absorption, connectivity infrastructure, organizational infrastructure and sophistication of use- and found that China exceeded or at least equaled India on each dimension. However, in terms of the Economist Intelligence Unit's (EIU) "E-readiness" ranking, India has been ahead of China (Ebusineeforum.com 2001b). The E-readiness ranks of India and China were 50 and 51 out of the 60 main economies studied by the EIU in 2000. In 2001, India's new rank of 45 took it in the group of “E-business followers” (Rogers, 1995) such as Greece, Czech Republic and Hungary. China’s new rank of 49 in 2001, on the other hand, put it in the group of “E-business laggards” (Rogers, 1995) such as Kazakhstan, Vietnam and Pakistan. Dutta and Roy (2003, 2004); Kshetri (2002); and Dholakia et al. (2003) also compared internet diffusion in India and China. They proposed that policies for stimulating internet diffusion must address both, infrastructure expansion as well as sectoral absorption in a balanced manner. For infrastructure expansion policies need to be crafted to stimulate private sector investment. They also proposed that attention devoted to internet infrastructure expansion needs to be matched by efforts directed at stimulating sectoral absorption of the technology. Kshetri (2002) examined the current stages of internet and e-commerce in China and India. They proposed a causal model with three levels of causes to explain internet diffusion in the two countries - deep structural causes, contextual causes and triggering causes. In doing so, the study also addresses to calls for research dealing with width and depth of innovation adoption and the way how people incorporate the internet into their lives and which of their previous activities are substituted or complemented with internet use. 26 The working paper Dholakia et al. (2003) examined several factors that are likely to influence the broadband-potential in the two countries. Their analysis indicates that factors such as higher-income, higher propensity of Chinese consumers adopt new technologies, higher-investment in the telecom sector (and a significant proportion of it going to the most modern technology), and much higher mobile phone and cable penetration favor China in terms of the demand and cost conditions affecting the potential of broadband. On the other hand, India’s position in the global IT map as a major provider of IT services is likely to trigger the demand for broadband. The competition levels in the broadband and traditional telecom sectors are comparable in the two economies; with India faring slightly better. As a result, the broadband subscription costs are declining rapidly in both economies, which are likely to further drive the demand for broadband technology. 1.2.2 Investigating Theory of Reasoned Action Sheppard et al. (1988) investigated the effectiveness of the model proposed by Fishbein and Ajzen in 1975 and conducted two meta-analyses- one with a sample of 87 separate studies of the individuals' intentions and performance (I-B) relationship and the second with a sample of 87 separate studies of the individuals' attitudes and subjective norms and their intentions (A+SN-I) relationship and found that the predictive ability of the model was strong (Sheppard et al., 1988). The study also found that the predictive ability of the Theory of Reasoned Action is not valid if the behavior is not under full volitional control. However there were two limitations. First, a variety of factors in addition to one's intentions determine whether the behavior is performed. Second, there is no provision in 27 the model for considering either the probability of failing to perform one's behavior or the consequences of such failure in determining one's intentions (Chang, 1998). Shimp and Kavas (1984) confirmed the validity of the theory. According to Shimp and Kavas, the Theory of Reasoned Action is useful in specifying the "antecedents" of coupon usage for grocery shopping (Bagozzi et al., 1992). Bagozzi et al. (1992) also proved the ability of the theory in specifying antecedents of coupon usage. However, their study also showed two other important variables that affect consumers' behavior. First, they found that prior behavior is a significant determinant of the decision of coupon usage. Secondly, the study proved that the factor of state versus action orientation of customers had affected the influence of attitudes and subjective norms on intentions. One study about sales promotion, including coupon usage, conducted in Taiwan, Thailand and Malaysia raised a problem about an application of Ajzen and Fishbein's model in collectivist societies where the influence of reference groups and opinion leaders affected individuals' attitudes directly (Huff and Alden, 1998). Munch et al. (1993) found consistency between their findings and the theory. They confirmed that beliefs about product benefits, not necessarily product features or performance consequences, are key determinant of product attitude. Moreover, they suggested that marketing communications should emphasize product benefits explicitly in order to build favorable attitudes toward products. On the contrary, many studies doubted the application of Ajzen and Fishbein's theory to persuasive communication. For example, as Grunert (1996) criticized, attitude models of the Fishbein type are not clear with regard to which types of cognitive processes lead from the information in the cognitive structure to the evaluation. James and Hensel (1991), however, found the Theory of Reasoned Action inappropriate for explaining or 28 predicting the impact of negative advertising. It was because under the theory, the customer's level of involvement, the feelings or emotions elicited by the advertising, and the attitude toward the ad and the sponsor of the ad would not be considered as the factors influencing customers' purchase intentions. Yet, behavioral (purchase) intention, a variable claimed to have immediate relationships with (purchasing) behavior in Ajzen and Fishbein's model, remains one of the most widely used variables to measure effectiveness of advertisements (Peterson et al., 1992). There are many extensions and proposed alternatives to the Theory of Reasoned Action. Funkhouser and Parker (1999) pointed out two different points of view regarding the extensive modification and extensions of the Theory of Reasoned Action. On the one hand, it confirms Fishbein's recognition (in the theory of reasoned action) of the importance of intentions as a mediator between attitudes and behaviors. On the other hand, it often sidesteps serious questions as to the relationships (if any) between intentions and behavior" (Funkhouser and Parker, 1999). Among these, the most widely known extension of the Theory of Reasoned Action is the Theory of Planned Behavior proposed by Ajzen in 1985 (Taylor and Todd, 1995). The Theory of Planned Behavior has been found more valid in predicting behavior in some studies, compared to the Theory of Reasoned Action. Chang (1998), in his comparison study of the Theory of Reasoned Action and the Theory of Planned Behavior, found that the Theory of Planned Behavior can be used successfully to predict the intention to perform unethical behavior, and that it is better than the Theory of Reasoned Action, which does not take the resource and opportunity into account, in predicting unethical behavior. However, some other studies also suggested that crossover effects and decomposition of the belief structures be allowed to improve the validity of behavioral prediction of Ajzen's model (Taylor and Todd, 1995). 29 Another extension of the Theory of Reasoned Action is the Theory of Trying developed by Bagozzi and Warshaw in 1990. This theory emphasizes customer uncertainty when achievement of a consumption objective is not entirely within one's volitional control (Funkhouser and Parker, 1999). Funkhouser and Parker proposed another alternative to understanding the persuasion process. The focus of this theory, called the Action Theory of Persuasion (ATP), is shifted from attitude change to action. Because of its achievement in developing a model to predict behavior, the Theory of Reasoned Action has been the basis of researches and studies in a wide variety of fields, including psychology, management, and marketing. Thus, the theory has been used as a basis of countless researches in a wide range of areas related to psychology and marketing. One of the most important topics in marketing research to which the theory can be applied is consumer behavior. However, although there were problems arising from applying the theory to behavioral prediction, the theory is still considered the "reference point" for most persuasion related research (Funkhouser and Parker, 1999). So far this theory has not been applied for exploring internet shopping intentions and actions but forms a strong base for developing theories and models for predicting user acceptance of internet shopping based on beliefs, attitudes and intentions. 1.2.3 Studies Using Theory of Planned Behavior As already mentioned, Theory of Planned Behavior, which has evolved from TRA, is considered better in determining behavior. Researchers have extensively used this theory for exploring individual differences in predicting behavior from behavioral intentions, which in turn follows attitudes and subjective norms. TPB has also been applied for predicting customers’ intentions and actions about adopting technical products (for example, internet shopping, mobile services etc.) DeBono (1993) used 30 TPB for studying individual differences in predicting behavioral intentions from attitude and subjective norms. It also highlights an analysis of how these attitudes and subjective norms affect behavioral intentions differently or similarly. Lado et al. (2003) used TPB to study attitudinal predictors of interest in and intention of enrolling in online masters. Three components of the respondents’ beliefs about online Masters Degree were identified, which are the difference in concerns between online and face-to-face Masters Degrees, the mistrust about online masters Degrees and the attrition concerns in pursuing online Masters Degrees. Ristola (2004) used TPB for predicting and understanding customer acceptance of mobile services and found it theoretically applicable. Cho and Cheung (2003) examined the determinants of customer adoption of the online legal services in the B2C e-commerce market in Hong Kong. In this research drawing from the Technology Acceptance Model (TAM), TPB, TRA, Triandis Model and IDT, an extended model of TAM (ETAM) was developed. Thus TPB, although not used widely for studying acceptance of internet and related applications, has been extensively used for studying the acceptance of other technologies. In this sense it is a useful extension from TRA leading towards development of very specific models for studying the intention-action relation in the context of internet shopping in conjunction with other theories and models. 1.2.4 Applicability of Technology Acceptance Model (TAM) in Predicting Acceptance of Internet Shopping TRA and TPB are general models for understanding relationship between attitudes and behaviors and IDT is a general model for studying diffusion of innovation. TAM, originally developed to understand the causal link between external variables and user acceptance of PC-based applications, has been widely used as theoretical framework in 31 recent studies in conjunction with constructs drawn from TRA, TPB and IDT to explain technology acceptance, including the internet and electronic shopping. Gefen and Straub (1997) used TAM to study gender differences in the perception and use of e-Mail and to examine the effect of gender on TAM. TAM is incomplete in the sense that it doesn’t account for social influence in the acceptance and utilization of new Information Systems (IS). Malhotra and Galletta (1999) operationalized the construct of social influence in terms of internalization, identification and compliance. Analysis of field study data provided evidence of the reliability and validity of the proposed constructs, factor structures and measures. TAM has been extensively used to study acceptance of internet and its applications, particularly for studying intentions and actions regarding internet shopping. (eg. Moon and Kim, 2001; Childers et al., 2001; Chen et al., 2002; Chen et al., 2003; Park and Jun, 2002; McCloskey, 2004; Leelayoutha and Lawley, 2004). Moon and Kim (2001) provided an extension of the TAM for a world-wide-web context. Perceived playfulness, the extended part of their model, operationalized the question of how intrinsic motives affect the individual’s acceptance of the WWW. McCloskey (2004) evaluated electronic commerce acceptance with the TAM. The research added ‘security concerns’ construct, which had two items determining credit card security and disclosure of personal information in addition to ease of use and usefulness constructs. Surprisingly, security and privacy concerns did not have an impact on electronic commerce participation. One important innovation attribute that is not studied in TAM is compatibility. Chen et al. (2002) studied impact of compatibility between using a virtual store and a customer’s belief, values and needs on his or her attitude toward using virtual store. They found that both compatibility and PEOU influence PU of virtual stores. In another research Chen et al. (2003) proposed a theoretical model and critical success factors for virtual stores by 32 expanding TAM and IDT. They found that compatibility, perceived service quality and perceived trust in addition to PU and PEOU were having important effects on attitude toward using. In addition to this, product offering and compatibility were found to have effects on PU but PEOU and information richness were not found to have effects on PU. Lastly usability of storefront was found to have a positive effect on PEOU. Leelayouthayotin and Lawley (2004) in their conceptual model for internet purchasing intention, dropped the attitude construct of TAM and added product and company attributes, perceived risk and customer experience. Like Moon and Kim (2001), Childers et al. (2001) added enjoyment as one of the constructs in their proposed model and confirmed that internet shopping enjoyment is a significant predictor of attitude toward interactive shopping. Lin et al. (2007) proposed an integrated model for explaining consumers’ intention to use online stock trading system. Based on related theoretical backgrounds, the study integrated technology readiness with the TAM, and theorized that the impact of technology readiness on use intention is completely mediated by both perceptions of usefulness and ease of use. Although initially developed for studying the acceptance of IS acceptance in an organization, which is an internal process within the boundaries of an organization, TAM has been used extensively for studying diffusion and acceptance of internet shopping. Researchers have found the application of the model well acceptable for the internet shopping context. As mentioned above, researchers have also provided genuine extensions and modifications of the model, which have increased the acceptability of TAM’s application to the internet shopping context. In a way TAM has established itself as a widely acceptable model for studying diffusion and acceptance of internet shopping. 33 Appendix B gives the summary of researches, which have applied one or more of the above theories for exploring internet shopping and related technologies. As shown, TRA, TPB, TAM and IDT are among the most influential theories in explaining and predicting acceptance and diffusion of IT in general and internet shopping specifically. TAM, especially, has been often used to study the acceptance of internet applications. Therefore, this research considers TAM as the base model for exploring internet shopping acceptance in India. 1.3 Literature Review on Shopping Orientations As a shopping behavior measure, shopping orientations are intended to capture the motivations of shoppers and/or the desired experiences and goals they seek when completing their shopping activities (Stone, 1954). For example, an in-home shopper may be motivated by convenience, while a personalizing shopper may value the interaction experience with a known sales clerk. Shopping orientations have also emerged as reliable discriminators for classifying different types of shoppers based on their approach to shopping activities (Gehrt and Carter, 1992; Lumpkin and Burnett, 1991-92). Researchers have tapped into shopper orientations to study patronage behavior among elderly consumers, catalog shoppers, outshoppers, and mall shoppers (Bloch et al., 1994; Evans et al., 1996; Gehrt and Shim, 1998; Korgaonkar, 1984; Lumpkin, 1985; Lumpkin et al., 1986; Shim and Mahoney, 1992). It is becoming increasingly clear that in order to survive and more importantly to succeed, online merchants should embrace and actively pursue fundamental principles of good retailing that apply to any medium. One of these principles is knowledge about existing and potential customers and their preferences and behaviors. Shopping orientations have been shown to be reliable predictors of customer patronage behavior 34 in other retail formats such as catalog and mall shopping. Therefore, it is expected that the study of shopping orientations can also help electronic retailers identify and understand those consumers who prefer to shop online and the reasons why. Stone (1954) proposed the idea that shoppers can be classified based on their approach to shopping activities. He identified four types of shoppers - economic, personalizing, ethical, and apathetic. Economic shoppers would attempt to maximize their returns by carefully evaluating price, quality, and value. This type of shoppers can be expected to spend a considerable amount of time collecting information about the available alternatives before making a purchase decision. The personalizing shoppers would be inclined to build close relationship with the store personnel and tend to make purchases close to home. For shoppers who fall under this category, shopping at stores where they can interact with salespeople and clerks on a personal level is important. If a shopper makes it a point to shop at stores in his immediate neighborhood with the objective of keeping the monies within the community, he can be labeled an ethical shopper. In order to preserve and build his community, this shopper would feel obligated to patronize local stores. Finally, an apathetic shopper disdains shopping, and would try and find ways to minimize the effort involved in completing a shopping activity. In addition to the above four orientations, other classifications for shoppers have also been suggested. For example, Bellenger and Korgaonkar (1980) identified a socializing shopper as someone who views shopping as a social activity. Typically, this type of shop have proposed classifying shoppers based on preferences for in-home shopping and mall shopping (Darden and Reynolds, 1971; Lumpkin et al., 1986). Korgaonkar (1981) collected data through personal interviews from 486 adult shoppers and tested hypothesized relationships between shopping orientations and preference for shopping 35 at catalog showrooms. It was concluded that patrons of catalog showrooms were more likely to have an economic rather than socializing or in-home shopping orientation. Shim and Mahoney (1991) studied consumer acceptance and use of videotex, a term used to describe electronic communication devices and services that provided access to email, news, and shopping (Goldstucker et al., 1986; Moschis et al., 1985). Shim and Mahoney’s (1991) findings from data collected through a survey of 132 videotex subscribers, who were also electronic shoppers; echo the results of Bickle and Shim (1993). It was found that price-conscious shoppers (labeled as conservative/worried shoppers) were the least satisfied with electronic shopping. In contrast, the comparative/user-friendly shoppers and recreative/innovative shoppers were more enthusiastic towards electronic shopping. More recently, researchers have extended the shopping orientations construct to the examination of electronic shopping on the internet. Analyzing data collected from an online survey of 999 U.S. internet users, Li et al. (1999) concluded that Web buyers were more convenience and less experientially oriented than non-Web buyers. However, no significant difference between the two groups was found on socializing and economic orientations. Vijayasarathy and Jones (2000) conducted a quasi-experimental study involving 201 student subjects and found that in-home shopping and mall shopping orientations were significant discriminators between low and high intentions to shop online. Another study carried out by Vijayasarathy (2001) also collected data from students in an experimental setting showed that in-home shopping orientation was a significant predictor of both attitude towards and intentions to use online shopping. On a normative level, Paden and Stell (2000) contend that the customization of Web design and content based on a person’s shopping orientation would be crucial for attracting and retaining customers. 36 Under Indian context the study done by Sinha (2000) classified shoppers into 26 segments based on their behaviour. The study concluded that shoppers do not portray all kinds of behavior at every store. Every retailer would need to find out its major set of buyers and develop its strategies accordingly. Sinha (2003) generated 13 orientations towards shopping. The findings of the study revealed that the Indian shoppers seek emotional value more than the functional value of shopping. The study also indicated that though there are some similarities in the orientation of Indian shoppers and shoppers from developed countries, there are some significant differences too. The Indian shoppers show an orientation that is based more on the entertainment value than on the functional value. Parikh (2006a) aimed at identifying various shopping orientations prevailing among the internet users and classified internet users into five shopping profiles: socializing, home, mall, economic and civil. Even after so much research has already undergone in exploring the internet shopping phenomenon, the fact remains that not all the limitations (specified in a prior section) of these models and theories are seriously looked into for solutions making the models robust. For example, consumer personality and its impacts on behavioural intention and actual behaviour have been looked at very high level and needs to be investigated further. Additionally, there is a dearth of research in Indian context exploring the acceptance of internet shopping in India. Therefore the next chapter focuses on these research gaps and outlines specific objectives for this research. Additionally, it proposes the research model and critical hypothesis to be tested by the research. 37