models of technology acceptance

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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



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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.



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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)

Normative Beliefs andMotivation to comply

(Σ nbi mc i)

Attitude TowardBehavior (A)

Subjective Norm(SN)

BehavioralIntention (BI)

ActualBehavior

FIGURE 1. Theory of Reasoned Action (TRA)

(Ajzen and Fishbein, 1980)



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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 (b i) 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 mc i (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



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(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



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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.

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

Behavioral

Beliefs (b)

Attitude Toward the

Behavior (A)

Intention

(BI)

Behavior

(B)

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.)

Normative

Beliefs (n)

Subjective Norm

(SN)

Control

Beliefs (c)

Perceived Behavioral

Control (PBC) Actual Behavioral

Control (ABC)



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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



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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).



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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 = w3 A + w4SN + W5PBC



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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 i th belief

ci Control belief strength of i th 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).



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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

PerceivedUsefulness(PU)

Perceived Ease

of Use

(PEOU)

AttitudeTowards Using

(A)

BehavioralIntention to

Use (BIU)

Actual

Use

FIGURE 3. Technology Acceptance

Model (TAM)

(Davis, F. D. (1989))

External

Variables



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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)



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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 open-

pollinated 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



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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;



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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



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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



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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



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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



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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).



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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



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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.



26

(1999) analyzed internet diffusion in China and India in terms of six dimensions-

pervasiveness, 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.



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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



28

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



29

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).



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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



31

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 PredictingAcceptance 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



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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



33

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.



34

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



35

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



36

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.



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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.

models of technology acceptance

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