Download Chapter- I REVIEW OF LITERATURE

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)
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)
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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 (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
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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.
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
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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 = w3A + w4SN + W 5PBC
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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 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).
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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
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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 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
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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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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
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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.
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(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.
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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
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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
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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
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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
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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
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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.
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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
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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.
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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.

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