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- An interval entropic estimation of consumer priority in multi-attribute behavioural environment – a case study of financial investment instruments in an urban vista
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- International Journal of Management (IJM)
Volume 8, Issue 6, Nov–Dec 2017, pp. 136–151, Article ID: IJM_08_06_015
Available online at
http://www.iaeme.com/ijm/issues.asp?JType=IJM&VType=8&IType=6
Journal Impact Factor (2016): 8.1920 (Calculated by GISI) www.jifactor.com
ISSN Print: 0976-6502 and ISSN Online: 0976-6510
© IAEME Publication
AN INTERVAL ENTROPIC ESTIMATION OF
CONSUMER PRIORITY IN MULTI-ATTRIBUTE
BEHAVIOURAL ENVIRONMENT – A CASE
STUDY OF FINANCIAL INVESTMENT
INSTRUMENTS IN AN URBAN VISTA
Dr. Ayan Chattopadhyay
Associate Professor – Marketing,
Army Institute of Management Kolkata, India
Pawan Gupta
Sales Trainee (Trade Marketing & Distribution) - ITC Ltd.
Independent Researcher; MBA 19 - Army Institute of Management Kolkata
ABSTRACT
The service sector in India has witnessed revolutionary change after liberalization
of the economy in early 90s and the financial investment sector too experienced
exponential growth with the continuous emergence of new financial instruments and
rapid change in consumer behaviour. One has witnessed a radical shift in retail
investment pattern from the conventional fixed deposits in a bank to different financial
instruments - equity, mutual funds, insurance, PPF, real estate, debentures or bonds,
precious items to name a few. However, reports on retail investment in different
instruments show big disparity. Though considerable research have been found on the
role and importance of such financial instruments, existing literature shows the dearth
of studies on behavioural aspect, especially related to attitude and preference or
consumer priority towards multi-attributes that influence behaviour or preference
comparison between different financial investment instruments. The researchers in the
present paper first explore consumer behaviour through attitude and preference study
towards four financial investment instruments; namely fixed deposit, equity, mutual
fund and insurance. Secondly, comparison between the four instruments has been
done. Both these studies were done using a highly popular method of Semantic
Differential Scaling. The researchers also make a modest effort in predicting the
extent to which the consumer priority in a multi-attribute behavioural environment
fluctuates for each of the four instruments and applied the unique interval entropy
approach as a potent method towards measuring the same. Primary survey forms the
basis of this study and Kolkata city is chosen as the urban vista.
Key words: Consumer attitude, Preference, Interval entropy Approach, Semantic
differential scaling, financial investment instrument.
http://www.iaeme.com/IJM/index.asp 136 editor@iaeme.com
- An Interval Entropic Estimation of Consumer Priority in Multi-Attribute Behavioural Environment –
A Case Study of Financial Investment Instruments in an Urban Vista
Cite this Article: Dr. Ayan Chattopadhyay and Pawan Gupta, An Interval Entropic
Estimation of Consumer Priority in Multi-Attribute Behavioural Environment – A
Case Study of Financial Investment Instruments in an Urban Vista. International
Journal of Management, 8 (6), 2017, pp. 136–151.
http://www.iaeme.com/IJM/issues.asp?JType=IJM&VType=8&IType=6
1. INTRODUCTION
The past few decades has witnessed a radical shift in the way financial markets have evolved.
This has not just been a phenomenon in select geographies but a global phenomenon. The
world has seen great innovations in the financial investment instruments which have benefited
consumers in borrowing, transacting and investing; resulting in a great upsurge of the retail
financial business. However, the scenario in India has started changing only after the
economic reforms of 1991. The pre-reforms period since independence saw a very limited
opportunity in retail financial sector. The market consisted of a mix of public and private
enterprises operating under the state control and after nationalization of banks in India it was
the public sector that remained dominant in this sector. The major operators were the public
sector banks, post offices and Life Insurance Corporation of India, who had limited offerings
for the consumers. Traditional instruments dominated this phase which included savings bank
account, fixed deposit and recurring deposits from banks while the post offices offered term
deposit, National Savings Certificate, Kishan Vikash Patra and LIC offering life insurance
endowment plans primarily. In India, the post liberalized era since 1991 have also promoted
the boom in financial investment sector with multiple financial companies grappling against
each other by extending the best of offers and services to consumers to attract investment.
Consumer service has witnessed a new dimension altogether. Opening up of the economy has
also made technology transfer enter India with ease which also has made drastic changes in
the financial sector. Both service orientation and technological advancement in the financial
sector has made possible for consumers opening bank accounts, operating them, and making
transactions at the click of a button if one just looks at the banking system. The phenomenon
is not just restricted to banking system but spread across diverse financial investment
instruments. Consumers have plethora of choice before they make any financial investment
decision. The risk-return equation, the ease of investment, the ease of payment or the multiple
payment option modes are all being looked into by today‟s consumers. Even many of the
investment options are so flexible that it suits every Indian pocket, as if it is a tailor made
offer. Many new instruments have been introduced which Indian consumers have never heard
off; mutual funds, unit linked mutual funds, children‟s education plan, and education loans to
name a few. This has been possible by participation of private and foreign players over and
above the public sector enterprises. The entire portfolios of financial investment instruments
have been targeted at the both urban and rural Indian consumers, but the spread or business
share is primarily restricted to the urban area. The new and innovative instruments have
gained more popularity in the urban vista compared to rural India. This may be attributed to
the concentrated activities of the financial sector players in urban areas, lower level of
awareness and education in the rural areas and a traditional mindset in rural areas that is
averse to risk taking. But with nearly two-thirds of the population still living in rural areas in
India the focus has started shifting towards rural India too.
Performance of different or new or relatively new financial investment instruments have
not been the same even in urban areas; and many organizations are finding it hard to make an
inroad in the consumer‟s consideration set. The widely varying consumer behavioural
response is a matter not fully discovered by most of the financial services companies. While
such organizations are going all out to increase awareness, extend greater benefits, ensure
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- Dr. Ayan Chattopadhyay and Pawan Gupta
service guarantee, improvise on the intangible components and so on; yet the thorough
understanding of the consumer behaviour and attitude towards investments is still a matter of
concern. Attitude and behaviour of consumers, their ability to risk the newer forms of
investment is on a constant change path. But what does one understand from the word attitude
or behaviour? Or link between the two. One may understand attitude as "a relatively enduring
organization of beliefs, feelings, and behavioral tendencies towards socially significant
objects, groups, events or symbols" (Hogg & Vaughan 2005); "a psychological tendency that
is expressed by evaluating a particular entity with some degree of favor or disfavor" (Eagly &
Chaiken, 1993). One of the underlying assumptions about the link between attitudes and
behavior is that of consistency. This means that we often or usually expect the behavior of a
person to be consistent with the attitudes that they hold which is also called the principle of
consistency. The principle of consistency reflects the idea that people are rational and attempt
to behave rationally at all times and that a person‟s behavior should be consistent with their
attitude(s). The strength with which an attitude is held is often a good predictor of behavior.
The stronger the attitude the more likely it should affect behavior.
The strength of the attitude is related to its personal relevance which means how
significant the attitude is for the person and relates to self-interest, social identification and
value. If an attitude has a high self-interest for a person, it is going to be extremely important.
As a consequence, the attitude will have a very strong influence upon a person's behavior. By
contrast, an attitude will not be important to a person if it does not relate in any way to his/
her life. Attitudes influence the way we think and behave and are therefore important for the
marketers who study them to understand how a consumer behaves. The formation of attitude
is dependent on numerous attributes; also known as attitude builders. Thus in a multi attribute
behavioural environment, some attitude builder has a greater impact on the overall attitude
formation or behavioural outcome i.e. attitude builders have a priority that is individual
specific. In the present paper, a modest effort to measure attitude towards different financial
investment instruments have been undertaken along with finding the priority of attitude
building attributes. Past research works form the backbone of evaluating and identifying the
parameters that constitute the multi-attribute behavioural environment.
2. EXPLORING PAST STUDIES
Study of past studies (literature) on financial investment instruments and that too related to
consumer attitude and behaviour suggests that while considerable studies in Indian context
have been made on the core product, studies on consumer behavioural aspect have received
low focus. This may be because of the fact that the onset of globalization that had brought in a
drastic change in the financial investment domain is not a very old phenomenon in India
compared to the other developed countries across the globe. In this context it is worth
mentioning some of the related researches. Francis J. C. (1986) revealed the importance of the
rate of return in investments which primarily guides consumer buying. Singh P. (1986) opined
that understanding and measuring return and risk is fundamental to the investment process by
consumers. According to her, most investors are 'risk averse' and to have a higher return the
investor has to face greater risks. Madhusudhan V. (1996) conducted a study on mutual funds
that reveals that investors look for safety of their invested amount, liquidity and growth or
appreciation of their capital in the order of importance which acts as a major differentiating
factor in the selection of mutual fund schemes. Study by Sikidar S. (1996) on the behavioural
aspects of the consumers in the North Eastern India, primarily towards equity and mutual
funds investment reveal that these instruments are merely viewed as tax savings instruments.
Goetzman and Peles (1997) established that there is evidence of investor psychology affecting
fund/scheme selection and switching. Chakrabarti A. & Rungta H. (2000) stressed the
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- An Interval Entropic Estimation of Consumer Priority in Multi-Attribute Behavioural Environment –
A Case Study of Financial Investment Instruments in an Urban Vista
importance of brand effect in determining the consumer behaviour towards mutual fund
scheme buying. Shanmugham (2000), studied the perceptions of various investment strategy
dimensions and the factors motivating investment decisions. The study highlights that among
the various factors, psychological and sociological factors dominated the economic factors in
investment decisions. Martenson R. (2005), describes that consumer knowledge, involvement,
and risk are central concepts in consumer behavior research in financial investments. The
hypothesized importance of domain specific knowledge was confirmed and a mediation
analysis showed the relations of involvement and risk willingness to knowledge and returns.
According to Jain R.( 2005), government backed savings instruments that offer a high rate of
assured returns and safety assurance of investors capital are preferred to mutual funds and
equity. The study also points out that Indians still have a risk-averse mentality that keeps
majority of the investments away from mutual funds and equity. Omar and Frimpong (2006)
stressed the importance of life insurance and regarded it as a saving medium, financial
investment, or a way of dealing with risks. Alinvi & Babri (2007) are of view that customers‟
preferences change on a constant basis, and organizations adjust in order to meet these
changes to remain competitive and profitable. Das B. et. al. (2008) made the behavioural
analysis of retail investors with reference to mutual funds and life insurance. The study
reveals that majority of the people (35%) are investing with the objective of capital growth,
followed by tax saving (28%) and only 17% are investing for the retirement plan. Maximum
investors (30%) like to invest in life insurance followed by mutual fund (20%) & Government
saving schemes (18%). O‟Donnel N. (2011) highlighted the importance of Risk as a
determinant to attitude formation in investment decision. While safety, security, complexity
(or convenience) are few of the attitude builders in financial investments as suggested by
Sarkar et. al. (2012), transparency and flexibility in financial investment instruments was
identified by Brian D (2010). Singh J. et. al. (2004) describes investor‟s perception and
attitude formation is dominantly guided by small investment option possibility and SIP is an
innovative option in that. Past studies explored provided not only valuable insights about
financial investment instruments in Indian context but also helped in building a foundation for
exploring the research gap.
3. RESEARCH GAP
Financial investment instruments have always been an area of interest in both academic as
well as industry fraternity. Umpteen number of research work have been conducted across the
globe, especially in the developed economies on the core instruments itself, factors promoting
consumer investments in financial instruments, consumer behaviour and psychology towards
different investment instruments in the developed economies. Even in Indian context one may
find considerable researches on the benefits or utility of such financial investment
instruments, especially how one instrument is different from the other and the differential
advantages of these instruments. However, the number of studies related to consumer attitude
and behaviour towards such instruments, primarily those which have gained prominence in
the globalized era are limited in Indian context. The researchers have identified the limited
study on the attitudinal and behavioural aspect towards the different financial investment
instruments as the gap area for their present study also restricted their study to four financial
investment instruments, namely Mutual Fund, Fixed Deposit, Equity and Insurance and
further restricted the geographic domain within the urban vista of Kolkata city.
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- Dr. Ayan Chattopadhyay and Pawan Gupta
4. RESEARCH OBJECTIVES
The researchers, on the basis of the research gap have framed two objectives in the present
study. Comparing overall consumer attitude towards different financial investment
instruments.
Evaluating and understanding the priority or weights of each of the multi-attribute attitude
building parameters that guides consumer behaviour for all the financial investment
instruments separately.
5. RESEARCH FRAMEWORK
The researchers in the ensuing study chose descriptive research design and cross sectional
study were preferred to longitudinal study since the objectives set suit the former study design
more. The research uses interval entropic method to determine the priority of consumers
towards different attitude forming attributes. This method assumes probabilistic or random
nature of drawing samples from the population. In order to bring in randomness in the sample
selection process, random sampling method was adopted so as to meet the criteria of the
interval entropic method. For the purpose of random sampling, sampling frame is required
from which the sample is to be drawn. Telephone directory of Kolkata forms the sample
frame to source the name and phone numbers of the probable respondents. Randomness in
selection process has been maintained by using the random number table. From the random
number table the researcher chose the random numbers between the Kolkata telephone
directory page range; 8 to 784. Pages with those numbers were selected for drawing the
sample. Then the first two names and the last two names from each page were taken along
with the respective telephone numbers from all the randomly selected pages. Thus for
Kolkata, 280 names with their telephone numbers were listed. In order make a comparative
analysis about consumers' attitude towards the different financial investment instruments,
primary survey method using questionnaire forms an integral part of the research design.
Before the full scale survey was rolled out with the chosen sample, a pilot survey was
administered to identify and eliminate the flaws. This survey had 30 respondents selected on
the basis of the fact that they are regular investors in multiple financial investment
instruments. Pilot survey helped in concluding that the questionnaire was good enough to be
used for the full scale survey and it is simplification of language that was required to bring in
clarity to certain questions. For the purpose of data collection, individuals were contacted and
time sought from them after explaining in brief the objective of the study. The response was
mixed as the probable respondents accepted and also declined in some cases to participate in
the survey. In most cases the respondents were comfortable in filling up the questionnaire
online and the link of the questionnaire (google form) was sent to such respondents for their
responses. For others, the questionnaire was filled through face to face interview. The filled in
questionnaires were then scrutinized and the incomplete ones rejected. It was found out that
on an average the respondents took 20 minutes to fill up the questionnaire. The sample size
was calculated to be 137 using the standard sample size calculator (Bartlett, J. E, et al., 2001).
230 number of questionnaires was distributed out which 121 filled in valid questionnaires
were received, thus getting a 52.6 % rate of return. It took 4 months to complete the survey. In
social science research, where an attitude scale or some other rating scales are being used, it is
very important that the investigator evaluates the extent to which random error effects the
measurement. Reliability Tests gives a measure of the extent to which the results are free
from experimental or measurement errors. Out of the various methods of measuring the
reliability of an instrument, the researcher used internal consistency coefficients. Cronbach‟s
(1951) alpha reliability coefficient was used to estimate the reliability in the present case and
its value was calculated using SPSS Ver. 20. The reliability statistics output reveal a value of
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- An Interval Entropic Estimation of Consumer Priority in Multi-Attribute Behavioural Environment –
A Case Study of Financial Investment Instruments in an Urban Vista
0.82 which falls within the good category; thereby suggesting that the items have high
internal consistency i.e. the instrument used for study is reliable.
6. METHODOLOGY
In the present study, an attempt is first made to understand the consumer attitude towards
different financial investment instruments using Semantic Differential Scale (SDS). The SDS
has been used as a measure of attitude in a wide variety of projects and is found to be applied
frequently. Osgood, et al., (1957) reports exploratory studies where SDS was used to assess
attitude change as a result of mass media programs. SDS has also been used by other
investigators to study attitude formation. The results in many studies support the validity of
SDS as a technique for attitude measurement. Semantic Differential Scale (SDS) measures
people's reactions to stimulus words and concepts in terms of ratings on bipolar scales defined
with contrasting adjectives at each end. Example of SDS scale is shown below where the
respondents are exposed to the bipolar statements and asked to comment on their agreement
or disagreement towards the same:
Good _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ Bad
+3 +2 +1 0 -1 -2 -3
OR
Passive _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ Active
1 2 3 4 5 6 7
This scale rates the object under study on a number of itemized rating scales bounded by a
set of bipolar adjectives. Osgood, Suci & Tannenbaum (1957) were the first to propose the
same. The classical Semantic Differential Scale is seven point scales, with a neutral middle
point. The scale assumes that the raw data are interval scaled. Usually, the position marked 0
is labeled "neutral," the 1 positions are labeled "extremely," the 2 positions "quite," and the 3
positions "slightly." The same 7 point scale may be constructed from 1 to 7 with 4 as the
neutral point. A scale like this measures the directionality of a reaction (e.g., good versus bad)
and also intensity (slight through extreme). It measures the direction and intensity of attitudes
to the object in question on three sets of dimension, evaluative (good vs. bad), activity (active
vs. passive) and potency (strong vs. weak). A classical Semantic Differential Scale would not
use less than seven adjectives, covering all these three dimensions. They are either expressed
as words, or preferably phrases, but it must be noted that the labels at the two extreme poles
of scale are truly bi-polar. The analysis of Semantic Differential Scale involves averaging the
response to each item across all respondents and plotting the average graphically. The average
value of all items is joined in what is usually referred to as the “Ladder” or “Snake Chart”.
The graphic representation of the Semantic Differential Scale makes it possible to compare
the consumer attitude (perception) towards different financial investment instruments and
hence the overall consumer preference towards them. Attitude comparison between four
different financial investment instruments has been made by computing the Sum of the
average rating points for each of the bi-polar parameters considered. Here, Sum value = ∑
Avg. Ratings for each financial investment instruments. The scale used considers 1 as a
measure of very strong agreement of the positive polarity of the attribute statement while 7
indicates a measure of very strong agreement of the negative polarity of the attribute
statement. The mid-point value of 4 indicate neither towards the positive or negative polarity
the attribute statement Thus, lower sum value score may be interpreted as a measure of strong
attitude towards the positive polarity of the attributes while higher values indicate deviation
from the such attitude i.e. higher sum values may be interpreted as a measure of weak attitude
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- Dr. Ayan Chattopadhyay and Pawan Gupta
towards the positive polarity of the attributes or strong attitude towards the negative polarity
of the attributes.
The Sum Value approach of SDS assumes all parameters having equal priorities or
weights or importance; however in reality there is always a differential priority towards
multiple attributes that consumers have in their consideration set which ultimately guides their
behaviour. This means the multiple parameters cannot have equal priority; in fact they will
have varying priority. Also from the company or brand‟s perspective, it is of utmost
importance to know how and to what degree consumers value the attributes in a multi
attribute behavioural environment. It is basis such valuable information on the ever changing
or dynamic consumer attitude that the service providers keep upgrading and modifying their
offerings. Such analyses set the direction for the service providers to prioritize action plans for
the attitude building attributes i.e. less important parameters can be dealt relatively later while
the most important ones need immediate attention. Basis the literature surveyed, ten attributes
have been identified that have been considered important in building consumer attitude and
behaviour towards financial investment instruments. These include Safety, High Returns,
High Liquidity, Easy Investment Process, Convenience in Redemption, Transparency in Fund
Profile, Availability of SIP, Flexibility of Funds, Guaranteed Returns and Small Amount
Investment Possibility.
There are many methods of evaluating priority or weights of attitude building parameters
that one may find in literature; most of which are categorized as either subjective or objective
weights. While subjective weights are found out using preference of the decision makers only,
objective weights do not consider such decision maker‟s preference and are calculated by
solving mathematical models. Examples of subjective weight determination includes methods
like AHP, Weighted Least Square Method, Delphi method etc. while objective methods
include entropy method, principle element analysis, multiple objective programming etc.
Objective methods are more useful and deployed when getting reliable decisions maker‟s
preference becomes difficult. One of the most popular objective weighting methods is the one
proposed by Shannon & Weaver (1947) that is based on entropy concept. Entropy weight
method was originally a concept of thermodynamics, that later included Information Theory
of CE Shannon and presently applies to various disciplines including that of engineering,
social economy, management etc. Entropy, as a concept of thermodynamics, is a measure of
system‟s disorder and higher value of it indicates higher disorder and higher stability of the
system and vice-versa. Information entropy on the other hand is a measure of the system‟s
orderly state. Smaller the value of information (Shannon‟s) entropy indicates greater
information provided by the indicators. Thus, indicators or attributes having lower entropies
provide greater information about the system and thus their importance or weights are higher.
The absolute values of both entropy and Shannon‟s entropy are the same but the symbol/ sign
is not. Shannon‟s entropy is a measure of uncertainty in a discrete distribution based on
Boltzmann‟s entropy or entropy of classical statistical mechanics. In social science and
management, Shannon‟s entropy has found a unique position as it serves the objective of
finding out attributes or parameters that gives maximum information about a system. In other
words, attributes that gives maximum information about a system is most important. The
relative importance or weights of attributes can be evaluated using this method.
In many real life situations, discrete data cannot be obtained precisely and other data types
like interval data, fuzzy data etc. may be available i.e. data is available in judgemental forms
rather than in number form. In such a situation when data is not discrete or precise,
measurement of priority or weights from such data would not be discrete; rather it would also
be in interval form. Shannon‟s entropy weight determination has been extended for interval
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- An Interval Entropic Estimation of Consumer Priority in Multi-Attribute Behavioural Environment –
A Case Study of Financial Investment Instruments in an Urban Vista
data (Interval Shannon‟s Entropy Weight) as well by Lotfi & Reza (2010). In fact when all
data is in discrete / deterministic form, interval entropy weight leads to usual entropy weight.
7. INTERVAL SHANNON’S ENTROPY
Shannon‟s entropy is a highly established and popular method of weight determination in a
multi-attribute or multi-criteria environment. The original procedure of Normal Shannon‟s
Weight (NSW) determination involves a series of sequential steps as described below.
NSW1. Normalization of the data matrix as ∑
, j = 1, 2, ….., m & I = 1,2,…., n
Raw data normalizing is done to eliminate the anomalies of disparate units of
measurement so as allow comparison on a similar platform.
NSW2. Entropy Ei is calculated as ∑
i.e. ∑
∑ ∑
, i = 1,2, …,n and
is the entropy constant and is defined as ( )
NSW3. Defining as and
NSW4. Defining Shannon‟s Entropy Weight as ∑
When there is interval data and the value of the attribute can change within the interval
range, it is logical to consider that even the weights would change in the interval range across
attributes with a uniform distribution. The extended Shannon‟s entropy for interval data i.e.
Interval Shannon‟s Entropy (ISE) also follows a series of a bit more complex sequential steps.
ISE1. Normalizing the values for lower and upper boundaries as:
∑
for the lower boundary value and ∑
for the upper boundary value
ISE2. Lower bound and upper bound entropies are calculated as:
{ ∑ ∑ }
i.e. { ∑ ∑ }
∑ ∑ ∑ ∑
and { ∑ ∑ }
i.e. { ∑ ∑ }
∑ ∑ ∑ ∑
where ( ) and is defined as 0 if = 0 or =0
ISE3. The lower and the upper bound interval diversification and follows as:
& , i = 1,2,…n
ISE4. The lower and upper bound of interval Shannon‟s entropy weights are evaluated as:
∑
and ∑
where, i = 1,2,…n
8. FINDINGS
Findings of Research Objective 1
The overall attitude of consumers towards the four financial investment instruments have
been found out by calculating the sum value for each of the attitude builders for the entire set
of respondents who participated in the study. Since the SDS considered 1 as a measure of
very strong agreement of the positive polarity of the attribute statement while 7 indicated a
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- Dr. Ayan Chattopadhyay and Pawan Gupta
measure of very strong agreement of the negative polarity of the attribute statement, the lower
the sum value score; more is the positive attitude towards a particular financial investment
instrument. Mutual Funds with the lowest overall „sum value score‟ (Fig. 1) has the highest
consumer preference while Equity is considered next best to mutual funds followed by Fixed
Deposits and Insurance in decreasing order of consumer preference. Fig. 2 represents the
Ladder Chart which is a graphical representation of the consumer attitude. The chart allows
one to compare the four investment instruments against each of the attitude building
parameters. It is evident that consumers have higher positive attitude towards investing in
mutual funds or mutual funds are more preferred to others instruments in five out of ten
attitude builders considered in the study. The chart also shows that mutual funds have
uniformity in the attitudinal pattern while other instruments show a higher irregularity.
Attitude Building Attributes MF FD E I
Safety 1.7 1.5 5.7 2.7
High Returns 1.9 3.6 2.5 4.1
High Liquidity 2.2 4.2 2.6 4.3
Easy Investment Process 1.6 1.5 1.4 4.6
Convenience in redemption 2.2 4.4 1.7 4.6
Transparency in Fund profile 2.2 3.6 3.3 2.9
Availability of SIP option 1.9 3.9 4.3 3.0
Flexibility of fund 2.7 5.0 1.5 3.5
Guaranteed returns 2.4 2.2 4.6 4.0
Small Amount Investment Possibility 1.7 3.1 3.7 3.0
Total SUM Value Score 20.4 32.8 31.3 36.7
Figure 1: Sum Value Score of Financial Investment Instruments (Source: Primary Survey)
MF: Mutual Fund, FD: Fixed Deposit, E: Equity, I: Insurance
Figure 2 Ladder Chart of Financial Investment Instruments (Source: Primary Survey)
Findings of Research Objective 2
The priority or weights of each of the multi-attribute attitude building parameters that guides
consumer behaviour have been calculated for all the four financial investment instruments
separately. For mutual funds, the highest & lowest limit of weights (or priorities), midpoint
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- An Interval Entropic Estimation of Consumer Priority in Multi-Attribute Behavioural Environment –
A Case Study of Financial Investment Instruments in an Urban Vista
weight and diversification is presented in Fig. 3 and graphically represented in Fig. 4. It is to
be noted that higher the mid-point weight of an attribute, greater is the consumer priority
towards that attribute in choosing an instrument for their investment. The attributes that have
been considered most important for mutual funds investment by consumers include SIP
option and small investment option possibility. Guarantee on return and return (high or low)
are also considered by important by them which makes them the 2nd and 3rd most important
attributes towards consumer attitude development. It is to be noted that priority or weights
should not be viewed in isolation. The degree of diversification of priority (Fig. 5) i.e. gap
between maximum and minimum value of priority must also be taken into account. Higher
diversification indicates higher fluctuation of priority i.e. an unpredictable nature. Thus, SIP
option and small investment option possibility might evolve as the most important attributes
guiding consumer behaviour, but their diversification is on the higher side thus indicating that
even if consumer considers these attributes as most important ones while investing in mutual
funds, the importance level can vary widely, thus making them more unpredictable than other
attributes. This calls for understanding the consumer priority in conjunction with
diversification. Attributes with low diversification, viz. flexibility and liquidity indicates that
they are more predictable than the rest of the attributes even though their priority might be
lower compared to other attributes.
Interval Entropic Priority Estimation (Mutual Fund)
Small
Investment Redemption SIP Guarantee
Safety Return Liquidity Transparency Flexibility Investment
Process Process Option on Returns
Possibility
Sum [LN{PLij}*{PLij}] -4.23 -3.50 -3.74 -4.23 -2.89 -2.89 -2.89 -3.74 -4.09 -3.74
h0 = - LN(m) -4.80 -4.80 -4.80 -4.80 -4.80 -4.80 -4.80 -4.80 -4.80 -4.80
ELi 0.88 0.88 0.78 0.88 0.60 0.60 0.60 0.78 0.85 0.78
U
E i 0.73 0.46 0.85 0.60 0.78 0.85 0.46 0.85 0.46 0.46
min {ELi , EUl} 0.73 0.46 0.78 0.60 0.60 0.60 0.60 0.78 0.46 0.46
max {ELi , EUl} 0.88 0.88 0.85 0.88 0.78 0.85 0.85 0.85 0.85 0.78
dLi - 1 - EUl 0.12 0.12 0.15 0.12 0.22 0.15 0.15 0.15 0.15 0.22
dUi - 1 - ELl 0.27 0.54 0.22 0.40 0.40 0.40 0.40 0.22 0.54 0.54
WLi 3% 3% 4% 3% 5% 4% 5% 4% 4% 5%
WUi 17% 34% 14% 25% 25% 25% 34% 14% 34% 34%
Mid Point Wt. 10% 18% 9% 14% 15% 14% 20% 9% 19% 20%
Diversification 14% 31% 10% 22% 19% 21% 28% 10% 30% 28%
Figure 3 Interval Entropic Estimation of Consumer Priority towards Mutual Funds (Source: Primary
Survey)
Figure 4 Consumer Priority Graph for Mutual Funds (Source: Primary Survey)
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- Dr. Ayan Chattopadhyay and Pawan Gupta
Figure 5 Attribute Priority & Diversification for Mutual Funds (Source: Primary Survey)
Study of highest & lowest limit of weights (or priorities), midpoint weight and
diversification for fixed deposits is presented in Fig. 6 and graphically represented in Fig. 7.
The attributes that have been considered most important while choosing fixed deposit as their
investment instrument include transparency, guarantee on returns, easy investment process
and return in decreasing order of consumer priority. The degree of diversification is shown in
Fig. 8. It must also be noted that all the attributes with higher consumer priority also have
higher degree of diversification or unpredictability. The study also indicated that if SIP
options of investment in fixed deposits are introduced, it might also have a higher influence in
the preference building process for the instrument. Liquidity and safety are two attributes that
have emerged low in terms of diversification i.e. higher predictability but have lower
influence on consumer behavior towards investment.
Interval Entropic Priority Estimation (Fixed Deposit)
Investment Redemption Transparenc SIP Guarantee Small Investment
Safety Return Liquidity Flexibility
Process Process y Option on Returns Possibility
Sum [LN{PLij}*{PLij}] -4.09 -4.09 -3.74 -4.23 -2.89 -2.20 -4.09 -4.23 -3.74 -4.23
h0 = - LN(m) -4.80 -4.80 -4.80 -4.80 -4.80 -4.80 -4.80 -4.80 -4.80 -4.80
ELi 0.85 0.85 0.78 0.88 0.60 0.46 0.85 0.88 0.78 0.88
EUi 0.85 0.46 0.85 0.46 0.85 0.73 0.46 0.73 0.46 0.73
min {ELi , EUl} 0.85 0.46 0.78 0.46 0.60 0.46 0.46 0.73 0.46 0.73
max {ELi , EUl} 0.85 0.85 0.85 0.88 0.85 0.73 0.85 0.88 0.78 0.88
L U
d i- 1 - E l 0.15 0.15 0.15 0.12 0.15 0.27 0.15 0.12 0.22 0.12
dUi - 1 - ELl 0.15 0.54 0.22 0.54 0.40 0.54 0.54 0.27 0.54 0.27
WL i 4% 4% 4% 3% 4% 7% 4% 3% 5% 3%
WUi 9% 34% 14% 34% 25% 34% 34% 17% 34% 17%
Mid Point Wt. 6% 19% 9% 19% 14% 21% 19% 10% 20% 10%
Diversification 6% 31% 10% 32% 22% 28% 31% 14% 29% 14%
Figure 6 Interval Entropic Estimation of Consumer Priority towards Fixed Deposits (Source: Primary
Survey)
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- An Interval Entropic Estimation of Consumer Priority in Multi-Attribute Behavioural Environment –
A Case Study of Financial Investment Instruments in an Urban Vista
Figure 7 Consumer Priority Graph for Fixed Deposits
Figure 8 Attribute Priority & Diversification for Fixed Deposits (Source: Primary Survey)
Study of highest & lowest limit of weights (or priorities), midpoint weight and
diversification for equity is presented in Fig. 9 and graphically represented in Fig. 10. The
attributes that have been considered most important while choosing equity as their investment
instrument include SIP & guarantee on returns. This means that consumers would invest more
(a direct bearing on the consumer behaviour) if equity offers such attributes. Also liquidity,
small investment options, returns and safety are the next important attributes considered by
consumers. The degree of diversification is shown in Fig. 11. The study also indicated that
attributes that have higher priority while considering an investment have emerged low in
terms of diversification i.e. consumer predictability is high.
Figure 9 Interval Entropic Estimation of Consumer Priority towards Equity (Source: Primary Survey)
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- Dr. Ayan Chattopadhyay and Pawan Gupta
Figure 10 Consumer Priority Graph for Equity
Figure 11 Attribute Priority & Diversification for Equity (Source: Primary Survey)
Study of highest & lowest limit of weights (or priorities), midpoint weight and
diversification for insurance is presented in Fig. 12 and graphically represented in Fig. 13.
The attributes that have been considered most important in framing their attitude towards
insurance as their investment instrument include liquidity & flexibility. This means that
insurance schemes offering such attributes would have a direct impact on the consumer
behaviour. Also SIP and safety are the next important attributes considered by consumers.
The degree of diversification is shown in Fig. 14. The study also indicated that attributes that
have higher priority in attitude formation towards insurance investment have high
diversification. This means consumer predictability is low for such attributes. It is also
noticed that most of the attributes have higher unpredictability barring three (ease of
investment and redemption process and guarantee on returns).
Figure 12 Interval Entropic Estimation of Consumer Priority towards Insurance (Source: Primary
Survey)
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- An Interval Entropic Estimation of Consumer Priority in Multi-Attribute Behavioural Environment –
A Case Study of Financial Investment Instruments in an Urban Vista
Figure 13 Consumer Priority Graph for Insurance
Figure 14 Attribute Priority & Diversification for Insurance (Source: Primary Survey)
9. CONCLUSIONS
The present study reveals that consumer attitude and behaviour is not affected or influenced
equally for different financial instruments studied. Consumer attitudes have been found to
vary with change in the instrument. SIP option and small investment possibility for mutual
funds; transparency and guarantee on returns for fixed deposits; SIP & guarantee on returns
for equity while liquidity & flexibility for insurance have been found out to be the attributes
that have maximum priority in attitude formation and hence consumer behaviour. Also, the
predictability of an attribute‟s priority while investing has been found to vary; in fact,
attributes with higher priority have been found to have low predictability thereby indicating
that both priority and diversification needs to be looked into simultaneously. It must also be
noted that consumers have indicated higher priority to an attribute or attributes that the
instruments do not have currently. This indicates that consumers would be more influenced to
invest if such attributes are modified or included in the investment instrument.
10. LIMITATIONS & SCOPE FOR FURTHER RESEARCH
Since the present research was conducted in urban city of Kolkata, the results of the study
cannot be generalized for other urban cities in India. Also, the sample size was restricted due
to feasibility issues. Research work on similar lines to that of the present study may be done
for other metro cities, semi-urban and rural India. Comparison between different urban
consumer behaviour in India could yield interesting view points and so is the comparison
between urban and semi-urban or rural areas. Age specific studies may also be conducted to
find out the change in behaviour of senior age group people vis-à-vis new age consumers. The
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- Dr. Ayan Chattopadhyay and Pawan Gupta
scope mentioned above is indicative only and researchers may consult further literature to
identify research gaps.
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