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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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.

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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 http://www.iaeme.com/IJM/index.asp 137 editor@iaeme.com
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 http://www.iaeme.com/IJM/index.asp 138 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 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. http://www.iaeme.com/IJM/index.asp 139 editor@iaeme.com
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 http://www.iaeme.com/IJM/index.asp 140 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 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 http://www.iaeme.com/IJM/index.asp 141 editor@iaeme.com
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 http://www.iaeme.com/IJM/index.asp 142 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 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 http://www.iaeme.com/IJM/index.asp 143 editor@iaeme.com
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 http://www.iaeme.com/IJM/index.asp 144 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 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) http://www.iaeme.com/IJM/index.asp 145 editor@iaeme.com
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) http://www.iaeme.com/IJM/index.asp 146 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 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) http://www.iaeme.com/IJM/index.asp 147 editor@iaeme.com
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) http://www.iaeme.com/IJM/index.asp 148 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 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 http://www.iaeme.com/IJM/index.asp 149 editor@iaeme.com
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