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CHAPTER 5

The Impact of Level of Purchase Decision

Involvement on Investment Behaviour of Mutual

Fund Investors

5.1 Introduction

The mutual fund (MF) industry is considered amongst the most

successful financial innovation (Khorana, Servaes and Tufano, 2005).

Over the past ten years, Indian MF industry has been among the fastest

growing segment of the Indian financial markets. The CAGR in the total

net asset of Mutual Fund Companies (MFCs) was 34.72 % from 2000 to

200774

. There is an exponential growth in the number of MFCs and

schemes being offered by them. This has increased the competition

amongst MFCs as they have to vie with each other to attract the existing

and prospective investors towards their schemes. As a result, MFCs have

been spending heavily on marketing (development, launch, promotion,

distribution and maintenance) of their new and existing schemes. The

successful marketing of MF schemes requires a clear understanding of

how the level of investors‟ purchase decision involvement (PDI)

influences their investment behaviour. Yet the MF marketers have only

74

www.ici.org/stats/mf/arcglo/index.html retrieved on 17/05/2009

175

a naïve understanding of above relationship. While there is a dearth of

studies which have directly focused on understanding the impact of the

level of investors‟ PDI on their investment behaviour, several studies in

the marketing and consumer behaviour literature suggest that the level of

investors‟ involvement should influence a person‟s search behaviour and

information processing behaviour (Celsi and Olson, 1988; Kassarjian,

1981; Lamb, Hair and McDaniel, 2004; Maheswaran, Mackie and

Chaiken, 1992; Swinyard, 1993).

This chapter draws upon the existing relevant literature to

propose a series of hypotheses related to the influence of level of PDI on

the investment behaviour of MF investors. These hypotheses put

forward the possible impact of PDI on the information search behaviour

and information processing behaviour of MF investors. The specific

research issues examined through these hypotheses are: How the level of

PDI impacts the width of information search by MF investors? How the

level of PDI impacts the depth of information search by MF investors?

How the level of PDI impacts the width of information processing by

MF investors? How the level of PDI impacts the depth of information

processing by MF investors?

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The proposed hypotheses are tested empirically using the data

collected through a survey wherein a pretested structured questionnaire

was administered to the 268 respondents. The findings of the study

suggest that level of PDI significantly predicted the investment

behaviour of MF investors. Further the results suggest significant

difference in the width and depth of information search and information

processing by low PDI MF investor and high PDI MF investors.

The rest of this chapter is organized in the following way. In

Section 5.2, the existing literature related to the influence of level of PDI

on investment behaviour is critically reviewed and the hypotheses are

proposed. Section 5.3 discusses the methodology and the constructs used

in the study. Section 5.4 presents the results of the study. The

conclusion and the managerial implications of the study are discussed in

Section 5.5. Section 5.6 discusses the limitations of this research and

possible scope of future research in this area.

5.2 Literature review

Over the past two decades, MFs have been the focal point of an

increasing number of research studies being conducted in the field of

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finance. However, there has been a dearth of studies done specifically to

understand the investment behaviour of MF investors (Warneryd, 2001).

This void in the finance research is partially filled in by the perspectives

provided by the contemporary consumer behaviour models available in

the consumer behaviour literature. Most of the contemporary consumer

behaviour models implicitly and explicitly acknowledge the impact of

consumers‟ involvement on the buying decision process (Howard, 1994;

Huffman and Houston, 1993).

The level of involvement is a source to explain the difference in

the degree of both the mental and physical efforts of consumers in their

purchase decision making (Beharrell and Denison, 1995). The concept

of involvement was originally investigated in the field of social

psychology by Sherif and Cantril (1947) who viewed it as the relation

between ego and an object. Later Sherif, Carolyn and Nebergall (1965)

referred to it as the centrality of beliefs involved with an object. From

the consumer behaviour perspective there is a consensus that high

involvement means (approximately) personal relevance or importance

(Greenwald and Leavitt, 1984). Involvement is often conceptualized to

be a dichotomous variable i.e. consumers are considered to depict only

high or low involvement (Day et al., 1995). However, as discussed by

most of the researchers involvement is more likely to be a continuum

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with high and low as two extremes of the continuum (Krugman, 1965;

Laurent and Kapferer, 1985; Zaichkowsky, 1985). Involvement has been

widely researched and it has resulted in multiple definitions and

dimensions like purchase decision involvement, product involvement,

situational involvement, enduring involvement, felt involvement,

message involvement etc.

In this study our research issues specifically deal only with the

purchase decision involvement (PDI), which can be defined as the extent

of interest and concern that a consumer brings to bear upon a purchase

decision task (Mittal, 1989). Hence PDI is a measure of the mindset

rather than response behaviour manifested in purchase decision making

(Foxall and Pallister, 1998; Mittal, 1989). High PDI is defined as a state

in which consumer brings high extent of interest and concern upon a

purchase decision task and low PDI is defined as a state in which

consumer brings low extent of interest and concern upon a purchase

decision task. In spite of the fact that purchase of MFs involve

substantial amount of money and risk, investor is likely to show

different level of PDI due to the extent to which MF investor attach

personal relevance to the purchase of MFs (Mittal, 1995). The level of

PDI is likely to impact purchase behaviour specifically information

search behaviour and information processing behaviour (Andrews,

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Durvasula and Akhter, 1990). Even though the above proposition is

supported by large volume of empirical work, most of them were

developed in the context of studying the purchase of tangible goods

(Sally, 1992) rather than intangible financial products (Gabbott and

Hogg, 1994). MFs are intangible financial goods (Lin and Lee, 2004)

having characteristics which are different from tangible goods or

services.

The focus of this chapter is to investigate the impact of level of

purchase decision involvement on investment behaviour of MF

investors. Specifically, the impact of level of PDI on the information

search behaviour and information processing behaviour of MF investors

is examined. This is due to the fact that most of the marketing activities

are directed to influence consumer purchase decision. Hence, knowing

how level of PDI impacts the investment behaviour of MF investors is of

a great relevance to the MF marketers as well as researchers.

Rest of this section is organized as follows. First, the impact of

level of PDI on the information search behaviour of MF investors is

discussed and a series of hypotheses are proposed, each conjecturing the

possible influence of level of investors‟ PDI on a specific facet of their

search behaviour. Next, the impact of level of PDI on the information

180

processing behaviour is discussed and a series of hypotheses are

proposed, each conjecturing the possible influence of level of investors‟

PDI on a specific facet of their information processing behaviour.

5.2.1 Information search behaviour

Three widely recognized contemporary consumer behaviour

model i.e. Nicosia (1966) model, Howard and Sheth model (1969) and

Engel- Blackwell- Minard model (Engel et al., 1995) considers

consumer search for information as an important prepurchase activity.

Since investment generally involves substantial amount of money and

risk, information search is therefore an important activity for many

investors before making investment decisions (Lin and Lee, 2004). The

search behaviour of investor primarily includes the width of information

search and depth of information search.

5.2.1.1 Width of information search

Width of information search can be primarily measured on the

basis of the number of information sources used by MF investors.

Broadly the investors use two types of information sources viz.,

interpersonal communications; and impersonal (mass) communications

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(Aldlaigan and Buttle, 2001). The interpersonal communications include

information received through informal (e.g. family and friends) and

formal (e.g. broker‟s recommendation) sources (Alexander et. al., 1997;

Urbany, Dickson and Wilkie, 1989). The impersonal communications

include information available from the financial magazines and

newsletters, published historical return performance, fund advertisings

and analyst reports (Capon et al, 1996; Jain and Shuang Wu, 2000).

Literature in consumer behaviour suggests that high involvement

consumers will attend to product related advertisements and magazines

and consult the reference groups on ongoing basis. During purchase,

high PDI consumer will use more than one source of information

(Alexander et al, 1997; Assael, 1981; Richins and Bloch, 1986; Wright,

1974). Hence it is hypothesized that

H1: Level of PDI has a positive impact on the number of information

sources used by MF investors.

5.2.1.2 Depth of information search

The depth of information search can be measured in terms of the

extent to which information was sought from each source and/or group

of sources. The extent to which information is sought from different

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sources is likely to be decided by the nature of information an investor is

looking for. The selection of search strategies (i.e. the extent to which

information will be gathered from individual/group of sources) is likely

to be impacted by the level of PDI of MF investors. It has been

suggested that consumers with high PDI rely less on distribution

channels for information (Alexander et al, 1997; Assael, 1981; Richins

and Bloch, 1986; Wright, 1974). Hence it is likely that the level of PDI

will have negative impact on the extent to which MF investors use

distribution channels like MF sales agent, bank etc. as a source of

information.

High PDI consumers have a tendency to seek advanced

information and process all relevant information‟s in detail ((Beatty and

Smith, 1987; Gensch, Javalgi and Rajshekhar, 1987). Hence, high PDI

MF investors are more likely to search for information sources providing

detailed MF information than do low PDI MF investors. With the

advancement of information technology, financial portals have become

an important source of detailed information related to MFs. This is due

to the depth of available information on these financial portals for

comparing and selecting MF schemes. Hence it is expected that high

PDI MF investors will rely more on financial portal for their extent of

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prepurchase information search as compared to the low PDI MF

investors. The study hypothesizes the following:

H2a: Level of PDI has a negative impact on the extent of use of MF

sales agent as source of investment advice by MF investors.

H2b: Level of PDI has a negative impact on the extent of use of banks as

source of investment advice by MF investors.

H2c: Level of PDI has a positive impact on the extent of use of financial

portal as source of information by MF investors.

5.2.2 Information processing behaviour

Information processing is an important prepurchase activity

which includes identification of key attribute(s) for comparing the

various brands of products to be purchased and comparison of the

available brands on the basis of identified attributes. The information

processing behaviour of MF investors primarily includes width of

information processing and depth of information processing.

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5.2.2.1 Width of information processing

The width of information processing by MF investors may be

measured in terms of the number of attributes used to compare MF

schemes. During the purchase of MF schemes investors rely on various

performance and non-performance attributes while making their

decisions. For example, historical risk and returns, turnover, load, fund

management reputation, fund family reputation, transparency in the

accounting statement, tax concession, fund size, origin of managers,

fund manager‟s age, experience, investment style, fund advertisement

expense, asset under management etc. are a few commonly used

attributes indicated in some of the earlier studies (Huhmann and

Bhattacharya, 2005; Patel, Zeckhauser and Hendricks, 1992; Sikdar and

Singh, 1996). In the consumer behaviour literature, the level of PDI is

found to have significant influence on the number of attributes

information used by consumers to compare the alternate product brands

(Engel and Blackwell, 1982; Hawkins, Best and Coney, 1986; Howard

and Sheth, 1969). It has been suggested that the investors with high PDI

are likely to seek information on numerous attributes (Assael, 1981;

Mitchell, 1981; Sherif and Cantril, 1947; Sherif et al., 1965). Hence the

study hypothesizes that

185

H3: Level of PDI has a positive impact on the number of attributes

information used by MF investors.

5.2.2.2 Depth of information processing

High PDI consumers have clearly articulated attributes to

compare brands (Lee and Marlowe, 2003). They engage in extensive

evaluation of attributes and products (Hansen, 2005). High PDI

consumers will have a high degree of cognitive activity and will make

strong efforts in evaluating and comparing products before reaching a

reasoned decision (Dawar and Parker, 1994). However, low PDI

consumers may use simple decision rules in arriving at judgments. Cue

utilization theory suggest that low PDI consumers may try to reduce the

risk of purchase by using cues which they consider are indicators of

quality of products or services (Hansen, 2005) . Hence it is hypothesized

that

H4: Level of PDI has a positive impact on the extent of information

processing by MF investors.

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5. 3 Methodology

5.3.1 Sample

The data was collected through a survey wherein a pretested

structured questionnaire was administered to the 350 respondents. The

survey respondents were conveniently selected from those who invested

in one or more MF scheme(s) within one month period prior to the

survey date and were based in the Jammu region of J&K (India). The

condition that respondent must have invested in one or more MF

scheme(s) within one month period prior to the survey date was applied

so as to more clearly measure the respondent‟s purchase decision

involvement75

. Since it was hard to reach the population, we used

convenience sampling method (Mittal and Vyas, 2008) and identified

respondents for the study by reaching to the place where they are

reasonably expected to gather. The local offices of the five Asset

Management Companies located in Jammu region, Jammu & Kashmir

(India) was identified for this purpose. A self administered survey

questionnaire was used to collect data.

75

Mittal (1989) argued that purchase decision involvement should be measured as

close to the time of purchase as possible.

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5.3.2 Survey Design

This study was conducted in four stages. In the first stage

(Section 5.2), relevant literature was reviewed to propose a series of

hypotheses on the impact of level of PDI on the investment behaviour of

MF investors. In the second stage, structured questionnaire was

developed and pretested for validity and reliability. The constructs used

in the pretested questionnaire are described below in Section 5.3.3. In

the third stage, the data was collected through a survey wherein a

pretested structured questionnaire was administered to the 350

respondents. The total 276 respondents (78.25%) returned the filled

questionnaire. Among the 276 responses received, 8 responses were

found to be incomplete and were not included in the study. The total

usable responses were 268 (76.57 % of the sample size) which forms the

basis for the study. The final (fourth) stage involved following two

steps. In step 1, the dimensionality and reliability of the constructs was

tested. The exploratory factor analysis (EFA) was used to assess the

dimensionality of the constructs (Hair et al., 2005). Cronbach‟s alpha

was used to test the reliability of the constructs used in this study (Hair

et al., 2005). In step 2, the proposed hypotheses were tested. In this step,

first the impact of level of PDI on the investment behaviour of MF

investors was analyzed statistically using bivariate regression. The

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measures of the investment behaviour were structured as dependent

variables, with level of PDI as independent variables. Next, the

independent sample t-test was conducted to compare the part of the

sample that is one standard deviation below the PDI mean (N1= 34) and

one standard deviation above the PDI mean (N2= 52) to find whether

significant difference exist in the investment behaviour of the above two

categories (i.e. high PDI and low PDI respectively) MF investors.

5.3.3 Constructs used in the pretested questionnaire

This section describes the operationalization of various

constructs used in the pretested questionnaire.

5.3.3.1 Level of purchase decision involvement (PDI)

To measure PDI, Laurent and Kapferer abridged Consumer

Involvement Profile (CIP) was used (Laurent and Kapferer, 1985). The

details of the measure used to operationalize the level of involvement

construct are shown in table 5.1. The PDI was measured by 3 items five

point Likert scale. The scores of the individual items were added to find

the overall score.

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5.3.3.2 Width of information search

This study measures the width of information search by counting

the number of sources which respondents used (from the given possible

twelve sources) while gathering information before the purchase of the

recent MF schemes. The possible twelve source included in the study are

interpersonal informal source (e.g. family, friends and peer group),

interpersonal formal source (e.g. MF sales agent, brokers, banks)

(Alexander et al., 1997; Urbany, Dickson and Wilkie, 1989), non

electronic source (e.g. newspaper, magazines, store display), and

electronic source (e.g. television, financial portal, mail) (Capon et al,

1996; Jain and Shuang Wu, 2000).

5.3.3.3 Depth of information search

Depth of information search is conceptualized as the extent to

which information is sought from each source and/or group of sources.

In this study depth of information search was measured by three

variables. The three variables selected to measure depth of information

search were based on its relevance to the study context and the proposed

hypotheses (Alexander et al., 1997; Beatty and Smith, 1987; Cho and

Lee 2006). First variable measured the extent of MF sales agent as a

190

source of investment advice/information. It was measured on a five

point Likert scale; where 1= did not use and 5=used a lot. Second

variable measured the extent of bank as a source of investment

advice/information. It was measured on a five point Likert scale; where

1= did not use and 5=used a lot. Third variable measured the extent of

financial portal as a source of investment advice/information. It was

measured on a five point Likert scale; where 1= did not use and 5=used

a lot.

5.3.3.4 Width of information processing

This study measures the width of information processing by counting the

attributes (from the given possible 12 attributes) which respondent have

used for comparing MF schemes before/at the time of purchase of the

scheme. The possible twelve attributes included in the study are fund

historical performance, fund size, scheme entry and exit load, scheme

investment portfolio, favorable rating of fund scheme, clarity of

accounting statement, reputation of fund manager, fund manager

background, investment style, scheme tax benefit, pending legislation

and fund advertisement expense, a few commonly used attributes

indicated in some of the earlier studies (Huhmann and

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Bhattacharya,2005 ; Patel, Zeckhauser and Hendricks, 1992; Sikdar and

Singh,1996).

5.3.3.5 Depth of information processing

The depth of information processing construct was used to

measure the level of effort done by the respondents to evaluate and

compare the MF schemes before reaching to decision. It was measured

by 12 items five point Likert scale; where 1= did not use and 5= used a

lot. Each item represented some particular attribute of the MF schemes

which investors can possibly use to compare the various schemes before/

at the time of purchase of the scheme(s). The scores of the individual

items were added to find the overall score.

5.4 Results

The demographic profiles of the sample MF investors are same

as shown in table 3.1. In this section the results of the study are

discussed. This section is organized as follows. First, the dimensionality

and reliability of each of the constructs used in this study is discussed.

Next, the test results of the hypotheses of the study are discussed.

192

5.4.1 Test of dimensionality and reliability of the constructs

The test results of dimensionality and reliability of all research

constructs are shown in Table 5.1. The dimensionality is assessed using

exploratory factor analysis (EFA), a method based on the pattern of

correlations between the questionnaire item scores. If all items share

moderate to strong correlations, this produces a single 'factor' and

suggests that the scale measures a single dimension. Several groups of

such items produce several factors, suggesting that several dimensions

are being measured through a single construct. Principal component

analysis with varimax rotation was conducted to identify the number of

dimensions (factors).

To determine the appropriateness of factor analysis, the (KMO)

and the Bartlett‟s test of Sphericity are examined. The Kaiser-Meyer-

Olkin (KMO) measure of sampling adequacy for all the constructs is

above minimum acceptable value of 0.60, which means data is adequate

for EFA (Tabachnick and Fidel, 1989). The Bartlett‟s test of Sphericity

is also found to be significant (p < .001). A significant Bartlett‟s test of

Sphericity indicates that each factor identified by EFA has only one

dimension and each attributes load only on one factor.

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Principal components analysis of purchase decision involvement

construct reveals the unidimensional nature of the construct as all the

items are loaded into one single factor. Next, principal component

analysis with varimax rotation results of extent of information

processing construct reveals that: the construct „extent of information

processing‟ is bidimensional (constitute two factors). A careful analysis

of items under construct „extent of information processing‟ indicates that

the items under factor 1 and 2, respectively, measure the extent of non

performance attributes processing and the extent of performance

attributes processing by MF investors. Further for each of the construct,

the reported eigen value is greater than 1 and the factor loadings exceed

the acceptable threshold level of 0.4 (Hair et al., 2005).

After identifying the dimensionality, the reliability of the

constructs is measured using the Cronbach‟s Alpha (α). Table 5.1 shows

that the reliability of each of the constructs as measured by the

Cronbach‟s α is above the recommended minimum acceptable value of

0.6 (Hair et al., 2005). Based on the above results it is concluded that the

measures are adequate to measure the constructs of the study.

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Table 5.1

Dimensionality and reliability of latent research constructs (N=268)

Construct Items Loadings Eigen

Value

KMO Cronbach’s

alpha

Factor 1 Factor

2

Level of

involvement

2.247 0.719 0.826

I choose mutual

fund schemes

very carefully

0.850

Which mutual

fund scheme I

buy matters to

me a lot

0.883

Choosing mutual

fund scheme is

an important

decision for me

0.864

Extent of

information

processing

Fund historical

performance

0.873 3.006 0.807 0.921

Fund size 0.835

Scheme entry

and exit load

0.444

Scheme

investment

portfolio

0.631

Favourable rating

of MF scheme

0.554

Clarity of

accounting

statement

0.711

Reputation of

fund manager

0.768

Fund manager

background

0.838

Investment style 0.742

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Scheme tax

benefit

0.623

Pending

legislation

0.812

Fund

advertisement

expense

0.699

5.4.2 Test results of the hypotheses of the study

The primary goal of the study is to investigate the impact of level

of PDI on the investment behaviour of MF investor, specifically

information search behaviour and information processing behaviour.

Data were analyzed using bivariate regression analysis and independent

samples t-test. Table 5.2 summarizes the bivariate regression results and

table 5.3 summarizes the results of independent sample t-test. In this

section, first the test results of the bivariate regression are discussed.

Next, test results of the independent sample t-tests are discussed.

5.4.2.1 Test results of bivariate regression analysis

Bivariate regression was employed to determine the impact of

level of PDI on the various facets of investment behaviour. One extreme

multivariate outlier was identified using Mahalanobois‟ distance

196

(D²=6.89). The respondent reported very low level of overall PDI (Level

of PDI =3). However, the case appeared to have a reasonably

understandable and logical range of responses. Further, (D²/f) value,

where „f‟ is the number of variables involved (in this case 2), was also

not significant at .01 levels (Hair et al., 2005).Hence no case was deleted

from the subsequent analysis. Homoscedasticity was examined through

several scatter plot and these indicated reasonable consistency of spread

through distribution.

The bivariate regression results (table 5.2) suggest that level of

PDI significantly predicted the various facets of investment behaviour

except the extent to which MF sales agent are used as a source of

investment advice (H2a). Results indicated that level of PDI have

statistically significant impact on the width of information search (ß=

.28, t(266) = 4.87, p < .01), extent of bank as a source of investment

advice/information(ß = -.24, t(266) = -4.10, p < .01), extent of financial

portal as a source of investment advice/information (ß = .29, t(266) =

5.10, p < .01), width of information processing (ß = .43, t(266) = 7.44, p

< .01), and depth of information processing (ß = .47, t(266) = 8.73, p <

.01).

197

Specifically, it is found that (a) during information search low

PDI mutual fund investors are likely to use fewer number of

information source to collect information, rely less on source providing

detailed information related to MFs ( for example financial portal etc.)

and rely more on banks as a source of investment advice in comparison

to high PDI MF investors; and (b) during information processing low

PDI mutual fund investors are likely to use information on fewer

number of attributes to compare MFs, do not process MFs related

attribute information in details in comparison to high PDI mutual fund

investors.

The low value of adjusted R² suggests that there is a need to

capture other variables predicting the various facets of investment

behaviour.

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Table 5. 2

Bivariate regression results: Facets of investment behaviour

predicted by level of PDI (N=268)

Hypotheses

No.

Dependent

variable:

Facets of

investment

behaviour

Independent variable:

Level of PDI

Standardize

d regression

coefficient

T

value

Adjuste

d R²

F

ratio

Width of

information

search

H1 No. of

informatio

n source

0.28 4.87*

*

0.07 23.79*

*

Depth of

information

search

H2a Extent of

MF sales

agent

0.02 .402 0.00 0.16

H2b Extent of

banks

-0.24 -

4.10*

*

0.05 16.82*

*

H2c Extent of

financial

portal

0.29 5.10*

*

0.08 26.09*

*

Width of information

processing

H3 No. of

attribute

informatio

n

0.43 7.44*

*

0.18 60.31*

*

Depth of information

processing

H4a Extent of

informatio

n

processing

0.47 8.73*

*

0.22 76.23*

*

Note. * p < .05. ** p < .01.

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5.4.2.2 Test results of independent sample t-tests

To further verify the above results independent sample t-tests is

conducted. This is to compare the part of the sample that is one standard

deviation below the PDI mean with the part of sample one standard

deviation above the PDI mean to find whether significant difference

exist in the investment behaviour of the above two categories (i.e. low

PDI and high PDI respectively) MF investors. The results show that (a)

significant difference exists in all but one facets of investment behaviour

of low PDI MF investors and high PDI MF investors; (b) no significant

difference exists in „extent to which MF sales agent are used as a source

of investment advice‟ by the low PDI MF investor and high PDI MF

investors.

200

Table 5.3

Results of independent sample t-test: Investment behaviour of Low

PDI MF investors vs. High PDI MF investors

Hypotheses No. Low PDI MF

investor

(N=34)

High PDI MF

investor

(N=52)

T ratio Test

assumption

Mean S.D Mean S.D

Width of information

search

H1 4.05 2.26 6.71 3.18 -

4.50**

Unequal

variance

Depth of information

search

H2a 2.61 1.92 2.78 1.71 -0.42 Unequal

variance

H2b 3.41 1.47 2.17 1.45 3.84** Equal

variance

H2c 1.50 1.08 2.61 1.72 -

3.68**

Unequal

variance

Width of information

processing

H3 5.55 4.84 12.42 3.58 -

7.54**

Equal

variance

Depth of information

processing

H4a 21.47 9.15 36.73 9.1 -

7.58**

Equal

variance

H4b 10.52 5.00 17.36 4.58 -

6.52**

Equal

variance

H4c 10.67 5.38 18.76 7.57 -

5.78**

Unequal

variance Note. * p < .05. ** p < .01.

201

5.5 Conclusion and Managerial Implications

This study assesses the impact of level of PDI on the investment

behaviour of MF investors. Specifically, the information search and

information processing behaviour of MF investors is investigated with

the help of data collected through a structured questionnaire

administered on the 268 MF investors located in Jammu region, Jammu

& Kashmir, India. The bivariate regression analysis is used to measure

the impact of level of PDI on the investment behaviour of MF investors.

Independent sample t- test is conducted to find the difference in the

investment behaviour of high PDI and low PDI MF investors.

The findings of the study suggest that level of PDI significantly

impacts information search and information processing behaviour of MF

investors. Also significant difference exists in the investment behaviour

of the two categories (i.e. low PDI and high PDI respectively) MF

investors.

During information search, level of PDI significantly impacts the

width as well as depth of information search. Low PDI mutual fund

investors is likely to use fewer number of information source to collect

202

information and also rely less on source providing detailed information

related to MFs ( for example financial portal etc.).Further, study

suggests that low PDI MF investors rely more on banks as a source of

investment advice in comparison to high PDI MF investors. On the other

hand, high PDI MF investors use large numbers of sources to collect

information, and rely more on those source that provide detailed

information related to MFs.

Level of PDI also significantly impacts the width as well as

depth of information processing by MF investors. Low PDI mutual fund

investors are likely to use information on fewer numbers of attributes to

compare MFs. They also do not process MFs related attribute

information in detail in comparison to high PDI mutual fund investors.

On the other hand high PDI MF investors use large number of attributes

information to compare MFs. They extensively process the selected

attributes before reaching a reasoned decision.

The study also suggests that low PDI MF investors extensively

use bank as a source of investment advice. The above finding has

significant importance for banks. Low PDI MF investors can be

persuaded by banks through effective investor relation strategy using

their asset management desk.

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The study suggests that the nature of information looked in by

the low PDI MF investors and high PDI MF investors are different. For

example low PDI MF investors will rely less on extensive information

search with very little or no analysis of attributes information and

primarily use recommendations from others (like banks, brokers, MF

agents etc.) to select MFs. This behaviour of low PDI MF investors can

facilitate investor relation functions of MFCs. This may be particularly

of greater importance for low/ moderate performing MFs / companies as

they can effectively use distribution channels to promote their new and

existing schemes by targeting them towards low PDI MFs investors.

Apart from the use of distribution channel to promote their MFs, these

MFCs can also leverage on their brand awareness to tap these categories

of MF investors. In comparison to low PDI MF investors, high PDI MF

investors engage in extensive search process and use large number of

attribute information to compare MFs. Earlier research in consumer

behaviour suggests that cognitive limitations of the consumers force

them to adopt heuristics, restricting their attention to minimum amount

of data which is necessary for satisfactory decision (Bettman, 1974). As

high PDI MF investor use large number of attributes information, they

are likely to restrict their attention to fewer number of brands during the

choice of a MF (i.e. will use brand by brand comparison) (Powell and

Ansic, 1997). Hence those MFs/companies which are part of the

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consideration set will have more probability for being selected in

comparison to those which are not. Hence while targeting these

categories of MF investors MFCs faces a challenge to be among the top

four or five names so that they become the part of the consideration set

of these categories of MF investors.

The study also suggests that low PDI MF investors do not

process attributes information extensively while comparing/ evaluating

MFs. Hence they are likely to rely on decision heuristics to select MFs

(Dawar and Parker, 1994). These investors use various kinds of cues as

an indicator of quality of MFs. They also use these cues to reduce their

purchase risk. Hence these basic characteristics of low PDI MF investors

can be used by the mutual fund marketers while designing and

promoting MFs. Further as low PDI MF investors show passive

behaviour in information processing, MF companies should promote

index funds among these segment of investors.

Today advertisements has become important marketing

instruments to attract fund sales and MFCs need to clearly outline their

expectations from advertising strategy when it is targeted to low PDI

MF investors and high PDI MF investors. It seems that the role of

advertisement is somewhat limited in the decision process of high PDI

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MF investors. However it is likely to play substantial role on the

decision making of low PDI MF investors due to the nature of

information used and information processing style of this category of

investors.

High PDI MFs investors have been found to use large numbers

of attributes to compare MFs and use the relevant attributes in detail.

This shows that high PDI MF investors have confidence in their

knowledge (Park and Lessig 1981) . Hence these investors themselves

are likely to act as a source of information. MFCs can explore the role of

these investors‟ in their sales/ communication strategy. Further aligning

company communication strategy with investor‟s source requirement

will effectively supplement sales strategy.

5.6 Limitations and Direction of Future Research

The premise of this chapter is that the level of PDI significantly

impacts the investment behaviour of MF investors. However the

findings of the study are constrained by the following limitations.

First, the study focuses on the prepurchase information search

behaviour and information processing behaviour of MF investors and

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the impact of PDI on the ongoing search behaviour and post purchase

behaviour is not discussed.

Second, depth of information processing behaviour has been

discussed with reference to the extent to which each type of attributes

information are used by MF investors before/at the time of purchase of

MF schemes. However more insights regarding the impact of level of

PDI on depth of information processing behaviour of MF investors can

be achieved through study on experimental design. It will help in

developing an understanding of the sequence in which attributes

information are used, the number of stages in which the decision is taken

and the number of MFs considered at various stages of information

processing.

Third, the sample selected for the study is through convenience

sampling due to non avaibility of data on MF investors. Due

consideration was given to check the validity and internal consistency of

the data however care has to be taken while generalizing upon the

results. Further use of random sampling method to select respondents

would have further improved the statistical validity of the findings.

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Fourth, the study is conducted using the respondents from the

Jammu region, J&K (India). It is possible that if the study is conducted

on the residents of other region, states and countries, the direction of the

relationship may be different. Hence further generalization of the results

is contingent upon securing the further empirical evidence in other

states/ countries.

Fifth, the study is confined to individual investors of MFs and

institutional investors are excluded from the study. Therefore, including

other categories of investors‟ might lead to different behavioural

outcome.

Sixth, the results of the study imply that MF companies can

segment investors on the basis of their level of PDI and develop

different marketing strategies to attract both the categories of investors.

However, there should be a way out to assess whether an investor is high

or low on PDI gives rise to a further scope of research in this field. A

further study conducted to do the profiling of investors based on their

PDI can certainly be useful in doing a proper segmentation of MF

investors.

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Seventh, the low adj R-square of our model indicates that in

future one need to capture the other influences (variables) to complete

the model.

Despite the above limitations, the findings of the study improves

consumer behaviour theory by deepening our understanding of how

level of PDI impact the investors‟ investment decisions for the

intangible financial products. As MF industry spends heavily on

developing, maintaining, distributing and promoting funds to these

investors, assumption about investor behaviour is very crucial for them.

The findings of this study will help MF marketers to design and deliver

different products, formulate different advertising and promotion

strategies and use different distribution innovations for high and low

PDI MF investors.