chapter 5 the impact of level of purchase decision ...the impact of level of purchase decision...
TRANSCRIPT
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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
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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
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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
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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.
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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
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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.
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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
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(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).
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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.
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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.
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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
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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.