What Drives Investments into Mutual Funds?
Applying the Theory of Planned Behaviour to Individuals’
Willingness and Intention to Purchase Mutual Funds
Nicolas Schmidt 1
First Draft: July 2010
Current Draft: November 2010
______________________
1
WHU - Otto Beisheim School of Management, Burgplatz 2, D-56179 Vallendar, Germany, nicolas.schmidt@whu.edu, +49 177 705 705 1
1
What Drives Investments into Mutual Funds?
Applying the Theory of Planned Behaviour to Individuals’
Willingness and Intention to Purchase Mutual Funds
NICOLAS SCHMIDT
ABSTRACT
With decreasing generosity of public pension schemes, private retirement savings and
participation in capital markets becomes more important. However, only parts of the
population engage in financial markets. This effect is known as “participation
puzzle” and widely researched. This paper adds to literature on household finance
and portfolio choice. It improves the understanding of factors influencing the
decision to participate in capital markets through mutual funds. To examine the
predictors, we employ the Theory of Planned Behaviour (TPB), a largely elaborated
and widely used framework to measure behavioural intentions. For analysis we use
unique survey data with 1.673 participants from Germany. The results indicate that
TPB components account for 67% of variance of willingness and 71% of variance of
the intention to invest in mutual funds. We find that social pressure, attitude and
perceived behavioural control show significant and positive influence on both
dependent variables. Results support our model and associated hypotheses based on
TPB, making it useful to assist in further research. Our findings are also in line with
existing results within household finance. We conclude with implications for mutual
fund companies and industry associations and give suggestions for future avenues of
research.
JEL Classification: D03, D14, G11, G15, G23
Key Words: household finance, portfolio choice, mutual funds selection, private investor
behaviour, stock market participation, smart decision making, fund selection criterion,
mutual funds purchase intention
2
What Drives Investments into Mutual Funds?
Applying the Theory of Planned Behaviour to Individuals’
Willingness and Intention to Purchase Mutual Funds
NICOLAS SCHMIDT
ABSTRACT
With decreasing generosity of public pension schemes, private retirement savings and
participation in capital markets becomes more important. However, only parts of the
population engage in financial markets. This effect is known as “participation
puzzle” and widely researched. This paper adds to literature on household finance
and portfolio choice. It improves the understanding of factors influencing the
decision to participate in capital markets through mutual funds. To examine the
predictors, we employ the Theory of Planned Behaviour (TPB), a largely elaborated
and widely used framework to measure behavioural intentions. For analysis we use
unique survey data with 1.673 participants from Germany. The results indicate that
TPB components account for 67% of variance of willingness and 71% of variance of
the intention to invest in mutual funds. We find that social pressure, attitude and
perceived behavioural control show significant and positive influence on both
dependent variables. Results support our model and associated hypotheses based on
TPB, making it useful to assist in further research. Our findings are also in line with
existing results within household finance. We conclude with implications for mutual
fund companies and industry associations and give suggestions for future avenues of
research.
With regard to decreasing generosity of public pension systems, private retirement
investment of individuals becomes increasingly important in many industrialized countries
and especially in Western Europe. Also with regard to inflation, it evolves as crucial for
individuals to somehow participate in capital markets for retirement investments as those
provide returns above savings accounts and inflation. As direct holdings in stocks and bonds
oblige to have distinct expert financial market knowledge, individuals also have the
opportunity to participate indirectly via mutual funds. With mutual funds, individuals can
reach diversification across regions, industries and different companies even with low
investment and savings volumes and without the need for superior financial literacy. There
are several articles existent dealing with reasons for holding mutual funds. Customer services,
low transaction costs, diversification and professional management are commonly stated
reasons, why one should have mutual funds (e.g. Gruber 1996 citing Sirri and Tufano 1992).
Despite these arguments and the necessity to participate in capital markets, not everybody
holds mutual funds. It is puzzling, why especially in some developed regions percentage of
mutual funds shareholders within population is low.
As a result of rising importance but also due to significant growth in some countries, mutual
funds have increasingly become a core area in academic research. Research categorizes along
3
its focus on mutual funds industry itself, performance and persistence, mutual funds holder
characteristics and on mutual fund decision making.
Literature on mutual funds industry includes research on conflicts of interest and competition
(Khorana and Servaes 2004, Feirreira and Ramos 2009), mutual fund fees around the world
(Khorana, Servaes and Tufano 2009) and comparing developments of the industry in
different regions (e.g. Otten and Schweitzer 2002, Klapper, Sulla and Vittas 2004). Khorana,
Servaes and Tufano (2005) search for explanations of the size of the industry globally and
find that rules and norms of the country affect the financial development. With mutual funds
being “advanced” financial products, they prosper in more developed economies.
Furthermore, fund industry is larger where per capita GDP is higher and is also impacted by
investor wealth and education. Ramos (2009) finds that competition through fewer barriers to
entry is positively associated with larger industry and more efficiency in terms of returns and
fees.
One of the largest areas of research however, questions, whether mutual funds outperform
their benchmarks and whether this performance is persistent over time. Initiated by Grinblatt
and Titman (1992), who find that performance differences between funds persist, and
confirmed by Elton et al. (1996) applying risk-adjusted measures, a large number of research
with differing results on persistence followed (e.g. Carhart 1997, Hendricks et al. 1993, Chen
et al. 2000, Wermers 2000). For further reading, Anderson and Schnusenberg (2005) provide
a comprehensive overview on performance persistence literature.
Another field of literature researches mutual funds investor characteristics and their trading
behaviour, using questionnaires or portfolio transaction data. Capon, Fitzsimons and Prince
(1996) build clusters among mutual funds investors along their information sources used and
selection criteria. Alexander, Jones and Nigro (1998) find that typical mutual funds holder are
older, wealthier and better educated than average. Regarding trading behaviour, Goetzmann
and Peles (1997) identify that cognitive dissonance also exists in mutual funds trading and
switching. Lenard et al. (2003) develop a model that can assist in predicting investors’
switching behaviour of mutual funds and find that investors consider investment risk, fund
performance, investment mix and capital base of the fund before switching. Bailey, Kumar
and Ng (2009) apply behavioural finance originated anomalies to mutual funds trading and
find that better-informed and experienced investors make better use of mutual funds.
Very closely related to characteristics and trading behaviour is the decision making and
selection behaviour of mutual funds investors, which has also been widely researched. One
key question analyzes whether investors have selection ability and are able to identify
superior mutual funds. The hypothesis known as “smart money effect” states that money
flows to funds that will outperform in the future. Thus, flows inherit predictive power over
future returns. While Gruber (1996) and Zheng (1999) find selection ability and evidence that
funds with high inflows show better performance, a controversial debate followed. Sapp and
Tiwari (2004) find that smart money does not hold when controlling for stock momentum.
Frazzini and Lamont (2008) even find that fund inflows are associated with low future
returns, while outflows are associated with high returns. In contrast, Keswani and Stolin
(2008) examine, in a recent paper, U.K. data and provide robust evidence again for smart
money in US and UK. Hackethal et al. (2009) conclude that smart investment decisions are
made by investors that are holder, more experienced and wealthier.
4
Another question within decision making focuses on criteria by which mutual funds are
selected. To identify determinants, academic research uses flow data, questionnaires or
portfolio transaction data. Thereby, most researchers find evidence that mutual funds’ past
performance influences mutual fund flows (e.g. Ippolito 1992, Bogle 1992, Gruber 1996,
Sirri and Tufano 1998, Chevalier and Ellison 1997, Bergstresser and Poterba 2002, Sapp and
Tiwari 2004 or Ber, Kempf and Ruenzi 2007). Gruber (1996) finds two different mutual
clienteles: sophisticated investors using past performance and disadvantaged clienteles using
advice and advertising for selection. Wilcox (2003) employs conjoint analysis to analyze
preferences for stock mutual funds and also identifies performance as most important
purchase determinant. Recently, Ivkovich and Weisbenner (2009) find evidence that inflows
are influenced by relative performance measures while outflows are related to absolute
performance. Also linked to performance, Del Guercio and Tkac (2002) find that Morningstar
ratings also have power to influence asset flows.
Another criterion widely researched involves mutual funds fees. Sirri and Tufano (1998)
document a negative relation between fund fees and inflows. Barber, Odean, Zheng, (2005)
find that investors are sensitive to the form of charging fund expenses and find negative
relations between flows and front-end-load fees. Gruber (1996) as well as Elton, Gruber and
Busse (2004) find that individual investors buy funds with high fees. In particular, this holds
for investors residing in less affluent and less educated neighbourhoods (Malloy and Zhu
2004) and for overconfident investors (Bailey, Kumar and Ng 2009). Müller and Weber
(2009) find a positive relationship between financial literacy and the likelihood of investing
passively. However, neither a high IQ (Grinblatt et al. 2007) nor high education levels and
experience (Engström 2007) holds investors off purchasing high-fee funds. Zhao (2004)
concludes that brokers and financial advisors ultimately minister as decision makers to
investments into load funds. Schmidt (2005) conducts a preference-based segmentation of
mutual fund purchasers within in private banking clientel via conjoint-analysis and also finds
that recommendation of advisors is most important. In contrast, brand seems to be highly
unimportant within mutual funds selection.
However, prior to researching selection criterion and ability, it is important to gain an
understanding of participation decisions even before, i.e. why individuals decide for or
against purchasing mutual funds. A similar question is addressed within a broad debate on
private household finance and portfolio choice initiated by Campbell (2006) dealing amongst
others with nonparticipation in risky asset markets and investment mistakes. One aspect that
has caused a wide array of studies is described as stock market participation puzzle, which
Haliassos (2002) summarizes as the analysis what keeps the majority of households out of the
stock market, even though if one can expect to earn more by holding stocks than by holding
riskless financial assets. Results are manifold.
Most intuitive factors result from the risks associated with capital market participation.
Barsky et al. (1997) show that risk tolerance measures significantly relate to holding stocks.
Barberis, Huang and Thaler (2006) analyse and find risk aversion in combination with
narrow-framing as potential reason for lack of participation. Dimmock and Kouwenberg
(2008) confirm a decreasing probability of equity participation for higher loss aversion. This
holds to a greater extent for direct stock holdings than for mutual funds. Cognitive abilities
(Christelis et al. 2006), individual’s IQ (Grinblatt et al. 2010) as well as educational
5
background (e.g. Bertraut 1998 or Guiso, Haliassos and Jappelli 2003) are also found
associated with the decision to invest in stocks directly or indirectly. Cole and Shastry (2009)
provide causal estimates and find large effects of education on participation. Rooij, Lusardi
and Alessie (2007) conclude that a lacking understanding of economics and finance is a
significant deterrent to stock ownership. Results from Guiso and Jappelli (2005) indicate that
a lack of awareness of existing investment vehicles does also limit stock market participation.
Trust is another explanation found by Guiso, Sapienza and Zingales (2008). Puri and
Robinson (2007) relate participation to general optimism. Other results deal with social
interaction and peer effects. Hong, Kubik and Stein (2004) find that social households –
defined as those who interact with their neighbours or who attend church – are indeed
substantially more likely to invest in the stock market than non-social households. Brown and
Taylor (2010) confirm with evidence from UK. Brown et al. (2005) quantify that a 10%
increase of equity market participation of the members of one’s community causes 4%
increase of probability to invest in stocks. Other findings include relations to health (Rosen
and Wu 2004), cultural background (Grinblatt and Keloharju 2001) and internet use (Bogan
2008). Regarding the choice of investing directly or indirectly, Zhu (2005) finds an influence
of households’ costs of time: busy individuals rather invest in mutual funds than in single
stocks.
To sum up, research approaches within stock market participation puzzle primarily focus
stock holdings, either direct or indirect. Distinct research on mutual fund purchasing in
general and viewed as vehicle independently from asset class is rather scarce. Thus, the main
question remains, namely why some households and individuals are generally willing and
intending to invest in mutual funds, while others categorically deny. Reasons behind this
decision are still unclear. As Keswani and Stolin (2008) stated, “gaining insight into mutual
fund investor behaviour continues to be an exciting area for future research”. Especially
from professional industry perspective, it becomes crucial to find out more, on what drives
individuals to purchase or to not purchase mutual funds.
The purpose of this paper is to improve understanding of willingness and intention to invest
in mutual funds and thus to identify key factors that determine this willingness and intention.
Furthermore, we want to find a model that can assist as basis for further research questions in
this field.
To examine the predictors of willingness and intention to invest in mutual funds, we
established a research model using the Theory of Planned Behaviour (TPB), a largely
elaborated and widely used framework to measure intentions. Theory of Planned Behaviour
(TPB) is an attitude-behaviour framework that has been developed from earlier Theory of
Reasoned Action (TRA) by Ajzen and Fishbein (1980, Fishbein and Ajzen 1975). TRA
proposes that an individuals’ behaviour or action is most proximally determined by his
intention to engage in that behaviour. Consequently, intention is regarded as the immediate
antecedent of behaviour. Intention on the other hand is expected to be a function of three
components: attitude, subjective norm and perceived behavioural control. While attitude
describes the cognitive or affective evaluation of a behaviour, subjective norm can be
interpreted as perceived social pressure towards this behaviour (Ajzen 2002, Armitage and
6
Connor 2001). The perceived control represents the confidence about the ability of
performing that certain behaviour. Several meta-analytic reviews of the Theory of Planned
Behaviour have proven its validity in predicting behaviours within different contexts and
research fields (e.g. Sutton 1998, Armitage and Connor 2001, Downs and Hausenblas 2005).
Armitage and Conner (2001) oversaw more than 150 independent studies and reported that
the “TPB accounted for 27% and 39% of the variance in behaviour and intention”.
Examples of using Theory of Planned Behaviour (TPB) in an investment decision context are
scarce. Firstly to be mentioned is East (1993), who applied TPB to decision making for
investing in certain shares, specifically in newly issued shares. His results indicate that
application for shares was predicted by intention and intention by the attitude, subjective
norm, perceived control and past behaviour. The findings showed strong influence of friends
and relatives and the importance of easy access to funds within investment decision making.
Secondly, Hofmann et al. (2008) applied different behavioural models, including TPB, to
explain effects for variations in bidding behaviour within social responsibility investment
investing. Results indicate that moral considerations influence investment decisions. To best
of our knowledge, so far no other studies tested TPB regarding general willingness and
intention to invest in mutual funds.
For our research, we constructed a questionnaire based on TPB and collected data through a
primary online survey, conducted in Germany in December 2009. Germany is appropriate for
analyzing mutual funds willingness and purchase intention since it should provide great
potential for mutual funds industry. Its economy belongs to the largest in the world in PPP
terms and it accommodates one of the largest stock markets worldwide. Furthermore, German
households are historically savers and own substantial wealth in terms of real estate and
financial assets. Here, mutual funds industry has experienced significant growth – especially
after 1990s – and has become one of the largest mutual funds markets in the world, managing
651.6bn EUR. However, in relation to population and potential, German market is
substantially behind other G7 countries, supporting the research question what drives
willingness and intention to purchase mutual funds.
Final dataset used for analysis consisted of 1.673 participants – being representative for
German population and suitable for our research approach. With the full dataset, we
calculated structural equation models in order to estimate measurement and structural causal
paths simultaneously, to explicitly account for measurement error and receive indices to
describe the fit of the models. As covariance-based approach is most appropriate in research,
in which well-established causal theories, such as TPB, are available and have to be
validated, we employed most widely used Maximum Likelihood (ML) for estimation of
parameters.
We find that the three key components of TPB, being subjective norm, attitude and perceived
behavioural control, are correlated and all show positive and significant influence on both
dependent variables, the general willingness and the intention to purchase mutual funds. The
results support our model and the associated hypotheses based on TPB, making it useful to
assist in further research questions. Our results are consistent with findings from TPB
research in other fields, as the model fits are good and coefficients of the three determinants
are positive and significant and attitude is strongest predictor for both – willingness and
intention. Also correlations between TPB variables are consistent with similar approaches.
7
We can also confirm findings of East (1993) with subjective norm showing significant impact
on intention. This is also in line with comparable research within stock market participation
by Hong, Kubik and Stein (2004), Brown and Taylor (2010), Brown et al. (2005).
The remainder of this paper is organized as follows. In Section I, we provide background on
mutual fund industry in Germany, an explanation of the Theory of Planned Behaviour as
research framework and derive our hypothesises and research models. In Section II, we
present the method including approach, data and key measures. Section III shows results and
robustness checks and in section IV, we conclude.
I.
Theoretical background and hypotheses
A. The German Mutual Fund Industry
With a population just over 80 million, Germany is Europe’s most populous country after
France and the UK and is a key member of the European Union and its economic, political
and defense organizations. There are several reasons, why Germany provides great conditions
and thus potential for mutual funds industry. Firstly, its economic situation: German economy
is the fifth largest economy in the world in PPP (purchasing power parity) terms and with a
GDP of USD 2.811 trillion in 2009E, European Union’s largest economy accounting for
approx. 20% of its 2009E GDP.1 With 34.121 USD also Germany’s GDP in PPP per capita is
above European Union average and higher than in France, Japan or Italy, but below levels in
US, UK and Canada.2 Secondly, Germany accommodates the seventh largest stock market
worldwide and the third largest in Europe after UK and France in terms of market value of
publicly traded shares.3 Market value amounts to USD 1.107 trillion after significant decrease
of share prices following 2008 financial crisis. Thirdly, German individuals and households
are historically significant savers and have accumulated large amounts of financial assets. In
2008, Germany had the second highest net saving rate in terms of percentage of disposable
income within the European Union: private households saved about 11.2% of their disposable
income – compared to about 4% in Japan and 3% in the United States.4 With regard to
financial assets, Germany continuously ranks high within international wealth tables with
private households owning more than EUR 4.400 billion. According to World Wealth Report
2009, Germany domiciled the third largest number of HNWIs globally and together with US
and Japan accounts for about 54% of 2008 world’s HNWI population.
Despite the solid economic foundation and the mentioned prerequisites of its private
households, it is a key feature of Germany to lack an equity culture and to have low stock
market participation. After decades of lag, German capital market awaked during the period
of New Economy. Especially with some major initial public offering, such as Deutsche
Telekom in November 1996, more Germans became stockholders resulting in approx. 9.7%
1
CIA World Factbook (2010), https://www.cia.gov/library/publications/the-world-factbook/geos/gm.html,
accessed on May, 29th, 2010. Also see International Monetary Fund (2010): World Economic Outlook Database,
April 2010.
2
CIA World Factbook (2010), https://www.cia.gov/library/publications/the-world-factbook/geos/gm.html,
accessed on May, 29th, 2010.
3
Worldbank (2010): http://data.worldbank.org/indicator/CM.MKT.LCAP.CD
4
Source: Savings rate 2008 according to Eurostat Data and central banks
8
of population holding stocks in 2000. After these record levels and the severe stock market’s
decline, the proportion drastically decreased to 5.2% holding shares in 2009. This percentage
is significantly below other G7 countries, e.g. Japan: 27.7%, USA: 25.4%, UK: 23.0% or
France with 14.5%.
Instead of direct ownership of stocks, individuals in Germany can also participate in capital
markets indirectly via mutual funds. First open-ended mutual funds appeared in Germany in
1949, about two decades later than in UK (1930s) or US (1920). In 1955 further mutual fund
investment companies arose to offer mutual funds and in 1970 the investment association BVI
was founded to further promote investments in mutual funds. However, German mutual funds
industry has one feature which makes it different to US or UK mutual funds market.
Germany was historically and still is one of the most heavily banked economies. Universal
banks, being engaged in all activities including investment advice, dominate the financial
system. As a consequence, most important distribution channel for mutual funds are banks
and their network of branches throughout Germany. According to BVI (2008), around 72
percent of all mutual fund sales were administered through banks and bank advisors. Despite
that fact, the importance of other distribution channels including mutual funds companies,
direct banks and distribution platforms, and independent financial advisors has slightly
grown.
Nevertheless, mutual funds industry experienced fast growth – especially after 1990s. Total
assets under management in mutual funds have grown with a compound annual growth rate
of 14.3% from solely 4.9bn EUR in 1970 to 71.1bn in 1990, and until 2000 with a CAGR of
20.1% p.a. even faster resulting in 444.5bn EUR. Though growth partly originated in stock
market, private households had also transferred savings from other vehicles into mutual
funds. After the bear market and the years 2000 and 2001, industry has recovered its all time
high, but with a second stock market downturn during the financial crisis 2008, assets under
management have only grown 4.4% p.a. until 2009. By year end 2009, 87 companies
managed 651.6bn EUR in mutual funds assets, making it one of the largest industries in the
world. When looking at the market and the demand for mutual funds, it has to be taken into
account that some funds domiciled abroad but are primarily sold in the home country of the
provider. Doing so, Germany has the second largest mutual funds market in Europe after
France.5
Despite growth and absolute size, in relation to its population and economic potential,
German mutual funds market is still substantially behind international comparisons. Only
10.2% of total German population hold mutual funds, which shows a significant decline from
records in 2001 when percentage was 15.2%. Also the share of household financial assets
held in mutual funds of approximately 15 % is relative low in comparison to a share of more
than 23 % in the US (BVI 2008, ICI 2008). Further proof is the average amount of assets a
household invested in mutual funds 2009, which is 7.946 EUR and thus significantly below
average of other G7 countries, e.g. US with 25.608 EUR, France with 20.314 EUR or Canada
with 11.923 EUR and UK with 8.746 EUR. Only Japan exhibits a lower rate with 3.611
EUR.
5
Source: Data Assets end 2008 taking into account funds domiciled abroad and promoted by national providers
in their own country, the foreign-domiciled funds promoted by foreign providers in each country and the homedomiciled funds sold abroad, cf. efama fact Book, 7th edition 09, p. 40.
9
To conclude, Gremillion (2005) attests Germany one of the largest gaps between wealth and
actual mutual fund penetration of all countries. He attributes this to strong conservatism of
German investors, who have traditionally favored savings instruments such as CDs and
passbook accounts and the lack and delayed introduction of a government provided tax
benefit for long term savings with mutual funds like US 401k or UK PEPs and ISAs. A
further reason could be the historically given relative generosity of German public pensions
system, which in the past rendered own investments unnecessary. Also low financial
knowledge of German investors could have caused widespread non-participation. Moreover,
recent developments in financial markets have not promoted mutual funds. Many German
private investors have experienced significant losses during financial crisis resulting in
increased risk and loss aversion. Additionally, general trust in banks and financial institutions
has diminished. As a consequence, German mutual funds market experienced major outflows
in 2008 being even larger than in US or UK for 2008.
At the same time, private individual retirement investments will become more important
within the next years, since public pension schemes become increasingly less generous. It
remains unclear, how German individuals will behave and mutual funds market will develop.
Further insight into behaviour and behavioural intentions would be helpful. In this context,
the Theory of Planned Behaviour (TPB) as attitude-behaviour model provides a conceptual
framework to analyse mutual funds purchase willingness and intention.
B. Theory of Planned Behaviour and mutual fund purchasing
In general, attitude-behaviour models can serve as frameworks to explain intentions and
behaviours. One widely used approach in consumer research and in predicting intentions and
behaviour is the Theory of Planned Behaviour (TPB) by Ajzen/Fishbein. According to Ajzen
(1988), TPB forms a “[…] conceptual framework for the prediction of specific action
tendencies, a framework that deals with a limited set of dispositional antecedents assumed to
guide specific action tendencies”. The main idea is, that a person’s intention to engage in a
certain behaviour constitutes the closest determinant of that behaviour. Intention is thus
assumed to be the immediate antecedent of behaviour (Ajzen 1991). This means that an
action will be performed, if the individual holds a strong intention to perform this action.
Intention in turn is a function of three determinants: the attitude towards behaviour, the
subjective norm reflecting a person’s perception of social pressure regarding the behaviour
and the perceived behavioural control. Figure 1 visualizes the major relations within the
Theory of Planned Behaviour.
Figure 1 Theory of Planned Behaviour (Ajzen 1991, Ajzen and Driver 1992, Ajzen 2006)
Subjective Norm
(SN)
Attitude towards a
behaviour (ATT)
Intention
(INT)
Perceived
behavioral control
(PBC)
10
Behaviour
The Theory of Planned Behaviour was proposed in 1988 by Ajzen. It emerged from previous
Theory of Reasoned Action (TRA), that was developed and introduced by Ajzen and
Fishbein (1980, Fishbein and Ajzen 1975) and has roots in various theories of attitude, such
as Learning Theories, Expectancy-Value Theories, Consistency Theories and Attribution
Theory. The key concept reflected in TRA is that attitude and subjective norm are direct
antecedents of intention and thus influence behaviour additively. According to TRA, if an
individual evaluates a certain behaviour as positive (meaning a positive attitude) and if he
thinks that his relevant peers would approve of such a behaviour (meaning subjective norm),
it will result in higher motivation and thus intention to actually perform that meant behaviour.
The link between attitude and subjective norm and intention and thereafter between intention
and behaviour has been proven in many studies.6
However, critics stated, that TRA can not deal with behaviours that require resources,
cooperation or certain skills, meaning behaviours that individuals do not have fully under
control. Ajzen (1988) confirmed that “The TRA was developed explicitly to deal with purely
volitional behaviours” describing behaviours that only require the formation of an intention.
As a consequence, Ajzen (1985, 1988, 1991, 2002) proposed the Theory of Planned
Behaviour as an adjusted model reflecting the idea that intentions are also impacted by a
person’s resources and opportunities available. To cope with these, he added – as stated
above – the perceived behavioural control.
TPB has been applied to studies of the relations among beliefs, attitudes, behavioural
intentions and behaviours in several fields such as leisure activities, healthcare, marketing,
advertising, public relations and economics.7 Numerous meta-analytic reviews of the TPB
have been conducted to prove its validity in predicting intentions and behaviours (for reviews
see Sutton 1998, Armitage and Connor 2001, Downs and Hausenblas 2005). Sutton (1998)
find that TRA and TPB explain on average between 19% and 38% of variance in behaviour
and 40% to 50% of variance in intention. Armitage and Conner (2001) reviewed 185
independent studies and summarized that 39% of the variance in behavioural intention and
27% of the variance in the respective behaviour can be attributed to TPB. This confirms the
effectiveness of the framework’s approach in a wide variety of contexts. As Armitage and
Christian (2003) call TPB “the most dominant model of attitude-behaviour relations” at
present, it should also be suitable for researching investment decision making.
However, in financial markets and investment behavioural research we could only find two
studies – already mentioned in the beginning – that employed TPB as research framework.
First is East (1993), researching decision-making for investing in certain shares and second is
Hofmann et al. (2008) who applied different behavioural models, including TPB, to explain
effects for variations in bidding behaviour within social responsibility investing.
Therefore, in our context of investing in mutual funds the validity of the TPB has yet to be
demonstrated. According to Ajzen (1991), attitudes, subjective norms and perceived
behavioural control as main components of the TPB can predict intentions to perform certain
behaviours with high accuracy.
For deriving our hypotheses and research models, a more detailed view on these three
antecedents of intention is necessary.
6
7
Sheeran (2002) reviewed several studies and found a mean correlation of .53 between intention and behaviour.
For an overview of applications see Ajzen (2001) or Ajzen (2010).
11
Subjective norm (SN)
There are some situations where behaviour is not only depending on an attitude of the
individual, but also on the expectation that others – like family and friends – have. Subjective
norm refers to the perceived influence that these significant others have on a persons
intentions and behaviour (Ajzen and Driver 1992b). It reflects the person’s perception
whether most people who are important to him think that he should or should not perform the
behaviour in question (Fishbein and Ajzen 1975). Thus, subjective norm is a function of
beliefs about the expectations of important referent others (Ajzen and Fishbein 1974) and a
persons motivation of complying with these. It is thus intended to measure the social
influences of family and friends on a person’s behaviour. Subjective norm can be interpreted
as a perceived social pressure to perform or not to perform the behaviour (Ajzen 1991, Beck
and Ajzen 1991, Armitage and Connor 2001). Subjective norm is measured by selfassessment of statements, e.g. “most people who are important to me approve/disapprove of
my engaging in this activity” and “most people who are important in my life think I should
engage on this activity”.
Results on significance of the relationship between subjective norm and intention differ
among studies that have employed TPB as research framework. As also stated by Ramayah et
al. (2008), some studies can not find a significant relationship between subjective norm and
intention (e.g. Lewis et al. 2003), while the majority finds significant positive relationships
(e.g. East 1993, Gumussoy and Calisir 2009, Jimmieson et al. (2008), Lin (2010), Schreurs et
al. 2009, Jen-Ruei et al. 2006, Ramayah et al. 2009, Xiao and Wu 2006).8 Also see Manning
(2009) for a meta-analysis on the effects of subjective norm.
Intuitively, subjective norm should also be influential with regard to investment decision
making. The more popular investing in financial products is within an individual’s peer
group, the higher the likelihood of the individual to also invest in financial products. This
relation has also been confirmed empirically in research on portfolio choice and stock market
participation. Hong, Kubik and Stein (2004) find that social households – defined as those
who interact with their neighbors or who attend church – are substantially more likely to
invest in the stock market than non-social households. Brown et al. (2005) even quantify and
find that a 10%- increase in equity market participation of the members of one’s community
increases likelihood of an individual to buy stocks by 4%. Transferred to the context of
mutual fund purchase willingness und intention, individuals are more likely to be willing and
intending to buy mutual funds, if their peer group or family is also favouring and engaging in
buying mutual funds. Thus, we formulate the following theses to be tested:
H1a: Subjective norms (SN) will influence private investor general willingness to
purchase of mutual funds (WNG).
H1b: Subjective norms (SN) will influence private investor intention to purchase of
mutual funds (INT).
8
For an overview of relations, also see Ajzen (2010) or Ramayah et al. (2009).
12
Attitude (ATT)
Attitude in general is a disposition to act favourably ore unfavourably to an object, person,
institution or event (Ajzen 1988, 1991). According to Ajzen (1985, 2006) it is the overall
positive or negative evaluation of performing a certain behaviour. Overall evaluation consists
of assessing two components. First is of an instrumental nature describing whether an action
is valuable, harmful and important. The second has experiential quality reviewing whether
behaviour is pleasant or enjoyable (Ajzen 2006). Intuitively and as described by Armitage
and Connor (2001), generally the more positive the attitude towards a certain behaviour is,
the stronger should be the individual’s intention to perform the behaviour. For measuring
attitude toward the behaviour, the respondents are usually asked to evaluate the behaviour in
question along several attributes on bipolar scales.
The great majority of studies have confirmed that attitude does have a significant and positive
impact on intention. For examples see East (1993), Schreurs et al. (2009), Cronan and AlRafee (2008), Gopi & Ramayah (2007), Jimmieson et al. (2008), Ramayah et al. (2008,
2009), Rhodes & Courneya (2003), Lin (2010), Van Dam and Heijden (2009), Fang, Shao
and Lan (2009), Teo & Pok (2003), Xiao and Wu (2006).9 Just like for the subjective norm,
also the attitude should be positively related to intentions with regard to investment decision
making. The better the attitude on a certain financial product, the higher should be the
likelihood of buying such product.
This can be caused by numerous factors, such as positive experiences made and
communicated by others – which could also indicate a link to subjective norms – positive
own experiences or even through media coverage. Transferred to our research context,
individuals are more likely to be willing and intending to invest in mutual funds, if they have
a positive attitude towards buying those. We therefore formulate the following theses to be
tested:
H2a: Attitude towards investing in mutual funds (ATT) will influence private
investor general willingness to purchase of mutual funds (WNG).
H2b: Attitude towards investing in mutual funds (ATT) will influence private
investor intention to purchase of mutual funds (INT).
Perceived behavioural control (PBC)
Some behaviours do not only require a positive attitude and the perceived backing of social
peer-group, but also skills and abilities (Ajzen 1991). Thus, perceived behavioural control
was added to TRA as third determinant of intention to resolve critics and to cover incomplete
volitional control. Ajzen (1988) defines the perceived behavioural control as “the perceived
ease or difficulty of performing the behaviour and it is assumed to reflect past experience as
well as anticipated impediments and obstacles” (Ajzen 1991, see also Ajzen and Driver
1992b).
The construct of perceived behavioural control reflects beliefs regarding access to resources
and fulfilment of requirements needed to perform a certain behaviour covering two
components: firstly, controllability needed to engage in the behaviour, e.g. external
constraints; secondly, the focal persons self-confidence or self-efficacy in the skills and
9
For an overview of relations, also see Ajzen (2010) or Ramayah et al. (2009).
13
ability to perform the behaviour in question, representing internal resources. Usually
respondents are asked to questions such as “For me to engage in this activity, it is easy…”.
Several studies in different contexts have empirically confirmed that perceived behavioural
control relates to intention. For examples of TPB studies with positive influences see East
(1993), Gummussoy and Calisir (2009), Jimmieson et al. (2008), Blanchard et al. (2008),
Gopi & Ramayah (2007), Cronan and Al-Rafee (2008), Jen-Ruei et al. (2006), Ramayah et al.
(2008, 2009), Fang, Shao and Lan (2009), Lin (2010), Van Dam and Heijden (2009), Teo &
Pok (2003), Xiao and Wu (2006).10 According to Armitage and Connor (2001), introducing
perceived behavioural control has in many studies improved prediction of intention.
Intuitively, PBC will also have a positive influence on willingness and intentions in an
investment decision making context. For engaging in financial markets and selecting
financial products, a basic financial education and literacy is necessary. Low financial
literacy however, as found in many studies, will deteriorate self-efficacy, resulting in lower
likelihood of participation. This thought is in-line with research on financial literacy,
portfolio choice and stock market participation. Christelis et al. (2006) find that cognitive
abilities are strongly associated with decision to invest in stocks. Additionally, Bertraut
(1998) and Cole and Shastry (2009) find that likelihood of stockholding increases amongst
other factors with education and Rooij, Lusardi and Alessie (2007) confirm an independent
effect of financial literacy on likelihood of participation in stock market. Thus, regarding the
investment in mutual funds, perceived behavioural control should be positively related to
willingness and intentions. With higher financial literacy, better understanding of mutual
funds and higher self-confidence to be able to select mutual funds, willingness and intention
of individuals will become more likely. We therefore formulate the following theses to be
tested:
H3a: Perceptions of their ability to control investment in mutual funds (PBC) will
influence private investor general willingness to purchase mutual funds
(WNG)
H3b: Perceptions of their ability to control investment in mutual funds (PBC) will
influence private investor intention to purchase mutual funds (INT)
To summarize, the application of the TPB in the context of mutual fund investing assumes
the following behavioural basis: general willingness and intention to invest in mutual funds
drive the actual purchase of mutual funds. Furthermore, individuals are more likely to be
willing and intending to purchase mutual funds if their attitudes, social norm and perceived
behavioural control are positive.
Extending the TPB induced research hypotheses, we also include a test on the relationship
between general willingness and intention. Intuitively, individuals with higher general
willingness to purchase mutual funds will also more likely intend to purchase those, resulting
in the following hypothesis to be tested:
H4:
10
General willingness to purchase mutual funds (WNG) will influence concrete
intention to invest in mutual funds (INT)
For an overview of relations, also see Ajzen (2010) or Ramayah et al. (2009).
14
Taken together, our hypotheses directly result from the postulated structure of the TPB and
are summed up in Table I.
Table I: Overview of hypothesis
H1a:
H1b:
H2a:
H2b:
H3a:
H3b:
H4:
Subjective norms (SN) will influence private investor general willingness (WNG) to purchase of mutual funds.
Subjective norms (SN) will influence private investor intention (INT) to purchase of mutual funds.
Attitude towards investing in mutual funds (ATT) will influence private investor general willingness (WNG) to purchase of mutual
funds.
Attitude towards investing in mutual funds (ATT) will influence private investor intention (INT) to purchase of mutual funds.
Perceptions of their ability to control investment in mutual funds (PBC) will influence private investor general willingness (WNG) to
purchase mutual funds
Perceptions of their ability to control investment in mutual funds (PBC) will influence private investor intention (INT) to purchase
mutual funds
General willingness to purchase mutual funds will influence concrete intention to invest in mutual funds
Based on the explanatory notes on TPB above and stated hypotheses, three research models
are developed for this study and visualized in Figure 2.
Model 1:Structural model with
willingness
SN
Figure 2: Research models
Model 2:Structural model with
intention
SN
H1a
H2a
ATT
SN
H1b
H2b
WNG
H3a
PBC
Model 3:Structural model with
willingness and intention
ATT
PBC
INT
H3b
ATT
WNG
H4
INT
PBC
II. Methodology and data
A. Procedure
Main objective is to analyze the determinants of general purchase willingness and intention
and thus to gain further insight into mutual fund investor purchase decision making. We
apply Theory of Planned Behaviour, a conceptual framework for relations between attitude
and behavioural intentions, which has been used widely in various contexts of social science
research. Though there is a broad body of literature on TPB, its application in mutual funds
purchasing context forms a novel approach and thus required the combination of theoretical
literature review with exploratory methods and qualitative pre-tests.
In a first stage, we formulated research hypotheses and constructed the study questionnaire.
Therefore, we firstly reviewed relevant literature, especially on household finance,
behavioural finance and related puzzles, such as the stock market participation or active
management. To gain better understanding of relations in order to formulate research model
and hypotheses, we additionally used focus group discussion, as well as numerous individual
15
and expert interviews. We then constructed a questionnaire based on recommendations of
Ajzen (2006) and previous TPB studies. We made several adjustments and modifications to
fit our research context and to comply with findings from our preliminary works. Where
possible we used existing measures – slightly modified to our research questions. For
subjective norm (SN), attitude (ATT) and perceived behavioural control (PBC) we composed
a pool of items and conducted a quantitative pre-test on the measures only. Through
exploratory factor analysis (EFA) we identified the factors, which we then validated with
confirmatory factor analysis (CFA). Thereafter, we asked 14 individuals to evaluate the
questionnaire regarding processing time and especially on ease of understanding and
clearness of the questions asked. Several minor adjustments were made to finalize the study
questionnaire.
In a second stage we collected the data. In order to avoid self-presentational biases and
socially desirable responding, we used a self-administered online questionnaire. Additionally,
several precautions were taken. Questionnaire instructions highlighted that participation was
voluntary and that survey participants could stop at any time and finish the questionnaire
later. Since the questionnaire also contained personal socioeconomic questions, it was made
clear that data is collected for research purposes only and that results would not be passed on
to a third party.
In the third stage of analysis, we calculated and evaluated the research models. First, we
conducted exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to again
validate the measures with the complete final dataset and setup a well fitting measurement
model. Secondly, we analyzed the data with commonly used covariance-based structural
equation modelling software MPLUS for several reasons.
As stated by Diamantopoulos and Siguaw (2000), Mueller (1996), Weiber and Mühlhaus
(2009) and Nachtigall et al. (2003), it is appropriate to select structural equation modelling
when components of the theoretical model, such as an attitude, are latent and have to be
measured through a set of observed variables. Furthermore, using a set of observed variables
to measure a construct increases the validity of the research model. To do so, structural
equation models are able to estimate measurement and structural causal paths simultaneously,
rather than one at a time sequentially. Another advantage of structural equation modelling is
the ability to explicitly account for measurement error (Steenkamp and Baumgartner 1998,
Homburg and Klarmann 2006). Measurement errors for observed variables are isolated and
do not influence relations of structural model between independent and dependent
constructs.11 At last, structural equation models deliver indices to describe the fit of the model
to empirical data. As Bollen (1989) concludes, research models can thus be tested and also be
rejected. The disadvantages of needed large samples and underlying distribution assumptions
can be opted, since we have a large sample and also control for distribution within robustness
check.12
For estimation, we elected covariance-based instead of variance-based approach for multiple
reasons. First, it is an integrated approach, estimating parameters simultaneously based on
11
Regression analyses, for example, is based on the assumption, that the predictors are free of measurement
error. If that is not the case in the data, regression weights typically underestimate the true effects.
12
Nachtigall et al. (2003) provide a brief overview on advantages and disadvantages of using SEM.
16
empirical variance-covariance-matrix rather than in multiple stages. Within covariance-based
approach, estimation of relations in structural model only contains factor variance, while
measurement errors of observed variables are not integrated. Secondly, the approach is
appropriate for large samples. Finally, it furnishes expressive evaluative measures. As a
consequence and thus most important in our context, covariance-based approach is most
appropriate in research, in which well-established causal theories, such as TPB, are available
and have to be validated with data.
Within covariance-based approach, we use Maximum Likelihood (ML) for estimating the
parameters, as it is the most widely used fitting function for general structural equation
models, whose estimators are consistent and robust, unbiased in large samples and deliver
various fit indices.13 Nonetheless an ML estimation assumes multivariate normal distribution
for the underlying data, and we therefore also conducted a robustness check in order to
validate the ML estimate.14
Though, as stated by Hooper et al. (2008), “structural equation modelling (SEM) has become
the techniques of choice for researchers across disciplines and increasingly is a ‘must’ for
researchers in the social sciences”, there is no broad consensus on the evaluation of how
theoretical models reflect the data. Several indices exist for describing fit of measurement and
structural models. As other researchers before, we followed the recommendation of Bollen
(1990) to examine multiple indices of model fit. We based our selection on recommendations
of Hu and Bentler (1995, 1999), Mueller (1996), Schermelleh-Engel et al. (2003) and Hooper
et al. (2008). Thus, we use Wheaton et al.’s (1977) relative/normed chi-square (χ2/df) due to
large sample size (cited in Hooper et al. 2008), the comparative fit index (CFI), the goodnessto-fit index (GFI), the nonnormed fit index (NNFI), as well as the root mean square error of
approximation (RMSEA) and the standardised root mean square residual (SRMR).
As the last stage in our analysis, we use alternative estimation methods as robustness check to
account for several potential problems, such as underlying distribution functions (in case of
non-multivariate distribution) or the influence of additional covariates. For the case that the
underlying dataset does not follow a multivariate normal distribution, we use Robust
Maximum Likelihood (MLR) employing the sandwich estimator for estimating standard
errors (Caroll, Ruppert and Stefanski 1995). Additionally, we employ non-parametric
bootstrap resampling (ML with bootstrap) based on Efron (1979) to derive robust estimation
of standard errors. To check path coefficients, we also calculate regular multiple regression
model. Therefore the indicator variables are summed up and used as predictors in the
regression analysis. To further increase complexity of the models and to check for possible
confounding variables, we included age, sex and a composite indicator for income/wealth as
additional covariates in each of the models.
B. Data
The sample used for analysis is a unique dataset drawn from a primary survey in December
2009. Over 20.000 potential participants from a panel database being representative for the
German population were selected, contacted and asked for participation. Within the time span
13
See Nevit and Hancock (2001) for further information on bootstrapping and approaches to model test
statistics and parameter standard error estimation in structural equation modelling.
14
Also see Nachtigall et al. (2003) on bootstrapping solutions if distributional assumptions are violated.
17
of the study from November 17 to December 8 2009, 2.130 (10.65%) followed the
invitational link, viewed and actually started to answer the online questionnaire. From these,
236 participants have quitted or broken up the questionnaire during the answering session.
Thus, 1.894 persons completed the questionnaire with an average processing time of 14
minutes and 42 seconds. Missing data led to the exclusion of 78 participants. In order to
ensure and further increase data quality, we first evaluated total processing times and
excluded outlier values in both directions. Then, we eliminated data records with implausible
processing times on certain survey pages. In total, 221 participants were excluded due to
missing data or implausible processing times. The final dataset used for analysis consisted of
1.673 participants. Average time of completion was 15 minutes and 16 seconds with a
standard deviation of 8 minutes and 34 seconds. Table II summarizes key attributes of the
dataset.
Of the respondents included in the final sample, 49.2% were male and 50.8% female. The age
ranged between 17 and 71 with an average of 39.8, a median of 39.0 and a standard deviation
of 12.2. 18.7% have completed German Abitur (equivalent to the A-Levels/high school
graduation), 27.2% have finished a vocational training and 30.4% have university degree or
even a PhD-programme. At the time of the survey, about 78% of the participants were either
employed or self-employed, only 7.4% were unemployed; the rest were either students or
retired. With these characteristics, the dataset is representative for Germany.
Further attributes of the dataset reveal high relevance for analysing the willingness and
intentions to purchase mutual funds. About 66% of the participants ranged between 26 and 55
years, representing the phase of life, in which people work and build their wealth status.
Furthermore, almost all of the participants are involved in the financial decision making of
their household: either solely or with partner. Even more important is that investment
potential is given: over 40% stated a wealth status of above 10.000 EUR and over 60% of
participants stated to have more than 100 EUR for free disposal for savings every month.
About 49% of the participants have never bought or owned mutual funds before, 12% had
owned mutual funds but not anymore and about 39% of the participants actually held mutual
funds.
Potential biases in the dataset, e.g. resulting from using an online questionnaire are discussed
later.
Table II: Description of dataset
Attributes
Gender
Age
(Mean = 39,8; Median = 39,0)
Marital status
Children
Living status
Male
Female
< 25 years
26-35 years
36-45 years
46-55 years
> 55 years
n/a
Married
Not married, but living with a partner
Single / unmarried
Separated / divorced
Widowed
Yes
No
Living alone.
18
Sample (1.673)
49,19%
50,81%
12,00%
27,10%
27,60%
22,20%
11,00%
0,30%
46,08%
25,76%
19,78%
6,81%
1,55%
51,70%
48,30%
21,40%
Highest educational degree
Involvement in financial
decision making
Employment status
Monthly Household Net Income
Monthly disposable for savings
Wealth status
Mutual funds ownership
Living in apartment-sharing community
Living with partner
Living in household with children
Living at parents house
No degree
Hauptschulabschluss (secondary school)
Realschulabschluss (secondary school)
Abitur (equivalent to A-levels / graduation)
Vocational training
University degree / PhD
Yes, solely.
Yes, together with partner.
Not involved
Worker (blue-collar)
Employee (white-collar)
Public Services
Self-employed / freelancer
Retired
Student
Unemployed / welfare recipient
< 1.000 EUR
1.000 - 2.000 EUR
2.000 - 3.000 EUR
3.000 - 4.000 EUR
< 4.000 EUR
< 100 EUR
100 - 300 EUR
300 - 500 EUR
500 - 700 EUR
700 - 1.000 EUR
> 1.000 EUR
n/a
< 5.000 EUR
5.000 - 10.000 EUR
10.000 - 25.000 EUR
25.000 - 50.000 EUR
50.000 – 250.000 EUR
> 250.000 EUR
n/a
No / Never had
Currently not anymore
Yes
3,29%
41,30%
28,75%
5,26%
0,54%
4,24%
18,77%
18,71%
27,20%
30,54%
51,29%
47,28%
1,43%
7,83%
50,39%
7,77%
11,60%
6,22%
8,79%
7,41%
9,6%
25,5%
30,5%
20,7%
13,7%
37,5%
30,8%
14,7%
5,8%
4,5%
5,8%
0,8%
39,39%
15,06%
15,06%
12,73%
13,27%
2,33%
2,15%
48,7%
11,6%
39,7%
C. Measures
Most of TPB variables cannot be observed fully, but latently. As a result, we used multi-item
scales with manifest indicator items to measure these TPB constructs. All indicator items
were constructed along the Theory of Planned Behaviour and to ensure consistency, we
followed recommendations of Ajzen (2002, 2006) on wording and suggested format. The
self-report items measures were additionally retrieved from studies by Ajzen and other
existing studies and modified to investment and mutual fund purchasing context. As
recommended by Ajzen (2006) and applied to various own empirical studies (e.g. Ajzen and
Driver 1992),15 most of the variables utilized a 7-point Likert scale (e.g. 1=strongly agree to
7=strongly disagree) to allow broader range of differentiation. For each of the independent
and dependent variables within research model, we used several indicator items based on
15
For further reading on scale development, see Rossiter (2002).
19
TPB and qualitative pre-tests. As described above, with the final dataset, we first conducted
exploratory factor analysis (EFA) to choose most fitting items for each constructs and then
conducted confirmatory factor analysis (CFA) to identify and validate good measures for
each of the research models components. Indicator item reliability is commonly considered
adequate when it has a factor loading of 0.7 or above. As a result, some items had to be
eliminated. Construct reliability is considered sufficient when composite reliability is 0.7 or
above. All constructs are reliable by those criteria.
Subjective Norm (SN)
The subjective norm was measured with three items adapted from recommendation of Ajzen
(2006). From a larger set of statements that respondents were asked to respond to, we
conducted exploratory and confirmatory factor analysis to find best fitting factor. The final
measure consisted of the questions: „most people that are important to me, invest in mutual
funds theirselves“, „most people that are important to me, regard mutual funds as positive“
and „most people whose opinion I highly respect, would approve and recommend me to
invest in mutual funds“. The average standardized item loading was 0.93 and Cronbachs
alpha for this measure was 0.95. Table III provides an overview of the scale.
Attitude (ATT)
For measuring attitude, as most commonly used and recommended by Ajzen (2006, Ajzen
and Driver 1992b) largely due to its ease of construction, we used a set of semantic
differential questions. The set consisted of bipolar adjective pairs containing a negative
evaluation the behaviour in question on one end and a positive evaluation on the other. As
described above, overall evaluation of a behaviour contains two separable components. For
the instrumental component sample bipolar items are “important-unimportant”, “valuableworthless”, “harmful-beneficial” and for experiential quality sample items are “pleasantunpleasant” or “enjoyable-unjoyable”. Furthermore, we selected further adjectives pairs
based on qualitative pre-test and wordings of interviewed people. Since it was a new measure
in our research context, we conducted EFA and CFA to identify most relevant items. For the
final measure, we used respondents rating of investing in mutual funds along the adjective
pairs „good-bad“, „important-unimportant“, “beneficial-not beneficial” and “pleasantunpleasant”. The average standardized item loading onto the factor was 0.90 and reliability
of scale was 0.945. Table III provides an overview of the scale.
Perceived behavioural control (PBC)
Again, the measure was also constructed based on recommendations of Ajzen (2006).
Measure should capture people’s confidence that they are capable of performing the
behaviour and the controllability of the behaviour. Furthermore we relied on the qualitative
pre-test and wordings of interviewed people and set up an item-pool. Since it is a new
measure in this context we conducted exploratory and confirmatory factor analysis to derive
final measure. Finally, perceived behavioural control was measured with three items. First
item regards respondents overall confidence and ability to engage in mutual funds investing.
Since it might be one of the key hurdles to invest in mutual funds, we also included capability
of selecting the right mutual funds. Finally, an item on understanding the development of
mutual funds was contained, since focus groups revealed low understanding of price
20
movements of stock markets and mutual funds as potential hurdle. The average standardized
item loading on perceived behavioural control was 0.93, while the reliability of the scale was
also high with a Cronbach’s alpha of 0.946. Table III provides an overview of the scale.
Since subjective norm, attitude and perceived behavioural control are three new measures,
we conducted confirmatory factor analysis to verify the fit of the measures. Due to sample
size, one-factor models were fully identified and no fit indices were generated. In
consequence, we used a three-factor model – equal to the measurement model. The analysis
shows a good fit: X2/df=2.3, Root Mean Square Error of Approximation (RMSEA) = 0.028,
Standardized Root-Mean-Square Residual (SRMR) = 0.011, Comparative Fit Index (CFI) =
0.998, Non-Normed Fit Index (NNFI) = 0.997. The average standardized item loading onto
each of the factors were as follows: subjective norm (SN) = 0.93, attitude (ATT) = 0.90,
perceived behavioural control (PBC) = 0.93.
Table III: Overview of latent measures SN, ATT and PBC
CONSTRUCT SUBJECTIVE NORM (SN)
Most people that are important to me,
SN 3
also invest in mutual funds themselves.
Most people that are important to me,
SN 4
regard mutual funds as positive.
Most people whose opinion I highly
SN 5
respect, would approve and recommend
me to invest in mutual funds.
CONSTRUCT ATTITUDE (ATT)
Good Bad
ATT SM 1
Important Unimportant
ATT SM 2
ITTC (Item
to Total
Correlation)
Factor
loading
(EFA)
Standardized
factor loading
(unstandardized
standard error)
0.880
0.924
0.909
(0.000)
0.921
0.926
0.963
(0.015)
0.886
0.906
0.919
(0.017)
0.894
0.856
0.923
0.871
0.933
0.889
(0.000)
(0.016)
ATT SM 3
Pleasant Unpleasant
0.846
0.886
0.874
(0.015)
ATT SM 6
Beneficial Not beneficial
0.879
0.924
0.909
(0.014)
0.911
0.940
(0.000)
0.951
0.923
(0.018)
0.912
0.940
(0.016)
CONSTRUCT PERCEIVED BEHAVIOURAL CONTROL (PBC)
I feel very confident to be able to
0.878
PBC FO 1
engage in mutual funds investing.
I am capable of selecting mutual funds.
0.907
PBC FO 2
PBC FO 3
I understand and can easily follow the
development of mutual funds.
0.878
Unstandar
dized
t-value
(p-value)
Cronbachs
Alpha
0.950
71.389
(0.000)
62.844
(0.000)
0.945
60.399
(0.000)
57.549
(0.000)
64.396
(0.000)
0.946
53.188
(0.000)
61.368
(0.000)
Intention (INT)
For measuring the intention to buy mutual funds, we asked respondents to self-assess several
intention-oriented statements. From a larger set of statements, we conducted exploratory and
confirmatory factor analysis to find best fitting factor. The final measure consisted of three
questions: the probability of investment in mutual funds within 12 months and current plans
to increase existing mutual fund investments. To also account for longer term intention
beyond current overview on funds free for disposal, we asked for intention to purchase
mutual funds as soon as there is money free for disposal. The average standardized item
loading on perceived behavioural control was 0.87, while the reliability of the scale was also
high with a Cronbach’s alpha of 0.95. Table IV provides an overview of the scale.
21
Table IV: Latent factor INT
CONSTRUCT INTENTION (INT)
Item
INT 4
INT 5
INT 7
It is very likely, that I will invest in
mutual funds within the next 12 months.
I plan to extend investments in mutual
funds in 2010.
I intend to invest in mutual funds, as
soon as I have money free for disposal..
ITTC (Item to
Total
Correlation)
Factor
loading
(EFA)
Unstandard
ized
t-value
(p-value)
0.907
Standardized
factor loading
(unstandardized
standard error)
0.933
(0.000)
0.809
0.773
0.842
0.805
(0.017)
0.761
0.813
0.871
(0.020)
51.384
(0.000)
45.125
(0.000)
Cronbachs
Alpha
0.950
Willingness (WNG)
In order to not only cover pure intention, we included a statement on general willingness to
purchase mutual funds. Respondents were asked the following question on a 7-point Likert
scale: “Generally speaking, I can imagine to invest in mutual funds.”
Further variables
Given importance in prior research within investment decision making context, we also
included the further measures for analysis. Participants were asked to give answers on
socioeconomics, e.g. age, gender, education, marital status, household income and wealth
(e.g. Cole and Shastry 2009, Guiso, Haliassos and Jappelli 2003, Bertraut 1998, VissingJorgensen 2002). Furthermore, we asked for the employment status and risk of a job loss
(Heaton and Lucas 2000), risk or loss aversion (e.g. Barsky et al. 1997, Ang et al. 2005 or
Dimmock and Kouwenberg 2008), trust (e.g. Guiso, Sapienza and Zingales 2008) and
whether recent financial crisis has effected the respondent.
For further analysis, we also included answers on ownership, knowledge and experience with
mutual funds and market expectations.
III. Results
A. Descriptive statistics
Participants show low financial literacy with only 28.1% of participants stating to have good
financial knowledge and only 30.2% assessing their knowledge on investment decisions as
good. Despite this fact, participants demonstrate high self-efficacy, with about half of
respondents being able to plan and implement their financial affairs solely. About 49% of the
participants have never bought or owned mutual funds before, 12% had owned mutual funds
but not anymore and about 39% of the participants actually held mutual funds. From those,
only 40% have personally made positive experience. The levels of trust in financial system
and regarding financial advisors are with 17% and 14.6% very low.
Means, standard deviations, and bivariate correlations among the observed variables for our
research model are reported in Table V. The table shows significant correlations between the
indicator variables of the TPB constructs. Due to these expected high correlations and to
avoid common problem of multi-collinearity, we use structural equation models and form
composite indicators (factor scores) for the regression model for robustness check.
22
Table V: Means, standard deviations and correlations
Bivariate correlations (Pearson)
Sex
Age SN 3 SN 4 SN 5
Variable
Mean SD
Sex
1,508 0,500
Age
39,842 12,193
SN 3
5,087 1,723
SN 4
4,747 1,767
SN 5
4,762 1,815
ATT SM 1
3,619 1,645
ATT SM 2
3,922 1,629
ATT SM 3
3,712 1,530
ATT SM 6
3,641 1,565
PBC Fo 1
4,231 1,917
PBC Fo 2
4,297 1,879
PBC Fo 3
4,310 1,906
WNG 1
3,399 1,962
INT 4
4,582 2,110
INT 5
5,330 1,752
INT 7
4,448 2,025
1
ATT ATT ATT ATT
SM 1 SM 2 SM 3 SM 6
PBC
Fo 1
PBC
Fo 2
PBC
Fo 3
WNG INT 4 INT 5 INT 7
1
-0,047 ,106** ,111** ,112** ,096** ,080** ,059* ,066** ,239** ,233** ,216** ,119** ,153** ,140** ,130**
1
0
0,034
1
,877** ,832** ,577** ,565** ,516** ,536** ,548** ,536** ,547** ,539** ,613** ,565** ,568**
1
,053*
0,04
0,002
0,008
,061*
-0,04 -0,023 -0,022 ,127** ,055* ,090** ,064**
,885** ,624** ,595** ,553** ,584** ,551** ,541** ,565** ,615** ,637** ,573** ,628**
1
,617** ,594** ,558** ,584** ,535** ,526** ,541** ,603** ,626** ,564** ,609**
1
,829** ,815** ,847** ,548** ,531** ,527** ,768** ,677** ,547** ,678**
1
,771** ,810** ,529** ,515** ,503** ,688** ,657** ,547** ,622**
1
,802** ,511** ,500** ,492** ,683** ,593** ,489** ,612**
1
,512** ,506** ,504** ,736** ,635** ,513** ,628**
1
,867** ,830** ,576** ,623** ,553** ,534**
1
,868** ,562** ,607** ,553** ,525**
1
,565** ,606** ,542** ,534**
1
,735** ,565** ,719**
1
,753** ,735**
1
,686**
1
**. Correlation on 0,01 (2-seitig) significant.
*. Correltaion on 0,05 (2-seitig) significant..
B. Model fits
As described in Section II, we use Wheaton et al.’s (1977) relative/normed chi-square (χ2/df)
due to large sample size (as stated in Hooper et al. 2008), the comparative fit index (CFI), the
goodness-to-fit index (GFI), the nonnormed fit index (NNFI), as well as the root mean square
error of approximation (RMSEA) and the standardised root mean square residual (SRMR) for
evaluation of model fit. Table VI summarizes selected fit indices on the measurement model
and the three structural models.
Table VI: Model fit indices
MODEL
Measurement model CFA (3 TPB components)
Model 1: Structural model with willingness (WNG)
Model 2: Structural model with intention (INT)
Model 3: Structural model with willingness (WNG)
and intention (INT)
χ2
72.389
131.914
196.258
312.795
p-Value
< .001
< .001
< .001
< .001
df
31
36
55
64
χ2 / df
2.335
3.664
3.568
4.887
CFI
0.998
0.995
0.994
0.990
NNFI
0.997
0.993
0.991
0.986
RMSEA
0.028
0.040
0.039
0.048
SRMR
0.011
0.011
0.016
0.019
For relative/normed chi-square (χ2/df) recommended values for describing a good fit range
from 5.0 to as low as 2.0.16 Especially for large samples, higher relative indices are
acceptable. The measurement model as well as the first two structural models show values up
to 3.6 and thus good fits. With a value of 4.8, model 3 – combining intention and willingness
– is still acceptable, but not as well fitting as model 1 and 2. For root mean square error of
16
Hooper et al. (2008) provide an overview of cut-off values including Wheaton et al. (1977) and Tabachnick
and Fidell (2007).
23
approximation (RMSEA), Steiger and Lind (cited in Steiger 1990 and Hooper et al. 2008)
recommend cut-off values close to 0.06, or 0.08. For SRMR, Hu and Bentler (1999) suggest
values below 0.09 and for GFI and incremental fit indices CFI and NNFI values should be
above 0.95. All tested models show values within recommended ranges and thus have very
good fits. Table VII provides an overview of the measurement model including
unstandardized and standardized factor loadings for each TPB measure. Average
standardized factor loadings were 0.92. Average standardized item loading onto subjective
norm (SN), attitude (ATT) and perceived behavioural control (PBC) were 0.93, 0.90 and
0.93. Composite reliabilities (Cronbachs Alpha) were 0.950, 0.945 and 0.946.
Table VII: Measurement model
PREDICTOR
Item
SN
SN 3
SN 4
SN 5
ATT SM 1
ATT SM 2
ATT SM 3
ATT SM 6
PBC FO 1
PBC FO 2
PBC FO 3
ATT
PBC
Factor loading
t-valuea
(standard error)
1.000
1.087
(0.015)
71.389
1.065
(0.017)
62.844
1.000
0.943
(0.016)
60.399
0.870
(0.015)
57.549
0.927
(0.014)
64.396
1.000
0.962
(0.018)
53.188
0.994
(0.016)
61.368
all values significant on p < 0.001
a
Standardized factor
loading
0.909
0.963
0.919
0.933
0.889
0.874
0.909
0.940
0.923
0.940
C. Research hypothesis testing
Results, including path coefficients (standardized), standard errors and t-value
(unstandardized) obtained from the structural equation models are presented in Table VIII.
Table VIII: Structural models
MODEL
PREDICTOR
Model 1: Structural model
with willingness (WNG)
SN
ATT
PBC
Model 2: Structural model
with intention (INT)
SN
ATT
PBC
Willingness (WNG)
Intention (INT)
Standardized
Estimate
S.E. 1
t-value2
0.096
0.640
0.158
0.029
0.030
0.022
4.192
26.942
7.667
Model 3: Structural model
with willingness (WNG)
and intention (INT)
SN
0.030
0.105
ATT
0.029
0.633
PBC
0.022
0.158
WNG
1 Unstandardized Standard Error
2 Unstandardized t-value, all values significant on p < 0.001
4.530
27.382
7.755
Standardized
Estimate
S.E. 1
t-value2
0.275
0.433
0.255
0.029
0.030
0.023
11.909
18.290
11.949
0.235
0.198
0.181
0.364
0.028
0.034
0.022
0.025
10.921
7.555
9.224
15.005
The full structural models produced a strong data fit. All standardized estimates are positive
and significant on p<0.001 and thus have an influence on the dependent variables willingness
(WNG) or intention (INT). The results support our model and the associated hypotheses
based on TPB. To gain a better overview of relations between TPB variables, willingness and
24
intentions, results on the three research models as well as on the measurement model are
visualized in Figure 3.
Figure 3: Path models for the structural model (standardized coefficients) for the four tested models:
a) CFA model for the three predictors; b) Structural model with willingness;
c) Structural model with intention; d) complete model with both dependent variables.
a) Measurement model CFA (3 TPB components)
b) Model 1: Structural model with willingness (WNG)
SN
SN
0.686
(0.077)
0.622
(0.087)
0.621
(0.087)
ATT
0.610
(0.084)
0.685
(0.078)
0.625
(0.087)
INT
PBC
ATT
0.198
(0.034)
0.633
(0.029)
WNG
0.364
(0.025)
R = 0.670
0.606
(0.084)
0.255
(0.023)
Standardized coefficients (unstandardized standard error)
all values significant on p < 0.001
0.235
(0.028)
0.105
(0.030)
INT
2
R2 = 0.712
0.606
(0.085)
0.158
(0.022)
SN
0.689
(0.077)
ATT
= 0.669
Standardized coefficients (unstandardized standard error)
all values significant on p < 0.001
d) Model 3: Structural model with willingness (WNG) and
intention (INT)
0.275
(0.029)
0.433
(0.030)
WNG
R2
PBC
Standardized coefficients (unstandardized standard error)
all values significant on p < 0.001
c) Model 2: Structural model with intention (INT)
0.622
(0.087)
0.640
(0.030)
ATT
0.609
(0.085)
PBC
SN
0.096
(0.029)
0.687
(0.077)
0.158
(0.022)
PBC
R2 = 0.726
0.181
(0.022)
Standardized coefficients (unstandardized standard error)
all values significant on p < 0.001
Both, measurement model as well as the structural models indicate high correlations between
the three TPB constructs. This is in line with findings of applications of TPB in other fields
and simple to be explained. Individuals might have a more positive attitude towards investing
in mutual funds once his personal environment shows a positive attitude and approves this
kind of investment. Family and friends apparently have an influence on shaping the
individuals’ attitude, as well as the other way around. Also once an individual perceives to
have good understanding and control over investing in mutual funds, he might as well have a
more positive attitude on mutual funds investing.
Determinants of Willingness
Based upon results in structural model 1 in Table VIII and visualized in Figure 3, support was
found for hypotheses H1a, H2a and H3a, which relate the TPB constructs to willingness.
These hypotheses predict a positive relationship between the predictors subjective norm,
attitude and perceived behavioural control onto the willingness to purchase mutual funds.
Results indicate that 67% of variance in the general willingness to invest in mutual funds can
be explained by all three TPB determinants. All standardized path coefficients are positive
and significant. However, not all of the TPB constructs are equally important. By far the most
significant and strongest predictor of general willingness to purchase mutual funds in the
25
structural equation model was the attitude, which shows a path coefficient of 0.64. This is
consistent with a simple story: the better and more positive an individuals evaluates investing
in mutual funds, the more likely will he be willing to actually invest in mutual funds. Second
strongest predictor for general willingness, being still significant, was the perceived
behavioural control with a path coefficient of 0.158. Feeling in control and being able to
make the right decisions also has an influence on individuals’ willingness to investing in
mutual funds. The influence of subjective norm on the general willingness is still positive, but
with 0.096 rather small compared to the attitude and perceived behaviour control.
Determinants of Intention
Hypotheses H1b, H2b and H3b predict a positive relationship between the predictors
subjective norm, attitude and perceived behavioural control onto the concrete intention to
purchase mutual funds. Results for structural model 2 regarding the purchase intention as in
Table VIII and visualized in Figure 3, indicate support for these hypotheses.
For the intention, the variance explained by all three TPB determinants is 71% and thus even
higher than for general willingness. In terms of impact, attitude again was most influential
followed by subjective norm and perceived behavioural control. Again, this is in line with
many TPB studies, in which the attitude variable has shown strongest relationships with
measured intention. However, path coefficients of the three TPB determinants for the
intention – ranging from 0.25 (PBC) to 0.43 (attitude) – are much closer to each other than
for general willingness. In consequence, for the concrete intention to invest in mutual funds,
attitudes and opinions communicated by friends or family members are more important than
just for the willingness to purchase mutual funds. Same applies for the behavioural control.
Determinants of Willingness and Intention
Structural model 3 combines models 1 and 2 within one model and includes hypothesis H4,
which predicts a mediating relationship between general willingness and intention. Path
coefficients in model 3 as in Table VIII and visualized in Figure 3 are all positive and
significant and indicate support for hypotheses H1a/b, H2a/b and H3a/b and H4. TPB
variables explained 67.0% of the variance in willingness and if all taken together, results
indicate that 72.6% of mutual fund purchase intention can be explained. Similar to model 1,
quantitative results indicate that attitude is associated closest with purchase willingness. For
intention general willingness to purchase mutual funds is strongest predictor, followed by
subjective norm and perceived behavioural control.
Summarizing comparisons
All three structural models produced a strong data fit with significant and positive path
coefficients. Variance in dependent variables can be explained to a large extent. Thus, TPB
can be confirmed in this context and can serve as a basis model for research in this field.
Furthermore, based upon results, support was found for all hypotheses analyzed. The three
TPB variables attitude, subjective norm and perceived behavioural control have positive
coefficients and are quantitatively associated with general willingness and concrete intention
to purchase mutual funds. However, absolute influential weights differ between willingness
and intention. Regarding impact, attitude has produced strongest relationships especially on
general willingness. This finding is in line with many studies TPB, where attitude variable
26
has shown strongest relationships with intention. Subjective norm has a visibly smaller
impact on general willingness than for intention. So when it comes to actually intending to
purchase mutual funds, family and friends play a larger role. Perceived behavioural control
influences both – willingness and intention, but for intention to a lower extent than subjective
norm.
To sum up, we provide an overview of the results on tested hypotheses in Table IX.
Table IX: Summary of the Tests of Hypotheses
Hp
Path Specification
H1a:
Subjective norms (SN) will influence private investor general
willingness (WNG) to purchase of mutual funds.
Subjective norms (SN) will influence private investor intention
(INT) to purchase of mutual funds.
Attitude towards investing in mutual funds (ATT) will influence
private investor general willingness (WNG) to purchase of
mutual funds.
Attitude towards investing in mutual funds (ATT) will influence
private investor intention (INT) to purchase of mutual funds.
Perceptions of their ability to control investment in mutual funds
(PBC) will influence private investor general willingness (WNG)
to purchase mutual funds
Perceptions of their ability to control investment in mutual funds
(PBC) will influence private investor intention (INT) to purchase
mutual funds
General willingness to purchase mutual funds will influence
concrete intention to invest in mutual funds
H1b:
H2a:
H2b:
H3a:
H3b:
H4:
Standardized
path coefficient
S.E. (unstandardized)
0.096
0.029
Support / not reject
0.275
0.030
Support / not reject
0.640
0.030
Support / not reject
0.433
0.030
Support / not reject
0.158
0.022
Support / not reject
0.255
0.023
Support / not reject
0.364
0.025
Support / not reject
Hp Support
D. Robustness checks
As described before, we employed alternative estimation methods to check for robustness of
research structural models. To account for potentially non-multivariate normal distributed
variables, we use robust maximum likelihood (MLR) and maximum likelihood with nonparametric bootstrap (ML with bootstrap). To check path coefficients we alternatively use
regression model. Furthermore, we included income, age and sex as additional covariates in
the models. Results from robustness checks, including estimates, standardized estimates and
standard error are listed in Table X.
Table X: Overview structural models for willingness (WNG) and intention (INT) including covariates
ML / MLR / ML with Bootstrap1
PREDICTOR
Willingness (WNG)
ML with
ML
MLR
Bootstrap
Estimate
Standard.
Estimate
S.E:
0.029*
0.043
0.110
0.008*
0.029*
0.197
0.592
0.007*
0.026*
0.025*
0.061
0.167
0.007*
0.023*
0.023*
0.023*
-0.015
-0.050
0.005*
0.117
0.002*
0.002*
0.003*
0.019
0.119
0.002*
0.009
0.059
0.059
0.056
0.052
0.013
0.059
Estimate
Standard. Estimate
SN
0.101
0.081
0.028*
0.028*
ATT
0.812
0.643
0.030*
0.030*
PBC
0.153
0.140
0.024*
INCOME
-0.081
-0.065
AGE
0.019
SEX
0.034
2
68.4%
R
Regression
S.E:
65.5%
27
ML / MLR / ML with Bootstrap1
PREDICTOR
ML
Intention (INT)
ML with
MLR
Bootstrap
Regression
Estimate
Standard.
Estimate
S.E.
0.032*
0.298
0.283
0.021*
0.030*
0.346
0.384
0.018*
0.027*
0.026*
0.239
0.244
0.020*
0.023*
0.024*
0.024*
-0.039
-0.048
0.013*
0.046
0.002*
0.002*
0.002*
0.031
0.070
0.006*
0.086
0.059
0.060
0.062
0.279
0.026
0.159
Estimate
Standard. Estimate
S.E.
SN
0.333
0.265
0.029*
0.033*
ATT
0.550
0.439
0.030*
0.031*
PBC
0.254
0.233
0.025*
INCOME
-0.075
-0.060
AGE
0.010
SEX
0.093
72.3%
65.7%
R2
1 Estimates and standardized estimates same for ML, MLR and ML with Bootstrap, only differing in standard error;
* significant on p < 0.001
Estimates for the different estimation methods differ slightly from findings with previous ML
estimation since we included additional covariates as predictors. Missing data were observed
for one indicator of the income variable (current wealth) and the missing values were
replaced using the EM algorithm (Dempster, Laird and Rubin 1977). The amount of missing
data in this indicator variable was 4.37% and could be assumed to be missing at random
given the covariate information of the other indicator variables, especially those for the
income variable.
Despite alternative estimation methods and additional covariates, results remain similar to
results from regular ML estimation. Though influential path coefficients are slightly lower,
the three predictors: subjective norm, attitude and perceived behavioural control show
significant relations to willingness and intention. For all estimation methods, attitude is the
strongest predictor for both – willingness and intention. The inclusion of the covariates did
not lead to different results than the models presented in section III.C and therefore confirms
the appropriateness of the models. Explained variance of willingness and intention is lower
for regression compared to structural equation models, resulting from measurement errors
that have not been isolated. The structural coefficients in the regression model are especially
lower for the attitude on willingness and intention.
IV. Discussion and Conclusions
Summary of results and implications for practice
The purpose of this study was to identify key determinants of the willingness and intention to
invest in mutual funds and to establish a basis model for further research in this field. To
examine the predictors of willingness and intention to invest in mutual funds, we use Theory
of Planned Behaviour, an elaborated and widely used framework to measure intentions.
To summarize our results, we find that the three key components of TPB, subjective norm
(SN), attitude (ATT) and perceived behavioural control (PBC), are correlated and all show
positive and significant influence on both, the general willingness and the intention to
purchase mutual funds.
28
These three results are each simple to understand. Once an individual has a positive attitude
towards investing in mutual funds, he will also be more willing to purchase mutual funds in
general. Thus, probability to actually intending or planning to purchase mutual funds will be
higher. Same effect applies for his social environment, since especially family and friends
have an impact on his thoughts and behaviour. Once family and friends inherit and
communicate a rather negative or sceptical view towards mutual funds, the individual will
probably also be more critical and might adjust his willingness or intention to purchase
mutual funds. This link also explains high correlations found between subjective norm and
attitude, as individuals will adjust their attitude based on communicated attitudes of their
social environment. The third result addresses a common opinion, that investing in mutual
funds belongs to the complex financial matters, that most people do not feel comfortable or
literate enough with. General willingness and especially plans to invest in mutual funds will
be lower for individuals that have low conviction and self-efficacy to make the appropriate
financial decisions and to select the right mutual funds compared to individuals that fully
trust in their abilities. Again, the link to attitude is obvious: once an individual perceives
himself in control of the financial decisions and able to select the right mutual funds, he will
likely feel more comfortable with investing and have a more positive attitude on this activity.
These results reveal insight into the complex decision making framework of potential mutual
funds buyers and thus propose some implications for practice, as mutual funds purchasing is
crucial for growth and success of asset managers worldwide. In general, it becomes necessary
to establish a positive attitude and perception towards mutual funds within the target group.
This need weighs even stronger in combination with social interaction of potential target
group, since positive attitudes are shared with peers, friends or family and those also have
influential effects on purchasing behaviours. Campaigns targeting an improvement of attitude
and generation of positive associations can set the basis. Furthermore, financial literacy and
thus the abilities to handle investments in mutual funds have to be developed to further
increase behavioural control and thus a positive gut feeling associated with it. Both, the
creation of a broader positive awareness as well as an improved knowledge on mutual funds
investing, would positively influence the willingness and intention to buy mutual funds. This
is a crucial requirement, that industry associations in the different countries and regions, as
well as mutual funds companies should target directly.
Strengths of approach and contribution to literature
This study’s approach shows some major strengths and contributes in several ways to
academic literature.
With the Theory of Planned Behaviour (TPB), we have employed a very strong theoretical
model to predict behavioural intentions. It has been applied and validated in various fields of
researching intentions over the past decades. Furthermore, we used a unique and highly
appropriate dataset for analysis. The sample is sufficiently large and representative, serving
well as basis for the research design. The combination of theoretical research framework and
dataset produced a very strong data fit with high explanatory power. Results supported our
model based on TPB and proved its validity as explanatory frame in this research context.
29
Altogether, as proposed by Keswani and Stolin (2008), this paper adds insights into mutual
fund investor behaviour in context of portfolio choice and adds to a better understanding of
what factors drive participation in capital markets via mutual funds. Within this field of
researching private households’ financial participation, we have used a novel approach by
applying the well established Theory of Planned Behaviour to mutual funds purchasing
decision. Since we could confirm the TPB model and found good results, we also have given
support to it to assist as basis model for further research in this field.
Additionally, as Theory of Planned Behaviour is a theoretical framework originating in
psychology, this paper further continues the trend of the linkage of finance and psychological
research, known as behavioural finance literature. Furthermore, with our approach we also
extended and specified literature around portfolio choice, household finance and stock market
participation puzzle to focus mutual funds in general. This is of great importance as mutual
funds are even more relevant for private investors with low sophistication than actually
participating directly via stocks. Next to the contribution to literature through the application
of TPB as model and extending portfolio choice to mutual funds, we can also confirm
empirical findings in TPB as well as related researches that use other methodological
approaches – especially on stock market participation. Our findings of social environment,
especially family and friends, influencing purchase willingness and intention is in line with
East (1993), Brown and Taylor (2010), Brown et al. (2005) and Hong, Kubik and Stein
(2004), who find that subjective norm, “neighbourhood” or “social interaction” have an effect
on buying shares. Same applies to our findings that the perceived behavioural control
resulting from knowledge and ability to invest relates to intention. This is in line with
Bertraut (1998) or Cole and Shastry (2009) who find that likelihood of stockholding increases
with education, Rooij, Lusardi and Alessie (2007) who confirm an independent effect of
financial literacy on likelihood of participation in stock market and Christelis et al. (2006)
who find that cognitive abilities are strongly associated with the decision to invest in stocks.
Limitations and outlook for further research
Just like for other studies, this one is not without limitations either.
First of all, we cannot draw any conclusions on actual mutual funds purchasing and thus have
no measurement of the direct link between the intention to invest in mutual funds and the
actual behaviour. It remains open, how this link is and whether additional influential or
mediating factors are relevant. Furthermore, it is obvious that Theory of Planned Behaviour is
a model with low complexity by focussing on three main predictors. There might well be
other covariates or factors influencing the willingness and intention, such as trust, personal
experience or risk awareness.
Additionally, there are three potential biases within the analysis that should be mentioned, but
cannot be accounted for or corrected explicitly.
One potential bias results from recent financial crisis, that occurred prior to survey period and
might have caused changes in attitudes, control beliefs and especially willingness and
intention to invest in mutual funds. Participants could have a positive attitude towards mutual
funds or even feel able to make the right financial decisions, but still have low willingness
and intention to invest in mutual funds because media coverage has influenced them
negatively.
30
Another bias results from collecting data via online questionnaire. Thereby, population parts
that do not use the internet are underrepresented in the dataset. Based on common market
research, this especially applies to the group of elderly or at least of advanced age. Since this
group typically has greater wealth on average and thus forms a major target group for mutual
funds, it might have an effect on results. However, this effect may be offset by the third
potential bias resulting from nature of mutual funds business.
As described in Section I, most important distribution channel for mutual funds in Germany
are banks and their advisors. Thus, it is not unusual, that bank advisors give the
recommendation to purchase mutual funds, even if the private investor does not have the
relevant background. Thus, individuals, that majorly rely on their bank advisors and follow
their advice, might be willing or intending to purchase mutual funds even if their social
environment would disapprove, they do not have a positive attitude or do not feel in control
of selecting the right mutual funds. However, this bias might not be too severe for two
reasons: First, the advice of advisors will probably more affect the actual purchase behaviour
than the intention to do so. Furthermore, commonly it is the group of elderly that strongly
relies and trusts in their bank advisors, for which the bias would be stronger. Those however,
might be underrepresented in our dataset due to using online questionnaire as described
before. Therefore, the previous two mentioned biases may offset each other.
This study opened a few different avenues for future research and provides a foundation for
expanding literature on three aspects. First, to gain more insight into the framework for
decision making and building of the purchase intention, relations and explanations within the
TPB model should be further analyzed. Interesting in this context remains the identification
of determinants of the attitude and perceived behavioural control as key influencing factor to
willingness and intention. Several determinants, such as past experience, trust, education or
socio demographics, can be thought of being related. Furthermore, it might be worth – as
done in other TPB studies – to analyze interaction effects or moderating roles of additional
variables onto the linear relation of TPB components and intention. Especially the availability
of income and wealth, as well as other factors such as reliance on financial advisors, trust,
evaluation of market development or risk awareness might influence or mediate the identified
relations. At last, it remains open, whether found paths are of linear nature or whether there
exists non-linearity. It might be worth to better understand, whether the impact of the attitude
on willingness and intention is greater for very positive attitudes than for very negative
attitudes.
Secondly, to further validate and understand mutual funds purchases, cohesion between
intention and actual mutual funds purchase should be analyzed. There might as well be
interaction effects within this relation. This analysis, however, requires a complex dataset
consisting of both individuals’ questionnaire and portfolio data.
Thirdly, findings on attitude, subjective norm and intentions to purchase mutual funds raise
the questions, whether population can be separated into certain segments or clusters and
whether there are different types of mutual funds buyers. Especially from a practical
perspective, it is worth knowing, who it is, that has a positive attitude and thus willingness
and intention to invest in mutual funds and how those potential buyers cluster along socio
demographics, such as sex, age, income or investment related preferences, such as risk
awareness and return expectations.
31
REFERENCES
Ajzen, I.: From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckman
(Eds.), Action-control: From cognition to behavior (pp. 11-39). Heidelberg: Springer
(1985).
Ajzen, I.: Attitudes, personality and behavior. Chicago: Dorsey Press (1988).
Ajzen, I.: The theory of planned behavior. Organizational Behavior and Human Decision
Processes, 50, 179-211 (1991).
Ajzen, I.: Nature and operation of attitudes. Annual Review of Psychology, 52, 27-58 (2001).
Ajzen, I.: Perceived behavioral control, self-efficacy, locus of control, and the theory of
planned behavior. Journal of Applied Social Psychology, 32, 665-683 (2002).
Ajzen, I.: Constructing a TpB Questionnaire. Conceptual and Methodological Considerations,
www.people.umass.edu/aizen/pdf/ tpb.measurement.pdf [24.09.2007] (2006).
Ajzen, I.: http://www.people.umass.edu/aizen/tpbrefs.html; accessed: July 2010 (2010).
Ajzen, I., Driver, B. L.: Application of the theory of planned behavior to leisure choice.
Journal of Leisure Research, 24, 207- 224 (1992).
Ajzen, I., Driver, B. L.: Contingent Value Measurement: On the Nature and Meaning of
Willingness to Pay, Journal of consumer psychology 1(4), 297-316 (1992).
Ajzen, I., Fishbein, M.: Factors influencing intentinos and the intention-behavior relation,
Human Relations 27(1), 1 (1974).
Ajzen, I., Fishbein, M.: Attitude-behaior relations: A theoretical analysis and review of
empirical research. Psychological Bulletin, 84, 888-918 (1977).
Ajzen, I., Fishbein, M.: Understanding Attitudes and Predicting Social Behavior, Englewood
Cliffs (1980).
Alexander, G. Jones, J., Nigro, P.: Mutual fund shareholders: characteristics, investor
knowledge, and sources of information, Financial Services Review, 7, issue 4, p. 301316 (1998).
Anderson, S. C., Schnusenberg, O.: A review of studies in mutual fund performance, timing,
and persistence, Working Paper (2005).
Ang, A., Geert, B., Liu, J.: Why stocks may disappoint, Journal of Financial Economics 76,
471-508 (2005).
Armitage, C.J., Christian, J.: From Attitudes to Behaviour: Basic and Applied Research on
the Theory of Planned Behaviour, Current Psychology: Development, Learning,
Personality, Social. Vol. 22, No. 3, pp. 187-195 (2003)
Armitage, C. J., Conner, M.: Efficacy of the theory of planned behaviour: A meta-analytic
review. British Journal of Social Psychology, 40, 471 - 499 (2001).
32
Bailey, W., Kumar, A., Ng, D.: Behavioral Biases and Mutual Fund Clienteles, Working
Paper, Cornell University (2009).
Barber, B., Odean, T., Zheng, L.: Out of sight, out of mind: the effects of expenses on mutual
fund flows, Journal of Business, Vol. 78, pp. 2095–2120 (2005).
Barberis, N., Huang, M., Thaler, R.H.: Individual Preferences, Monetary Gambles, and Stock
Market Participation: A Case for Narrow Framing, American Economic Review,
American Economic Association, vol. 96(4), pages 1069-1090 (2006).
Barsky, R.B., Juster, F.T., Kimball, M.S., Shapiro, M.D.: Preference Parameters and
Behavioral Heterogeneity: An Experimental Approach in the Health and Retirement
Study, Quarterly Journal of Economics, 537–579 (1997).
Beck, L., Ajzen, I.: Predicting dishonest actions using the theory of planned behavior.
Journal of Research in Personality, 25, 285-301 (1991).
Ber, S., Kempf, A., Ruenzi, S.: Determinanten Der Mittelzuflüsse Bei Deutschen
Aktienfonds, Schmalenbachs Zeitschrift für betriebswirtschaftliche Forschung, Vol.
59, No. 2, pp. 35-60 (2007).
Bergstresser, D., Poterba, J.: Do after-tax returns affect mutual fund inflows? Journal of
Financial Economics 63, 381-414 (2002).
Bertaut, C.C.: Stockholding Behavior Of U.S. Households: Evidence From The 1983-1989
Survey Of Consumer Finances, The Review of Economics and Statistics, MIT Press,
vol. 80(2), pages 263-275, May (1998).
Blanchard, C., Fisher, J., Sparling, P., Nehl, E.: Understanding physical activity behavior in
African American and Caucasian college students: An application of the Theory of
Planned Behavior. Journal of American College Health, 56(4), 341-347 (2008).
Bogan, V.: Stock Market Participation and the Internet. Journal of Financial and
Quantitative Analysis, 43, pp 191-211 (2008).
Bogle, J.C.: Selecting equity mutual funds, The Journal of Portfolio Management, Vol. 18,
No. 2: pp. 94-100 (1992)
Bollen, K.A.: Structural Equation Models with Latent Variables, New York (1989).
Bollen, K.A.: Overall fit in covariance structure models: Two types of sample size effects.
Psychological Bulletin, 107, 256−259 (1990).
Brown, S., Taylor, T.: Social Interaction and Stock Market Participation: Evidence from
British Panel Data, Discussion Paper No. 4886 (2010).
Brown, J.R., Ivković, Z., Smith, P.A., Weisbenner, S.: Neighbors matter: Causal community
effects and stock market participation, Journal of Finance 63, 1509-1531 (2005).
Campbell, J.: Household finance, Journal of Finance 61, 1553-1604 (2006).
Capon, N., Fitzsimmons, G., Prince, R.: An individual level analysis of the mutual fund
investment decision, Journal of Financial Services Research 10, 59–82 (1996).
33
Carhart, M.: On Persistence in Mutual Fund Performance, Journal of Finance 52, 57-82
(1997).
Caroll, R.J., Ruppert, D., Stefanski, L.A.: Measurement error in nonlinear models (1. Aufl.).
London: Chapman and Hall (1995).
Chen, H.L., Jegadeesh, N., Wermers, N.: The Value of Active Mutual Fund Management: An
Examination of the Stockholdings and Trades of Fund Managers, Journal of
Financial & Quantitative Analysis, Vol. 35, No. 3, pp. 343-368 (2000).
Chevalier, J. A., Ellison, G.D.: Risk taking by mutual funds as a response to incentives,
Journal of Political Economy 105, 1167–1200 (1997).
Christelis, D., Jappelli, T., Padula, M.: Cognitive Abilities and Portfolio Choice, Manuscript,
University of Salerno (2006).
Cole, S., Shastry, G. K.: Smart Money: The Effect of Education, Cognitive Ability, and
Financial Literacy on Financial Market Participation, Working Paper, Harvard
Business School (2009).
Cronan, T. P., Al-Rafee, S.: Factors that influence the intention to pirate software and media.
Journal of Business Ethics, 78, 527-545 (2008).
Del Guercio, D., Tkac, P.: The determinants of the flow of funds on managed portfolios:
Mutual funds vs. pension funds, Journal of Financial and Quantitative Analysis 37,
523–557 (2002).
Dempster, A. P., Laird, N. M., Rubin, D. B.: Maximum Likelihood from Incomplete Data via
the EM Algorithm, Journal of the Royal Statistical Society. Series B, Vol. 39, No. 1,
pp. 1-38 (1977).
Diamantopoulos, A., Siguaw, J.A.: Introducing LISREL. London: Sage Publications (2000).
Dimmock, S.G., Kouwenberg, R.R.P.: Loss-Aversion and Household Portfolio Choice:
http://ssrn.com/abstract=680783 (2009).
Downs, D.S., Hausenblas, H.A.: The theories of reasoned action and planned behavior
applied to exercise: A meta-analytic update. Journal of Physical Activity and Health,
2, 76-97 (2005)
East, J.R.: Investment decisions and the theory of planned behaviour, Journal of Economic
Psychology 14 (1993), 337-375, (1993).
Efron, B.: Bootstrap methods: Another look at the jackknife, Annals of Statistics, 7, 1-26
(1979).
Elton, E.J., Gruber, M., Blake, C.: The persistence of risk-adjusted mutual fund performance.
J. Bus. 69 (2), 133–157 (1996).
Elton, E.J., Gruber, M.J., Busse, J.A.: Are investors rational? Choices among index funds,
Journal of Finance 59, 261–288 (2004).
34
Engström, S.: Preferences and Characteristics of Mutual Fund Investors, Working Paper,
Stockholm School of Economics and Agenta (2007).
Fang, J., Shao, P., & Lan, G. (2009). Effects of innovativeness and trust on web survey
participation. Computers in Human Behavior, 25, 144-152. [Abstract]
Ferreira, M.A., Ramos, S.B.: Mutual Fund Industry Competition and Concentration:
International Evidence (January 01, 2009). http://ssrn.com/abstract=1343096
Fishbein, M., Ajzen, I.: Belief, Attitude, Intention and Behavior. An Introduction to Theory
and Research, Reading (1975).
Frazzini, A., Lamont, O.A.: Dumb money: Mutual fund flows and the cross-section of stock
returns, Journal of Financial Economics, Elsevier, vol. 88(2), pages 299-322, May
(2008).
Goetzmann, W. N., Peles, N.: Cognitive Dissonance and Mutual Fund Investors, Journal of
Financial Research 20, 145-158 (1997).
Gopi, M., Ramayah, T.: Applicability of theory of planned behavior in predicting intention to
trade online: Some evidence from a developing country. International Journal of
Emerging Markets, 2(4), 348-360 (2007).
Gremillion, L.: Mutual Fund Industry Handbook: A Comprehensive Guide for Investment
Professionals, Boston Institute of Finance Series, Boston, Wiley, (2005).
Grinblatt, M.: How Distance, Language, and Culture Influence Stockholdings and Trades,
Journal of Finance, American Finance Association, vol. 56(3), pages 1053-1073, 06
(2001).
Grinblatt, M., Sheridan T. : The Persistence of Mutual Fund Performance, Journal of Finance
47, 1977-1984 (1992).
Grinblatt, M., Ikaheimo, I., Keloharju, M.: Are Mutual Fund Fees Competitive? What IQRelated Behavior Tells Us, Working Paper, University of California, Los Angeles
(2007).
Grinblatt, M., Keloharju, M., Linnainmaa, J.T.: IQ and Stock Market Participation. CRSP
Working Paper; AEA 2010 Atlanta Meetings Paper; Western Finance Association
2010 Meetings Paper; Chicago Booth Research Paper No. 09-27.
http://ssrn.com/abstract=14415
Gruber, M. J.: Another Puzzle: The Growth in Actively Managed Mutual Funds, Journal of
Finance 51, 783-810 (1996).
Guiso, L., Jappelli, T.: Awareness and stock market participation, Review of Finance 9, 537567 (2005).
Guiso, L., Haliassos, M., Jappelli, T.: Household Stockholding in Europe: Where Do We
Stand and Where Do We Go? Economic Policy, Vol. 18, No. 36, pp. 125-170 (2003).
35
Guiso, L., Sapienza, P., Zingales, L.: Trusting the stock market, Journal of Finance 63, 25572600 (2008).
Gumussoy, C.A., Calisir, F.: Understanding factors affecting e- reverse auction use: An
integrative approach. Computers in Human Behavior, 25, 975-988 (2009).
Hackethal, A., Niebling, F., Meyer, S.: Whose money is smart? Mutual fund purchases of
private investors; Working Paper (2009).
Haliassos, M.: Stockholding: Recent Lessons from Theory and Computations (2002).
doi:10.2139/ssrn.312899
Heaton, J., Lucas, D.: Portfolio Choice in the Presence of Background Risk, The Economic
Journal, 110, 1–26 (2000).
Hendricks, D., Patel, J. Zeckhauser, R.: Hot hands in mutual funds: Short-run persistence of
relative performance 1974–1988, Journal of Finance 48, 93–130 (1993).
Hofmann, E., Hoelzl, E., Kirchler, E.: A comparison of models describing the impact of
moral decision making on investment decisions. Journal of Business Ethics, 82, 171187 (2008).
Homburg, C., Klarmann, M.: Die Kausalanalyse in der empirischen betriebswirtschaftlichen
Forschung. Problemfelder und Anwendungs-empfehlungen, Die Betriebswirtschaft,
66, 6, S. 727-748 (2006).
Hong, H.G., Kubik, J.D., Stein, J.C.: Social interaction and stock market participation,
Journal of Finance 59, 137-163 (2004).
Hooper, D., Coughlan, J., Mullen, M.: Structural Equation Modelling: Guidelines for
Determining Model Fit. Electronic Journal of Business Research Methods, 6(1), 5360 (2008).
Hu, L., Bentler, P.M.: Evaluating model fit. In R. H. Hoyle (Ed.), Structural equation
modeling. Concepts, issues, and applications (pp. 76−99). London: Sage (1995).
Hu, L., Bentler, P.M.: Cutoff Criteria for Fit Indices in Covariance Structure Analysis.
Conventional Criteria versus New Alternatives, in: Structural Equitation Modelling, 6,
1, S. 1-55 (1999).
Investment Company Institute (2008), Investment Company Fact Book.
Ippolito, R. A.: Consumer reaction to measures of poor quality: Evidence from the mutual
fund industry, Journal of Law and Economics 35, 45–70 (1992).
Ivkovića, Z., Weisbenner, S.: Individual investor mutual fund flows; Journal of Financial
Economics, Volume 92, Issue 2, Pages 223-237, May (2009).
Jen-Ruei, F., Cheng-Kiang, F., Wen-Pin, C.: Acceptance of electronic tax filing: A study of
taxpayer intentions. Information & Management, 43, 109-26 (2006).
36
Jimmieson, N.L., Peach, M., White, K.M.: Utilizing the theory of planned behavior to inform
change management: An investigation of employee intentions to support
organizational change. Journal of Applied Behavioral Science, 44, 237-262 (2008).
Keswani, A., Stolin, D.: Which Money is Smart? Mutual Fund Buys and Sells of Individual
and Institutional Investors, Journal of Finance 63, 85-118 (2008).
Khorana, A., Servaes, H.: Conflicts of Interest and Competition in the Mutual Fund Industry
(July 2004). doi:10.2139/ssrn.240596
Khorana, A., Servaes, H. Tufano, P.: Explaining the size of the mutual fund industry around
the world, Journal of Financial Economics 78, 145–185 (2005).
Khorana, A., Servaes, H. Tufano, P.: Mutual Funds Fees Around the World, Review of
Financial Studies 22, 1279-1310 (2009).
Klapper, L., Sullas, V. Vittas, D.: The development of the mutual funds around the world,
Emerging Markets Review, Vol. 5, pp. 1–38 (2004).
Lenard, M.J., Akhter, S. H., Alam, P.: Mapping Mutual Fund Investor Characteristics and
Modeling Switching Behavior, Financial Services Review Volume 12, Volume 12
Number 1, pp. 39-59 (2003).
Lewis, W., Agarwal, R., Sambamurthy, V.: Sources of influence on beliefs about information
technology use: An empirical study of knowledge workers. MIS Quarterly, 27(4),
657-678 (2003).
Lin, H.-F.: Applicability of the extended theory of planned behavior in predicting job seeker
intentions to use job-search websites. International Journal of Selection and
Assessment, 18, 64-74 (2010).
Malloy, C.J., Zhu, N.: Mutual Fund Choices and Investor Demographics, Working Paper
(2004).
Manning, M.: The effects of subjective norms on behaviour in the theory of planned
behaviour: A meta-analysis. British Journal of Social Psychology, 48, 649-705
(2009).
Mueller, R.O.: Basic principles of structural equation modeling: An introduction to LISREL
and EQS. New York: Springer (1996).
Müller, S., Weber, M.: Financial Literacy and Mutual Fund Investments: Who Buys Actively
Managed Funds?, Schmalenbach Business Review, 62, 126-153 (2010).
Nachtigall, C., Kroehne, U., Funke, F., Steyer, R.: (Why) Should We Use SEM? Pros and
Cons of Structural Equation Modeling, Methods of Psychological Research Online, 8,
2, S. 1-22 (2003).
Nevit, Hancock: Performance of bootstrapping Approaches to Model Test Statistics and
Parameter Standard Error Estimation in Structural Equation modelling, in: Structural
Equation Modeling, 8 (3), 133-145 (2001).
37
Otten, R., Schweitzer, M.: A comparison between the European and the US mutual fund
industry, Managerial Finance, Vol. 28, pp. 14–35 (2002).
Puri, M., Robinson, D.T.: Optimism and Economic Choice, Journal of Financial Economics
86, 71-99 (2007).
Ramayah, T., May, O. S., Omar, A.: Behavioral determinants of online banking adoption:
Some evidence from a multicultural society. I-manager’s Journal of Management,
2(3), 29-37 (2008).
Ramayah, T., Y.M., Jamaludin, N., Ibrahim, A.: Applying the Theory of Planned Behavior
(TPB) to Predict Internet Tax Filing Intentions, International Journal of Management
Vol. 26 No. 2 (2009).
Ramos, S. B.: The Size and Structure of the World Mutual Fund Industry. European
Financial Management, 15: 145–180 (2009).
Rhodes, R.E., Courneya, K.S.: Investigating multiple components of attitude, subjective
norm, and perceived control: An examination of the theory of planned behaviour in
the exercise domain. The British Journal of Social Psychology, 42, 129-146 (2003).
Rooij, M., Lusardi, A., Alessie, R.:Financial Literacy and Stock Market Participation,
Working Paper, National Bureau of Economic Research, Cambridge (2007).
Rosen, H. S., Wu, S.: Portfolio Choice and Health Status, Journal of Financial Economics,
72, 457–484 (2004).
Rossiter, J.R.: The C-OAR-SE Procedure for Scale Development in Marketing, International
Journal of Research in Marketing, Vol. 19, pp. 305-335 (2002).
Sapp, T. Tiwari, A.: Does Stock Return Momentum Explain the Smart Money Effect?,
Journal of Finance 59, 2605-2622 (2004).
Schermelleh-Engel, K., Moosbrugger, H., Müller, H.: Evaluating the Fit of Structural
Equation Models. Tests of Significance and Descriptive Goodness-of-Fit Measures,
Methods of Psychological Research Online, 8, 2, S. 28-74 (2003).
Schmidt, R.: Präferenzbasierte Segmentierung von Fondskäufern: Verbesserung der
Kundenorrientierung im Fondsvertrieb. Deutscher Universitäts-Verlag | GWV
Fachverlage GmbH, Wiesbaden (2006).
Schreurs, B., Derous, E., Hooft, E.A.J., Proost, K., Witte, K.: Predicting applicant's job
pursuit behavior from their selection expectations: the mediating role of the theory of
planned behavior, Journal of Organizational Behavior, 30, 761-783 (2009).
Sheeran, P.: Intention-behavior relations: A conceptual and empirical review. European
review of social psychology, Vol. 12, pp 1-36 (2002)
Sirri, E. R., Tufano, P.: The demand for mutual fund services by individual investors,
Working Paper, Harvard Business School (1992).
38
Sirri, E. R., Tufano, P.: Costly Search and Mutual Fund Flows, Journal of Finance 53, 15891622 (1998).
Steenkamp, J.-P. / Baumgartner, H.: Assessing Measurement Invariance in Cross-National
Consumer Research, Journal of Consumer Research, 25, 1, S. 78-90 (1998).
Steiger, J.H.: Structural Model Evaluation and Modification. An Interval Estimation
Approach, Multivariate Behavioural Research, 25, 2, S. 173-180 (1990).
Sutton, S.: Predicting and explaining intentions and behavior: How well are we doing?
Journal of Applied Social Psychology, 28, 1317-1338 (1998).
Teo, T.S.H., Pok, S. H.: Adoption of WAP-enabled mobile phones among internet users. The
International Journal of Management Science, 31, 483-498 (2003).
Van Dam, K., Van der Vorst, J.D.M., Van der Heijden, B.I.J.M.: Employees' intentions to
retire early: A case of planned behavior and anticipated work conditions. Journal of
Career Development, 35, 265- 289 (2009).
Vissing-Jørgensen, A.: Towards an Explanation of Household Portfolio Choice
Heterogeneity: Nonfinancial Income and Participation Cost Structures. Chicago:
Univ. Chicago, Dept. Econ., (2002).
Weiber, R., Mühlhaus, D.: Strukturgleichungsmodellierung: Eine anwendungsorientierte
Einführung in die Kausalanalyse. Springer, Berlin; (2009).
Wermers, R.: Mutual Fund Performance: An Empirical Decomposition into Stock-Picking
Talent, Style, Journal of Finance, Vol. 55, No. 4, pp. 1655-1695 (2000).
Wheaton, B., Muthen, B., Alwin, D.F., Summers, G.: Assessing Reliability and Stability in
Panel Models, Sociological Methodology, 8 (1), 84-136 (1977).
Wilcox, R.T.: Bargain Hunting or Star Gazing? Investors' Preferences for Stock Mutual
Funds, Journal of Business, University of Chicago Press, vol. 76(4), pages 645-664,
October (2003).
Xiao, J., Wu, J.: Applying the Theory of Planned Behaviour to Retain Credit Counseling
Clients. TCAI Working Paper. (2006)
Zhao, X.: The Role of Brokers and Financial Advisors Behind Investments into Load Funds,
Working Paper (2004).
Zheng, L.: Is Money Smart? A Study of Mutual Fund Investors’ Fund Selection Ability,
Journal of Finance, 54, 901-933 (1999).
Zhu, N.: Cost of Time and Household Choice between Direct and Delegated Investment.
http://ssrn.com/abstract=686569 (2005).
39