Study of satisfaction, loyalty, and market share in Kuwait banks offering mutual fund services.
Al-Wugayan, Adel ; Pleshko, Larry P.
INTRODUCTION
Although attaining growth and profitability is of paramount
importance to businesses, systematic sector analysis aimed at
identifying the relationship among key marketing outcome variables is
scant at best. According to the basic consumer model, improved value
should enhance brand choice and generate favorable satisfaction
judgments, resulting in repeat purchase (brand loyalty), and ultimately
to overall firm performance (Cooil et al. 2007, Leverin & Liljander
2006, Story & Hess 2006, Reinartz, Werner & Kumar 2000, Pleshko
& Cronin 1997, Rust & Zahoric 1993).
Expecting these general premises to hold for every industry or
under every condition is generally met with justified skepticism. The
marketing literature offers many examples showing that better product
does not always translate into higher sales or larger market shares.
Intervening variables such as poor marketing, poor relative value, or a
variety of macro-firm factors can weaken the logical relationship
between marketing effectiveness variables. Additionally, satisfaction
with a service or a usage occasion is not sufficient to ensure customer
loyalty or higher profitability even when satisfaction leads to improved
market shares (Mitchell & Kiral 1998, Pleshko & Cronin 1997,
Reichheld & Sasser 1990). Despite the positive effects of buyer
loyalty on market share, loyalty is often found to be more prevalent in
firms with smaller market shares (Ehrenberg & Goodhardt 2002, Knox
& Denison 2000, Reichheld & Sasser 1990). Additional research is
needed to verify the association between marketing effectiveness outcome
variables.
This study investigates the interrelationships among consumer
satisfaction, consumer loyalty, and market share in the mutual funds
industry. Kuwaiti banks, local pioneers in offering mutual fund
services, are considered to be concentrated yet operating in a
monopolistically competitive industry (Al-Mutairi & Al-Omar 2009).
Mutual funds are a unique service-type in that the investments
themselves are potentially quite durable (lengthy) while still offering
quick dissolution if necessary. Although banks have been the focus of a
variety of studies, however investigations into the mutual funds service
have been lacking.
CUSTOMER SATISFACTION
In general terms, satisfaction can be defined as the summary
judgments formed after consumption. Although many models have been
postulated to explain satisfaction, this study conceptualizes
satisfaction to result from a comparison of mutual fund service and
product expectations to the performance of the banks on these salient
components (Al-Weqaiyan 1998, Churchill & Surprenant 1982). Meeting
or exceeding initial expectations should lead to satisfaction whereas
falling short of expected performance will generate dissatisfaction.
User satisfaction judgments have been shown to impact various
attitudinal and behavioral tendencies toward chosen brands (Breivik
& Thorbjornsen 2008).
Customer satisfaction and buyer retention are generally considered
among the most important long term objectives of firms (Cooil et al.
2007). Satisfied buyers should be more likely to repurchase again, or at
least, consider repurchasing again than those with undesired service
experiences (Kotler 1977, Keith 1960, Leavitt 1960). Importantly,
satisfied buyers are known to provide important positive word-of-mouth
communication (DeMatos & Rossi 2008). According to Reichheld and
Sasser (1990), repeat customers can benefit a firm's cost structure
through reduced costs per visit when compared to new customers.
Additionally, maximizing customer retention rates and minimizing
customer defections are primary strategic objectives for most firms
emphasizing the maintenance of market share through customer
relationship management (Ching et al. 2004, Verhoef 2003). Thus,
previously satisfied buyers may result in both reduced marketing costs
and more stable sales/share levels if a large enough proportion of those
satisfied buyers are retained as customers. Additionally, new buyers
satisfied with their experiences can be expected to consider using the
product/brand again in the future, possibly resulting in continued
repeat patronage and increased shares.
BUYER LOYALTY
Brand loyalty is perhaps one of the oldest concepts of interest to
marketing scholars. In the past, researchers have used different aspects
of loyalty including "purchase possibility", "purchase
frequency", "awareness", and "long-term
trust/commitment" (Farley 1964, Brody & Cunningham 1968, Twedt
1967, Story & Hess 2006, Oliver 1999). Dick and Basu (1994)
challenged the traditional view of brand loyalty as
"repurchase-related behavior" and offered to define loyalty as
an interrelation between both purchase behavior and brand attitudes. In
this perspective, which has empirical support, true brand loyalty
requires both repeat purchase behavior as well as significant
psychological attachment to the chosen brand (Kerin et al. 2006, Pleshko
& Heiens 1997, Pleshko & Heiens 1996).
Marketers contend that business performance is associated with
maintaining adequately high levels brand loyalty. In fact, corporate
incentives-based loyalty programs may lead to immediate increases in
buyer loyalty, but with no guarantees that repurchase will continue in
the long term due to a lack of psychological attachment (Story &
Hess 2006). The association between loyalty and repurchase frequency has
been complicated by the rise of buyer switching behavior due to reasons
both at the individual level and the market level, as exhibited in
variety seeking and aggressive promotional programs (Breivik &
Thorbjornsen 2008, Al-Weqaiyan 2005). This is relevant to the company
since marketing performance depends partially on managing both market
penetration and customer retention (McDowell & Dick 2001, Lehmann
& Winner 1997).
INTERRELATIONSHIP BETWEEN CUSTOMER SATISFACTION AND BUYER LOYALTY
It often suggested that strong loyalty is a major determinant of
customer-business interaction (Cooil et al 2007, Tellervision 2006). If
buyers are satisfied with their chosen brands, increasingly they will be
inclined to re-purchase and eventually become loyal buyers exhibiting
strong customer goodwill. Leverin & Liljander (2006) found that
satisfaction with banks in Finland played an important role in
determining loyalty, especially in less profitable sectors.
However, many researchers question the reliability of using
satisfaction as a predictor of loyalty (Story & Hess 2006, Reichheld
2003, Oliver 1999). This alternative perspective notes that satisfaction
may be an important requirement for loyalty, yet it may not be
sufficient by itself to generate loyal customers. This view leads to the
possibility of loyalty being classified as either of spurious or latent,
rather than real. One can conclude from the work of Dick and Basu (1994)
that truly loyal buyers must exhibit both behavioral loyalty and
psychological loyalty. Latent loyalty is evident when buyers have
favorable attitudes but exhibit a weak brand repurchase pattern. In
contrast, when buyers with high brand repurchase lack preference towards
that brand, this is referred to as spurious loyalty. The absence of
either of these loyalty factors in empirical studies may explain the
variability in the satisfaction-loyalty relationships.
From a conceptual viewpoint, the directionality of the
satisfaction-loyalty relationships can take one of three possibilities:
(a) satisfaction predicts loyalty, (b) loyalty predicts satisfaction, or
(c) there no real correlation between satisfaction and loyalty. A
related research question pertains to the identification of conditions
under which each of these satisfaction-loyalty relationship
possibilities exists. It is not the intention of the present study to
address either of these major issues, but rather simply looks to
determine if a relationship does indeed exist between loyalty and
satisfaction. In this study, where highly involved product decisions
have consequential results from very few possible repurchase occasions,
it is proposed that satisfaction is expected to precede loyalty. Hence,
the following hypothesis is formulated:
H1: Customer satisfaction and buyer loyalty should be positively
associated in banks offering mutual funds investment services.
MARKET SHARE AND ITS RELATIONSHIPS WITH CUSTOMER SATISFACTION AND
BUYER LOYALTY
Market share is a critical marketing outcome variable that should
be influenced by both the satisfaction a buyer perceives from purchase
and usage experience and the continued patronage of loyal buyers.
According to recent empirical research, as satisfaction improves,
consumers are more likely to engage in repeat purchases and positive
word-of-mouth (De Matos & Rossi 2008). Higher levels of loyalty are
found to be associated with positive word-of-mouth, an increased
inclination to repurchase, and at the firm level, and an increase in
profits per customer due to lower retention costs (Zeithaml 2000, Tellis
1988, Rosenberg & Czepial 1983). In the long run, the value of any
business should increase as management reinvests those higher relative
profits back into the firm (Day & Wensley 1988).
To management, the combined effects of psychological and behavioral
loyalty provide many advantages. As markets mature, market share
maintenance and growth strategies become exceedingly costly in order to
combat rivals' competitive pressures. This may be overcome by
creating and maintaining a loyal buying base (Gounaris &
Stathakopoulos 2004). Many previous studies have provided evidence
relating loyalty to either market share or other performance indicators
(Leverin & Liljander 2006, Fader & Schmittlein 1993, Colombo
& Morrison 1989, Raj 1985, Robinson 1979). Many studies have found a
positive relationship, where brands with large market shares usually
have the most brand loyal buyers while brands with small market share
suffer from low loyalty levels (Ehrenberg and Goodhardt 2002, Badinger
& Rubinson 1997, McPhee 1963). This share-loyalty linkage is termed
'double jeopardy' and has be attributed to a variety of
explanations, including switching gains or distribution channel
advantages or popularity (Kumcit 2008, Pleshko & Souiden 2007,
Caminal & Vives 1996).
Assessing the loyalty-share relationship is of prime interest in
the present investigation. Earlier studies suggest that the prevailing
direction of this relationship a positive relationship. The following
hypothesis is coined accordingly as:
H2: Customers loyalty and market share are positively related in
banks offering mutual funds investment services.
Repatronage in the service industry, and especially in the banking
sector, depends largely on the level of customers' satisfaction.
Previous research in the banking industry has shown that higher levels
of customer satisfaction are associated with higher market shares
(Pleshko & Cronin 1997). Failure to achieve relatively high
satisfaction levels would lead to customer defections to better
positioned competitors, resulting in eroded market shares over time. In
the financial services industries, buyers will be dissatisfied if
expectations regarding high levels of service, security, and safety are
not met (Adams 2007, Tellervision 2006). Additionally, brand switching
is easier as customers can transfer their business to better-serving
institutions with convenience and few switching costs (Leverin &
Liljander 2006). In light of this, the following hypothesis is advanced:
H3: There is a positive relationship between customer satisfaction
and market share in banks offering mutual fund investment services.
Figure 1 summarizes the proposed hypotheses. The reader may also
use Figure 1 for general information about the indicators used to
represent each construct in the study.
DATA COLLECTION
In testing the hypotheses, data is collected from investors with
mutual funds at the various banks in the state of Kuwait. The sampling
frame is comprised of bank customers in Kuwait and is derived from lists
provided by the Central Administration of Statistics for the State of
Kuwait. Data are collected from self-administered questionnaires
collected from interview visits to households of both local citizens and
foreign residents. A multi-stage sampling procedure is employed in order
to maintain an adequate representation of bank users in Kuwait. In
addition, the sample closely matches the distribution of residences over
the six districts of Kuwait. This procedure generated three hundred
thirty mutual fund investors which are included in the study. The
non-response rate is lower than ten percent, an acceptable number given
that the information gathered is considered private and sensitive. Any
respondents unwilling to share information about their banking
activities were dropped from the study.
[FIGURE 1 OMITTED]
While many financial services companies operate in Kuwait, only
those offering mutual funds are included in this study. There are
thirty-six companies offering mutual fund services. However, most of the
investment activity, nearly eighty-five percent, is handled through the
ten major banks of Kuwait. Therefore, the ten banks are each included in
the study as individual entities while the remaining twenty-six
providers are grouped together into an 'other' category due to
the small market shares evident with mutual funds. For the purpose of
our analysis, there are eleven 'banks' or entities that will
be included in the analyses; the ten major banks by market share, along
with an 'other' category which includes the averages of
twenty-six banks and financial services companies. Table 1 summarizes
the banks and investors data derived from the respondents.
From Table 1 many items are noted regarding the sample: (i) the
banks are identified in the first column, (ii) the number of investors
for each bank are shown in the second column, (iii) the number of
investors in the banks where the investors have the most mutual fund
money invested is shown in column three, (iv) the total number of mutual
fund accounts held by each bank is shown in column four, and finally (v)
the total mutual funds investments in Kuwaiti dinars is revealed in
column five.
It is worthwhile to note that there is not a numerical
correspondence between the number of investors, investor accounts, and
respondents. In other words, (i) investors may have more than one mutual
fund investment account and (ii) investors may have mutual fund
investments at more than one bank. Thus, the numbers will show that
there are more investors (530 total in column two) and accounts (732
total in column four) than total responding investors (326 total in
column three). However, column three shows the bank where the customers
have the majority of their investment money and those numbers will add
approximately to the number of respondents: two hundred forty seven
(three not grouped) associated with the ten major banks and eighty with
the other banks: a total of three hundred and twenty seven
investors/respondents. The mutual fund investments of the sample total
approximately KD 16,538,179 which is split as eighty-four percent with
the ten major banks and the remaining sixteen percent invested in the
other twenty-six banking entities.
MEASUREMENT
Several indicators are used to measure the three constructs
investigated in this study: market share, satisfaction, and consumer
loyalty. The loyalty indicators are described below and are derived from
research in other industries where similar measures are shown to be
reliable and valid (Pleshko 2006). The satisfaction indicator is
commonly used in the literature to measure general satisfaction with a
service (Pleshko & Cronin 1997). Also, the market share indicators
fit the common definition of share as a percentage of a total related to
an item of relevance, such as customers, sales, market size, or
accounts. The aggregate market share, the aggregate loyalty indictors,
and the satisfaction indicators are revealed in Table 2 for each of the
banks. Note that to gather the data, the respondents were asked to write
the bank, investment amount, satisfaction, and year initiated for each
of their mutual fund investments.
Three indicators are used to measure aggregate market share. The
first indicator, (MSFI), refers to the share of investors that each bank
holds out of a total of five hundred thirty. Thus, MSFI is calculated as
follows: [MSFI.sub.i] = Si/530, where 'S' refers to the data
from column two in Table 1 and 'i' refers to the specific
bank. So, regarding Bank 33 for example: [MSFI.sub.33] = 74/530 =
13.96%. From Table 2, it is noted that the range of MSFI is from a low
of 0.78% for 'other' banks to a high of 17.74% for Bank 17.
The second market-share indicator (MSFA) refers to the share of
mutual fund accounts/investments that each bank holds out of a total of
seven hundred thirty two. Thus, MSFA is calculated as follows:
[MSFA.sub.i] = [T.sub.i]/732, where 'T' refers to the data
from column four in Table 1 and 'i' refers to the specific
bank. So, regarding Bank 33 for example: [MSFA.sub.33] = 99/732 =
13.52%. From Table 2, it is noted that the range of MSFA is from a low
of 0.76% for 'other' banks to a high of 19.13% for Bank 17.
The third market-share indicator (MSVA) refers to the share of
money invested that each bank holds. Note that the total value of the
respondents' investments in mutual funds is Kd16,538,179. Thus,
MSVA is calculated as follows: [MSVA.sub.i] = [Z.sub.i]/16,538,179,
where 'Z' refers to the data from column five in Table 1 and
'i' refers to the specific bank. So, for Bank 33 for example:
MSVA33 = 3,083,503/16,538,179 = 18.64%. From Table 2, it is noted that
the range of MSVA is from a low of 0.61% for 'other' banks to
a high of 23.61% for Bank 17.
There is strong evidence to support the internal structure of the
market share construct as the three market share indicators are
significantly and positively related using the Spearman rank-order test.
The relationships are as follows: MSFI-MSFA: rho=0.97 with p<.01,
MSFI-MSVA: rho=0.90 with p<.01, and MSFA-MSVA: rho=0.88 with
p<.01.
Loyalty is assessed using two indicators. The first loyalty
indicator (LOYF) refers to the number of mutual fund investors at each
bank. Specifically, LOYF is defined as the number of investors at each
bank, where the investors are assigned to the specific bank where they
have the largest investment in mutual funds (see column three, Table 1).
This is adjusted by the total number of investors in the sample: three
hundred and twenty-seven classified. So, regarding Bank 33 for example:
[LOYF.sub.33] = 45/327 = 13.76%. From Table 2, it is noted that the
range of LOYF is from a low of 0.91% for 'other' banks to a
high of 14.06% for Bank 17.
The second loyalty indicator (LOYP) also refers to the number of
mutual fund investors assigned to each bank as discussed above, but as a
percentage of the total investors at each bank (see columns two and
three, Table 1) rather than the total sample. So, regarding Bank 33 for
example: [LOYP.sub.33] = 45/74 = 60.81%. From Table 2, it is noted that
the range of LOYP is from a low of 2.81% for 'other' banks to
a high of 88.89% for Bank 27. In other words, LOYP is the percentage of
a bank's (Bank A) customers classified as loyal to the bank. (Bank
A loyal)
The study includes a single indicator of consumer satisfaction with
banks' mutual funds services. An overall indicator (SATB) is
measured using a single item with ratings from very satisfied [5] to not
at all satisfied [1] for each bank where respondents have investments.
This measurement procedure is similar to that used in other studies in
the financial services industry to measure general satisfaction (Pleshko
& Cronin 1997, Dawes & Smith 1985). The satisfaction responses
are aggregated to the specific bank and then averages are calculated.
For the banks, the eleven averages of SATB have an average of 3.60 with
a standard deviation of 0.21 and range from 3.35 to 3.93.
ANALYSIS/RESULTS
The analysis proceeds in two steps. Initially, the explained
variance is derived for each relationship in the model. This will be
accomplished using Spearman's (1904) rank-order test, which is
explained in the next paragraph. As the indicators are aggregated across
the banks and are not specific to each customer, Spearman's test is
appropriate due to the small number of observations; the eleven
'banks'. The eleven observations are not of adequate size to
perform parametric testing (Stevens 1986). Secondly, the model paths are
analyzed to determine if they are significant in relation to the overall
model as presented in Figure 1. This will be accomplished using path
analysis, as explained in later paragraphs.
The Spearman rank correlation coefficient is calculated as follows.
The test statistic, rho or "r", is calculated with data taken
from 'n' pairs (Xi,Yi) of ordered observations from the
respondents on the same objects: the banks. Each of the two bank
variables is ordered from smallest to largest and assigned relative
ranks from one (lowest; in this case one) to n (highest; in this case
eleven). Ties are assigned the average ranking value. These
"rankings pairs", the two ordered variables of interest
(Xi,Yi), are then used to calculate the test statistic, which is derived
as follows: r=1-6[Sum([d.sup.2])/n([n.sup.2]-1)]. In the equation,
'n' equals the number of paired rankings for each bank
(eleven) and 'd' equals the absolute differences between the
rankings for each bank (Xi-Yi). The rho values to be used in the model
and shown in Table 3 (and Table 4) and have theoretical ranges between
+1 (perfect positive association) and -1 (perfect negative association).
Next, path analysis is used to test the overall relationships among
the identified research constructs presented in Figure 1. Path analysis
is a relevant technique for testing causal models using regression
analyses (Pedhazur 1982). The technique allows the researcher to
determine if the hypothesized model is consistent with the variable
intercorrelations -the rho statistics in this study or whether the
null/full model is more relevant.
Using path analysis, our hypothesized/reduced model (Ha) is
compared with a null/full model inclusive of all the possible paths (the
Ho) using Equation one identified below (Pedhazur 1982, p. 619). The
calculated test statistic, 'W', is distributed as a
chi-square, with 'm' degrees of freedom. If the null
hypothesis is rejected, then support is provided for the hypothesized
model. This means that, with a rejected null hypothesis, the tested path
cannot be excluded from the model. On the other hand, if Ho is not
rejected, then evidence does not support including the tested path(s) in
the model.
In our model, Equation two and Equation three summarize the basic
mathematical relationships where the Spearman rho statistics will be
used. The relative weights are represented by the eleven relevant
Spearman rho statistics: [A.sub.1], [A.sub.2] through [A.sub.11] in the
two equations. In order to test the hypotheses, it is necessary to
determine the significance of the predictors by comparing a full model
to a reduced model, wherein some of the predictors will be removed. Due
to the multiple indicators for market share and loyalty, more than one
indicator will be evident for each construct in the full model and more
than one indicator will be removed from the equations when dealing with
the reduced models. Therefore, the full model will include all of the
[A.sub.i] (the [A.sub.i]), while the reduced model will be without those
[A.sub.i] pertaining to a specific relationship-pair of constructs:
either satisfaction-loyalty ([A.sub.1], [A.sub.2]), or loyalty-share
([A.sub.4], [A.sub.5], [A.sub.7], [A.sub.8], [A.sub.10], [A.sub.11]), or
satisfaction-share ([A.sub.3], [A.sub.6], [A.sub.9]).
Equation 1: W = - (n-m) [ln (1 - [R.sup.2].sub.o]) / (1 -
[R.sup.2.sub.a])]
Where
W = [X.sup.2] statistic
n = number of observations = 327 respondents
m = d.f. = model paths hypothesized to be zero
ln = natural log
[R.sup.2.sub.o] = R2full = 1-[(1-[r.sup.2.sub.i])
(1-[r.sup.2.sub.ii]) (1-[r.sup.2.sub.iii]) (etc.)]
[R.sup.2.sub.a] = R2reduced = 1-[(1-[r.sup.2.sub.i])
([r.sup.2.sub.ii] (etc.)]
[r.sub.i] = Spearman rho statistics = explained inter-correlations
Equation 2a: LOYF = [A.sub.1] * SATB + error
Equation 2b: LOYP = [A.sub.2] * SATB + error
Equation 3a: MSFA = [A.sub.3] * SATB + [A.sub.4] * LOYF + [A.sub.5]
* LOYP + error
Equation 3b: MSFI = [A.sub.6] * SATB + [A.sub.7] * LOYF + [A.sub.8]
* LOYP + error
Equation 3c: MSVA = [A.sub.9] * SATB + [A.sub.10] * LOYF +
[A.sub.11] * LOYP + error
The results of the path analyses tests are shown in Table 4, which
reveals that none of the overall paths should be excluded from the
model. For H1, the satisfaction-loyalty proposal, the table shows that
W=19.74 (p=<.005), indicating that satisfaction is an important
predictor of loyalty with a positive relationship and must be kept in
the study. However, the goodness-of-fit index, Q = .941, suggests that
the effect size for satisfaction-loyalty is minimal, about three percent
(([0.0568.sup.2] + [0.2364.sup.2]) / 2 = 0.02956). For H2, the
loyalty-share proposal, the table shows that W = 945.06 (p<.001),
indicating that loyalty is an important predictor of market share with a
positive relationship and must be kept in the study. The goodness-of-fit
index, Q = .053, suggests that the effect size for loyalty-share is
large, nearly seventeen percent (([0.7932.sup.2] + [0.6795.sup.2] +
0.85232 + [0.0341.sup.2] + [0.1636.sup.2] + [0.0909.sup.2]) / 6 =
0.1667). For H3, the satisfaction-share proposal, the table shows that
W=101.15 (p<.001), suggesting that satisfaction is an important
predictor of market share with a negative relationship and must be kept
in the study. The goodness-of-fit index, Q = .732, suggests that the
effect size for satisfaction-share is moderate, about ten percent
(([0.3159.sup.2] + [0.3727.sup.2] + [0.2364.sup.2])) / 3 = 0.0982).
Therefore, the path analysis results support the model as stated in
Figure 1, except for the direction of H3, the satisfaction-share
relationship, which is negative.
DISCUSSION
The general objective of the study is to determine if the general
model presented in Figure 1 is valid in Kuwait banks offering mutual
fund services. The results indicate that the model is appropriate, since
all three relationships are found to be important and none of the
constructs should be excluded from the model. As expected, higher
customer satisfaction leads to (minimally) higher levels of customer
loyalty (lending support to H1) and higher levels of loyalty lead to
(greatly) more market share, supporting H2. The only unexpected result
is that higher levels of customer satisfaction are associated with
(moderately) lower levels of market share, hence the size, but not the
direction, of H3 is supported.
The findings suggest the importance of developing customer loyalty
in investment bank services. This item is congruent with many other
studies across industries and supports literature linking loyalty to
performance in financial institutions (Reinartz & Kumar 2002). As
the results suggest, the relationship between buyer loyalty and market
share is found to be positive with an estimate of effect size to be
nearly seventeen percent. In one direction, loyalty leads to an increase
of market share while in the other direction, market share may have a
positive effect on loyalty (Reinartz & Kumar 2002, Hellofs &
Johnson 1999, Fader 1993, Colombo & Morrison 1989). The
directionality of this relationship was not addressed by the current
study. If the relationship conforms to the standard temporal precedence
where aggregate loyalty leads to more aggregate buying, then firms
wishing to increase market share performance would be wise to invest in
loyalty-developing programs.
Our finding of a positive relationship between satisfaction and
loyalty is consistent with previous literature where satisfied customers
are more likely to become frequent users of a specific service brand
than customers with dissatisfying experiences. While the magnitude of
the satisfaction-loyalty association is significantly different from
zero, this effect is low and estimated to be only about three percent.
In a sense, higher levels of satisfaction seem insufficient to generate
strong loyalty tendencies. It should be noted that satisfaction is only
one of the many variables that contributes to the ultimate formation of
buyers' loyalty (Oliver 1999). Oftentimes, other variables
including situational, psychological, or even socio-cultural influences
might lead a satisfied buyer to purchase different brands on a regular
basis. The interplay of these moderating (or predictor) variables may
act in a manner to dilute the satisfaction-loyalty relationship.
The direction of the relationship between satisfaction and market
share is inconsistent with our prediction in H3. The relationship
between satisfaction and market share is definitely significant, but
negative. The estimated effect size is nearly ten percent. These
findings underscore the transient nature of the impact of satisfaction
on buying behaviors. Behaviors (market share) can be influenced by many
exogenous and indigenous factors. Specifically, the level of marketing
spending, the lack of a strong brand image, disadvantages in bank
locations, or the existence of very large and powerful competitors might
all contribute to low levels of market share even in the face of high
satisfaction levels. More likely in this instance, it may be that large
banks with larger client bases do not (or can not) provide the kind of
customized services desired by customers, that is without increasing
marketing mix expenditures and becoming less profitable. It is often the
case where smaller firms specialize and/or provide better service by
focusing on smaller and more manageable markets or segments. Possibly,
in this market, larger banks had better customer satisfaction levels
when they were smaller in size and thereby attracted more customers.
But, over time cumulative satisfaction judgments of the banks diminished
as growth occurred and buyers experienced different (decreased) levels
of service.
Additionally, it is possible that the effect of satisfaction is
mediated through loyalty. That is, a portion of the large main effect
which loyalty has towards market share is due to an indirect effect from
satisfaction. If this were the case, it might be estimated that an
additional half percent ((([0.0568.sup.2] + [0.2364.sup.2])/2) *
(([0.7932.sup.2] + [0.6795.sup.2] + [0.8523.sup.2] + [0.0341.sup.2] +
[.1636.sup.2] + [0.0909.sup.2]) / 6) = 0.00493) be attributed to the
effects of satisfaction on market share. This very small increase in
explained variance (0.493%) doesn't seem to be very relevant to the
model and seems to rule out any indirect (mediating) effect of
satisfaction on share through loyalty.
In summary, the model in Figure 1 is supported with the following
findings. First, as satisfaction increases, then loyalty increases
slightly. This is in line with basic marketing premises and suggests
that banks must continually emphasize satisfying the customer in
marketing efforts. Secondly, as loyalty increases, then market share
increases a fairly large amount. This also supports the basic marketing
theory and offers that a secondary effort should focus on turning
satisfied customers into loyal buyers because increases in loyalty are
associated with larger market shares. Third, increases in market share
are associated with a moderate decrease in satisfaction (or vice versa).
This item reveals the importance of customer service to any service
organization. It may be that larger share banks do not offer the level
of service that smaller share banks offer, with the result being less
satisfaction with larger banks. In summary, a bank wishing to increase
its share of the market might focus on expanding programs aimed at
improving customer loyalty. These efforts targeting improved loyalty
should include as the focus an emphasis on service to effectively
increase customer satisfaction.
LIMITATIONS
The readers must wonder if the current findings are indicative of
general tendencies or simply a characteristic of this limited study in
the Kuwait market. Larger studies with more respondents taken over time
are probably needed to truly identify the scope of the outlined model in
banking. Additionally, this study only addressed banks as related to
mutual funds services: no evidence is provided that these findings apply
to other banking services, such as investment accounts, credit cards, or
money transfers. Future research might also include both different
target respondents as well as different product-markets, both in the
banking sector and elsewhere across the GCC or other regions.
The question of measures may also be relevant. The use of other
performance measures, such as profitability, as well as alternative
indicators of each construct may add variety to the findings. Also,
different types of loyalty might be included (true, spurious, latent,
cognitive, affective, conative, and action) in order to capture a better
explanation of the relationship among loyalty, customer satisfaction and
market share (Oliver 1999, Dick & Basu 1994). Finally, satisfaction
might be measured using the various dimensions of specific services,
rather than as a global indicator.
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Table 1: Investor/Account Information for Mutual Funds
Bank# * # Investors # Investors ** # Mutual Total Kd
w/ most Kd Accounts Funds
33 74 45 99 3,083,503
17 94 46 140 3,905,120
27 45 40 50 1,269,456
20 45 20 63 1,677,000
03 30 20 41 700,000
12 20 16 24 692,667
32 22 16 26 633,000
28 26 15 39 448,000
15 24 14 34 504,500
34 42 14 71 995,000
Others (avg) 4.2 3.0 5.6 101,151
Others (tot) 108 80 145 2,629,933
* 36 total banks: 10 shown + 26 'Others'
** referred to as LOY1F
Table 2: General Bank Statistics
Bank# * MSFI MSFA MSVA LOYF LOYP SATB
33 .1396 .1352 .1864 .1376 .6081 3.354
17 .1774 .1913 .2361 .1407 .4894 3.546
27 .0849 .0683 .0768 .1223 .8889 3.454
20 .0849 .0861 .1014 .0612 .4444 3.690
03 .0566 .0560 .0423 .0612 .6667 3.935
12 .0377 .0328 .0419 .0489 .8000 3.854
32 .0415 .0355 .0383 .0489 .7273 3.685
28 .0491 .0533 .0271 .0459 .5769 3.837
15 .0453 .0464 .0305 .0428 .5833 3.373
34 .0792 .0970 .0602 .0428 .3333 3.367
Others (avg) .0078 .0076 .0061 .00920 .0281 3.494
Others (tot) .2038 .1976 .1586 .2447 .7306 3.494
* 36 total banks: 10 shown + 26 'Others'
* Others averages are used in the analyses
Table 3: Spearman Rho Statistics
Indicator MSFA MSVA LOYF LOYP SATB
MSFI rho= +.9705 +.9068 +.7932 -.0341 -.3159
MSFA rho= n/a +.8818 +.6795 -.1636 -.3727
MSVA rho= n/a +.8523 +.0909 -.2364
LOYF rho= n/a +.4159 +.0568
LOYP rho= n/a +.2364
Table 4: Path Analysis Statistics
Path Path Rho Sign m [R.sup.2]o [R.sup.2]a
Analysis Eliminated
I H1: Sat-Loy + 2 0.964 0.961
SATB-LOYF .0568 +
SATB-LOYP .2364 +
II H2: Loy-MSh + 6 0.964 0.311
LOYF-MSFI .7932 +
LOYF-MSFA .6795 +
LOYF-MSVA .8523 +
LOYP-MSFI .0341 -
LOYP-MSFA .1636 -
LOYP-MSVA .0909 +
III H3: Sat-MSh - 3 0.964 0.950
SATB-MSFI .3159 -
SATB-MSFA .3727 -
SATB-MSVA .2364 -
Path Q W 'p' Conclusion
Analysis
I 0.941 19.7 <.005 keep H1 path
II 0.053 945.1 <.001 keep H2 path
III 0.732 101.1 <.001 keep H3 path
