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Study of satisfaction, loyalty, and market share in Kuwait banks offering mutual fund services.


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.


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.


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).


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 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.


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.


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.


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.


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])]


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.


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.


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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Adel Al-Wugayan, Kuwait University Larry P. Pleshko, Kuwait University
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

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
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Author:Al-Wugayan, Adel; Pleshko, Larry P.
Publication:Journal of International Business Research
Geographic Code:7KUWA
Date:Jul 1, 2011
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