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REGULAR ARTICLE

Fuzzy importance-performanceanalysis for determiningcritical service attributes

Wei-Jaw DengGraduate School of Business Administration,

Chung Hua University, Hsinchu, Taiwan

Abstract

Purpose Thepurpose of this paper is to propose a novel approach of fuzzy importance-performanceanalysis (FIPA) to replace conventional importance-performance analysis (IPA) for determining criticalservice attributes those really need to improve for achieving superior customer satisfaction.

Design/methodology/approach First, referring numerous studies, conventional IPA has someerroneous assumptions, the customer satisfaction of attribute performance has the characteristic ofthree-factor theory and the novel approach which integrates natural logarithmic transformation andpartial correlation analysis is feasible for acquiring the implicitly derived importance of attributes.Second, according the fact and nature of fuzziness in human perception, this study applies fuzzy settheory to revise conventional IPA. Finally, the FIPA is proposed and subsequently implemented in aTaiwanese hot spring hotel case study.

Findings The implementation of FIPA shows the determined critical service attributes are almostcompletely different from those attributes acquired by conventional IPA. Hence, the application ofconventional IPA may cause practitioners make incorrect decisions of improvement priorities forservice attributes and direct unsuitable quality-based marketing strategies.

Originality/value The proposed FIPA which integrates fuzzy set theory, three-factor theory,partial correlation analysis and natural logarithmic transformation avoids the erroneous assumptionsof conventional IPA, considers the nature of fuzziness in human perception and includes the actualimportance of service attributes. Therefore, the proposed FIPA can effectively assist businessmanagers in determining critical service attributes to improve service quality or customer satisfactionand to achieve competitive advantage.

Keywords Fuzzy logic, Customer services quality, Performance measures

Paper type Research paper

1. IntroductionDelivering superior customer value and satisfaction are crucial to the competitive edge of afirm (Kotler and Armstrong, 2000; Weitz and Jap, 1995). Undoubtedly, service quality and

customer satisfaction are principal drivers of financial performance. Matzler et al. (2004a)contended that customer satisfaction increases customer loyalty, reduces price sensitivity,increases cross-buying and increases positive word of mouth. Hansemark and Albinsson(2004) alsonoted that customer satisfaction directly influences customer retention and firmmarket share. Numerous empirical studies haveconfirmed the positive correlation betweencustomer satisfaction and profitability (Anderson etal., 1994; Hallowell, 1996; Johnsonetal.,1996; Eklof et al., 1999; Zeithaml, 2000). Therefore, improving customer satisfactionis a critical issue for business managers in todays competitive global marketplace.

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0956-4233.htm

IJSIM19,2

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Received 26 September 2006Revised 9 March 2007Accepted 9 March 2007

International Journal of Service

Industry Management

Vol. 19 No. 2, 2008

pp. 252-270

q Emerald Group Publishing Limited

0956-4233

DOI 10.1108/09564230810869766


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With this goal in mind, numerous business managers are continually attempting toidentify critical service attributes that generate customer satisfaction and loyalty in orderto stay abreast of competitors.

Numerous practitioners and researchers have applied importance-performance

analysis (IPA) to identify the critical performance attributes in customer satisfactionsurvey data for products and services (Hawes and Rao, 1985; Yavas and Shemwell,1997; Tikkanen et al., 2000; Chu and Choi, 2000; Huana et al., 2002; Zhang and Chow,2004; ONeill and Palmer, 2004; Enright and Newton, 2004). Hansen and Bush (1999)pointed out that IPA is a simple and effective technique that can assist practitioners inidentifying improvement priorities for service attributes and direct quality-basedmarketing strategies. Practitioners apply IPA to analyze two dimensions of serviceattributes: performance level (satisfaction); and, importance to customers. Analyses ofthese dimension attributes are then integrated into a matrix that helps a firm identifyprimary drivers of customer satisfaction and, based on these findings, set improvementpriorities (Matzler et al., 2004a). Hence, following a customer satisfaction survey andIPA, business managers can make rational decisions about how to best deploy scarceresources to attain the highest degree of customer satisfaction.

Although IPA is an extremely valuable method, previous studies have severalimportant shortcomings. For example, Matzler et al. (2004a) noted the original IPA hastwo implicit assumptions:

(1) attribute performance and attribute importance are independent variables; and

(2) the relationship between attribute performance and overall performance islinear and symmetrical.

These assumptions are erroneous in the realworld, the relationship between attribute-levelperformance and overall customer satisfaction (OCS) is asymmetrical (Kano et al., 1984;Matzler and Sauerwein, 2002; Ting and Chen, 2002; Matzler et al., 2003, 2004a) and the

relationship between attribute performance and attribute importance is causal (Sampsonand Showalter, 1999; Oh, 2001; Ryan and Huyton, 2002; Matzler et al., 2004a).

Berman (2005) noted that customer delight is not same as customer satisfaction.Customer delight requires that customer receive a positive surprise that exceeds theirexpectations. Berman also mentioned that the must-be, satisfier, and delight attributecategorization system developed by Kano et al. (1984) is a popular approach for betterunderstanding customer delight. However, other studies of customer satisfaction haveindicated that satisfaction attributes can be understood using three categories: basicfactors, performance factors, and excitement factors (Brandt, 1988; Johnston, 1995;Matzler et al., 1996; Oliver, 1997; Matzler and Hinterhuber, 1998; Anderson and Mittal,2000; Matzler and Sauerwein, 2002). The impact of satisfaction attribute performanceon OCS differs according to category. For example, if delight (excitement) attributes are

not met, customers do not feel dissatisfied. However, if delight (excitement) attributesare met, the result is customer delight. Therefore, practitioners of IPA must considerthree-factor theory to determine critical service attributes that capable of generatingcustomer satisfaction, delight and loyalty.

Customer service perceptions are characterized by uncertainty and fuzziness.Traditional assessments of service quality or customersatisfaction that used a Likert scale(equal-space crisp number) (Yang et al., 2004; Behara et al., 2002) to represent customerperceptions based on linguistic assessments (for example, Very satisfied 5,

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satisfied 4, fair 3, unsatisfied 2, very unsatisfied 1) in surveyquestionnaire are impractical. Human perceptions and attitudes are subjective andvague. Furthermore, variations in individual perceptions and personality mean that thesame words can indicate very different perceptions (Chiou et al., 2005). Consequently, the

use of binary logic and crisp numbers to describe human perceptions or attitudes fails toaddress fuzziness (Zadeh, 1965). Zadeh (1965) noted that fuzzy set theory can deal withproblems involving uncertainty and fuzziness. Fuzzy number is considered moreappropriate than crisp number to represent the linguistic term scale about thecustomers perception of delivered-service (Chien and Tsai, 2000; Wu et al., 2004).Therefore, practitioners of IPA require a psychometrically valid and practical measure ofattribute performance before determining real critical service attributes.

This study proposes a revised IPA approach that comprises fuzzy set theory,three-factor theory, partial correlation analysis and natural logarithmic transformation.The proposed fuzzy importance-performance analysis (FIPA) avoids two shortcomingsof traditional IPA and considers the nature of fuzziness in human perception. Theproposed FIPA which includes the actual importance of service attributes, assistsbusiness managers in determining critical service attributes to improve service qualityor customer satisfaction and to achieve competitive advantage.

The remainder of this paper is organized as follows. Section 2 reviews the relevantliterature particularly that about IPA, three-factor theory of customer satisfaction,fuzzy set theory and assessment of service attributes implicitly derived importance.To elucidate the real importance of attributes, Section 3 introduces a FIPA approach.Next, Section 4 demonstrates the implementation of the proposed FIPA approach todetermine critical service attributes and enhance customer satisfaction at a Taiwanesehot spring hotel. Finally, Section 5 draws conclusions.

2. Literature review

2.1 Importance-performance analysisIPA has been applied as an effective means of evaluating a firms competitive position inthe market, identifying improvement opportunities, and guiding strategic planning efforts(Martilla and James, 1977; Hawes and Rao, 1985; Myers, 1999). IPA, first introducedby Martilla and James (1977), identifies which product or service attributes a firm shouldfocus on to enhance customer satisfaction (Matzler et al., 2004a). Typically, data fromcustomer satisfaction surveys or service quality surveys (using SERVPERF model(Cronin and Taylor, 1992)) with pre-consuming measurement of customer attributeimportance are utilized to construct a two-dimensioned matrix. In this matrix,attribute importance is depicted along the x-axis and attribute performance (satisfactionor service quality) is depicted along the y-axis. Attribute importance is measured usingsome form of self-stated importance (e.g. rating scales, constant sum scales, etc.) or

implicitly derived importance (e.g. multiple regression weights, structural equationmodeling weights or partial correlation weights). The means of performance andimportance, commonlyutilizedin practice,divide the matrix intofourquadrants(Figure1).

Based on this analysis, particular improvement opportunities are determined.For example, researchers commonly suggest that major weaknesses (Quadrant IV) shouldbe top priority and targeted for immediate improvement efforts (Martilla and James, 1977).Conversely, attributes deemed major strengths (Quadrant I) should be maintained,leveraged, and heavily promoted (Lambert and Sharma, 1990).

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Some studies have modified and extended IPA. However, the basic framework has largelyremained the same (Sampson and Showalter, 1999). For example, OLeary and Adams(1982) presented a method for generating importance measures as a composite ranking ofmedian importance scores and Pearson correlation coefficients. Dolinsky and Caputo(1991) only surveyed consumers to obtain attribute performance ratings for derivingimportance indicators. Performance scores for attributes were then regressed on scores forOCS and the standard regression coefficients were used as measures of attributeimportance. A minor variation on this approach is found in basic conjoint analysis thatuses dummy variable regression to derive coefficients for specific attributes levels, anddetermines importance as a range of coefficients for each attribute (Liljander andStrandvik, 1993; Malhotra, 1996). Matzler et al. (2003) propose a revised IPA in whichattribute importance is derived by partial correlation analysis between attributeperformance and OCS. Therefore, two erroneous assumptions of traditional IPA (whichare described in Section 1) had been discussed and criticized in his literature.

2.2 Three-factor theory of customer satisfactionKano et al. (1984) developed a model that distinguishes between different qualityattribute types. Kanos model divides product or service quality attributes into fivedistinct categories (attractive, one-dimensional, must-be, indifference, and reverse),each of which influences customer satisfaction differently. Other studies of customersatisfaction, however, suggest that service attributes can be understood using three

categories: basic factors, performance factors, and excitement factors (Brandt, 1988; Johnston, 1995; Matzler et al., 1996; Oliver, 1997; Matzler and Hinterhuber, 1998;Anderson and Mittal, 2000; Matzler and Sauerwein, 2002). The basic factors are similarto must-be quality elements. The performance factors are similar to one-dimensionquality elements. The excitement factors are similar to attractive quality elements.

Matzler et al. (2004a) elucidate these three factors. Basic factors (dissatisfiers) areminimum requirements that produce consumer dissatisfaction when not fulfilled, butdo not result in customer satisfaction when fulfilled or exceeded; that is, negative

Figure 1.Importance-performance

analysis

H

L

L H

"Possible Overkill"

Quadrant II

"Keep Up the Good Work"

Quadrant I

"Low Priority"

Quadrant III

"Concentrate Here"

Quadrant IV

Importance

Performance


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performance for these attributes has a greater impact on overall satisfaction thanpositive performance. Excitement factors (satisfiers) are attributes that increasecustomer satisfaction when delivered, but cause no dissatisfaction when not delivered.That is, positive performance for these attributes has a stronger influence on overall

consumer satisfaction than negative performance. Performance factors producesatisfaction when performance is high and dissatisfaction when performance is low.The relationship between service attribute performance and OCS is nonlinear andasymmetrical for basic and excitement attributes. For performance attributes, therelationship between service attribute performance and overall satisfaction is linearand symmetrical (Ting and Chen, 2002; Matzler et al., 2004a).

Consequently, service attributes have two key characteristics in three-factor theory:

(1) Importance of a basic or excitement attribute is based on its performance.Basic attributes are crucial when performance is low and are unimportant whenperformance is high. Excitement factors are critical when performance is highand are irrelevant when performance is low (Sampson and Showalter, 1999;

Ting and Chen, 2002; Matzler et al., 2004a).(2) The relationship between attribute performance and OCS is asymmetrical.

Consequently, the applicability of the traditional IPA model that utilizes explicitcustomer self-stated importance requires modification.

2.3 Fuzzy set theoryFuzzy set theory was introduced by Zadeh (1965) to deal with problems involvinguncertainty and fuzziness. The basic definition and concept of fuzzy set theory can befound on Zadeh (1965), Chen (1996), Tsaur et al. (1997), Chien and Tsai (2000), Hsieh et al.(2004) and Wu et al. (2004). Numerous studies have applied fuzzy set theory to researchproblems involving uncertainty. For example, Chien and Tsai (2000) used fuzzy number

to assess perceived service quality and clarify the strengths and weaknesses ofTaiwanese retail stores. Furthermore, Wu et al. (2004) proposed a fuzzy set theory-baseddecision model for determining market position and developing improvement strategyfor hospital service quality. In decision-making research field, fuzzy multiple criteriadecision making (fuzzy MCDM) was introduced to replace traditional MCDM. Numerousstudies regarding fuzzy MCDM have been published (Chiou et al., 2005; Chu and Lai,2005; Hsieh et al., 2004). In above researches, the qualitative data or linguistic terms usedto represent imprecise assessments of decision criteria or performance attributes are allexpressed using fuzzy number. Consequently, researchers or practitioners shouldconsider the application of fuzzy set theory into IPA.

2.4 Assessment of service attributes implicitly derived importance

Since changes to attribute performance influences the relative importance of attributes,the self-stated importance explicitly stated by customers for each attribute is notpractically feasible. Implicitly derived importance based on the attribute performancescorrelation with OCS already includes the characteristic of attribute category inthree-factor theory and is superior to self-stated attribute importance. Thus, Matzlerand Sauerwein (2002) and Matzler et al. (2004a) implicitly derived dimension attributeimportance using a multiple regression analysis with OCS as a dependent variable anddimension attributes performance as independent variables.

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Recently, Matzler et al. (2004b) noted between the single attribute variables a ratherstrong multicollinearity is to be expected. Therefore, he determines the potentialinfluence of multicollinearity on regression coefficient estimation. Consequently, hedeclares multiple regression analysis is an inappropriate tool for deriving reliable

impact measures when multicollinearity exists within independent variables. Assuggested by Hair et al. (1995), partial correlation analysis is more suitable thanregression analysis for quantifying the influence of independent variables on dependentvariables when multicollinearity exists within independent variables. Therefore,Matzler et al. (2004b) used dichotomized partial correlation analysis with dummyvariables to identify the three factors category of each single attribute.

A regression model utilizing natural logarithmic transformation of independentvariables can capture more diminishing return or sensitivity for independent variables(Anderson and Sullivan, 1993). Thus, Brandt (1988), Mittal et al. (1998), Anderson andMittal (2000) and Ting and Chen (2002) utilize multiple regression analysis with naturallogarithmic dummy variables to determine the asymmetric influence of attributeperformance on OCS.

3. Methodology of the fuzzy importance-performance analysis3.1 Assignment of triangular fuzzy number to indicate the perceptions of respondentsGenerally, surveys examining customer perceptions of satisfaction or service quality haveused questionnaires in which respondents indicate their feelings with reference to selectedlinguistic terms. But human judgments of events may vary significantly according to thesubjective perceptions or personality of individuals, even when the same linguistic term isused (Chiou et al., 2005). Thus, when using fuzzy number to represent specific linguisticterms, researchers must consider differences among survey respondents.

This study used a triangular fuzzy number to represent the linguistic term ofrespondents perception of customer satisfaction or service quality. Moreover, the

linguistic terms from amongwhich respondents chose to indicate their perception towardsservice were very satisfied, satisfied, fair, unsatisfiedand very unsatisfied.In thefirst part in the survey questionnaire of this study, respondents were asked to complete thequestion about the range of each linguistic term based on their own subjective decision.For example, one respondent gave triplets (0, 0, 25), (0, 25, 50), (25, 50, 75), (50, 75, 100) and(75, 100, 100) meaning very unsatisfied, unsatisfied, fair, satisfied, and verysatisfied,, respectively, (Figure 2). Another respondent gave triplets (0, 0, 30), (0, 30, 50),(30, 50, 70), (50, 70, 100) and (70, 100, 100) meaning very unsatisfied, unsatisfied, fair,satisfied, and very satisfied, respectively. Finally, this study aggregated respondentopinions regarding specific linguistic terms by calculating the average triangular fuzzy

Figure 2.The ith respondents

linguistic term0

VUS US F S VS

0.0

1.0

fA (x)

25 50 75 100

U


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number for all respondents. Consequently, the final average triangular fuzzy number ofeach linguistic term is decided and used for the subsequent assignment of a triangularfuzzy number indicating respondent perceptions (Tsaur et al., 1997). The integrationformula is as follows:

~Ak avg

Xni1

~Ai

k

n

Xni1

aik1

;Xni1

aik2

;Xni1

aik3

!

n; i 1; 2; . . . ; n; k 1; 2; 3; 4; 5 1

where ~Ai

k is the triangular fuzzy number of kth linguistic term under ith respondent;

aik1 ; aik2 and a

ik3 represent the lower, the moderate and the upper values of the support

of ~Ai

k, respectively; n denotes the total number of respondent; k denotes the number oflinguistic term and there are five linguistic terms in this study, including veryunsatisfied, unsatisfied, fair, satisfied, and very satisfied.

3.2 Fuzzy number arithmetic and defuzzification for respondent perceptionsAfter all respondent perceptions are assigned triangular fuzzy number, the necessaryarithmetic and defuzzification can be performed. The necessary triangular fuzzynumber arithmetic and defuzzification are as follows:

. Average jth attribute performance:

~Ajavg

Xni1

~Ai

j

n

Xni1

aij1 ;Xni1

aij2 ;Xni1

aij3

!

n; i 1;2; . . .;n; j 1;2; . . . ;m 2

where ~Ai

j is the triangular fuzzy number of jth attribute performance under ith

respondent; a

i

j1 , a

i

j2 and a

i

j3 represent the lower, the moderate and the uppervalues of the support of ~A

i

j, respectively; n denotes the total number ofrespondents; m is the total number of attributes.

. Average OCS:

~Oavg

Xni1

~Oi

n

Xni1

Oi1 ;Xni1

Oi2 ;Xni1

Oi3

!

n; i 1; 2; . . . ; n 3

where ~Oi is the triangular fuzzy number of OCS under ith respondents perception;

Oi1 , Oi2 and O

i3 represent the lower, the moderate and the upper values of the

support of~

Oi

, respectively; n denotes the total number of respondents.. Defuzzification of triangular fuzzy number:

As Kaufmann and Gupta (1991), Chen (1996) and Chien and Tsai (2000) noted,the defuzzification formula for triangular fuzzy number is:

V~A a1 2a2 a3

44

where V~A is the crisp number of~A triangular fuzzy number (a1, a2, a3).

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Crisp numbers derived from the defuzzification of attribute performance and OCS canbe used to obtain the implicitly derived importance of service attributes and are plottedin the FIPA matrix.

3.3 Acquiring the implicitly derived importance of attributesAs previous studies described in Section 2.4, this study presents a novel method formeasuring the implicitly derived importance of attributes that combines partialcorrelation analysis and natural logarithmic transformation. The proposed methodcomprises three steps:

(1) Transform all attribute performance (APij ) into a natural logarithmic form:

APij ! lnAPij i 1; 2; . . . ; n;j 1; 2; . . .m 5

where APij is the crisp number of attribute performance on jth attribute underith respondent; n denotes the total number of respondents; m is the total numberof attributes.

(2) Natural logarithmic attribute performance (ln(APij)) and OCS (the crisp numberof OCS) are included in a multivariate normal correlation model as variables(total m 1 variables). Each variable has total n data. In the practicalapplication via feasible statistical software, participators just enter all ln(APij)and OCSi in data sheet. Columns denote variables and rows denote respondents.About the concept of multivariate normal correlation model, participators canrefer the Chapter 15 (Normal correlation models) of Applied Linear StatisticalModels (Neter et al., 1985) or other feasible statistics book.

(3) Perform partial correlation analysis of each ln(APj ) with OCS via feasiblestatistical software (e.g. SPSS; SAS; STATISTICA). The coefficient of partialcorrelation between OCS and ln(APj) is the implicitly derived importance of jth

attribute.For example, the formula of coefficient of partial correlation between OCS andln(AP1) given fixed ln(AP2), ln(AP3), . . . ln(APm) is as follows (Neter et al., 1985):

rOCS lnAP1 lnAP2 lnAP3 ... lnAPm

sOCS lnAP1 lnAP2 lnAP3 ... lnAPm

sOCSlnAP2lnAP3 ... lnAPm slnAP1 lnAP2lnAP3 ... lnAPm

6

where sOCS lnAP1 lnAP2lnAP3 ... lnAPm is the standard deviation of theconditional joint distribution of OCS and ln(AP1 ) when ln(AP2 ), ln(AP3), . . .ln(APm ) are fixed; sOCSlnAP2 lnAP3... lnAPm is the standard deviation of theconditional distribution of OCS when ln(AP2 ), ln(AP3),. . . ln(APm ) are fixed;

slnAP1 lnAP2 lnAP3...

lnAPmis the standard deviation of the conditional

distribution of ln(AP1 ) when ln(AP2), ln(AP3), . . . ln(APm ) are fixed.

Because proposed method does not apply common multiple regression analysis to acquirethe implicitly derived importance of service attribute, then it can eliminate the potentialproblem of a linear and symmetrical relationship between attribute performance and OCS.Unlike the self-stated importance which do not consider the influence of service attributespresent performance, the implicitly derived importance of service attributes derived viaproposed method can consider the relationship between OCS and attributes present


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performance and can include the attribute category characteristic of three-factor theory.Furthermore, the potential problem of multicollinearity among independent variableswhen employing multiple regression analysis to measure implicitly derived attributeimportance can also be eliminated via partial correlation analysis (Hair et al., 1995).

Notably, the natural logarithmic transformation of attributes performance used in thismethod also captures more sensitivity for correlation model variables (Anderson andSullivan, 1993).

3.4 FIPA approachThe implicitly derived importance is calculated using the method proposed in abovesubsections and then inputted into the FIPA matrix. The information acquired usingthis FIPA is extremely useful for managers for using in relation to customersatisfaction or service quality improvements. The FIPA comprises seven steps:

(1) Gather customer perception for attribute performance and OCS for focalservices. A questionnaire survey based on linguistic variables is commonlyused for this step. Notably, in the first part of questionnaire, the question aboutthe range of each linguistic term must be designed.

(2) Assign a final average triangular fuzzy number to customer perceptions ofattribute performance and OCS as mentioned in Section 3.1.

(3) Transform the fuzzy numbers of customer perceptions for attributeperformance and OCS into crisp numbers by performing some necessaryarithmetic and conducting defuzzification as mentioned in Section 3.2.

(4) Obtain the implicitly derived importance of each attribute through naturallogarithmic transformation and partial correlation analysis as mentioned inSection 3.3.

(5) Use the mean of all implicitly derived degrees of importance for attributes and

the mean of all performance for attributes to divide the FIPA matrix into fourquadrants.

(6) Plot all attributes on the FIPA matrix.

(7) According to the management scheme of each quadrant, FIPA practitionersdetermine a reasonable action plan for each attribute in each quadrant.Particularly, service attributes in Quadrant IV (management scheme action isconcentrate here) is the critical service attributes that can be improved forhigher customer satisfaction and competitive advantage. Moreover, theimprovement priority for critical service attributes is the sequence ofimplicitly derived importance for attributes. Restated, improvement priorityis based on the degree of attribute importance.

The proposed FIPA approach allows practitioners to consider the nature of fuzziness inhuman perception or attitude and the three-factor theory of customer satisfaction. Thus,an appropriate, effective and reasonable action plan for each attribute can be obtained.

4. Case studyIn this section, an example case is presented to demonstrate the implementation of theproposed FIPA approach to determine critical service attributes that can enhancecustomer satisfaction at a Taiwanese hot spring hotel.

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4.1 Gather the data of customer perception about focal delivered serviceThis section shows the Step 1 of FIPA approach. The questionnaire in this case studycomprised four parts. The first part contained 1 statement about the range of eachlinguistic term. The second part contained 20 statements reflecting the dimensionality

of service attribute performance in focal hot spring hotel. To provide a comparisonbetween IPA and FIPA, the answer column of self-stated raw importance is also designin second parts statements. But when the actual application of FIPA is performed,statements of self-stated raw importance are unnecessary. The third part contained1 statement reflecting the dimensionality of OCS. The fourth part included respondentdemographic information. The scale of answer is five linguistic terms (very satisfied,satisfied, fair, unsatisfied and very unsatisfied) in parts 2 and 3. The statements ofquestionnaire are closed-response questions and are developed based on a review ofhotel customer satisfaction literatures and practical hot spring hotel circumstance(Miyoung and Haemoon, 1998; Tsang and Qu, 2000; Antony et al., 2004; Lau et al., 2005;Mohsin and Ryan, 2005).

Customers with who had consumed services from the focal hot spring hotel wereasked to help the survey. Firstly, they fill up the triplet of each linguistic term by theirown subjective decision. Secondly, they rate their degree of satisfaction and self-statedimportance for each attribute. Thirdly, they rate their degree of OCS for focal hotelsservice. Lately, they provide their demographic information. A total 324 validquestionnaires were collected for analysis.

To verify the reliability and construct validity of the formal questionnaire beforesurvey data can use for subsequent analysis, factor analysis was performed using thedefuzzification crisp number of attribute performance to verify the construct validityand the Cronbachs a value for each dimension was calculated to verify the reliability.The factor analysis was based on the principal component analysis with varimaxrotation, eigenvalue exceeding 1 and factor loadings exceeding 0.4. The test value of the

Kaiser-Meyer-Olkin was 0.910, and the p value of the Bartletts sphericity test wasalmost zero. Furthermore, the cumulative explained variance is 60.297 percent.Consequently, the construct validity of the questionnaire was quite good (Kaiser, 1974).The 20 customer satisfaction statements regarding the service of hot spring hotels inTaiwan were classified into three dimensions, namely empathy and assurance,responsibility and reliability, and tangibility. Cronbachs a values for eachdimension of hot spring hotel service satisfaction ranged from 0.8239 to 0.8915 (Table I).This range demonstrates that the scales of the formal questionnaire have good reliability(with Cronbachs a values for each dimension exceeding 0.7) (Nunnally, 1978).

4.2 Triangular fuzzy number assignment, arithmetic and defuzzificationThis section shows the Steps 2 and 3 of FIPA approach. Using survey data of the

triplet of each linguistic term, the final average triangular fuzzy number of eachlinguistic term is calculated by equation (1) in Section 3.1 and listed in Table II. Use

fifth linguistic term Very Satisfied as an example, the ~A5avg isP324

i1~A

i

5=324 and

equal to (78.0159, 95.9524, 100) with ~A1

5 80; 100; 100;~A

2

5 70; 90; 100; . . .~A

324

5 75; 100; 100. Subsequently, perceptions of service attribute performance andOCS expressed using linguistic terms are transferred into triangular fuzzy numbersusing the final average triangular fuzzy number for each linguistic term. Finally, fuzzy


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Extracteddimension Statement

Factorloading Eigenvalue

Varianceexplained(percent)

Cronbachsa

Responsibility 20 Personal warm care given by staff 0.698and empathy 18 Have customers best interest at

heart 0.67417 Easy to get staffs attention & help 0.65910 Readiness to respond to customers

requests 0.65715 Knowledgeable to answer

customers request 0.647 5.254 26.268 0.898414 Courtesy and friendliness of staff 0.64616 Individual attention for customer 0.62012 Willingness to help customers 0.62019 Understand the specific needs of

customers 0.570

13 Provision of safe environment andequipment 0.532Reliability 07 Provision of services as promised 0.737and assurance 08 Dependability in handling

customers service problem 0.70506 Reasonable price 0.679 3.577 17.886 0.851511 Prompt reply to customers 0.56809 Perform service right at the first

time 0.509Tangibility 01 The physical facilities are visually

appealing 0.83902 Multiple hot spring facilities 0.76005 Availability of adequate fire &

first aids facilities and instructions 0.626 3.229 16.143 0.8407

03 Cleanness of hot spring facilities 0.61004 Convenient hotel location 0.402

Cumulative variance explained 60.297 percent

Table I.Results of factor analysis

by using defuzzificationcrisp number ofattributes performance

Linguistic term-very unsatisfied L M HAverage triplet 0 2.381 21.8254Linguistic term-unsatisfied L M H

Average triplet 5.5556 24.9206 46.746Linguistic term-fair L M HAverage triplet 27.6984 49.2857 69.7619Linguistic term-satisfied L M HAverage triplet 52.619 73.4127 92.2222Linguistic term-very satisfied L M HAverage triplet 78.0159 95.9524 100

Notes: L the low bound; M the median; H the high bound

Table II.Average triangular fuzzynumber of eachlinguistic term

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numbers representing customer perceptions for service attribute performance and OCSwere transformed into crisp numbers via equations (2)-(4) mentioned in Section 3.2.Table III shows the average triangular fuzzy number and defuzzification crisp numberfor the service attribute performance of focal hotel in columns 3 and 4. To provide a

comparison, average self-stated raw importance and average performance (in Likertfive-point scale) of each attribute of focal hotel are listed in columns 5 and 6 in Table III.The bottom two rows of Table III list the average triangular fuzzy number and theaverage defuzzification crisp number of OCS.

4.3 Attributes implicitly derived importance and IPAThis section shows the Steps 4-7 of FIPA approach. By using the defuzzification crispnumber of attribute performance and OCS, The implicitly derived importance ofservice attributes is acquired by approach described in Section 3.3. After doing naturallogarithmic transformation and partial correlation analysis via SPSS software, theresults of partial correlation coefficient are represented the implicitly derived

importance of service attributes and are listed in column 3 of Table IV. For example,the 17th customer attribute Easy to get staffs attention & help has a partialcorrelation coefficient of 0.244. Figure 3 shows the SPSS output for partial correlationcoefficients for the 17th service attribute. Moreover, column 4 of Table IV lists theranking of the implicitly derived importance of service attributes.

Subsequently, traditional IPA matrix was constructed using a Likert scale basedself-stated raw importance and attitude performance to represent the x- and y-axes,respectively. Mean self-stated raw importance and mean performance of attitude wereused to separate the axes. After plotting 20 attributes self-stated raw importance andattribute performance into IPA matrix. Figure 4 shows the traditional IPA matrix forthe example case. To build FIPA matrix, this study uses total average implicitlyderived importance of attribute and average OCS to separate the x- and y-axes,

respectively, and plot 20 attributes implicitly derived importance and performanceinto FIPA matrix. Figure 5 shows the FIPA matrix for the example case.

Based on traditional IPA results, business managers should concentrate oncustomer attributes 7, 15, and 18. However, analytical results for the FIPA indicate thatbusiness managers should concentrate on customer attributes 6, 17, 18, and 19 whichare the critical service attributes of focal hotel. The improvement priority for thesecritical service attributes is 6, 17, 19, and 18. That is, service attribute 6 is the mostimportant and service attribute 18 is the least important in Quadrant IV (Figure 5).Therefore, the determination result of critical service attributes is mostly different.

Furthermore, business managers may utilize different management strategies forthe same service attribute. For example, service attribute 17, Easy to get staffsattention & help, was located in Quadrant III in the traditional IPA matrix and is a

minor weaknesses and do not require additional effort. The management schemeaction is low priority. However, based on the FIPA matrix, service attribute 17 waslocated in Quadrant IV and is a competitive disadvantage for customer satisfaction andthe management scheme action is concentrate here. Service attribute 9, Performservice right at the first time, as another example, was located in Quadrant II in thetraditional IPA matrix and is the minor strength attribute for improving customersatisfaction. The management scheme action is possible overkill. Managers candecide to redirect business resources to other attributes that require resources.


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No.

Serviceattribute

Focalhotelsperformance

(intriangularfuzzy

number)

Focalhotels

performance

(indefuzzification

crispnumber)

Self-stated

importance

Focalhotels

performance

(infive-point

Likertscale)

1

Thephysicalfacilitiesarevisuallyappealing

(45.4

0,

65.8

6,

83.90)

65.25

4.08

3.6

9

2

Multiplehotspringfacilities

(43.3

3,

63.8

4,

82.21)

63.30

4.14

3.6

1

3

Cleannessofhotspringfacilities

(47.6

4,

67.9

2,

84.62)

67.03

4.27

3.7

8

4

Convenienthotellocation

(44.4

0,

65.0

1,

82.56)

64.25

4.02

3.6

6

5

Availabilityofade

quatefire&firstaidsfacilitiesandinstructions

(46.6

2,

66.8

2,

83.57)

65.96

4.09

3.7

4

6

Reasonableprice

(39.5

9,

59.7

6,

77.07)

59.04

3.94

3.4

5

7

Provisionofservic

esaspromised

(43.3

1,

63.2

2,

80.83)

62.65

4.18

3.5

8

8

Dependabilityinh

andlingcustomersserviceproblem

(45.3

3,

65.1

2,

81.50)

64.27

4.28

3.6

7

9

Performservicerightatthefirsttime

(45.4

3,

65.7

3,

82.98)

64.97

4.09

3.6

9

10

Readinesstorespo

ndtocustomersrequests

(43.4

2,

63.5

1,

80.65)

62.77

4.08

3.6

0

11

Promptreplytocu

stomers

(38.5

9,

58.5

4,

76.37)

58.01

4.08

3.3

9

12

Willingnesstohelpcustomers

(45.4

4,

65.5

6,

82.57)

64.78

4.18

3.6

8

13

Provisionofsafee

nvironmentandequipment

(43.3

3,

63.5

8,

81.23)

62.93

4.07

3.6

0

14

Courtesyandfrien

dlinessofstaff

(48.8

9,

69.1

0,

84.83)

67.98

4.30

3.8

4

15

Knowledgeableto

answercustomersrequest

(42.5

7,

62.9

6,

80.50)

62.25

4.21

3.5

8

16

Individualattentio

nforcustomer

(38.5

2,

58.7

2,

76.59)

58.14

3.94

3.4

0

17

Easytogetstaffs

attention&help

(41.1

8,

61.3

5,

79.24)

60.78

3.96

3.5

1

18

Havecustomersb

estinterestatheart

(41.6

4,

61.4

9,

78.92)

60.88

4.18

3.5

2

19

Understandthesp

ecificneedsofcustomers

(42.3

6,

62.7

7,

80.22)

62.03

4.11

3.5

7

20

Personalwarmcaregivenbystaff

(45.1

8,

65.1

8,

82.19)

64.43

4.27

3.6

7

Totalaverage

(43.6

1,

63.8

0,

81.13)

63.09

4.12

3.6

1

AverageOCS(intriangularfuzzynumber)

(44.9

9,

65.5

3,

83.89)

AverageOCS(indefuzzificationcrispnumber)

64.9

9

Table III.Average performanceand average self-statedraw importanceof attributes

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However, in the FIPA matrix, service attribute 9 was located in Quadrant I and is anopportunity for achieving or maintaining competitive advantage and is a majorstrength. The management scheme action is keep up the good work. That is, theconsequences of final management action would be inappropriate. Thus, managersmust note that traditional IPA did not consider the nature of fuzziness in humanperception and three-factor theory of customer satisfaction. The referential information

No. Service attribute

Implicitlyderived

importance Ranking

1 The physical facilities are visually appealing 0.006 182 Multiple hot spring facilities 0.220 43 Cleanness of hot spring facilities 0.006 194 Convenient hotel location 0.010 175 Availability of adequate fire & first aids facilities and instructions 0.001 206 Reasonable price 0.259 17 Provision of services as promised 0.012 168 Dependability in handling customers service problem 0.129 89 Perform service right at the first time 0.179 6

10 Readiness to respond to customers requests 0.052 1211 Prompt reply to customers 0.071 1112 Willingness to help customers 0.073 1013 Provision of safe environment and equipment 0.031 14

14 Courtesy and friendliness of staff 0.232 315 Knowledgeable to answer customers request 0.020 1516 Individual attention for customer 0.100 917 Easy to get staffs attention & help 0.244 218 Have customers best interest at heart 0.133 719 Understand the specific needs of customers 0.191 520 Personal warm care given by staff 0.048 13Total average 0.101

Table IV.Implicitly derived

importance of serviceattributes

Figure 3.Partial correlation

coefficient of 17th serviceattribute

Controlling for

LOGP17 1.0000

( 0)

P= .

0.2441

( 304)

P= .010

OCSCRISP

(Coefficient / (D.F.) / 2-tailed Significance)

" . " is printed if a coefficient cannot be computed

0.2441

( 304)

P= .010

1.0000

( 0)

P= .

LOGP1

LOGP7

LOGP13LOGP20

LOGP17 OCSCRISP

- - - PARTIAL CORRELATION COEFFICIENTS - - -

LOGP2

LOGP8

LOGP14

LOGP3

LOGP9

LOGP15

LOGP4

LOGP10

LOGP16

LOGP5

LOGP11

LOGP18

LOGP6

LOGP12

LOGP19


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15/19

acquired by traditional IPA can cause managers take incorrect actions whenattempting to improve service quality or customer satisfaction.

5. ConclusionsTraditional IPA was developed as a tool to facilitate prioritization of improvementsand resource allocation. The three-factor theory of customer satisfaction indicates the

Figure 4.Traditional IPA matrix forattributes

H

L

L H

"possible overkill"

Self-Stated Importance

Note: The number in grid is the statement number of questionnaire (see Table III)

"keep up the

good work"

"low priority"

"concentrate here"

4.12

1

17

16

6

1310

19

18

7 15

2

5

914

1220 8

3

14

3.61

Actualperformance

Figure 5.

FIPA matrix for attributes

H

L

L H

"possible overkill"

Implicitly Derived Importance

"keep up the

good work"

"low priority""concentrate here"

0.101

1611

15

7 13 10

20 124

15

3

89

14

2

19

18 17

6

0.631

(%)

FuzzySetTheoryBa

sedPerformance

Notes: 1.The number in grid is the statement number of questionnaire (see Table III);

2.The value of performance is presented in percentage

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existence of a nonlinear relationship between attribute performance (satisfaction) andimportance; however, this theory creates questions regarding the applicability of IPAand resulting managerial recommendations. Managers must be aware that changes toattribute performance (satisfaction) are associated with changes to attribute

importance (Matzler et al., 2003). Typically, managers must work with limitedresources in competitive business environments. Restated, potential serviceimprovements must be prioritized, with resources allocated to facilitate changes toachieve competitive advantage (Beach and Burns, 1995).

This study presented a FIPA integrating fuzzy set theory, three-factor theory,partial correlation analysis and natural logarithmic transformation. The importance ofservice attributes is implicitly derived via natural logarithmic transformation andpartial correlation analysis. The partial correlation coefficient represents the actualimportance of attribute that had considered the attribute category in three-factortheory. The application of fuzzy set theory enables practitioners to consider the natureof fuzziness in human perceptions or attitudes. Furthermore, from the perspective ofworkload in questionnaire survey (20 customer satisfaction statements plus 1 OCS

statement in FIPA approach; 20 customer satisfaction statements plus20 pre-consuming self-stated attributes importance statements in IPA approach forcompleting analysis), the FIPA approach avoids the task need for measuring thepre-consuming importance of attributes (almost 50 percent questionnaire surveyworkload saving). This unnecessary process is time-consuming for both analysts andrespondents. Consequently, business managers can effectively determine criticalservice attributes of focal service and obtain an appropriate action plan for eachcritical service attribute via proposed FIPA approach to improve service quality orcustomer satisfaction and to achieve competitive advantage.

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Corresponding authorWei-Jaw Deng can be contacted at: [email protected]

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