research article a decision method to maximize service...

Research ArticleA Decision Method to Maximize ServiceQuality under Budget Constraints The Kano Studyof a Chinese Machinery Manufacturer

Qingliang Meng12 Xiaochao Wei3 and Wen Meng4

1School of Management amp Economics Jiangsu University of Science and Technology Zhenjiang Jiangsu 212003 China2Business School Hunan University Changsha Hunan 410082 China3School of Economics Wuhan University of Technology Wuhan Hubei 430070 China4Marketing Department Xuzhou Construction Machinery Group Co Ltd Xuzhou Jiangsu 221004 China

Correspondence should be addressed to Qingliang Meng mengzhi007163com

Received 27 June 2016 Accepted 5 September 2016

Academic Editor Xiaofeng Xu

Copyright copy 2016 Qingliang Meng et alThis is an open access article distributed under theCreativeCommonsAttribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Manufacturers are increasingly facing keen competition and improving customersrsquo position in the value chain Many of themhave made efforts to promote service quality and enhance customer satisfaction Research is lacking in considering the trade-offbetween service costs and customer satisfaction when tackling service quality issues in the machinery industry A decision methodis proposed for maximizing service quality in machinery industry under budget constraints from the perspective of enterprisecapacity and customer satisfaction Due to the strength of the Kano model in acknowledging the nonlinear impacts of qualityelements on customer satisfaction we formalize the relationship between customer satisfaction and sufficiency of service qualityelements quantitatively And then we develop a novel nonlinear programming model to maximize service quality under budgetconstraints In particular we implement our model at Xuzhou ConstructionMachinery Group Co Ltd one of the largest Chineseconstruction machinery companies to validate the efficacy of the method

1 Introduction

To meet complex customer requirements and respondquickly to increasing competition and technological devel-opments the machinery industry has recognized customervalue as a source of competitive advantage [1]Themachineryindustry produces and maintains machines for consumersthe industry and other companies Many machinery manu-facturers transform themselves from being product-orientedcompanies to being service providers [2ndash4] They believeeffective service planning and high level of quality servicedelivery are necessary to improve operations efficiency andenhance competitive edge [5]

Indeed the changing environment has led to a lot ofmachinery manufacturers to rethink their service strategiesThe concept of service is not limited to after-sale serviceit will be extended to cover the whole lifetime cycle This

includes the service components integrated into the processof delivery consumption and use Researchers have usedvarious expressions to refer to the escalation of servicesfor example ldquoservitisationrdquo [6] ldquointegrated solutionsrdquo [7]and product-service systems (PSS) [8ndash11] Obviously ser-vice is crucial for machine manufacturers to enhance cus-tomer value and gain additional business To gain customerendorsement and loyalty firms need to put efforts into servicedesign to improve service quality [12]

Oliva and Kallenberg (2003) [2] highlighted the signifi-cance of service quality in satisfying and retaining customersin manufacturing industries while Izogo and Ogba (2015)[12] believe customerrsquos satisfaction of service quality is thedeterminant of customer loyalty However manufacturersoffer services of varying scopes and degrees and the servicequality is significantly related to the specific service processesand service participants To achieve high service quality

Hindawi Publishing CorporationScientific ProgrammingVolume 2016 Article ID 7291582 12 pageshttpdxdoiorg10115520167291582

2 Scientific Programming

one needs to understand the relationships between variousservice quality elements and other related elements forexample processes personnel and resources [13]

The literatures have mainly focused on categorizing cus-tomer service requirements planning and designing properservice products to assure customer satisfaction There isa lack of a systematic and quantitative verification schemefor service quality improvement especially in the machineryindustry In general services influence costs and high servicequality often leads to high service costs It is therefore vitalto strike a good balance between customer satisfaction andservice costs when improving service quality in machineryindustries while researchers have not yet addressed thisimportant issue enough To make contributions to the exist-ing literatures we try to find out a solution of service qualityimprovement in machinery industries to leverage customersatisfaction and budget constraints from bilateral perspec-tives (customer satisfaction and service providerrsquos capacity)Thus the relationships between customer satisfaction servicecosts and service quality elements will be identified andthen a novel quantitative decision method will be set up todetermine the priorities of service quality elements in qualityimprovement

In an effort to propose a decision method to maximizethe overall customer satisfaction under budget constraintswe formulate two research issues

(1) In themachinery industry there is a limit on resourcesallocation to services Most resources are focused ontangible products There is still a need for a continu-ous improvement process in regard to service qualitySo based on a bilateral prospective how can this helpto develop a systemic and quantitative frameworkto identify key service quality elements detect therelationship among quality elements and determinethe improvement priorities for service transformationin machinery industries

(2) The level of customer satisfaction depends on ade-quate supply of the service components which relieson large monetary investment Then how can thishelp to develop a mathematical model balancingcustomer satisfaction and service costs and validatethe results empirically

The paper proceeds as follows Section 2 covers a reviewof the literatures Section 3 proposes the research frameworkWe implement the proposed model and its solution method-ology to Xuzhou Construction Machinery Group Co Ltd(XCMG) the largest construction machinery company inChina in Section 4 and prove its validity Research conclu-sions and future research are discussed in Section 5

2 Literature Review

21 Kanorsquos Model Kano et al (1984) [14] proposed the Kanomodel adaptingHerzbergrsquos ldquomotivation-hygiene theoryrdquo [15]This model demonstrates the nonlinear relationship between

customer satisfaction and the performance of quality ele-ments And it classifies quality elements into five dimen-sions must-be one-dimensional attractive indifferent andreverse as shown in Figure 1

The Kano model has been widely used for customersrsquoneeds analysis decision-making analysis and other manage-ment practices [16ndash18] However it has some disadvantagesthat is it uses qualitative analysis techniques and does notdefine the classification criteria explicitly in quality elementsrsquoclassification It also fails to account for the providerrsquosconcerns in terms of the capacity to fulfill the customerrequirements [17]

Many studies tried to improve Kanorsquos model for support-ing product and service design in different ways Throughthe quantitative analysis of Kanorsquos model Berger et al (1993)[19] used two indicators of customer satisfaction (CS) andcustomer dissatisfaction (DS) to indicate the average impactof customer requirements on customer satisfaction Thisimproves Kanorsquos model in understanding the influence aboutdifferent categories of customer requirements on CS [20]FurtherWang and Ji (2010) [21] proposed a novel approach toidentify the relationships between customer satisfaction andthe fulfillment of customer requirements (S-CR) in Kanorsquosmodel and it provided an effective way to integrate Kanorsquosmodel to other mathematical model in engineering designprocess

In addition the traditional Kano survey forces customersto choose one answer for a particular question This ignoresthe fuzzy and uncertainty factors related to human thinkingwhen the questionnaire is being designed Thus some schol-ars proposed the fuzzy Kanomodel using integrated fuzzy settheory [22ndash24]

To improve Kanorsquos model for decision support literaturesdiscussed the issues of integrating Kano model with othertechniques These techniques include SERVQUAL [25 26]QFD [27ndash29] Importance-Performance Analysis (IPA) [30]and Kaisei engineering [31 32] It is very beneficial forimproving Kano methodology [33]

22 Kanorsquos Model in Service Quality Management Under-standing each category of the quality elements will be helpfulin service quality management Thus the Kano model hasbeen applied in various industries to classify service qualityelements andmake decisions in improving service quality Anoverview of the reviewed literature in this theme is given inTable 1 encompassing authors research focus industry andmodel type (theory quantitative method or empirical)

Previous research in the area has shown focus on servicequality elements identification evaluation and classificationThere has not been an emphasis on the decision-makingmethod of service quality improvement The small amountof research in this area dealt with qualitative analysis andthere is a dearth of quantitative research Table 1 showsthat the Kano model has a wide application in improvingservice quality in many industries However most of theseapplications are in the service business and little research isfocused on service quality in machinery enterprises

Scientific Programming 3

SatisfiedY

Dysfunctional

Dissatisfied

Must-be quality element

One-dimensional quality element

Functional

Attractive qualityelement

Reverse quality element

X

Degree of achievement

Customer satisfaction

Indifferent quality element

Figure 1 An overview of Kano model

23 Conclusions on the Literatures From the review of relatedliteratures the following conclusions were drawn

(1) The Kano model is an effective tool to identify andclassify service quality elements whether in serviceproduct development or service quality improve-ment But Kano model is still a qualitative method innature and setting up a quantitative Kano method-ology would be beneficial to provide support fordecisions more effectively

(2) Service quality research has largely been focusedon service business leading to a dearth of researchon service quality in machinery industries Servicequality is a critical element for machinery industriesto gain and maintain competitive advantage so it ismeaningful to extend the application to machineryindustries

(3) Researchers stressed the importance of understand-ing service quality elements that have the greatestimpact on customer satisfaction and promoting thepriority of these quality elements In the decision-making process for service quality improvement theservice providerrsquos capacity needs to also be con-sidered Cost constraints are usually accommodatedin the decision-making The goal of the decision-making process is to seek a solution of service qualityimprovement that leverages customer satisfaction andthe service providerrsquos resource constraints

3 Methodology

In this section an integrated framework is proposed tomaximize machinery industry service quality under budgetconstraints by considering the trade-off between customersatisfaction and service costs Figure 2 shows the proposedresearch framework with its subprocesses These subpro-cesses include identification of service quality elements divi-sion of market segmentation Kano model survey quantita-tive analysis of Kanomodel and decisionmodel formulation

31 Identification of Service Quality Elements Service qualityelements extraction and identification are the foundation notonly of the Kano model but also of decision analysis in ser-vice quality improvement Service qualities are different fromgeneral products with the distinctive features of intangibilityinseparability heterogeneity and perishability [53] So it isvery necessary to find an effective way to translate customerrequirements into a set of service quality elements from thecustomer perspective There are several methods that can beused to identify customer service quality elements such asSERVQUAL model and QFD which are currently thoughtof as useful methods to extract service quality elements bylistening to the voice of customers [29]

32 Division of Market Segments The division of marketsegments aims to find the appropriate target customers in thedecision analysis process As a rule different customers havedifferent utility expectations of service quality At this pointthe customer base is divided into several customer segments


Table 1 An overview of the reviewed literatures in this theme

Authors Research focus Industry Model typeTan and Shen (2000) [27] Integrating Kano model of QFD Web page Quantitative methodErto and Vanacore (2002)[34] Service quality measurement Hotel service quality Empirical

Ting and Chen (2002) [35] Quality elements identification and classification Hypermarket Empirical

Kuo (2004) [30] Classification of service quality attributes Virtual communitywebsites Empirical

Yang (2005) [36] Classification of service quality attributes Technical products TheoryNilsson Witell and Fundin(2005) [37] Dynamics of service attributes E-service Theory empirical

Chang et al (2006) [38] Service quality improvement from customersrsquosatisfaction perspective Hospital services Quantitative method

Chen et al (2010) [39] Service quality improvement by integrating Kano modelof Six-Sigma Stationary industry Quantitative method

Chen and Lee (2009) [40] Quality elements identification and classification Chain convenient stores Method empirical

Kuo et al (2009) [41] Service quality measurement and classification Mobile value-addedservices Theory empirical

Chen et al (2009) [42] Attractive quality elements discovering Massively multiplayeronline role-playing game Method empirical

Yang (2011) [43] Quality elements identification International certificationservice Theory empirical

Xie et al (2010) [44] Quality elements evaluation NPO products EmpiricalChen and Kuo (2011) [45] Quality elements identification and classification E-learning service Empirical

Tsai et al (2011) [46] Potential service quality gaps discovery Human resource serviceonline agency Method empirical

Kuo et al (2012) [47] Service quality improvement decision using IPA-Kanomodel Mobile service Method empirical

Florez-Lopez andRamon-Jeronimo (2012)[48]

Quality elements identification and classification usingfuzzy Kano model Logistics service Quantitative method

Tontini and DagostinPicolo (2014) [49]

Quality elements interaction analysis based onpsychological foundations

Pizzerias and video rentalstores Theory empirical

F-Y Chen and S-H Chen(2014) [50] Service quality improvement Hot spring industry Method empirical

Bandyopadhyay et al(2015) [51] Classification of service quality attributes Banking Empirical

Esmaeili et al (2015) [52] A fuzzy method to assess and identify service qualityattributes Logistics service Quantitative method

Meng et al (2015) [24] Decision method for service quality improvement Logistics service Method empirical

according to their demographic information psychographicdata and purchase behaviors The different customer seg-ments have distinguished characteristics about service qualityperceptions Effective marketing research and customer-tailored management actions are taken for each segmentguaranteeing an optimal level of customer satisfaction indifferent customer segments under budget constraints Manymethods and tools are available to assist the process includ-ing clustering classification self-organizing maps (SOM)evolutionary algorithms interaction detection methods andartificial neural networks [54]

33 Classification of Service Quality Elements Using the FuzzyKano Model The Kano model provides a systematic way ofservice quality elements classification It is based on three

tools the Kano questionnaire the Kano evaluation table andthe Kano final results table [14] The Kano questionnaireexamines each service quality element with a pair of ques-tions functional and dysfunctional There are five possibleanswers for each question that is ldquolikerdquo ldquomust-berdquo ldquoneutralrdquoldquolive-withrdquo and ldquodislikerdquo [14] According to the Kano evalua-tion table for each respondent the service quality element isclassified as one of Kano categories Generally according tothe Kano final results table the most frequent observationsof the sample set of responses are considered as the finalcategory for the service quality element [14]

Generally the traditional Kano model is lacking in pro-cessing the vagueness and uncertainties in human judgmentand decision-makingwhen surveying Fuzzy set theory intro-duced by Zadeh (1965) has many advantages in analyzing


(5) Decision model formulation

(1) Identification of service quality elements

CR SR

(3) Classification of service qualityelements based on fuzzy Kano model (2) Division of market segments

(i) Determining CS and DS points(ii) Identifying relationship

functions between service sufficiency and satisfaction

(4) Quantitative analysis of Kano model

X

Y

Kano quantitative analysis

Relationship betweenservice sufficiency andinvestment

Decision

Budget constraints

(i) Fuzzy Kano questionnaire(ii) Fuzzy Kano statistics

S = si | i = 1 2 I

15

2032

1518

Figure 2 The proposed research framework

fuzzy linguistics and fuzzy inference and it is successfullyused in fuzzy decision-making [55] Thus some scholarssuggested incorporating the fuzzy set theory intoKanomodelto accommodate linguistic properties of subjective and vaguehuman perception [22ndash24] The fuzzy Kano model has beenfound capable of mimicking a realistic cognition process inpractice An evaluatorrsquos multiple feelings can be expressed bythe possibility degrees in the fuzzy Kanomodel In this studywe are inclined to use fuzzy Kano model to elicit customersrsquoperception and gain classification results of service qualityelements [55]

34 Quantitative Analysis of the KanoModel After the classi-fication results based on the fuzzy Kano model are obtainedthe quantitative analysis is conducted as the literature ofWang and Ji (2010) [21]

Based on the findings of service quality elements classifi-cation it is possible to calculate two values namely customersatisfaction (CS) and customer dissatisfaction (DS) [19]

CS119894 = 119891119860 + 119891119874119891119860 + 119891119874 + 119891119872 + 119891119868

DS119894 = 119891119874 + 119891119872119891119860 + 119891119874 + 119891119872 + 119891119868 (1)

Let 119891119860 denote the number of attractive quality elements 119891119874the number of one-dimensional quality elements 119891119872 thenumber of must-be quality elements and 119891119868 the number ofindifferent quality elements

Consequently the two points define the customer satis-faction if a quality element can be fully fulfilled or completelynonfulfilled These points can be plotted as (1CS119894) and(0 minusDS119894) [21] The relationship function between customersatisfaction and service quality element fulfillment can beidentified given the category of the quality element The

relationship function can be expressed as 119878 = 119891(119909 119886 119887)where 119878 denotes the customer satisfaction 119909 denotes thefulfillment level and 119886 and 119887 are adjustment parameters forthe Kano categories of service quality elements For one-dimensional quality elements the function is 119878 = 1198861119909+1198871 andthe shape of the linear curve must pass the CS and DS pointsThen we can get 1198861 = CS119894 minus DS119894 and 1198871 = DS119894 Therefore thefunction for one-dimensional quality elements is

119878119900119894 = (CS119894 minus DS119894) 119909119900119894 + DS119894 (2)

For attractive quality elements the function can be seen to beexponential 119878 = 1198862119890119909 + 1198872 Substituting (1CS119894) and (0 minusDS119894)into the equation we can get 1198862 = (CS119894 minus DS119894)(119890 minus 1) and1198872 = minus(CS119894 minus 119890DS119894)(119890 minus 1) The function for attractive qualityelements is therefore

119878119886119894 = CS119894 minus DS119894119890 minus 1 119890119909119886119894 minus CS119894 minus 119890DS119894119890 minus 1 (3)

Using the similar approach for must-be quality elements thefunction is modified to be 119878 = minus1198863119890minus119909+1198873 and we can acquire1198863 = 119890(CS119894 minusDS119894)(119890 minus 1) and 1198873 = (119890CS119894 minusDS119894)(119890 minus 1) Thenthe function for must-be quality elements is

119878119898119894 = minus119890 (CS119894 minus DS119894)119890 minus 1 119890minus119909119898119894 + 119890CS119894 minus DS119894119890 minus 1 (4)

35 Decision Model Formulation In this stage a decisionmodel for maximizing service quality under budget con-straints is developed based on the quantitative Kano modelDifferent service quality element categories have differentinfluence on satisfaction Fulfilling customer expectations ofservice quality to a great extent does not necessarily guaranteea higher customer satisfaction Sufficient provision will leadto increased costs It is a very important task to seek foran efficient way to leverage the customer satisfaction and


Customersrsquo satisfaction

Sufficiency ofone-dimensional elementsa b

a998400

b998400

Figure 3 Sufficiency of one-dimensional elements and customersrsquosatisfaction

cost constraints This will be done using a service qualitymaximization model and its construction will be shownin the following section Firstly the relationships betweensufficiencies of different service quality elements categoriesand customer satisfactions are discussed

(i) One-Dimensional Quality Elements Figure 3 shows thefunction for one-dimensional quality elements identified inSection 34 When the sufficiency is provided by 119909119900119894 fromlevel 119886 to 119887 resulting from one unit of monetary investmentthe increased satisfaction 119860119900 is the area 119860119900 surrounded bypoints 119886 119887 1198861015840 and 1198871015840 and it can be computed as

119860119900 = int119887

119886[(CS119894 minus DS119894) 119909119900119894 + DS119894] 119889119909119900119894 (5)

When an enterprise provides 119899 one-dimensional servicequality elements and if the sufficiency provided by each 119909119900119894increases from 119886119894 to 119887119894 then 119879119860119900 the level of satisfactionincreased by 119899 one-dimensional elements can be expressed as

119879119860119900 =119899sum119894=1

119860119900119894 =119899sum119894=1

int119887119894119886119894

[(CS119894 minus DS119894) 119909119900119894 + DS119894] 119889119909119900119894119894 = 1 2 119899

(6)

For simplicity assuming that 1198861 = 1198862 = sdot sdot sdot = 119886119899 = 119886 1198871 = 1198872 =sdot sdot sdot = 119887119899 = 119887 the total increased customer satisfaction 119879119860119900provided by 119899 one-dimensional elements can be computed as

119879119860119900 =119899sum119894=1

int119887119886[(CS119894 minus DS119894) 119909119900119894 + DS119894] 119889119909119900119894 (7)

(ii) Attractive Quality Elements Figure 4 shows the exampleof an attractive quality element The relationship functionis 119878119886119894 = ((CS119894 minus DS119894)(119890 minus 1))119890119909119886119894 minus (CS119894 minus 119890DS119894)(119890 minus 1)and when the sufficiency is provided by 119909119886119894 from level 119888to 119889 resulting from one unit of monetary investment theincreased satisfaction 119860119886 surrounded by points 119888 119889 1198881015840 and1198891015840 can be computed as

119860119886 = int119889

119888(CS119894 minus DS119894119890 minus 1 119890119909119886119894 minus CS119894 minus 119890DS119894119890 minus 1 ) 119889119909119886119894 (8)


Sufficiency of attractive elementsc d

c998400d998400

Figure 4 Sufficiency of attractive elements and customersrsquo satisfac-tion


Sufficiency of must-be elements

e998400

e

f998400

f

Figure 5 Sufficiency of must-be elements and customersrsquo satisfac-tion

If 119899 attractive quality elements provided with the sufficiencyof each 119909119886119894 increase from 119888119894 to 119889119894 the increased satisfaction119879119860119886 can be expressed as

119879119860119886 =119899sum119894=1

119860119886

= 119899sum119894=1

int119889119894119888119894

(CS119894 minus DS119894119890 minus 1 119890119909119886119894 minus CS119894 minus 119890DS119894119890 minus 1 ) 119889119909119886119894119894 = 1 2 119899

(9)

For the ease of manifestation assuming that 1198881 = 1198882 = sdot sdot sdot =119888119899 = 119888 1198891 = 1198892 = sdot sdot sdot = 119889119899 = 119889 the total increased satisfaction119879119860119886 can be simplified as

119879119860119886 =119899sum119894=1

int119889119888(CS119894 minus DS119894119890 minus 1 119890119909119886119894 minus CS119894 minus 119890DS119894119890 minus 1 ) 119889119909119886119894 (10)

(iii) Must-Be Quality Elements As Figure 5 shows therelationship function for must-be quality element is 119878119898119894 =minus(119890(CS119894minusDS119894)(119890minus1))119890minus119909119898119894+(119890CS119894minusDS119894)(119890minus1) andwhen thesufficiency is provided by 119909119898119894 from level 119890 to 119891 resulting fromone unit of monetary investment the decreased dissatisfac-tion 119860119898 surrounded by points 119890 119891 1198901015840 and 1198911015840 is computed as

119860119898 = int119891

119890(minus119890 (CS119894 minus DS119894)119890 minus 1 119890minus119909119898119894 + 119890CS119894 minus DS119894119890 minus 1 )119889119909119898119894 (11)


If 119899 must-be quality elements provided with the sufficiencyprovided by each 119909119898119894 increase from 119890119894 to 119891119894 then thedecreased dissatisfaction 119879119860119898 can be expressed as

119879119860119898 =119899sum119894=1

119860119898

= 119899sum119894=1

int119891119894119890119894

(minus119890 (CS119894 minus DS119894)119890 minus 1 119890minus119909119898119894 + 119890CS119894 minus DS119894119890 minus 1 )119889119909119898119894119894 = 1 2 119899

(12)

Assuming that 1198901 = 1198902 = sdot sdot sdot = 119890119899 = 119890 1198911 = 1198912 = sdot sdot sdot = 119891119899 = 119891the total decreased dissatisfaction 119879119860119898 is simplified as

119879119860119898= 119899sum119894=1

int119891119890(minus119890 (CS119894 minus DS119894)119890 minus 1 119890minus119909119898119894 + 119890CS119894 minus DS119894119890 minus 1 )119889119909119898119894 (13)

The equations above now can be applied to improve themachinery service qualityThere are many factors that can berelated to the machinery service quality and some fall intomust-be elements and others are one-dimensional or attrac-tive quality elements Moreover the increased satisfactionresulting from investment onmust-be one-dimensional andattractive quality elements is certainly different Thus theproblem is how to properly allocate the budget to servicequality elements in order to maximize the overall customersatisfaction At this point the following model can beproposed

119872 max 119862119900 (119879119860119900) + 119862119886 (119879119860119886) + 119862119898 (119879119860119898)st 119862119900 + 119862119886 + 119862119898 le 119861

0 le 119862119900 le 1198610 le 119862119886 le 1198610 le 119862119898 le 119861119862119900 = 1198961 (119887 minus 119886)119862119886 = 1198962 (119889 minus 119888)119862119898 = 1198963 (119891 minus 119890)0 le 119886 le 119887 le 119863 le 10 le 119888 le 119889 le 119864 le 10 le 119890 le 119891 le 119865 le 1

(14)

where 119862119900 119862119886 and 119862119898 denote the individual budget allocatedto one-dimensional attractive and must-be quality elementsand these are the decision variables 119861 is the total budget toimprove the overall service quality When budgeted 119862119900 canbe increased from 119886 to 119887 with 119863 being the upper limit Withthe same principles 119862119886 can be increased from 119888 to 119889 with 119864being the upper limit and 119862119898 can be increased from 119890 to 119891with 119865 being the upper limitThe coefficients of 1198961 1198962 and 1198963

stand for the relationship between budget allocated and thelevel of quality improvement The nonlinear mathematicalmodel can be solved by optimization software Lingo and thedetails will be shown in the following illustrative example

4 An Empirical Study

To demonstrate the performance of the proposed methodan empirical study in Xuzhou Construction MachineryGroup Co Ltd (XCMG) is given in this section XCMGis the largest construction machinery company in China Itsproducts cover road machineries crane trucks heavy-dutyroad rollers and so on which include 75 series and 330varieties The product sales network of XCMG covers morethan 170 countries and regions and the annual export valuehas exceeded $16 Billion USD But in recent years with thescale enlargement of markets and the implementation of theldquoGoing Globalrdquo strategy XCMG is facing much greater vari-ety and uncertainty in customer requirement XCMG nowneeds to explore the appropriate strategy on the servicequality management to gain competitive advantage Since2011 XCMG launched the project of ldquoHanfeng Programrdquo toestablish a control system for service quality improvementAnd the decision method proposed in this paper is impliedin the program

41 Service Quality Elements Identification in XCMG Basedon the SERVQUAL model several experts in XCMG andsome users are investigated by the teammembers and twelveservice quality elements and benefits provided for customersin XCMG are extracted in Table 2

42 Data Collection and Analysis According to the twelveservice quality elements the fuzzy Kano questionnaire isdesigned which is divided into two parts in the firstpart there is some demographic information of respondentssuch as organization characters (state-owned business pri-vate business foreign-funded enterprise or joint ventureenterprise) and types of customers (general customers keyaccounts or strategic customers) In the second part thequestionnaire focuses on a set of 12 items of service qualityelements and the form of each item is presented with bothfunctional and dysfunctional forms but unlike traditionalKano model using binary data the fuzzy Kano model ques-tionnaire allows respondents to express theirmultiple feelingsby the possibility degrees among multiple items Additionalinformation on the fuzzy Kano questionnaire can be foundin the literature [55]

The fuzzy Kano questionnaire is distributed randomlyface-to-face by the employees from marketing departmentand after-sale service support department and teammembersin the program The first step is to provide respondents witha brief introduction of Kano model and then customers areasked to give multiple answers of the pair questions aboutservice quality elements From September 15 to December 152012 250 copies of the questionnaire have been issued and 196completed copies are retrieved This is a reasonable responserate of 784 The sample demographic characteristics areshown in Table 3


Table 2 Service quality elements and benefits provided for cus-tomers in XCMG

Service qualityelements Description and specification Benefits

provided

SQ1Fulfill service commitmentsmaintenance service in PLC

Reliabilitysafety

SQ2Quick response torequirements and complaints Speediness

SQ3Consultation and solutionscustomization

Empathy addedvalue

SQ4Repair and maintenanceservice covers widely

Safetyassurance andreliability

SQ5

Machinery vehicles statusmonitoring and warning longrange informationtransmission and diagnosis

Safetyconvenience

SQ6 Value-added service Added valueempathy

SQ7 Network and online service Conveniencespeediness

SQ8Used machinery vehiclesbusiness

Convenienceempathy

SQ9High quality service staffsrsquoprofessional and technicalabilities

Pleasureassurance

SQ10 Compensation for delaying Reliabilitysafety

SQ11Proactive services providedregularly and periodically

Conveniencereliability

SQ12Customer participation inservice process

Added valueempathy

Table 3 Demographic characteristics of respondents

Demographic characteristics Number Percentage ()Organization charactersState-owned business 54 2755Private business 43 2194Foreign-funded enterprise 47 2398Joint venture enterprise 52 2653

Types of customersGeneral customers 79 4031Key accounts 65 3316Strategic customers 52 2653

43 Service Quality Elements Classification Based on fuzzyKano survey and statistical analysis the classification resultsof service quality elements in XCMG are given from thepreliminary qualitative results by the author [55] as is shownin Table 4

44 Decision Results According to results of service qualityelements classification the service quality elements SQ11(proactive services provided regularly and periodically) andSQ12 (customer participation in service process) are classi-fied as indifferent quality elements They are not included in

the further analysis due to the low impact on customer sat-isfaction At this point the proposed quantitative analysis inSection 3 is applied Firstly the values of CS and DS are com-puted for each service quality element as shown in Table 5Based on the classification results in Table 4 the values of 119886and 119887 are calculated to determine the function for each servicequality element Accordingly all the relationship functions ofthree category quality elements are estimated in Table 5

Given the data of cost for each service quality elementdrawn from investigation in XCMG as is shown in Table 6the total budget for service quality improvement is yen150000

Assuming 1198961 = 150 1198962 = 300 1198963 = 100 119886 = 055 119888 =055 119890 = 055 and 119863 = 09 119864 = 08 and 119865 = 1 Then themodel becomes

119872 max 119862119900 (119879119860119900) + 119862119886 (119879119860119886) + 119862119898 (119879119860119898)st 119862119900 + 119862119886 + 119862119898 le 150

0 le 119862119900 le 1500 le 119862119886 le 1500 le 119862119898 le 150119862119900 = 150 (119887 minus 055)119862119886 = 300 (119889 minus 055)119862119898 = 100 (119891 minus 055)055 le 119887 le 09055 le 119889 le 08055 le 119891 le 10119879119860119900 = 20151198872 minus 203119887 + 0507119879119860119886 = 132119890119889 minus 225119889 minus 10504119879119860119898 = 888119890minus119891 + 551119891 minus 81538

(15)

The nonlinear programming model is solved by Lingo 110and the results are obtained as 119862119900 = 525 119862119886 = 525and 119862119898 = 45 119879119860119900 = 031 119879119860119886 = 004 and 119879119860119898 =062 and the overall customer satisfaction is 4672 It appearsthat XCMG should allocate yen52500 to improve the one-dimensional quality elements with the level of service qualitybeing improved from055 to 09 yen52500 of the budget shouldbe allocated for the attractive quality elements with the levelof service quality being improved from 055 to 0725 Andyen45000 should be allocated for themust-be quality elementsand it will improve the level of service quality from 055 to 1

To examine the appropriateness of the results threedifferent parameter values of coefficients 119896119894 are applied inthe model for a sensitivity analysis Due to the fact that thecategories of service quality elements are the most effectivefactors on resource utilization the sensitivity analysis focusesmainly on the coefficients 119896119894 Table 7 shows the results of thesensitivity analysis

The results in Table 7 show that the coefficients 119896119894 havesome effect on the decision-making results In the three


Table 4 Service quality elements classification results from Kano model

Service qualityelements (number)

Traditional Kano model Fuzzy Kano model119872 119860 119868 119874 Category 119872 119860 119868 119874 Category

SQ1 88 32 25 51 119872 106 38 18 44 119872SQ2 78 45 23 50 119872 98 42 20 51 119872SQ3 45 38 20 93 119874 51 43 25 99 119874SQ4 89 51 11 45 119872 95 56 15 37 119872SQ5 40 82 24 50 119860 46 88 27 53 119860SQ6 45 90 16 45 119860 49 92 18 48 119860SQ7 84 39 26 47 119872 95 52 24 38 119872SQ8 37 90 21 48 119860 39 48 19 92 119874SQ9 49 90 19 38 119860 54 46 24 95 119874SQ10 51 80 15 50 119860 84 56 13 47 119872SQ11 38 59 80 19 119868 43 64 86 25 119868SQ12 55 47 73 21 119868 56 48 78 22 119868

Table 5 Quantitative results of service quality elements based on Kano model

Number CS DS CS point DS point 119886 119887 119891(119909) 119878 = 119886119891(119909) + 119887SQ1 040 minus073 (1 04) (0 minus073) 178 105 119890minus119909 119878 = minus178119890minus119909 + 105SQ2 044 minus071 (1 044) (0 minus071) 181 111 119890minus119909 119878 = minus181119890minus119909 + 111SQ3 065 minus069 (1 065) (0 minus069) 134 minus069 119909 119878 = 134119909 minus 069SQ4 046 minus065 (1 046) (0 minus065) 175 110 119890minus119909 119878 = minus175119890minus119909 + 110SQ5 066 minus046 (1 066) (0 minus046) 065 minus112 119890119909 119878 = 065119890119909 minus 112SQ6 068 minus047 (1 068) (0 minus047) 067 minus113 119890119909 119878 = 067119890119909 minus 113SQ7 043 minus064 (1 043) (0 minus064) 169 105 119890minus119909 119878 = minus169119890minus119909 + 105SQ8 071 minus066 (1 071) (0 minus066) 137 minus066 119909 119878 = 137119909 minus 066SQ9 064 minus068 (1 064) (0 minus068) 132 minus068 119909 119878 = 132119909 minus 068SQ10 052 minus066 (1 052) (0 minus066) 185 120 119890minus119909 119878 = minus185119890minus119909 + 12

Table 6 Cost for each service quality element

Service qualityelements(number)

Description and specification Costs(yen1000)

SQ1Fulfill service commitmentsmaintenance service in PLC 4800

SQ2Quick response to requirements andcomplaints 295

SQ3Consultation and solutionscustomization 400

SQ4Repair and maintenance service coverswidely 4300

SQ5Machinery vehicles status monitoringand warning long range informationtransmission and diagnosis

360

SQ6 Value-added service 200SQ7 Network and online service 170SQ8 Used machinery vehicles business 215

SQ9High quality service staffsrsquo professionaland technical abilities 310

SQ10 Compensation for delaying 460

conditions the must-be quality elements will first achievetheir targets (always 119891 = 1) and they must be allocated tothe budget to be fully fulfilled while the one-dimensionaland attractive quality elements are quite differentThe perfor-mance of must-be quality elements has the priority in servicequality improvementWith a limited budget (yen150000) it canonly be guaranteed that the must-be and one-dimensionalquality elements achieve their goals while the attractivequality elements cannot be assured

45 Suggestions From the results it appears that XCMGshould firstly invest in must-be quality elements to assure anentirely fulfilled system with limited resources SpecificallyXCMG will establish a service standard system to fulfillservice commitments provide the maintenance service in aproductrsquos life cycle and assure these quality elements to ahigh level XCMGmust guarantee a quick response to servicerequirements and complaints from customers and this canbe done through the use of both a Customer RelationshipManagement (CRM) system andMobile Internet Technology(MIT) The service quality element ldquorepair and maintenanceservice covers widelyrdquo will be improved in a large scale


Table 7 Solutions with different parameter values of 119896119894Number 1198961 1198962 1198963 119862119900 119862119886 119862119898 119879119860119900 119879119860119886 119879119860119898 119887 119889 119891 maxCS1 150 300 100 525 525 45 031 004 062 09 073 1 46722 100 150 300 0 15 135 375 lowast 10minus5 002 062 055 065 1 84343 300 100 150 10273 002 4725 030 0 062 089 055 1 6028

with the business expansion Meanwhile ldquothe network andonline servicerdquo needs also to be investigated within thecompany as poor service quality for this element leads tostrong dissatisfaction If the must-be quality elements are notfulfilled it would be essential to provide compensation tocustomers that place complaints

The one-dimensional elements of ldquoconsultation and solu-tions customizationrdquo ldquoused machinery vehicles businessrdquoand ldquohigh quality service staffsrsquo professional and technicalabilitiesrdquo should also be improved to a high level to maximizecustomer satisfaction for XCMG Considering customersrsquoheterogeneities service customization and service specializa-tion will be the general trend in the futureThe service qualityis significantly related with service staffs and XCMG mustmake sure that there are a group of service employees withhigh levels of professional and technical abilities In additionto the focus on must-be and one-dimensional elements it iswise to seek for some effective ways to improve the attractivequality elements if there is potential for additional monetaryinvestment

The decision method proposed is also practical andfunctional for other enterprises to improve service qualityFirstly it is critical to better identify and understand servicequality elements from customersrsquo perspective Secondly itis necessary to account for the budget constraints in thedecision-making of service quality improvement Thirdlywith the limited budget to maximize the overall customersatisfaction the must-be quality elements must be allocatedto the budget to be fully fulfilled and to achieve the targets

5 Conclusions

Service quality has become one of the sources for competitiveadvantage especially for some machinery industries trans-forming into product-service system providers And moreimportantly service quality is the crucial determinant ofcustomer satisfaction and customer loyalty This integratedframework proposed for maximizing machinery industryservice quality under budget constraints involves servicequality elements identification and classification quantitativeanalysis of Kano model and decision model formulationIt provides a guidance principle of ldquoorigin from customersapplication for customersrdquo for enterprises to manage servicequality in the customers-dominated logic era As Wangand Ji (2010) [21] stated before it is important to providea way for quantitative Kano model to be integrated withother mathematical models for optimizing customer-focusedproduct or service design

The contributions of this research can be summarizedas follows First the decision method proposed provides asystematic and quantitative solution for improving service

quality in machinery manufacturers to balance customersatisfaction and service costs Second the integrated frame-work gives a practical roadmap for enterprises to improveservice quality with limited budgets and it involves severalkey processes service quality elements identification classi-fication and service quality improvement decision-makingFinally the results provide empirical evidence that must-bequality elements have higher priorities in budget allocationfor quality improvement compared with one-dimensionaland attractive quality elements under the limited budgets

Yet there are some limitations in this paper Firstlyin the nonlinear programming model (14) the coefficientsparameters which related the budgets to the level of qualityimprovement should be discussed further using a largeamount of historical data in the future Secondly in theempirical research the perceptions varieties in classificationof service quality elements from different customer segmentsare not considered And it is significant for the enterprises tomake decisions with different customer orientation Finallymore empirical studies on the decision method should beconducted to test the applicability

Competing Interests

The authors declare that there are no competing interestsregarding the publication of this paper

Acknowledgments

The support in language modification from Mikhail Gor-don doctoral candidate of Joseph M Katz Graduate Schoolof Business and College of Business Administration fromUniversity of Pittsburgh is acknowledged The research issupported by the National Social Science Fund of Chinaunder Grant 14CGL014 and China Postdoctoral ResearchFund under Grant 2013M530353

References

[1] H Gebauer and E Fleisch ldquoAn investigation of the relationshipbetween behavioral processes motivation investments in theservice business and service revenuerdquo Industrial MarketingManagement vol 36 no 3 pp 337ndash348 2007

[2] R Oliva and R Kallenberg ldquoManaging the transition fromproducts to servicesrdquo International Journal of Service IndustryManagement vol 14 no 2 pp 160ndash172 2003

[3] S L Vargo and R F Lusch ldquoEvolving to a new dominant logicformarketingrdquo Journal ofMarketing vol 68 no 1 pp 1ndash17 2004

[4] H Gebauer R Krempl E Fleisch and T Friedli ldquoInnovationof product-related servicesrdquo Managing Service Quality vol 18no 4 pp 387ndash404 2008


[5] M Alsmadi B Lehaney and Z Khan ldquoImplementing six sigmain Saudi Arabia an empirical study on the fortune 100 firmsrdquoTotal Quality Management and Business Excellence vol 23 no3-4 pp 263ndash276 2012

[6] T Baines H Lightfoot J Peppard et al ldquoTowards an operationsstrategy for product-centric servitizationrdquo International Journalof Operations amp ProductionManagement vol 29 no 5 pp 494ndash519 2009

[7] A Davies ldquoMoving base into high-value integrated solutions avalue stream approachrdquo Industrial and Corporate Change vol13 no 5 pp 727ndash756 2004

[8] S Johnstone A Dainty and A Wilkinson ldquoIntegrating prod-ucts and services through life an aerospace experiencerdquo Inter-national Journal of Operations and ProductionManagement vol29 no 5 pp 520ndash538 2009

[9] K S Pawar A Beltagui and J C K H Riedel ldquoThe PSO trian-gle designing product service and organisation to create valuerdquoInternational Journal of Operations amp Production Managementvol 29 no 5 pp 468ndash493 2009

[10] V Martinez M Bastl J Kingston and S Evans ldquoChallengesin transforming manufacturing organisations into product-service providersrdquo Journal of Manufacturing Technology Man-agement vol 21 no 4 pp 449ndash469 2010

[11] M Szwejczewski K Goffin and Z Anagnostopoulos ldquoProductservice systems after-sales service and new product develop-mentrdquo International Journal of Production Research vol 53 no17 pp 5334ndash5353 2015

[12] E E Izogo and I-E Ogba ldquoService quality customer sat-isfaction and loyalty in automobile repair services sectorrdquoInternational Journal of Quality and Reliability Managementvol 32 no 3 pp 250ndash269 2015

[13] F Jacob and W Ulaga ldquoThe transition from product to servicein businessmarkets an agenda for academic inquiryrdquo IndustrialMarketing Management vol 37 no 3 pp 247ndash253 2008

[14] N Kano N Seraku F Takahashi and S H Tsjui ldquoAttractivequality and must-be qualityrdquo Hinshitsu vol 14 no 2 pp 147ndash156 1984

[15] F Herzberg ldquoThe motivation to work among finnish supervi-sorsrdquo Personnel Psychology vol 18 no 4 pp 393ndash402 1965

[16] M Lofgren and L Witell ldquoTwo decades of using Kanorsquostheory of attractive quality a literature reviewrdquo The QualityManagement Journal vol 15 no 1 pp 59ndash75 2008

[17] A Shahin M Pourhamidi J Antony and S Hyun ParkldquoTypology of Kano models a critical review of literature andproposition of a revisedmodelrdquo International Journal of Qualityamp Reliability Management vol 30 no 3 pp 341ndash358 2013

[18] T Luor H-P Lu K-M Chien and T-C Wu ldquoContribution toquality research a literature review of Kanorsquos model from 1998to 2012rdquo Total Quality Management amp Business Excellence vol26 no 3-4 pp 234ndash247 2015

[19] C Berger R Blauth D Boger et al ldquoKanorsquos methods for under-standing customer-defined qualityrdquo The Center for Quality ofManagement Journal vol 2 pp 3ndash36 1993

[20] K Matzler and H H Hinterhuber ldquoHow to make productdevelopment projects more successful by integrating Kanorsquosmodel of customer satisfaction into quality function deploy-mentrdquo Technovation vol 18 no 1 pp 25ndash38 1998

[21] T Wang and P Ji ldquoUnderstanding customer needs throughquantitative analysis of Kanorsquos modelrdquo International Journal ofQuality and Reliability Management vol 27 no 2 pp 173ndash1842010

[22] Y-C Lee and S-Y Huang ldquoA new fuzzy concept approach forKanorsquos modelrdquo Expert Systems with Applications vol 36 no 3pp 4479ndash4484 2009

[23] C-H Wang ldquoIncorporating customer satisfaction into thedecision-making process of product configuration a fuzzy kanoperspectiverdquo International Journal of Production Research vol51 no 22 pp 6651ndash6662 2013

[24] Q Meng X Jiang and L Bian ldquoA decision-making methodfor improving logistics services quality by integrating fuzzyKanomodel with importance-performance analysisrdquo Journal ofService Science andManagement vol 8 no 3 pp 322ndash331 2015

[25] KC Tan andTA Pawitra ldquoIntegrating SERVQUALandKanorsquosmodel into QFD for service excellence developmentrdquoManagingService Quality An International Journal vol 11 no 6 pp 418ndash430 2001

[26] B Baki C Sahin Basfirinci A R Ilker Murat and Z CilingirldquoAn application of integrating SERVQUAL and Kanorsquos modelinto QFD for logistics services a case study from Turkeyrdquo AsiaPacific Journal of Marketing and Logistics vol 21 no 1 pp 106ndash126 2009

[27] K C Tan and X-X Shen ldquoIntegrating Kanorsquos model in theplanning matrix of quality function deploymentrdquo Total QualityManagement vol 11 no 8 pp 1141ndash1151 2000

[28] G Tontini ldquoIntegrating the Kanomodel andQFD for designingnew productsrdquo Total Quality Management amp Business Excel-lence vol 18 no 6 pp 599ndash612 2007

[29] P Ji J Jin T Wang and Y Chen ldquoQuantification and integra-tion of Kanorsquos model into QFD for optimising product designrdquoInternational Journal of Production Research vol 52 no 21 pp6335ndash6348 2014

[30] Y-F Kuo ldquoIntegrating Kanorsquos model into web-community ser-vice qualityrdquo Total Quality Management amp Business Excellencevol 15 no 7 pp 925ndash939 2004

[31] MHartono andT K Chuan ldquoHow theKanomodel contributesto Kansei engineering in servicesrdquo Ergonomics vol 54 no 11pp 987ndash1004 2011

[32] C Llinares and A F Page ldquoKanorsquos model in Kansei Engineeringto evaluate subjective real estate consumer preferencesrdquo Inter-national Journal of Industrial Ergonomics vol 41 no 3 pp 233ndash246 2011

[33] L Witell M Lofgren and J J Dahlgaard ldquoTheory of attractivequality and the Kano methodologymdashthe past the present andthe futurerdquo Total Quality Management and Business Excellencevol 24 no 11-12 pp 1241ndash1252 2013

[34] P Erto and A Vanacore ldquoA probabilistic approach to measurehotel service qualityrdquo Total Quality Management vol 13 no 2pp 165ndash174 2002

[35] S-C Ting and C-N Chen ldquoThe asymmetrical and non-lineareffects of store quality attributes on customer satisfactionrdquoTotalQuality Management vol 13 no 4 pp 547ndash569 2002

[36] C-C Yang ldquoThe refinedKanorsquosmodel and its applicationrdquoTotalQuality Management and Business Excellence vol 16 no 10 pp1127ndash1137 2005

[37] L Nilsson Witell and A Fundin ldquoDynamics of serviceattributes a test of Kanorsquos theory of attractive qualityrdquo Interna-tional Journal of Service Industry Management vol 16 no 2 pp152ndash168 2005

[38] W-K Chang C-C Wei and N-T Huang ldquoAn approach tomaximize hospital service quality under budget constraintsrdquoTotal Quality Management and Business Excellence vol 17 no6 pp 757ndash774 2006


[39] L-S Chen C-H Liu C-C Hsu and C-S Lin ldquoC-kanomodela novel approach for discovering attractive quality elementsrdquoTotal Quality Management and Business Excellence vol 21 no11 pp 1189ndash1214 2010

[40] J-K Chen and Y-C Lee ldquoA new method to identify thecategory of the quality attributerdquo Total Quality Management ampBusiness Excellence vol 20 no 10 pp 1139ndash1152 2009

[41] Y-F Kuo C-MWu andW-J Deng ldquoThe relationships amongservice quality perceived value customer satisfaction and post-purchase intention in mobile value-added servicesrdquo Computersin Human Behavior vol 25 no 4 pp 887ndash896 2009

[42] S C Chen L Chang and T H Huang ldquoApplying six-sigmamethodology in the Kano quality model an example of thestationery industryrdquo Total Quality Management and BusinessExcellence vol 20 no 2 pp 153ndash170 2009

[43] C-C Yang ldquoIdentification of customer delight for qualityattributes and its applicationsrdquo Total Quality Management andBusiness Excellence vol 22 no 1 pp 83ndash98 2011

[44] Y Xie C L Hui and S F Ng ldquoThe evaluation of qualityattributes of NPO products a case in medical garmentsrdquo TotalQuality Management and Business Excellence vol 21 no 5 pp517ndash535 2010

[45] L-H Chen and Y-F Kuo ldquoUnderstanding e-learning servicequality of a commercial bank by using Kanorsquos modelrdquo TotalQuality Management amp Business Excellence vol 22 no 1 pp99ndash116 2011

[46] M-C Tsai L-F Chen Y-H Chan and S-P Lin ldquoLooking forpotential service quality gaps to improve customer satisfactionby using a new GA approachrdquo Total Quality Management ampBusiness Excellence vol 22 no 9 pp 941ndash956 2011

[47] Y-F Kuo J-Y Chen and W-J Deng ldquoIPA-Kano model a newtool for categorising and diagnosing service quality attributesrdquoTotal Quality Management and Business Excellence vol 23 no7-8 pp 731ndash748 2012

[48] R Florez-Lopez and J M Ramon-Jeronimo ldquoManaging logis-tics customer service under uncertainty an integrative fuzzyKano frameworkrdquo Information Sciences vol 202 pp 41ndash57 2012

[49] G Tontini and J Dagostin Picolo ldquoIdentifying the impactof incremental innovations on customer satisfaction using afusion method between importance-performance analysis andKano modelrdquo International Journal of Quality amp ReliabilityManagement vol 31 no 1 pp 32ndash52 2014

[50] F-Y Chen and S-H Chen ldquoApplication of importance andsatisfaction indicators for service quality improvement of cus-tomer satisfactionrdquo International Journal of Services Technologyand Management vol 20 no 1ndash3 pp 108ndash122 2014

[51] N Bandyopadhyay H Estelami and K Eriksson ldquoClassifica-tion of service quality attributes using Kanorsquos model a study inthe context of the Indian banking sectorrdquo International Journalof Bank Marketing vol 33 no 4 pp 457ndash470 2015

[52] A Esmaeili R A Kahnali R Rostamzadeh E K Zavadskasand B Ghoddami ldquoAn application of fuzzy logic to assessservice quality attributes in logistics industryrdquo Transport vol30 no 2 pp 172ndash181 2015

[53] W J Regan ldquoThe service revolutionrdquoThe Journal of Marketingvol 27 no 3 pp 57ndash62 1963

[54] H Gucdemir and H Selim ldquoIntegrating multi-criteria decisionmaking and clustering for business customer segmentationrdquoIndustrialManagement ampData Systems vol 115 no 6 pp 1022ndash1040 2015

[55] Q Meng X Jiang L He and X Guo ldquoIntegration of fuzzytheory into Kano model for classification of service qualityelements a case study in a machinery industry of ChinardquoJournal of Industrial Engineering and Management vol 8 no5 pp 1661ndash1675 2015

Submit your manuscripts athttpwwwhindawicom

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Artificial Intelligence

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Electrical and Computer Engineering

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Human-ComputerInteraction

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one needs to understand the relationships between variousservice quality elements and other related elements forexample processes personnel and resources [13]

The literatures have mainly focused on categorizing cus-tomer service requirements planning and designing properservice products to assure customer satisfaction There isa lack of a systematic and quantitative verification schemefor service quality improvement especially in the machineryindustry In general services influence costs and high servicequality often leads to high service costs It is therefore vitalto strike a good balance between customer satisfaction andservice costs when improving service quality in machineryindustries while researchers have not yet addressed thisimportant issue enough To make contributions to the exist-ing literatures we try to find out a solution of service qualityimprovement in machinery industries to leverage customersatisfaction and budget constraints from bilateral perspec-tives (customer satisfaction and service providerrsquos capacity)Thus the relationships between customer satisfaction servicecosts and service quality elements will be identified andthen a novel quantitative decision method will be set up todetermine the priorities of service quality elements in qualityimprovement

In an effort to propose a decision method to maximizethe overall customer satisfaction under budget constraintswe formulate two research issues

(1) In themachinery industry there is a limit on resourcesallocation to services Most resources are focused ontangible products There is still a need for a continu-ous improvement process in regard to service qualitySo based on a bilateral prospective how can this helpto develop a systemic and quantitative frameworkto identify key service quality elements detect therelationship among quality elements and determinethe improvement priorities for service transformationin machinery industries

(2) The level of customer satisfaction depends on ade-quate supply of the service components which relieson large monetary investment Then how can thishelp to develop a mathematical model balancingcustomer satisfaction and service costs and validatethe results empirically

The paper proceeds as follows Section 2 covers a reviewof the literatures Section 3 proposes the research frameworkWe implement the proposed model and its solution method-ology to Xuzhou Construction Machinery Group Co Ltd(XCMG) the largest construction machinery company inChina in Section 4 and prove its validity Research conclu-sions and future research are discussed in Section 5

2 Literature Review

21 Kanorsquos Model Kano et al (1984) [14] proposed the Kanomodel adaptingHerzbergrsquos ldquomotivation-hygiene theoryrdquo [15]This model demonstrates the nonlinear relationship between

customer satisfaction and the performance of quality ele-ments And it classifies quality elements into five dimen-sions must-be one-dimensional attractive indifferent andreverse as shown in Figure 1

The Kano model has been widely used for customersrsquoneeds analysis decision-making analysis and other manage-ment practices [16ndash18] However it has some disadvantagesthat is it uses qualitative analysis techniques and does notdefine the classification criteria explicitly in quality elementsrsquoclassification It also fails to account for the providerrsquosconcerns in terms of the capacity to fulfill the customerrequirements [17]

Many studies tried to improve Kanorsquos model for support-ing product and service design in different ways Throughthe quantitative analysis of Kanorsquos model Berger et al (1993)[19] used two indicators of customer satisfaction (CS) andcustomer dissatisfaction (DS) to indicate the average impactof customer requirements on customer satisfaction Thisimproves Kanorsquos model in understanding the influence aboutdifferent categories of customer requirements on CS [20]FurtherWang and Ji (2010) [21] proposed a novel approach toidentify the relationships between customer satisfaction andthe fulfillment of customer requirements (S-CR) in Kanorsquosmodel and it provided an effective way to integrate Kanorsquosmodel to other mathematical model in engineering designprocess

In addition the traditional Kano survey forces customersto choose one answer for a particular question This ignoresthe fuzzy and uncertainty factors related to human thinkingwhen the questionnaire is being designed Thus some schol-ars proposed the fuzzy Kanomodel using integrated fuzzy settheory [22ndash24]

To improve Kanorsquos model for decision support literaturesdiscussed the issues of integrating Kano model with othertechniques These techniques include SERVQUAL [25 26]QFD [27ndash29] Importance-Performance Analysis (IPA) [30]and Kaisei engineering [31 32] It is very beneficial forimproving Kano methodology [33]

22 Kanorsquos Model in Service Quality Management Under-standing each category of the quality elements will be helpfulin service quality management Thus the Kano model hasbeen applied in various industries to classify service qualityelements andmake decisions in improving service quality Anoverview of the reviewed literature in this theme is given inTable 1 encompassing authors research focus industry andmodel type (theory quantitative method or empirical)

Previous research in the area has shown focus on servicequality elements identification evaluation and classificationThere has not been an emphasis on the decision-makingmethod of service quality improvement The small amountof research in this area dealt with qualitative analysis andthere is a dearth of quantitative research Table 1 showsthat the Kano model has a wide application in improvingservice quality in many industries However most of theseapplications are in the service business and little research isfocused on service quality in machinery enterprises


SatisfiedY

Dysfunctional

Dissatisfied



Functional



X









3 Methodology



































CR SR





X

Y



Decision

Budget constraints


S = si | i = 1 2 I

15

2032

1518





CS119894 = 119891119860 + 119891119874119891119860 + 119891119874 + 119891119872 + 119891119868

DS119894 = 119891119874 + 119891119872119891119860 + 119891119874 + 119891119872 + 119891119868 (1)




119878119900119894 = (CS119894 minus DS119894) 119909119900119894 + DS119894 (2)









a998400

b998400




119860119900 = int119887

119886[(CS119894 minus DS119894) 119909119900119894 + DS119894] 119889119909119900119894 (5)


119879119860119900 =119899sum119894=1

119860119900119894 =119899sum119894=1

int119887119894119886119894

[(CS119894 minus DS119894) 119909119900119894 + DS119894] 119889119909119900119894119894 = 1 2 119899

(6)


119879119860119900 =119899sum119894=1

int119887119886[(CS119894 minus DS119894) 119909119900119894 + DS119894] 119889119909119900119894 (7)


119860119886 = int119889




c998400d998400




e998400

e

f998400

f



119879119860119886 =119899sum119894=1

119860119886

= 119899sum119894=1

int119889119894119888119894


(9)


119879119860119886 =119899sum119894=1



119860119898 = int119891




119879119860119898 =119899sum119894=1

119860119898

= 119899sum119894=1

int119891119894119890119894


(12)


119879119860119898= 119899sum119894=1



119872 max 119862119900 (119879119860119900) + 119862119886 (119879119860119886) + 119862119898 (119879119860119898)st 119862119900 + 119862119886 + 119862119898 le 119861


(14)











provided


Reliabilitysafety



Empathy addedvalue



SQ5


Safetyconvenience




Convenienceempathy


Pleasureassurance





Added valueempathy









119872 max 119862119900 (119879119860119900) + 119862119886 (119879119860119886) + 119862119898 (119879119860119898)st 119862119900 + 119862119886 + 119862119898 le 150


(15)



















360











5 Conclusions





Competing Interests


Acknowledgments


References

































































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




SatisfiedY

Dysfunctional

Dissatisfied



Functional



X









3 Methodology



































CR SR





X

Y



Decision

Budget constraints


S = si | i = 1 2 I

15

2032

1518





CS119894 = 119891119860 + 119891119874119891119860 + 119891119874 + 119891119872 + 119891119868

DS119894 = 119891119874 + 119891119872119891119860 + 119891119874 + 119891119872 + 119891119868 (1)




119878119900119894 = (CS119894 minus DS119894) 119909119900119894 + DS119894 (2)









a998400

b998400




119860119900 = int119887

119886[(CS119894 minus DS119894) 119909119900119894 + DS119894] 119889119909119900119894 (5)


119879119860119900 =119899sum119894=1

119860119900119894 =119899sum119894=1

int119887119894119886119894

[(CS119894 minus DS119894) 119909119900119894 + DS119894] 119889119909119900119894119894 = 1 2 119899

(6)


119879119860119900 =119899sum119894=1

int119887119886[(CS119894 minus DS119894) 119909119900119894 + DS119894] 119889119909119900119894 (7)


119860119886 = int119889




c998400d998400




e998400

e

f998400

f



119879119860119886 =119899sum119894=1

119860119886

= 119899sum119894=1

int119889119894119888119894


(9)


119879119860119886 =119899sum119894=1



119860119898 = int119891




119879119860119898 =119899sum119894=1

119860119898

= 119899sum119894=1

int119891119894119890119894


(12)


119879119860119898= 119899sum119894=1



119872 max 119862119900 (119879119860119900) + 119862119886 (119879119860119886) + 119862119898 (119879119860119898)st 119862119900 + 119862119886 + 119862119898 le 119861


(14)











provided


Reliabilitysafety



Empathy addedvalue



SQ5


Safetyconvenience




Convenienceempathy


Pleasureassurance





Added valueempathy









119872 max 119862119900 (119879119860119900) + 119862119886 (119879119860119886) + 119862119898 (119879119860119898)st 119862119900 + 119862119886 + 119862119898 le 150


(15)



















360











5 Conclusions





Competing Interests


Acknowledgments


References

































































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in


































CR SR





X

Y



Decision

Budget constraints


S = si | i = 1 2 I

15

2032

1518





CS119894 = 119891119860 + 119891119874119891119860 + 119891119874 + 119891119872 + 119891119868

DS119894 = 119891119874 + 119891119872119891119860 + 119891119874 + 119891119872 + 119891119868 (1)




119878119900119894 = (CS119894 minus DS119894) 119909119900119894 + DS119894 (2)









a998400

b998400




119860119900 = int119887

119886[(CS119894 minus DS119894) 119909119900119894 + DS119894] 119889119909119900119894 (5)


119879119860119900 =119899sum119894=1

119860119900119894 =119899sum119894=1

int119887119894119886119894

[(CS119894 minus DS119894) 119909119900119894 + DS119894] 119889119909119900119894119894 = 1 2 119899

(6)


119879119860119900 =119899sum119894=1

int119887119886[(CS119894 minus DS119894) 119909119900119894 + DS119894] 119889119909119900119894 (7)


119860119886 = int119889




c998400d998400




e998400

e

f998400

f



119879119860119886 =119899sum119894=1

119860119886

= 119899sum119894=1

int119889119894119888119894


(9)


119879119860119886 =119899sum119894=1



119860119898 = int119891




119879119860119898 =119899sum119894=1

119860119898

= 119899sum119894=1

int119891119894119890119894


(12)


119879119860119898= 119899sum119894=1



119872 max 119862119900 (119879119860119900) + 119862119886 (119879119860119886) + 119862119898 (119879119860119898)st 119862119900 + 119862119886 + 119862119898 le 119861


(14)











provided


Reliabilitysafety



Empathy addedvalue



SQ5


Safetyconvenience




Convenienceempathy


Pleasureassurance





Added valueempathy









119872 max 119862119900 (119879119860119900) + 119862119886 (119879119860119886) + 119862119898 (119879119860119898)st 119862119900 + 119862119886 + 119862119898 le 150


(15)



















360











5 Conclusions





Competing Interests


Acknowledgments


References

































































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in






a998400

b998400




119860119900 = int119887

119886[(CS119894 minus DS119894) 119909119900119894 + DS119894] 119889119909119900119894 (5)


119879119860119900 =119899sum119894=1

119860119900119894 =119899sum119894=1

int119887119894119886119894

[(CS119894 minus DS119894) 119909119900119894 + DS119894] 119889119909119900119894119894 = 1 2 119899

(6)


119879119860119900 =119899sum119894=1

int119887119886[(CS119894 minus DS119894) 119909119900119894 + DS119894] 119889119909119900119894 (7)


119860119886 = int119889




c998400d998400




e998400

e

f998400

f



119879119860119886 =119899sum119894=1

119860119886

= 119899sum119894=1

int119889119894119888119894


(9)


119879119860119886 =119899sum119894=1



119860119898 = int119891




119879119860119898 =119899sum119894=1

119860119898

= 119899sum119894=1

int119891119894119890119894


(12)


119879119860119898= 119899sum119894=1



119872 max 119862119900 (119879119860119900) + 119862119886 (119879119860119886) + 119862119898 (119879119860119898)st 119862119900 + 119862119886 + 119862119898 le 119861


(14)











provided


Reliabilitysafety



Empathy addedvalue



SQ5


Safetyconvenience




Convenienceempathy


Pleasureassurance





Added valueempathy









119872 max 119862119900 (119879119860119900) + 119862119886 (119879119860119886) + 119862119898 (119879119860119898)st 119862119900 + 119862119886 + 119862119898 le 150


(15)



















360











5 Conclusions





Competing Interests


Acknowledgments


References

































































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in





119879119860119898 =119899sum119894=1

119860119898

= 119899sum119894=1

int119891119894119890119894


(12)


119879119860119898= 119899sum119894=1



119872 max 119862119900 (119879119860119900) + 119862119886 (119879119860119886) + 119862119898 (119879119860119898)st 119862119900 + 119862119886 + 119862119898 le 119861


(14)











provided


Reliabilitysafety



Empathy addedvalue



SQ5


Safetyconvenience




Convenienceempathy


Pleasureassurance





Added valueempathy









119872 max 119862119900 (119879119860119900) + 119862119886 (119879119860119886) + 119862119898 (119879119860119898)st 119862119900 + 119862119886 + 119862119898 le 150


(15)



















360











5 Conclusions





Competing Interests


Acknowledgments


References

































































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in






provided


Reliabilitysafety



Empathy addedvalue



SQ5


Safetyconvenience




Convenienceempathy


Pleasureassurance





Added valueempathy









119872 max 119862119900 (119879119860119900) + 119862119886 (119879119860119886) + 119862119898 (119879119860119898)st 119862119900 + 119862119886 + 119862119898 le 150


(15)



















360











5 Conclusions





Competing Interests


Acknowledgments


References

































































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in


















360











5 Conclusions





Competing Interests


Acknowledgments


References

































































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in








5 Conclusions





Competing Interests


Acknowledgments


References

































































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in































































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in



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