HOW TO MEASURE AND IDENTIFY THE ULTIMATE IMPROVEMENT REQUIRED FOR CUSTOMER SATISFACTION.

HAERYIP SIHOMBING1_{, YUHAZRI}2_{, M.Y.and YAHAYA}3_{, S.H.} 1, 2, 3

Faculty of Manufacturing Engineering Universiti Teknikal Malaysia Melaka Malaysia

17609, Durian Tunggal, Hang Tuah Jaya, Melaka, MALAYSIA 1

iphaery@utem.edu.my

1.0 INTRODUCTION

In cur r ent hyper competitive mar kets, the companies’ sur vival in the mar ket and, thus becoming successful in their business field ar e depends on many factor s contr ibuted. Since the main objective of a business is, basically, to cr eate pr ofit by satisfying the customer s, accor ding to Schnaar s (1991), thr ough the cr eation of customer satisfaction w ill enable the company to gener ate some benefits to them, including the r elationship w ith the customer s as a good foundation for the cr eation of loyalty and r epeat pur chase of customer s. On this per spective, Rahman (2004:426) stated the customer satisfaction constitutes as a car dinal indicator . By consider ing on this point of view , customer r elationship development and management systems ar e, ther efor e, need to be focused heavily by companies (Ver hoef et al., 2002), w hile the development of effective customer r elationships need to be r ecognized as an essential component of mar keting strategies (Lymper opoulos et al., 2006:366). Tow ar d this idea w her e ser vice quality as becoming a pr imar y competitive w eapon, then the quality of ser vices is globally r emained as a cr itical point for businesses str ategy to a comparative advantage in the mar ketplace (Hossain & Leo, 2009:338; Staffor d, 1996:6)

The facts, w hat the companies faced to the challenges and competitions in the mar ket ar e not only on how to identify w hat the customer satisfaction and r equir ements. This is due to the actual manifestation of the state of satisfaction is var y fr om per son to per son, also against pr oducts or ser vices. In addition, the customer satisfaction is an ambiguous and abstract concept (Kanojia and Yadav, 2012). Whether they have been or might be successfully implementing or not, accor ding to Kultanan et al., (2006), the customer r equir ements ar e much mor e technically complex than in consumer mar ket, especially in ser vice sector . In pr oduct quality measur ement, even they ar e still in scientific debate on super ior ity of one method over another . This is due to the methods used ar e usually not tr eated as complement ar y, r ather as alter native tools (Zelma, 2008). Hence, as pr eviously w as under lined by Grigor oudis et al., (2002:1), a number of measurable par ameter s that dir ectly linked to sever al aspects of company’s pr oducts/ ser vices or elsew her e r emained as an abstr act and intangible notion. In addition, ther e ar e a common pr oblems occur r ed w hile analyzing data fr om customer satisfaction sur veys w hich is car r ied out by compar ing the stated and der ived impor tance for a set of satisfaction dimension (Gr igor oudis and Spyr idaki , 2003:229; Kano et al., 1984). Ther efor e,

(i) Fir st, the companies need to alw ays taken their business str ategy into account in pr oviding goods

and ser vices to satisfy the customer s by inter pr eting today's competitive mar ket as a cr ucial effor t in cr eating a loyal customer , w hich involves of captur ing and r etaining them. In this per spective,

In current hypercompetitive markets, customer satisfaction is one of major requirements that enable the company to generate some benefits to their business. This is also including the relationship with the customers as a good foundation for the creation of loyalty and repeat purchase of customers. However, the customer requirements are much more technically complex than in consumer market, especially in service sector. On the other hand, the analysis of importance and performance assumed on Kano method to measure customer satisfaction also lead to misleading the implications of customer satisfaction since some scholars use this technique into symmetric and linear relationships between attribute level performances. In this study, we poposes the approach on how to find the priority improvement or the most significant element required for improvement based on Kano method as a measurement basis ; using Kano manipulating graph, ranking level, and then a simplification approach toward the graphs to enable the priorities / significant element required for improvement determined and justified. To justify the approach proposed, some trials carried out in service companies as the case of study.

Key w ords: Customer satisfaction, Kano method, Kano manipulating Graph, priorities for improvement required.

A BSTRA CT

HOW TO MEASURE AND IDENTIFY THE ULTIMATE

IMPROVEMENT REQUIRED FOR CUSTOMER SATISFACTION

w hen customer s ar e satisfied, they ar e mor e likely to r et ur n. While they ar e dissatisfied, how ever , the customer s ar e mor e likely to go elsew her e. Ger son (2003) stated the under standings of customer ’s expectation depend on the fulfilment of customer ’s need and the existence of the pr oduct or ser vices per for mance deliver ed to them. Besides the quality and pr ice factor s, since the r etention of ver y loyal customer is a key to or ganizational sur vival, the other s impor tance factor that should be ther efor e consider ed by them is also how to attr act the customer to buy the pr oduct (Jones & Sasser , 1995). Hence, it can be concluded that a major outcome of mar keting activities ar e not only r elated t o how much pr ofit can be achieved, but also on how high customer satisfaction r esulting fr om company’s activities. This can be exper ienced in a var iety of situations and connected to both goods and ser vices (CSSP,2007:6 ; Cengiz, 2010:78).

(ii) Second, Tse and Wilton (1988) ar gues that the customer r esponse should be consider ed to the mismatches (disconfir mation) per ceived betw een pr ior expectations and actual per for mance of a pr oduct. A compar ison of the per ceived per for mance against the expectations is as a level of customer feelings (Kotler , 2003) w her e the expectation, as asser ted by Hsu and Cai (2009:5), is as a cr itical antecedent of satisfaction that becomes a deter minant of attitude. The customer satisfaction has r elated to an emotional challenge of the exper ience tow ar ds the consumption of a pr oduct / ser vice, the pur chase evaluation, accor ding to Wilkie (1990), is ther efor e r equir ed against the customer 's expectations and dissatisfaction of the selected alter natives. Especially, w hen the expectations r esults (outcome) w er e not met (Engel, 1990). Hence, due to the customer satisfaction is gr eatly affected by customer expectations (CSSP, 2007:6), how customer s, accor ding to Gr önr oos (1998:329), per ceived good the pr oduct quality they should be based on the measur ement against w hat the appr oaches of attitude deter minant of customer satisfaction r elated to the ser vice per ceptions and expectations value. To addr ess this issue, scholar s discussed about as follow s: (a) the pr ocess of cr eating and deliver ing the value to customer s in the mar ketplace as the

combination of customer satisfaction and pr ice (Collier , 1995).

(b) how to cr eate the ser vice values w ith the aim of satisfying customer w her e the company must to cor r ectly attr ibute the factor s r elated of the identified quality so that cor r ect decision can be made (Chen and Lee, 2007).

(c) the assumption that a customer w ill lear n fr om exper ience, w her e the decr easing levels of

expectations disconfir mation against goods and ser vices should affect customer satisfaction (McQuitty et al., 2000).

(d) the using of satisfaction r atings as the per for mance indicator of pr oducts and ser vices deliver ed, beside the indicator of the company’s futur e (Matzler and Hinter huber , 1988). (iii) Thir d, accor ding to Kumar et al., (2008:176-177), they ar e, how ever , not clear ly to differ entiate

the ser vice quality constr ucted; distinguishing betw een functional ser vice quality (FSQ) w hich means doing things nicely and technical ser vice quality (TSQ) is as doing things r ight. Specifically in ser vice business, most of the case discussed the ser vice per ceptions and expectations. (i.e. bank (Alhemoud, 2010; Naeem & Saif 2010; Guo et al., 2008; Jabnoun & Al-Tamimi, 2002), financial and loan funding (Gottschalk, 2008), hospital (Br ennan, 1995; Williams et al., 1998; Peltola et al., 2007; Padma et al., 2009), public ser vice (Rodr íguez et al., 2009), secur ity firm (Xu and Goedegebuur e, 2005), air lines (Gustafsson et al., 1999; Fr ost and Kumar , 2001), education (Joseph et al., 2005; García-Ar acil, 2009), etc.). In or der to incr ease the company’s competitiveness, ther efor e the companies should pay gr eater attention to customer ser vice quality and customer satisfaction thr ough the der egulation of the total per ception r elated to the quality of a ser vice as the outcome (technical quality), rather than simply addr essing ser vice quality fr om a functional per spective (Gr önr oos, 1998:329; Kumar et al., 2008:183; Kang and James, 2004:266). Steve et al. (2001) in their r esear ch added that in satisfying the customer s ar e also gr eatly depends on a smooth r unning pr ocess appr oach to successfully completing a customer tr ansaction. Briefly to say, ther e is a positive linear r elationship betw een staff satisfaction, ser vice quality and customer satisfaction leading to pr ofitability (Hallow ell et al., 1996; Yee et al., 2009).

Based on pr oblems afor ementioned, each of scholar s pr oposed the appr oaches how to measur e the customer satisfaction. (i.e. IPA to impr ove or der -w inner cr iter ia and w in or der (Lee et al., 2009), IPA w ith str ength and w eakness (Zemla, 2008), IPA w ith Kano Model and Dematel (Hu et al., 2009), MUSA method (Gr igor oudis and Spyr idaki, 2003), str uctur ing the customer r equir ement model w ith Quality Function Deployment (Kultanan et al., 2006; Matzler and Hinter huber , 1998; Lai et al., 2004), the w eighted aver age scor e

model tow ar d Kano model (Bhattachar yya and Rahman, 2004; Xu et al., 2009), customer satisfaction thr ough

cr eating loyal customer s (Rahman, 2004), the influences the components of pr oducts and ser vices (Sauer w in et al., 1996; Sauer w ein, 1999), the impor tance of quality attr ibutes using 8 categor ies of Kano model (Yang, 2005),

potential benefit acquir ed w ith quality elements classified into 3 categor ies of Kano models (Hsu et al., 2007), inter activity-satisfaction r elationship (Zhao and Dholakia, 2009), integr ated appr oach of Kano model and ANOVA technique (Lai and Wu, 2011), SERVPEX (Robledo, 2001), integr ative configur ation of customer value by 3-angle view (Khlaifa, 2004), to conver t of attr active quality attr ibute to must-be quality attr ibute and one-dimensional quality attr ibute (Lee et al., 2009), integr ation of FMEA and Kano model (Shahin, 2003), integr ating

the Kano model, AHP, and planning matrix (Bayraktağlu and Özgen, 2007), the multistage method for w eighting

customer satisfaction (Cr ostack et al., 2010), Br ady and Cr onin’s model tow ar d Kano Model (Högstr om et al.,

2010), better -w or se diagr am of Kano model (Witell and Löfgr en, 2007), the dynamic of ser vice attr ibutes of attr active quality in Kano model (Witell and Fundin, 2005), integr ation of Kano model and exit-voice theor y (Lee

et al., 2009), life cycle design (Er nzer and Kopp, 2003; etc). They ar e, as commented by Tontini and Silveir a (2007:497), how ever not dir ectly discuss the aspects of per for mance-impor tance infer r ed to the pr ior ities of impr ovement fr om the attr ibutes’ positioning (based on cur r ent level per for mance) r elated t o the differ ent of Kano quality elements r esult ed (in w hich impr ovement pr ior ities can be infer r ed by analyzing the cur r ent level of performance). In this sense, as was underlined by Mikulić (2007), they would potentially lead to misleading the implications of customer satisfaction. Especially, tow ar d the analysis of impor tance and per for mance assumed as the technique of symmetr ic and linear r elationships betw een attr ibute level per for mance and OSC (asymmetr ic impact on over all customer satisfaction). Ther efor e, since the major assumptions of the Kano model is, actually, pointing out on the phenomenon of pr oduct/ ser vice attr ibutes that can be infer r ed by the cur r ent level of per for mance - that show ing as an asymmetr ic and nonlinear impact to the cer tain pr oduct/ ser vice attr ibutes (quality elements) - a fur ther step in identifying the Kano quality elements that pr imar ily have an impact on cr eating satisfaction thr ough the impr ovement pr ior ities finding, accor ding to Sihombing et al., (2012), is ther efor e r equir ed.

2.0 LITERATURE REVIEW

The “Voice of the Customer ” (VOC) is a pr ocess used to captur e the r equir ements or feedback fr om the customer in or der to pr ovide best-in-class ser vice or pr oduct quality. The using of VOC is to descr ibe the stated and unstated needs or r equir ements of the customer in a var iety of w ays such as dir ect discussion or inter view s, sur veys, focus gr oups, customer specifications, obser vation, w ar ranty data, field r epor ts, and complaint logs.

2.1 Kano Method

The Kano model offer s some insight into the pr oduct attr ibutes per ceived to be impor tant to customer s. Kano’s model employed is as a star ting point of the pr oposed quantitative analysis that involves the conducting of pr eliminar y study, developing, and administr ating the Kano questionnair e. In this method, the most fr equent obser vations of the sample set of r esponses ar e consider ed as the final Kano categor y for CR (customer r equir ements) (Kano et al., 1984), w her e;

(i) Quantitative analysis of customer satisfaction into Kano’s model is car r ied out by calculating

tw o values w hich ar e “better ” and “w or se” in or der to r eflect the aver age impact of a CR on customer satisfaction (CS) or dissatisfaction (DS) of all customer s (Ber ger et al., 1993) as follow s:

(ii) In making decisions about pr oduct developments, the featur es that have to be taken into consider ation for impr ovement ar e the featur es that has the gr eatest influence on the per ceived pr oduct quality (Sauerw ein et al., 1996; 1999), w her e their evaluation r ule as follow s :

M > O> A >I (3) In this for mula, M stands for ‘Must-be’ r equir ements, O for ‘One-dimensional’ r equir ements, A for ‘Attr active’ r equir ement and I stands for ‘Indiffer ent’ r equir ements. It means that the

(a) Coefficient of cause of satisfaction (CS):

O + A (1) M + O + A + I

(b) Coefficient of cause of dissatisfaction (DS):

O + M (2) M + O + A + I

r ange of ‘Must-be’ attr ibute have the lar gest range and it is lar ge than the other attr ibute. This evaluation r ule r ecommends the fir st taking those pr oduct r equir ements into consider ation, w hich ar e allocated to the r equir ement Kano’s method categor y M because disr egar ding of such elementar y basic elements cr eates dissatisfaction (Zanger and Baier , 1999). The ‘Indiffer ent’ attr ibute has the least acuteness because it has only minor influence on the employee’s satisfaction. If this attr ibute did not being fulfill, the employees w ill does not feel dissatisfy. Table 1 show s the six categor ies quality attr ibutes influenced to the customer satisfaction.

Table 1: Kano’s eval uat ion table

F U N C T IO N A L DYSFUNCTI ONAL 1. Like 2. Must-be 3. Neutr al 4. Live with 5. Dislike

1. Like Q A A A O

2. Must-be R I I I M

3. Neutr al R I I I M

4. Live with R I I I M

5. Dislike R R R R Q

A = Attr active ; M = Must- be; R = Rever se; O = One- dimensional ; I = Indiffer ent; Q = Questionable

(a) Must-be Requir ements (Thr eshold/ Basic attr ibutes). If these r equir ements ar e not fulfilled, the customer w ill be extr emely dissatisfied. The must-be r equir ements ar e basic cr iter ia of a pr oduct. Fulfilling the must-be r equir ements w ill only lead to a state of “not dissatisfied”. Must-be r equir ements ar e in any case a decisive competitive factor , and if they ar e not fulfilled, the customer w ill not be inter est ed in the pr oduct at all.

(b) One-dimensional Requir ements (Per for mance/ Linear). With r egar d to these

r equir ements, customer satisfaction is pr opor tional to the level of fulfilment – the higher the level of fulfilment, the higher the customer ’s satisfaction and vice ver sa. One-dimensional r equir ements ar e usually explicitly demanded by the customer .

(c) Attr active Requir ements (Exciter s/ Delighter s). These r equir ements ar e the pr oduct cr iter ia w hich have the gr eatest influence on how satisfied a customer w ill be w ith a given pr oduct. Attr active r equir ements ar e neither explicitly expr essed nor expected by the customer . Fulfilling these r equir ements leads to mor e than pr opor tional satisfaction. If they ar e not met, how ever , ther e is no feeli ng of dissatisfaction.

(d) Indiffer ent Attr ibutes. The customer does not car e about this featur e. Means that the

customer is not concer ned w ith this pr oduct attr ibute and is not ver y int er ested w hether it is pr esent or not.

(e) Questionable Attr ibutes. It is unclear w hether the customer expects this attr ibute. This situation occur s if ther e is a contr adiction in the customer s’ answ er s to the pair ed questions. A questionable r ating indicates incor r ectly phr ased question, misunder standing of a question, or an incor r ect r esponse.

(f) Rever se Attr ibutes: Means that some of the r espondents’ satisfaction decr eases w ith

the existence of this r equir ement, but they also expect the r ever se of it.

(iii) Categor y Str ength (CA) Value. This categor y str ength (CAT) method is a suitable method in

deter mining the pr ior ities w ithin a r equir ements categor y. Fr om the value of CAT, it is also can be r anking in or der to know n w hich categor y have to be focus fir st. Usually, the maximum value of CAT is placed at the fir st place w hich means it has the pr ior ity to be focus among the other r equir ement. Besides, the low er the per centage of the CAT value means that the r equir ement that being pr ovided ar e satisfy the customer or employee feeling. The CAT index can be calculated using the CAT for mula as follow :

CAT = 1st _{most fr equently-given nomination (%) – 2}nd _{most fr equently nomination (%) (4)}

(iv) Categor y Fuzzy Kano. Lee and Huang (2009:4479 and 4481) said that traditional Kano

questionnair e (TKQ) unable to sufficiently r eflect the complex thought of an individual since Kano’s model ar e alw ays lack of consider ing the fuzzy and uncer tainty of mentality and affection w hen devising questionnair e. In addition, in Kano’s tr aditional evaluation sheet, all quality attr ibute str engths ar e unequal; it is unr easonable and not precise to sum up

equivalently each r esponse fr equency of ever y quality attr ibute to evaluate the influences of quality attr ibutes (Lee et al., 2011:180). Ther efor e, simply using a mode statistic as the classification cr iter ion is not appr opr iate. It is necessar y to adopt a ‘continuous’ appr oach for Kano’s model to quantitatively analyse the aver age impact of a CR on the over all customer satisfaction (OCS) (Wu and Wang, 2012:536). To over come these difficulties, accor ding to Mikulic´ and Dar ko Pr ebežac (2011:50), it should be r ecognised that the key issue that deter mines the Kano categor y of an attr ibute is not the per for mance of that attr ibute; r ather , it is actually the pr ovision (or non-pr ovision) of a mor e-or -less expected benefit. On this, to fur ther incr ease the r eliability of attr ibute categor isations, Kano’s method should r efer to the pr ovision (or non-pr ovision) of the benefits to be expected thr ough the pr ovision of an attr ibute r ather than the pr ovision of the attr ibute itself.

(a) Fuzzy Kano Questionnair e (Lee and Huang, 2009:4481)

Table 2: Fuzzy Kano’s evaluation tabl e Fuzzy Kano Questionnair e

Like Must-Be Neutr al Live - W ith Dislike

Functional 20% 50% 30% - -

Dysfunctional - - - 50% 50%

(b) Matr ix calculation to compar e and evaluate “need pr ofiles” based on functional and dysfunctional. On this, FI (functional scor e: satisfaction degr ee assessing the existence of the ∑need or suf iciency), DI (dysfunctional score: dissatisfaction degr ee assessing the inexistence of the need or insufficiency), and RI (dissatisfaction degr ee r elated to existence and measur ing a r ever se index) (Rejeb et al., 2008).

Table 3: Revision of Kano’s eval uat ion t able

F U N C T IO N A L DYSFUNCTI ONAL 1. Like 2. Must-be 3. Neutr al 4. Live with 5. Dislike

+2 +1 0 -1 -2

1. Like +2

2. Must-be +1

3. Neutr al 0

4. Live with -1

5. Dislike -2

A = Attr active ; M = Must- be; R = Rever se; O = One- dimensional ; I = Indiffer ent; Q = Questionable

FI = [ Degr ee of satisfaction w ith existence / Number of r esponse X 2]

(only the ≥ 0 at the functional questions) (5) DI = [ Degr ee of dissatisfaction w ith inexistence / Number of r esponse X 2]

(only the ≥ 0 at the functional questions) (6) RI = [ Degr ee of dissatisfaction w ith existence / Number of r esponse X 2]

(only the ≥ 0 at the functional questions) (7) (v) Kano Manipulating Graph. Since in the r anking system based on pairw ise value, it can be assumed that the functional value is r ever se of dysfunctional value and vice ver sa (Sihombing et al., (2012a,b) as follow s:

F = ~ DF or DF = ~ F

 F = DF’ or DF = F’

The r anking value based on this appr oach is as compar ison betw een F vs. DF and DF’ vs. F. This appr oach also can be constr ucted into the gr aph as for consider ation taken for impr ovement r equir ed. Based on this r eason, the modification is car r ied out tow ar d equation (5) and equation (6) w her e SSI is sum of satisfaction existence and DDI is dissatisfaction existence. Below is the for mula of how to deter mine the r anking values:

(8)

3.0 METHODOLOGY

This study is car r ied out on how to find out the customer satisfaction position and the pr ior ities impr ovement r equir ed. The data and infor mation r elated to their satisfaction per for mance is categor ized into the impor tant level based on r anking level. The r anking level used is t o gener ate the impor tance level to meet the customer needs, w hile Kano model is to deter mine w hat the factor s that satisfy the customer .

Figur e 1 : Flow to fi nd the pr ior ities i mpr ovements usi ng Kano

Figur e 1 show s the fr amew or k to find the impr ovement pr ior ities based on the questionnair e developed r efer s to Par asuraman’s Ser vqual dimension (Parasur aman et al., 1988) and Her zber g’s Hygiene-Motivator factor s (Her zber g et al., 1959) using Kano method. Each element of Ser vqual r elated to ser vice quality attr ibutes and Her zber g theor y r elated to Hygiene – Motivator factor s ar e gener ated into Kano pair w ise of questions for mulated. The fir st question concer ns to the r eaction of the customer r elated to functional for m (F) of the question, w hile for the second question concer ns to the r eaction of dysfunctional for m (DF) of the question.

4.0 RESULT AND DISCUSSION

Based on 5 cases taken (Table 4) against the sur vey questionnair e gener ated w ith quality attr ibutes cr iter ia r efer s to Kano model as show n in Table 5a~ 5e ar e as follow ing:

(i) Ther e ar e many of “Must-be” attr ibutes pr esent in the cases of Event Or ganizer and Cour ier Ser vice.

(ii) Only one (1) quality attr ibutes w ith “Must-be” criter ia existed in case of Hospital ser vice.

(iii) In cases of Hotel and Bank ser vice show s that many of “Indiffer ent” and “One-Dimensional” quality

attr ibutes existed

Table 4: Case St udy of Ser vice Company

No Exter nal Customer

Satisfaction Samples

1 Bank 60

2 Event Or ganizer 30

3 Cour ier Ser vice 138

4 Hospital 56

5 Hotel 145

To addr ess such cases of how to find the impr ovement pr ior ities, the appr oach of Saur w ein et al., (1996, 1999) using M>O>A>I face the difficulties since the r esponse data of sur vey pr esence w ith the similar quality attr ibutes. While to find the pr ior ities using other s Kano method (par t 2b~ 2e) for the impr ovement r equir ed by each of the companies ser vices ar e in ambiguity since each of cases show s the differ ent r anking values of the methods used. This means that the method pr oposed in par t 2a ~ 2e to pr ocess the data for finding the

SATISFACTION

HYGIENE-MOTIVATOR [ Employee Satisfaction] SERVQUAL

[ Customer Sat isfaction]

Pr ocess for findi ng the pr ior ities for i mpr ovement

Kano Mani pulati ng Gr aph

Impr ovement pr ior it ies for sat isfact ion KANO Met hod

pr ior ities impr ovement r equir ed by the ser vice company is in contr adiction since they ar e not consistent to each other . Ther efor e, to find the pr ior ities for impr ovement elements of ser vice r equir ed ar e as follow s:

(i) Fir st, deter mine w hich of r esponse against the aspects/ elements gener ated in the questionnair es

r esulting w ith quality attr ibutes based on Sauer w ein appr oach (M>O>A>I). (See the yellow mar ks in the Table 5a~ 5e).

(ii) Second, compar e each the elements of the pr evious quality attr ibutes based on r anking level using

Kano method in par t 2. Point to note, if such elements having same of the higher r anking (the r anking level is 1), this can be deter mined as the top pr ior ities for impr ovement r equir ed. How ever , if the r anking against the element is differ ent , then find w hich of the elements having mor e top r anking.

(iii) Thir d, to justify w hich of the elements r equir ed for impr ovement pr ior ities, then as follow s:

(a) Compar e the elements based on quadr ant position of the gr aph (called Kano Manipulating

Gr aph) as follow s:

Method F vs. DF DF’ vs. F’ SSI vs. DDI Cs vs. DS K CAT FI vs. DI

Quadr ant 2 2 &4 4 2 & 4 4 4 1 & 3

(b) Find w hich of the elements having most fr equently pr esented in the quadr ant mentioned above (Table 7).

(c) Compar e the most fr equently elements existed w ith the most of higher r anking (Table 5a~ 5e). In table 6 show s that the r esult of the impr ovement pr ior ities r equir ed is in gr een mar ks.

4.1 Cases ( Based on Table 4)

(i) Only 1 “Must-be” quality attr ibutes existed. Table 5a show s the feeling of customer r equir es the

ser vice w ith only 1 of “Must-be” quality attr ibutes existed (see a case of the Hospital ser vice). The r anking level using F vs. DF; CS vs. DS; FI vs. DI (RI) and K is no.1 as the higher pr ior ity. In this case, Sauer w ein appr oach using M>O>A>I (Sauerw ein et al., 1996; 1999) and Fuzzy Kano as pr oposed by Lee and Huang (2009) is consistent.

(ii) Many of “Must-be” quality attr ibutes existed. The customer r equir es the ser vice w ith mor e of “Must-be” quality attr ibutes existed ar e as follow ing:

(a) In case of the Event Or ganizer (Table 5b), ther e ar e “Must-be” quality attr ibutes on K1, K2,

K13, K14, K15, K16, and K29. How ever , based on r anking levels show that only K1 and K15 w hich having mor e of the top ranking level. Using F vs. DF; SSI vs. DDI; CS vs. DS; FI vs. DI (RI) ; K and CAT, the ranking level of K15 is no.3, 2,2,1,3, and 16 , w hile the r anking level of K1 is No. 4,1,1,4,4, and 1 r espectively. Since both of these elements having mor e of the top r anking level, the justification in deter mining w hich one of these elements ar e as the most pr ior ities using quadr ant position of Kano Manipulating Gr aph as mentioned pr eviously. Table 6 show s that K15 is the top pr ior ity since this element is fr equent ly existed in the cer tain quadr ant pr er equisite of gr aph. (K15 fr equently in 6 times, w hile K1 only in 4 times) (b) In case of the Cour ier Ser vice (Table 5c) , ther e ar e “Must-be” quality attributes on K3, K6,

K7, K8, K9, K10, K11, K12, K13, K15, K18, K19, K20, K24, AND K25. Among of these elements w hich having the higher r anking is K3, K7, K8, K11, K13, and K20. Based on the quadr ant position of gr aph, w e can justify that the most pr ior ities among these elements is on K8. This element fr equently found in 7 times.

(b) Ther e is no “Must-be” quality attr ibutes existed, only “Indiffer ent” and “One-Dimensional”

attr ibutes.

(i) Table 5d show s the customer r equir es the ser vice w ith mor e of “One Dimensional” quality

attr ibutes existed (case of the Bank ser vice), that is K1, K2, K5, K6, K8, and K9. How ever , based on r anking levels only K5 and K6 w hich having mor e of the top r anking level. Using SSI vs. DDI; CS vs. DS; FI vs. DI (RI) ; K and CAT, the r anking level of K5 is no.1, 1, 6, 1, and 1, w hile the r anking level of K6 is No. 2, 3, 1, 2, and 5 r espectively. Based on quadr ant position of Kano Manipulating Gr aph, w e found that K5 is the top pr ior ity since this element fr equently existed in 4 times and having the most of top r anking level).

(b) In case of the Hotel Ser vice (Table 5e), ther e is “Indiffer ent” quality attr ibutes existed on all the elements of ser vice gener ated in the questionnair es. How ever , only one element w hich having the higher r anking, that is K8. (The r anking level of F vs. DF; CS vs. DS, and K is no. 1, 2, and 1). Based on the quadr ant position of gr aph, this element (K8) also having fr equently occur r ed, that is 6 times. Ther efor e, K8 is as the top pr ior ity for impr ovement r equir ed by the company.

4.2 Trials ( Based on Table 4)

Using the method discussed above, the tr ials car r ied out ar e against employees’ satisfaction of Hotel Ser vice (Table 6a) and customer satisfaction of Pos Ser vice (Table 6b) as below :

(i) In the case of employees’ satisfaction measur ement using Her zber g r elated to hygiene and motivator factor s, 150 sur vey r espondents ar ticulate their feeling mor e on elements K12 and K20 that having “One-dimensional” quality attr ibutes. These elements ar e also having mor e of the top r anking levels. This is consistent w ith Sauerw ein approach using M>O>A>I since ther e ar e no “Must-be’ quality attr ibutes existed. Both of these elements ar e having same fr equently occur r ence in the quadr ant of gr aph method w ith the quality attr ibutes stated about the satisfaction. The pr ior ities impr ovement r equir ed in this case is on how they ar e existed in the factor s of Her zber g’s motivator s. This is due to fulfil the motivator s factor s w ill deter mine how satisfy the employees after the hygiene factor s had alr eady fulfilled.

(ii) In the POS ser vice case, 180 r espondents answ er the sur vey w her e mor e of “Must-be” quality attr ibutes existed (K3, K6, K8, K9, K17, K18, K19, K20, K22, and K25). Based on r anking level, element K17 having mor e of the top r anking level. Using F vs. DF ; SSI vs. DDI; CS vs. DS; FI vs. DI (RI) ; K and CAT, the r anking level of K17 is no.1, 2, 3, 1, 1, and 4 r espectively. While based on quadr ant position of Kano Manipulating Gr aph, although K9 found is mor e fr equent ly than K7 (7 ver sus 6 times), element K7 is, how ever , having the most of top r anking level. Ther efor e, the decision for pr ior ity impr ovement r equir ed is on element K7.

5.0 CONCLUSION

To deter mine the top or fir st pr ior ities for impr ovement r equir ed by the company thr ough Kano method is not alw ays easy to answ er , especially if they pr esence w ith many of the same quality attr ibutes. Based on cases discussed, Sauer w ein appr oach using M>O>A>I to decide the impor tance based on Kano method and Ber ger appr oach thr ough CS vs. DS graph or CS-DS for r anking level faced the ambiguity to justify w hich the element r equir ed for fir st impr ovement pr ior ity. The other s method, such as Fuzzy Kano, Tontini appr oach thr ough CAT, and Rejeb pr oposal ar e actually also make the justification for finding the pr ior ity for impr ovement r equir ed becoming mor e confusing. Consider ing on this r eason, the Kano manipulating graph pr oposed thr ough DF’ vs. F’ against F vs. DF as w ell as K r anking to enr ich the pictur e of customer expectation thr ough Kano method ar e actually enable us t o find w hat the pr ior ity r equir ed for impr ovement since it can extent the pictur es of customer need based on data collected.

In this point of view , to find the most impor tant for impr ovement pr ior ities, Sauer w ein appr oach tow ar d r anking levels and the gr aphs need a simplification of data obser vation. Fir st, the simplification tow ar d the gr aphs as pr oposed in par t 4c that concentr ate on cer tain quadr ant of the gr aphs. Second, the simplification tow ar d the r anking level by only consider ing the most of top r anking level fr om the methods pr oposed in par t 2. Based on both simplifications, w e can justify and choose w hich of the most pr ior ity element for impr ovement taken as having pr oven in tr ial cases.

In this study, since the appr oach to det er mine customer r equir ements by extending the Kano method need mor e calculation car r ied out and then both simplification should be done to pr ocess the justification for pr ior ity impr ovement r equir ed, fur ther study on how deter mine the pr ior ity for impr ovement w ith simply appr oach based on Kano method ar e how ever r equir ed.

ACKNOWLEDGMENT

The author s w ould like to thank CRIM-UTeM. This pr oject is suppor ted by CRIM thr ough PJP/ 2011/ FKP (11D) S00878.

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Table 5a: Customer Sati sfaction: Case of Hospi tal Ser vice

Table 5c: Customer Satisfaction: Case of Cour i er Ser vice

Table 5d: Customer Sati sfaction: Case of Bank Ser vice

Table 6a: Customer Sati sfaction: Case of Hotel Employee’s Satisfaction

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pp.176-187.

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Table 5a: Customer Sati sfaction: Case of Hospi tal Ser vice

Table 5c: Customer Satisfaction: Case of Cour i er Ser vice

Table 5d: Customer Sati sfaction: Case of Bank Ser vice

Table 6a: Customer Sati sfaction: Case of Hotel Employee’s Satisfaction