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Kano model lectura_2

  1. 1. Identification of satisfaction attributes using competitive analysis of the improvement gap Ge´rson Tontini and Ame´lia Silveira Department of Business Management, Regional University of Blumenau – FURB, Blumenau, Brazil Abstract Purpose – To analyze the limitations of two methods used in the identification of satisfaction attributes in products and services – importance performance analysis (IPA) and Kano method – and to propose a new method for identification of improvement opportunities based on the competitive analysis of the improvement gap. Design/methodology/approach – A case analyzing attributes of the service “rodizio de pizzas” a kind of pizzeria found in Brazil, was used to illustrate the proposed method. Resulting from a focus group, four attributes, one of them being an innovation, were specifically chosen to include the different categories of the Kano model: basic, performance and excitement attributes. A survey was conducted with a random sample of 110 undergraduate students that eat regularly at pizzerias. Findings – As a major limitation, IPA leads to different conclusions depending on how an attribute’s importance is figured. Also, it does not take into consideration the non-linear relationship between the performance of the attributes and customer satisfaction, possibly misleading improvement decisions and hindering the introduction of innovations. The Kano method identifies the non-linear relationship between performance and satisfaction, but it does not take into consideration the current level of attributes’ performance in the analysis. The proposed method successfully identified improvement opportunities in a service case, including the possible impact of including a new attribute, i.e. an innovative attribute, overcoming limitations of the IPA and of the Kano method. Originality/value – The paper provides an intuitive and simple method that correctly identified improvement decisions in the case studied, including the introduction of an incremental innovation. Keywords Customer satisfaction, Service industries, Brazil, Operations management Paper type Research paper Introduction Considering that customer loyalty is a key factor for business success in a competitive market, companies should find out how to increase and sustain it in the long-term. Service quality and customer satisfaction have been recognized as the main antecedents of customer loyalty. (Anderson and Mittal, 2000; Wilkins et al., 2007; Grace and O’Cass, 2005; Karatepe et al., 2005; Chow et al., 2007; Brady et al., 2002; Hume et al., 2006; Stuart and Tax, 2004). In fact, the dominant literature also suggests that quality is the main antecedent of customer satisfaction (Cronin and Taylor, 1992; Anderson and Sullivan, 1993; Caruana, 2002; Brady et al., 2002). Thus, continuously improved quality should be the focus for any company. Different from goods, almost all aspects of service operations directly impact customers and their evaluation of the service quality. Thus, any initiative to reduce costs or to improve the service provided needs to consider the impact on quality. The current issue and full text archive of this journal is available at www.emeraldinsight.com/0144-3577.htm IJOPM 27,5 482 International Journal of Operations & Production Management Vol. 27 No. 5, 2007 pp. 482-500 q Emerald Group Publishing Limited 0144-3577 DOI 10.1108/01443570710742375
  2. 2. Owing to services characteristics, firms must rely on customers’ perceptions of service quality to identify strengths and/or weaknesses and design appropriate strategies. It is argued that service quality is viewed as being more closely linked to the actual service provision and, thus, is multidimensional by nature (Grace and O’Cass, 2005). Most of the studies found in the literature try to explain the dimensions of the quality construct or to design instruments to be applied across different services (Gro¨nroos, 1984; Parasuraman et al., 1988; Brady and Cronin, 2001; Caro and Garcı´a, 2007). Usually, these generic scales need to be adapted to each situation since, regardless of the scale used to measure service quality, it is based on the evaluation of service attributes that are grouped in different dimensions. Since, these attributes are context specific they should be identified for each service (Karatepe et al., 2005). Service quality, and customer satisfaction, can be improved by managing the performance of the service attributes. Since, not all attributes have the same role in satisfying customer needs, it becomes important to find out how their performance impacts on customer satisfaction. It means that a company should evaluate the importance of the service attributes for the customers and evaluate the current performance of these attributes to plan quality improvements. Most of the traditional techniques that aim to find out the relative importance and performance of attributes assume that customers have previous knowledge about the service (Deszca et al., 1999), hindering the introduction of innovations. Besides, they assume that there is a linear relationship between attribute performance and customer satisfaction, which may lead to wrong decisions about which attributes should be improved or offered to increase customer satisfaction (Huiskonen and Pirttila¨ 1998). The Kano model of customer satisfaction (Kano et al., 1984; Berger et al., 1993; Nilson-Witell and Fundin, 2005; Yang, 2005) proposes that the relationship between an attribute’s performance and satisfaction is non-linear. This model classifies product and service attributes as basic, performance and excitement factors, but it does not take into consideration the present level of attributes’ performance, being limited as a tool to find out improvement opportunities. Although there has been debate in the literature about the problems regarding the importance performance analysis – IPA (Ting and Cheng, 2002; Matzler and Sauerwein, 2002; Matzler et al., 2003, 2004; Eskildsen and Kristensen, 2006), there is a lack of studies that try to integrate the Kano model with the IPA to overcome some of the problems of both methods. This work has as an objective to evaluate the limitations of the IPA and of the traditional Kano model analysis, proposing a new approach to identify the impact of variations in attributes’ performance on customer satisfaction. The results presented in this paper are of particular interest to operations managers involved in the design of services and their components. The paper is structured as follows: first we explain the IPA and explore its limitations based on a literature review. Then, we present the Kano model and explore the possible mistakes that a company may incur by not considering the non-linear relationship between attribute performance and customer satisfaction. Following that, we present the methodology used to illustrate the proposed method and, using the research data, we analyze the limitations of IPA and Kano model analysis, confirming some of the results found in the literature. Finally, we introduce and illustrate the new method, showing that in the studied case it overcame some of the limitations of the IPA and of the Kano model analysis. Identification of satisfaction attributes 483
  3. 3. The importance performance analysis The IPA, introduced originally by Martilla and James (1977), allows a company to identify which attributes of its product or service should be improved to become more competitive in the market. Typically, data coming from customer satisfaction surveys are used to build a matrix, where the importance is shown by the y-axis and the performance of the attribute by the x-axis (Figure 1). In the traditional IPA (Figure 1(a)), the matrix is divided into four quadrants. An attribute located in quadrant I has high importance and high performance, representing a possible competitive advantage. An attribute that has high importance, but low performance, should receive immediate attention (quadrant II). Quadrant III contains the attributes with low importance and low performance, not requiring additional effort on them. Quadrant IV concentrates the attributes with high performance but with low importance. In this case the company can be wasting resources that could be better used in another place. One possible disadvantage of this quadrant approach is that “a minor change in the position of an attribute can lead to a dramatic change in the attribute’s inferred priority” (Eskildsen and Kristensen, 2006, p. 42). Slack (1994) proposes a different way to analyze the IPA matrix, dividing it into non-symmetrical action zones (Figure 1(b)). In Figure 1(b) the importance and performance axes in Slack’s proposal were inverted to become compatible with the traditional IPA (Figure 1(a)). Slack’s approach allows for a more continuous transition in the inferred priorities (Eskildsen and Kristensen, 2006) and the reasoning behind it is that customers could accept lower performance in less important attributes and require higher performance of more important attributes. Evaluating attributes’ performance The evaluation of attribute performance in IPA may be carried out through the disconfirmation paradigm (Parasuraman et al., 1988) or through the use of performance only scales. Several works found that performance only scales outperform difference scales (perceived – expected performances) in predicting the overall service quality (Cronin and Taylor, 1992; Parasuraman et al., 1994; Dabholkar et al., 2000; Brady et al., 2002; Paje and Spreng, 2002). Thus, this work used a performance only scale in the research. Another topic to be addressed is how the performance is assessed in IPA. Garver (2003) found in the literature four ways of analyzing the performance in the IPA. Figure 1. Importance – performance analysis matrix (IPA) Urgent action Improve Appropriate Excess Low Performance High LowImportanceHigh 0 1 2 3 4 5 6 7 8 9 10 Traditional IPA Modified IPA 0 1 2 3 4 5 6 7 8 9 10 Performance QUADRANT II QUADRANT III QUADRANT IV (major weakness) ( major Strength ) (minor weakness) (minor strength) Importance QUADRANT I Source: (a) Garver (2003); (b) Adapted from Slack (1994) (a) (b) IJOPM 27,5 484
  4. 4. The first method is the “gap analysis” – subtracting the best competitor’s performance from the focal firm’s performance. According to Garver (2003, p. 456) “one limitation of this method is that the majority of customer satisfaction attributes are deemed as equal to the competition, when significant differences in performance may actually exist”. In the second method, performance ratios are calculated by dividing the focal firm’s mid-range performance by the best competitor’s mid-range performance. One possible point to be addressed in this method is the identification of the true size of the parity zone. Another problem is that the company has to interview their competitors’ customers, which is not always possible. The third approach to capture relative performance is to measure comparative performance directly by survey (Harding, 1998). The scale is designed to ask the respondents to evaluate the attributes as, for example “much better than competition” “equal to competition” and “much worse than competition”. The advantage of this method is that the number of questions in the questionnaire is reduced. Three limitations may be pointed out in this approach: (1) if all competitors have low performance in some attribute, this approach will not detect this deficiency; (2) customers may not be aware of the competitor’s performance; and (3) customers also cannot evaluate performance of innovative attributes. In the fourth approach, satisfaction scores are simply plotted on the performance axis. In this method two ways are typically used to split the performance axis: either the mid-point of the measurement scale or the mean performance for all attributes. According to Garver (2003, p. 457), citing Peterson and Wilson (1992), “satisfaction scores are often inflated and mid-range performance scores actually signify poor performance” possibly leading all attributes to the high performance quadrant. Also, dividing the performance axis by the mean performance of all attributes would guarantee attributes in the high and low performance quadrants, independent of whether to the customers they represent weaknesses or strengths. Evaluating attributes’ importance Two ways are commonly used to estimate the importance of attributes: stated importance and statistically inferred importance. In the stated importance method customers are asked to rate the importance of the attribute typically ranging from “not important at all” to “very important” in a Likert scale. This method has some limitations: . Consumers tend to give higher importance to attributes that represent the basic functions of a service (Garver, 2003). . Usually, stated importance tends to have low discrimination power and customers tend to find everything important. This occurs because customers are reasoning about the importance of the attributes, which may result in socially acceptable or status quo answers (Gustafsson and Johnson, 2004). For the statistically inferred importance, customers are asked to rate both their satisfaction with the current performance of the different attributes and their general satisfaction with the service under study. Several methods may be used to identify the importance such as: multiple regression, normalized pair wise estimation, partial least Identification of satisfaction attributes 485
  5. 5. squares with reflective or formative attribute specification and principal components regression (Gustafsson and Johnson, 2004). In this paper, we use the multiple regression approach. In this approach, a linear multiple regression equation is adjusted between the satisfaction with the individual attributes (independent variables) and the general satisfaction (dependent variable). Attributes with higher regression coefficients are more important than others. This method eliminates the tendency of finding all attributes important and discriminates better the relative importance between them (Garver, 2002). However, it is not free of deficiencies. Multicollinearity among independent variables tends to be a problem and the relationship between the satisfaction with an attribute and general satisfaction is not always linear (Ting and Cheng, 2002; Matzler and Sauerwein, 2002; Matzler et al., 2004). Also, customers tend to say that they are “satisfied” when actually they are in a neutral state, leading to a bias in the data. Besides, the problems described above, IPA assumes that the relationship between attribute performance and its importance is independent. Many studies have found that the importance of an attribute may change with performance (Mittal et al., 1999; Sampson and Showalter, 1999; Bacon, 2003; Matzler et al., 2003, 2004; Picolo, 2005). The lack of independence may lead to an excess of attributes in quadrant III (minor weaknesses) and quadrant I (major strengths). Also, as cited above, several studies suggest that the relationship between attribute performance and satisfaction is nonlinear. As will be seen later in this paper, the use of IPA without considering this nonlinear relationship may lead to wrong decisions in the improvement of the service. Kano model of excitement and basic quality The Kano model of excitement and basic quality (Kano et al., 1984; Berger et al., 1993; Nilson-Witell and Fundin, 2005) brings a different perspective for the analysis of improvement opportunities in products and services, exactly because it takes into consideration the non-linear relationship between performance and satisfaction. The Kano model classifies the attributes of products and services in three categories: (1) Basic attributes. These attributes fulfill the basic functions of a product. If they are not present or their performance is insufficient, customers will be extremely dissatisfied. On the other hand, if they are present or have sufficient performance, they do not bring satisfaction. Customers see them as prerequisites. (2) Performance attributes. As for these attributes, satisfaction is proportional to the performance level – the higher the performance, the higher will be the customer’s satisfaction and vice-versa. Usually, customers explicitly demand performance attributes. (3) Excitement attributes. These attributes are key factors for customer satisfaction. If they are present or have sufficient performance, they will bring superior satisfaction. On the other hand, if they are not present or their performance is insufficient, customers will not get dissatisfied. These attributes are neither demanded nor expected by customers. Two other attributes may be identified in the Kano model: neutral and reverse attributes. Neutral attributes bring neither satisfaction nor dissatisfaction. Reverse attributes bring more satisfaction if absent than if present. IJOPM 27,5 486
  6. 6. The non-linear relationship is confirmed by other studies. Anderson and Mittal (2000) make an extensive literature review exploring the connection of individual attribute performance, customer satisfaction, customer loyalty and companies’ profit. Ting and Chen (2002) use regression analysis to show the non-linear relationship of the performance of different attributes in supermarkets and customer satisfaction. Nilson-Witell and Fundin (2005) study how the attributes of an e-service have different classification in the Kano model depending on how often customers have contact with them, as well as their comfort and tendency in using technology. The Kano model and IPA Sauerwein (1999) demonstrates that customers tend to give more importance to basic factors, decreasing the importance given to performance, excitement and neutral attributes, respectively. Since, superior performance in basic attributes does not bring additional satisfaction to customers, IPA may lead a company to concentrate efforts on attributes that will not increase general satisfaction. On the other hand, the low importance given to excitement attributes may lead a company to despise attributes that could bring a differential in the market. Besides, the tendency of customers to give more importance to basic attributes, Matzler et al. (2004), using the statistically inferred importance method, show evidence that importance may vary with performance. For basic attributes, importance decreases as performance increases. For excitement attributes, importance increases with an increase in performance because customers already know the benefits of the attribute, having become used to it. It leads to another conclusion: the importance will be lower and lower the more innovative is the attribute. Since, the benefits of an innovation are unknown to customers, its introduction in the service tends to be hindered if the analysis is based only on IPA. Based on the paper by Huiskonen and Pirttila¨ (1998), Table I shows the wrong decisions that the IPA may lead to if the Kano model classification is not taken into consideration. The Kano method may identify the non-linear relationship between performance and satisfaction, possibly avoiding the problems presented in Table I. But, it does not take into consideration the current performance of the attributes in relation to competitors, being limited as far as discovering improvement opportunities if used alone. Research methodology A survey with customers of pizzerias was used to illustrate the limitations of the studied methods, as well as to illustrate the proposed alternative approach. The kind of pizzerias studied in this paper is called “Rodizio de Pizzas” and it is found only in Brazil. It is an “all you can eat” pizzeria where customers are continuously served from a wide variety of pizzas, pasta and other kinds of food, being very popular among young people and families. The research was carried out in two phases: one exploratory, qualitative, using a focus group and the other a quantitative, descriptive, survey type. The focus group was composed of six voluntary undergraduate business students that eat in these kinds of pizzerias at least twice a month. The purpose of the study was explained to the participants and several important attributes of the service under study were identified by conducting the focus group. Then, the participants were asked to classify the Identification of satisfaction attributes 487
  7. 7. attributes according to the Kano categories. Although different attributes could be used, four were specifically chosen because they represent the different categories of the Kano model: cleanness (basic attribute), courtesy (performance attribute), choice of pasta besides pizza (excitement attribute) and diversified filled border, i.e. filling the border with the same topping of the pizza (excitement attribute). “Diversified filled border” was a new attribute, not offered in the market at the time of the study. From now on we refer to the choice of pasta as “pasta offering” and the diversified filled border simply as “filled border”. The quantitative questionnaire comprised four parts. In the first part respondents were asked to rate their satisfaction with imaginary situations regarding sufficient and insufficient performance for each attribute. Then, using a Likert scale ranging from very dissatisfied (23) to very satisfied (þ3), they were asked to rate their satisfaction with the performance of the attributes in the last visited pizzeria. Also, the respondents where asked to rate the importance of each attribute in a scale ranging from 1 to 5, being 1 “not important at all” and 5 “very important”. Finally, it was asked how often they ate in pizzerias and the name of the last visited pizzeria. The questionnaire was tested with a pilot sample of 24 students. The survey was conducted with a random sample of 161 students, from a population of 560 undergraduate business students of the Regional University of Blumenau, located in the southern part of Brazil. The sample was composed of the students that were present in the classroom and that agreed to take part in the research. A total of 51 respondents (31 percent of the sample) did not indicate the name of the last visited pizzeria and were not considered in the research, yielding a final sample of 110 usable questionnaires. From this sample, 68 percent of the respondents were under 25 years old, 46 percent were female, 82 percent single, 71 percent eat in this kind of pizzeria at Kano model classification Customer’s importance Competitive performance IPA decision Reasons for the mistake Result Basic High Equal Improve Improving above this level will not increase satisfaction Waste of resources Basic Low Superior Abandon Good previous experiences and good average market performance lead customers to rate it as not important Critical attribute left unattended or abandoned, leading to dissatisfaction Basic High Inferior Improve Correct decision Excitement Low Inferior or equal Abandon Customers do not know the benefits of the attribute Relevant improvement opportunity left unnoticed Excitement High Equal or inferior Improve Previous good experiences lead customer to rate it as important Improvement of a non-critical attribute Excitement High Superior Keep Correct decision Source: Adapted from Huiskonen and Pirttila¨ (1998) Table I. Mistakes of IPA due to the non-linear relationship between performance and satisfaction IJOPM 27,5 488
  8. 8. least once a month and 38 percent twice or more than that a month. Figure 2 shows the frequency distribution for the last pizzerias visited by the respondents of the survey. The data of the two most frequented pizzerias were used to illustrate the problems with the IPA and the competitive analysis in the proposed method. Although the number of questionnaires is not enough to make an evaluation of the level of customer satisfaction, the sample is large enough to make a comparative evaluation and was considered adequate for the purpose of this study. For the Kano model analysis, all 110 questionnaires were used for the classification of the attributes. Importance-performance analysis and Kano model analysis for the studied case This section uses empirical data of the case study to show some of the problems of the IPA and of the Kano method presented in the literature. The main purpose is to make clear, for operations managers involved in the design or improvement of services, the need for the development of more robust methods that take into consideration the non-linear relationship between attributes’ performance and customer satisfaction. IPA for the studied case In this paper, the performance was evaluated by the average customer satisfaction with each attribute, using the relative performance method to compare pizzerias A and B. The statistical significance was used to identify the difference in attributes £ performance between the two pizzerias. Table II displays the average satisfaction and the p-values for pizzerias A, B, market average satisfaction (average Attribute Pizzeria A Pizzeria B Market average Satisf. A/B (percent) p-value (A ¼ B) p-value (A ¼ Market) Cleanness 1.45 1.40 1.51 104 0.400 0.22 Courtesy 1.18 1.69 1.36 70 0.055 0.25 Pasta offering 1.09 1.51 1.00 72 0.091 0.38 Filled border 20.51 20.53 20.51 96 0.480 0.50 Table II. Average satisfaction for the studied attributes Figure 2. Frequency distribution for visited pizzerias 39 33 11 6 3 18 51 0 10 20 30 40 50 60 A B C D E Others Didn´t indicate Identification of satisfaction attributes 489
  9. 9. satisfaction among all subjects) and the relative position between pizzerias A and B for each studied attribute. Table II shows that customers of pizzeria A have the same satisfaction as the market average in all attributes (p . 0.1). Comparing pizzeria A with pizzeria B, its largest competitor, we see that customers of pizzeria A are less satisfied than customers of pizzeria B in the attributes “courtesy” and “pasta offering” with at least 90 percent confidence. If an isolated analysis of customer satisfaction is used as an indicator of the areas or attributes that operations managers should improve, Table I suggests that pizzeria A should improve its “courtesy” and its “pasta offering”. The stated importance’s for the attributes studied in this paper are shown in Table III. “Cleanness” was considered the most important attribute, followed by “courtesy” “pasta offering” and “filled border” respectively. But, to what extent could “cleanness” be considered a differential for attracting customers? Would “courtesy” not be more effective in satisfying them? Besides, since the value “3” corresponds to “important” in the scale, the tendency of customers in finding everything important is evidenced, as cited by Garver (2003). Table IV displays the results for the statistically inferred importance. The most important attribute was “pasta offering” followed by “courtesy” “cleanness” and “filled border”. Statistically inferred importance depends on the previous experience of the customers with the service. As all competitors reached a satisfactory level in “cleanness” the variation in satisfaction is small, making the coefficient b1 smaller and this attribute less important. Also, since the respondents of the survey do not know “filled border” they are neither satisfied nor dissatisfied, thus making the coefficient b4 insignificant and this attribute not important at all. These results are different from those of stated importance (Table III). This difference is due to the fact that stated importance for each attribute is strongly dependent on customers’ perceptions arising from personal and social aspects. For instance, in most cases, in a restaurant, “cleanness” will tend to be considered as the Attribute b coefficients p-value Significance b0 (constant) 0.87 ,0.001 b1 (Cleanness) 0.16 ¼ 0.037 b2 (Courtesy) 0.27 ,0.001 b3 (Pasta offering) 0.30 ,0.001 b4 (Filled border) 0.02 ¼ 0.69 Not significant R 2 0.46 Table IV. Statistically inferred importance Attribute Average importance Cleanness 4.77 Courtesy 4.29 Pasta offering 3.02 Filled border 2.94 Table III. Stated importance IJOPM 27,5 490
  10. 10. attribute with highest stated importance due to the negative image consumers have in the case of its absence. Figure 3 shows the IPA for the case studied in this paper. A key point in the IPA is the position of the lines dividing the quadrants. In this paper, the dividing line of the “stated importance” axis will be set to “important” (3), and the dividing line of the “performance” axis will be set to “equal to the strongest competitor” using a relative performance scale (Figure 3(a)). Considering only the stated importance, Figure 3(a) and Table III, indicate that the weak points of pizzeria A in relation to its strongest competitor are “courtesy” and “pasta offering”. Since, the performance of pizzeria A is lower than pizzeria B, and the importance is high for both attributes, pizzeria A should improve them. As for “cleanness” and “filled border” there is no significant difference in performance for pizzerias A and B (p-value ¼ 0.4 and p-value ¼ 0.48, respectively). In this case, since the importance of “filled border” is below 3, it should not be offered. In the case of “cleanness” since importance is high and performance is equal, pizzeria A could be tempted to improve it. Obviously, since the competitors achieved a satisfactory performance level in this attribute, it would be a wrong decision, confirming some of the findings of Huiskonen and Pirttila¨ (1998), presented in Table I. If, instead of using stated importance, the statistically inferred importance was adopted, the results would be different (Figure 3(b)). Now, the dividing line of importance was set equal to the average of the b values (0.19). In this case, the weak points of pizzeria A continue to be the same ones (“pasta offering” and “courtesy”), the decision to abandon “filled border” is reinforced and “cleanness” does not need to be improved. The divergent results of the stated and the statistically inferred importance methods put in check the decisions to be made during the design of a product or service. The previous analyses show that the IPA leads to different decisions depending on how the importance weights are estimated. Kano model analysis for the case studied The nonlinear relationship between attribute performance and customer satisfaction may be illustrated by answering a simple question: if pizzeria A offers superior cleanness, above the market average and above its strongest competitor, will this improvement bring more satisfaction to its customers and differentiate itself in the market? The answer seems obvious: it depends on the current satisfaction with “cleanness” not only for pizzeria A, but also with the general level of the market. Figure 3. IPA for “Pizzeria A” using stated importance and inferred importance Pasta Offering Courtesy Cleanness Filled Border 0 0.19 0.38 60% 100% 140% Relative performance Inferredimportance Pasta offering Courtesy Cleanness Filled border 1 3 5 60% 100% 140% Relative performance Statedimportance (a) (b) Identification of satisfaction attributes 491
  11. 11. If “cleanness” among competitors is below a minimum acceptable level, improving it can differentiate pizzeria A and attract more customers. However, if the average cleanness in the market is considered adequate, improving it above this level will not bring a differential. Yet, if “cleanness” is below this level, it will bring dissatisfaction, greatly decreasing the competitiveness of pizzeria A. If this non-linear relationship exists, how can it be taken into consideration in the analysis of improvement opportunities? To classify the attributes according to the Kano model, Berger et al. (1993) proposed the customer satisfaction coefficient (CS-coefficient). The CS-coefficient calculates the percentage of customers that are satisfied with the presence of an attribute and the percentage that are dissatisfied with the absence of an attribute. These two percentages are plotted in a scatter diagram divided into four quadrants. The quadrants classify the attributes as neutral, excitement, performance and basic factors. Using the CS-coefficient, Figure 4 shows that the presence of “pasta offering” and “diversified filled border” brings satisfaction to a greater number of customers, while their absence brings dissatisfaction to fewer customers, being classified as excitement attributes. “Courtesy” and “cleanness” bring both satisfaction and dissatisfaction to a greater number of customers, being classified as performance attributes. The identification of “filled border” as an excitement attribute indicates that it could become a differential if offered by pizzeria A. This result is different from the IPA that indicated this attribute should not be offered. Innovative attributes, which the consumer has little experience with or that they do not know the benefits of, tend to be considered of very little importance, if at all. Then, innovative ideas may be despised in the IPA but could be identified through the use of the Kano method. Garver (2003) and Matzler and Sauerwein (2002) use the stated and inferred importance in an importance grid to classify the attributes into key (performance), basic, amplifier (excitement) and secondary ones. Attributes that receive high importance in both methods are considered key attributes. Similarly, attributes that receive low importance in both methods are considered secondary. Those attributes that receive high importance in the stated importance method and low importance in the statistical method are considered basic. Those that receive low importance in the stated importance method and high importance in the statistical method are considered as “amplifiers.” Using the importance grid method for the pizzerias case, “cleanness” is Figure 4. Customer satisfaction coefficient for the pizzeria’s attributes Filled border Excitement Cleanness 0.00 50% 100% 100%50%0.00 Percentage of dissatisfied customers Basic Percentageofsatisfiedcustomers Neutral Courtesy Performance Pasta IJOPM 27,5 492
  12. 12. considered basic, “courtesy” is a key attribute, “pasta offering” is an amplifier attribute and “filled border” is a secondary one (Figure 5). In this case, the average of the b coefficients (0.19) was used as the dividing line between high and low importance in the statistical method, and “important” (3) as the dividing line in the stated importance method. Based solely on the conclusions of Figure 5, pizzeria A should concentrate on having superior courtesy, offer an excellent pasta option, avoid cleanness problems and not be concerned about offering a filled border. As can be seen, the importance grid also leads to different results in the classification of the attributes when compared to the classification using the CS-coefficient. “Cleanness” is considered a performance attribute by the CS-coefficient and a basic one by the Importance Grid. One possible problem with the traditional Kano method is that customers tend to say they become “satisfied” with the positive questions and “dissatisfied” with the negative questions regardless of the attribute being evaluated, leading to a high number of performance attributes. On the other hand, “filled border” was classified as a secondary (neutral) attribute by the importance grid, while classified as an excitement one by the CS-coefficient. Here, there seems to be a problem regarding the importance grid: it depends on the previous experience of the customers with the attributes being analyzed, and their being unable to identify innovative attributes. Operations managers seeking to improve service and customer satisfaction need to consider that IPA and the traditional Kano method may lead to different decisions depending on how the data of importance and performance are calculated and analyzed. Of special interest is the fact that the IPA tends to put more emphasis on basic attributes and tends to despise innovative ones. The traditional Kano model analysis may classify basic attributes as performance ones and also does not take into consideration competitors’ performance. The following section presents a method that overcomes some of these limitations. Competitive analysis of the improvement gap To direct the improvement efforts it is essential to evaluate how satisfied the customers are with the current competitors in the market and what the competitive position of the company is in relation to them. Also, it should be determined what additional Figure 5. Attribute classification using the importance grid method PastaFilled Border Courtesy Cleanness 1 2 3 4 5 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 Statedimportance BASIC KEY SECONDARY AMPLIFIER Inferred importance Identification of satisfaction attributes 493
  13. 13. satisfaction an increase in performance may provide. To do that, we propose the use of a Likert scale in the Kano questionnaire (Figure 6). For each possible answer a numeric value varying from 23 (very dissatisfied) to þ3 (very satisfied) is assigned. Value zero is assigned for the answer “neutral” or “it should be like this”. The average satisfaction with the positive (Kano þ ) and negative questions (Kano 2 ), and with the current level of an attribute’s performance in the market is calculated. The results are plotted in a graph (Figure 6). This method makes clear the increase or decrease in satisfaction that can be obtained by varying the performance of the attribute. Table V shows the p-values for the differences in the average satisfaction considering equivalent variances for the attributes studied in this paper. Figure 7 shows the graphic results. Table V and Figure 7 show that there are no significant differences in satisfaction with “cleanness” between pizzerias A, B and the market average. Also, since the difference in satisfaction between Kano (þ) and pizzeria A is small (p ¼ 0.069), if pizzeria A improves its performance in “cleanness” the satisfaction will not be greatly increased, but a reduction in its performance would bring great dissatisfaction to customers. So, this attribute may be classified as a basic factor and the decision for pizzeria A would be to keep its current performance. As for “courtesy” the results indicate that pizzeria B is better than pizzeria A (p ¼ 0.05). Also, an increase in the performance of this attribute may bring a substantial increase in satisfaction ( p , 0.001). On the other hand, decreasing it would cause great dissatisfaction. Then, “courtesy” may be considered as a performance factor. Pizzeria A should concentrate on improving this attribute that may be negatively affecting its competitiveness. Table V and Figure 7 show that pizzeria A has the same performance as the average of the market in the attribute “pasta offering”. Also, since the difference in satisfaction between Kano (þ) and pizzeria A is large, if pizzeria A improves the performance of this attribute it will increase customer satisfaction. If “pasta offering” is not present, Figure 7 shows that the average customer satisfaction would be between neutral (0) and slightly dissatisfied (21). So, although this attribute is present in all pizzerias in the market, its absence would not bring great dissatisfaction. This result leads to the conclusion that “pasta offering” is an excitement factor in the Kano method. Although it brings satisfaction if present, it will not bring dissatisfaction if absent, raising the possibility of being substituted by a less expensive excitement factor. Figure 6. Proposed modified Kano questionnaire Verydissatisfied Dissatisfied Slightly dissatisfied Neutralorshould bethatway Slightlysatisfied Satisfied Verysatisfied Verydissatisfied Dissatisfied Slightly dissatisfied Neutralorshould bethatway Slightlysatisfied Satisfied Verysatisfied If you go to a pizzeria and you find better courtesy than the market average, what do you feel? If you go to a pizzeria and you find worse courtesy than the market average, what do you feel? Courtesy DissatisfactionSatisfaction Worse Current Better IJOPM 27,5 494
  14. 14. CleannessCourtesy Averagesatisfy PizzeriaA 1.45 Pizzeria B1.4Market1.51 Kanoþ 1.8Averagesatisfy PizzeriaA 1.18 PizzeriaB 1.69 Market 1.36 Kanoþ 2.41 PizzeriaBp¼0.40–PizzeriaBp¼0.05– Marketp¼0.22p¼0.299–Marketp¼0.251p¼0.078– Kanoþp¼0.069p¼0.088p¼0.002–Kanoþp,0.001p,0.001p,0.001– PastaofferingDiversifiedfilledborder Averagesatisfy PizzeriaA 1.09 PizzeriaB 1.51Market1 Kanoþ 1.76Averagesatisfy PizzeriaA 20.51 PizzeriaB 20.53Market20.51 Kanoþ 2.08 PizzeriaBp¼0.091–PizzeriaBp¼0.48– Marketp¼0.376p¼0.027–Marketp¼0.496p¼0.479– Kanoþp¼0.005p¼0.15p,0.001–Kanoþp,0.001p,0.001p,0.001– Table V. P-values for differences in satisfaction considering equivalent variances Identification of satisfaction attributes 495
  15. 15. “Filled border” is not offered by any pizzeria in the market, being an innovation. In the Kano questionnaire, customers were asked to rate their satisfaction with the existence of “filled border” and with its absence. Figure 7 shows that the current satisfaction with the filled border for all competitors and the satisfaction with its absence are close to neutral. Its presence would bring great increase in satisfaction. Thus, this attribute may be classified as an excitement factor. This result shows a clear advantage of this new method: incremental innovations, which customers perceive the benefits of, are identified and enhanced. Since, this attribute is much less expensive, pizzeria A could analyze the possibility of offering it instead of pasta. Conclusions Fulfilling customers needs and exceeding their expectations is fundamental for the success of a company in the long-term. The fulfillment of customer needs depends on the performance of the product or service in its different attributes. So, the identification of the relationship between attribute performance and customer satisfaction becomes key to business success. Since, the needs of customers change over time, the improvement in performance and the introduction of innovations must be a continuous task for any company. Based on the literature and on empirical data, this paper analyzed the limitations of two methods used to identify improvement Figure 7. Performance £ satisfaction for the attributes Courtesy 2.42 −2.21 1.36 −3.00 −2.00 −1.00 0.00 1.00 2.00 3.00 Cleanness 1.80 −2.17 1.40 −3.00 −2.00 −1.00 0.00 1.00 2.00 3.00 −2 −1 0 1 2 Performance −2 −1 0 1 2 Performance Pasta Offering 1.84 1,00 −3.00 −2.00 −1.00 0.00 1.00 2.00 3.00 −2 −0.591 0 1 2 Performance Performance SatisfactionSatisfaction SatisfactionSatisfaction Kano Pizzaria B Pizzaria A Market Diversified Filled Border 2.09 −0.23 - 0.52 −3.00 −2.00 −1.00 0.00 1.00 2.00 3.00 −1 IJOPM 27,5 496
  16. 16. opportunities in services: IPA and the Kano method. The main problems with the IPA are that: . it leads to different results depending on how the importance and performance of the attributes are calculated; and . it does not take into consideration the possible non-linear effect of an attribute’s performance on customer satisfaction, hindering the introduction of innovations and possibly misleading the improvement efforts. As for the Kano method, the main limitation is that it does not take into consideration the competitive position of the company, being a limited tool for finding improvement opportunities. To overcome the limitations of IPA and of the traditional Kano method, this paper introduces competitive analysis of the improvement gap. Using a modified Kano questionnaire, the gaps, “expected satisfaction with the superior performance – current satisfaction” and “current satisfaction – expected satisfaction with inferior performance” and the gap between current satisfaction and average market satisfaction, are used to classify the attributes according to the Kano categories and to direct the efforts for service improvement. The proposed method successfully identified improvement opportunities in a service case, including the possible impact of an innovative attribute. To some extent, the positive improvement gap of the proposed method is similar to the gap “desired-perceived” of SERVQUAL (Parasuraman et al., 1991). Although some similarity may exist, the method proposed in this paper differs from SERVQUAL because it takes in consideration not only the positive gap, but also the negative and the competitive gaps in the analysis. Also, SERVQUAL is a model to measure service quality with a fixed set of attributes, not measuring the impact of improving or abandoning an existing or innovative attributes in customer satisfaction. The main contribution of this work is the proposal of a simple method that combines the Kano method with IPA, overcoming at least two of the possible mistakes of using IPA pointed out by Huiskonen and Pirttila¨ (1998): (1) the tendency to direct efforts for the improvement of basic attributes above the competitors, when in fact it would not lead to an increase in customer satisfaction; and (2) the disregarding of innovative excitement attributes that could bring a differential in the market. Also, through the use of a didactic case with empirical data, this work reinforces the debate about the possible problems with the IPA (Huiskonen and Pirttila¨ 1998; Anderson and Mittal; 2000; Matzler and Sauerwein, 2002; Garver, 2003; Matzler et al., 2004; Eskildsen and Kristensen, 2006). The managerial implication of this work is that companies wishing to improve their competitive position in the market should pay attention to the fact that not all improvement efforts will lead to meaningful results due to the possible non-linear effects of the attributes on customer satisfaction. The competitive analysis of the improvement gap, presented in this paper, may supply managers with a simple and intuitive tool to direct improvement efforts. Identification of satisfaction attributes 497
  17. 17. In spite of presenting progress in relation to IPA and to the traditional Kano model analysis, the proposed method still presents limitations. When the customer answers about their satisfaction with the current performance of an attribute, or when they answer about their satisfaction with an increase or decrease in performance, they may be thinking about the satisfaction with the individual attribute, not with the service or product as a whole. The improvement of an attribute can cause an increase in the satisfaction with that attribute, but its effect on the general satisfaction may be small due to interactions among the different attributes. Besides, although the new method was successful in the identification of the impact of an innovative attribute in the case studied, it should be further tested in the identification of other innovative attributes in other situations. Also, all the analysis carried out in this paper had the customer evaluation of “satisfaction” as a central point. Johnston (2004) suggests that satisfaction should be “expressed in terms of emotions” because when the customer is asked about their satisfaction with a situation they make a rational judgment. When asked about their “emotions” in a scale ranging from “delight to outrage” for example, the consumer answers more deeply about human feelings, being a better predictor of loyalty than satisfaction (Yu and Dean, 2001). So, further research should be conducted to identify the influence of the interaction of different attributes in customer satisfaction, the power of this new method in the identification of innovative attributes and the impact of an “emotional” scale in the methods studied in this paper. References Anderson, E.W. and Mittal, V. (2000), “Strengthening the satisfaction-profit chain”, Journal of Service Research, Vol. 3 No. 2, pp. 107-1290. Anderson, E.W. and Sullivan, M. (1993), “The antecedents and consequences of customer satisfaction for firms”, Marketing Sciences, Vol. 12, pp. 125-43. Bacon, D.R. (2003), “A comparison of approaches to importance-performance analyses”, International Journal of Market Research, Vol. 45 No. 1, pp. 55-71. Berger, C., Blauth, R. and Boger, D. et al. (1993), “Kano’s methods for understanding customer-defined quality”, Journal of the Japanese Society for Quality Control, Vol. 23 No. 2, pp. 3-35. Brady, M.K. and Cronin, J.J. Jr (2001), “Some new thoughts on conceptualizing perceived service quality: a hierarchical approach”, Journal of Marketing, Vol. 65, pp. 34-49. Brady, M.K., Cronin, J.J. Jr and Brand, R.R. (2002), “Performance-only measurement of service quality: a replication and extension”, Journal of Business Research, Vol. 55, pp. 17-31. Caro, L.M. and Garcı´a, J.A.M. (2007), “Measuring perceived service quality in urgent transport service”, Journal of Retailing and Consumer Services, Vol. 14 No. 1, pp. 60-72. Caruana, A. (2002), “Service loyalty: the effects of service quality and the mediating role of customer satisfaction”, European Journal of Marketing, Vol. 36 Nos 7/8, pp. 811-30. Chow, I.H., Lau, V.P., Lo, T.W., Sha, Z. and Yun, H. (2007), “Service quality in restaurant operations in China: decision- and experiential-oriented perspectives”, International Journal of Hospitality Management, p. 13, available at: www.sciencedirect.com/science/ journal/02784319 (accessed August 17, 2006). Cronin, J.J. and Taylor, S.A. (1992), “Measuring service quality: a re-examination and extension”, Journal of Marketing, Vol. 56, pp. 55-68. IJOPM 27,5 498
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  20. 20. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.