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PROOF COVER SHEET Author(s): Josip Mikulic´ Article Title: Rethinking the importance grid as a research tool for quality managers Aritcle No: CTQM593857 Enclosures: 1) Query sheet 2) Article proofs Dear Author, 1. Please check these proofs carefully. It is the responsibility of the corresponding author to check these and approve or amend them. A second proof is not normally provided. Taylor & Francis cannot be held responsible for uncorrected errors, even if introduced during the production process. Once your corrections have been added to the article, it will be considered ready for publication. For detailed guidance on how to check your proofs, please see http://journalauthors.tandf.co.uk/production/checkingproofs.asp. 2. Please review the table of contributors below and confirm that the first and last names are structured correctly and that the authors are listed in the correct order of contribution. This check is to ensure that your name will appear correctly online and when the article is indexed. Sequence Prefix Given name(s) Surname 1 Josip Mikulic´ 2 Darko Prebezˇac Suffix Queries are marked in the margins of the proofs. Unless advised otherwise, submit all corrections and answers to the queries using the CATS online correction form, and then press the “Submit All Corrections” button. AUTHOR QUERIES General query: You have warranted that you have secured the necessary written permission from the appropriate copyright owner for the reproduction of any text, illustration, or other material in your article. (Please see http://journalauthors.tandf.co.uk/preparation/permission.asp.) Please check that any required acknowledgements have been included to reflect this. QUERY NO. QUERY DETAILS AQ1 Please check the edit made in the sentence ‘However, this paper argues. . .’. AQ2 The following references Oliver (1997) and Oh (2001) are cited in text but not there in the list. Please add to the list or delete the citations. AQ3 The sentence ‘However, for attributes categorised. . .’ does not seem very clear. Please check. AQ4 Please check the edit made in the sentence ‘On the one hand. . .’. AQ5 Is ‘attribute relevance’ the expansion of the acronym AR? If not, can we change ‘relevance’ to AR throughout the file, please suggest. AQ6 Is ‘attribute determinance’ the expansion of the acronym AD? If not, can we change ‘determinance’ to AD throughout the file, please suggest. AQ7 Please check the edit made in the sentence ‘However, airlines are. . .’. AQ8 Please check the edit made in the sentence ‘It is important to note. . .’. AQ9 Please check the edit made in the sentence ‘Since the AP level of. . . ’. AQ10 Please check the edit made in the sentence ‘The case study in this paper. . .’. AQ11 Please provide in-text citation for the references Fu¨ller & Matzler (2008), Mikulic´ & Prebezˇac (2011a). AQ12 Please expand the acronyms IAA, DAI. AQ13 We have changed the values with dot separator to comma separator. Please check. CTQM593857 Techset Composition Ltd, Salisbury, U.K. 6/13/2011 Total Quality Management Vol. 00, No. 0, Month 2011, 1 –14 5 Rethinking the importance grid as a research tool for quality managers Josip Mikulic´∗ and Darko Prebezˇac Department of Tourism, Faculty of Economics and Business, University of Zagreb, J.F. Kennedy Square 6, 10000 Zagreb, Croatia 10 The importance grid (IG) is a research tool developed for the purpose of categorising product/service attributes according to the Kano model, thus making it a tempting technique for quality managers. However, this paper argues that the IG lacks a clear theoretical foundation, which is why it is not recommended for its intended purpose. Nevertheless, it is shown that a reinterpretation of the IG can provide valuable information for the purpose of prioritising product/service attributes for improvement. It is further suggested to regard the IG and the penalty-reward contrast analysis (PRCA) not as competing techniques, as it is usually assumed in the literature, but rather as complementary approaches. The managerial value of the rethought IG in combination with a modified PRCA (determinance-asymmetry analysis) is demonstrated in a case study on airline passenger satisfaction with airport services. 15 20 Keywords: importance grid; attribute importance; customer satisfaction; Kano model Introduction 25 30 QA: Coll: 35 CE: RK/RI 40 The importance grid (IG) was developed by Harvey Thompson, an IBM consultant, for the purpose of categorising product/service attributes according to the Kano model (Kano, Seraku, Takahashi, & Tsuji, 1984). In the scholarly literature, the technique was first mentioned by Vavra (1997), and ever since it has been applied to explore the Kano model in various product settings (e.g. Matzler & Hinterhuber, 1998; Yang, 2005; Riviere, Monrozier, Rogeaux, Pages, & Saporta, 2006) and services settings (e.g. Martensen & Gronholdt, 2001; Fuchs, 2002; Fuchs & Weiermair, 2003, 2004; Matzler, Sauerwein, & Heischmidt, 2003; Bartikowski & Llosa, 2004; Busacca & Padula, 2005). To classify product/service attributes into the different Kano categories, the IG uses scores of explicit and implicit attribute importance (AI). Explicit AI, also referred to as stated importance, is obtained directly from the customer (e.g. by means of direct rating, ranking- or constant-sum scales), whereas implicit AI is obtained indirectly, most usually by regressing attribute-level performance against a global measure of performance (e.g. overall satisfaction). This type of importance thus is also referred to as derived importance. The IG literature reports the use of standardised regression coefficients (Fuchs, 2002; Matzler & Sauerwein, 2002; Matzler, Sauerwein, & Stark, 2002; Busacca & Padula, 2005; Peters, 2005), partial correlation coefficients (Matzler et al., 2002, 2003; Fuchs & Weiermair, 2003, 2004; Bartikowski & Llosa, 2004), and zero-order correlation coefficients (Matzler et al., 2002). The two AI measures are then used to construct a two-dimensional ∗ 45 Corresponding author. Email: [email protected] ISSN 1478-3363 print/ISSN 1478-3371 online # 2011 Taylor & Francis DOI: 10.1080/14783363.2011.593857 http://www.informaworld.com Q1 2 50 55 60 65 70 75 J. Mikulic´ and D. Prebezˇac grid, which is divided into four quadrants, most frequently by using grand means of implicit and explicit AI scores as thresholds. Depending on the attributes’ positionings across the grid, three distinct categories of attributes can be identified: basic factors (BFs), excitement factors (EFs) and performance factors (PFs) (Figure 1). BFs, which are also referred to as must-be requirements or hygiene factors, are attributes with a strong negative impact on overall satisfaction (OS) in case of low-level performance, but which do not have a significant positive impact on OS when performance is high. An example of a BF mentioned by several authors is airline safety (e.g. Matzler & Sauerwein, 2002; Mikulic´ & Prebezˇac, 2008). If safety performance on a flight was low, this would certainly have a strong negative impact on the passenger’s OS. Conversely, high performance would not be likely to affect the passenger’s OS in a significant way, because all airlines are usually expected to be safe, and they usually are. In contrast to BFs, EFs are attributes with a positive impact on OS when provided at a satisfactory or higher level, but which do not have a significant negative impact on OS when absent, or when objective performance is low. These attributes are also referred to as attractive quality elements or value-enhancing factors. Using the same example of a passenger flight, an EF might be a diverse offer of in-flight movies. If delivered, this attribute would be likely to contribute to the creation of passenger satisfaction (positive impact on OS); however, when only a small choice of movies was provided (or when not provided at all), this attribute would not necessarily be likely to significantly impact the passenger’s OS in a negative way. Due to these performance-level-dependent dynamics in the attributes’ impact on OS, both BFs and EFs are said to have an asymmetric impact on OS. In contrast, PFs, which are also referred to as one-dimensional quality elements or linear factors, have a symmetric impact on OS – that is, high performance positively impacts OS and low performance negatively impacts OS. A distinction is further made between PFs with high importance (PF+ , high explicit and implicit AI) and PFs with low importance (PF2 , low explicit and implicit AI). The latter category of attributes is considered less important in explaining customer satisfaction and dissatisfaction. The managerial value of information about key drivers of customer satisfaction, such as provided by the IG, is immense. However, due to a lack of theoretical foundation, and the lack of convergent validity between the IG and other methods for assessing the Kano model (e.g. Matzler & Sauerwein, 2002; Fuchs & Weiermair, 2003; Busacca & Padula, 80 85 90 Figure 1. The importance grid. Total Quality Management 95 100 105 110 115 120 125 130 135 3 2005; Witell & Lo¨fgren, 2007), the technique has never gained greater popularity among practitioners and researchers. Following the call of Matzler, Bailom, Hinterhuber, Renzl and Pichler (2004), this paper thus aims to discuss the technique’s underlying assumptions and to pinpoint crucial logical and conceptual shortcomings. Based on insights from the discussion, a reinterpretation of the IG is put forward, and a case study is used to show how the ‘reinterpreted IG’ in combination with a determinance-asymmetry analysis (DAA) (Mikulic´ & Prebezˇac, 2008) can provide valuable guidance to quality managers. Another important implication of the discussion is that the IG and the penalty-reward contrast analysis (PRCA) should not be regarded as competing or conflicting techniques for operationalising the Kano model, as it is generally assumed in the literature, but rather as complementary approaches which, in combination, provide managers with surplus information in prioritising product/service attributes for improvement. Theoretical foundations of the IG When using the IG for categorising product/service attributes according to the Kano model, several implicit assumptions are made. Thus, in order to theoretically validate the technique, it is necessary to assess whether its underlying assumptions are logical and theoretically grounded. Assumption 1: Explicit and implicit measures of AI assess different concepts. Since the IG uses two different AI measures to determine an attribute’s category according to the Kano model, the most basic assumption is that measures of explicit and implicit AI assess different concepts. The literature dealing with the measurement of AI provides theoretical confirmation for this assumption, as it is acknowledged that importance is a multidimensional concept, and that different importance measures assess its different dimensions (Myers & Alpert, 1968, 1977; Jaccard, Brinberg, & Ackerman, 1986; Van Ittersum, Pennings, Wansink, & van Trijp, 2007). Moreover, several studies failed to confirm nomological (e.g. Harte & Koele, 1995) and convergent validity (e.g. Wiley, MacLachlan, & Moinpour, 1977) between derived measures (i.e. implicit AI) and direct ratings (i.e. explicit AI), which are the two most frequently used measures in the IG. Hence, the assumption that explicit and implicit measures assess different concepts (or different dimensions of AI) is also empirically grounded. Moreover, it is noteworthy that several authors have raised serious concerns about the reliability of AI measurement in customer satisfaction research because of the ambiguity of AI measures (Oliver, 1997; Q2 Oh, 2001; Matzler & Sauerwein, 2002). This reinforces the recommendation not to regard Q2 explicit and implicit measures of AI as exchangeable measures for the same concept. Assumption 2: Explicit AI is an indicator of an attribute’s dissatisfaction-generating potential (DGP). When explicit AI is high, the attribute is either categorised as a BF or a PF with a high degree of importance (PF+). These are the two attribute categories according to the Kano model which strongly impact OS in a negative way when performing low. Put the other way round, a necessary precondition for an attribute to have a strong negative impact on OS when performing low is that the attribute has a high level of explicit AI. The relevant literature does not explicitly confirm this assumption, but it seems reasonable that attributes which are perceived to be very important by consumers (i.e. have high explicit AI) negatively impact their OS when absent or performing low. However, this should not be taken as a rule, but rather as a rule of thumb. Moreover, there is no theory explaining that attributes which are perceived less important by customers do not bear (a large) potential to generate dissatisfaction when performing low, which is 4 140 145 150 155 160 165 J. Mikulic´ and D. Prebezˇac also implicitly being assumed in the IG. The assumption of explicit AI to be an indicator of DSG should therefore be considered with care. Assumption 3: Implicit AI is an indicator of an attribute’s satisfaction-generating potential (SGP). When implicit AI is high, the attribute is either categorised as an EF or a PF+. These are the two attribute categories with a strong positive impact on OS when performance is high. Put the other way round, a necessary precondition for an attribute to have a strong positive impact on OS when performing high is that it has a high level of implicit AI. This assumption also seems reasonable since implicit AI is in fact a measure of an attribute’s impact on OS. However, for attributes categorised as EFs in the IG, it is being neglected that a high level of implicit AI does not necessarily imply that the attribute’s impact on OS is unidirectional – that is, that high performance positively impacts OS, but low performance does not negatively impact it. Consider the illustrative Q3 example of only one attribute impacting OS (Figure 2). The unstandardised regression coefficient (i.e. implicit AI) in both the relationships is b ¼ 0.5. However, attribute A clearly is an EF (the impact increases towards higher levels of attribute performance – i.e. positively asymmetric relationship), whereas attribute B is a BF (the impact increases towards lower levels of attribute performance – i.e. negatively asymmetric relationship). Accordingly, the assumption of implicit AI being an indicator of an attribute’s SGP should be made with care, because it could as well be an indicator of an attribute’s DGP. Therefore, one should rather regard implicit AI as an indicator of an attribute’s overall impact on OS, encompassing its potentials to generate both satisfaction and dissatisfaction (Mikulic´ & Prebezˇac, 2008). In order to analyse whether an attribute has an equal, larger or smaller SGP than DGP, the attribute’s overall impact on OS could be split up into its impacts in cases of low-level performance and high-level performance, as proposed in the penalty-reward contrast approach introduced by Brandt (1987), or using the moderated regression approach proposed by Lin, Yang, Chan and Sheu (2010). Assumption 4: Relative attribute positionings reveal the different attribute categories according to the Kano model. Since the IG applies a data-centred approach to determine the threshold values of the different Kano categories (i.e. the crosshairs that divide the IG into four quadrants), for an attribute all the other attributes which are included in the analysis represent the reference points in determining its own category. A logical consequence of such an approach is that the analysis will always yield a classification of attributes into EFs, BFs and PFs when there are any differences in both explicit and implicit AI of attributes, which is usually the case. In their empirical comparison of different methods for performing Kano classifications, Witell and Lo¨fgren (2007) highlight this to be a serious problem tackling the validity of the IG. Another consequence of a data-centred 170 175 180 Figure 2. Statistically derived importance weights: BFs versus EFs. Total Quality Management 185 5 approach is that the category of an attribute may change with different sets of analysed attributes, as has been empirically confirmed by Mikulic´ and Prebezˇac (2011b). However, in order to make sense, it is obvious that the classification of an attribute should be robust and consistent across any set of analysed attributes. Thus, a datacentred quadrant analysis, such as the IG, is applicable only if it is used to draw conclusions based on relative attribute positionings, but not for performing classifications of attributes into absolute, predefined attribute categories. Consequently, this assumption should be regarded as a major reason for a lack of convergent validity with other methods for assessing the Kano model in earlier studies. 190 Reinterpretation of the IG 195 200 205 210 215 220 225 The previous discussion revealed that one of four implicit assumptions of the IG is highly problematic (assumption 4), whereas two should be taken with care (assumptions 2 and 3). Consequently, it is not recommended to use the IG for categorising product/service attributes according to the Kano model. The questions which remain are how to properly interpret IG results, and whether these results have any managerial value. To provide answers to these questions, it is first necessary to specify what is exactly assessed with the measures typically used in the technique (i.e. direct AI ratings and statistically derived AI), and to evaluate their informational value. On the one hand, such direct ratings assess the customer-perceived importance of attributes, which can be described as an attitude-like importance statement that is based on personal values and desires (Batra, Homer, & Kahle, 2001). This type of importance is also referred to as rel- Q4 evance (AR) by several authors (e.g. Myers & Alpert, 1977; Van Ittersum et al., 2007). On Q5 the other hand, statistically derived AI measures, such as regression-based weights, indicate an attribute’s importance in explaining variations in an outcome variable (e.g. OS), based on experiential data. This type of importance is also referred to as determinance (AD) (Myers & Alpert, 1977). Now, if one departs from these definitions, it does, in Q6 fact, not seem unreasonable to assume a strong positive correlation between AR and AD – that is, attributes that are perceived to be more important (high AR) should have a stronger impact on OS (high AD), and vice versa. If this was truly the case, it would, in fact, be unnecessary to use both types of AI measures, because they provide equivalent information. However, when reasoning this way, it must not be neglected that calculations of AD indicators are usually based on data from single case studies, which has important implications for the AR – AD relationship. Such case-based data do not always cover the whole range of possible levels of objective attribute performance (AP), and neither do the data always cover significant variations in objective AP for all attributes that are subject to analysis. Accordingly, relatively low AD of an attribute with relatively high AR may simply be attributed to a lack of variation in objective AP, whereas relatively high AD of an attribute with relatively lower AR might be attributed to relatively higher variation in objective AP. The first situation might be the case with core product/service attributes, which are usually rated highly important, and which are typically provided at a satisfactory (high) level by most competing product/service providers in a market. Furthermore, it is also important to consider that variations in AP, as assessed in case-based studies, may for some attributes be within the customer’s zone of tolerance (ZOT), thus deflating their impact on OS, but for others be outside the ZOT, thus inflating their impact (Johnston, 1995). To illustrate the points made, let us reconsider the earlier mentioned example of flight safety as an attribute of an airline. It is rather obvious that this attribute would yield very high importance ratings in a passenger survey (i.e. high AR), and it would 6 J. Mikulic´ and D. Prebezˇac 230 235 Figure 3. The RDG. 240 245 250 255 260 265 270 probably emerge as the most ‘important’ attribute. However, airlines are typically safe and do not vary in safety performance, which is why the attribute would not necessarily be likely to exhibit a proportionately strong impact on OS (i.e. relatively low AD if the flight was safe). Consequently, it should not be surprising if an attribute with relatively Q7 lower AR (e.g. in-flight service) showed a relatively higher impact on OS than the safety attribute, especially if the AP of the lower relevance attribute crossed the passenger’s ZOT, either in a positive or a negative direction. However, conversely, if there actually were safety problems during a flight, the highly relevant attribute would certainly become a highly determinant one (in accordance with its high AR), and all other attributes might probably become less- or even in-determinant. Although this is a hypothetical and extreme example, it makes clear that measures of AR and AD assess quite distinct concepts, and that it is not reasonable to always assume a strong positive correlation between them in case-based studies. On the one hand, AD is a highly dynamic concept, as it is a function of its own relevance, performance and, possibly, even the performance of other attributes, whereas AR, on the other hand, is a relatively stable attitude-like concept that may, however, change during time. Now, if we return to our question about the informational value of these two types of AI measures, it can be concluded that the measures are complementary, rather than conflicting or competing, as already acknowledged in the literature (e.g. Myers & Alpert, 1977; Van Ittersum et al., 2007). Such measures of AD help to reveal more or less active key drivers of customer satisfaction in a particular research setting, whereas measures of AR reveal those attributes that generally have a larger or smaller potential to affect the customer’s OS, if not performing in accordance with the customer’s expectations. From a managerial perspective, the two measures thus provide valuable surplus information in combination, because they help to uncover those attributes that are both highly relevant and determinant, and that should therefore have highest priority in improvement strategies. Accordingly, the value of the IG should not be sought in its questionable ability to operationalise the Kano model, but rather in its high reliability for the purpose of prioritising product/service attributes for improvement. Moreover, if combined with data on AP, the IG, in fact, becomes a three-dimensional importance – performance analysis (IPA; Martilla & James, 1977) with enhanced reliability compared to traditional approaches that employ unidimensional operationalisations of AI. In particular, the reinterpreted IG, which will subsequently be Total Quality Management 7 referred to as relevance – determinance grid (RDG), facilitates a classification of attributes into four categories with different relative priority levels (Figure 3). . Higher impact core attributes (high AR/high AD): These attributes are perceived as highly important by customers, and they have a strong influence on OS. The management should therefore assign this attribute category highest general priority in improvement strategies. In order to achieve a competitive advantage, the management should primarily focus on this attribute category. . Higher impact secondary attributes (low AR/high AD): These attributes are perceived as less important for providing the core product/service, but they have nevertheless a large influence on OS. Attributes from this category usually form the augmented product/service. Managers who are seeking opportunities to differentiate themselves from the competition should focus on this category. It is important to note that the importance of these attributes may be underestimated, if only a measure of AR is used as decision criterion. . Lower impact core attributes (high AR/low AD): These attributes are perceived as very important by the customer, but they have a relatively lower impact on OS. Attributes from this category are fully expected by the customer, and they are usually provided by all competing product/service providers at a satisfactory level. Managers should treat such attributes with care, because they might, in fact, be latent dissatisfiers with a strong negative impact on OS in case of performance failures. In this regard, these attributes are similar to BFs in the original IG. In general, managers should track innovations regarding these attributes, as they could result in a competitive advantage. Lower priority attributes/lower impact secondary attributes (low AR/low AD): Compared with other attributes, these attributes have lower levels of both AR and AD. The management should assign this attribute category lower general priority than the other three categories in improvement strategies. Managers should, however, be aware that this category may comprise latent satisfiers that have not fully expanded their potentials, because objective AP is low, and/or because more relevant and/or determinant attributes perform below customer-desired levels. 275 280 285 290 . 295 300 305 310 315 It is important to note that the RDG should only be used to derive managerial implications based on the relative positioning of attributes with regard to each other. This is Q8 especially important if attributes are located close to each other, but in different quadrants of the grid. A certain degree of flexibility should thus be retained when interpreting RDG results. However, although the RDG helps to reveal the most critical attributes that need to be improved, a shortcoming is that the analysis does not provide insight into possible asymmetric effects in the AP – AD relationship, which is particularly valuable information in cases when two or more attributes with similar AP levels are located nearby in the RDG. It is thus suggested to use the RDG in combination with a DAA (Mikulic´ & Prebezˇac, 2008), an extension of Brandt’s PRCA, that facilitates revealing satisfiers (positive determinance-asymmetry (DA)), dissatisfiers (negative DA) and hybrid (linear) attributes (zero DA) (Brandt, 1987). As a rule of thumb, in the case of similar AP and location in the RDG, dissatisfiers should have higher priority than satisfiers, whereas it should be the other way round when AP is high. Since such an approach accounts for possible diminishing and increasing returns in OS caused by rising AP perceptions, it is supposed to result in more effective increases of OS (Mikulic´ & Prebezˇac, 2008). 8 320 325 330 335 J. Mikulic´ and D. Prebezˇac Case study To demonstrate the value of a combination of the RDG and DAA in prioritising quality improvements, data from an airport satisfaction survey are used. Improvement priorities are derived in two steps. In the first step, the RDG is used to infer general priorities based on AR, AD and AP, whereas a DAA (Mikulic´ & Prebezˇac, 2008, 2011b) is used in a second step to refine the prioritisation, if necessary. Since the DAA is basically an extension of the PRCA (Brandt, 1987), it is noteworthy that the (reinterpreted) IG and the PRCA appear as highly complementary approaches for the purpose of attribute prioritisation, rather than as competing or conflicting approaches for operationalising the Kano model, as it is generally assumed in the literature (e.g. Fuchs & Weiermair, 2004). Measures and sample The data were collected in face-to-face interviews by means of a standardised questionnaire at a Croatian international airport over a period of 1 week in fall 2008. The questionnaire encompassed eight service attributes: (1) ‘ease of way-finding’, (2) ‘availability of flight information’, (3) ‘check-in efficiency’, (4) ‘dining/drinking possibilities’, (5) ‘shopping possibilities’, (6) ‘comfort level of the building’, (7) ‘courtesy of airport staff’ and (8) ‘offer of flights’. AP and OS with passenger services offered by the airport were measured with rating scales from 1 (‘very low’) to 5 (‘very high’). AR (i.e. customer-perceived importance) was measured with Likert scales from 1 (‘I do not agree at all’) to 5 (‘I completely agree’). In total, 1017 fully completed and usable questionnaires entered the data analysis. Analysis and results 340 345 In the first step, the input data for the RDG and DAA were calculated. For the RDG, AP scores were regressed against OS with the airport services to obtain indicators of AD (R2 ¼ 0.535). Arithmetic means were further calculated to obtain indicators of AR and AP. For the DAA, another multiple regression analysis was performed with two sets of binary-coded AP ratings as predictors and OS the criterion variable (R2 ¼ 0.453) (Equation 1): OS = b0 + 350 355 360 (pi dp,i + ri dr,i ) + 1 ∀i [ I, (1) where b0 is the constant, pi the incremental change in OS as a consequence of very low AP ratings of attribute i, i [ I (penalty score), ri the incremental change in OS as a consequence of very high AP ratings of attribute i, i [ I (reward score), dp,i the dummy variable for attribute i, i [ I, with a value of 1 for lowest AP ratings and a value of 0 for all other ratings, dr,i the dummy variable for attribute i, i [ I, with a value of 1 for highest AP ratings and a value of 0 for all other ratings, and 1 the error term. A comparison of pi and ri then reveals the direction of DA (dai ): . pi . ri : negative dai attribute i has a stronger effect on OS when its AP is perto be low than when it is perceived to be high. ceived . pi ri : symmetric dai ; attribute i has approximately equal effects on OS when its AP is perceived to be low and when it is perceived to be high. . pi , ri : positive dai ; attribute i has a weaker effect on OS when its AP is perceived to be low than when it is perceived to be high. Total Quality Management 365 9 It should be noted that statistical insignificance of pi scores may be attributed to generally satisfactory objective AP, resulting in very few or no cases of lowest AP ratings. In case of both insignificant and high pi scores, an attribute is likely to be a latent dissatisfier with a potentially strong negative impact on OS in case of low objective AP. Indicators of the degree of DA (DAIi) in the range [21,1], can be obtained as follows (Mikulic´ & Prebezˇac, 2008): ri − pi DAIi = pi + ri ∀i [ I, (2) 370 . . 375 . 380 385 390 A value of 21 means that low AP perceptions cause a decrease in OS, but high AP perceptions do not cause an increase in OS (perfect dissatisfier). A value of 0 means that high AP perceptions cause an equal increase in OS, but low AP perceptions cause a decrease in OS (perfect hybrid). A value of 1 means that high AP perceptions cause increase in OS, but low AP perceptions do not decrease OS (perfect satisfier). The calculated indices are presented in Table 1. In the next step, the RDG was constructed using scores of AR and AD. The threshold values for dividing the grid into four quadrants were set at the grand means of AR (ARGM ¼ 4.25) and AD (ADGM ¼ 0.111). Furthermore, service attributes performing below average (i.e. below the grand mean of AP scores; APGM ¼ 3.94) were marked with a minus (2), and attributes performing above average were marked with a plus (+) (Figure 4). The RDG reveals that the (6) ‘comfort level of the building’ and the (8) ‘offer of flights’ should be assigned highest priority in improvement strategies. Both attributes are categorised as higher impact core attributes and both perform below average. In a next step, the airport management should focus on (5) ‘shopping possibilities’ and (4) ‘dining/drinking possibilities’, which are categorised as higher impact secondary attributes that perform below average. It is noteworthy that the improvement priority of these two attributes would have been completely underestimated if only a measure of Table 1. Input data for the RDG and IAA . Service attribute AR 395 400 405 1. Ease of way-finding 2. Availability of flight information 3. Check-in efficiency 4. Dining/drinking possibilities 5. Shopping possibilities 6. Comfort level of the building 7. Courtesy of airport staff 8. Offer of flights Grand means Note: ns, not significant. ∗ p , 0.1. ∗∗ p , 0.01. ∗∗∗ p , 0.001. 4.51 4.23 4.50 3.58 3.63 4.47 4.64 4.40 4.25 Q12, Q13 AD ∗ 0.056 0.038 (ns) 0.135∗∗∗ 0.117∗∗ 0.124∗∗∗ 0.150∗∗∗ 0.146∗∗∗ 0.132∗∗∗ 0.111 AP DGP SGP DAI 4.37 4.26 4.32 3.18 3.49 3.85 4.17 3.89 3.94 20.689 20.588 20.491 20.505 20.496 20.477 20.364 20.479 20.689 0.311 0.412 0.509 0.495 0.504 0.523 0.636 0.521 0.311 20.378 20.176 0.018 20.010 0.008 0.046 0.273 0.041 20.378 10 J. Mikulic´ and D. Prebezˇac 410 415 420 425 430 435 440 445 450 Figure 4. RDG results. Note: AP scores are shown in brackets. Attributes marked with a + perform above average and attributes marked with a 2 perform below average. AR had been used as a decision criterion, since both attributes have relatively high AD, but the lowest AR scores in the analysed attribute set. Conversely, if only AD had been used as decision criterion, the improvement priority of these two attributes would have been overestimated, because they are the two worst performing attributes, but have similar AD like the two higher impact core attributes (i.e. (6) and (8)). The remaining four attributes are less problematic as they perform above average, but they can nevertheless be prioritised. Accordingly, the next two attributes to be considered for improvement should be (7) ‘courtesy of airport staff’ and (3) ‘check-in efficiency’, which are categorised as higher impact core attributes; (1) ‘ease of way-finding’, which is categorised as a lower impact core attribute, should follow, and lowest priority should be assigned to (2) ‘availability of flight information’, which is categorised as a lower priority attribute. Next, the DAA was conducted to test for possible asymmetries in the AP –AD relationship and to refine the prioritisation obtained through the RDG, if necessary (Figure 5). The DAA reveals an approximately symmetric AP – AD relationship for five attributes (SGP ≈|DGP|) – that is, for (3) ‘check-in efficiency’, (4) ‘dining/drinking possibilities’, (5) ‘shopping possibilities’, (6) ‘comfort level of the building’ and (8) ‘offer of flights’. These attributes could be described as hybrid factors, meaning that rising AP perceptions cause approximately linear returns in OS. However, significant asymmetries are present with regard to the remaining three attributes. On the one hand, (1) ‘ease of way-finding’ and (2) ‘availability of flight information’ cause diminishing returns in OS as AP perceptions rise. Since the AP level of these two attributes is relatively high (P1 ¼ 4.37 and P2 ¼ 4.26, respectively), improving these attributes would not be likely to cause significant increases in OS, which is why they do not necessitate particular attention. On the other Q9 hand, (7) ‘staff courtesy’ causes increasing returns in OS as AP perceptions rise. Its AP level is quite high (P7 ¼ 4.17), but since it is a highly determinant satisfier (AD7 ¼ 0.146), increasing its AP would be likely to cause further significant increases in OS. Accordingly, this attribute should be considered for improvement right after the four attributes that perform below average. However, the same recommendation has been provided earlier by the RDG. Total Quality Management 11 455 460 465 Figure 5. DAA results. 470 475 To conclude, although the DAA revealed several significant asymmetric effects, there is no need for any refinements of the prioritisation obtained by the RDG. However, a noteworthy finding is that (7) ‘staff courtesy’, which is categorised as a satisfier, has a larger absolute negative impact on OS when AP perceptions are low (PC7 ¼ 20.092) than both the identified dissatisfiers – that is, (1) ‘ease of wayfinding’ (PC1 ¼ 20.062) and (2) ‘availability of flight information’ (PC2 ¼ 20.040). Accordingly, if all these attributes performed low, unlike common opinion the satisfiers, rather than the dissatisfiers, should be improved first, which reinforces the recommendation made by Mikulic´ and Prebezˇac (2008) not to use DA (i.e. a categorisation of attributes into satisfiers, dissatisfiers and hybrids) as a first-order criterion in decisionmaking about improvement priorities. 480 485 490 495 Conclusions The IG was developed as a research tool for categorising product/service attributes in the Kano model. A discussion of the technique’s underlying implicit assumptions, however, failed to validate it as a technique for its originally intended purpose. Nevertheless, based on a specification of the informational value of the measures typically used in the IG, a logical and theoretically grounded reinterpretation of IG results was put forward in this paper. In a case example on passenger satisfaction with airport services, the reinterpreted IG, referred to as RDG, facilitated the identification of four attribute categories with different general priority levels: (i) ‘higher impact core attributes’, (ii) ‘higher impact secondary attributes’, (iii) ‘lower impact core attributes’ and (iv) ‘lower priority attributes’. To derive improvement priorities with the objective of effectively increasing overall passenger satisfaction, the DAA was paired with data on AP, thus turning it into an IPA with enhanced reliability compared with traditional approaches that apply unidimensional operationalisations of AI. Moreover, a DAA was used in a second step to refine the prioritisation obtained from the RDG, however, the analysis revealed that refinements were not necessary. 12 500 505 510 515 520 525 530 535 540 J. Mikulic´ and D. Prebezˇac Implications for managers Managers who use measures of AI in prioritising product/service attributes for improvement should be aware that explicit AI measures (e.g. direct importance ratings) and implicit AI measures (e.g. coefficients obtained by regressing AP data against a global performance measure) assess quite distinct concepts. Explicit AI measures assess the relevance of attributes, whereas implicit AI measures assess the determinance of attributes. In fact, it is not unlikely that attributes which are not perceived important by customers (low relevance) emerge as having strong impact on OS in case-based studies (high determinance), and vice versa. It is thus suggested to combine both types of AI measures in decision-making, since very different implications regarding the prioritisation of attributes may emerge depending on the type of AI measure used. Moreover, managers who base their decisions about improvement priorities on a classification of product/service attributes into satisfiers, dissatisfiers and hybrids should be aware that implications might be misleading if absolute levels of determinance remain unconsidered. The case study in this paper revealed that the only attribute which was categorised as a satisfier (‘courtesy of the airport staff’) actually had a larger absolute potential to create dissatisfaction, than both the identified dissatisfiers (‘ease of wayfinding’ and ‘availability of flight information’). Consequently, if all these attributes per- Q10 formed low, and if the primary objective was to decrease overall dissatisfaction, unlike common opinion the satisfier should be assigned highest improvement priority rather than the dissatisfiers. The authors of this study thus suggest prioritising attributes for improvement in two steps. In the first step, an RDG should be used to obtain a general prioritisation of attributes, based on their relevance, determinance and performance, whereas a DAA should be used, in a second step, to gain insight into possible increasing or diminishing returns in OS caused by rising levels of performance ratings. Results from the latter analysis can then be used to refine the prioritisation of attributes with similar performance levels, relevance and determinance, if necessary. A noteworthy practical advantage of the proposed analytical framework is that it can be applied to data which are collected in typical customer satisfaction surveys. 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