- Email: [email protected]
Revisiting importance–performance analysis
Tourism Management 22 (2001) 617–627
Revisiting importance–performance analysis Haemoon Oh* Hotel, Restaurant, and Institution Management, Iowa State University, 11 MacKay Hall, Ames, IA 50011-1120, USA Received 3 February 2000; accepted 20 September 2000
Abstract As a tool to develop marketing strategies, importance–performance analysis has gained popularity among hospitality and tourism researchers for its simplicity and ease of application. This article revisited several conceptual and methodological issues inherent, but often overlooked, in using the importance–performance analysis framework. The author conducts a critical review of past studies, reanalyzes published data to raise questions, and develops suggestions for future hospitality and tourism research applying importance–performance analysis. The primary goal of this article was to stimulate further discussion and research on the validity and reliability of widely adopted importance–performance analysis. r 2001 Elsevier Science Ltd. All rights reserved. Keywords: Importance; Performance; Importance–performance analysis; Tourism; Hospitality; Validity; Reliability; Attribute
1. Introduction Importance–performance analysis (IPA) has been used in hospitality and tourism research for years. Since the seminal work by Martilla and James (1977), the IPA framework has gained popularity among researchers in service quality (e.g., Ennew, Reed, & Binks, 1993), travel and tourism (e.g., Evans & Chon, 1989; Go & Zhang, 1997), leisure and recreation (e.g., Guadagnolo, 1985; Hollenhorst, Olson, & Fortney, 1992), education (e.g., Alberty & Mihalik, 1989; Ortinau, Bush, Bush, & Twible, 1989), and healthcare marketing (e.g., Dolinsky, 1991; Dolinsky & Caputo, 1991; Hawes & Rao, 1985). Anecdotal evidence also suggests that studies applying IPA are frequently presented at various hospitality and tourism conferences. As Martilla and James (1977) emphasized, ease of application and the appealing methods of presenting both data and strategic suggestions seem to be the factors, among others, that contribute to wide acceptance of the technique. To date, few studies have critically considered the conceptual validity of IPA. Most IPA studies have attempted to apply the same technique in different subject areas. Although several researchers have tried to extend the original IPA method (e.g., Dolinsky & Caputo, 1991; Vaske, Beaman, Stanley, & Grenier, *Tel.: +1-515-294-7409; fax: +1-515-294-8551. E-mail address: [email protected] (H. Oh).
1996), the focus of their efforts was on modifying the analysis methods by adding more information to the original IPA framework. Not sufficiently addressed in past studies, however, were fundamental conceptual and practical issues underlying the IPA research methodology. Although ease of application or simplicity may be one important criterion for wide acceptance of a research framework such as IPA, it certainly should not be interpreted as an indicator of validity of the method that overrides logical justifications. The goals of this article are to (1) raise several conceptual and practical issues inherent, but often overlooked, in using the IPA technique and (2) develop suggestions for future research to improve the utilities of IPA. Although a few researchers have raised several similar issues covered in this article along with their empirical applications of IPA, they have touched upon mostly local issues and, thus, their discussion has been somewhat fragmentary (Tzung-Cheng, Beaman, & Shelby, in press). Accordingly, this article attempted to include various issues, with a particular intention to raise those issues that have not been addressed previously. First, this article briefly revisits the IPA methodology and its critical assumptions. Questions are then raised and the IPA literature is critically reviewed to address several conceptual and practical issues that warrant further attention and research. The article concludes with suggestions for appropriate applications of the IPA technique, as it is in its current form, in hospitality and tourism research.
0261-5177/01/$ - see front matter r 2001 Elsevier Science Ltd. All rights reserved. PII: S 0 2 6 1 - 5 1 7 7 ( 0 1 ) 0 0 0 3 6 - X
618
H. Oh / Tourism Management 22 (2001) 617–627
2. Concept of IPA Martilla and James (1977) first proposed IPA as a tool to develop firms’ management strategies. In its essence, IPA combines measures of attribute importance and performance into a two-dimensional grid (see Fig. 1) in an effort to ease data interpretation and derive practical suggestions. According to their suggestions, a set of key attributes of a target product is generated and subjects rate each attribute for its importance to a purchase decision. For example, Martilla and James (1977) used 14 attributes to understand the importance of an automobile dealer’s service. Ideally, attribute importance needs to be measured prior to, rather than after, an actual purchase experience, because IPA in general pursues understanding the role of the key selected attributes in a purchase decision. Performance is then measured using the same set of attributes so that importance and performance can be directly compared within the same attributes via the IPA plot (or grid). The mean values of importance and performance scores are then used as the crossing point in constructing the IPA grid. Note that Martilla and James (1977) also recommended use of the median, instead of mean, values for the importance axis when there is an insufficient amount of variance or when the importance ratings show a non-normal distribution pattern. Interpretation of the IPA plot is straightforward. As shown in Fig. 1, IPA generates four different suggestions based on importance–performance measures. Interpretations follow the combination of importance and
performance scores of each attribute. The first quadrant, ‘keep up the good work’, captures the attributes that customers think are important to their purchase decision and on which customers also perceive the company (or product) performs well. Likewise, ‘possible overkill’ in Quadrant 2 indicates that the attributes falling in this quadrant are relatively less important but the company performs well on the attributes. Some attributes may fall in the third quadrant, ‘low priority’, because both importance and performance ratings of the attribute are lower than the average. These items are likely to receive a low priority in resource allocation decisions. The attributes that are important to customers’ purchase decisions but on which the company does not perform well are classified into Quadrant 4, ‘concentrate here’. The company needs to focus on improving its performance on these attributes. In essence, IPA provides an attractive snapshot of how well the company meets customers’ important concerns on selected attributes and, at the same time, offers guidelines for the company’s future resource allocation decisions.
3. Revisit to IPA 3.1. Conceptual issues The IPA literature does not provide a clear definition of attribute importance. Most IPA studies in the hospitality and tourism arena have tended to operationalize importance simply as the level of salience of an
Fig. 1. Traditional importance–performance grid.
H. Oh / Tourism Management 22 (2001) 617–627
attribute that is likely to be a determinant of an imminent purchase decision. Few researchers have considered the multiple roles of the concept in consumer decision-making. Jaccard, Brinberg, and Ackerman’s (1986) review, for example, suggested at least five additional definitions of importance that are associated with different measurement methods. One is importance that can be derived from memory-based free elicitation. According to this line of reasoning, attributes that are important to a purchase decision are believed to be salient (Kaplan & Fishbein, 1969) and provide a ready access in memory (Tversky & Kahneman, 1973). Another type of importance is reflected in goal-oriented search attributes that consumers actively look for in the target product and consider when making a purchase decision (Jacoby, 1975). Jaccard and Wood (1986) presented a utility-type importance that could be obtained by conjoint measures. In addition, the consumer’s subjective conditional probability of willingness to consider a certain attribute characteristic in a purchase decision appeared to be another operationalization of importance (Wyer, 1974). Thurstonian measures of importance, which derives from pair-wise comparisons of relative attribute importance, also received attention in attitude research (Fishbein & Ajzen, 1975). Nevertheless, a confirmatory factor analysis by Jaccard et al. (1986) indicated that these definitions were not unidimensional, which means that depending on the type of conceptualization the importance concept could offer different implications to the users of IPA. Similar to Jaccard et al.’s study, Lego and Shaw (1992) tested, both at the individual attribute and aggregate levels, the convergent validity of four methods of measuring attribute importance associated with a tourist product. Their results, based on a correlational approach, confirmed the findings of Jaccard et al. (1986). An implication is that the multidimensional nature of the importance concept could cause, when not properly controlled, weakening the validity and reliability of importance measures, especially in field survey settings. The absence of a clear criterion concept that could be related to attribute importance is another issue. In his recent review of related research, Oliver (1997, p. 34) raised a question: Important for what? Although researchers could elicit importance in terms of a product purchase, importance in product choice is an inappropriate context when the research goal is to understand post-purchase decision processes (Oliver, 1997). This is a typical problem of determining a common criterion variable or target concept (e.g., consumer satisfaction) for theoretically related variables (i.e., importance and performance). Thus, Oliver (1997) suggests that consumer satisfaction be a target criterion variable when measuring attribute importance and performance (Oliver, 1997). In a similar reasoning,
619
Martilla and James (1977) suggested that the disparity between importance and performance be an indicator of customer dissatisfaction, while Guadagnolo (1985) maintained that IPA be a tool to assess consumer satisfaction. In a recent tourist motivation study, Ryan and Huyton (in press) also inferred a functional relationship between importance and both performance perceptions and satisfaction. Nevertheless, use of any criterion variable was not a tradition in past IPA studies. Oh and Parks (1997) pointed out another critical issue related to frequent confusion among researchers between the concepts of importance and expectation. Martilla and James (1977, p. 77) hinted at a subtle difference between the two concepts in that ‘‘y consumer satisfaction is a function of both expectations related to certain important attributes and judgments of attribute performance’’. A number of IPA researchers, however, have often used the two concepts interchangeably when measuring and interpreting importance (e.g., Chon, Weaver, & Kim, 1991; Evans & Chon, 1989; Hollenhorst et al., 1992; Tzung-Cheng et al., in press). If they purported to measure expectations, interpretations of the results and thus marketing suggestions should have been different from what they offered. This mixed use of the two concepts seems to have originated in the similarity between IPA and other popular consumer research models such as expectancy-value theory (Fishbein & Ajzen, 1975) and SERVQUAL (Parasuraman, Zeithaml, & Berry, 1988) that involve similar concepts. The SERVQUAL model, for example, operationalizes service quality as a function of expectations and performance. Consumer satisfaction is also modeled as a function of the disparity between expected and experienced performance in expectancy-disconfirmation theory (see Oh & Parks, 1997, for a comprehensive review). As in the case of importance, however, the concept of expectation has also been heavily criticized for its ambiguous, multidimensional definitions in previous studies (see Teas, 1993). Some evidence exists to support the conceptual difference of the two concepts. In his review of service quality research, Ryan (1999), for example, distinguished importance from expectation such that the former is a desired outcome and the latter a tolerated outcome in consumer perceptions of service quality. In addition, Myers and Alpert (1977) contend that salience, importance, and determinance be distinguished in attitude research. Finally, the predictive validity of absolute versus relative importance has not received sufficient consideration in previous IPA studies. In traditional IPA studies, the respondents are typically directed to consider one attribute at a time when providing importance ratings. Unfortunately, this method is likely to inflate importance ratings of most attributes, which, in turn, restricts the variation in importance scores. This
620
Author
Subjects
Alberty and Mihalik (1989) Mengak (1985)
Adult education 93 students
Hollenhorst et al. (1993)
Chon et al. (1991)
Fletcher, Kaiser, and Groger (1992) Dolinsky and Caputo (1991)
Importance scales used
Crosshairs
Percentage of attributes by scale meansa
Percentage of attributes by actual meansa
23
5 point Likert
Actual means
Positive 100%
255 travelers to a visitor center
34
5 point Likert
Actual means
Positive 100%
522 cabin renters
24
7 point
Actual means
Park visitors
14
7 point
Actual means
401 Virginia residents travelling to the Norfolk-area Residents from northern Virginia Residents from Richmond Residents from Southwestern Virginia Residents from Hampton Roads
15
Actual means
15
5 point, 1-not important and 5-very important Same as above
Positive 75%, negative 21% Positive 36%, negative 50% Positive 100%
Actual means
Positive 100%
15
Same as above
Actual means
Positive 100%
15
Same as above
Actual means
Positive 100%
15
Same as above
Actual means
Positive 100%
14
5 point, 1-very unimportant and 5-very important 4 point, 1-very satisfied and 4-very dissatisfied
Statistically derived means N/A
Positive 57%, negative 14% N/A
Positive 74%, negative 22% Positive 71%, negative 24% Positive 58%, negative 38% Positive 71%, negative 21% Positive 40%, negative 40% Positive 40%, negative 53% Positive 40%, negative 40% Positive 60%, negative 27% Positive 40%, negative 53% Positive 93%, negative 7% N/A
573 park and recreation directors 622 fee-for-service health care users
Number of Attributes
6
H. Oh / Tourism Management 22 (2001) 617–627
Table 1 Review of the importance–performance literature
Hawes and Rao (1985)
Dolinsky (1991)
254 women who had given birth at two local hospitals within the previous year 787 HMO members 787 HMO members
Ortinau et al. (1989)
20
7 6 14
End of semester
14
Clients of travels agent and tour operators: 133 pre-trip clients 89 post-trip clients
20
Guadagnolo (1985)
N/A
Uysal, Howard, and Jamrozy (1991) Go and Zhang (1997)
117 ski resort visitors
Duke and Persia (1996)
Evans and Chon (1989)
Scale means
Positive 90%, negative 5%
Positive 70%, negative 30%
4 point, 1-very satisfied and 4-very dissatisfied 3 point, 1-HMOs better and 3-traditional fee-for-service better 6 point, 1-not at all important to me and definitely disagree and 6-extremely important to me and definitely agree Same as above
N/A
N/A
N/A
N/A
N/A
N/A
Actual means
Positive 100%
Positive 71%, negative 21%
N/A
Positive 93%
Positive 71%, negative 14% Positive 75%, negative 10% Positive 85%, negative 10% Positive 68%, negative 32% Positive 67%, negative 33% Positive 64%, negative 27% Positive 64%, negative 27% Positive 45%, negative 55% Positive 40%, negative 60%
Actual means
Positive 100%
20
4 point, 4-very important and 1-unimportant Same as above
Actual means
Positive 100%
31
7 point
Actual means
117
5 point
Actual means Arbitrary
Positive 90%, negative 10% Positive 89%, negative 11% Positive 100%
N/A
Positive 100% Positive 45%, negative 55% Positive 50%, negative 50%
59 international meeting planners Industry factor
11 11
5 point, 1-least important and 5-extremely important 5 point
400 travelers
11
7 point, 7-high and 1-low
Scale means
400 travelers
10
Same as above
Scale means
a ‘Positive’ indicates the percentage of attributes in either Quadrants 1 or 3; ‘Negative’ indicates the percentage of attributes in either Quadrants 2 or 4. When the study used actual means for the IPA grid, the percentage was recalculated based on a reconstruction of the grid using the scale means; and vice versa.
H. Oh / Tourism Management 22 (2001) 617–627
146 business students for course evaluations: beginning of semester
6 point, 1-least important and 6-most important
621
622
H. Oh / Tourism Management 22 (2001) 617–627
type of ‘ceiling effects’ is highly likely, particularly because researchers tend to use a selected set of keyFtherefore, ‘important’ already in its own rightFattributes to measure importance. Martilla and James (1977) and Hollenhorst et al. (1992) contend that this is a frequent problem occurring in empirical applications of IPA. Moreover, it is questionable how meaningful this type of absolute importance would be in developing a firm’s action plans, because this kind does not reflect competitive business environments. Evidently, consumer preferences for a product or brand are formed based upon not only a trade-off or comparison among attributes within the focal product, but also a comparison between the same attributes across competing products. Hence, even if measurement would be a challenge, the market-oriented relative importance of an attribute may be a more valid, competitive representation of attribute importance than attribute-specific absolute importance. Neslin (1981) showed that statistically derived relative importance had superior predictive validity than self-reported absolute importance. 3.2. Relationships among variables Many IPA studies have not considered potential relationships between importance and performance. Although the method of constructing the IPA grid itself, as shown in Fig. 1, imposes a statistical relationship (i.e., correlation at least) to the two concepts, few IPA studies have attempted to examine the implications of the potential relationship. As implied in Martilla and James’ (1977) statement cited above, importance has a close relationship with expectations in a way that the two concepts are antecedents of performance perceptions. In addition, previous studies showed that importance could be used as a weighting variable of performance (e.g., Cronin & Taylor, 1992; Oh & Parks, 1998; Tse & Wilton, 1988). The implication is that importance may possess some additive and/or interactive effects on performance ratings. In other words, it is plausible that customers’ evaluations of a firm’s attribute-specific performance are affected by how important the customer thinks the attribute is to him or her (Ryan & Huyton, in press). The plausible causal relationship between importance and performance perceptions can be justified theoretically beyond the relationship reflected in the IPA grid. Borrowing first from the theory of expectancy disconfirmation (see Oh & Parks, 1997, for a review), it can be shown that importance is positively related to performance; the more important the attribute is to the customer, the more likely the customer perceives the attribute performance favorably and thus higher satisfaction. In fact, when one of the recently published lodging data (Oh & Parks, 1998) was reanalyzed via confirmatory factor analysis, the strength of the under-
lying correlation between importance and performance was as high as 49 percent. Similarly, a reanalysis of Ryan and Huyton’s data (Table 1, in press) indicated that importance and satisfaction was indeed highly correlated (r ¼ 0:78). This relationship can also be explained by generalizability theory (see Oliver, 1980 for details). The theory posits that consumers tend to generalize their experience (i.e., feelings arising from perceptions of performance) in the direction of their evaluation outcome, especially for important product/ service attributes. Applied to the measurement of importance and performance, this implies that the two concepts tend to generate either positive or negative correlation. Given a causal relationship between importance and performance, the traditional IPA grid could offer serious misinformation depending upon the nature and magnitude of the relationship. Consider first a high positive correlation between the two concepts, as Hollenhorst et al. (1992) empirically observed in their study. Mathematically, a high positive correlation will cause the attributes to spread along the solid line stretching through Quadrants 1 and 3 as illustrated in Fig. 1, whereas a high negative correlation between the two variables causes the attributes to scatter along the dotted line. This suggests that a positive correlation between the two variables tends to result in prescribing more suggestions of ‘keep up the good work’ (Quadrant 1) or ‘low priority’ (Quadrant 3). In contrast, a negative correlation will tend to cause over-prescribing ‘possible overkill’ (Quadrant 2) or ‘concentrate here’ (Quadrant 4). Therefore, as much as importance and performance ratings covary, the IPA grid becomes ineffective as a basis of developing management suggestions. The reason is that the suggestions tend to be affected by the causal relationship between importance and performance, as well as the main effects of the two concepts. A critical review of past IPA studies, shown in Table 1, supports the arguments made above. The last two columns in the table show the proportion of attributes reflecting positive (i.e., the number of attributes falling in Quadrants 1 and 3) and negative (i.e., the number of attributes falling in Quadrants 2 and 4) correlations between the importance and performance of the attributes, based upon the type of ‘cross-hair’ points (to be discussed later). For example, in Alberty and Mihalik’s study (1989) using actual importance and performance mean scores as the cross-hair points (see the last column), about 74 percent of the attributes fell in the quadrants of positive correlation and about 22 percent in the negative correlation quadrants. Only the remaining 4 percent fell along the crosshairs, defying a classification. However, when their IPA grid was reconstructed using the scale mean scores as the crosshair points (see the second-to-the-last column), all attributes fell in the positive correlation zones. Similarly,
H. Oh / Tourism Management 22 (2001) 617–627
in the majority of the other studies reviewed in Table 1, patterns of positive correlation were prevalent especially when the importance–performance scale mean scores were used as the cross points. This finding reflects that a causal system may be under operation between importance and performance perceptions. Besides the between-concepts correlation, correlations among the attributes, both within importance and within performance, could have contributed to the patterned dispersions of attributes on the IPA grid. Although most IPA studies have measured importance and performance using each attribute independently, some items always tend to correlate with each other. A luxury gourmet restaurant, for example, may perform well on courtesy and this may cause customers to infer high responsiveness of employees even if the latter might have not been experienced directly. This type of between-attributes correlation is likely to result in causing the two attributes to fall in the same quadrant. The same concern is applicable to other pairs of attributes, thereby potentially undermining the validity of IPA-produced managerial suggestions.
4. Practical issues The level of abstraction in determining a set of measurement attributes becomes an issue when employing IPA. A typical IPA study begins with a fairly large number of attributes on which to measure importance and performance. Most researchers, however, initially reduce the number of attributes in the pretest stage so as to achieve a parsimonious research framework and to minimize redundancy, if any; a trade-off occurs between exhaustiveness and practicality of a research model. In this case, ‘‘survey length restrictions and the inability to adequately sample the full population of consumers, particularly in the pretest stages of survey construction, are likely to cause the researcher to overlook some critical elements of the purchase which will not be represented in the feature list’’ (Oliver, 1997, p. 34). This problem may be addressed by using a proper, typically high, level of feature abstraction when the attribute list is prepared, at the expense of specificity. Is the concept of importance unidirectional or bidirectional? As shown in the fourth column of Table 1, many researchers measured importance unidirectionally by using a scale anchored with ‘no importance’ to ‘very (or extremely) important’. Other researchers, however, used a bi-directional measure of importance as labeled ‘very unimportant’ and ‘very important’. Little psychometric reasoning has been given to the property of the concept. Provided that the concept of importance reflects the ‘level’ or ‘strength’, rather than evaluations of goodness or badness, of the attribute characteristic, the unidirectional scale seems to make sense more than
623
the bi-directional one. Nevertheless, the bi-directional scale with modified wordings may be useful when measuring relative importance. Another notable inconsistency in the results of past IPA studies arises from the location of the horizontal and vertical axes or ‘cross-hair’ points of the grid. As Martilla and James (1977) prescribed, the majority of researchers have used the mean values of observed (i.e., self-reported) actual importance and performance ratings when determining the cross-hair point in the IPA grid (see the fifth column in Table 1). Other researchers, however, used the mean values of the scales employed for measuring importance and performance (e.g., Evans & Chon, 1989; Hawes & Rao, 1985). The last two columns of Table 1 show that the study results and interpretations could be dramatically different depending upon whether the actual or scale means were used as the cross-hair point in constructing the IPA grid. For example, when the actual means were used, about 40 percent of attributes fell in Quadrants 1 or 3 and another 40 percent in Quadrants 2 and 4 in the first study of Chon et al. (1991). When the grid was reconstructed with the scale means as the cross-hair point, however, all attributes fell in Quadrant 1. These conflicting results imply that careful interpretations are necessary when using the actual means because the range of the original scale tends to be truncated, requiring different interpretations. Researchers need to caution readers and their research consumers about this hidden problem. The traditional IPA grid provides limited understanding of product performance. Although the IPA grid classifies all IP-paired attributes into one of the four categories (i.e., quadrants), this categorization could result in serious misinformation. Consider Fig. 1 again, in which hypothetical attributes A, B, and C are plotted. According to the IPA approach, attributes A and B are grouped into the same category and point to the same suggestion, ‘keep up the good work’, while attribute C belongs to a different category and is likely to receive ‘low priority’ in the future. Ironically, attributes A and B have a greater psychometric (e.g., Euclidian) distance than attributes B and C. It is, therefore, highly probable that popular statistical techniques, such as factor analysis, multidimensional scaling, and cluster analysis, will yield attributes B and C as more likely to fall in the same group (i.e., category) than attributes A and B. Furthermore, extracting the identical marketing suggestions from attributes A and B may cause loss of information that is specific to each attribute and, thus, may not be a valid interpretation of the data. This categorization problem reveals that IPA may not be sophisticated enough to precisely represent the data structure. It is important that IPA be sensitive to problem areas in order to maximize its effectiveness (Hollenhorst et al., 1992).
624
H. Oh / Tourism Management 22 (2001) 617–627
Finally, the goal of IPA is inconsistent with the strategic philosophy of most firms in today’s marketplace. Today, firms are striving to deliver not only products and services but also high quality and customer satisfaction along with the products (Oh & Parks, 1997). According to consumer satisfaction theories (Oliver, 1997), perceptions of high quality and customer satisfaction are achieved when firms perform better than what customers want. However, IPA views firms’ better-than-wanted performance as ‘overkill’. In the hospitality business, extra performance often yields customer satisfaction without committing the overkill of resources. Most intangible service attributes, such as courtesy and politeness that are furnished through employees’ behaviors, qualify for this reasoning. Consider also a hypothetical attribute A in Fig. 1 again. It is of high importance but the firm performs relatively low on it. Yet, IPA would tell management to ‘keep up the good work’, rather than urging them to ‘concentrate on it’. Strategically, IPA does not effectively reflect firms’ performance directed at customer satisfaction and market retention. Moreover, from a practical standpoint, it is difficult for a firm to always perform exactly at the importance level of the attribute in order to prevent possible overkill or to reduce needs for additional resource allocation to the attribute. Performance that exceeds the level of importance may be desirable, especially in the competitive hospitality market where extra performance often does not require corresponding input resources.
5. Summary Hospitality and tourism researchers continue to employ IPA without giving critical considerations to its conceptual and practical validities. This article attempted to raise such issues in an effort to provide a starting point of additional debate and research on ways to improve the value of IPA as a framework for continued research in hospitality and tourism. Understanding what works and what does not in IPA is critical in that it has established a unique tradition of hospitality and tourism research. The issues addressed thus far also deserve close attention because IPA seems to continue playing an important role in the graduate education of hospitality and tourism. Nevertheless, this critical review should be understood as a beginning point to stimulate related discussions and, thus, is far from the exhaustive coverage of many more issues awaiting investigation. The issues tackled in this article are summarized as follows: (1) (2)
Lack of a clear definition for the concept of importance. Absence of a clear criterion variable for the IPA framework as a whole.
(3) Mixed uses of importance and expectation. (4) Lack of research on absolute versus relative importance. (5) The implications of relationships between importance and performance and among the attributes. (6) Absence of guidelines for developing a set of attributes to be used. (7) Use of unidirectional versus bi-directional measurement scales for the concept of importance. (8) Use of actual means versus scale means in constructing the IPA grid. (9) Potential misclassifications of attributes on the IPA grid. (10) A philosophical issue related to strategic suggestions. Easy applicability of research methods that can efficiently explain complex consumer behaviors is always welcome. Yet, simplicity and convenience should not be the predominant criteria when selecting and using a research framework. Firms cannot sustain their competitive position based on information obtained through a research method that lacks validity and reliability. Rigorous reconsideration of IPA as a tool for developing marketing programs is necessary.
6. Suggestions for future IPA studies In conclusion, this article addresses a few pending issues that should be considered by researchers so as to increase the utilities of IPA. Definition of the concepts and corresponding interpretations: A clear definition of importance and performance is a prerequisite to future IPA studies. Jaccard et al. (1986) and Lego and Shaw (1992) provide a useful starting point for considering several possibilities in this effort. Another approach to the definition of importance would be a two-step approach in which, first, the level of salience of a particular attribute characteristic is determined in general, and then the strength of the salient attribute(s) is assessed in terms of the focal decision. Myers and Alpert (1977) also offer a direction the concept of importance can be defined along with similar concepts and relevant measurement methods. Determining the measurement scale should be done along the definitional efforts. For the definition of performance perceptions, the consumer satisfaction and service quality literature can be a good source (Oh & Parks, 1997; Oliver, 1997; Parasuraman et al., 1988). Clear definitions will not only eliminate confusion between importance and expectation, but also provide a basis for precise interpretations of study results, thereby improving the value of the obtained information.
H. Oh / Tourism Management 22 (2001) 617–627
Specification of a common criterion concept: The specification of a criterion variable(s) should, in part, be a statement of study goals. When the concepts of importance and performance are measured toward different criterion concepts, it is not logical to plot them together, as in the conventional IPA grid. Many useful criterion concepts are available in the marketing literature. Oliver (1997) suggests that consumer satisfaction and/or return intention could well be such a variable, while Ryan and Huyton (in press) models tourist satisfaction over time as a function of importance and the tourist’s socio-demographic background. Perceived value may be another good criterion for measuring importance and performance. Depending on study objectives, researchers may also utilize prepurchase criterion variables, such as perceived quality and/or willingness to buy. By relating both importance and performance concepts to one or more criterion variables, the comparability between importance and performance can be improved. In addition, this approach allows more useful correlational data analysis methods. Causal modeling of attribute importance: Provided importance judgments are an antecedent of performance perceptions in consumer decision-making, direct predictions of performance, as well as other criterion variables, are feasible based on importance ratings. This direct causal modeling of importance within prevailing consumer models, such as those on service quality and consumer satisfaction, may offer a fresh opportunity to explain and understand consumer behavior. To this end, cognitive dissonance theory (Festinger, 1957) or adaptation-level theory (Helson, 1964) may be instrumental in that importance plays a role as a predetermined standard of performance in consumer decisions. Ryan (1999) suggests that importance be used as a normative standard of performance. Use of importance either as a weighting variable (i.e., typically multiplied with other variables: see Oh & Parks, 1998, for a review and practice) or as a stand-alone construct in a causal model (i.e., typically additive in nature) is an issue to be considered in future studies. The causal modeling approach will contribute to clarifying the role of importance as compared to that of other similar concepts, such as expectations, in consumer decisionmaking behavior. This also will offer an opportunity to better understand the implication of covariation between importance and performance, the opportunity that has been lost in past IPA studies. It is further likely that pre-purchase perceptions of attribute importance need certain mediating variables, such as ‘‘relevance’’, that may facilitate inferring a logical connection between importance and key criterion variables in causal modeling. Absolute versus relative importance: Relative importance is a crucial concept in the highly competitive
625
hospitality industry. When the market is saturated and thus increasingly competition-oriented, merely knowing and meeting customers’ wants may not promise repeat purchases because customers will constantly compare market offerings in search of a better choice. Research is needed to assess the utilities of absolute versus relative importance (cf. Oh & Parks, 1998). The concept of relative importance is likely to be multidimensional; for example, it can be modeled across a set of attributes under study (a) within the focal product and (b) across different market offerings in the same target market. Neslin (1981) illustrated methods of deriving relative importance. The two-dimensional IPA framework also needs to be developed further to encompass relative importance in its grid. Determination of a set of attributes: A careful selection of attributes for measuring importance and performance is critical because follow-up management decisions are expected to rely on the information based on the selected attributes. One issue involved in this task is the use of the same set of attributes for measuring both importance and performance. Many service attributes are experiential by nature (Parasuraman et al., 1985); that is, consumers may not know how important a particular attribute is to their decisions until they actually experience the attribute. Many service failures become important only when they occur, for example. It is recommended that researchers develop attributes by considering not only the characteristics of the firm’s product or service but also the market demands shared by competing companies. Involving managers and consumer panels in the early process of IPA research is desirable, especially for developing a set of strategically important attributes. The level of abstraction in specifying attributes should be commensurate with the target level of follow-up management decisions. In essence, selection of attributes must be based on a priori judicious trade-off between exhaustiveness and practicality of the information to be obtained. Scale construction. A unidirectional rating scale was recommended earlier for measuring importance. In practice, however, both unidirectional and bi-directional scales are likely to produce similar results because importance ratings will almost always be highly skewed in the negative direction, regardless of the scale used. Nevertheless, using a bi-directionally worded scale is likely to cause a stronger negative skewness than a unidirectional scale. The scale of performance perceptions does not seem to be problematic in previous IPA studies as the concept is strictly evaluative. When constructing measurement scales, however, the comparability between the scales for importance and performance should receive additional attention. It is also recommended that scale means be used as the cross-hair point when constructing the IPA grid and that using actual means be viewed as a special case or an
626
H. Oh / Tourism Management 22 (2001) 617–627
extension of the former. Potential ineffectiveness and misinformation of IPA research findings were illustrated earlier in terms of using either the scale or actual mean scores as the cross-hair point. If the spirit of IPA lies with comparing importance to performance, or vice versa, using scale means is likely to provide a simpler description than using actual means. The reason is that the meaning of the original scales provides easy and valid interpretation of the data. The actual means of importance and performance are likely to differ in most cases, requiring (a) study-specific adjustments to the scales in interpreting importance and performance ratings (see Tzung et al., in press, for an illustrative example) and (b) a relative interpretation of attributes within importance and performance, respectively. Note that using different means has different implications for management decisions. Modifications. As illustrated earlier in Fig. 1, the original IPA grid provides a basis for further elaboration in many directions. For example, building additional cross-hairs into each quadrant of the standard IPA grid may improve accuracy in classifying attributes and deriving strategic suggestions (e.g., Dolinsky, 1991; Dolinsky & Caputo, 1991). This type of modification results in corresponding modifications in the labels of the original quadrants. As some attributes, especially non-cost attributes, are desired to exceed the level customers want, separate considerations of cost versus non-cost attributes in the same or separate grids may prove useful. Finally, IPA needs more empirical reports on its actual contribution to strategic decisions in industry. Case study reports will provide excellent opportunities to assess the practical value of IPA.
Acknowledgements The author wishes to thank the editor for the constructive comments and suggestions on the earlier version of this paper.
References Alberty, S., & Mihalik, B. (1989). The use of importance–performance analysis as an evaluative technique in adult education. Evaluation Review, 13(1), 33–44. Chon, K., Weaver, P. A., & Kim, C. (1991). Marketing your community: image analysis in Norfolk. Cornell Hotel and Restaurant Administration Quarterly, February, 31–37. Cronin Jr., J. J., & Taylor, S. A. (1992). Measuring service quality: an examination and extension. Journal of Marketing, 56(2), 55–68. Dolinsky, A. L. (1991). Considering the competition in strategy development: an extension of importance–performance analysis. Journal of Health Care Marketing, 11(1), 31–36. Dolinsky, A. L., & Caputo, R. K. (1991). Adding a competitive dimension to importance–performance analysis: an application to
traditional health care systems. Health Care Marketing Quarterly, 8(3/4), 61–79. Duke, C. R., & Persia, M. A. (1996). Performance–importance analysis of escorted tour evaluations. Journal of Travel & Tourism Marketing, 5(3), 207–223. Ennew, C. T., Reed, G. V., & Binks, M. R. (1993). Importance– performance analysis and the measurement of service quality. European Journal of Marketing, 27(2), 59–70. Evans, M. R., & Chon, K. (1989). Formulating and evaluating tourism policy using importance–performance analysis. Hospitality Education and Research Journal, 13(3), 203–213. Festinger, L. (1957). A theory of cognitive dissonance. Stanford, CA: Stanford University Press. Fishbein, M., & Ajzen, I. (1975). Beliefs, attitudes, intentions, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley. Fletcher, J. E., Kaiser, R. A., & Groger, S. (1992). An assessment of the importance and performance of park impact fees in funding park and recreation infrastructure. Journal of Park and Recreation Administration, 10(3), 75–87. Go, F., & Zhang, W. (1997). Applying importance–performance analysis to Beijing as an international meeting destination. Journal of Travel Research, Spring, 42–49. Guadagnolo, F. (1985). The importance–performance analysis: an evaluation and marketing tool. Journal of Park and Recreation Administration, 2, 13–22. Hawes, J. M., & Rao, C. P. (1985). Using importance–performance analysis to develop health care marketing strategies. Journal of Health Care Marketing, 5(4), 19–25. Helson, H. (1964). Adaptation-level theory. New York: Harper & Row. Hollenhorst, S., Olson, D., & Fortney, R. (1992). Use of importance– performance analysis to evaluate state park cabins: the case of the West Virginia state park system. Journal of Park and Recreation Administration, 10(1), 1–11. Jaccard, J., Brinberg, D., & Ackerman, L. J. (1986). Assessing attribute importance: A comparison of six methods. Journal of Consumer Research, 12, 463–468. Jaccard, J., & Wood, G. N. (1986). An idiothetic analysis of behavioral decision making. In: D. Brinberg, & R. J. Lutz (Eds.), Perspectives on methodology in consumer research. New York: Springer. Jacoby, J. (1975). The emerging behavioral process technology in consumer decision-making. In: W. D. Perreault Jr. (Ed.), Advances in consumer research, vol. 4, (pp. 263–265). Atlanta, GA: Association for Consumer Research. Kaplan, K. J., Fishbein, M. (1969). The source of beliefs, their saliency, and prediction of attitude. Journal of Social Psychology, 78, 63–74. Lego, R., Shaw, R. N. (1992) Convergent validity in tourism research: an empirical analysis. Tourism Management, 13(4), 387–393. Martilla, J. A., & James, J. C. (1977). Importance–performance analysis. Journal of Marketing, January, 77–79. Mengak, K. K. (1985). Use of Importance–performance analysis to evaluate a visitor center. M.S. thesis, Clemson University, Department of Parks, Recreation, and Tourism Management, Clemson, SC. Myers, H. H., & Alpert, M. I. (1977). Semantic confusion in attitude research: Salience vs. importance vs. determinance. In: W. D. Perrault (Ed.), Advances in consumer research, vol. 4 (pp. 106–110). Atlanta, GA: Association for Consumer Research. Neslin, S. A. (1981). Linking product features to perceptions: selfstated versus statistically revealed importance weights. Journal of Marketing Research, 18, 80–86. Oh, H., & Parks, S. C. (1997). Customer satisfaction and service quality: a critical review of the literature and research implications for the hospitality industry. Hospitality Research Journal, 20(3), 35–64.
H. Oh / Tourism Management 22 (2001) 617–627 Oh, H., & Parks, S. C. (1998). Evaluating the role of attribute importance as a multiplicative weighting variable in the study of hospitality consumer decision-making. Journal of Hospitality & Tourism Research, 21(3), 61–80. Oliver, R. L. (1997). Satisfaction: a behavioral perspective on the consumer. Irwin: McGraw-Hill Company. Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17, 46–49. Ortinau, D. J., Bush, A. J., Bush, R. P., & Twible, J. L. (1989). The use of importance–performance analysis for improving the quality of marketing education: interpreting faculty-course evaluations. Journal of Marketing Education, Summer, 78–86. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A conceptual model of service quality and its implications for future research. Journal of Marketing, 49, 41–50. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64, 12–40. Ryan, C. (1999). From the psychometrics of SERVQUAL to sexFmeasurements of tourist satisfaction. In: A. Pizam, & Y. Mansfeld (Eds.), Consumer behavior in travel & tourism (pp. 267–286). Binghamton, NY: Haworth Press.
627
Ryan, C., & Huyton, J. Balanda visitors to Central AustraliaFtheir perceptions. Annals of Tourism Research, in press. Teas, R. K. (1993). Consumer expectations and the measurement of perceived service quality. Journal of Professional Services Marketing, 8(2), 33–53. Tse, D. K., & Wilton, P. C. (1988). Models of consumer satisfaction formation: An extension. Journal of Marketing Research, 25, 204–212. Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5, 207–232. Tzung-Cheng, H., Beaman, J., & Shelby, L. B. Using action-grids. Tourism Management, in press. Uysal, M., Howard, G., & Jamrozy, U. (1991). An application of importance–performance analysis to a ski resort: A case study in North Carolina. Visions in Leisure and Business, 10, 16–25. Vaske, J. J., Beaman, J., Stanley, R., & Grenier, M. (1996). Importance–performance and segmentation: Where do we go from here? Journal of Travel & Tourism Marketing, 5(3), 225–240. Wyer, R. S. (1974). Cognitive organizations and change: An information processing approach. Potomac, MD: Erlbaum.
Revisiting importance–performance analysis Haemoon Oh* Hotel, Restaurant, and Institution Management, Iowa State University, 11 MacKay Hall, Ames, IA 50011-1120, USA Received 3 February 2000; accepted 20 September 2000
Abstract As a tool to develop marketing strategies, importance–performance analysis has gained popularity among hospitality and tourism researchers for its simplicity and ease of application. This article revisited several conceptual and methodological issues inherent, but often overlooked, in using the importance–performance analysis framework. The author conducts a critical review of past studies, reanalyzes published data to raise questions, and develops suggestions for future hospitality and tourism research applying importance–performance analysis. The primary goal of this article was to stimulate further discussion and research on the validity and reliability of widely adopted importance–performance analysis. r 2001 Elsevier Science Ltd. All rights reserved. Keywords: Importance; Performance; Importance–performance analysis; Tourism; Hospitality; Validity; Reliability; Attribute
1. Introduction Importance–performance analysis (IPA) has been used in hospitality and tourism research for years. Since the seminal work by Martilla and James (1977), the IPA framework has gained popularity among researchers in service quality (e.g., Ennew, Reed, & Binks, 1993), travel and tourism (e.g., Evans & Chon, 1989; Go & Zhang, 1997), leisure and recreation (e.g., Guadagnolo, 1985; Hollenhorst, Olson, & Fortney, 1992), education (e.g., Alberty & Mihalik, 1989; Ortinau, Bush, Bush, & Twible, 1989), and healthcare marketing (e.g., Dolinsky, 1991; Dolinsky & Caputo, 1991; Hawes & Rao, 1985). Anecdotal evidence also suggests that studies applying IPA are frequently presented at various hospitality and tourism conferences. As Martilla and James (1977) emphasized, ease of application and the appealing methods of presenting both data and strategic suggestions seem to be the factors, among others, that contribute to wide acceptance of the technique. To date, few studies have critically considered the conceptual validity of IPA. Most IPA studies have attempted to apply the same technique in different subject areas. Although several researchers have tried to extend the original IPA method (e.g., Dolinsky & Caputo, 1991; Vaske, Beaman, Stanley, & Grenier, *Tel.: +1-515-294-7409; fax: +1-515-294-8551. E-mail address: [email protected] (H. Oh).
1996), the focus of their efforts was on modifying the analysis methods by adding more information to the original IPA framework. Not sufficiently addressed in past studies, however, were fundamental conceptual and practical issues underlying the IPA research methodology. Although ease of application or simplicity may be one important criterion for wide acceptance of a research framework such as IPA, it certainly should not be interpreted as an indicator of validity of the method that overrides logical justifications. The goals of this article are to (1) raise several conceptual and practical issues inherent, but often overlooked, in using the IPA technique and (2) develop suggestions for future research to improve the utilities of IPA. Although a few researchers have raised several similar issues covered in this article along with their empirical applications of IPA, they have touched upon mostly local issues and, thus, their discussion has been somewhat fragmentary (Tzung-Cheng, Beaman, & Shelby, in press). Accordingly, this article attempted to include various issues, with a particular intention to raise those issues that have not been addressed previously. First, this article briefly revisits the IPA methodology and its critical assumptions. Questions are then raised and the IPA literature is critically reviewed to address several conceptual and practical issues that warrant further attention and research. The article concludes with suggestions for appropriate applications of the IPA technique, as it is in its current form, in hospitality and tourism research.
0261-5177/01/$ - see front matter r 2001 Elsevier Science Ltd. All rights reserved. PII: S 0 2 6 1 - 5 1 7 7 ( 0 1 ) 0 0 0 3 6 - X
618
H. Oh / Tourism Management 22 (2001) 617–627
2. Concept of IPA Martilla and James (1977) first proposed IPA as a tool to develop firms’ management strategies. In its essence, IPA combines measures of attribute importance and performance into a two-dimensional grid (see Fig. 1) in an effort to ease data interpretation and derive practical suggestions. According to their suggestions, a set of key attributes of a target product is generated and subjects rate each attribute for its importance to a purchase decision. For example, Martilla and James (1977) used 14 attributes to understand the importance of an automobile dealer’s service. Ideally, attribute importance needs to be measured prior to, rather than after, an actual purchase experience, because IPA in general pursues understanding the role of the key selected attributes in a purchase decision. Performance is then measured using the same set of attributes so that importance and performance can be directly compared within the same attributes via the IPA plot (or grid). The mean values of importance and performance scores are then used as the crossing point in constructing the IPA grid. Note that Martilla and James (1977) also recommended use of the median, instead of mean, values for the importance axis when there is an insufficient amount of variance or when the importance ratings show a non-normal distribution pattern. Interpretation of the IPA plot is straightforward. As shown in Fig. 1, IPA generates four different suggestions based on importance–performance measures. Interpretations follow the combination of importance and
performance scores of each attribute. The first quadrant, ‘keep up the good work’, captures the attributes that customers think are important to their purchase decision and on which customers also perceive the company (or product) performs well. Likewise, ‘possible overkill’ in Quadrant 2 indicates that the attributes falling in this quadrant are relatively less important but the company performs well on the attributes. Some attributes may fall in the third quadrant, ‘low priority’, because both importance and performance ratings of the attribute are lower than the average. These items are likely to receive a low priority in resource allocation decisions. The attributes that are important to customers’ purchase decisions but on which the company does not perform well are classified into Quadrant 4, ‘concentrate here’. The company needs to focus on improving its performance on these attributes. In essence, IPA provides an attractive snapshot of how well the company meets customers’ important concerns on selected attributes and, at the same time, offers guidelines for the company’s future resource allocation decisions.
3. Revisit to IPA 3.1. Conceptual issues The IPA literature does not provide a clear definition of attribute importance. Most IPA studies in the hospitality and tourism arena have tended to operationalize importance simply as the level of salience of an
Fig. 1. Traditional importance–performance grid.
H. Oh / Tourism Management 22 (2001) 617–627
attribute that is likely to be a determinant of an imminent purchase decision. Few researchers have considered the multiple roles of the concept in consumer decision-making. Jaccard, Brinberg, and Ackerman’s (1986) review, for example, suggested at least five additional definitions of importance that are associated with different measurement methods. One is importance that can be derived from memory-based free elicitation. According to this line of reasoning, attributes that are important to a purchase decision are believed to be salient (Kaplan & Fishbein, 1969) and provide a ready access in memory (Tversky & Kahneman, 1973). Another type of importance is reflected in goal-oriented search attributes that consumers actively look for in the target product and consider when making a purchase decision (Jacoby, 1975). Jaccard and Wood (1986) presented a utility-type importance that could be obtained by conjoint measures. In addition, the consumer’s subjective conditional probability of willingness to consider a certain attribute characteristic in a purchase decision appeared to be another operationalization of importance (Wyer, 1974). Thurstonian measures of importance, which derives from pair-wise comparisons of relative attribute importance, also received attention in attitude research (Fishbein & Ajzen, 1975). Nevertheless, a confirmatory factor analysis by Jaccard et al. (1986) indicated that these definitions were not unidimensional, which means that depending on the type of conceptualization the importance concept could offer different implications to the users of IPA. Similar to Jaccard et al.’s study, Lego and Shaw (1992) tested, both at the individual attribute and aggregate levels, the convergent validity of four methods of measuring attribute importance associated with a tourist product. Their results, based on a correlational approach, confirmed the findings of Jaccard et al. (1986). An implication is that the multidimensional nature of the importance concept could cause, when not properly controlled, weakening the validity and reliability of importance measures, especially in field survey settings. The absence of a clear criterion concept that could be related to attribute importance is another issue. In his recent review of related research, Oliver (1997, p. 34) raised a question: Important for what? Although researchers could elicit importance in terms of a product purchase, importance in product choice is an inappropriate context when the research goal is to understand post-purchase decision processes (Oliver, 1997). This is a typical problem of determining a common criterion variable or target concept (e.g., consumer satisfaction) for theoretically related variables (i.e., importance and performance). Thus, Oliver (1997) suggests that consumer satisfaction be a target criterion variable when measuring attribute importance and performance (Oliver, 1997). In a similar reasoning,
619
Martilla and James (1977) suggested that the disparity between importance and performance be an indicator of customer dissatisfaction, while Guadagnolo (1985) maintained that IPA be a tool to assess consumer satisfaction. In a recent tourist motivation study, Ryan and Huyton (in press) also inferred a functional relationship between importance and both performance perceptions and satisfaction. Nevertheless, use of any criterion variable was not a tradition in past IPA studies. Oh and Parks (1997) pointed out another critical issue related to frequent confusion among researchers between the concepts of importance and expectation. Martilla and James (1977, p. 77) hinted at a subtle difference between the two concepts in that ‘‘y consumer satisfaction is a function of both expectations related to certain important attributes and judgments of attribute performance’’. A number of IPA researchers, however, have often used the two concepts interchangeably when measuring and interpreting importance (e.g., Chon, Weaver, & Kim, 1991; Evans & Chon, 1989; Hollenhorst et al., 1992; Tzung-Cheng et al., in press). If they purported to measure expectations, interpretations of the results and thus marketing suggestions should have been different from what they offered. This mixed use of the two concepts seems to have originated in the similarity between IPA and other popular consumer research models such as expectancy-value theory (Fishbein & Ajzen, 1975) and SERVQUAL (Parasuraman, Zeithaml, & Berry, 1988) that involve similar concepts. The SERVQUAL model, for example, operationalizes service quality as a function of expectations and performance. Consumer satisfaction is also modeled as a function of the disparity between expected and experienced performance in expectancy-disconfirmation theory (see Oh & Parks, 1997, for a comprehensive review). As in the case of importance, however, the concept of expectation has also been heavily criticized for its ambiguous, multidimensional definitions in previous studies (see Teas, 1993). Some evidence exists to support the conceptual difference of the two concepts. In his review of service quality research, Ryan (1999), for example, distinguished importance from expectation such that the former is a desired outcome and the latter a tolerated outcome in consumer perceptions of service quality. In addition, Myers and Alpert (1977) contend that salience, importance, and determinance be distinguished in attitude research. Finally, the predictive validity of absolute versus relative importance has not received sufficient consideration in previous IPA studies. In traditional IPA studies, the respondents are typically directed to consider one attribute at a time when providing importance ratings. Unfortunately, this method is likely to inflate importance ratings of most attributes, which, in turn, restricts the variation in importance scores. This
620
Author
Subjects
Alberty and Mihalik (1989) Mengak (1985)
Adult education 93 students
Hollenhorst et al. (1993)
Chon et al. (1991)
Fletcher, Kaiser, and Groger (1992) Dolinsky and Caputo (1991)
Importance scales used
Crosshairs
Percentage of attributes by scale meansa
Percentage of attributes by actual meansa
23
5 point Likert
Actual means
Positive 100%
255 travelers to a visitor center
34
5 point Likert
Actual means
Positive 100%
522 cabin renters
24
7 point
Actual means
Park visitors
14
7 point
Actual means
401 Virginia residents travelling to the Norfolk-area Residents from northern Virginia Residents from Richmond Residents from Southwestern Virginia Residents from Hampton Roads
15
Actual means
15
5 point, 1-not important and 5-very important Same as above
Positive 75%, negative 21% Positive 36%, negative 50% Positive 100%
Actual means
Positive 100%
15
Same as above
Actual means
Positive 100%
15
Same as above
Actual means
Positive 100%
15
Same as above
Actual means
Positive 100%
14
5 point, 1-very unimportant and 5-very important 4 point, 1-very satisfied and 4-very dissatisfied
Statistically derived means N/A
Positive 57%, negative 14% N/A
Positive 74%, negative 22% Positive 71%, negative 24% Positive 58%, negative 38% Positive 71%, negative 21% Positive 40%, negative 40% Positive 40%, negative 53% Positive 40%, negative 40% Positive 60%, negative 27% Positive 40%, negative 53% Positive 93%, negative 7% N/A
573 park and recreation directors 622 fee-for-service health care users
Number of Attributes
6
H. Oh / Tourism Management 22 (2001) 617–627
Table 1 Review of the importance–performance literature
Hawes and Rao (1985)
Dolinsky (1991)
254 women who had given birth at two local hospitals within the previous year 787 HMO members 787 HMO members
Ortinau et al. (1989)
20
7 6 14
End of semester
14
Clients of travels agent and tour operators: 133 pre-trip clients 89 post-trip clients
20
Guadagnolo (1985)
N/A
Uysal, Howard, and Jamrozy (1991) Go and Zhang (1997)
117 ski resort visitors
Duke and Persia (1996)
Evans and Chon (1989)
Scale means
Positive 90%, negative 5%
Positive 70%, negative 30%
4 point, 1-very satisfied and 4-very dissatisfied 3 point, 1-HMOs better and 3-traditional fee-for-service better 6 point, 1-not at all important to me and definitely disagree and 6-extremely important to me and definitely agree Same as above
N/A
N/A
N/A
N/A
N/A
N/A
Actual means
Positive 100%
Positive 71%, negative 21%
N/A
Positive 93%
Positive 71%, negative 14% Positive 75%, negative 10% Positive 85%, negative 10% Positive 68%, negative 32% Positive 67%, negative 33% Positive 64%, negative 27% Positive 64%, negative 27% Positive 45%, negative 55% Positive 40%, negative 60%
Actual means
Positive 100%
20
4 point, 4-very important and 1-unimportant Same as above
Actual means
Positive 100%
31
7 point
Actual means
117
5 point
Actual means Arbitrary
Positive 90%, negative 10% Positive 89%, negative 11% Positive 100%
N/A
Positive 100% Positive 45%, negative 55% Positive 50%, negative 50%
59 international meeting planners Industry factor
11 11
5 point, 1-least important and 5-extremely important 5 point
400 travelers
11
7 point, 7-high and 1-low
Scale means
400 travelers
10
Same as above
Scale means
a ‘Positive’ indicates the percentage of attributes in either Quadrants 1 or 3; ‘Negative’ indicates the percentage of attributes in either Quadrants 2 or 4. When the study used actual means for the IPA grid, the percentage was recalculated based on a reconstruction of the grid using the scale means; and vice versa.
H. Oh / Tourism Management 22 (2001) 617–627
146 business students for course evaluations: beginning of semester
6 point, 1-least important and 6-most important
621
622
H. Oh / Tourism Management 22 (2001) 617–627
type of ‘ceiling effects’ is highly likely, particularly because researchers tend to use a selected set of keyFtherefore, ‘important’ already in its own rightFattributes to measure importance. Martilla and James (1977) and Hollenhorst et al. (1992) contend that this is a frequent problem occurring in empirical applications of IPA. Moreover, it is questionable how meaningful this type of absolute importance would be in developing a firm’s action plans, because this kind does not reflect competitive business environments. Evidently, consumer preferences for a product or brand are formed based upon not only a trade-off or comparison among attributes within the focal product, but also a comparison between the same attributes across competing products. Hence, even if measurement would be a challenge, the market-oriented relative importance of an attribute may be a more valid, competitive representation of attribute importance than attribute-specific absolute importance. Neslin (1981) showed that statistically derived relative importance had superior predictive validity than self-reported absolute importance. 3.2. Relationships among variables Many IPA studies have not considered potential relationships between importance and performance. Although the method of constructing the IPA grid itself, as shown in Fig. 1, imposes a statistical relationship (i.e., correlation at least) to the two concepts, few IPA studies have attempted to examine the implications of the potential relationship. As implied in Martilla and James’ (1977) statement cited above, importance has a close relationship with expectations in a way that the two concepts are antecedents of performance perceptions. In addition, previous studies showed that importance could be used as a weighting variable of performance (e.g., Cronin & Taylor, 1992; Oh & Parks, 1998; Tse & Wilton, 1988). The implication is that importance may possess some additive and/or interactive effects on performance ratings. In other words, it is plausible that customers’ evaluations of a firm’s attribute-specific performance are affected by how important the customer thinks the attribute is to him or her (Ryan & Huyton, in press). The plausible causal relationship between importance and performance perceptions can be justified theoretically beyond the relationship reflected in the IPA grid. Borrowing first from the theory of expectancy disconfirmation (see Oh & Parks, 1997, for a review), it can be shown that importance is positively related to performance; the more important the attribute is to the customer, the more likely the customer perceives the attribute performance favorably and thus higher satisfaction. In fact, when one of the recently published lodging data (Oh & Parks, 1998) was reanalyzed via confirmatory factor analysis, the strength of the under-
lying correlation between importance and performance was as high as 49 percent. Similarly, a reanalysis of Ryan and Huyton’s data (Table 1, in press) indicated that importance and satisfaction was indeed highly correlated (r ¼ 0:78). This relationship can also be explained by generalizability theory (see Oliver, 1980 for details). The theory posits that consumers tend to generalize their experience (i.e., feelings arising from perceptions of performance) in the direction of their evaluation outcome, especially for important product/ service attributes. Applied to the measurement of importance and performance, this implies that the two concepts tend to generate either positive or negative correlation. Given a causal relationship between importance and performance, the traditional IPA grid could offer serious misinformation depending upon the nature and magnitude of the relationship. Consider first a high positive correlation between the two concepts, as Hollenhorst et al. (1992) empirically observed in their study. Mathematically, a high positive correlation will cause the attributes to spread along the solid line stretching through Quadrants 1 and 3 as illustrated in Fig. 1, whereas a high negative correlation between the two variables causes the attributes to scatter along the dotted line. This suggests that a positive correlation between the two variables tends to result in prescribing more suggestions of ‘keep up the good work’ (Quadrant 1) or ‘low priority’ (Quadrant 3). In contrast, a negative correlation will tend to cause over-prescribing ‘possible overkill’ (Quadrant 2) or ‘concentrate here’ (Quadrant 4). Therefore, as much as importance and performance ratings covary, the IPA grid becomes ineffective as a basis of developing management suggestions. The reason is that the suggestions tend to be affected by the causal relationship between importance and performance, as well as the main effects of the two concepts. A critical review of past IPA studies, shown in Table 1, supports the arguments made above. The last two columns in the table show the proportion of attributes reflecting positive (i.e., the number of attributes falling in Quadrants 1 and 3) and negative (i.e., the number of attributes falling in Quadrants 2 and 4) correlations between the importance and performance of the attributes, based upon the type of ‘cross-hair’ points (to be discussed later). For example, in Alberty and Mihalik’s study (1989) using actual importance and performance mean scores as the cross-hair points (see the last column), about 74 percent of the attributes fell in the quadrants of positive correlation and about 22 percent in the negative correlation quadrants. Only the remaining 4 percent fell along the crosshairs, defying a classification. However, when their IPA grid was reconstructed using the scale mean scores as the crosshair points (see the second-to-the-last column), all attributes fell in the positive correlation zones. Similarly,
H. Oh / Tourism Management 22 (2001) 617–627
in the majority of the other studies reviewed in Table 1, patterns of positive correlation were prevalent especially when the importance–performance scale mean scores were used as the cross points. This finding reflects that a causal system may be under operation between importance and performance perceptions. Besides the between-concepts correlation, correlations among the attributes, both within importance and within performance, could have contributed to the patterned dispersions of attributes on the IPA grid. Although most IPA studies have measured importance and performance using each attribute independently, some items always tend to correlate with each other. A luxury gourmet restaurant, for example, may perform well on courtesy and this may cause customers to infer high responsiveness of employees even if the latter might have not been experienced directly. This type of between-attributes correlation is likely to result in causing the two attributes to fall in the same quadrant. The same concern is applicable to other pairs of attributes, thereby potentially undermining the validity of IPA-produced managerial suggestions.
4. Practical issues The level of abstraction in determining a set of measurement attributes becomes an issue when employing IPA. A typical IPA study begins with a fairly large number of attributes on which to measure importance and performance. Most researchers, however, initially reduce the number of attributes in the pretest stage so as to achieve a parsimonious research framework and to minimize redundancy, if any; a trade-off occurs between exhaustiveness and practicality of a research model. In this case, ‘‘survey length restrictions and the inability to adequately sample the full population of consumers, particularly in the pretest stages of survey construction, are likely to cause the researcher to overlook some critical elements of the purchase which will not be represented in the feature list’’ (Oliver, 1997, p. 34). This problem may be addressed by using a proper, typically high, level of feature abstraction when the attribute list is prepared, at the expense of specificity. Is the concept of importance unidirectional or bidirectional? As shown in the fourth column of Table 1, many researchers measured importance unidirectionally by using a scale anchored with ‘no importance’ to ‘very (or extremely) important’. Other researchers, however, used a bi-directional measure of importance as labeled ‘very unimportant’ and ‘very important’. Little psychometric reasoning has been given to the property of the concept. Provided that the concept of importance reflects the ‘level’ or ‘strength’, rather than evaluations of goodness or badness, of the attribute characteristic, the unidirectional scale seems to make sense more than
623
the bi-directional one. Nevertheless, the bi-directional scale with modified wordings may be useful when measuring relative importance. Another notable inconsistency in the results of past IPA studies arises from the location of the horizontal and vertical axes or ‘cross-hair’ points of the grid. As Martilla and James (1977) prescribed, the majority of researchers have used the mean values of observed (i.e., self-reported) actual importance and performance ratings when determining the cross-hair point in the IPA grid (see the fifth column in Table 1). Other researchers, however, used the mean values of the scales employed for measuring importance and performance (e.g., Evans & Chon, 1989; Hawes & Rao, 1985). The last two columns of Table 1 show that the study results and interpretations could be dramatically different depending upon whether the actual or scale means were used as the cross-hair point in constructing the IPA grid. For example, when the actual means were used, about 40 percent of attributes fell in Quadrants 1 or 3 and another 40 percent in Quadrants 2 and 4 in the first study of Chon et al. (1991). When the grid was reconstructed with the scale means as the cross-hair point, however, all attributes fell in Quadrant 1. These conflicting results imply that careful interpretations are necessary when using the actual means because the range of the original scale tends to be truncated, requiring different interpretations. Researchers need to caution readers and their research consumers about this hidden problem. The traditional IPA grid provides limited understanding of product performance. Although the IPA grid classifies all IP-paired attributes into one of the four categories (i.e., quadrants), this categorization could result in serious misinformation. Consider Fig. 1 again, in which hypothetical attributes A, B, and C are plotted. According to the IPA approach, attributes A and B are grouped into the same category and point to the same suggestion, ‘keep up the good work’, while attribute C belongs to a different category and is likely to receive ‘low priority’ in the future. Ironically, attributes A and B have a greater psychometric (e.g., Euclidian) distance than attributes B and C. It is, therefore, highly probable that popular statistical techniques, such as factor analysis, multidimensional scaling, and cluster analysis, will yield attributes B and C as more likely to fall in the same group (i.e., category) than attributes A and B. Furthermore, extracting the identical marketing suggestions from attributes A and B may cause loss of information that is specific to each attribute and, thus, may not be a valid interpretation of the data. This categorization problem reveals that IPA may not be sophisticated enough to precisely represent the data structure. It is important that IPA be sensitive to problem areas in order to maximize its effectiveness (Hollenhorst et al., 1992).
624
H. Oh / Tourism Management 22 (2001) 617–627
Finally, the goal of IPA is inconsistent with the strategic philosophy of most firms in today’s marketplace. Today, firms are striving to deliver not only products and services but also high quality and customer satisfaction along with the products (Oh & Parks, 1997). According to consumer satisfaction theories (Oliver, 1997), perceptions of high quality and customer satisfaction are achieved when firms perform better than what customers want. However, IPA views firms’ better-than-wanted performance as ‘overkill’. In the hospitality business, extra performance often yields customer satisfaction without committing the overkill of resources. Most intangible service attributes, such as courtesy and politeness that are furnished through employees’ behaviors, qualify for this reasoning. Consider also a hypothetical attribute A in Fig. 1 again. It is of high importance but the firm performs relatively low on it. Yet, IPA would tell management to ‘keep up the good work’, rather than urging them to ‘concentrate on it’. Strategically, IPA does not effectively reflect firms’ performance directed at customer satisfaction and market retention. Moreover, from a practical standpoint, it is difficult for a firm to always perform exactly at the importance level of the attribute in order to prevent possible overkill or to reduce needs for additional resource allocation to the attribute. Performance that exceeds the level of importance may be desirable, especially in the competitive hospitality market where extra performance often does not require corresponding input resources.
5. Summary Hospitality and tourism researchers continue to employ IPA without giving critical considerations to its conceptual and practical validities. This article attempted to raise such issues in an effort to provide a starting point of additional debate and research on ways to improve the value of IPA as a framework for continued research in hospitality and tourism. Understanding what works and what does not in IPA is critical in that it has established a unique tradition of hospitality and tourism research. The issues addressed thus far also deserve close attention because IPA seems to continue playing an important role in the graduate education of hospitality and tourism. Nevertheless, this critical review should be understood as a beginning point to stimulate related discussions and, thus, is far from the exhaustive coverage of many more issues awaiting investigation. The issues tackled in this article are summarized as follows: (1) (2)
Lack of a clear definition for the concept of importance. Absence of a clear criterion variable for the IPA framework as a whole.
(3) Mixed uses of importance and expectation. (4) Lack of research on absolute versus relative importance. (5) The implications of relationships between importance and performance and among the attributes. (6) Absence of guidelines for developing a set of attributes to be used. (7) Use of unidirectional versus bi-directional measurement scales for the concept of importance. (8) Use of actual means versus scale means in constructing the IPA grid. (9) Potential misclassifications of attributes on the IPA grid. (10) A philosophical issue related to strategic suggestions. Easy applicability of research methods that can efficiently explain complex consumer behaviors is always welcome. Yet, simplicity and convenience should not be the predominant criteria when selecting and using a research framework. Firms cannot sustain their competitive position based on information obtained through a research method that lacks validity and reliability. Rigorous reconsideration of IPA as a tool for developing marketing programs is necessary.
6. Suggestions for future IPA studies In conclusion, this article addresses a few pending issues that should be considered by researchers so as to increase the utilities of IPA. Definition of the concepts and corresponding interpretations: A clear definition of importance and performance is a prerequisite to future IPA studies. Jaccard et al. (1986) and Lego and Shaw (1992) provide a useful starting point for considering several possibilities in this effort. Another approach to the definition of importance would be a two-step approach in which, first, the level of salience of a particular attribute characteristic is determined in general, and then the strength of the salient attribute(s) is assessed in terms of the focal decision. Myers and Alpert (1977) also offer a direction the concept of importance can be defined along with similar concepts and relevant measurement methods. Determining the measurement scale should be done along the definitional efforts. For the definition of performance perceptions, the consumer satisfaction and service quality literature can be a good source (Oh & Parks, 1997; Oliver, 1997; Parasuraman et al., 1988). Clear definitions will not only eliminate confusion between importance and expectation, but also provide a basis for precise interpretations of study results, thereby improving the value of the obtained information.
H. Oh / Tourism Management 22 (2001) 617–627
Specification of a common criterion concept: The specification of a criterion variable(s) should, in part, be a statement of study goals. When the concepts of importance and performance are measured toward different criterion concepts, it is not logical to plot them together, as in the conventional IPA grid. Many useful criterion concepts are available in the marketing literature. Oliver (1997) suggests that consumer satisfaction and/or return intention could well be such a variable, while Ryan and Huyton (in press) models tourist satisfaction over time as a function of importance and the tourist’s socio-demographic background. Perceived value may be another good criterion for measuring importance and performance. Depending on study objectives, researchers may also utilize prepurchase criterion variables, such as perceived quality and/or willingness to buy. By relating both importance and performance concepts to one or more criterion variables, the comparability between importance and performance can be improved. In addition, this approach allows more useful correlational data analysis methods. Causal modeling of attribute importance: Provided importance judgments are an antecedent of performance perceptions in consumer decision-making, direct predictions of performance, as well as other criterion variables, are feasible based on importance ratings. This direct causal modeling of importance within prevailing consumer models, such as those on service quality and consumer satisfaction, may offer a fresh opportunity to explain and understand consumer behavior. To this end, cognitive dissonance theory (Festinger, 1957) or adaptation-level theory (Helson, 1964) may be instrumental in that importance plays a role as a predetermined standard of performance in consumer decisions. Ryan (1999) suggests that importance be used as a normative standard of performance. Use of importance either as a weighting variable (i.e., typically multiplied with other variables: see Oh & Parks, 1998, for a review and practice) or as a stand-alone construct in a causal model (i.e., typically additive in nature) is an issue to be considered in future studies. The causal modeling approach will contribute to clarifying the role of importance as compared to that of other similar concepts, such as expectations, in consumer decisionmaking behavior. This also will offer an opportunity to better understand the implication of covariation between importance and performance, the opportunity that has been lost in past IPA studies. It is further likely that pre-purchase perceptions of attribute importance need certain mediating variables, such as ‘‘relevance’’, that may facilitate inferring a logical connection between importance and key criterion variables in causal modeling. Absolute versus relative importance: Relative importance is a crucial concept in the highly competitive
625
hospitality industry. When the market is saturated and thus increasingly competition-oriented, merely knowing and meeting customers’ wants may not promise repeat purchases because customers will constantly compare market offerings in search of a better choice. Research is needed to assess the utilities of absolute versus relative importance (cf. Oh & Parks, 1998). The concept of relative importance is likely to be multidimensional; for example, it can be modeled across a set of attributes under study (a) within the focal product and (b) across different market offerings in the same target market. Neslin (1981) illustrated methods of deriving relative importance. The two-dimensional IPA framework also needs to be developed further to encompass relative importance in its grid. Determination of a set of attributes: A careful selection of attributes for measuring importance and performance is critical because follow-up management decisions are expected to rely on the information based on the selected attributes. One issue involved in this task is the use of the same set of attributes for measuring both importance and performance. Many service attributes are experiential by nature (Parasuraman et al., 1985); that is, consumers may not know how important a particular attribute is to their decisions until they actually experience the attribute. Many service failures become important only when they occur, for example. It is recommended that researchers develop attributes by considering not only the characteristics of the firm’s product or service but also the market demands shared by competing companies. Involving managers and consumer panels in the early process of IPA research is desirable, especially for developing a set of strategically important attributes. The level of abstraction in specifying attributes should be commensurate with the target level of follow-up management decisions. In essence, selection of attributes must be based on a priori judicious trade-off between exhaustiveness and practicality of the information to be obtained. Scale construction. A unidirectional rating scale was recommended earlier for measuring importance. In practice, however, both unidirectional and bi-directional scales are likely to produce similar results because importance ratings will almost always be highly skewed in the negative direction, regardless of the scale used. Nevertheless, using a bi-directionally worded scale is likely to cause a stronger negative skewness than a unidirectional scale. The scale of performance perceptions does not seem to be problematic in previous IPA studies as the concept is strictly evaluative. When constructing measurement scales, however, the comparability between the scales for importance and performance should receive additional attention. It is also recommended that scale means be used as the cross-hair point when constructing the IPA grid and that using actual means be viewed as a special case or an
626
H. Oh / Tourism Management 22 (2001) 617–627
extension of the former. Potential ineffectiveness and misinformation of IPA research findings were illustrated earlier in terms of using either the scale or actual mean scores as the cross-hair point. If the spirit of IPA lies with comparing importance to performance, or vice versa, using scale means is likely to provide a simpler description than using actual means. The reason is that the meaning of the original scales provides easy and valid interpretation of the data. The actual means of importance and performance are likely to differ in most cases, requiring (a) study-specific adjustments to the scales in interpreting importance and performance ratings (see Tzung et al., in press, for an illustrative example) and (b) a relative interpretation of attributes within importance and performance, respectively. Note that using different means has different implications for management decisions. Modifications. As illustrated earlier in Fig. 1, the original IPA grid provides a basis for further elaboration in many directions. For example, building additional cross-hairs into each quadrant of the standard IPA grid may improve accuracy in classifying attributes and deriving strategic suggestions (e.g., Dolinsky, 1991; Dolinsky & Caputo, 1991). This type of modification results in corresponding modifications in the labels of the original quadrants. As some attributes, especially non-cost attributes, are desired to exceed the level customers want, separate considerations of cost versus non-cost attributes in the same or separate grids may prove useful. Finally, IPA needs more empirical reports on its actual contribution to strategic decisions in industry. Case study reports will provide excellent opportunities to assess the practical value of IPA.
Acknowledgements The author wishes to thank the editor for the constructive comments and suggestions on the earlier version of this paper.
References Alberty, S., & Mihalik, B. (1989). The use of importance–performance analysis as an evaluative technique in adult education. Evaluation Review, 13(1), 33–44. Chon, K., Weaver, P. A., & Kim, C. (1991). Marketing your community: image analysis in Norfolk. Cornell Hotel and Restaurant Administration Quarterly, February, 31–37. Cronin Jr., J. J., & Taylor, S. A. (1992). Measuring service quality: an examination and extension. Journal of Marketing, 56(2), 55–68. Dolinsky, A. L. (1991). Considering the competition in strategy development: an extension of importance–performance analysis. Journal of Health Care Marketing, 11(1), 31–36. Dolinsky, A. L., & Caputo, R. K. (1991). Adding a competitive dimension to importance–performance analysis: an application to
traditional health care systems. Health Care Marketing Quarterly, 8(3/4), 61–79. Duke, C. R., & Persia, M. A. (1996). Performance–importance analysis of escorted tour evaluations. Journal of Travel & Tourism Marketing, 5(3), 207–223. Ennew, C. T., Reed, G. V., & Binks, M. R. (1993). Importance– performance analysis and the measurement of service quality. European Journal of Marketing, 27(2), 59–70. Evans, M. R., & Chon, K. (1989). Formulating and evaluating tourism policy using importance–performance analysis. Hospitality Education and Research Journal, 13(3), 203–213. Festinger, L. (1957). A theory of cognitive dissonance. Stanford, CA: Stanford University Press. Fishbein, M., & Ajzen, I. (1975). Beliefs, attitudes, intentions, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley. Fletcher, J. E., Kaiser, R. A., & Groger, S. (1992). An assessment of the importance and performance of park impact fees in funding park and recreation infrastructure. Journal of Park and Recreation Administration, 10(3), 75–87. Go, F., & Zhang, W. (1997). Applying importance–performance analysis to Beijing as an international meeting destination. Journal of Travel Research, Spring, 42–49. Guadagnolo, F. (1985). The importance–performance analysis: an evaluation and marketing tool. Journal of Park and Recreation Administration, 2, 13–22. Hawes, J. M., & Rao, C. P. (1985). Using importance–performance analysis to develop health care marketing strategies. Journal of Health Care Marketing, 5(4), 19–25. Helson, H. (1964). Adaptation-level theory. New York: Harper & Row. Hollenhorst, S., Olson, D., & Fortney, R. (1992). Use of importance– performance analysis to evaluate state park cabins: the case of the West Virginia state park system. Journal of Park and Recreation Administration, 10(1), 1–11. Jaccard, J., Brinberg, D., & Ackerman, L. J. (1986). Assessing attribute importance: A comparison of six methods. Journal of Consumer Research, 12, 463–468. Jaccard, J., & Wood, G. N. (1986). An idiothetic analysis of behavioral decision making. In: D. Brinberg, & R. J. Lutz (Eds.), Perspectives on methodology in consumer research. New York: Springer. Jacoby, J. (1975). The emerging behavioral process technology in consumer decision-making. In: W. D. Perreault Jr. (Ed.), Advances in consumer research, vol. 4, (pp. 263–265). Atlanta, GA: Association for Consumer Research. Kaplan, K. J., Fishbein, M. (1969). The source of beliefs, their saliency, and prediction of attitude. Journal of Social Psychology, 78, 63–74. Lego, R., Shaw, R. N. (1992) Convergent validity in tourism research: an empirical analysis. Tourism Management, 13(4), 387–393. Martilla, J. A., & James, J. C. (1977). Importance–performance analysis. Journal of Marketing, January, 77–79. Mengak, K. K. (1985). Use of Importance–performance analysis to evaluate a visitor center. M.S. thesis, Clemson University, Department of Parks, Recreation, and Tourism Management, Clemson, SC. Myers, H. H., & Alpert, M. I. (1977). Semantic confusion in attitude research: Salience vs. importance vs. determinance. In: W. D. Perrault (Ed.), Advances in consumer research, vol. 4 (pp. 106–110). Atlanta, GA: Association for Consumer Research. Neslin, S. A. (1981). Linking product features to perceptions: selfstated versus statistically revealed importance weights. Journal of Marketing Research, 18, 80–86. Oh, H., & Parks, S. C. (1997). Customer satisfaction and service quality: a critical review of the literature and research implications for the hospitality industry. Hospitality Research Journal, 20(3), 35–64.
H. Oh / Tourism Management 22 (2001) 617–627 Oh, H., & Parks, S. C. (1998). Evaluating the role of attribute importance as a multiplicative weighting variable in the study of hospitality consumer decision-making. Journal of Hospitality & Tourism Research, 21(3), 61–80. Oliver, R. L. (1997). Satisfaction: a behavioral perspective on the consumer. Irwin: McGraw-Hill Company. Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17, 46–49. Ortinau, D. J., Bush, A. J., Bush, R. P., & Twible, J. L. (1989). The use of importance–performance analysis for improving the quality of marketing education: interpreting faculty-course evaluations. Journal of Marketing Education, Summer, 78–86. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A conceptual model of service quality and its implications for future research. Journal of Marketing, 49, 41–50. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64, 12–40. Ryan, C. (1999). From the psychometrics of SERVQUAL to sexFmeasurements of tourist satisfaction. In: A. Pizam, & Y. Mansfeld (Eds.), Consumer behavior in travel & tourism (pp. 267–286). Binghamton, NY: Haworth Press.
627
Ryan, C., & Huyton, J. Balanda visitors to Central AustraliaFtheir perceptions. Annals of Tourism Research, in press. Teas, R. K. (1993). Consumer expectations and the measurement of perceived service quality. Journal of Professional Services Marketing, 8(2), 33–53. Tse, D. K., & Wilton, P. C. (1988). Models of consumer satisfaction formation: An extension. Journal of Marketing Research, 25, 204–212. Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5, 207–232. Tzung-Cheng, H., Beaman, J., & Shelby, L. B. Using action-grids. Tourism Management, in press. Uysal, M., Howard, G., & Jamrozy, U. (1991). An application of importance–performance analysis to a ski resort: A case study in North Carolina. Visions in Leisure and Business, 10, 16–25. Vaske, J. J., Beaman, J., Stanley, R., & Grenier, M. (1996). Importance–performance and segmentation: Where do we go from here? Journal of Travel & Tourism Marketing, 5(3), 225–240. Wyer, R. S. (1974). Cognitive organizations and change: An information processing approach. Potomac, MD: Erlbaum.