1. Importance–performance analysis
Prof Ivan Lai

2. Importance-Performance Analysis
Importance-performance analysis (IPA) has been applied to different areas in the services industries since it was introduced by Martilla and James (1977) in the 1970s.
In IPA studies, respondents are asked to rate the importance and performance of selected attributes in relation to their experiences.
The direct measurement results were plotted on four quadrants of importance–performance mapping (I–P mapping).
The ‘data-centred quadrants approach’ has been widely used to display the empirical data as cross-points.
Lai and Hitchcock (2015a)

3. I-P Mapping

4. Example of Direct IPA Approach
Lai and Hitchcock (2015a)

5. Indirect IPA
In the indirect IPA method, the relationship between the attribute’s importance and customer satisfaction is statistically inferred, commonly using two approaches.
The first is the ‘regression coefficient approach’ that calculates the relative importance of an attribute by using a multiple regression with attribute performance (independent variables) and overall customer satisfaction (dependent variable).
The second is the ‘partial correlation method’ that evaluates the relative importance of an attribute by analysing the correlation between attribute performance and overall satisfaction.
Lai and Hitchcock (2015a)

6. Example of Indirect IPA Approach
Lai and Hitchcock (2015a)

7. Different I-P Mapping Approaches
Scale-centered quadrants approach (Martilla and James, 1977)
Data-centered quadrants approach (Alberty and Mihalik, 1989)
Diagonal line model (Hawes and Rao, 1985)
Scale-centered diagonal line model (Abalo, Varela, and Rial, 2006)
Means and diagonal line model (Rial, Rial, Varela, and Real, 2008)
Three-factor theory model (Fallon and Schofield, 2006)

8. Different I-P Mapping Approaches
Lai and Hitchcock (2015b)

9. Reliability and Validity Issues
Due to the lack of comprehensive guidelines, researchers over the years have pursued IPA in various ways.
Common questions:
Is it necessary to build a new set of attributes?
Which scale is the best one for IPA studies?
What should be the appropriate sample size for IPA study?
Which sampling method is preferable? And how should data be collected?
In what situation(s) should factor analysis be performed? Should it be EFA or CFA?
Is it necessary to calculate the difference between importance and performance with t-value? And if so, why?
Is I-P mapping an effective tool for setting strategic actions and how may it be presented graphically?
Lai and Hitchcock (2015b)

10. IPA Research Framework
Lai and Hitchcock (2015b)

11. Research Direction
In cases where importance and performance of attributes are designed to suit an individual firm (e.g., Holiday Inn), the aim of this approach is to provide guidance for a given company on how to improve its weaknesses. (XX)
If the importance of the attributes is measured in an industrial setting (e.g., the hotel industry in Macau) and the performance of attributes is measured for an individual firm (e.g., Venetian Macao-Resort-Hotel), then the outcome of this setting is to show how individual firms may enhance competitiveness. (X)
Using IPA to evaluate different segments' preferences could generate more meaningful results. (√)
Cross-cultural comparisons. (√)
Well-developed attributes deriving from previous studies. (X)
Own unique set of attributes for the study. (√ √)
Lai and Hitchcock (2015b)

12. Questionnaire Design
Unique set of attributes
Qualitative studies such as focus groups and/or unstructured personal interviews to generate their unique set of attributes.
For performing indirect performance measures, measurable items for overall satisfaction should be included.
Likert-type scale
Using 5-point or 7-point Likert-type scales
7-point Likert-type scale shows more reliable results in measuring the gaps between importance and performance of the attributes
Asking importance-performance sequences
simultaneous sequence: a monotonous task which may lead respondents to produce stereotyped and unreliable information (X)
separate sequence: respondents to initially rate the importance of all attributes and then rate the performance of all attributes
back translation was used in order to ensure that there was no translation bias
Lai and Hitchcock (2015b)

13. Data Collection Sample size Pilot studies Sampling methods Time frame
should be conducted to test content validity
minimum sample size is 30
Sample size
the item-to-response ratios should be 1:20
for 15 measurable items, 300 samples
Sampling methods
Convenience, random, full
Systematic random sampling (√)
Time frame
measuring importance occurs before the product or service is obtained
ascertaining performance is after consumption
Lai and Hitchcock (2015b)

14. Multivariate Normality
measured by the use of Mardia's multivariate kurtosis
cutoff value of 3 for skewness
cutoff value of 10 for kurtosis
the value of multivariate kurtosis should not be 30 or greater
Lai and Hitchcock (2015b)

15. Data Analysis Process Step 1 – descriptive
mean and standard deviation of attributes (not enough)
skew and kurtosis distribution
a multivariate normality test
Eliminating these abnormal attributes in order to obtain a multivariate normal distribution
detect influential multivariate outliers by using the Mahalanobis distance approach as a precursor for removing abnormal data
keeping non-normal attributes and perform bootstrapping to overcome multivariate normality problems for indirect measures (details will be discussed later)
Lai and Hitchcock (2015b)

16. Data Analysis Process Step 2 – reliability and validity
Exploratory Factor Analysis (EFA) provides evidence of construct validity
Convergent EFA is recommended for refining new attributes
Confirmatory Factor Analysis (CFA) is conducted to assess the quality of the factor structure
CFA provides further evidence of the construct validity of the new attributes
Cronbach's alpha is commonly used to test internal consistency reliability
The average variance extracted (AVE) and composite reliability (CR) are used for accessing convergent validity
Lai and Hitchcock (2015b)

17. Exploratory Factor Analysis
A Kaiser-Meyer-Olkin (KMO) test of sample adequacy (>0.70)
EFA is based on the principal component analysis with varimax rotation and eigenvalue exceeding 1 with factor loadings exceeding 0.4
Lai and Hitchcock (2015b)

18. Confirmatory Factor Analysis
The CFA fit indices include:
Chi-square
relative/normal Chi-square (<3)
RMSEA (<0.08)
CFI (>0.90)
NFI (>0.90)
Lai and Hitchcock (2015b)

19. Cronbach's alpha, AVE, CR, and Correlation Coefficients
The square-root of the construct's AVE exceeds its correlations with other dimensions and thus the discriminant validity of the latent dimensions is also valid.
Lai and Hitchcock (2015b)

20. Data Analysis Process Step 3 - measure the levels of importance
it is assumed that the levels of importance of attributes for products or services are different
repeated measures ANOVA will help to find the rating of importance among the attributes
Post Hoc tests can be applied in case the researcher wants to further elucidate the differences between attributes
Lai and Hitchcock (2015b)

21. Mauchly’s Test of Sphericity
If the test of sphericity is significant (i.e., p-value < 0.05), then the assumption of null hypothesis is met.
For the tests of Within-Subject Effects, the main effect is significant (p-value < 0.05).
Lai and Hitchcock (2015b)

22. Data Analysis Process
Step 4: measure the gaps between importance and performance
individual paired-samples t-test should be used to confirm that there are significant differences among the levels of importance of the attributes and their respective performances
gap analysis can also be viewed as an effective means of benchmarking against competitors
Lai and Hitchcock (2015b)

23. Individual Paired-samples t-test
the attributes with p-value higher than 0.05 should be eliminated
and should not be plotted in IeP mapping for further
interpretation
Lai and Hitchcock (2015b)

24. Data Analysis Process Step 5: interpret the results of IPA
The most common direct measurement method is the ‘data-centered quadrants approach’ in I-P mapping.
‘regression coefficient approach’ has a significant advantage because researchers can perform a multiple regression analysis by using standard statistical tools (such as SPSS, LISREL, AMOS, and PLS).
bootstrapping can be performed to adjust for distributional problem in case a multivariate normality problem exists.
The Partial Least Squares (PLS) approach is recommended because it is more robust with small sample sizes and non-normal data
Lai and Hitchcock (2015b)

25. I-P Mapping for Direct Measures
Lai and Hitchcock (2015b)

26. PLS Results
Lai and Hitchcock (2015b)

27. Latent Variable Correlations
Lai and Hitchcock (2015b)

28. I-P Mapping for Indirect Measures
Lai and Hitchcock (2015b)

29. Dimensions Distribution
researchers should provide a dimensions (factors/constructs) distribution in I-P mapping after showing the attributes‘ spread in cases where the attributes can be classified into different dimensions
Lai and Hitchcock (2015b)

30. Data Analysis Process Step 6: practical recommendation
The strategic actions should relate to the existing market position of the
Lai and Hitchcock (2015b)

31. Flexible Data-Centered Diagonal Line Model
Lai and Hitchcock (2015b)

32. Flexible Data-Centered Diagonal Line Model
Lai and Hitchcock (2015b)

33. Q & A

34. References
Abalo, J., Varela, J., & Rial, A. (2006). El Analisis de Importancia-Valoracion aplicado a la gestion de servicios. Psicothema, 18(4),
Alberty, S., & Mihalik, B. (1989). The use of importance-performance analysis as an evaluative technique in adult education. Evaluation Review, 13(1),
Fallon, P., & Schofield, P. (2006). The dynamics of destination attribute importance. Journal of Business Research, 59(6),
Han, S., Ham, S. S., Yang, I., & Baek, S. (2012). Passengers’ perceptions of airline lounges: Importance of attributes that determine usage and service quality measurement. Tourism Management, 33(5), 1103–1111.
Hawes, J. M., & Rao, C. P. (1985). Using importance-performance analysis to develop health care marketing strategies. Journal of Health Care Marketing, 5(3),
Lai, I.K.W., & Hitchcock, M. (2015a). A Consideration of Normality in Importance-Performance Analysis, Current Issues in Tourism, Jan 2015 online publication.
Lai, I.K.W., & Hitchcock, M. (2015b). Importance-Performance Analysis in Tourism: A Framework for Researchers, Tourism Management, 48, 242–267.
Martilla, J. A., & James, J. C. (1977). Importance-performance analysis. Journal of Marketing, 41(1),
O’Leary, S., & Deegan, J. (2005). Ireland’s image as a tourism destination in France: Attribute importance and performance. Journal of Travel Research, 43(3), 247–256.
Rial, A., Rial, J., Varela, J., & Real, E. (2008). An application of importance-performance analysis (IPA) to the management of sport centres. Managing Leisure, 13(3/4),