IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v48y2014i6p3053-3064.html

Model-based approach for importance–performance analysis

Author

Listed:
  • Federica Cugnata

  • Silvia Salini

Abstract

The importance that users or customers attach to various services and products is an essential part of customer satisfaction surveys. Some proposals for linking satisfaction and importance can be found in available literature. The objective is to identify and understand the dimensions with high importance but low perceived quality. These dimensions are primary candidates for focused improvement initiatives. In this study, we propose to apply a class of statistical models, denoted as CUB models, generally used to estimate the feeling and the uncertainty, to measure the importance of items on observed overall satisfaction. A questionnaire with explicit variables of importance for each dimension is considered to compare the obtained ranks with the observed ones. Then the estimated importance and the perceived quality, both obtained with the CUB models, will be jointly analyzed in different datasets coming from various fields. This approach will be compared with some others reported in the literature. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Federica Cugnata & Silvia Salini, 2014. "Model-based approach for importance–performance analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3053-3064, November.
    • Handle: RePEc:spr:qualqt:v:48:y:2014:i:6:p:3053-3064
    DOI: 10.1007/s11135-013-9940-3
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11135-013-9940-3
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11135-013-9940-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Ron S. Kenett & Galit Shmueli, 2014. "On information quality," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(1), pages 3-38, January.
    2. Federico ANDREIS & Pier Alda FERRARI, 2015. "Customer Satisfaction Evaluation Using Multidimensional Item Response Theory Models," Departmental Working Papers 2015-25, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    3. Silvia Salini & Ron Kenett, 2009. "Bayesian networks of customer satisfaction survey data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(11), pages 1177-1189.
    4. Ron S. Kenett & Silvia Salini, 2011. "Modern analysis of customer satisfaction surveys: comparison of models and integrated analysis," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 27(5), pages 465-475, September.
    5. Cicia, Gianni & Corduas, Marcella & Del Giudice, Teresa & Piccolo, Domenico, 2010. "Valuing Consumer Preferences with the CUB Model: A Case Study of Fair Trade Coffee," International Journal on Food System Dynamics, International Center for Management, Communication, and Research, vol. 1(01), pages 1-12.
    6. Pier Ferrari & Silvia Salini, 2011. "Complementary Use of Rasch Models and Nonlinear Principal Components Analysis in the Assessment of the Opinion of Europeans About Utilities," Journal of Classification, Springer;The Classification Society, vol. 28(1), pages 53-69, April.
    7. Maria Iannario, 2010. "On the identifiability of a mixture model for ordinal data," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 87-94.
    8. Jiunn-I Shieh & Hsin-Hung Wu, 2011. "Applying information-based methods in importance–performance analysis when the information of importance is unavailable," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(3), pages 545-557, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Stefania Capecchi & Domenico Piccolo, 2017. "Dealing with heterogeneity in ordinal responses," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(5), pages 2375-2393, September.
    2. Zhou Lu & Zhuyao Zhuo, 2021. "Modelling of Chinese corporate bond default – A machine learning approach," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(5), pages 6147-6191, December.
    3. Marcella Corduas & Alfonso Piscitelli, 2017. "Modeling university student satisfaction: the case of the humanities and social studies degree programs," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 617-628, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Domenico Piccolo & Rosaria Simone, 2019. "The class of cub models: statistical foundations, inferential issues and empirical evidence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 389-435, September.
    2. Corduas, Marcella, 2015. "A statistical model for consumer preferences: the case of Italian extra virgin olive oil," 143rd Joint EAAE/AAEA Seminar, March 25-27, 2015, Naples, Italy 202701, European Association of Agricultural Economists.
    3. Giancarlo MANZI & Pier Alda FERRARI, "undated". "Statistical methods for evaluating satisfaction with public services Abstract: Contrary to private enterprises, public enterprises can be unaware of the impact of their performance when providing serv," CIRIEC Working Papers 1404, CIRIEC - Université de Liège.
    4. Maria Iannario & Marica Manisera & Domenico Piccolo & Paola Zuccolotto, 2012. "Sensory analysis in the food industry as a tool for marketing decisions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(4), pages 303-321, December.
    5. Inbal Yahav & Galit Shmueli, 2014. "Outcomes matter: estimating pre-transplant survival rates of kidney-transplant patients using simulator-based propensity scores," Annals of Operations Research, Springer, vol. 216(1), pages 101-128, May.
    6. Manisera, Marica & Zuccolotto, Paola, 2015. "Identifiability of a model for discrete frequency distributions with a multidimensional parameter space," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 302-316.
    7. Pierpaolo D’Urso & Vincenzina Vitale, 2020. "Bayesian Networks Model Averaging for Bes Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 151(3), pages 897-919, October.
    8. Maria Iannario, 2012. "Preliminary estimators for a mixture model of ordinal data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(3), pages 163-184, October.
    9. F. Cugnata & G. Perucca & S. Salini, 2017. "Bayesian networks and the assessment of universities' value added," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(10), pages 1785-1806, July.
    10. Galeotti, Marzio & Rubashkina, Yana & Salini, Silvia & Verdolini, Elena, 2018. "Environmental policy performance and its determinants: Application of a three-level random intercept model," Energy Policy, Elsevier, vol. 114(C), pages 134-144.
    11. Volker Lingnau & Florian Fuchs & Florian Beham, 2019. "The impact of sustainability in coffee production on consumers’ willingness to pay–new evidence from the field of ethical consumption," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 30(1), pages 65-93, April.
    12. Gennaro Punzo & Rosalia Castellano & Mirko Buonocore, 2018. "Job Satisfaction in the “Big Four” of Europe: Reasoning Between Feeling and Uncertainty Through CUB Models," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 139(1), pages 205-236, August.
    13. Federico ANDREIS & Pier Alda FERRARI, 2015. "Customer Satisfaction Evaluation Using Multidimensional Item Response Theory Models," Departmental Working Papers 2015-25, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    14. Leonelli, Manuele & Varando, Gherardo, 2024. "Robust learning of staged tree models: A case study in evaluating transport services," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    15. Pedro Costa & Helena Rodrigues, 2024. "The ever-changing business of e-commerce-net benefits while designing a new platform for small companies," Review of Managerial Science, Springer, vol. 18(9), pages 2507-2545, September.
    16. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Risks, MDPI, vol. 6(2), pages 1-20, April.
    17. Hellberg-Bahr, Anneke & Pfeuffer, Martin & Spiller, Achim & Brümmer, Bernhard, 2011. "Using Price Rigidities to Explain Pricing Strategies in the Organic Milk Chain," 2011 International European Forum, February 14-18, 2011, Innsbruck-Igls, Austria 122003, International European Forum on System Dynamics and Innovation in Food Networks.
    18. Ryan H. L. Ip & K. Y. K. Wu, 2024. "A mixture distribution for modelling bivariate ordinal data," Statistical Papers, Springer, vol. 65(7), pages 4453-4488, September.
    19. Mohamed Hanafy & Ruixing Ming, 2021. "Machine Learning Approaches for Auto Insurance Big Data," Risks, MDPI, vol. 9(2), pages 1-23, February.
    20. Shu-Yun Yin & Kuan-Kai Huang & Jiunn-I Shieh & Yu-Huei Liu & Hsin-Hung Wu, 2016. "Telehealth services evaluation: a combination of SERVQUAL model and importance-performance analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(2), pages 751-766, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:qualqt:v:48:y:2014:i:6:p:3053-3064. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.