IDEAS home Printed from https://ideas.repec.org/a/eee/touman/v33y2012i3p713-716.html
   My bibliography  Save this article

Using dummy regression to explore asymmetric effects in tourist satisfaction: A cautionary note

Author

Listed:
  • Mikulić, Josip
  • Prebežac, Darko

Abstract

This research note addresses the misuse of standardized weights as measures of effect in dummy regression, which is the most frequently used technique for assessing asymmetric effects in the formation of tourist satisfaction. Unlike in regular regressions, standardized weights have no straightforward interpretation in dummy regressions, but they only carry the risk of providing misleading implications in theory building and guiding managerial action. To empirically underpin the arguments put forward in this note, an illustrative case example is used that provides insight into the underlying statistical mechanisms that cause unstandardized and standardized weights to provide significantly different implications in dummy regressions. The findings of this note should help to prevent bad practice in future studies that make use of the technique in assessments of asymmetric effects in customer satisfaction and/or the three-factor structure of customer satisfaction. However, the points put forward hold for dummy regressions in general.

Suggested Citation

  • Mikulić, Josip & Prebežac, Darko, 2012. "Using dummy regression to explore asymmetric effects in tourist satisfaction: A cautionary note," Tourism Management, Elsevier, vol. 33(3), pages 713-716.
    • Handle: RePEc:eee:touman:v:33:y:2012:i:3:p:713-716
    DOI: 10.1016/j.tourman.2011.08.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0261517711001580
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.tourman.2011.08.005?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
    ---><---

    References listed on IDEAS

    as
    1. Boris Bartikowski & Sylvie Llosa, 2004. "Customer satisfaction measurement: comparing four methods of attribute categorisations," The Service Industries Journal, Taylor & Francis Journals, vol. 24(4), pages 67-82, July.
    2. Mikulić, Josip & Prebežac, Darko, 2011. "Evaluating hotel animation programs at Mediterranean sun-and-sea resorts: An impact-asymmetry analysis," Tourism Management, Elsevier, vol. 32(3), pages 688-696.
    3. Howard D. Bondell & Brian J. Reich, 2009. "Simultaneous Factor Selection and Collapsing Levels in ANOVA," Biometrics, The International Biometric Society, vol. 65(1), pages 169-177, March.
    4. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
    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. Rong-Chang Jou & Yeong-Jia Day, 2021. "Application of Revised Importance–Performance Analysis to Investigate Critical Service Quality of Hotel Online Booking," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    2. Lai, Ivan Ka Wai & Hitchcock, Michael, 2017. "Sources of satisfaction with luxury hotels for new, repeat, and frequent travelers: A PLS impact-asymmetry analysis," Tourism Management, Elsevier, vol. 60(C), pages 107-129.
    3. Sanela Škorić & Josip Mikulić & Petra Barišić, 2021. "The Mediating Role of Major Sport Events in Visitors’ Satisfaction, Dissatisfaction, and Intention to Revisit a Destination," Societies, MDPI, vol. 11(3), pages 1-14, July.

    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. Albayrak, Tahir & Caber, Meltem, 2015. "Prioritisation of the hotel attributes according to their influence on satisfaction: A comparison of two techniques," Tourism Management, Elsevier, vol. 46(C), pages 43-50.
    2. Justin B. Post & Howard D. Bondell, 2013. "Factor Selection and Structural Identification in the Interaction ANOVA Model," Biometrics, The International Biometric Society, vol. 69(1), pages 70-79, March.
    3. Jeon, Jong-June & Kwon, Sunghoon & Choi, Hosik, 2017. "Homogeneity detection for the high-dimensional generalized linear model," Computational Statistics & Data Analysis, Elsevier, vol. 114(C), pages 61-74.
    4. Groll, Andreas & Hambuckers, Julien & Kneib, Thomas & Umlauf, Nikolaus, 2019. "LASSO-type penalization in the framework of generalized additive models for location, scale and shape," Computational Statistics & Data Analysis, Elsevier, vol. 140(C), pages 59-73.
    5. Gerhard Tutz & Jan Gertheiss, 2014. "Rating Scales as Predictors—The Old Question of Scale Level and Some Answers," Psychometrika, Springer;The Psychometric Society, vol. 79(3), pages 357-376, July.
    6. Xian Ji & Long Shao & Yu Du, 2020. "Collaborating with Local Communities to Identify Improvement Priorities for Historic Urban Landscape Based on Residents’ Satisfaction: An Application of Asymmetric Impact-Performance Analysis in Dando," Sustainability, MDPI, vol. 12(4), pages 1-16, February.
    7. Chen, Pin-Zheng & Liu, Wan-Yu, 2019. "Assessing management performance of the national forest park using impact range-performance analysis and impact-asymmetry analysis," Forest Policy and Economics, Elsevier, vol. 104(C), pages 121-138.
    8. Lai, Ivan Ka Wai & Hitchcock, Michael, 2017. "Sources of satisfaction with luxury hotels for new, repeat, and frequent travelers: A PLS impact-asymmetry analysis," Tourism Management, Elsevier, vol. 60(C), pages 107-129.
    9. Benjamin G. Stokell & Rajen D. Shah & Ryan J. Tibshirani, 2021. "Modelling high‐dimensional categorical data using nonconvex fusion penalties," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 579-611, July.
    10. Gerhard Tutz & Margret-Ruth Oelker, 2017. "Modelling Clustered Heterogeneity: Fixed Effects, Random Effects and Mixtures," International Statistical Review, International Statistical Institute, vol. 85(2), pages 204-227, August.
    11. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    12. Jun Yan & Jian Huang, 2012. "Model Selection for Cox Models with Time-Varying Coefficients," Biometrics, The International Biometric Society, vol. 68(2), pages 419-428, June.
    13. Ye, Ya-Fen & Shao, Yuan-Hai & Deng, Nai-Yang & Li, Chun-Na & Hua, Xiang-Yu, 2017. "Robust Lp-norm least squares support vector regression with feature selection," Applied Mathematics and Computation, Elsevier, vol. 305(C), pages 32-52.
    14. Gerhard Tutz & Moritz Berger, 2018. "Tree-structured modelling of categorical predictors in generalized additive regression," 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. 12(3), pages 737-758, September.
    15. Guillaume Sagnol & Edouard Pauwels, 2019. "An unexpected connection between Bayes A-optimal designs and the group lasso," Statistical Papers, Springer, vol. 60(2), pages 565-584, April.
    16. Bakalli, Gaetan & Guerrier, Stéphane & Scaillet, Olivier, 2023. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Journal of Econometrics, Elsevier, vol. 237(2).
    17. Shuichi Kawano, 2014. "Selection of tuning parameters in bridge regression models via Bayesian information criterion," Statistical Papers, Springer, vol. 55(4), pages 1207-1223, November.
    18. Peng, Heng & Lu, Ying, 2012. "Model selection in linear mixed effect models," Journal of Multivariate Analysis, Elsevier, vol. 109(C), pages 109-129.
    19. Yize Zhao & Matthias Chung & Brent A. Johnson & Carlos S. Moreno & Qi Long, 2016. "Hierarchical Feature Selection Incorporating Known and Novel Biological Information: Identifying Genomic Features Related to Prostate Cancer Recurrence," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1427-1439, October.
    20. G. Aneiros & P. Vieu, 2016. "Sparse nonparametric model for regression with functional covariate," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(4), pages 839-859, October.

    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:eee:touman:v:33:y:2012:i:3:p:713-716. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/tourism-management .

    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.