IDEAS home Printed from https://ideas.repec.org/a/spr/infott/v24y2022i2d10.1007_s40558-022-00226-9.html
   My bibliography  Save this article

Asymmetrical impact of service attribute performance on consumer satisfaction: an asymmetric impact-attention-performance analysis

Author

Listed:
  • Mengqiang Pan

    (Beijing Technology and Business University)

  • Nao Li

    (Beijing Technology and Business University)

  • Xiankai Huang

    (Beijing Technology and Business University)

Abstract

Analyzing the asymmetrical effects of service attribute performance on customer satisfaction can identify the different effects of service attributes on overall satisfaction at different levels of performance; it can compensate for the shortcomings of conventional linear regression analysis. Previous research on the asymmetrical relationship between restaurant attribute performance and satisfaction has lacked an assessment of market segmentation differences; it has also adopted an attribute priority division method based on the asymmetrical relationship, ignoring consumers’ attention to attributes. This paper presents a method to analyze the asymmetrical impact of service attributes performance on satisfaction based on online reviews. First, the sentiment score of the restaurant attributes were calculated by aspect-based sentiment classification and a clustering algorithm. Second, the asymmetrical influence of attribute performance on satisfaction was explored according to the sentiment score of the attributes. Finally, consumers’ attention to attributes was measured according to the frequency of attribute mentions and the priority of attribute promotion was investigated by integrating the asymmetrical impact-attention-performance analysis (AIAPA) model of attention. Based on the analysis of 92,904 online restaurant reviews in Tokyo collected on TripAdvisor using the proposed method, this research founds that the performance of most restaurant attributes has an asymmetrical effect on satisfaction. The asymmetrical effect of restaurant attribute performance on satisfaction varies by seasons and market segments. Using the AIAPA model, this research identifies the attribute priority of different restaurant types in different seasons.

Suggested Citation

  • Mengqiang Pan & Nao Li & Xiankai Huang, 2022. "Asymmetrical impact of service attribute performance on consumer satisfaction: an asymmetric impact-attention-performance analysis," Information Technology & Tourism, Springer, vol. 24(2), pages 221-243, June.
    • Handle: RePEc:spr:infott:v:24:y:2022:i:2:d:10.1007_s40558-022-00226-9
    DOI: 10.1007/s40558-022-00226-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40558-022-00226-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40558-022-00226-9?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Michael Anderson & Jeremy Magruder, 2012. "Learning from the Crowd: Regression Discontinuity Estimates of the Effects of an Online Review Database," Economic Journal, Royal Economic Society, vol. 122(563), pages 957-989, September.
    2. 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.
    3. Geetha, M. & Singha, Pratap & Sinha, Sumedha, 2017. "Relationship between customer sentiment and online customer ratings for hotels - An empirical analysis," Tourism Management, Elsevier, vol. 61(C), pages 43-54.
    4. Xiangbin Yan & Jing Wang & Michael Chau, 2015. "Customer revisit intention to restaurants: Evidence from online reviews," Information Systems Frontiers, Springer, vol. 17(3), pages 645-657, June.
    5. Boris Bartikowski & Sylvie Llosa, 2004. "Customer satisfaction measurement: comparing four methods of attribute categorisations," Post-Print hal-01822828, HAL.
    6. Daniel M. Ringel & Bernd Skiera, 2016. "Visualizing Asymmetric Competition Among More Than 1,000 Products Using Big Search Data," Marketing Science, INFORMS, vol. 35(3), pages 511-534, May.
    7. 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.
    8. Guo, Yue & Barnes, Stuart J. & Jia, Qiong, 2017. "Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation," Tourism Management, Elsevier, vol. 59(C), pages 467-483.
    9. 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.
    10. Mochen Yang & Gediminas Adomavicius & Gordon Burtch & Yuqing Rena, 2018. "Mind the Gap: Accounting for Measurement Error and Misclassification in Variables Generated via Data Mining," Information Systems Research, INFORMS, vol. 29(1), pages 4-24, March.
    11. Rita Ferreira Gomes & Beatriz Casais, 2018. "Feelings generated by threat appeals in social marketing: text and emoji analysis of user reactions to anorexia nervosa campaigns in social media," International Review on Public and Nonprofit Marketing, Springer;International Association of Public and Non-Profit Marketing, vol. 15(4), pages 591-607, December.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. Ahani, Ali & Nilashi, Mehrbakhsh & Yadegaridehkordi, Elaheh & Sanzogni, Louis & Tarik, A. Rashid & Knox, Kathy & Samad, Sarminah & Ibrahim, Othman, 2019. "Revealing customers’ satisfaction and preferences through online review analysis: The case of Canary Islands hotels," Journal of Retailing and Consumer Services, Elsevier, vol. 51(C), pages 331-343.
    4. Henner Gimpel & Dominikus Kleindienst & Niclas Nüske & Daniel Rau & Fabian Schmied, 2018. "The upside of data privacy – delighting customers by implementing data privacy measures," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(4), pages 437-452, November.
    5. Lionel Nicod & Élodie Mallor & Sylvie Llosa, 2023. "L’influence de l’aide à participer en magasin sur la satisfaction client : une approche par le modèle tétraclasse," Post-Print hal-04311121, HAL.
    6. Junge, Hendrik & Vollmar, Bernhard, 2021. "Autokauf der Zukunft: Eine Darstellung und Analyse des gegenwärtigen und zukünftigen Kaufentscheidungsprozesses von Automobilen unter Berücksichtigung aktueller Trends in der Automobilindustrie," PFH Forschungspapiere/Research Papers 2021/001, PFH Private University of Applied Sciences, Göttingen.
    7. Giglio, Simona & Pantano, Eleonora & Bilotta, Eleonora & Melewar, T.C., 2020. "Branding luxury hotels: Evidence from the analysis of consumers’ “big” visual data on TripAdvisor," Journal of Business Research, Elsevier, vol. 119(C), pages 495-501.
    8. 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.
    9. Christèle Camelis & Sylvie Llosa & Cécile Maunier, 2015. "Gestion de la satisfaction et de l’insatisfaction des touristes : les apports du modèle Tétraclasse," Post-Print hal-01243401, HAL.
    10. Nilashi, Mehrbakhsh & Ahmadi, Hossein & Arji, Goli & Alsalem, Khalaf Okab & Samad, Sarminah & Ghabban, Fahad & Alzahrani, Ahmed Omar & Ahani, Ali & Alarood, Ala Abdulsalam, 2021. "Big social data and customer decision making in vegetarian restaurants: A combined machine learning method," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    11. Darko Prebežac & Josip Mikulić, 2008. "Destination image and key drivers of perceived destination attractiveness," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 20(2), pages 163-178.
    12. Finn, Adam, 2011. "Investigating the non-linear effects of e-service quality dimensions on customer satisfaction," Journal of Retailing and Consumer Services, Elsevier, vol. 18(1), pages 27-37.
    13. Susan (Sixue) Jia, 2021. "Analyzing Restaurant Customers’ Evolution of Dining Patterns and Satisfaction during COVID-19 for Sustainable Business Insights," Sustainability, MDPI, vol. 13(9), pages 1-15, April.
    14. W. Sabadie & I. Prim-Allaz & S. Llosa, 2006. "Contribution des elements de gestion des reclamations a la satisfaction: les apports de la theorie de la justice," Post-Print hal-01822842, HAL.
    15. Xue Bai & James R. Marsden & William T. Ross & Gang Wang, 2020. "A Note on the Impact of Daily Deals on Local Retailers’ Online Reputation: Mediation Effects of the Consumer Experience," Information Systems Research, INFORMS, vol. 31(4), pages 1132-1143, December.
    16. Zajadacz Alina & Minkwitz Aleksandra, 2020. "Using Social Media Data to Plan for Tourism," Quaestiones Geographicae, Sciendo, vol. 39(3), pages 125-138, September.
    17. Zielke, Stephan, 2008. "Exploring asymmetric effects in the formation of retail price satisfaction," Journal of Retailing and Consumer Services, Elsevier, vol. 15(5), pages 335-347.
    18. Yi Luo & Xiaowei Xu, 2019. "Predicting the Helpfulness of Online Restaurant Reviews Using Different Machine Learning Algorithms: A Case Study of Yelp," Sustainability, MDPI, vol. 11(19), pages 1-17, September.
    19. 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.
    20. Nilashi, Mehrbakhsh & Abumalloh, Rabab Ali & Minaei-Bidgoli, Behrouz & Abdu Zogaan, Waleed & Alhargan, Ashwaq & Mohd, Saidatulakmal & Syed Azhar, Sharifah Nurlaili Farhana & Asadi, Shahla & Samad, Sar, 2022. "Revealing travellers’ satisfaction during COVID-19 outbreak: Moderating role of service quality," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).

    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:infott:v:24:y:2022:i:2:d:10.1007_s40558-022-00226-9. 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.