IDEAS home Printed from https://ideas.repec.org/a/taf/servic/v42y2022i3-4p206-224.html
   My bibliography  Save this article

Qualitative analysis of big data in the service sectors

Author

Listed:
  • Chun-Che Huang
  • Wen-Yau Liang
  • Dan-Wei (Marian) Wen
  • Ping-Ho Ting
  • Meng-Ying Shen

Abstract

Nowadays, service sectors are facing a data tsunami. Previous studies on service sectors have been conducted using questionnaire surveys and have been subjectively analyzed using statistical analysis techniques. Such techniques make it difficult for non-statisticians to integrally explore the overall nature of questionnaire data in the big data paradigm. To further discover the quantitative and qualitative nature of a data set, granularity computing is used to make up for the weaknesses of statistical techniques and a rough set (RS) based solution approach is proposed. The Multi-Value Rule Generation (MVRG) algorithm is developed to analyze questionnaire data and deal with the roughness problem of multiple-values in outcome features. The rules resulting from the MVRG algorithm exhibit both the relationships between dependent and independent variables and the content of the relationships. Rules, rather than numerical charts, can be understood by non-statisticians. Two cases of tourism and hospitality are restudied and comparisons between the proposed approach and traditional analytical techniques are made to validate the complementary benefits of traditional statistical analysis. This comparison shows that the proposed solution approach provides further hidden knowledge behind the data set. The MVRG algorithm can complement statistical methods in finding hidden knowledge and providing comprehensive rules to non-statisticians.

Suggested Citation

  • Chun-Che Huang & Wen-Yau Liang & Dan-Wei (Marian) Wen & Ping-Ho Ting & Meng-Ying Shen, 2022. "Qualitative analysis of big data in the service sectors," The Service Industries Journal, Taylor & Francis Journals, vol. 42(3-4), pages 206-224, March.
    • Handle: RePEc:taf:servic:v:42:y:2022:i:3-4:p:206-224
    DOI: 10.1080/02642069.2018.1509957
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02642069.2018.1509957
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02642069.2018.1509957?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.

    Citations

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


    Cited by:

    1. Minjun Kim & Silvana Trimi, 2023. "Transforming data into information for smart services: integration of morphological analysis and text mining," Service Business, Springer;Pan-Pacific Business Association, vol. 17(1), pages 257-280, March.

    More about this item

    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:taf:servic:v:42:y:2022:i:3-4:p:206-224. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/FSIJ20 .

    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.