IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0313450.html
   My bibliography  Save this article

What do part-time employees in Japanese chain restaurants talk about when dissatisfied? Applying Structural Topic Modeling to employee reviews

Author

Listed:
  • Hiroki Takahashi

Abstract

While part-time employees constitute the primary workforce in the chain restaurant industry, their retention has become crucial in developed countries, especially Japan, due to labor shortages resulting from the declining birthrate and aging population. Analyzing employee reviews is an effective method for understanding factors that decrease employee satisfaction. However, while many analyses are focusing on full-time employees, there is insufficient analysis focusing on part-time employees, whose employment status and motivations differ from those of full-time employees. This study employs a Structural Topic Model to correlate latent topics from 4511 online text reviews with a 5-point scale of part-time employee satisfaction scores in Japanese chain restaurants. The study identifies 20 topics, including management systems and key employee interests. Especially digital communication and interview processes frequently appeared when satisfaction was low, which are unique to part-time employees in chain restaurants and had been overlooked in previous analyses. Further analysis links 20 topics to four 5-point scale HRM metrics (compensation satisfaction, workplace environment, motivation, and interpersonal relationships), enabling deeper analysis of the relationships between topics and HRM metrics. These insights contribute to the development of strategies to enhance part-time employee satisfaction in chain restaurants.

Suggested Citation

  • Hiroki Takahashi, 2024. "What do part-time employees in Japanese chain restaurants talk about when dissatisfied? Applying Structural Topic Modeling to employee reviews," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-18, December.
    • Handle: RePEc:plo:pone00:0313450
    DOI: 10.1371/journal.pone.0313450
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0313450
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0313450&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0313450?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. Margaret E. Roberts & Brandon M. Stewart & Edoardo M. Airoldi, 2016. "A Model of Text for Experimentation in the Social Sciences," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 988-1003, July.
    2. Xinxin Li & Lorin M. Hitt, 2008. "Self-Selection and Information Role of Online Product Reviews," Information Systems Research, INFORMS, vol. 19(4), pages 456-474, December.
    3. Heejung Chung, 2020. "Gender, Flexibility Stigma and the Perceived Negative Consequences of Flexible Working in the UK," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 151(2), pages 521-545, September.
    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. Symitsi, Efthymia & Stamolampros, Panagiotis & Daskalakis, George & Korfiatis, Nikolaos, 2021. "The informational value of employee online reviews," European Journal of Operational Research, Elsevier, vol. 288(2), pages 605-619.
    2. Zhou, Chi & Yu, Jing & Qian, Yong, 2024. "Should live-streaming platforms nonexclusively promote brands from traditional retail platforms?," Journal of Retailing and Consumer Services, Elsevier, vol. 80(C).
    3. Diwanji, Vaibhav S. & Cortese, Juliann, 2020. "Contrasting user generated videos versus brand generated videos in ecommerce," Journal of Retailing and Consumer Services, Elsevier, vol. 54(C).
    4. Hui, Xiang & Klein, Tobias & Stahl, Konrad, 2022. "Learning from Online Ratings," CEPR Discussion Papers 17006, C.E.P.R. Discussion Papers.
    5. Sandra Wankmüller, 2023. "A comparison of approaches for imbalanced classification problems in the context of retrieving relevant documents for an analysis," Journal of Computational Social Science, Springer, vol. 6(1), pages 91-163, April.
    6. Rosillo-Díaz, Elena & Muñoz-Rosas, Juan Francisco & Blanco-Encomienda, Francisco Javier, 2024. "Impact of heuristic–systematic cues on the purchase intention of the electronic commerce consumer through the perception of product quality," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    7. Sallin, Aurelién, 2021. "Estimating returns to special education: combining machine learning and text analysis to address confounding," Economics Working Paper Series 2109, University of St. Gallen, School of Economics and Political Science.
    8. Kunpeng Zhang & Wendy Moe, 2021. "Measuring Brand Favorability Using Large-Scale Social Media Data," Information Systems Research, INFORMS, vol. 32(4), pages 1128-1139, December.
    9. Das, Kallol & Patel, Jayesh D. & Sharma, Anuj & Shukla, Yupal, 2023. "Creativity in marketing: Examining the intellectual structure using scientometric analysis and topic modeling," Journal of Business Research, Elsevier, vol. 154(C).
    10. Ting Li & Robert J. Kauffman & Eric van Heck & Peter Vervest & Benedict G. C. Dellaert, 2014. "Consumer Informedness and Firm Information Strategy," Information Systems Research, INFORMS, vol. 25(2), pages 345-363, June.
    11. Dimitrios Kanelis & Pierre L. Siklos, 2025. "The ECB press conference statement: deriving a new sentiment indicator for the euro area," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 30(1), pages 652-664, January.
    12. Davide Crapis & Bar Ifrach & Costis Maglaras & Marco Scarsini, 2017. "Monopoly Pricing in the Presence of Social Learning," Management Science, INFORMS, vol. 63(11), pages 3586-3608, November.
    13. Dehler-Holland, Joris & Schumacher, Kira & Fichtner, Wolf, 2021. "Topic Modeling Uncovers Shifts in Media Framing of the German Renewable Energy Act," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 2(1).
    14. Minnema, Alec & Bijmolt, Tammo H.A. & Gensler, Sonja & Wiesel, Thorsten, 2016. "To Keep or Not to Keep: Effects of Online Customer Reviews on Product Returns," Journal of Retailing, Elsevier, vol. 92(3), pages 253-267.
    15. Shan, Wei & Qiao, Tong & Zhang, Mingli, 2020. "Getting more resources for better performance: The effect of user-owned resources on the value of user-generated content," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    16. Marcel Fratzscher & Tobias Heidland & Lukas Menkhoff & Lucio Sarno & Maik Schmeling, 2023. "Foreign Exchange Intervention: A New Database," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 71(4), pages 852-884, December.
    17. Arthur Dyevre & Nicolas Lampach, 2021. "Issue attention on international courts: Evidence from the European Court of Justice," The Review of International Organizations, Springer, vol. 16(4), pages 793-815, October.
    18. Azer, Jaylan & Anker, Thomas & Taheri, Babak & Tinsley, Ross, 2023. "Consumer-Driven racial stigmatization: The moderating role of race in online consumer-to-consumer reviews," Journal of Business Research, Elsevier, vol. 157(C).
    19. Li Tang & Jennifer Kuzma & Xi Zhang & Xinyu Song & Yin Li & Hongxu Liu & Guangyuan Hu, 2023. "Synthetic biology and governance research in China: a 40-year evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5293-5310, September.
    20. Han, Chunjia & Yang, Mu & Piterou, Athena, 2021. "Do news media and citizens have the same agenda on COVID-19? an empirical comparison of twitter posts," Technological Forecasting and Social Change, Elsevier, vol. 169(C).

    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:plo:pone00:0313450. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.