IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v129y2021icp860-877.html
   My bibliography  Save this article

Online critical review classification in response strategy and service provider rating: Algorithms from heuristic processing, sentiment analysis to deep learning

Author

Listed:
  • Zhu, John Jianjun
  • Chang, Yung-Chun
  • Ku, Chih-Hao
  • Li, Stella Yiyan
  • Chen, Chi-Jen

Abstract

This research proposes and tests mechanisms for defining and identifying the critical online consumer reviews that firms could prioritize to optimize their online response strategies, while incorporating the latest artificial intelligence (AI) technology to deal with the overwhelming volume of information. Three sets of analytical tools are introduced: Heuristic Processing, Linguistic Feature Analysis, and Deep Learning-based Natural Language Processing (NLP), to extract review information. Twelve algorithms to classify critical reviews were developed accordingly and empirically tested for their effectiveness. Our econometric analysis of 110,146 online reviews from a chain operation in hospitality industry over seven years identifies six outstanding algorithms. Firm value rating, comment length, valence, and certain consumer emotions, in addition to past comment-response behavior, are found to be superior in predicting incoming review criticality. However, the service attributes such as urgency to reply and the feasibility of actions to take are not as informative.

Suggested Citation

  • Zhu, John Jianjun & Chang, Yung-Chun & Ku, Chih-Hao & Li, Stella Yiyan & Chen, Chi-Jen, 2021. "Online critical review classification in response strategy and service provider rating: Algorithms from heuristic processing, sentiment analysis to deep learning," Journal of Business Research, Elsevier, vol. 129(C), pages 860-877.
    • Handle: RePEc:eee:jbrese:v:129:y:2021:i:c:p:860-877
    DOI: 10.1016/j.jbusres.2020.11.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296320307505
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbusres.2020.11.007?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. Zihao Zhang & Stefan Zohren & Stephen Roberts, 2018. "BDLOB: Bayesian Deep Convolutional Neural Networks for Limit Order Books," Papers 1811.10041, arXiv.org.
    2. Nath, Prithwiraj & Devlin, James & Reid, Veronica, 2018. "The effects of online reviews on service expectations: Do cultural value orientations matter?," Journal of Business Research, Elsevier, vol. 90(C), pages 123-133.
    3. Kim, Kun & Park, Oun-joung & Yun, Seunghyun & Yun, Haejung, 2017. "What makes tourists feel negatively about tourism destinations? Application of hybrid text mining methodology to smart destination management," Technological Forecasting and Social Change, Elsevier, vol. 123(C), pages 362-369.
    4. Xu, Xun, 2020. "Examining an asymmetric effect between online customer reviews emphasis and overall satisfaction determinants," Journal of Business Research, Elsevier, vol. 106(C), pages 196-210.
    5. Liu, Zhiwei & Park, Sangwon, 2015. "What makes a useful online review? Implication for travel product websites," Tourism Management, Elsevier, vol. 47(C), pages 140-151.
    6. Sparks, Beverley A. & So, Kevin Kam Fung & Bradley, Graham L., 2016. "Responding to negative online reviews: The effects of hotel responses on customer inferences of trust and concern," Tourism Management, Elsevier, vol. 53(C), pages 74-85.
    7. Gao, Baojun & Li, Xiangge & Liu, Shan & Fang, Debin, 2018. "How power distance affects online hotel ratings: The positive moderating roles of hotel chain and reviewers’ travel experience," Tourism Management, Elsevier, vol. 65(C), pages 176-186.
    8. Chebat, Jean-Charles & Slusarczyk, Witold, 2005. "How emotions mediate the effects of perceived justice on loyalty in service recovery situations: an empirical study," Journal of Business Research, Elsevier, vol. 58(5), pages 664-673, May.
    9. Voelpel, Sven & Leibold, Marius & Tekie, Eden & von Krogh, Georg, 2005. "Escaping the Red Queen Effect in Competitive Strategy:: Sense-testing Business Models," European Management Journal, Elsevier, vol. 23(1), pages 37-49, February.
    10. Weitzl, Wolfgang & Hutzinger, Clemens, 2017. "The effects of marketer- and advocate-initiated online service recovery responses on silent bystanders," Journal of Business Research, Elsevier, vol. 80(C), pages 164-175.
    11. Niall Piercy & Chris Archer-Brown, 2014. "Online service failure and propensity to suspend offline consumption," The Service Industries Journal, Taylor & Francis Journals, vol. 34(8), pages 659-676, May.
    12. Arun Rai, 2020. "Explainable AI: from black box to glass box," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 137-141, January.
    13. Ching-Chow Yang & Yung-Tsan Jou & Lai-Yu Cheng, 2011. "Using integrated quality assessment for hotel service quality," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(2), pages 349-364, February.
    14. Xitong Li, 2018. "Impact of Average Rating on Social Media Endorsement: The Moderating Role of Rating Dispersion and Discount Threshold," Information Systems Research, INFORMS, vol. 29(3), pages 739-754, September.
    15. Davide Proserpio & Georgios Zervas, 2017. "Online Reputation Management: Estimating the Impact of Management Responses on Consumer Reviews," Marketing Science, INFORMS, vol. 36(5), pages 645-665, September.
    16. Lastner, Matthew M. & Folse, Judith Anne Garretson & Mangus, Stephanie M. & Fennell, Patrick, 2016. "The road to recovery: Overcoming service failures through positive emotions," Journal of Business Research, Elsevier, vol. 69(10), pages 4278-4286.
    17. Chan, Kimmy Wa & Li, Stella Yiyan & Zhu, John Jianjun, 2018. "Good to Be Novel? Understanding How Idea Feasibility Affects Idea Adoption Decision Making in Crowdsourcing," Journal of Interactive Marketing, Elsevier, vol. 43(C), pages 52-68.
    18. Nie, Winter, 2000. "Waiting: integrating social and psychological perspectives in operations management," Omega, Elsevier, vol. 28(6), pages 611-629, December.
    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. Feng, Yi & Yin, Yunqiang & Wang, Dujuan & Dhamotharan, Lalitha, 2022. "A dynamic ensemble selection method for bank telemarketing sales prediction," Journal of Business Research, Elsevier, vol. 139(C), pages 368-382.
    2. Chong Guan & Yu-Chen Hung & Wenting Liu, 2022. "Cultural differences in hospitality service evaluations: mining insights of user generated content," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1061-1081, September.
    3. Azadi, Majid & Yousefi, Saeed & Farzipoor Saen, Reza & Shabanpour, Hadi & Jabeen, Fauzia, 2023. "Forecasting sustainability of healthcare supply chains using deep learning and network data envelopment analysis," Journal of Business Research, Elsevier, vol. 154(C).

    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. Koc, Erdogan & Hatipoglu, Sercan & Kivrak, Oguzhan & Celik, Cemal & Koc, Kaan, 2023. "Houston, we have a problem!: The use of ChatGPT in responding to customer complaints," Technology in Society, Elsevier, vol. 74(C).
    2. Surachartkumtonkun, Jiraporn (Nui) & Grace, Debra & Ross, Mitchell, 2021. "Unfair customer reviews: Third-party perceptions and managerial responses," Journal of Business Research, Elsevier, vol. 132(C), pages 631-640.
    3. Wen-Kuo Chen & Dalianus Riantama & Long-Sheng Chen, 2020. "Using a Text Mining Approach to Hear Voices of Customers from Social Media toward the Fast-Food Restaurant Industry," Sustainability, MDPI, vol. 13(1), pages 1-17, December.
    4. Azemi, Yllka & Ozuem, Wilson & Howell, Kerry E. & Lancaster, Geoff, 2019. "An exploration into the practice of online service failure and recovery strategies in the Balkans," Journal of Business Research, Elsevier, vol. 94(C), pages 420-431.
    5. Colmekcioglu, Nazan & Marvi, Reza & Foroudi, Pantea & Okumus, Fevzi, 2022. "Generation, susceptibility, and response regarding negativity: An in-depth analysis on negative online reviews," Journal of Business Research, Elsevier, vol. 153(C), pages 235-250.
    6. Zhou, Yuanyuan & Tsang, Alex S.L. & Huang, Minxue & Zhou, Nan, 2014. "Does delaying service-failure resolution ever make sense?," Journal of Business Research, Elsevier, vol. 67(2), pages 159-166.
    7. Chuanmin Mi & Yetian Chen & Chiung-Shu Cheng & Joselyne Lucky Uwanyirigira & Ching-Torng Lin, 2019. "Exploring the Determinants of Hot Spring Tourism Customer Satisfaction: Causal Relationships Analysis Using ISM," Sustainability, MDPI, vol. 11(9), pages 1-20, May.
    8. 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.
    9. Lu, Lu & Cai, Ruiying & King, Ceridwyn, 2020. "Building trust through a personal touch: Consumer response to service failure and recovery of home-sharing," Journal of Business Research, Elsevier, vol. 117(C), pages 99-111.
    10. Ao Shen & Peng Wang & Yongyuan Ma, 2022. "When crowding‐in and when crowding‐out? The boundary conditions on the relationship between negative online reviews and online sales," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(6), pages 2016-2032, September.
    11. Sheng, Jie, 2019. "Being Active in Online Communications: Firm Responsiveness and Customer Engagement Behaviour," Journal of Interactive Marketing, Elsevier, vol. 46(C), pages 40-51.
    12. Ku, Hsuan-Hsuan & Shang, Rong-An & Fu, Yi-Fan, 2021. "Social learning effects of complaint handling on social media: Self-construal as a moderator," Journal of Retailing and Consumer Services, Elsevier, vol. 59(C).
    13. Marius Johnen & Oliver Schnittka, 2019. "When pushing back is good: the effectiveness of brand responses to social media complaints," Journal of the Academy of Marketing Science, Springer, vol. 47(5), pages 858-878, September.
    14. T. Ravichandran & Chaoqun Deng, 2023. "Effects of Managerial Response to Negative Reviews on Future Review Valence and Complaints," Information Systems Research, INFORMS, vol. 34(1), pages 319-341, March.
    15. Xu, Xun & Lee, Chieh, 2020. "Utilizing the platform economy effect through EWOM: Does the platform matter?," International Journal of Production Economics, Elsevier, vol. 227(C).
    16. Yang, Luming & Xu, Min & Xing, Lin, 2022. "Exploring the core factors of online purchase decisions by building an E-Commerce network evolution model," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    17. Zhang, Ziqiong & Qiao, Shuchen & Chen, Ying & Zhang, Zili, 2022. "Effects of spatial distance on consumers' review effort," Annals of Tourism Research, Elsevier, vol. 94(C).
    18. Liao, Junyun & Chen, Jiawen & Zhao, Haichuan & Li, Muhua, 2023. "Fanning the flames: Transmitting negative word of mouth of rival brands," Journal of Business Research, Elsevier, vol. 154(C).
    19. Weitzl, Wolfgang J. & Einwiller, Sabine A., 2020. "Profiling (un-)committed online complainants: Their characteristics and post-webcare reactions," Journal of Business Research, Elsevier, vol. 117(C), pages 740-753.
    20. Kim, Minseong, 2021. "Conceptualization of e-servicescapes in the fitness applications and wearable devices context: Multi-dimensions, consumer satisfaction, and behavioral intention," Journal of Retailing and Consumer Services, Elsevier, vol. 61(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:eee:jbrese:v:129:y:2021:i:c:p:860-877. 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: http://www.elsevier.com/locate/jbusres .

    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.