IDEAS home Printed from https://ideas.repec.org/a/spr/binfse/v64y2022i4d10.1007_s12599-021-00737-5.html
   My bibliography  Save this article

Identifying Competitive Attributes Based on an Ensemble of Explainable Artificial Intelligence

Author

Listed:
  • Younghoon Lee

    (Seoul National University of Science and Technology)

Abstract

Competitor analysis is a fundamental requirement in both strategic and operational management, and the competitive attributes of reviewer comments are a crucial determinant of competitor analysis approaches. Most studies have focused on identifying competitors or detecting comparative sentences, not competitive attributes. Thus, the authors propose a method based on explainable artificial intelligence (XAI) that can detect competitive attributes from consumers’ perspectives. They construct a model to classify the reviewer comments for each competitive product and calculate the importance of each keyword in the reviewer comments during the classification process. This is based on the assumption that keywords significantly influence product classification. The authors also propose an additional novel methodology that combines various XAI techniques such as local interpretable model-agnostic explanations, Shapley additive explanations, logistic regression, gradient-based class activation map, and layer-wise relevance propagation to build a robust model for calculating the importance of competitive attributes for various data sources.

Suggested Citation

  • Younghoon Lee, 2022. "Identifying Competitive Attributes Based on an Ensemble of Explainable Artificial Intelligence," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(4), pages 407-419, August.
    • Handle: RePEc:spr:binfse:v:64:y:2022:i:4:d:10.1007_s12599-021-00737-5
    DOI: 10.1007/s12599-021-00737-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12599-021-00737-5
    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/s12599-021-00737-5?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. Swapnajit Chakraborti & Shubhamoy Dey, 2019. "Analysis of Competitor Intelligence in the Era of Big Data: An Integrated System Using Text Summarization Based on Global Optimization," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(3), pages 345-355, June.
    2. Lee, Mingook & Lee, Sungjoo, 2017. "Identifying new business opportunities from competitor intelligence: An integrated use of patent and trademark databases," Technological Forecasting and Social Change, Elsevier, vol. 119(C), pages 170-183.
    3. Davcik, Nebojsa S. & Sharma, Piyush, 2016. "Marketing resources, performance, and competitive advantage: A review and future research directions," Journal of Business Research, Elsevier, vol. 69(12), pages 5547-5552.
    4. Nadja Hatzijordanou & Nicolai Bohn & Orestis Terzidis, 2019. "A systematic literature review on competitor analysis: status quo and start-up specifics," Management Review Quarterly, Springer, vol. 69(4), pages 415-458, November.
    5. Kasturi Dewi Varathan & Anastasia Giachanou & Fabio Crestani, 2017. "Comparative opinion mining: A review," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(4), pages 811-829, April.
    6. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    7. Alobaidi, Mohammad H. & Chebana, Fateh & Meguid, Mohamed A., 2018. "Robust ensemble learning framework for day-ahead forecasting of household based energy consumption," Applied Energy, Elsevier, vol. 212(C), pages 997-1012.
    8. Hu, Chao & Youn, Byeng D. & Wang, Pingfeng & Taek Yoon, Joung, 2012. "Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 120-135.
    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. Baxendale, Shane & Macdonald, Emma K. & Wilson, Hugh N., 2015. "The Impact of Different Touchpoints on Brand Consideration," Journal of Retailing, Elsevier, vol. 91(2), pages 235-253.
    2. Wu, Xingli & Liao, Huchang, 2021. "Modeling personalized cognition of customers in online shopping," Omega, Elsevier, vol. 104(C).
    3. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    4. Sun, Wenbin & Price, Joseph & Ding, Yuan, 2019. "The longitudinal effects of internationalization on firm performance: The moderating role of marketing capability," Journal of Business Research, Elsevier, vol. 95(C), pages 326-337.
    5. Zheng, Xiujuan & Fang, Huajing, 2015. "An integrated unscented kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 74-82.
    6. Jiyeon Hong & Paul R. Hoban, 2022. "Writing More Compelling Creative Appeals: A Deep Learning-Based Approach," Marketing Science, INFORMS, vol. 41(5), pages 941-965, September.
    7. Gökçe Esenduran & James A. Hill & In Joon Noh, 2020. "Understanding the Choice of Online Resale Channel for Used Electronics," Production and Operations Management, Production and Operations Management Society, vol. 29(5), pages 1188-1211, May.
    8. Fang, Lei & He, Bin, 2023. "A deep learning framework using multi-feature fusion recurrent neural networks for energy consumption forecasting," Applied Energy, Elsevier, vol. 348(C).
    9. Schneider, Matthew J. & Gupta, Sachin, 2016. "Forecasting sales of new and existing products using consumer reviews: A random projections approach," International Journal of Forecasting, Elsevier, vol. 32(2), pages 243-256.
    10. Cao, Mengda & Zhang, Tao & Liu, Yajie & Zhang, Yajun & Wang, Yu & Li, Kaiwen, 2022. "An ensemble learning prognostic method for capacity estimation of lithium-ion batteries based on the V-IOWGA operator," Energy, Elsevier, vol. 257(C).
    11. Shen, Sheng & Sadoughi, Mohammadkazem & Li, Meng & Wang, Zhengdao & Hu, Chao, 2020. "Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 260(C).
    12. Wen, Pengfei & Zhao, Shuai & Chen, Shaowei & Li, Yong, 2021. "A generalized remaining useful life prediction method for complex systems based on composite health indicator," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    13. Marjeta Marolt & Hans-Dieter Zimmermann & Andreja Pucihar, 2022. "Social Media Use and Business Performance in SMEs: The Mediating Roles of Relational Social Commerce Capability and Competitive Advantage," Sustainability, MDPI, vol. 14(22), pages 1-14, November.
    14. Chien-Chang Hsu & Min-Sheng Chen, 2016. "Intelligent maintenance prediction system for LED wafer testing machine," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 335-342, April.
    15. Ali Rohan, 2022. "Holistic Fault Detection and Diagnosis System in Imbalanced, Scarce, Multi-Domain (ISMD) Data Setting for Component-Level Prognostics and Health Management (PHM)," Mathematics, MDPI, vol. 10(12), pages 1-22, June.
    16. Borchert, Philipp & Coussement, Kristof & De Weerdt, Jochen & De Caigny, Arno, 2024. "Industry-sensitive language modeling for business," European Journal of Operational Research, Elsevier, vol. 315(2), pages 691-702.
    17. Li, Zhixiong & Wu, Dazhong & Hu, Chao & Terpenny, Janis, 2019. "An ensemble learning-based prognostic approach with degradation-dependent weights for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 110-122.
    18. Le Son, Khanh & Fouladirad, Mitra & Barros, Anne & Levrat, Eric & Iung, Benoît, 2013. "Remaining useful life estimation based on stochastic deterioration models: A comparative study," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 165-175.
    19. Yunis, Manal & Tarhini, Abbas & Kassar, Abdulnasser, 2018. "The role of ICT and innovation in enhancing organizational performance: The catalysing effect of corporate entrepreneurship," Journal of Business Research, Elsevier, vol. 88(C), pages 344-356.
    20. Wu, Yingwen & Ji, Yangjian, 2023. "Identifying firm-specific technology opportunities from the perspective of competitors by using association rule mining," Journal of Informetrics, Elsevier, vol. 17(2).

    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:binfse:v:64:y:2022:i:4:d:10.1007_s12599-021-00737-5. 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.