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Identifying Competitive Attributes Based on an Ensemble of Explainable Artificial Intelligence

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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.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2020R1F1A1067914).

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Authors and Affiliations

  1. Department of Industrial Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul, 01811, Korea

    Younghoon Lee

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  1. Younghoon Lee
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Correspondence to Younghoon Lee.

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Accepted after three revisions by Stefan Lessmann.

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Lee, Y. Identifying Competitive Attributes Based on an Ensemble of Explainable Artificial Intelligence. Bus Inf Syst Eng 64, 407–419 (2022). https://doi.org/10.1007/s12599-021-00737-5

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