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Paper Number
2189
Paper Type
Complete
Description
Extensive efforts have been made by both academics and practitioners to understand interfirm competitive relationship. However, it has never been an easy task to fully characterize firms and assess their competitive relationship owing to the challenge of information heterogeneity. In this regard, we propose a novel IT artifact for firm profiling and interfirm competition assessment guided by Information System Design Theory (ISDT). We start by constructing a Heterogeneous Occupation Network (HON) using employees' occupation details and education attainments. Then we adopt a Methpath2Vec-based heterogeneous network embedding model to learn firms' latent profiles (embeddings). Using firm embeddings as input, we train multiple classifiers to assess the competitive relationship among the firms. We demonstrate the utility of our IT artifact with extensive experimental study and in-depth discussions. Our study also reveals that employees’ occupation and education information significantly contribute to the identification of the focal firm's potential competitors.
Recommended Citation
Zhong, Hao and Liu, Chuanren, "Firm Profiling and Competition Assessment via Heterogeneous Occupation Network" (2022). ICIS 2022 Proceedings. 8.
https://aisel.aisnet.org/icis2022/data_analytics/data_analytics/8
Firm Profiling and Competition Assessment via Heterogeneous Occupation Network
Extensive efforts have been made by both academics and practitioners to understand interfirm competitive relationship. However, it has never been an easy task to fully characterize firms and assess their competitive relationship owing to the challenge of information heterogeneity. In this regard, we propose a novel IT artifact for firm profiling and interfirm competition assessment guided by Information System Design Theory (ISDT). We start by constructing a Heterogeneous Occupation Network (HON) using employees' occupation details and education attainments. Then we adopt a Methpath2Vec-based heterogeneous network embedding model to learn firms' latent profiles (embeddings). Using firm embeddings as input, we train multiple classifiers to assess the competitive relationship among the firms. We demonstrate the utility of our IT artifact with extensive experimental study and in-depth discussions. Our study also reveals that employees’ occupation and education information significantly contribute to the identification of the focal firm's potential competitors.
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