Competitor identification: A review of use cases, data sources, and algorithms

Werle M, Laumer S (2022)

Publication Language: English

Publication Type: Journal article, Review article

Publication year: 2022


Book Volume: 65

Journal Issue: 102507


DOI: 10.1016/j.ijinfomgt.2022.102507


Businesses have to deal with and survive in competitive, constantly changing landscapes due to new entrants and exogenous shocks like the COVID-19 pandemic. Furthermore, decision-makers have to deal with staggering amounts of data produced in their corporate environment. Hence, research and practice have developed hybrid socio-technical systems of data mining and expert knowledge to identify competitors early on. To better understand the ergonomic design of such systems and how to integrate them in strategic management driven by artificial intelligence, we engaged in a systematic literature review to summarize the field and guide future research. We identified use cases, data source types, and algorithms that have been developed in 40 publications between 2002 and 2019. We found that existing approaches neglect to identify indirect and potential competitors at the periphery of a company’s vision. Such a blind spot exposes companies to increased risks of disruption, given that disruptive change often starts in the form of weak signals. Furthermore, we recommend using artificial intelligence to advance search strategies, allow document collection updates, and harmonize multiple data source types.

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How to cite


Werle, M., & Laumer, S. (2022). Competitor identification: A review of use cases, data sources, and algorithms. International Journal of Information Management, 65(102507).


Werle, Marcel, and Sven Laumer. "Competitor identification: A review of use cases, data sources, and algorithms." International Journal of Information Management 65.102507 (2022).

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