Data-Driven Technology Management Supported by Artificial Intelligence Solutions

The early detection of and an adequate response to meaningful signals of change have a defining impact on the competitive vitality and the competitive advantage of companies. For this strategically important task, companies apply corporate foresight, aiming to enable superior company performance. With the growing dynamics of global markets, the amount of data to be analyzed for this purpose is constantly increasing. As a result, these analyses are often performed with an unreasonably high investment of financial and human resources, or are even not performed at all. To address this challenge, this paper presents a machine-learning-based approach to help companies identify early signals of change with a higher level of automation than before. For this, we combine a newly-proposed quantitative approach with the existing qualitative approaches by Cooper (stage-gate model) and by Rohrbeck (corporate foresight process). After a search field of interest has been defined, the related data is collected from web news sites, early signals are identified and selected automatically, and domain experts then assess these signals with respect to their relevance and novelty. Once it has been set up, the approach can be executed iteratively at regular time intervals in order to continuously scan for new signals of change. By means of three case studies supported by domain experts we demonstrate the effectiveness of our approach. After presenting our findings and discussing possible limitations of the approach, we suggest future research opportunities to further advance this field.

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Innovation signals: leveraging machine learning to separate noise from news

Mühlroth

Kölbl

Grottke

2023

Scientometrics

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Data-Driven Technology Management Supported by Artificial Intelligence Solutions

Cited by 1 publication

References 28 publications

Innovation signals: leveraging machine learning to separate noise from news

Innovation signals: leveraging machine learning to separate noise from news

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