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Discovering technologies in the automotive industry

Sandberg, A. (2024) Discovering technologies in the automotive industry.

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Abstract:Companies increasingly face volatile, uncertain, complex and ambiguous environments. Firms need to adapt to the environment to stay competitive. To achieve this, companies invest in Research and Development. But firms have limited resources to spend, so the main problem is selecting the best innovations to pursue. To gain insights into future requirements, companies engage in Technological Foresight. This research proposes using text mining on patents to extract information for Technological Foresight. More specifically, the study intends to discover current technologies in the automotive industry, classify them based on uncertainty characteristics, and create an innovation portfolio uncertainty matrix. Topic Modeling was used on patent titles to uncover topics. These topics were then assigned uncertainty scores and plotted in the uncertainty matrix. The technologies discovered are diverse and vary in uncertainty, with advanced driver assistance systems and alternative power sources being the most uncertain ones. The uncertainty matrix shows the technologies’ distribution to be skewed towards low uncertainty. According to theory, the distribution should be optimized to include more uncertain technologies. It is discussed that this stipulation might not apply to the automotive industry to the same extent, due to industry-specific characteristics. This research demonstrated how text mining can be used in practice to discover insights.
Item Type:Essay (Master)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:85 business administration, organizational science
Programme:Business Administration MSc (60644)
Link to this item:https://purl.utwente.nl/essays/98369
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