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Exploring the integration of automated text classification solutions in roadmapping

Benerink, T. (2020) Exploring the integration of automated text classification solutions in roadmapping.

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Abstract:A powerful tool to enable and support successful management and planning of technology is a Technology Roadmap (Phaal et al., 2004). The roadmapping process has been described in detail and effective blueprints on how to roadmap are available, such as the T-Plan (Phaal et al., 2001) or the Scenario Driven Roadmap (Siebelink et al., 2016). Large firms with more diverse portfolios and capabilities will require more extensive roadmaps, increasing the overall scope of data processing. Can automated solutions be used to make processing of workshop outputs within the roadmapping process more effective and efficient? This thesis experimented with word frequency based machine learning classifiers & clustering and computer aided text analysis. The quality and sample size of the dataset provided challenges for the used automated solutions. Automated exploratory data analysis and the use of computer aided text analysis did enable overview and a priori creation of base categories. The lessons learned resulted in a suggestion to improve the data collection design in the preparation phase of the roadmapping process. These lessons can be used for near future iterations. This study suggests to effectively link the format of data collection in the workshop phase to the intended processing and selection.
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:http://purl.utwente.nl/essays/85372
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