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Organisational project evaluation via machine learning techniques: an exploration

Author

Listed:
  • Alon Yaakobi
  • Moshe Goresh
  • Iris Reychav
  • Roger McHaney
  • Lin Zhu
  • Hanoch Sapoznikov
  • Yuval Lib

Abstract

This study explores ways an organisation can save time; review all proposed innovative, internal ideas; and, identify relevant start-up companies able to bring these ideas to fruition within a knowledge management framework. It uses text-mining techniques, including Python for data extraction and manipulation and topic modelling with Latent Dirichlet Allocation and Jaccard similarity indexes as a basis for evaluation of potentially valuable project ideas. Results show that internal organisational project ideas can be automatically matched with external data regarding potential implementation partners using big data knowledge management approaches. This ensures internal ideas are not overlooked or lost, but rather considered further so potentially profitable and viable opportunities are not missed. Increased use of big data to predict innovation and add value opens new channels to utilise text analysis in organisations and ensure internal innovation through a sustainable knowledge management approach.

Suggested Citation

  • Alon Yaakobi & Moshe Goresh & Iris Reychav & Roger McHaney & Lin Zhu & Hanoch Sapoznikov & Yuval Lib, 2019. "Organisational project evaluation via machine learning techniques: an exploration," Journal of Business Analytics, Taylor & Francis Journals, vol. 2(2), pages 147-159, July.
    • Handle: RePEc:taf:tjbaxx:v:2:y:2019:i:2:p:147-159
    DOI: 10.1080/2573234X.2019.1675478
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