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How to improve a technology evaluation model: A data-driven approach

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  • Noh, Heeyong
  • Seo, Ju-Hwan
  • Sun Yoo, Hyoung
  • Lee, Sungjoo

Abstract

Academic research suggests a number of technology evaluation models. To ensure effective use, models need to be improved in accordance with changing internal and external environments. However, a majority of previous studies focus on model development, while a few emphasize their implementation or improvement. To fill this research gap, this study suggests a systematic approach to examining the validity of technology evaluation models and improving them. We consider three propositions as criteria for improvement: 1) the coherence of the evaluation results with the evaluation purpose, 2) the appropriateness of the evaluation methods, and 3) the concreteness of the evaluation model. Rather than using expert opinions, this study takes a data-driven approach, wherein we analyze actual evaluation results and determine whether the model produces the intended results. A case study of 291 technology evaluation results, all made by the South Korean government in support of technology-based small and medium-sized enterprises, is conducted to verify the suggested approach's applicability. This is one of the few studies to address issues regarding improvements to a technology evaluation model. Its approach can help to develop and continuously improve a valid technology evaluation model, thus leading to more effective practice.

Suggested Citation

  • Noh, Heeyong & Seo, Ju-Hwan & Sun Yoo, Hyoung & Lee, Sungjoo, 2018. "How to improve a technology evaluation model: A data-driven approach," Technovation, Elsevier, vol. 72, pages 1-12.
    • Handle: RePEc:eee:techno:v:72-73:y:2018:i::p:1-12
    DOI: 10.1016/j.technovation.2017.10.006
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    6. Juhyun Lee & Jiho Kang & Sangsung Park & Dongsik Jang & Junseok Lee, 2020. "A Multi-Class Classification Model for Technology Evaluation," Sustainability, MDPI, vol. 12(15), pages 1-16, July.

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