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Analyzing Metrics for Tech Mining Methodologies

Arho Suominen, Analyzing Metrics for Tech Mining Methodologies. In: Denise Chiavetta, Alan Porter (Eds.), 1st Global TechMining Conference, -, VP Institute, 2011.

Abstract:

In 1989, Ayres (1989) called for better methods of forecasting and planning for the future. Focusing on quantitative methods, Ayres thought that more accurate tools for decision making were needed. Since the development of different quantitative methods such as bibliometrics, extrapolations and patent analysis has been active.
Methodologies analyzing quantitative data can be roughly categorized as purely quantitative (Järvenpää, Mäkinen, & Seppänen, 2011), cluster analysis (Tseng, Lin, & Lin, 2007), trend extrapolations (Daim, Rueda, Martin, & Gerdsri, 2006), and causal relationships (Kajikawa, Takeda, & Matsushima, 2010). Porter and Newman (2011) frame similar approaches to inductive patterning, pattern analysis, trend projections, and blackspace mapping. In the abundance of different quantitative tech mining studies published, an internally consistent view on a specific case is often produced. This is often obtained by using several methods and validating the results by expert opinion. Tech mining metrics, which would consistent between technologies, are however lacking.
The study conducted uses two technologies, dye-sensitized solar cells and thermoelectricity, to study different approaches of modelling quantitative data. By using different approaches suggested in literature, such as clustering, extrapolations and causal relationships consistencies between technologies were studied in order to create consistent metrics.
The study found, that externally consistent metrics of measuring trajectories are challenging. The case technologies produced significantly different results, which suggest that while case sensitive trajectory is easily created, more general metrics might even be impractical. The results suggest that futher study on creating consistent analysis tools should be created to ensure the adoption in industry.

BibTeX entry:

@INPROCEEDINGS{inpSuominen_Arho11a,
  title = {Analyzing Metrics for Tech Mining Methodologies},
  booktitle = {1st Global TechMining Conference},
  author = {Suominen, Arho},
  editor = {Chiavetta, Denise and Porter, Alan},
  publisher = {VP Institute},
  pages = {-},
  year = {2011},
}

Belongs to TUCS Research Unit(s): TUCS Productization Laboratory

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