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Technological forecasting using mixed methods approach.

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Title: Technological forecasting using mixed methods approach.
Authors: Kucharavy, Dmitry1 (AUTHOR) dkucharavy@unistra.fr, Damand, David1 (AUTHOR), Barth, Marc2 (AUTHOR)
Source: International Journal of Production Research. Aug2023, Vol. 61 Issue 16, p5411-5435. 25p. 10 Diagrams, 8 Charts, 3 Graphs.
Subjects: Technological forecasting, Cognitive bias, Forecasting methodology, Copper mining, Technological innovations, Forecasting
Abstract: How can strategic decision-making be reinforced through reliable forecasts of technological change? Observations of strategic forecasts have shown that they mainly rely upon expert opinions. To turn these opinions into consistent knowledge about the future, we need to manage cognitive biases using provable models. Observed forecasting methods provide useful tools for exploiting expert knowledge and data, but management of cognitive bias remains underdeveloped. To improve the situation with cognitive biases in technology forecasting, the Researching Future method (RFm) offers a mixed methods approach. This article introduces RFm, a method that combines a problem-based approach and a logistic function, unified by an applied resources paradigm. A practical case study is described to illustrate and validate RFm, and the results, limitations, and perspectives of RFm are then examined. The article contributes to the technology forecasting methodology and is of interest to copper mining technology R&D specialists, among others. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Technological forecasting using mixed methods approach.
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  Data: <searchLink fieldCode="AR" term="%22Kucharavy%2C+Dmitry%22">Kucharavy, Dmitry</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> dkucharavy@unistra.fr</i><br /><searchLink fieldCode="AR" term="%22Damand%2C+David%22">Damand, David</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Barth%2C+Marc%22">Barth, Marc</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Aug2023, Vol. 61 Issue 16, p5411-5435. 25p. 10 Diagrams, 8 Charts, 3 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Technological+forecasting%22">Technological forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+bias%22">Cognitive bias</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting+methodology%22">Forecasting methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Copper+mining%22">Copper mining</searchLink><br /><searchLink fieldCode="DE" term="%22Technological+innovations%22">Technological innovations</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: How can strategic decision-making be reinforced through reliable forecasts of technological change? Observations of strategic forecasts have shown that they mainly rely upon expert opinions. To turn these opinions into consistent knowledge about the future, we need to manage cognitive biases using provable models. Observed forecasting methods provide useful tools for exploiting expert knowledge and data, but management of cognitive bias remains underdeveloped. To improve the situation with cognitive biases in technology forecasting, the Researching Future method (RFm) offers a mixed methods approach. This article introduces RFm, a method that combines a problem-based approach and a logistic function, unified by an applied resources paradigm. A practical case study is described to illustrate and validate RFm, and the results, limitations, and perspectives of RFm are then examined. The article contributes to the technology forecasting methodology and is of interest to copper mining technology R&D specialists, among others. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/00207543.2022.2102447
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      – Code: eng
        Text: English
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        PageCount: 25
        StartPage: 5411
    Subjects:
      – SubjectFull: Technological forecasting
        Type: general
      – SubjectFull: Cognitive bias
        Type: general
      – SubjectFull: Forecasting methodology
        Type: general
      – SubjectFull: Copper mining
        Type: general
      – SubjectFull: Technological innovations
        Type: general
      – SubjectFull: Forecasting
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      – TitleFull: Technological forecasting using mixed methods approach.
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              M: 08
              Text: Aug2023
              Type: published
              Y: 2023
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