A1 Refereed original research article in a scientific journal
Disaggregative policy Delphi Using cluster analysis as a tool for systematic scenario formation
Authors: Tapio Petri
Publisher: Elsevier
Publication year: 2003
Journal: Technological Forecasting and Social Change
Journal name in source: TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
Journal acronym: TECHNOL FORECAST SOC
Article number: PII S0040-1625(01)00177-9
Volume: 70
Issue: 1
First page : 83
Last page: 101
Number of pages: 19
ISSN: 0040-1625
eISSN: 1873-5509
DOI: https://doi.org/10.1016/S0040-1625(01)00177-9
Web address : https://doi.org/10.1016/S0040-1625(01)00177-9
Abstract
A critical phase of scenario making is the choosing of scenarios. In the worst case, a futures researcher creates scenarios according to his/her subjective views and cannot see the real quality of the study material. Oversimplification is a typical example of this kind of bias. In this study, an attempt towards a more data sensitive method was made using Finnish transport policy as an example. A disaggregative Delphi method as opposed to traditional consensual Delphi was applied. The article summarises eight Delphi pitfalls and gives an example how to avoid them. A two-rounded disaggregative Delphi was conducted, the panelists being representatives of interest groups in the traffic sector. Panelists were shown the past development of three correlating key variables in Finland in 1970-1996: GDP, road traffic volume and the carbon dioxide emissions from road traffic. The panelists were invited to give estimates of their organisation to the probable and the preferable futures of the key variables for 1997-2025. They were also asked to give qualitative and quantitative arguments of why and the policy instruments of how their image of the future would occur. The first round data were collected by a fairly open questionnaire and the second round data by a fairly structured interview. The responses of the quantitative three key variables were grouped in a disaggregative way by cluster analysis. The clusters were complemented with respective qualitative arguments in order to form wider scenarios. This offers a relevance to decision-making not afforded by a nonsystematic approach. Of course, there are some problems of cluster analysis used in this way: The interviews revealed that quantitatively similar future images produced by the panelists occasionally had different kind of qualitative background theory. Also, cluster analysis cannot ultimately decide the number of scenarios, being a choice of the researcher. Cluster analysis makes the choice well argued, however. (C) 2002 Elsevier Science Inc. All rights reserved.
A critical phase of scenario making is the choosing of scenarios. In the worst case, a futures researcher creates scenarios according to his/her subjective views and cannot see the real quality of the study material. Oversimplification is a typical example of this kind of bias. In this study, an attempt towards a more data sensitive method was made using Finnish transport policy as an example. A disaggregative Delphi method as opposed to traditional consensual Delphi was applied. The article summarises eight Delphi pitfalls and gives an example how to avoid them. A two-rounded disaggregative Delphi was conducted, the panelists being representatives of interest groups in the traffic sector. Panelists were shown the past development of three correlating key variables in Finland in 1970-1996: GDP, road traffic volume and the carbon dioxide emissions from road traffic. The panelists were invited to give estimates of their organisation to the probable and the preferable futures of the key variables for 1997-2025. They were also asked to give qualitative and quantitative arguments of why and the policy instruments of how their image of the future would occur. The first round data were collected by a fairly open questionnaire and the second round data by a fairly structured interview. The responses of the quantitative three key variables were grouped in a disaggregative way by cluster analysis. The clusters were complemented with respective qualitative arguments in order to form wider scenarios. This offers a relevance to decision-making not afforded by a nonsystematic approach. Of course, there are some problems of cluster analysis used in this way: The interviews revealed that quantitatively similar future images produced by the panelists occasionally had different kind of qualitative background theory. Also, cluster analysis cannot ultimately decide the number of scenarios, being a choice of the researcher. Cluster analysis makes the choice well argued, however. (C) 2002 Elsevier Science Inc. All rights reserved.
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