Scenario construction via Delphi and cross-impact analysis

Bañuls, Víctor A.; Turoff, Murray

doi:10.1016/j.techfore.2011.03.014

Cited by 108 publications

(87 citation statements)

References 30 publications

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“…Using Delphi and scenario planning, the qualitative and quantitative methods are complimentary to each other and will bring more value to study outcomes [23][24][25][26]. For this study, a combined approach to data collection and evaluating methods inspired by the Delphi method, as well as visualizing and testing the results using scenarios, was deployed.…”

Section: Methodsmentioning

confidence: 99%

Envisioning future innovative experimental ecosystems through the foresight approach. Case: Design Factory

2017

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Change makers are visionaries who wish to bring change to their respective fields. As technological change is accelerating, it is relevant to consider, how the way and what we teach can evolve with the future to remain meaningful and pioneering. Design Factory at Aalto University, as an innovative experimental ecosystem with interdisciplinary principles and new teaching methodologies has been successful in and at the forefront in educating the students to be change-makers. The paper presents a case study of holistically anticipating plausible futures for innovative experimental ecosystems utilizing a foresight approach. We analyze how the ways of working, spaces, and teaching methods of one such ecosystem, Design Factory at Aalto University in Finland, could support students learning in the year 20 × 6 {x = 2, 3}. We present the process of drawing virtual lines that connect trends, future drivers, visions, and scenarios using a contemporary approach that fuses qualitative and quantitative methods. The results from the study are six future scenarios for the Design Factory, that have implications for innovation ecosystems in general. These results are expected to further foster or trigger new research and development experiments, directions for building radical environments, new teaching methods and ways of working.

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Section: Methodsmentioning

confidence: 99%

Envisioning future innovative experimental ecosystems through the foresight approach. Case: Design Factory

2017

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“…Literature outlines that the use of Delphi method with other techniques (e.g. collaborative workshops, Nominal Group Technique, focus groups and face-to-face meetings) usually produce more satisfactory results (Bañuls and Turoff, 2011;Landeta et al, 2011) and make them more coherent (Nowack et al, 2011).…”

Section: Collaborative Risk Analysis In a Context Of Geospatial Databmentioning

confidence: 99%

Towards a Collaborative Knowledge Discovery System for Enriching Semantic Information About Risks of Geospatial Data

Grira

Bédard

Roche

et al. 2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The aim of this research is to design and implement a knowledge discovery system that facilitates, using a web 2.0 collaborative approach, the identification of new risks of geospatial data misuse based on a contributed knowledge repository fed by application domain experts. [Context/Motivation] This research is motivated by the irregularity of risk analysis efforts and the poor semantic of the collected information about risks. In the context of risk analysis during geospatial database design, the knowledge about risks of geospatial data misuse is typically held by domain application experts. The collection and record of that knowledge are usually considered as optional activities. It is usually performed through face-to-face risk assessment meetings and reports. Such techniques end up by restricting the scope of risk analysis to a set of obvious risks usually already identified. Besides, little consideration is devoted to the storage of risk information in an appropriate format for automatic reasoning and new risk information discovery. As a consequence, many foreseeable risky aspects inherent to the data remain overlooked leading to ill-defined specification and faulty decisions. [Principal ideas/results] In this paper, we present a contributed knowledge discovery system that aims at enriching the semantic information about risks of geospatial data misuse in order to identify foreseeable risks. The proposed web-based system relies on a systematic and more active involvement of users in risk analysis. The approach consists of 1) providing an overview of the related work in the domains of risk analysis within the context of geospatial database design, 2) presenting an ontology-based knowledge discovery system that helps experts in risks identification based on an upper-level risk ontology and on a structured representation of the domain-specific knowledge and, 3) presenting the components of the proposed system architecture and how it may be implemented and used in practice, and finally 4) we conclude by discussing the approach. [Contribution] A major outcome is that the proposed platform can help discovering implicit domain knowledge, and facilitating the identification of foreseeable risks of geospatial data misuse in a way to preventively improve the resulting fitness-for-use.

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“…This process is known as Cross Impact Analysis [6]. The final step was to graphically determine sets of risks that were tightly coupled into scenarios or Macro Events using Interpretive Structural Modeling [7].…”

Section: Introductionmentioning

confidence: 99%