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Semantic compared cross impact analysis

: Thorleuchter, Dirk; Poel, Dirk van den

Preprint urn:nbn:de:0011-n-2777732 (220 KByte PDF)
MD5 Fingerprint: ad45b49bc8a8147d7ff79f03fce86c5a
Created on: 19.2.2014

Expert Systems with Applications 41 (2014), No.7, pp. 3477-3483
ISSN: 0957-4174
Journal Article, Electronic Publication
Fraunhofer INT ()
cross impact analysis; latent semantic indexing; text mining; compared cross impact analysis; web mining

The aim of cross impact analysis (CIA) is to predict the impact of a first event on a second. For organizations strategic planning, it is helpful to identify the impacts among organizations internal events and to compare these impacts to the corresponding impacts of external events from organizations competitors. For this, literature has introduced compared cross impact analysis (CCIA) that depicts advantages and disadvantages of the relationships between organizations events to the relationships between competitors' events. However, CCIA is restricted to the use of patent data as representative for competitors events and it applies a knowledge structure based text mining approach that does not allow considering semantic aspects from highly unstructured textual information . In contrast to related work, we propose an internet based environmental scanning procedure to identify textual patterns represent competitors events. To enable processing of this highly unstructured textual information, the proposed methodology uses latent semantic indexing (LSI) to calculate the compared cross impacts (CCI) for an organization. A latent semantic subspace is built that consists of semantic textual patterns. These patterns are selected that represent organizations events. A web mining approach is used for crawling textual information from the internet based on keywords extracted from each selected pattern. This textual information is projected into the same latent semantic subspace. Based on the relationships between the semantic textual patterns in the su bspace, CCI is calculated for different events of an organization. A case study shows that the proposedapproach successfully calculates the CCI for technologies processed by a governmental organization. This enables decision makers to direct their investments more targeted.