Scanning World Wide Web documents with the vector space model

Aasheim, Cheryl L.; Kœhler, Gary J.

doi:10.1016/j.dss.2005.03.002

Cited by 16 publications

(6 citation statements)

References 29 publications

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“…With regard to the automation of ES activities, models have been available to detect events from online news sources for some time (Wei and Lee, 2004). Related research has demonstrated how mathematical models can be used to scan news sources and use the outcomes to predict share price performance (Aasheim and Koehler, 2006) or to quantify and organise outcomes from an automated scan of a given external issue (Tonn, 2008). More recently, complex models for scanning social media to support cross-border merger and acquisition decisions (Lau et al, 2012) and text-mining techniques for strategic forecasting (Kayser et al, 2014) have been developed.…”

Section: Advances In Es Technologymentioning

confidence: 99%

Consolidation and fragmentation in environmental scanning: A review and research agenda

Robinson

Ahmad

Simmons

2021

Long Range Planning

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Section: Advances In Es Technologymentioning

confidence: 99%

Consolidation and fragmentation in environmental scanning: A review and research agenda

Robinson

Ahmad

Simmons

2021

Long Range Planning

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“…The solution to this problem is the combination of, first, the theory and concept of scanning the macro‐environment; and second, new information technologies developed for electronic data treatment to extract a reduced and structured set of information. The studies of Liu (1998), Myers (1999), Wei and Lee (2004), Decker et al (2005), Aasheim and Koehler (2006) indicate that the electronic “scanning” of macro‐environmental dimensions tends to be a powerful tool to understand the current global environment. This study is the first systematic application of these tools to the area of liquid biofuels to date, combining text‐mining techniques with macro‐environmental scanning practices.…”

Section: Strategic Planning and Macro‐environmental Scanningmentioning

confidence: 99%

Scanning the macro‐environment for liquid biofuels

Talamini

Wubben

Padula

et al. 2013

Journal of Strategy and Management

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PurposeMacro‐environmental scanning is a first step in strategic planning, which is essential in an emerging industry such as liquid biofuels. The purpose of this paper is to identify the dimensions within which the governments of Brazil, the USA and Germany have constructed the macro‐environment for liquid biofuels over time and to test for similarities between the governments’ constructs.Design/methodology/approachDocumentary research was carried out on official public policies and program documents on the topic of liquid biofuels, covering a ten‐year period from 1997 to 2006. The database consisted of 624 documents from the Brazilian government, 854 from the American government and 168 documents from the German government. Text mining was used to extract information from the texts by applying a specific analysis structure that was built on macro‐environmental dimensions as expressed by their respective dimensional words “d‐words”. The “d‐words” were selected based on their usage frequency in the knowledge fields related to each dimension.FindingsThe results indicate that the macro‐environments for liquid biofuels, as configured by the governments under analysis, differ systematically and over time in their emphasis of specific macro‐environmental dimensions.Originality/valueThere are two primary aspects of this study which are original and valuable: the application of text‐mining techniques as a tool for strategic planning and the development of a particular tool to extract knowledge from text documents and to categorize them according to their macro‐environmental dimensions.

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“…Empirical studies were carried out to detect signals of interest from World Wide Web data to help an organisation to take effective decisions [16]. A combination of a vector space model, linear discriminant analysis, environmental scanning (a method for obtaining and using information from an organisation"s external environment) and text classification methods were studied to determine their effect in helping the decision making process of an organisation.…”

Section: Text Classification Background and Related Workmentioning

confidence: 99%