BizPro: Extracting and categorizing business intelligence factors from textual news articles

Chung, Wingyan

doi:10.1016/j.ijinfomgt.2014.01.001

Cited by 53 publications

(30 citation statements)

References 36 publications

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“…Text analytics engage statistical analysis, computational linguistics, and machine learning. Text analytics make possible businesses to change huge volumes of human-generated text into important summaries, which hold up evidence-based decisionmaking [30]. Text analytics methods are:…”

Section: Text Analytics Text Analytics (Text Mining) Arementioning

confidence: 99%

Access methods for Big Data: current status and future directions

Rashid

2017

ICST Transactions on Scalable Information Systems

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Heterogeneity, size, timeliness, difficulty & confidentiality problems with Big Data hinder advancement at all phases of the channel that can create value from data. Data analysis, organization, retrieval & modeling are initial challenges for Big Data. Data investigation is a clear traffic jam in many applications, both due to lack of scalability of the core algorithms and due to the difficulty of the data that needs to be analyzed. Despite this, the appearance of the results and its understanding by non-technical experts is vital to extracting actionable knowledge. To defeat these, there is a need for novel architectures, techniques, algorithms & analytics to deal with it as well as to retrieve the value and unseen knowledge. Further, we need to build up efficient and optimized access methods for countless reasons such as velocity of Big Data. In this article, we present a brief overview of the current status of access methods for Big data and discuss a few promising research directions.

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Section: Text Analytics Text Analytics (Text Mining) Arementioning

confidence: 99%

Access methods for Big Data: current status and future directions

Rashid

2017

ICST Transactions on Scalable Information Systems

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“…Lau, Liao, Wong, and Chiu (2012) implemented an adaptive business intelligence system to support evolutionary learning, domain-specific sentiment analysis, and business relation mining to aid decision makers under different mergers and acquisitions scenarios. Chung (2014) developed an intelligent system that extracts and categorizes factors that can influence market reactions. The system extracts these factors from textual papers and reports using text-mining procedures.…”

Section: Using Gss To Target Strategic Scanningmentioning

confidence: 99%

“…However, unveiling information needs is not an easy process (Choo, 1998). Sometimes, it means that managers who come from different units of the organization and who might initially share neither the same interests nor the same vocabulary or who might have a fragmentary and fuzzy understanding of the overall issues they need to scan need to interact with each other Organizations have already used computer-based systems for strategic scanning to support information scanning (e.g., CI Sider (Chen, Chau, & Zeng, 2002) and analysis (e.g., Abima (Lau, Liao, Wong & Chiu, 2012), BizPro (Chung, 2014)). However, no computer system currently provides support to target strategic scanning.…”

Section: Introductionmentioning

confidence: 99%

Getting a clean shot on a blurred target: Improving targeting for Strategic Scanning through action research in 10 French organizations

Loza-Aguirre¹,

Lesca²,

Haddad³

et al. 2016

CAIS

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Abstract:Targeting comprises defining the part of the business environment that corresponds to organizations' strategic objectives and priorities. Targeting is not an easy process because it includes the interaction of managers who come from different organizational units that might have a fragmentary and blurred understanding of the overall issue. Through an action research, we designed and evaluated a GSS to help managers target strategic scanning in fuzzy contexts. Evaluations through interventions in 10 French organizations allowed both participants to achieve relevant targets and researchers to propose four major improvements to targeting activities: 1) use suggested lists of actors and topics as starting points to trigger and facilitate discussions, 2) define actor and topic importance to produce useful targeting results, 3) evaluate the organization's perceived capacity to be informed early enough, and 4) define a mechanism to signal scanning relevancy in the short, mid-, or long term. From a management perspective, our results help managers in their strategic scanning activity by 1) identifying information needs for strategically scanning fuzzy subjects, 2) reducing risk of strategic scanning failure, 3) enabling organizations to assess their scanning capabilities, 4) identifying scanning priorities according to a temporal horizon, and 5) fostering teamwork participation.

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“…Gilad & Gilad, 1988;Choo, 1999) or technologies (i.e. Zhang, Dang et al, 2009;Chung 2014). More recent research shows how scanning practices contribute to generating representations of the external environment (Belmondo, 2008) and to strengthening the absorptive capacity of organizations (Amabile, Meissonier et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Drivers and barriers to pre-adoption of strategic scanning information systems in the context of sustainable supply chain

Lesca¹,

Caron-Fasan²,

Loza-Aguirre³

et al. 2016

Systèmes d'Information &Amp; Management

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This research is reporting on the pre-adoption of Strategic Scanning (S.Scan) information systems (IS). More specifically, it relates to the pre-adoption phase, that is, the emergence of the idea of such a system and the evaluation of its need for the organization, upstream of any technological consideration. The research question is the following: what are the drivers and barriers that influence the pre-adoption of a S.Scan IS? The objective of this research is to extend knowledge on a subject that has received little attention from the scholars. Research's originality relies on the use of isomorphic processes from neo-institutional framework to study pre-adoption in the field of S.Scan. On the basis of a multi-method research combining qualitative and quantitative exploratory studies in the specific field of sustainable supply chains (SSC), our results highlight 31 drivers and barriers to pre-adoption of S.Scan IS, ten of which have not been identified before, and five types of pressures. They therefore suggest that pre-adoption of S.Scan IS can be subject to both functional and institutional pressures. It can be driven either by competitiveness or conformism pressures, and hindered by performance objectives or lack of coercive pressures. Finally, these results put a question mark about the understanding of the strategic dimension of S.Scan IS by organisations, and the government's role and its responsibility for promoting SSC initiatives and for the adoption of S.Scan IS on this issue.

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BizPro: Extracting and categorizing business intelligence factors from textual news articles

Cited by 53 publications

References 36 publications

Access methods for Big Data: current status and future directions

Access methods for Big Data: current status and future directions

Getting a clean shot on a blurred target: Improving targeting for Strategic Scanning through action research in 10 French organizations

Drivers and barriers to pre-adoption of strategic scanning information systems in the context of sustainable supply chain

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