Business stakeholder analyzer: An experiment of classifying stakeholders on the web

Wingyan Chung, Hsinchun Chen, Edna Reid

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

As the Web is used increasingly to share and disseminate information, business analysts and managers are challenged to understand stakeholder relationships.Traditional stakeholder theories and frameworks employ a manual approach to analysis and do not scale up to accommodate the rapid growth of the Web. Unfortunately, existing business intelligence (BI) tools lack analysis capability, and research on BI systems is sparse. This research proposes a framework for designing BI systems to identify and to classify stakeholders on the Web, incorporating human knowledge and machine-learned information from Web pages. Based on the framework, we have developed a prototype called Business Stakeholder Analyzer (BSA) that helps managers and analysts to identify and to classify their stakeholders on the Web. Results from our experiment involving algorithm comparison, feature comparison, and a user study showed that the system achieved better within-class accuracies in widespread stakeholder types such as partner/sponsor/supplier and media/reviewer, and was more efficient than human classification. The student and practitioner subjects in our user study strongly agreed that such a system would save analysts' time and help to identify and classify stakeholders. This research contributes to a better understanding of how to integrate information technology with stakeholder theory, and enriches the knowledge base of BI system design.

Original languageEnglish (US)
Pages (from-to)59-74
Number of pages16
JournalJournal of the American Society for Information Science and Technology
Volume60
Issue number1
DOIs
StatePublished - Jan 2009

Fingerprint

Competitive intelligence
stakeholder
experiment
Industry
Managers
Experiments
Information technology
Websites
Systems analysis
manager
Students
World Wide Web
Stakeholders
Experiment
supplier
Business intelligence
information technology
Analysts
lack

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Information Systems
  • Human-Computer Interaction
  • Computer Networks and Communications

Cite this

Business stakeholder analyzer : An experiment of classifying stakeholders on the web. / Chung, Wingyan; Chen, Hsinchun; Reid, Edna.

In: Journal of the American Society for Information Science and Technology, Vol. 60, No. 1, 01.2009, p. 59-74.

Research output: Contribution to journalArticle

@article{360a0c2a53364e62bc130d3a9aa2329a,
title = "Business stakeholder analyzer: An experiment of classifying stakeholders on the web",
abstract = "As the Web is used increasingly to share and disseminate information, business analysts and managers are challenged to understand stakeholder relationships.Traditional stakeholder theories and frameworks employ a manual approach to analysis and do not scale up to accommodate the rapid growth of the Web. Unfortunately, existing business intelligence (BI) tools lack analysis capability, and research on BI systems is sparse. This research proposes a framework for designing BI systems to identify and to classify stakeholders on the Web, incorporating human knowledge and machine-learned information from Web pages. Based on the framework, we have developed a prototype called Business Stakeholder Analyzer (BSA) that helps managers and analysts to identify and to classify their stakeholders on the Web. Results from our experiment involving algorithm comparison, feature comparison, and a user study showed that the system achieved better within-class accuracies in widespread stakeholder types such as partner/sponsor/supplier and media/reviewer, and was more efficient than human classification. The student and practitioner subjects in our user study strongly agreed that such a system would save analysts' time and help to identify and classify stakeholders. This research contributes to a better understanding of how to integrate information technology with stakeholder theory, and enriches the knowledge base of BI system design.",
author = "Wingyan Chung and Hsinchun Chen and Edna Reid",
year = "2009",
month = "1",
doi = "10.1002/asi.20948",
language = "English (US)",
volume = "60",
pages = "59--74",
journal = "Journal of the Association for Information Science and Technology",
issn = "2330-1635",
publisher = "John Wiley and Sons Ltd",
number = "1",

}

TY - JOUR

T1 - Business stakeholder analyzer

T2 - An experiment of classifying stakeholders on the web

AU - Chung, Wingyan

AU - Chen, Hsinchun

AU - Reid, Edna

PY - 2009/1

Y1 - 2009/1

N2 - As the Web is used increasingly to share and disseminate information, business analysts and managers are challenged to understand stakeholder relationships.Traditional stakeholder theories and frameworks employ a manual approach to analysis and do not scale up to accommodate the rapid growth of the Web. Unfortunately, existing business intelligence (BI) tools lack analysis capability, and research on BI systems is sparse. This research proposes a framework for designing BI systems to identify and to classify stakeholders on the Web, incorporating human knowledge and machine-learned information from Web pages. Based on the framework, we have developed a prototype called Business Stakeholder Analyzer (BSA) that helps managers and analysts to identify and to classify their stakeholders on the Web. Results from our experiment involving algorithm comparison, feature comparison, and a user study showed that the system achieved better within-class accuracies in widespread stakeholder types such as partner/sponsor/supplier and media/reviewer, and was more efficient than human classification. The student and practitioner subjects in our user study strongly agreed that such a system would save analysts' time and help to identify and classify stakeholders. This research contributes to a better understanding of how to integrate information technology with stakeholder theory, and enriches the knowledge base of BI system design.

AB - As the Web is used increasingly to share and disseminate information, business analysts and managers are challenged to understand stakeholder relationships.Traditional stakeholder theories and frameworks employ a manual approach to analysis and do not scale up to accommodate the rapid growth of the Web. Unfortunately, existing business intelligence (BI) tools lack analysis capability, and research on BI systems is sparse. This research proposes a framework for designing BI systems to identify and to classify stakeholders on the Web, incorporating human knowledge and machine-learned information from Web pages. Based on the framework, we have developed a prototype called Business Stakeholder Analyzer (BSA) that helps managers and analysts to identify and to classify their stakeholders on the Web. Results from our experiment involving algorithm comparison, feature comparison, and a user study showed that the system achieved better within-class accuracies in widespread stakeholder types such as partner/sponsor/supplier and media/reviewer, and was more efficient than human classification. The student and practitioner subjects in our user study strongly agreed that such a system would save analysts' time and help to identify and classify stakeholders. This research contributes to a better understanding of how to integrate information technology with stakeholder theory, and enriches the knowledge base of BI system design.

UR - http://www.scopus.com/inward/record.url?scp=58449091305&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=58449091305&partnerID=8YFLogxK

U2 - 10.1002/asi.20948

DO - 10.1002/asi.20948

M3 - Article

AN - SCOPUS:58449091305

VL - 60

SP - 59

EP - 74

JO - Journal of the Association for Information Science and Technology

JF - Journal of the Association for Information Science and Technology

SN - 2330-1635

IS - 1

ER -