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Investigation of Information Retrieval Accuracy from Knowledge Management Systems Ryan C. LaBrie Department of Information Systems W. P. Carey School of.

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Presentation on theme: "Investigation of Information Retrieval Accuracy from Knowledge Management Systems Ryan C. LaBrie Department of Information Systems W. P. Carey School of."— Presentation transcript:

1 Investigation of Information Retrieval Accuracy from Knowledge Management Systems Ryan C. LaBrie Department of Information Systems W. P. Carey School of Business Arizona State University

2 Agenda Motivation Theory Measurement Method Other Issues

3 Motivation Conceptual framework Literature Review Research model Research method Hypotheses Data Measurement Implications

4 Motivation  Strategic importance of IT Standards  Lack of IT Standards research in IS area  Little empirical treatment on IT Standards research  Unbalanced research emphasis

5 Research Question  How are IT Standards created and formalized as a Knowledge Product for the community served?  What group dynamics drive Standards Organizations in their pursuit of the development of IT Standards?  Explore factors that impact IT Standards Setting Process in Web-based industry consortia.  Explore the relationship between the factors and group dynamics focusing on individual and thematic centrality.  Examine the transition of the centrality over different stages of standards setting process.

6 IT Standards  Emphasis Economic/Strategic focus Technical focus Organizational focus  Theoretical Treatment Game theory Installed base Switching cost (Klemperer, 1987) Lock-in (David, 1985) Network externalities (Katz & Shapiro, 1985) First mover advantage, RBV

7 IT Standards Organizations  Formal Standards Development Organization Highest level of Standards Setting de jure Standards ISO, ITU, IEEE and so on Participation is internationally recognized  Standards-oriented Industry Consortia National Cooperative Research Act (1984) de facto Standards OASIS, HR-XML Consortium, Industry specific Use the Internet

8 Thematic Centrality People- focused analysis People- focused analysis Theme- focused analysis Theme- focused analysis Time email data Conceptual Model Individual Centrality Group Heterogeneity (Proxy: Group composition) Standards Type (Infrastructure vs. Business process oriented)

9 Group Environment  Electronic Communities of Practice Communities of Practice (CoP) (Wenger, 1990; Lave and Wenger, 1990; Brown and Duquid, 1991) A group of experts with Informal, work-related social relationship Important for knowledge sharing and creation (Alavi and Leidner, 2001). The emergence of electronic or online CoP due to the Internet  IT Standards Development in Online Knowledge Communities (OKCs) Open Source Software (OSS) Development “The Cathedral and the Bazaar” (Raymond, 1999): Proprietary software development versus OSS project

10 Group Heterogeneity  Group Size Very large-scale conversation (100~200 participants) As group size increases, problem-solving becomes more efficient until some optimal size is reached (Shaw, 1981; Dennis et al., 1990) – the Law of Diminishing Returns  Use of Computer-Mediated Communication Help increase the size of a group (increase heterogeneity) Help reduce social queues, making OKCs more task-oriented Tends to cut across organizational boundaries, breaking down hierarchies (Kiesler 1986; McGrath, 1990)  Group Heterogeneity Large participants often leads to heterogeneity, which might cause low degrees of centrality The more numbers of participants, the lesser the centrality. Proxy: Group composition - Vendor and end-user organizations

11 Standards Types  Infrastructure-oriented IT Standards A group of core technologies to form a technical framework that provides an infrastructure on which organizations can perform business transactions. Compatibility is emphasized. Requires input from a lot of human resources Work best in decentralized organizational structure  Business process-oriented IT Standards The documented agreements that define procedures for horizontal and vertical business transactions performed over the Internet. Feasibility is emphasized. Requires central authority to collate multiple solutions Work best in centralized organizational structure

12 Group Process: Dynamic Centrality  Individual Centrality in Social Network The extent to which the individual is linked to others in the group: Central individuals exchange messages with a large number of members in a group  Thematic Centrality in Semantic Network The extent to which the theme or topic is linked to others in the discussion. Central themes or topics emerge much more times than less central themes.  Why Dynamic? A standards setting process is a sequential and iterative process consisting of distinct stages such as standards draft proposal, adaptation through feedback, and adoption of standards (Marpet, 1998). Not every group displays the same pattern over time: Poole’s (1983) contingency model of group development - Stages of standardization process as breakpoints

13 Centrality (Individual and Thematic) Group Heterogeneity Low High Business process oriented Infrastructure oriented Standards Type Step 1. Social Network Analysis (social and semantic network) Step 2. Factorial Design: 2 X 2 ANOVA design (Standards type X Group Heterogeneity) Centrality (Individual and Thematic) Centrality (Individual and Thematic) Centrality (Individual and Thematic) Research Method I (+ +)II(- +) III(+ -)IV(- -) Time: Standards setting process

14 Assumptions  Tasks are successfully implemented.  The individual centrality is positively related with the individual performance (Ahuja et al., 2003), and Individual performance tends to be consistent with group performance.  In OKCs, thematic centrality generally overpowers individual centrality. CMC helps reduce social queues, making OKCs more task-oriented CMC tends to cut across organizational boundaries, breaking down hierarchies (Kiesler 1986; McGrath, 1990)

15 H1: Creation of infrastructure oriented IT standards is negatively related to both individual and thematic centrality. H2: Creation of business process oriented IT standards is positively related to both individual and thematic centrality. H3: Increase in heterogeneity of a group is negatively related to both individual and thematic centrality. H4: Decrease in heterogeneity of a group is positively related to both individual and thematic centrality. H5: Creation of infrastructure oriented IT standards in a high heterogeneous environment is negatively related to both individual and thematic centrality. H6: Creation of business process oriented IT standards in a low heterogeneous environment is positively related to both individual and thematic centrality. H7: Variability of the centrality will decrease over time. H8: Individual centrality will be less stronger, while the thematic centrality will become more stronger over time. Hypotheses

16  Email archives from ebXML, DocBook, and Business Transaction NoProject TeamNo. of ParticipantNo. of Email MemberNon-MemberBy MemberBy Non-MemberTotal 1Transport, Routing & Packaging 24 (13%)160 (87%)1,908 (56%)1,507 (44%)3,415 2Core Components8 (4%)175 (96%)603 (38%)995 (62%)1,598 3Registry and Repository 4 (4%)105 (96%)481 (35%)875 (65%)1,356 4Business Process Methodology 12 (9%)125 (91%)806 (63%)483 (37%)1,289 5Technical Architecture22 (15%)123 (85%)648 (52%)610 (48%)1,258 6Marketing Awareness7 (9%)75 (91%)131 (33%)267 (67%)398 7ebXML Requirement12 (15%)67 (85%)247 (82%)54 (18%)301 Data

17  Standards Types  Degree of technical specification  Industry standards  Group Heterogeneity  Use group composition as proxy  Proportion of vendor and end-user organizations in a group  Individual Centrality  Degrees, Betweenness, and Closeness  Social Network Analysis Software (Social Network)  Thematic Centrality  Count of appearance of the theme  Social Network Analysis Software (Semantic Network) Measurement

18  For practitioners Understanding IT standards setting process in Web-based industry consortia  For researchers Understanding the group dynamics of collaborative decision making process in Online Knowledge Communities Implications

19 Thank you for your time. Questions, comments? ryan.labrie@asu.edu www.public.asu.edu/~rlabrie


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