TOWARD SYSTEMATIC DESIGN OF KNOWLEDGE-INTENSIVE SERVICE DELIVERY NETWORKS

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Dong, S. (2011). TOWARD SYSTEMATIC DESIGN OF KNOWLEDGE-INTENSIVE SERVICE DELIVERY NETWORKS. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

Effective management of IT-enabled services is becoming increasingly important. These services are often delivered by networks of knowledge workers who constitute Knowledge Intensive Service Delivery Networks (KISDN). This dissertation contributes to the effective design and management of KISDN by presenting two mixed integer programming models which integrate disparate streams of research. The first model facilitates analysis and managerial benchmarking of KISDN. We focus on how the performance of such networks depends on the interaction between workflow decisions, information flow networks (IFNs) structure and knowledge management decisions. We propose that knowledge about IFNs and worker competencies can be effectively used to make workflow decisions. Our results, based on the study of different IFN archetypes, illustrate practices for effective management of KISDN. Recognizing existing IFNs, increasing randomness in IFNs, nurturing weak or performative ties depending on the archetype, assigning tasks based on effective worker competence, and selectively delaying assignment of tasks to workers can enhance business value. The second model focuses on the design of IFNs. Organizations are increasingly creating and using IFNs to transfer knowledge. However, there is limited understanding of the design of IFNs to maximize knowledge sharing. Our results demonstrate the impact of worker competency heterogeneity, number of skills supported by the firm, and time (cost) associated with knowledge sharing on the design of efficient IFNs.

Details
Author

Dong, Su

Title

TOWARD SYSTEMATIC DESIGN OF KNOWLEDGE-INTENSIVE SERVICE DELIVERY NETWORKS

Physical Description

1 online resource (128 pages) : PDF

Date

2011

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

Effective management of IT-enabled services is becoming increasingly important. These services are often delivered by networks of knowledge workers who constitute Knowledge Intensive Service Delivery Networks (KISDN). This dissertation contributes to the effective design and management of KISDN by presenting two mixed integer programming models which integrate disparate streams of research. The first model facilitates analysis and managerial benchmarking of KISDN. We focus on how the performance of such networks depends on the interaction between workflow decisions, information flow networks (IFNs) structure and knowledge management decisions. We propose that knowledge about IFNs and worker competencies can be effectively used to make workflow decisions. Our results, based on the study of different IFN archetypes, illustrate practices for effective management of KISDN. Recognizing existing IFNs, increasing randomness in IFNs, nurturing weak or performative ties depending on the archetype, assigning tasks based on effective worker competence, and selectively delaying assignment of tasks to workers can enhance business value. The second model focuses on the design of IFNs. Organizations are increasingly creating and using IFNs to transfer knowledge. However, there is limited understanding of the design of IFNs to maximize knowledge sharing. Our results demonstrate the impact of worker competency heterogeneity, number of skills supported by the firm, and time (cost) associated with knowledge sharing on the design of efficient IFNs.

Genre

doctoral dissertations

Subjects--Topics

Management
Social structure
Information technology

Degree

Ph.D.

Keywords

Knowledge Management
Service Science
Social Network

Subject Area

Information Technology

Advisor(s)

Kumar, Ram
Johar, Monica

Committee Members

Park, Sungjune
He, Xiuli
Wilhelm, Bob

Degree Note

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2011.

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Rights Holder Information

Copyright is held by the author unless otherwise indicated.

Identifier

Dong_uncc_0694D_10201

Permalink

http://hdl.handle.net/20.500.13093/etd:317