A Benchmarking Model for
Management of KnowledgeIntensive Service Delivery
Networks
SU DONG, MONICA S. JOHAR, and RAM L. KUMAR
Journal of Management Information Systems / Winter 2011–12,
Vol. 28, No. 3, pp. 127–160
報告日期：2013.03.15(五)
系級：資管研二
學號：610039011
姓名：陳珮馨
Introduction
Introduction(1 / 3)
Organizations increasingly use knowledgeintensive information technology (IT) and ITenabled services delivered from multiple locations.
Employees providing such services may be at
different locations and interact with each other to
form a knowledge- intensive service delivery
network (KISDN).
 The author define KISDNs as networks of
knowledge workers who use their expertise and
professional relationships with other experts in an
IT-intensive environment to perform knowledgeintensive service tasks.

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Introduction(2 / 3)
In addition to IT services, KISDNs
include other knowledge-intensive
services that are facilitated by
sophisticated IT such as some types of
management services, financial services,
and engineering consulting services.
 It has been recognized that the real
benefits of providing these knowledgeintensive services include expert
knowledge and problem-solving capability.

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Introduction(3 / 3)

Research Question:
◦ How are knowledge diffusion and business
value affected by workflow decisions,
knowledge management decisions, and
organizational information networks in
KISDNs?
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Theoretical
Background
KISDN and Service Science(1 / 2)
This research studies KISDNs, which are
service systems with knowledge-intensive
service tasks and service-level agreements.
 Maglio and Spohrer define service science as
“the study of service systems which are
dynamic value creation co-configurations of
resources (people, technology, organizations
and information)” and suggest that a service
system may be considered a basic theoretical
construct for service science.

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KISDN and Service Science(2 / 2)
The goal of this research is to better
understand how people, technology,
organization, and shared information can
be brought together for dynamic value cocreation.
 Such a study is consistent with the service
science perspective of studying “how
service systems interact and evolve in
order to create value”.

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Social Networking and Knowledge
Sharing in Work Processes(1 / 3)
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CN
RN
SN
Information
diffusion
Strong link
Direct link
random
complex
Use example
Mining e-mail
network
Knowledgeintensive
industries
IT-intensive
work
environments
Social Networking and Knowledge
Sharing in Work Processes(2 / 3)



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In studying KISDNs, we recognize that workers
could seek help from co-workers in order to
improve their competence.
Strong ties occur between workers who know
each other directly through organizational
relationships that facilitate knowledge sharing.
Workers connected by weak ties do not know
each other directly but have strong ties with
another (intermediate) worker. This intermediate
worker plays a bridging role that allows the two
workers to get acquainted and share knowledge
with each other.
Social Networking and Knowledge
Sharing in Work Processes(3 / 3)
Workers may also consult experts who are
listed in the company’s internal directory,
and with whom they have no strong or
weak ties. Consultation with such an
expert is referred to as using a
performative tie, since the basis for
consultation is job performance (expertise)
 Workers with strong ties are the most
efficient and most likely to provide help

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Workflow Decisions, Knowledge
Management, and Business Value

This research studies the workflow of service
tasks in the context of knowledge workers.
◦ These workers can improve competence by
consulting co-workers using ties.

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The author study how different types of IFNs,
density of these networks, workforce
characteristics, and service task
characteristics affect organizational learning,
knowledge retrieval, and overall business
value.
Model
Development
Model Development
與IT有關，
像是DBM,
programming
非立即處理，
會造成成本
Request
對企業價值造成負面影響
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Model Formulation(1 / 4)
希望在這段
時間中能達到
企業價值最大化
support
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一項任務完成所需的時間
以及閒置工作者從閒置到
完成工作所需時間
Model Formulation(2 / 4)
員工薪水
技能強度:
0:專家
4:新手
工作者使用
技能在t時間段
完成工作的效率
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Model Formulation(3 / 4)
工作者k總共
可獲得的知識
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Model Formulation(4 / 4)
工作者分配到的任務量
以及任務收益
工作者閒置的成本
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KISDN最佳化
任務從執行到完成所需
的總成本
未分派任務所造成的成本
Simulation Design
Simulation Design
: making assignments in periods t and t + 1 successively
: waiting and making assignments only in period t + 1
: wait for one period
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Simulation Parameters(1 / 2)
1200時間區間
小組織
讓顧客等待所造成的成本
閒置員工的薪資成本比例
閒置員工的係數
使用S技能完成工作K
員工薪資
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Simulation Parameters(2 / 2)
任務類型
閒置員工從閒置到
完成工作的時間
Equal arrival rate
Knowledge retention coefficient
提供幫助的個體成本
向外求助
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Simulation Results
Impact of Network Topology and
Density(1 / 4)
Financial performance
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Operational performance
Impact of Network Topology and
Density(2 / 4)
These results are driven by knowledge-sharing
behavior, which in turn depends on network topology
and network density.
 The extent of knowledge exchange between two coworkers depends on the type of tie shared and the
competence difference between them.
 Since strong ties are the most effective means of
acquiring knowledge, this accounts for improved
financial and operational performance with increase in
network density.
 As network density increases to relatively high values,
the three network topologies tend to become similar,
reducing performance differences between them.

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Impact of Network Topology and
Density(3 / 4)
good
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Impact of Network Topology and
Density(4 / 4)
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Impact of Cost of Providing Help
on Relative Network Performance
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Effect of Various Parameters on
Assignment Decision Dynamics
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Model Extension: Impact of Training
on Knowledge Acquisition
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Conclusions
Conclusions(1 / 3)
In author’s opinion, managing KISDNs is
an important aspect of the emerging
discipline of service science, which is of
increasing interest to IS researchers.
 To the best of author’s knowledge,
author’s paper is the first to propose how
IFN structure information can be
combined with worker competence
information to improve operational and
financial performance of KISDNs.

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Conclusions(2 / 3)


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Future research could examine
interdependent task arrivals, for example, by
extending the unit of analysis in this paper (a
single KISDN) to multiple interrelated
KISDNs.
Author’s focus in this paper has been on
maximizing value. However, organizations
might be interested in other objectives such
as maximizing knowledge sharing for future
use. Alternative model formulations to study
this are interesting areas of future research.
Conclusions(3 / 3)

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This paper will serve as useful initial
framework for IS researchers as well as
practitioners interested in exploring this
nexus or its components in a service
science context.
Thank you!!