A Benchmarking Model for

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6 Νοε 2013 (πριν από 3 χρόνια και 10 μήνες)

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A Benchmarking Model for
Management of Knowledge
-
Intensive 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
(

)

系級:資管研二




610039011

姓名
:陳珮馨

Introduction

Introduction(1 / 3)


Organizations increasingly use
knowledge
-
intensive information technology (IT)
and
IT
-
enabled 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 knowledge
-
intensive service tasks.

2 / 33

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 knowledge
-
intensive services include
expert
knowledge

and
problem
-
solving capability
.


3 / 33

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?


4 / 33

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.


6 / 33

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 co
-
creation.


Such a study is consistent with the
service
science perspective

of studying “
how
service systems interact and evolve in
order to create value
”.

7 / 33

Social Networking and Knowledge
Sharing in Work Processes(1 / 3)

CN

RN

SN

Information
diffusion

Strong link

Direct link

random

complex

Use

example

Mining e
-
mail
network

Knowledge
-
intensive
industries

IT
-
intensive
work
environments

8 / 33

Social Networking and Knowledge
Sharing in Work Processes(2 / 3)


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.

9 / 33

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

10 / 33

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.


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.


11 / 33

Model
Development

Model Development

Request

非立即處理,

會造成成本


IT
有關,

像是
DBM
,

programming

對企業價值造成負面影響

13 / 33

Model Formulation(1 / 4)

希望在這段

時間中能
達到

企業價值
最大化

support

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

14 / 33

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

15 / 33

Model Formulation(3 / 4)

工作者
k
總共

可獲得的知識

16 / 33

Model Formulation(4 / 4)


工作者分配到的任務量

以及任務收益

任務從執行到完成所需

的總成本

工作者閒置的成本

未分派任務所造成的成本

KISDN
最佳化

17 / 33

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

19 / 33

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

20 / 33

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

21 / 33

Simulation Results

Impact of Network Topology and
Density(1 / 4)

Financial performance

Operational performance

23 / 33

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 co
-
workers 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.

24 / 33

Impact of Network Topology and
Density(3 / 4)


good

25 / 33

Impact of Network Topology and
Density(4 / 4)

26 / 33

Impact of Cost of Providing Help
on Relative Network Performance

27 / 33

Effect of Various Parameters on
Assignment Decision Dynamics

28 / 33

Model Extension: Impact of Training
on Knowledge Acquisition

29 / 33

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
.

31 / 33

Conclusions(2 / 3)


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.

32 / 33

Conclusions(3 / 3)


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.

33 / 33

Thank you!!