KSU - George Washington University

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Transcript KSU - George Washington University

A Method For Designing
Improvements in Organizations,
Products, and Services
Stuart Umpleby
Research Program in Social and
Organizational Learning
The George Washington University
Washington, DC USA
E-mail: [email protected]
Dragan Tevdovski
Mathematics, Statistics and
Informatics
University Sts. Cyril and Methodius
Skopje, Macedonia
E-mail: [email protected]
Second Conference of the Washington Academy of
Sciences
Washington DC, March 2006
Introduction
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A method for determining priorities for
improvement in an organization
Priority means high importance and low
performance
Quality Improvement Priority Matrix
The approach to design
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This approach to design is “piecemeal” rather
than “utopian”
It is “bottom up” rather than “top down”
It uses the judgments of employees or
customers
Features to improve are ranked by urgency
Several projects can be worked on
simultaneously
Quality Improvement Priority
Matrix
References
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The method was first described by the
specialists from GTE Directories
Corporation in 1995
Armstrong Building Products Operation
used the method in1996
Naoumova and Umpleby (2002) evaluation of visiting scholar programs
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Melnychenko and Umpleby (2001) and
Karapetyan and Umpleby (2002) used
QIPM in a university department
Prytula (2004) introduced the
importance / performance ratio
Dubina (2005) used cluster analysis and
proposed standard deviation as a
measure of agreement or disagreement
Goals of the Paper
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Understand more fully the priorities of
the Department of Management Science
at The George Washington University
(GWU), USA, and the Department of
Management at Kazan State University
(KSU), Kazan, Russia
Use and develop new methods to
compare QIPMs for two organizations
The Data
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A questionnaire was given to
management faculty members at both
GWU and KSU in 2002
The questionnaire contained 51
features of their departments
Importance and performance scales,
each ranging from 0 to 10
Evaluation
Range
Mean
Standard
Deviation
Importance
(GWU)
4.80
7.5408
1.25207
Performance
(GWU)
4.90
5.4890
1.18905
Importance (KSU)
6.00
7.3371
1.84934
Performance
(KSU)
8.39
4.3529
2.49989
Dispersion in the responses
Coefficient of
variation
Importance (GWU)
16.60%
Performance (GWU)
21.66%
Importance (KSU)
25.21%
Performance (KSU)
57.43%
Standardization of the importance
and the performance scores
Range
Min
Max
Mean
Std.
Deviation
Importance
Standardized
(GWU)
3.84
3.35
7.19
6.0225
1.00
Performance
Standardized
(GWU)
4.12
2.73
6.85
4.6157
1.00
Importance
Standardized
(KSU)
3.25
2.16
5.41
3.9661
1.00
Performance
Standardized
(KSU)
3.36
0.20
3.56
1.7408
1.00
GWU QIPM
KSU QIPM
Ranking the Priorities
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Standardized importance-performance
ratio (SIP)
Is
SIP 
Ps
Ranking GWU Priorities
According to SIP Ratio
Rank
GWU Priority Features
SIP
1
Office security
1.977
2
Building/ physical environment
Dept. organization to implement its
strategic plan
1.781
Dept. strategic plan
Help with writing research
proposals
1.729
3
4
5
1.756
1.724
Ranking KSU Priorities
According to SIP Ratio
Rank
KSU Priority Features
SIP
1
Funds to support research
24.197
2
Travel support
24.170
3
Office space for faculty
12.289
4
Projection equipment
9.387
5
Salaries
6.631
Clustering the Priorities
GWU Clusters Centers
Cluster
1
2
3
4
5
Importance
Standardized
(GWU)
7.15
4.92
5.83
4.3
4.22
Performance
Standardized
(GWU)
3.62
2.87
3.48
3.72
2.92
SIP
1.97
1.71
1.67
1.15
1.44
Clustering the Priorities
GWU Clusters Centers
Cluster
1
2
3
4
5
Importance
Standardized
(GWU)
7.15
4.92
5.83
4.22
4.3
Performance
Standardized
(GWU)
3.62
2.87
3.48
2.92
3.72
SIP
1.97
1.71
1.67
1.44
1.15
GWU Southeast Quadrant
KSU Clusters Centers
Cluster
1
2
3
4
5
6
7
Importance
Standardized
(KSU)
4.79
5.29
4.89
4.24 4.90
4.60 4.87
Performance
Standardized
(KSU)
0.30
0.71
1.27
2.00 2.38
3.01 3.40
15.97
7.45
3.85
2.12 2.06
1.53 1.43
SIP
KSU Southeast Quadrant
Review of what we did (1)
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We used 2002 data from GWU and KSU
We divided importance and performance
means by st. dev. in order to achieve a
common level of agreement among GWU and
KSU faculty members
Combining GWU and KSU data, we calculated
the nearest whole integer mean for
importance and performance
Review of what we did (2)
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These means were used to create a
common QIPM coordinate system
For each department the features in the
SE quadrant were clustered by
proximity
The clusters were ordered by average
SIP, a measure of urgency
Conclusions (1)
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Standardizing importance and performance
scores to achieve a common level of
agreement magnifies the differences between
the two departments
At KSU the average importance of the
features is lower than at GWU. This may
mean that KSU is still struggling with basics
such as salaries and office space. GWU has
the luxury of concern with travel and research
funds and the library collection
Conclusions (2)
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Faculty members at KSU evaluate the
performance of their department lower
than do GWU faculty members
At KSU high priority features are mostly
personal concerns such as salaries
At GWU high priority features are
organizational issues such as planning