Download Contributions from Class of 2008

Readings in MIS:
Key Articles Database and Analysis
Prepared By:
Mary Burns
Katherine Carl
Soo Mi Cheong
Jeisi Cheng
Koren Elder
Li Fan
Edward Huang
Brent Langhals
Matt Pickard
Nathan Twyman
Shou Zeng
Ray Zhao
MIS696A: Readings in MIS Fall 2008
Oversight by Dr. Jay F. Nunamaker, Jr.
Abstract
In this report, we summarize the contributions of previous classes, which become
a solid foundation which our project can be based on. With efforts by previous years and
the MIS definition (by Brabb) we accepted, we developed our research philosophy and
define our main contributions.
The main contributions of the class 2008 are that we developed a systematic
approach to build MIS research repository, evaluate the research according to predefined
dimensions, and automate the classification analysis. Further, a user-friendly interface
was constructed for future usage. Beside the system construction, we also extended the
previous MIS research collection. We interviewed facilities during the semester and
asked them to recommend studies that influenced them most. All these studies were
included into our repository to make the collection more complete.
Our project details are also described in this report.
We used three
methodologies which are Text Mining, Clustering Analysis and Citation analysis to get
better insights from our MIS paper repository. After that, the limitation and future
research are discussed. Future classes should read the Limitations and Future Study
section of this paper for important tips on how to conduct follow up research. In the end,
we conclude our finding of this project and the experiences we learned from this project.
Table of Contents
Abstract..........................................................................................................................................................2
Introduction ..................................................................................................................................................4
Contributions from Classes 1998 through 2007 ................................................................................4
Contributions from Class of 2008...........................................................................................................5
Definitions .....................................................................................................................................................7
Our Research Philosophy..........................................................................................................................9
Database System Development .............................................................................................................9
Database Design ....................................................................................................................................... 10
Testing ......................................................................................................................................................... 10
Research Methodologies / Results ..................................................................................................... 10
Text Mining ...................................................................................................................................... 11
Clustering Analysis of MIS Papers .............................................................................................. 15
1.
Introduction ...................................................................................................................... 15
2.
Experiment Design .......................................................................................................... 16
3.
Result Analysis................................................................................................................... 23
4.
Discussion and Future Work ......................................................................................... 25
Citation Analysis .............................................................................................................................. 26
Limitations and Future Research......................................................................................................... 34
Conclusion .................................................................................................................................................. 35
Appendix 1: User Reading Guide........................................................................................................ 36
Appendix 2: 2007 Classification Model ............................................................................................ 37
Appendix 3: General Sequel Queries ................................................................................................ 38
Appendix 4: Database Design.............................................................................................................. 41
Appendix 5: Data Mining Analysis – Data ......................................................................................... 43
Appendix 6: Clustering Analysis ........................................................................................................... 60
Appendix 7: Clustering Results............................................................................................................. 67
Introduction
The purpose of this research is to familiarize the reader with key researchers,
research categories, and the research articles crucial to a foundation in the Management
of Information Systems (MIS) discipline. Building on the analyses, models, and reports
from previous classes, we categorized 185 research articles we collected, built a
database, populated it with the articles in both PDF and text format, conducted a variety
of statistical and data/text mining analyses, and summarized our findings.
Contributions from Classes 1998 through 2007
Previous classes have compiled excellent research to familiarize readers with the
MIS domain, including key researchers and their respective research. The Class of 1998
began the process by listing seven subdomains of MIS. For each subdomain, they listed
over 45 influential researchers with a one paragraph biography. 1999’s class created a list
of 47 key researchers in MIS grouped by ten research areas described in several
paragraphs. The Class of 2000 expanded the research areas to 15, listing 90 key
researchers with highlighted research for those authors. In 2001, the class presented a
timeline of events in MIS and defined eight subdomains of MIS. The Class of 2002 recategorized MIS into nine subdomains with a visual representation of the subdomain
relationships. For each subdomain, they described seminal works. For the project in
2003, the class identified the top 101 MIS researchers, categorizing them by subdomain.
The class of 2003 also presented a three-dimensional model of MIS research
characteristics with axes representing behavior vs. technical, application vs. theory, and
rigor vs. relevance. Each seminal work was mapped onto the three-dimensional model
for enhanced visualization. Also, profiles of researchers with key contributions were
identified. Most importantly for future classes, the Class of 2003 developed an End Note
reference library of the research articles that they had collected. In 2004, the class added
to the body of knowledge by identifying U.S. departments of key researchers and their
corresponding key research. The Class of 2005 compiled the models of MIS from 2002,
2003, and 2004 into one comprehensive model. With this model, the class identified key
research. In addition, the Class of 2005 included charts explaining the quantitative
contribution of research from each subdomain. The class of 2006 extended the previous
projects by exploring various research methodologies and methodological paradigms.
The class of 2007 took the first steps toward automating the classification of MIS research
by developing an algorithm and decision tree (Appendix 2) to aid readers in defining and
identifying key MIS research articles and how they fit within the MIS continuum.
Contributions from Class of 2008
Early on, we decided that our key goals were to: categorize the foundation
articles based on the 2007 algorithm/decision tree, develop a database of the articles,
and perform analyses for our class project/report.
Description of our Project/Process: Each member in the class familiarized him/herself
with the materials/models from previous project reports. We learned that there was a
160 MIS ‘foundation’ research articles formerly stored in an End Note library that had
been developed by an earlier project group. While we could not find that entire library,
we did find subsets of the library that contained PDF versions of these papers. Therefore,
we needed to re-create the previous library as an early step.
In September, we selected a team leader, Brent Langhals, and a project leader,
Koren Elder. Both of them worked quickly to develop a project plan to which the rest of
the team agreed:
a. As one deliverable, we would deliver a database that contained all of the articles
from the previous library list in both PDF and text format for the article and its
accompanying bibliographic data. This database would be placed on a web site that
could be accessed not only by our class but also by future classes.
b. As part of that deliverable, we would identify other key research articles
recommended to us by faculty in the MIS department.
c. The articles would be read, categorized according to the existing decision tree,
and rated on a variety of dimensions, such as Theoretical, or Applied.
d. Another deliverable would consist of the analyses of the articles.
To start, we divided the existing list of articles among the class members. Each
class member needed to find the PDF version (if not already available via the remainder
of the earlier End Note library) of the articles and place those on a shared Website set up
for the class. Due to some duplication, erroneous End Note entries, or articles that were
impossible to find (within the scope of the project), the number of articles uploaded to
the Website is 160.
To augment the list of articles, the class, broken into four teams, interviewed
faculty members who recommended additional articles to be added to the existing 160
articles. 25 of these articles (in PDF format) were added to the Website.
As a class, we decided to categorize the articles by using the decision tree
developed by the class of 2007. We realized that we could re-develop that tree or
enhance it. However, as first year Ph.D. students, our limited knowledge and experience
would not permit us to develop a significantly better decision tree efficiently. Therefore,
to keep moving forward to the key contributions of the project, we decided not to
reinvent the wheel. Future classes can re-examine the decision tree, as necessary.
To support data mining and analyses, we chose to keep track of five key words
and a rating (on a scale of 1 to 5, with 5=highest rating) of each of the following domain
types: Theoretic, Application, Rigorous, Relevance, Review, Innovative, Technical, and
Behavioral, for each article. For each article, we obtained the citation counts from both
Web of Knowledge and Google Scholar. Finally, we asked each group to answer the
following question per article: “Should this article be considered for removal from the
corpus?”
For the data described above, Koren built a database that included attributes for
the various ratings, keywords, and citation counts. In addition, she provided attributes to
store text as well as PDF versions of the articles. The text version of the articles was
essential for the text mining analyses conducted at the end of the project. To access the
database, Koren, Li, and Ray developed a Web interface for team members to use to
update the fields for each article.
The four teams split 185 articles equally by random assignment. Each group
member read each of the articles in the subset and independently chose corresponding
classifications, ratings, and key words. Later, each group met to develop a group decision
about the categories and ratings for each article. Finally, the group updated the database
via the Web interface.
Once the database was set up, several teams conducted statistical analyses and
text mining. The results were collected for this report.
Definitions
While there is academic debate as to an all encompassing definition of MIS, this
study uses the succinct and widely accepted definition of Brabb1:
A management information system is the complement of people, machines, and
procedures that develops the right information and communicates it to the right
managers at the right time.
Using this definition, each of the previous classes from 1998 through 2005 have
created a conceptual model of MIS. Each class has subdivided MIS into various
subdomains, ranging from 7 to 9 subdomains. While each class has justified the
uniqueness of their findings, the distinguishing characteristics have become progressively
consubstantial. Therefore, until a completely novel model of MIS is developed, we
choose to follow the model presented by the Class of 2005. For simplicity’s sake, we
refer the reader to their paper for discussion, defense, and justification of the model (See
Figure “MIS Model by Class of 2005 “).
1Brabb, George J.
Computers and Information Systems in Business. Houghton Mifflin Co.,
Boston. 1976, pp. 26, 37.
While a difficult and potentially controversial task, defining “key” research and
“key” researchers has been documented in previous MIS696 class research projects. In
earlier classes, “key” represents those papers and individuals that have highly influenced
and contributed to the IS body of knowledge. To employ this definition, previous classes
have used interviews with established researchers and citation counts to guide their
selection. This paper accepts the definition and selection criteria of the past eight
projects. We will specifically use the list of key articles and researchers provided by the
Class of 2005’s research paper, with a few updates to account for recent research. The
focus of this paper is not to dispute the past papers’ findings, but instead to augment
their findings with a discovery and discussion of the categories of key research articles.
We wish we could include a much larger body of high quality research from IS in this
paper, but that would include thousands of papers. Realizing that this project has a
limited scope, we narrowed the number of papers to 185.
MIS Model by Class of 2005
Using the MIS model from previous classes allowed us to focus on a unique
contribution: to identify the categories of influential research papers. We hope that this
data will provide a foundation for future researchers in selecting and justifying a research
domain/subdomain.
Our Research Philosophy
Our research philosophy involved developing a tool that would not only
automate the classification of MIS research, but also offer a lasting repository of seminal
MIS literature that future classes could build upon. Our team determined that the best
way to accomplish this goal was to develop a relational database containing the text from
185 articles and key attributes of each article.
Database System Development
The team decided to load the paper metadata and content to a database in order
to use more sophisticated query and mining techniques. SQL Server was chosen as the
database management tool because the CMI lab had access to developer licenses, the
team had some expertise in SQL Server and the SQL Server text mining tools were going
to be used for one method of analysis. Working from an EndNote library, the team
loaded the paper metadata into SQL Server. Then, the full text was added for each paper
by the individual members of the team. Finally, the groups added category and
dimensions after reading the papers. Once the database was complete, queries to
extract data for analysis were run against the Papers table.
Database Design
Testing
We performed general testing of the database functionality by conducting
multiple queries based on potential research questions. It was determined that the
database is fully functional and all errors were resolved. A sample of the results of our
general queries is located in Appendix 3.
Research Methodologies / Results
The primary objective of our project was to create a complete a relational
database of the MIS corpus. However, our team sought to perform some analysis of the
data for the following reasons: 1) to validate the classification model from the 2007 class,
2) validate the database was capable of supporting future text mining and key word
analysis, and 3) use citation counts as a measure to validate that an article did belong in
the MIS corpus. The following sections describe the types of data analysis we performed
and the results we discovered.
Text Mining
Our goal for this project was to perform limited text mining analysis of the
articles, particularly in regards to the ability of text mining techniques to classify the
articles. Unfortunately our team had limited data mining experience, so the following is
an example of the methods we used and some preliminary results.
Process used to build mining model:
1. Build a dictionary
a. Using Fulltext field on Papers table - Noun and Noun Phrase (TFIDF
frequency = 10, length = 2) - to build Dictionary table
b. TFIDF (term frequency–inverse document frequency) - weight is a
statistical measure used to evaluate how important a word is to a
document in a collection or corpus. The importance increases
proportionally to the number of times a word appears in the
document but is offset by the frequency of the word in the corpus.
c. Ran mining models with full dictionary (no editing of words).
2. Build term vectors
a. Use “Term Lookup” transform to capture frequency and paper id into
TermVectors table.
3. Prepare train/test samples
a. Set sampling rate to 70%
b. Train sample 70% into TrainPapers
c. Test sample 30% into TestPapers
4. Build/Test/Refine data mining models
a. Use TermVectors, TrainPapers and TestPapers as input
b. Case table: TrainPapers
c. Nested table: TermVectors
d. Microsoft Decision Trees (PapersDM_DT)
e. Microsoft Naïve Bayes (PapersDM_NB)
f. Microsoft Logistic Regression (PapersDM_NN)
g. Micorsoft Clustering (PapersDM_CL)
5. Check Mining Accuracy
a. Case table: TestPapers
b. Nested table: TermVectors
6. Browse Models
The "Population correct" is shown in the lift chart legend if you don't specify
the predict value, thus showing overall correctness of the model (how many
predictions are correct, no matter of the state of the target variable).
Predict Probability – probability of the most popular prediction state for the
model. Score – The Score column in the legend shows you the overall quality of a model the higher the score is, the better the model is. So order of charts by score would be: Neural
Network, Logistic Regression, Decision Tree, Clustering, and then Naïve Bayes.
The following Lift Chart depicts clustering techniques looking to predict some
specific Category values. Target Population – Indicates how much of the target population
you capture at the gray intercept line. Predict Probability – shows the probability score
needed for each prediction to capture the shown target population. Score – used for
comparison with other models.
This means that by selecting all rows with the specified probability or higher, you
will capture that percentage of the total possible target rows. So, in the Naïve Bayes
model, selecting rows with .76% probability or higher would get 93.33% of the target
rows. Logistic Regression and Naïve Bayes are the best for predicting but Neural Network
is close behind.
Systems Analysis & Design is harder to predict. Logistic Regression and then
Neural Network models score the best.
Can get to 100% with the Logistic Regression if select rows with probability of
23.75% or higher and with Neural network if select rows with probability of 31.12% or
above
The dependency network displays the dependencies between the input
attributes and the predictable attributes in a model.
Additional data from our text mining analysis can be found in Appendix 5.
Clustering Analysis of MIS Papers
1. Introduction
1)
Purpose
The purpose of this clustering analysis is to classify the MIS papers from a
different aspect, and try to build a map from a different angle. Our analysis uses
unsupervised clustering method as the tool, and uses generalized characteristics of
papers such as the domain of general research (theoretical vs. applied), the research
methodology (rigor vs. relevance), the characteristic of the content (review vs.
innovation) and the track it belongs to (technical vs. behavior), other than specific
research domain and keyword, as the raw data. The methodology we use is Fuzzy kMeans Clustering method, which is suitable for unsupervised classification. The result of
this analysis will provide useful information to assist the trend analysis and prediction
about MIS research.
2)
Fuzzy k-Means Clustering Algorithm
Data clustering is the process of dividing data elements into classes or clusters so
that items in the same class are as similar as possible, and items in different classes are as
dissimilar as possible. Depending on the nature of the data and the purpose for which
clustering is being used, different measures of similarity may be used to place items into
classes, where the similarity measure controls how the clusters are formed. Some
examples of measures that can be used as in clustering include distance, connectivity,
and intensity.
In this case, we use Fuzzy k-Means Clustering method for clustering procedure,
which is one of the widely used clustering methods nowadays. In fuzzy clustering, data
elements can belong to more than one cluster, and associated with each element is a set
of membership values. So each point can belong to every cluster with a specific level, as
in fuzzy logic, rather than completely belongs to just one cluster. It indicates the strength
of the association between that data element and a particular cluster. Thus, points on the
edge of a cluster may belong to a cluster with a lower level than points in the center of
this cluster. Fuzzy clustering is the process of assigning these membership levels, and
then using them to assign data elements into a specific cluster.
The clustering procedure consists of three steps: fuzzy k-means clustering,
validation and cluster evaluation. In the fuzzy clustering procedure, the number of
clusters should be set by user. Because we do not know the best number of clusters for
this data set at the beginning, we should run the clustering procedure many times with a
series of different numbers of clusters one by one, to provide information for validation
procedure to determine the best number of clusters. In the validation procedure, we
choose a certain index to evaluate the performance of these clustering results, in order to
find out the best number of clusters for this data set. In the cluster evaluation procedure,
we re-clustered the data set with the best number of clusters, and labeled each paper
with the cluster number which it belongs to.
2. Experiment Design
1)
Coordinates System Definition
Each paper was scored for 8 different attributes, which can be categorized into 4
pairs: Theoretical vs. Applied (TA), Rigor vs. Relevance (RR), Review vs. Innovation (RI),
and Technical vs. behavior (TB). Four axes are used to represent these attributes, which
we called MIS-Paper Attributes Space. The coordinates system is defined as follows:
Applied
Review
X1
Behavior
X4
X2
Rigor
Relevance
X3
Technical
Innovation
Theoretical
Thus, all the 185 papers have their own coordinates in this 4-dimensional space,
which will be used for clustering.
2)
Data Processing
Before fuzzy clustering, the coordinates of each paper should be calculated from
the 8 attributes, so each paper can be represented as a point in the MIS-Paper Attributes
Space. Each score value of the paper is represented by the symbol:
scorei ,Attribute , i  1, 2... N
where i is the number of a paper, and Attribute is the name of the attribute
Because the score of each attribute of the paper ranges from 1 to 5, the score
range of two attributes will be distributed along the same axis symmetrically around the
origin, and the arithmetic mean of every pair of two scores is used as the coordinate on
the very axis. The coordinate of each paper is represented by the vector pi :
pi  ( xi1 , xi 2 , xi 3 , xi 4 )T
xi1 
( scorei ,Applied  scorei ,Theoretical )
xi 2 
2
( scorei ,Relevance  scorei ,Rigor )
2
xi 3 
xi 4 
( scorei ,Innovation  scorei ,Review )
2
( scorei ,Behavior  scorei ,Technical )
2
i  1, 2...N
3)
Clustering Procedure
The number of clusters is set by user during the clustering procedure. Because we
are not sure how many clusters is the best for this data set at the beginning, we execute
the clustering procedures many times with a series of different numbers of clusters,
which starts from a lower number to a high enough number. In this case, we choose the
numbers from 3 to 15 as the numbers of clusters. The result of clustering will later be
used in the validation procedure to determine the best number of clusters.
4)
Validation Procedure
Clustering validity refers to the problem whether a given fuzzy partition fits to the
data all. The clustering algorithm always tries to find the best fit for a fixed number of
clusters and the parameterized cluster shapes. However this does not mean that even
the best fit is meaningful at all. Either the number of clusters might be wrong or the
cluster shapes might not correspond to the groups in the data, if the data can be grouped
in a meaningful way at all.
Based on the result of a series of clustering experiments in the clustering
procedure, we use Partition Index as the index to evaluate the performance of the
clustering result. Partition Index (SC) is the ratio of the sum of compactness and
separation of the clusters. It is a sum of individual cluster validity measures normalized
through division by the fuzzy cardinality of each cluster.
c
SC (c)  
i 1

N
j 1
( ij ) m x j  vi
N i  k 1 vk  vi
c
2
2
where i is the cluster number
j is the object number
ij is the fuzzy membership value of object j belonging to cluster i
x j is the coordinates of the object j
vi , vk is the coordinates of the center of cluster i, k
N i is the number of objects that belong to cluster i
The goal of clustering is to categorize the objects with similar attributes into the
same group, so the reason we choose this index is that it covers the two aspects of our
goal: to group the papers with as many similarities as possible, and to separate different
groups as far away with each other as possible. According to the definition of the
Partition Index, a lower value indicates a better partition. The Partition Index is calculated
based on the series of results from the clustering procedure, with numbers of clusters
ranging from 3 to 15 in this case. The curve of Partition Index is shown in the following
figure:
Validation curve using Partition Index (SC)
3
2.8
Partition Index Value
2.6
2.4
2.2
2
1.8
X: 7
Y: 1.47
1.6
1.4
2
4
6
8
10
Number of Clusters
12
14
16
We want to cluster the data to achieve a better performance with as few clusters
as possible. If the number of clusters is too big, the number of papers in each cluster
would be too small to be used in generalizing the characteristics of each cluster as a
classification model for future publication analysis. A fact from the figure is that the
Partition Index decreases sharply with the numbers of clusters from 3 to 7, and decreases
tardily with the numbers of clusters greater than 7. Although cluster numbers that are
greater than 7 result in lower Partition Index values, the increase of performance is not as
prominent as the cluster numbers that is smaller than 7. So 7 is the “elbow” point of the
curve, where the absolute value of the slope suddenly drops. As noticed, although the
Partition Index would be the lowest among the points in this curve if the data set is
partitioned into 12 clusters, considering the 185 as the total number of papers in this
dataset and the generalization in characteristics of every cluster, we will choose 7 as the
best number of clusters.
Best Number of Clusters: 7
Partition Index Value for 7 Clusters: 1.4703
5)
Clustering Evaluation and Data Visualization
With the best number of clusters 7, the data set was re-clustered again. Each
paper will have 7 membership values that indicate its level of belonging to every cluster.
The sum of these 7 values should be 1. By selecting the greatest membership value
among the seven values, each paper was labeled with the number of category it belongs
to. We use 3-dimensional graphs to display the result of clustering, and 4 graphs are
drawn to cover all aspects of the MIS-Paper Attributes Space. In these graph, different
clusters are represented in different colors (Cluster 1 – Green, Cluster 2 – Blue, Cluster 3 –
Cyan, Cluster 4 – Red, Cluster 5 – Yellow, Cluster 6 – Purple, Cluster 7 - Black):
3. Result Analysis
The clustering result is a partition of all 185 papers based on the similarity of 4
groups of features: Theoretical vs. Applied, Rigor vs. Relevance, Review vs. Innovation,
and Technical vs. Behavior. Papers in the same cluster have similarities based on all the
features, all the papers can be categorized into 7 groups. By calculating the mean
coordinates of all the papers of each cluster, the center coordinates of each cluster can
be obtained, and the characteristics of each category can be shown based on the value of
different pair of features. The coordinates and the number of papers of each cluster are
shown as following:
v1  0.6052 0.5752 0.8217 1.1602 , N1  28
v2   1.2773 0.5368 0.2232 1.4327 , N 2  24
v3  0.2477 0.0712 0.3298 -1.3236 , N3  29
v4  -0.9300 -0.3595 0.9106 -1.7341 , N 4  26
v5  -0.1861 0.6770 -0.8188 -0.4133 , N5  28
v6  1.0888 0.6974 0.9997 -1.6909 , N6  29
v7  0.1839 0.9297 -0.8653 1.1222 , N 7  21
The characteristics of different clusters can be summarized as follow. In order to
describe the degree of features, we map the values with different descriptions. For the
absolute value of every attributes
[0, 0.1)
slightly
[0.1, 1)
moderately
[1, 1.5)
normal
[1.5, 2]
extreme
So the characteristics of each cluster could be translated into the following table:
Theoretical
Applied
Rigorous
Relevant
Review
Innovation
Technical
Behavior
Cluster1
---
Moderate
---
Moderate
---
Moderate
---
Normal
Cluster2
Normal
---
---
Moderate
---
Moderate
---
Normal
Cluster3
---
Moderate
Slight
Slight
---
Moderate
Normal
---
Cluster4
Moderate
---
Moderate
---
---
Moderate
Extreme
---
Cluster5
Moderate
---
---
Moderate
Moderate
---
Moderate
---
Cluster6
---
Normal
---
Moderate
---
Moderate
Extreme
---
Cluster7
---
Moderate
---
Moderate
Moderate
---
---
Normal
Further more, we mapped the clustering result with the categorized domain of
the papers:
Artificial Intelligence
Collaboration
Data Management
Decision Sciences
eCommerce
Ecomomics of Information
HCI
Information Assurance
Knowledge Management
Operations Management
Social Informatics
Supply Chain Management
System Analysis & Design
Workflow/Business Process
Management
OTHER
Sum
Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7
0
1
5
1
2
4
0
4
5
1
1
0
1
5
1
1
12
10
7
8
0
2
1
1
4
3
0
3
2
0
1
0
1
0
1
4
2
0
0
4
0
1
3
2
1
0
2
1
1
1
0
0
0
0
0
0
1
5
1
0
0
0
1
1
0
0
1
0
0
0
3
1
0
0
2
0
4
0
0
0
2
0
0
0
6
0
4
4
3
13
4
0
0
28
1
5
24
0
3
29
1
2
26
0
4
28
1
1
29
0
1
21
Sum
13
17
39
14
5
11
10
1
8
2
10
2
34
3
16
185
Pie charts (Appendix 6) are also generated to display the data explicitly. In these
pie chart, the percentage numbers represents the proportion of papers in the specific
domain which belong to different clusters.
From these pie charts and the table of characteristics description of every cluster,
one can easily check the attributes structure of papers in every MIS domain. Combining
with the detail information of every paper (Appendix 7), such as the author, the author’s
affiliation, and the journal, we can summarize the following information:
1) Author’s research map
By analyzing the author’s paper distribution among these pie charts and the
characteristics description table, domains and attributes of the research work of
this author can be extracted, where the attributes are defined the same as in
MIS-Paper-Space.
2) Research map of a university
By analyzing the authors that belong to the same department of a university, we
can also extract the domains and attributes of the research work of this
university.
3) Types of paper of a journal
By analyzing the paper distribution from the same journal, we can extract the
preference of its selection of papers.
4) Trend analysis and prediction
By adding time dimension to the above result, we can analyze and predict the
trends of the author’s research work, university’s research work and journal’s
preference. Also by adding other information such as the change of affiliation of
professors in university or editors of journal, the analysis can also cover the
sudden changes in the trends due to these unpredictable factors.
With the result of this analysis, we can easily catch the latest research hotspot in
every domain, follow the change of the preference of journals, acquire the real-time
information of the changes of universities’ and professors’ role in the MIS community,
and the most important, to discover the unexplored domain in MIS area, all in order to
make better decisions in choosing our research field and managing our research
portfolio.
4. Discussion and Future Work
There are two major difficulties in applying this analysis method. First, due the
unsupervised essence of clustering method, it needs information from other aspects to
make a reasonable explanation about the result. In the clustering procedure, the
algorithm will automatically “find” some patterns as the standard of partitioning, which is
often invisible. So the result may have no meaning unless it is analyzed with the help of
other information. Manual work is usually required in this analysis process. Second, the
attributes of every paper are scored by reviewing the paper, which will contain bias due
to the research domain and the familiarity to the paper’s research domain of the
reviewer. Because we use the attributes of papers as all the data in our clustering
analysis, the bias will affect the performance of the results.
The future work may contain these three aspects:
a)
Select new attributes to evaluate the paper, which may be more efficient and
unbiased
b)
Examine the effect of bias in paper rating of every attribute, and design a
better approach, either manual (decision tree) or automatic (text mining) to
rate the paper, to eliminate the bias
c)
Replace some of the manual analysis with automatic process, such as Text
Mining, and Social Network Analysis, to analyze the content of the paper and
the relationship between authors by machine
Citation Analysis
As one method to determine the completeness of our corpus, we looked at the number
of articles in our corpus which were published in each year. The chart below (see Figure
“Precentage of Articles in Corpus by Year Publiched”) suggests that our corpus might be
over-represented by articles published from the mid 1980’s through the 1990’s. Before
the mid-1980’s it could be argued that the MIS field was still very young so there were
fewer MIS articles published. After the 1990’s we are probably left to draw the
conclusion that our corpus needs to be updated with recent papers which have had
significant impact on the field. Of course, it could also be argued that papers published
after 2000 have not had significant time to have an impact on the field, but we feel that
the corpus probably needs updating.
Percentage of Articles in Corpus by Year Published
7.00%
6.00%
5.00%
4.00%
3.00%
2.00%
1.00%
2008
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
1978
1977
1976
1975
1974
1972
1971
1970
1969
1968
1967
1966
1963
1962
1960
1959
1945
1937
0.00%
Precentage of Articles in Corpus by Year Publiched
To further analyze the completeness and representation of our corpus, two
figures below show the article count by decade group by category. It is interesting to see
the growth in the data management and information assurance articles from the 1970’s
through the 1990’s. Again, from these charts we can see the dip in article representation
in the 2000’s.
14
Artificial Intelligence
12
Collaboration
Data Management
Decision Sciences
10
eCommerce
Economics of Information
8
HCI
Information Assurance
6
Knowledge Management
Operations Management
4
OTHER
Social Informatics
2
Supply Chain Management
System Analysis & Design
Workflow/Business Process Management
0
1930-1939
1950-1959
1960-1969
1970-1979
1980-1989
1990-1999
2000-2009
14
Artificial Intelligence
12
Collaboration
Data Management
Decision Sciences
10
eCommerce
Economics of Information
8
HCI
Information Assurance
6
Knowledge Management
Operations Management
4
OTHER
Social Informatics
2
Supply Chain Management
System Analysis & Design
Workflow/Business Process Management
0
1930-1939
1950-1959
1960-1969
1970-1979
1980-1989
1990-1999
2000-2009
Next, we wanted to analyze our corpus according to the citation counts from
Web of Knowledge and Google. Web of Knowledge is more specific to the social sciences
and applies more readily to the MIS field. In addition, Web of Knowledge limits the
citation counts to articles that are published in top journals, whereas Google counts any
citation that they find in their database. As such Web of Knowledge is probably a better
indicator of the importance and the depth of an article’s impact. The Google citation
count, on the other hand, seems to capture more of the breadth of an article’s influence.
In any case, we placed more emphasis on analyzing the Web of Knowledge citation
counts.
As can be seen from the pie charts below, in both Web of Knowledge (See Figures
“Top 10 Categories by % of WK Citations“ and “Top 10 Categories by Web of Knowledge
Citation Count“) and Google citations (See Figure “Top 10 Categories by % of Google
Citations”), the articles in the “other” category accounted for the largest majority of the
citations. In order to determine whether this result was because we grouped more
papers into the “other” category or whether the “other” papers were more heavily cited,
we looked at the number of papers from each category in our corpus. Figure “Top 10
Categories by % of Article Count “ and “Paper Counts in Categories“ show that the data
management category contained the most articles, account for roughly 23 percent of the
articles in the corpus; whereas, “other” papers accounted for only 9.3 percent of the
articles in the corpus. These findings may suggest that while the “other” papers may not
fit squarely into the current MIS field, they are very influential in defining the field.
Top 10 Categories by % of WK Citations
Artificial Intelligence, 6.30%
Social Informatics, 4.22%
Workflow/Business Process
Management, 8.60%
OTHER, 16.12%
Data Management, 14.67%
Decision Sciences, 8.84%
Economics of Information,
8.96%
System Analysis & Design,
12.14%
Collaboration, 9.38%
Knowledge Managment,
10.77%
Category
WK Citations
OTHER
Data Management
System Analysis & Design
Knowledge Managment
Collaboration
Economics of Information
Decision Sciences
Workflow/Business Process Management
Artificial Intelligence
Social Informatics
Top 10 Categories by Web of Knowledge Citation Count
2809
2556
2116
1877
1635
1561
1540
1499
1097
736
Top 10 Categories by % of Google Citations
Artificial Intelligence, 3.52%
HCI, 3.50%
Decision Sciences, 4.06%
Workflow/Business Process
Management, 4.42%
OTHER, 24.75%
Economics of
Information,
7.24%
Collaboration, 6.28%
Knowledge Managment,
11.19%
System Analysis & Design,
15.06%
Data Management, 19.97%
Top 10 Categories by % of Article Count
Social Informatics, 5.81%
Economics of Information,
6.40%
Knowledge Managment,
4.65%
HCI, 5.81%
Data Management, 22.67%
Artificial Intelligence, 7.56%
System Analysis & Design,
19.77%
Decision Sciences, 8.14%
OTHER, 9.30%
Collaboration, 9.88%
Category
Data Management
System Analysis & Design
Collaboration
OTHER
Decision Sciences
Artificial Intelligence
Economics of Information
HCI
Social Informatics
Knowledge Management
eCommerce
Workflow/Business Process Management
Supply Chain Management
Operations Management
Information Assurance
# of Papers
39
34
17
16
14
13
11
10
10
8
5
3
2
2
1
Paper Counts in Categories
To further explore the influence that the “other” articles have in our corpus and
in the MIS field we compiled the titles of the “other” articles into the chart below (see
Figure Articles Categorized as “Other”) along with citation counts from Web of
Knowledge and Google. If a citation count is listed as ‘-1’, then we didn’t find it in that
specific database. This chart could aid us in eliminating certain articles from the
database. For instance, those articles that did not appear in Web of Knowledge and had
low Google citation counts (less than 200) may be candidates for removal. Articles such
as Coase’s “Nature of the Firm” which did not appear in Web of Knowledge, but were
highly cited in Google, should most likely stay in the corpus assuming that they are
related to or influence the MIS field. In addition, Im’s 1998 article, though not cited a lot,
is probably referred (i.e. read) by many MIS researchers.
Author
Title
Year
Google
WK
Citations Citations
Dynamic capabilities and strategic
management
A Resource-Based Theory of the Firm:
Knowledge versus Opportunism
Teece et al
1997
1532
5536
Conner and Prahalad
1996
343
1219
Using technology and constituting
structures: A practice lens for studying
technology in organizations
A taxonomy of part-whole relations
Simula--an ALGOL-Based Simulation
Language
Design Science in Information Systems
Research
Dynamic Competition with Switching
Costs
A comparative analysis of disk
scheduling policies
Copyright's fair use doctrine and digital
data
On applications of differential equations
in general problem solving
Orlikowski
2000
251
791
Morton et al
Dahl and Nygaard
1987
1966
194
176
431
524
Hevner et al
2004
131
490
Farrell and Shapiro
1988
103
265
Teorey and Pinkerton
1972
56
187
Samuelson
1994
29
35
Kubik
1966
0
0
The Nature of the Firm
Studying information technology in
organizations: Research approaches and
assumptions
A Note on Two Problems in Connection
with Graphs
Systems Development in Information
Systems Research
Software Engineering Programs are not
Computer Science Programs
An Assessment of Individual and
Institutional Research Productivity in
MIS
R. H. Coase
Orlikowski and Baroudi
1937
1991
-1
-1
11151
953
Dijkstra
1959
-1
2190
Nunamaker et al
1991
-1
211
Parnas
1999
-1
103
Im et al
1998
-1
31
Articles Categorized as “Other”
Lastly, with regards to citation counts, we extracted several “Top 10” lists to
provide a few more insightful views. The first two charts below (see Figure “Top 10
Articles by Web of Knowledge Citations” and Figure “Top 10 Articles by Google Citations”)
show the top 10 most cited articles in Web of Knowledge and Google, respectively,
sorted by number of citations. The following two charts (see “Top 10 Articles by Average
Web of Knowledge Citations per Year” and Figure “Top 10 Articles by Average Google
Citations per Year”) show the top 10 cited articles by citations per year. We chose to run
the citations per year analysis in order to take into account when the article was
published. For instance, if article A was published in 1960 and has been cited one
thousand times, it probably is not as significant as article B which was published in 1999
and has been cited one thousand times.
Top 10 Articles by Web of Knowledge Citations
Title
Author
Year
Category
WK WK Citations per
Citations
Year
Dynamic capabilities and strategic management
A Relational Model of Data for Large Shared Data Banks
Organizational Information Requirements, Media Richness and
Structural Design
A Dynamic Theory of Organizational Knowledge Creation
On the Criteria To Be Used in Decomposing Systems into Modules
Teece et al
Codd
Daft and Lengel
1997 OTHER
1970 Data Management
1986 Workflow/Business Process Management
1532
1269
1100
139
33
50
Nonaka
Parnas
1994 Knowledge Managment
1972 System Analysis & Design
1098
680
78
19
Machine learning in automated text categorization
The Lagrangian Relaxation Method for Solving Integer Programming
Problems
Electronic Markets and Electronic Hierarchies
A Foundation for the Study of Group Decision Support Systems
Internet paradox: A social technology that reduces social involvement
and psychological well-being?
Sebastiani
Fisher
2002 Artificial Intelligence
1981 Decision Sciences
663
658
111
24
Malone et al
DeSanctis and Gallupe
Kraut et al
1987 Economics of Information
1987 Collaboration
1998 Social Informatics
575
543
509
27
26
51
Top 10 Articles by GoogleCitations
Title
Author
Year
The Nature of the Firm
Dynamic capabilities and strategic management
A Dynamic Theory of Organizational Knowledge Creation
The entity-relationship model toward a unified view of data
A Relational Model of Data for Large Shared Data Banks
As We May Think
Organizational Information Requirements, Media Richness and
Structural Design
A Spiral Model of Software Development and Enhancement
On the Criteria To Be Used in Decomposing Systems into Modules
Coase
Teece et al
Nonaka
Chen
Codd
Bush
Daft and Lengel
1937
1997
1994
1976
1970
1945
1986
Boehm
Parnas
A Note on Two Problems in Connection with Graphs
Dijkstra
Category
Google Google Citations
Citations
per Year
OTHER
OTHER
Knowledge Managment
Data Management
Data Management
Knowledge Managment
Workflow/Business Process Management
11151
5536
5121
4583
4197
2972
2853
157
503
366
143
110
47
130
1988 System Analysis & Design
1972 System Analysis & Design
2848
2729
142
76
1959 OTHER
2190
45
Top 10 Articles by Average Web of Knowledge Citations per Year
Title
Author
Year
Category
Review: Knowledge Management and Knowledge Management Systems:
Conceptual Foundations and Research Issues
Dynamic capabilities and strategic management
Machine learning in automated text categorization
A Dynamic Theory of Organizational Knowledge Creation
User acceptance of information technology: Toward a unified view
Internet paradox: A social technology that reduces social involvement and
psychological well-being?
Organizational Information Requirements, Media Richness and Structural
Design
A Relational Model of Data for Large Shared Data Banks
Frictionless Commerce? A Comparison of Internet and Conventional Retailers
Alavi and Leidner
2005 Knowledge Managment
Teece et al
Sebastiani
Nonaka
Venkatesh et al
Kraut et al
1997
2002
1994
2003
1998
Daft and Lengel
Design Science in Information Systems Research
WK WK Citations per
Citations
Year
418
139
1532
663
1098
379
509
139
111
78
76
51
1986 Workflow/Business Process Management
1100
50
Codd
Brynjolfsson and Smith
1970 Data Management
2000 eCommerce
1269
263
33
33
Hevner et al
2004 OTHER
131
33
OTHER
Artificial Intelligence
Knowledge Managment
HCI
Social Informatics
Top 10 Articles by Average Google Citations per Year
Title
Author
Year
Category
Google Google Citations
Citations
per Year
Review: Knowledge Management and Knowledge Management Systems:
Conceptual Foundations and Research Issues
Dynamic capabilities and strategic management
A Dynamic Theory of Organizational Knowledge Creation
Machine learning in automated text categorization
User acceptance of information technology: Toward a unified view
The Nature of the Firm
The entity-relationship model toward a unified view of data
A Spiral Model of Software Development and Enhancement
Frictionless Commerce? A Comparison of Internet and Conventional Retailers
Alavi and Leidner
2005 Knowledge Managment
Teece et al
Nonaka
Sebastiani
Venkatesh et al
R. H. Coase
Peter Pin-Shan Chen
Boehm
Brynjolfsson and Smith
1997
1994
2002
2003
1937
1976
1988
2000
Organizational Information Requirements, Media Richness and Structural
Design
Daft and Lengel
1986 Workflow/Business Process Management
OTHER
Knowledge Managment
Artificial Intelligence
HCI
OTHER
Data Management
System Analysis & Design
eCommerce
1593
531
5536
5121
2056
1055
11151
4583
2848
1129
503
366
343
211
157
143
142
141
2853
130
We also looked at the breakdown of categories and dimensions. Our biggest take
away here was basically a lesson learned. As can be seen in Figure below, all the
dimension assessments regressed toward three, the middle of the scale. We should have
either expanded the scale to a 7 or 9 point to allow for more spread, or kept the original
dimension opposites together (for instance, rigor vs. relevance). The dimension analysis
could also speak to the lack of knowledge and experience we have about the MIS field as
Ph.D. students.
Category
Artificial Intelligence
Collaboration
Data Management
Decision Sciences
eCommerce
Economics of Information
HCI
Information Assurance
Knowledge Management
Operations Management
OTHER
Social Informatics
Supply Chain Management
System Analysis & Design
Workflow/Business Process Mgt
Theoretical
Applied
Rigor
Relevance
Review
Innovation
Technical
Behavioral
2
3
2
3
2
3
3
1
3
3
2
3
3
3
2
4
3
3
3
3
2
3
4
1
3
3
3
3
3
3
2
2
3
4
3
4
2
2
2
2
3
3
2
4
3
3
2
3
3
3
2
3
2
3
3
3
3
4
2
4
1
3
1
5
3
2
2
3
2
3
1
3
3
3
3
3
2
4
3
3
3
2
2
3
3
2
2
2
2
3
2
4
3
2
1
4
4
2
5
2
1
5
5
1
2
3
2
3
2
3
3
2
3
3
3
4
3
3
3
2
Limitations and Future Research
Our research approach was to take the 185 key papers in MIS, add them to a
relational database in order to perform analysis such as grouping by category, text
mining, and citation analysis. The greatest limitation to our research was that we did not
have enough time to perform as thorough analysis as we would have liked. Our limited
knowledge of data mining techniques limited our ability to accomplish in depth analysis
of the key articles.
In our research, we have built a foundation for future work because the database
we created could save a lot of preliminary and repeating work. Future classes can make
use of the database information to conduct greater analysis and research on the key MIS
papers.
Conclusion
In this project, we have extracted key information in the 185 MIS papers, added
our own assessment on different character of these papers and then stored them into a
relational database. We conducted categorization, text mining and citation analysis based
on our database, in this process we have discovered some interesting and valuable
findings. However, due to lack of relevant experiences, there still exist some limitations in
our work. Since we have done much fundamental work for the future group, we believe
they can generate better ideas and more scientific model based on our contributions.
In the past semester, each team member has learned a lot from both individual
work and team collaboration. This project is extremely beneficial for us to get familiar
with the history, development and present in MIS. Finally, we should say thank you to Dr.
Nunamaker and Chris Diller for their valuable suggestion and sincerely help.
Appendix 1: User Reading Guide
Article Reading/Scoring Guide
As you are reading your articles, please attempt to extract the following data:
Article/Reviewer Data:
1) Articles Name:
______________________________________________
2) Primary Author:
______________________________________________
3) Team Member Reviewing Article: _________________________________
Information For the Database:
1) Decision Tree Result:
________________________________________
2) 5 Key Words that describe the content of the article:
_____________________
_____________________
_____________________
_____________________
_____________________
3) Domain Type: Based upon your interpretation of the article, please rate each of the
domains below by circling the number that most closely approximates your opinion.
Theoretic
1
2
3
4
5
Application
1
2
3
4
5
Rigorous
1
2
3
4
5
Relevance
1
2
3
4
5
Review
1
2
3
4
5
Innovative
1
2
3
4
5
Technical
1
2
3
4
5
Behavioral
1
2
3
4
5
4) Should this article be considered for removal from the corpus? Y
5) How many times has this article been cited?
______________
N
Appendix 2: 2007 Classification Model
Does the research describe, conceptualize or
theorize a technological component?
No
Yes
Is technological component treated as an
unchanging, discrete entity (black box)?
Is there a technological factor in the context,
motivation or background?
Yes
No
Yes
Is the focus on universal System Analysis and
Design Methodologies or a specific problem or a
specific system design and/or implementation?
Is technology a solely independent variable?
Yes
No
Yes
Is the focus on the application or the value of
the technological component?
No
Strategy
System Analysis and
Design
Meta
IS Design /
Implementation
IS Strategy
Research
About...
IS Meta
Research
About...
Distinction
according
to
functionality
Distinction
according
to
functionality
Distinction
according
to
functionality
Value
Does it focus on organizational or behavioral
aspects?
Application
Does the research focus on perception
of technology, diffusion or capital?
Behavioral
about MIS?
Specific
Universal
Is the component utilized or viewed as a tool?
No
Organizational Strategy or Meta Research
Organizational
No
Yes
Is the focus on interactions
between human and computers?
IS
Organizational
No
Research
Yes
No
Is the research focused on economic
impact?
IS Strategy
Research
About...
HCI
Is the focus on social impacts of the
technological component?
Distinction
according
to
functionality
Yes
Is the focus on interactions
between human and computers?
No
Distinction
according
to
functionality
Yes
No
HCI
Yes
Social Informatics
No
NOT MIS
Economics of Information
Is the focus on social impacts of the
technological component?
Social Informatics
Yes
Social Informatics
No
NOT MIS
Distinction according to
functionality
Data Management
Collaboration
Workflow / Business Process
Management
Bioinformatics
eCommerce
Decision Sciences
Artificial Intelligence
Healthcare Systems
Information Assurance
Supply Chain Management
Operations Management
Accounting Information
System
NOT MIS
Appendix 3: General Sequel Queries
Papers by Category
Category
Data Management
System Analysis & Design
Collaboration
OTHER
Decision Sciences
Artificial Intelligence
Economics of Information
HCI
Social Informatics
Knowledge Management
eCommerce
Workflow/Business Process Management
Supply Chain Management
Operations Management
Information Assurance
# of Papers
39
34
17
16
14
13
11
10
10
8
5
3
2
2
1
Average Dimension Ratings by Category
Category
Artificial
Intelligence
Collaboration
Data Management
Decision Sciences
eCommerce
Economics
of
Information
HCI
Information
Assurance
Knowledge
Management
Operations
Management
OTHER
Social Informatics
Supply
Chain
Management
System Analysis &
Design
Workflow/Business
Process
Management
Theoretical Applied Rigor Relevance Review Innovation Technical Behavioral
2
3
2
3
2
3
3
1
3
3
3
3
3
3
3
3
4
3
2
3
3
3
2
3
3
3
4
4
3
2
3
2
3
3
3
3
2
3
2
4
2
2
2
4
1
2
2
3
3
3
3
4
2
2
3
4
2
1
3
3
3
1
3
5
3
2
2
3
2
3
1
3
3
3
3
3
2
4
3
3
3
2
4
2
3
2
2
2
5
3
4
2
3
3
1
2
2
5
2
1
5
2
4
1
2
3
2
3
2
3
3
2
3
3
3
4
3
3
3
2
Category Counts by Decade
Decade
1930-1939
1940-1949
1950-1959
1960-1969
1960-1969
1960-1969
1960-1969
1960-1969
1960-1969
1970-1979
1970-1979
1970-1979
1970-1979
1970-1979
1970-1979
1970-1979
1980-1989
1980-1989
1980-1989
1980-1989
1980-1989
1980-1989
1980-1989
1980-1989
1980-1989
1980-1989
1980-1989
1980-1989
1990-1999
1990-1999
1990-1999
1990-1999
1990-1999
1990-1999
1990-1999
1990-1999
1990-1999
1990-1999
1990-1999
1990-1999
1990-1999
1990-1999
1990-1999
Category
OTHER
Knowledge Management
OTHER
Artificial Intelligence
Decision Sciences
HCI
Knowledge Management
OTHER
System Analysis & Design
Artificial Intelligence
Collaboration
Data Management
HCI
OTHER
Social Informatics
System Analysis & Design
Artificial Intelligence
Collaboration
Data Management
Decision Sciences
Economics of Information
HCI
Knowledge Management
Operations Management
OTHER
Social Informatics
System Analysis & Design
Workflow/Business
Process
Management
Artificial Intelligence
Collaboration
Data Management
Decision Sciences
eCommerce
Economics of Information
HCI
Information Assurance
Knowledge Management
Operations Management
OTHER
Social Informatics
Supply Chain Management
System Analysis & Design
Workflow/Business
Process
Management
Count of papers
1
1
1
1
1
2
1
2
7
1
1
10
1
1
1
8
2
6
12
4
3
3
1
1
2
1
7
1
6
9
13
6
2
6
2
1
3
1
7
5
1
7
2
2000-2009
2000-2009
2000-2009
2000-2009
2000-2009
2000-2009
2000-2009
2000-2009
2000-2009
2000-2009
2000-2009
2000-2009
Artificial Intelligence
Collaboration
Data Management
Decision Sciences
eCommerce
Economics of Information
HCI
Knowledge Management
OTHER
Social Informatics
Supply Chain Management
System Analysis & Design
3
1
4
3
3
2
2
2
2
3
1
5
Appendix 4: Database Design
 Database Design
 EndNote
1. Using EndNote, export the references to a .txt file using the “SQL Export” style. This
will create a text file of references in an xml format.
2. Add this as first line of text file: <?xml version="1.0" encoding="UTF-8"
?><xml><records>
3. Add this as last line of text file: </records></xml>
4. Rename file to .xml extension.
5. Open the file in an xml viewer and make sure the xml is good. The most likely
changes to fix the xml are:
a. Change & to &amp;
b. Change < in abstract text to &lt;
c. Change > in abstract text to &gt;
 Access
6. Open a new access database.
7. Select External Data, XML file for import and select the xml file from step 5 above.
Choose Structure only the first time because some of the field formats need to be
updated.
8. Open the record table in design view and change the following fields:
a. Change recnum to Number
b. Change abstract to Memo
c. Change note to Memo
d. Change keywords to Memo
9. Select External Data, XML file for import and select the XML file again. Choose to
Append file to existing table.
10. Save the database as an .mdb for importing to SQL if you are using Office 2007
 SQL
11. From the CodifyNet database, select Tasks, Import Data
12. Choose Access as the input data source and select the mdb database created above.
13. Choose the ADAR CodifyNet database as the destination data source (use a SQL login
if doing this remotely).
14. Choose ‘Copy data from one or more tables or views’
15. For a first time load:
a. You will see the Access record table mapped to a SQL record table. Change
the name of the destination table to Papers.
b. Choose to create a table
c. Select Edit mappings and change the following:
i. Reftype – change to nvarchar(50)
ii. Refyear – change to nvarchar(20)
iii. Volume – change to nvarchar(20)
iv. Number – change to nvarchar(20)
v. Pages – change to nvarchar(20)
16. For a subsequent load:
a. Select the Papers table from the dropdown
b. Choose to append data to the existing table
17. Run the package.
Appendix 5: Data Mining Analysis – Data
Naïve Bayes – Artificial Intelligence (Characteristics and Discrimination)
Naïve Bayes – Collaboration
Naïve Bayes – Data Management
Naïve Bayes – Decision Sciences
Naïve Bayes – ecommerce
Naïve Bayes – Economics of Information
Naïve Bayes – Human Computer Interaction
Naïve Bayes – Knowledge Management
Naïve Bayes – Operations Management
Naïve Bayes – Social Informatics
Naïve Bayes – Supply Chain Management
Naïve Bayes – Systems Analysis & Design
Naïve Bayes – Workflow/Business Process Management
Naïve Bayes - Other
Microsoft Clustering
Cluster1
Cluster2
Cluster3
Cluster 4
Appendix 6: Clustering Analysis
Economics of Information
Cluster 6
0%
Cluster 7
9%
Cluster 4
0%
Cluster 1
37%
Cluster 3
0%
Cluster 5
36%
Cluster 2
18%
Appendix 7: Clustering Results
Cluster No. No.
Title
Year
A Spiral Model of Software
Development and
1988
Enhancement
Authors
Domain
Barry W. Boehm
System Analysis &
Design
Erik Brynjolfsson,Lorin Hitt
Economics of
Information
Russell L Ackoff
System Analysis &
Design
R. B. Cooper,R. W. Zmud
Information
Assurance
Mendelson Haim
Economics of
Information
1
45
1
Paradox Lost? Firm-level
Evidence on the Returns to
217
1996
Information Systems
Spending
1
41
1
Information Technology
Implementation Research - a
232
1990
Technological Diffusion
Approach
1
218
1
A Response to "Assessing
Research Productivity:
119
Important But Neglected
Considerations"
1
Computer Support For
Meetings Of Groups
125 Working On Unstructtured 1988
Problems: A Field
Experiment
Sirkka L. Jarvenpaa, V. Srinivasan
Rao,George P Huber
Knowledge
Managment
1
Information technology
adoption across time: A
235 cross-sectional comparison 1999
of pre-adoption and postadoption beliefs
E. Karahanna, D. W. Straub,N. L.
Chervany
Social Informatics
1
133
Dongmin Kim,Izak Benbasat
eCommerce
1
An Experimental
Investigation of the Impact
192 of Computer Based Decision 1991
Aids on Decision Making
Strategies
Peter Todd,Izak Benbasat
Decision Sciences
1
194
M Turoff
Collaboration
Management
Misinformation Systems
1967
Pricing computer services:
1985
queueing effects
1998
Trust-Related Arguments in
Internet Stores: A
2003
Framework for Evaluation
Computer Mediated
1991
Kun Shin Im, Kee Young Kim,Joon S.
Social Informatics
Kim
Communication
Requirements for Group
Support
1
Using a GDSS to Facilitate
Group Consensus: Some
197
1988
Intended and Unintended
Consequences
1
219
1
139
1
A Theory of Attributed
Equivalence in Databases
146
1989 J. A. Larson, S. B. Navathe,R. Elmasri Data Management
with Application to Schema
Integration
1
148
1
150 Man-Computer Symbiosis 1960
1
151
Electronic Markets and
Electronic Hierarchies
1
164
The Usability Engineering
Life-Cycle
1
Richard T. Watson, Geraldine
Desanctis,Marshall Scott Poole
Collaboration
The Economics of
Organization: The
1981
Transaction Cost Approach
Oliver E. Williamson
Economics of
Information
Computerization and Social
1991
Transformations
Rob Kling
Social Informatics
Winning the Last Mile of E2001
Commerce
Hau L. Lee,Seungjin Whang
eCommerce
J. C. R. Licklider
System Analysis &
Design
1987
Thomas W. Malone, Joanne
Yates,Robert I. Benjamin
Economics of
Information
1992
J. Nielsen
HCI
165
Finding usability problems
1992
through heuristic evaluation
J. Nielsen
System Analysis &
Design
1
167
Improving system usability
1996
through parallel design
J. Nielsen,J. M. Faber
System Analysis &
Design
1
Future research in group
support systems: needs,
170
1997
some questions and possible
directions
J. F. Nunamaker
Collaboration
G Robertson, Allen Newell,K
Ramakrishna
HCI
ZOG: A Man-Machine
1977
Communication Philosophy
1
4
1
Managing the Development
7 of Large Software Systems: 1970
Concepts and Techniques
Winston W. Royce
System Analysis &
Design
1
Critical Success Factor
15 Analysis as a Methodology 1985
for MIS Planning
Michael E. Shank, Andrew C.
Boynton,Robert W. Zmud
Decision Sciences
1
Direct Manipulation - a Step
21
Beyond Programming1983
Languages
B. Shneiderman
HCI
1
27
Reducing Social Context
Cues: Electronic Mail in
Organizational
Communication
1
28
Thinking about
implementation
2
Testing the interactivity
model: Communication
230
processes, partner
1999
assessments, and the quality
of collaborative work
2
43
1945
Vannevar Bush
Knowledge
Managment
2
Evaluation of Strategic
63 Investments in Information 1991
Technology
Eric K Clemons
Economics of
Information
2
64
R. H. Coase
OTHER
2
A Resource-Based Theory of
222 the Firm: Knowledge versus 1996 Kathleen R. Conner,C. K. Prahalad
Opportunism
As We May Think
The Nature of the Firm
Effects of Anonymity and
Evaluative Tone on Idea
Generation in ComputerMediated Groups
1986
Lee Sproull,Sara Kiesler
Collaboration
1986
Lee S. Sproull,Kay R. Hofmeister
Operations
Management
J. K. Burgoon, J. A. Bonito, B.
Bengtsson, A. Ramirez, N. E.
Dunbar,N. Miczo
Collaboration
1937
OTHER
Terry Connolly, Leonard M.
Jessup,Joseph S. Valacich
Collaboration
2
Organizational Information
Requirements, Media
70
1986
Richness and Structural
Design
Richard L. Daft,Robert H. Lengel
Workflow/Business
Process
Management
2
MEDIA, TASKS, AND
COMMUNICATION
234
2008
PROCESSES: A THEORY OF
MEDIA SYNCHRONICITY
Allen R. Dennis, Robert M.
Fuller,Joseph S. Valacich
Collaboration
2
96
DC Englebart
Knowledge
Managment
2
98
Joseph Farrell,Carl Shapiro
OTHER
2
68
Augmenting Human
Intellect: A Conceptual
Framework
1990
1962
Dynamic Competition with
1988
Switching Costs
2
103 Effective View Navigation
2
106
2
George W. Furnas
Decision Sciences
Electronic Brainstorming and
1992
Group-Size
R. B. Gallupe, A. R. Dennis, W. H.
Cooper, J. S. Valacich, L. M.
Bastianutti,J. F. Nunamaker
Collaboration
The Impact of Information
111 Systems on Organizations 1991
and Markets
Vijay Gurbaxani,Seungjin Whang
Economics of
Information
Sirkka L. Jarvenpaa, Kathleen
Knoll,Dorothy E. Leidner
Collaboration
Wanda J Orlikowski
OTHER
Is anybody out there?
Antecedents of trust in
global virtual teams.
1997
2
124
2
Using technology and
constituting structures: A
175
2000
practice lens for studying
technology in organizations
2
182
Information foraging
1999
P. Pirolli,S. Card
Data Management
2
228
The Firm as a Distributed
Knowledge System: A
Constructionist Approach
1996
Haridimos Tsoukas
Knowledge
Managment
2
238
User acceptance of
information technology:
Toward a unified view
2003
V. Venkatesh, M. G. Morris, G. B.
Davis,F. D. Davis
HCI
2
The psychobiological model:
Towards a new theory of
236
computer-mediated
2004
communication based on
Darwinian evolution
N. Kock
HCI
Unraveling the temporal
fabric of knowledge
conversion: A model of
media selection and use
1998
2006 A. P. Massey,M. M. Montoya-Weiss
Knowledge
Managment
2
237
2
Computer Science as
162 Empirical Inquiry - Symbols 1976
and Search
A. Newell,H. A. Simon
Artificial Intelligence
2
A Dynamic Theory of
226 Organizational Knowledge 1994
Creation
Ikujiro Nonaka
Knowledge
Managment
2
10
Toward a new politics of
intellectual property
2001
Pamela Samuelson
Social Informatics
2
227
Dynamic capabilities and
strategic management
1997
David J. Teece, Gary Pisano,Amy
Shuen
OTHER
3
Frictionless Commerce? A
52 Comparison of Internet and 2000 Erik Brynjolfsson,Michael D. Smith
Conventional Retailers
System R: relational
approach to database
management
eCommerce
M. M. Astrahan, M. W. Blasgen, D.
D. Chamberlin, K. P. Eswaran, J. N.
Gray, P. P. Griffiths, W. F. King, R. A.
1976
Data Management
Lorie, P. R. McJones, J. W. Mehl, G.
R. Putzolu, I. L. Traiger, B. W.
Wade,V. Watson
3
59
3
Data Model Issues for
55
1987
Object-Oriented Applications
3
AI and computational
science: Implementing Fuzzy
60
2003 Elva. Corona Carlos A. Reyes-Garcia Artificial Intelligence
Expert System for intelligent
buildings
3
The entity-relationship
62 model toward a unified view 1976
of data
3
82
3
84
3
85
3
94
The category concept: an
extension to the entityrelationship model
1985 R. Elmasri, J. Weeldreyer,A. Hevner Data Management
3
211
Software reuse research:
status and future
2005
3
J. Banerjee, H. T. Chou, J. F. Garza,
K. Won, D. Woelk, N. Ballou,H. J. Data Management
Kim
Peter Pin-Shan Chen
Data Management
Thomas Devogele, Christine
Parent,Stefano Spaccapietra
Data Management
A Note on Two Problems in
1959
Connection with Graphs
Edsger W. Dijkstra
OTHER
Go To Statement Considered
1968
Harmful
Edsger W. Dijkstra
System Analysis &
Design
On spatial database
integration
1998
W. B. Frakes,Kang Kyo
System Analysis &
Design
102 Generalized fisheye views 1986
G. W. Furnas
HCI
3
Computer-Mediated
Communication for
105
1994
Intellectual Teamwork - an
Experiment in Group Writing
J. Galegher,R. E. Kraut
Collaboration
3
223
AR Hevner, March, S, Ram, S
OTHER
3
Joyce McDowell Kathleen Dahlgren,
131 Knowledge representation 1989
Artificial Intelligence
Edward P. Stabler
for commonsense reasoning
Design Science in
Information Systems
Research
2004
with text
On optimizing an SQL-like
nested query
3
134
3
The Inter-Database Instance
Identification Problem in
196
1989 Y. Richard Wang,Stuart E. Madnick Data Management
Integrating Autonomous
Systems
3
Embedding web-based
statistical translation models
Jian-Yun Nie Wessel Kraaij, Michel
198
2003
in cross-language
Simard
information retrieval
3
229
A taxonomy of part-whole
1987
relations
Morton E. Winston, Roger
Chaffin,Douglas Herrmann
OTHER
3
141
Dynamic Configuration for
1985
Distributed Systems
Jeff Kramer,Jeff Magee
System Analysis &
Design
3
143
Jeff Kramer, Jeff Magee,M. S.
Sloman
System Analysis &
Design
3
Allocating Data and
152 Operations to Nodes in
1995
Distributed Database Design
Salvatore T. March,Sangkyu Rho
Data Management
C. Mohan
Data Management
Managing Evolution in
Distributed Systems
1982
1989
Won Kim
Data Management
Decision Sciences
3
154
Distributed data base
management: Some
thoughts and analyses
3
159
Report on a general
problem-solving program
3
8
Automatic information
retrieval
1980
G Salton
Artificial Intelligence
3
12
Machine learning in
automated text
categorization
2002
Fabrizio Sebastiani
Artificial Intelligence
3
13
Knowledge compilation and
1996
theory approximation
Bart Selman,Henry Kautz
Knowledge
Managment
3
View Integration - a Step
24 Forward in Solving Structural 1994
Conflicts
S. Spaccapietra,C. Parent
Data Management
3
Model independent
25 assertions for integration of 1992
heterogeneous schemas
Stefano Spaccapietra, Christine
Parent,Yann Dupont
Data Management
3
189 Ontologies for conceptual 2002 Vijayan Sugumaran,Veda C. Storey Data Management
1980
1960 A. Newell, Shaw, J.C. & Simon, H.A Artificial Intelligence
modeling: their creation,
use, and management
4
Branch-and-Price: Column
Cynthia Barnhart, Ellis L. Johnson,
54 Generation for Solving Huge 1998 George L. Nemhauser, Martin W.P.
Integer Programs
Savelsbergh,Pamela H. Vance
4
47
4
57
4
221
4
Supply Chain Inventory
53 Management and the Value 2000 Gerard P. Cachon,Marshall L. Fisher
of Shared Information
4
50
4
System Test Planning of
209 Software: An Optimization 2006
Approach
K. Chari,A. Hevner
System Analysis &
Design
4
A Machine Learning
Approach to Inductive Query
by Examples: An Experiment
61
1998
Using Relevance Feedback,
ID3, Genetic Algorithms, and
Simulated Annealing
Chen H., Shankaranarayanan G.,
Iyer A., She L
Artificial Intelligence
4
65
A Relational Model of Data
1970
for Large Shared Data Banks
E. F. Codd
Data Management
4
66
Relational Completeness of
1972
Data Base Sublanguages
E. F. Codd
Data Management
4
Extending the database
67 relational model to capture 1979
more meaning
E. F. Codd
Data Management
4
72
1966
Ole-Johan Dahl,Kristen Nygaard
OTHER
4
73
Decomposition Principle for
1960
Linear Programs
George B. Dantzig,Philip Wolfe
Decision Sciences
4
239
The Working Set Model for
1968
Program Behavior
Peter J. Denning
System Analysis &
Design
DSS Design: A Systematic
View of Decision Support
1985
An evaluation of research
2000
productivity in academic IT
Why and Where: A
Characterization of Data
Provenance
2001
Operations
Management
Gad Ariav,Michael J. Ginzberg
Collaboration
Susan Athey,John Plotnicki
Decision Sciences
P Buneman
Data Management
Supply Chain
Management
SEQUEL: A structured English
Donald D. Chamberlin,Raymond F.
1974
Data Management
query language
Boyce
Simula--an ALGOL-Based
Simulation Language
4
The Lagrangian Relaxation
101 Method for Solving Integer 1981
Programming Problems
4
An Overview of Workflow
Management: From Process
Diimitrios Georgakopoulos, Mark
109
1995
Modeling to Workflow
Hornick,Amit P. Sheth
Automation Infrastructure
4
113
1996
Venky Harinarayan, Anand
Rajaraman,Jeffrey D. Ullman
Decision Sciences
4
Multi-User View Integration
114 System (MUVIS): An Expert 1990
System for View Integration
Stephen Hayne,Sudha Ram
System Analysis &
Design
4
145
On applications of
differential equations in
general problem solving
1966
Robert N. Kubik
OTHER
4
240
Some Approaches to the
Theory of Information
Systems
1963
Borje Langefors
System Analysis &
Design
4
Information Distortion in a
147 Supply Chain: The Bullwhip 1997
Effect
Hau L. Lee, V.
Padmanabhan,Seungjin Whang
Supply Chain
Management
4
The semantic data model: a
225 modelling mechanism for 1978 Hammer Michael,McLeod Dennis
data base applications
Data Management
4
InfoHarness: An Information
Integration Platform for
14
1999
Managing Distributed,
Heterogeneous Information
K. Shah,Amit P. Sheth
Data Management
4
23
J. M. Smith,D. C. P. Smith
Data Management
4
186
Michael Stonebraker
Data Management
4
A logical design
methodology for relational
191
databases using the
1986
extended entity-relationship
model
Toby J. Teorey, Dongqing
Yang,James P. Fry
Data Management
5
44
Anitesh Barua, Charles H
Kriebel,Tridas Mukhopadhyay
Economics of
Information
Implementing data cubes
efficiently
Database abstractions:
aggregation and
generalization
1977
The Design of the POSTGRES
1987
Storage System
Information technologies
and business value: An
analytic and empirical
1995
Marshall L. Fisher
Decision Sciences
Workflow/Business
Process
Management
investigation
5
A comparative analysis of
48 methodologies for database 1986 C. Batini, M. Lenzerini,S. B. Navathe Data Management
schema integration
5
58
5
46
5
The Evolution of Research on
Information Systems: A
216 Fiftieth-Year Survey of the 2004 Rajiv D. Banker,Robert J. Kauffman
Literature in Management
Science
Decision Sciences
5
Interactions between system
evaluation and theory
testing: A demonstration of
231
2006
the power of a multifaceted
approach to information
systems research
System Analysis &
Design
5
69
5
Information technology and
economic performance: A
74
2003
critical review of the
empirical evidence
5
Information technology and
83 productivity: Evidence from 2000 Sanjeev Dewan,Kenneth L Kraemer
country-level data
5
92
5
Dendral and Meta-dendral:
Roots of Knowledge Systems
99
1993
and Expert System
Applications
5
100
5
104
Kevin Bacon, Degrees-ofSeparation, and MIS
Research
2002
The productivity paradox of
1993
information technology
Process Modeling
Social Informatics
Erik Brynjolfsson
Economics of
Information
J. W. Cao, J. M. Crews, M. Lin, A.
Deokar, J. K. Burgoon,J. F.
Nunamaker
1992 Bill Curtis, Marc I. Kellner,Jim Over Data Management
Office Information Systems
1980
and Computer Science
Inconsistency Handling in
Multiperspective
Specifications
Paul Beckman,Asa Forsman
1994
Jason Dedrick, Vijay
Gurbaxani,Kenneth L Kraemer
Economics of
Information
Economics of
Information
Clarence A. Ellis,Gary J. Nutt
System Analysis &
Design
E.A Feigenbaum,B.G Buchanan
Artificial Intelligence
Anthony C. W. Finkelstein, Dov
Gabbay, Anthony Hunter, Jeff
Kramer,Bashar Nuseibeh
Data Management
The Vocabulary Problem in
G. W. Furnas, T. K. Landauer, L. M.
Human System
1987
Data Management
Gomez,S. T. Dumais
Communication
Decision Making and
Problem Solving
Robin Hogarth Herbert A. Simon
George B. Dantzig, Charles R. Piott,
1986 Howard Raiffa, Thomas C. Schelling, Decision Sciences
Kennth A. Shepsle, Richard Thaier,
Amos Tversky, and Sidney Winter
5
115
5
Semantic database
modeling: survey,
118
1987
applications, and research
issues
5
An Assessment of Individual
Kun Shin Im, Kee Young Kim,Joon S.
120 and Institutional Research 1998
Kim
Productivity in MIS
5
130
Natural Language Processing
1996
for Information Retrieval
K.S Jones
Artificial Intelligence
5
135
Comparing Data Modeling
1995
Formalisms
Y. G. Kim,S. T. March
Data Management
5
Systems Development in
172
Information Systems
Research
J. F. Nunamaker, Jr., Minder
Chen,Titus D.M. Purdin
OTHER
5
Software Engineering
180 Programs are not Computer 1999
Science Programs
David Lorge Parnas
OTHER
5
140
What is social informatics
and why does it matter?
Rob Kling
Social Informatics
5
149
Computer as a
Communication Device
1968 Licklide.Jc, R. W. Taylor,E. Herbert
HCI
157
A Brief history of Human
Computer Interaction
Technology
1998
Brad A. Myers
HCI
Sudha Ram
Data Management
Pamela Samuelson
OTHER
Ralph H. Sprague
Decision Sciences
Daniel Teichroew,John F. Lubin
System Analysis &
Design
5
19901991
1999
5
Guest Editor's Introduction:
1 Heterogeneous Distributed 1991
Database Systems
5
9
5
26
5
Computer Simulation -241 Discussion of the Technique 1966
and Comparison of
Copyright's fair use doctrine
1994
and digital data
A Framework for the
Development of Decision
Support Systems
1980
Richard Hull,Roger King
Data Management
OTHER
Languages
E-Commerce: Structures and
1996
Issues
5
207
6
Semantics and
implementation of schema
Jay Banerjee, Won Kim, Hyoung-Joo
56
1987
Data Management
evolution in object-oriented
Kim,Henry F. Korth
databases
6
Programming-in-the-Large
78 versus Programming-in-the- 1975
Small
6
86
6
95
6
97
6
Context interchange:
overcoming the challenges
110 of large-scale interoperable 1994
database systems in a
dynamic environment
6
210
Notes on Structured
Programming
1969
Vladimir Zwass
Frank DeRemer,Hans Kron
System Analysis &
Design
Edsger W. Dijkstra
System Analysis &
Design
Aspect-Oriented
Tzilla Elrad, Robert E. Filman,Atef
2001
Programming: Introduction
Bader
Display-Selection Techniques
1967
for Text Manipulation
eCommerce
System Analysis &
Design
W. K. English, Engelbar.Dc,M. L.
Berman
HCI
Cheng Hian Goh, Stuart E.
Madnick,Michael D. Siegel
Data Management
Service-oriented computing:
2005
key concepts and principles
M. N. Huhns,M. P. Singh
System Analysis &
Design
6
Information systems
224 interoperability: What lies 2004
beneath?
Park Jinsoo,Ram Sudha
Data Management
6
132
6
On the Criteria To Be Used in
177 Decomposing Systems into 1972
Modules
David Lorge Parnas
System Analysis &
Design
6
A Technique for Software
178 Module Specification with 1972
Examples
David Lorge Parnas
System Analysis &
Design
6
On the Design and
179 Development of Program
Families.
David Lorge Parnas
System Analysis &
Design
6
Machine learning
Lin Liang Patrick Suppes, Michael
181 comprehension grammars 1996
Artificial Intelligence
Buettner
for ten languages
Querying object-oriented
databases
1992
1976
Michael Kifer, Won Kim,Yehoshua
Data Management
Sagiv
6
A Language/Action
201 Perspective on the Design of 1988
Cooperative Work
Terry Winograd
System Analysis &
Design
6
The Evolving Philosophers
142 Problem: Dynamic Change 1990
Management
Jeff Kramer,Jeff Magee
System Analysis &
Design
6
The determination of
efficient record
153
1977
segmentations and blocking
factors for shared data files
Salvatore T. March,Dennis G.
Serverance
Data Management
6
The Draco Approach to
158 Constructing Software from 1984
Reusable Components
James M. Neighbors
System Analysis &
Design
6
Intelligent database design
2 using the unifying semantic 1995
model
Sudha Ram
Data Management
6
3
DENDRAL: A Case Study of
the First Expert System for
1993
Scientific Hypothesis
Formation
6
5
6
6
6
Semantic Content
Amit P. Sheth, C. Bertram, D. Avant,
18 Management for Enterprises 2002
Artificial Intelligence
B. Hammond, K. Kochut,Y. Warke
and the Web
6
Managing Heterogeneous
Multi-System Tasks to
19
1995
Support Enterprise-wide
Operations
6
Beyond the Chalkboard Computer Support for
M. Stefik, G. Foster, D. G. Bobrow,
184
1987
Collaboration and ProblemK. Kahn, S. Lanning,L. Suchman
Solving in Meetings
6
185
6
187
6
188 A Methodology for Learning 2002
Across Application Domains
Learning to reason
1997
Structured Analysis (SA): A
Language for
1976
Communicating Ideas
Structured Design
1974
Bruce G. Buchanan Robert K.
Lindsay, Edward A. Feigenbaum, Artificial Intelligence
Joshua Lederberg
Dan Roth Roni Khardon
Artificial Intelligence
Douglas T. Ross
System Analysis &
Design
Amit P. Sheth,N. Krishnakumar
W. P. Stevens, G. J. Myers,Larry L.
Constantine
Workflow/Business
Process
Management
Collaboration
System Analysis &
Design
The design and
Michael Stonebraker, Gerald Held,
1976
Data Management
implementation of INGRES
Eugene Wong,Peter Kreps
V. C. Storey,D. Dey
Data Management
for Database Design Systems
6
190
A comparative analysis of
disk scheduling policies
1972
Toby J. Teorey,Tad B. Pinkerton
OTHER
6
204
Program Development by
Stepwise Refinement
1971
Niklaus Wirth
System Analysis &
Design
7
32
Anchoring the Software
Process
1996
Barry W. Boehm
System Analysis &
Design
7
42 Software Risk Management 1997
Barry W. Boehm,Tom DeMarco
System Analysis &
Design
7
Review: Knowledge
Management and
Knowledge Management
220
2005
Systems: Conceptual
Foundations and Research
Issues
M. Alavi,D. E. Leidner
Knowledge
Managment
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