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Simulation & Gaming

41(6) 869 –897

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DOI: 10.1177/1046878110387539

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SG387539 SAG41610.177/1046878110387539Braggeet al.Simulation & Gaming

1Aalto University, Helsinki, Finland2Towson University, Towson, MD, USA3Turku School of Economics, Turku, Finland

Corresponding Author:

Johanna Bragge, Department of Business Technology, Aalto University School of Economics,

P.O. Box 21220, 00076 Aalto, Finland

Email: [email protected]

Profiling 40 Years

of Research in

Simulation & Gaming

Johanna Bragge1, Precha Thavikulwat2, and Juuso Töyli3

Abstract

The authors apply the research profiling method to review all the research that hasbeen published in Simulation & Gaming since the journal’s inauguration in 1970. Thedata include 2,096 articles, of which 1,046 are research articles. The authors identifythe prolific authors and their institutional affiliations. They tally referenced articles,title phrases, and descriptors. They find that the most prolific authors neither engagein more work division nor author more conventional thinking articles than less prolificauthors and that the 51 prolific authors fall into 7 to 11 clusters.

Keywordsaffinity propagation, bibliometrics, clusters of authors, conventional thinking, descrip-tors, hidden patterns, hot topics, knowledge creation, literature review, mapping,new thinking, profiling, prolific authors, research articles, research profiling, statisticalmethods, text mining, topic evolution, visualization, work division

A literature review is an essential part of every research initiative. Its purpose is to

assess and advance the current state of knowledge, identify uncovered areas and direc-

tions for future research, advance and facilitate theory development, and/or guide policy decisions (Guzzo, Jackson, & Katzell, 1987; Webster & Watson, 2002). The

review may serve either to synthesize a mature topic or to link an emerging issue to

theoretical foundations (Webster & Watson, 2002). Methodologically, it may be in the

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870 Simulation & Gaming 41(6)

form of a narrative review at one end and a quantitative meta-analysis at the other end

(King & He, 2005). In between these extremes are descriptive reviews that combine

verbal description with quantification, a notable example of which is Faria, Hutchinson,

and Wellington’s (2009) review of business games. Besides the methods discussed byKing and He (2005), review methods using computer intensive techniques, such as

text-mining and social network analysis, have recently received increasing interest

(Börner, Chen, & Boyack, 2003; Bragge, Korhonen, Wallenius, & Wallenius, 2010;

Porter, Kongthon, & Lu, 2002; Raghuram, Tuertscher, & Garud, 2010; SciMaps, 2005;

Small, 1999). These bibliometric approaches can reveal hidden patterns in the data and

allow the analysis of large data sets beyond what is feasible with more traditional

approaches.

In this study, we employ research profiling, a bibliometric method, first, to depict

and visualize the research that has appeared in Simulation & Gaming (S&G) since itsinauguration in 1970 and, second, to compare the patterns of work division and knowl-

edge creation among top-publishing authors and other prolific authors. Our data consist

of matrices of frequency counts among authors, author affiliations, references, titles,

and descriptors taken from the database of the publisher of S&G.

Research Profiling

Methodology

Research profiling (Porter et al., 2002) is based on bibliometrics, broadly defined as the

application of mathematical and statistical methods to books and other media of com-

munication (Hood & Wilson, 2001; Pritchard, 1969). Bibliometric studies typically

enhance general activity counts by examining item occurrences or co-occurrences,

inasmuch as terms appearing together more often than expected may reflect a signifi-

cant relationship (Porter et al., 2002). The most common bibliometric items studied are

references (Boyack, Klavans, & Börner, 2005; Culnan, 1986, 1987; Meyer, Lorscheid,

& Troitzsch, 2009; Raghuram et al., 2010). Research profiling extends the scope of

bibliometric studies by examining content words with text-mining tools (Yang, Akers,Klose, & Barcelon Yang, 2008).

Research profiling supplies answers to four type of questions, namely, who, what,

where, and when (Porter et al., 2002; Watts & Porter, 2007).

• Who are the prolific authors publishing research in a certain field?

• What are their specific research topics?

• Where are the research results published?

• When did each topic appear in the literature?

Additional questions, such as which institutions conduct research published in

the field, what are the hot topics in the area, and how have the topics evolved over

time also may be addressed. Answers are provided using simple frequency lists,







Bragge et al. 871

two-dimensional tables, and graphs of trends. In addition to these basic analyses, cor-

relational and factor analytic tools may be used find clusters and produce maps.Research profiling can be considered as an iterative problem-solving process.

Borrowing from Simon’s (1960) seminal work, Porter and Cunningham (2005) suggest

that the process includes three main phases: (A) intelligence, (B) analysis and design,

and (C) choice. Each phase includes two to four steps, as illustrated in Figure 1.

Following this process, we set out to profile all the research that has been published

in S&G during 1970-2009. S&G published four issues a year until 2008 and publishes

six issues a year since then. Although academic research on simulation/gaming has

been published in various other journals, this body of literature is fragmented. Thus,

we decided to make a journal-based study instead of a content-based study, becauseS&G is one of the top journals in this field and the most focused on the topic. Moreover,

40 years is such a long time for a single journal that it alone provides data abundantly

to be profiled with bibliometric methods.

Recently, Crookall (2009) gave a thorough overview of S&G’s history. In brief,

the journal was originally named Simulation & Games: An International Journal of

Theory, Design, and Research. It has been renamed twice, in 1990 and in 2000. The

current full name is Simulation & Gaming: An Interdisciplinary Journal of Theory,

Practice and Research. Besides research articles, the journal publishes reviews, asso-

ciation news and notes, ready-to-use games and simulations, reports and communica-tions, and biographies.

The bibliographic data for this study was acquired from S&G’s publisher, Sage

Publications. We collected data of all published items from 1970 (Volume 1, Number 1)

through 2009 (Volume 40, Number 6). In addition, we included 37 prepublished items

from Sage Publications’ OnlineFirst service, up to November 17, 2009. A complete set

of statistics was not available for the OnlineFirst articles, so we could not include them

in every analysis. Tables and figures wherein OnlineFirst articles are included show a

plus symbol in the range of years (i.e., 1970-2009+). Altogether, 2,096 published items

formed our final data set.Regarding publication type, Sage categorizes S&G articles into 13 different classes:

research article, review, editorial, about the author, news, conference, letter, erratum,

notes, obituary, bibliography, introduction, and other. We found many miscategorizations

Phase A: Intelligence

1. Issue identification

2. Selection of information sources

3. Search refinement and dataretrieval

4. Data cleaning

Phase B:

Analysis & Design

5. Basic analyses6. Advanced analyses

Phase C:

Choice

7. Representation8. Interpretation9. Utilization

Figure 1. Phases of the research profiling process









in the data (e.g., a news item is categorized as a journal article), so we resorted theitems into three main categories: research articles (1,046 items), editorials (120 items),

and others (930 items). We present first a basic bibliometric analysis on all publica-

tions, after which we present basic and advanced bibliometric analyses on the research

articles. Because of space limitations, we depict the analyses rather briefly in these

sections and elaborate on the results primarily in the discussion section that follows.

All Publications

Excluding OnlineFirst items, the number of research articles, editorials, and others aredepicted, year by year, in Figure 2. The number of research articles rose by 63% from

1970 to 2009, and the number of editorials increased by 530% during the same interval.

The others category include reviews as the largest type. The reviews averaged around

10 per year in the first three decades, but they dropped to about two per year during

2000-2009.

Table 1 presents the top 10 authors based on the total of all types of publications.

The last column depicts the authors’ most common recurring title words. From those

we may see that several of the top authors in this all-publication-types list have regu-

larly published news items related to one of the following associations: InternationalSimulation and Gaming Association (ISAGA), the North American Simulation and

Gaming Association (NASAGA), the Association for Business Simulation and

Experiential Learning (ABSEL), and the Japan Association of Simulation and Gaming

(JASAG). Also, the statistics on the number of pages per article (see Figure 3),

Figure 2. The number of published items (1970-2009)











research articles to S&G within the study period. The most prolific authors (Table 4),

having each contributed seven or more articles, represent 0.7% of all S&G article

authors but account for 12.8% of the S&G articles published. The top-3 authors

Table 2. Top-10 Referenced Authors of All Types of Publications (1970-2009)

Rank Referenced AuthorNumber of Referencing

ArticlesNumber of

Referencing Instances

1 Wolfe, J. 156 471

2 Greenblat, C. S. 104 174 3 Keys, B. 103 166

4 Crookall, D. 96 149

5 Duke, R. D. 88 183

6 Faria, A. J. 70 149

7 Boocock, S. S. 67 104

8 Coleman, J. 60 88

9 Kolb, D. A. 58 98

10 Guetzkow, H. 55 96

Figure 3. Pages per article across all types of publications (1970-2009)








Bragge et al. 875

(Wolfe, Jones, and Thavikulwat) in this list also appear in the top-10 author list of all

publication types (Table 1).

The average range of publication years is 22.5 for the top-10 authors (24.3, if

excluding Thatcher, whose seven publications appear in a single year, from the calcu-

lations). Martin Shubik has the longest publication year range (39 years), followed byJoseph Wolfe (34 years) and Jan Klabbers (30 years). It is a strength for the journal to

have so many active authors with an extensive perspective to the field.

Table 5 presents the authors’ top affiliations. The rank order is based on the number

of research articles, but for comparison we also present the number of instances, which

Table 3. Distribution of Research Articles by Number of Authors per Article (1970-2009+)

Number of Authors per Article Percentage of Articles

1 52.20 2 30.31

3 11.19

4 3.35

5 1.24

6 0.48

7 0.57

8 0.29

10 0.19

11 0.10

12 0.10

Table 4. Top-10 Authors of Research Articles (1970-2009+)

Rank Author Number of Articles Range of Publication Years

1 Wolfe, Joseph 23 1976-2009

2 Jones, Kena 17 1986-2004

3 Thavikulwat, Precha 13 1989-2009+

4 Gold, Steven C. 12 1984-2009

4 Klabbers, Jan H. G. 12 1980-2009

6 Faria, Anthony J. 11 1987-2009

7 Pray, Thomas F. 9 1984-2002

7 Shubik, Martin 9 1971-2009

9 Cannon, Hugh M. 8 2000-2009+

10 Duke, Richard D. 7 1974-2000

10 Lederman, Linda Costigan 7 1978-2001

10 Teach, Richard D. 7 1990-2007

10 Thatcher, Donald C. 7 1990

a. Eleven research articles by Ken Jones appear in the September 1998 issue, which was devoted to hissubstantial contributions over many years.









is larger when an article is coauthored by two authors from the same institution. The

majority of research published in S&G comes from U.S. universities. The only non-

U.S. institution among the top 10 is the University of Windsor, Canada. Other non-

U.S. institutions among the top-35 affiliations are Delft University of Technology, the

Netherlands (11th with 10 research articles); the National University of Singapore(also 11th with 10 research articles); and Tel-Aviv University, Israel (25th with 6

research articles).

Figure 4 depicts the distribution of the number of references, M = 22.6, of the 1,010

research articles (excluding OnlineFirst items). About one third of the articles have 10

or fewer references, and about half of the articles have 17 or fewer references.

Table 6 lists the 10 most referenced authors, ranked by the number of research

articles referencing each author. Nine of the top-10 referenced authors are the same as

in Table 2 for all publication types, with only Biggs replacing Guetzkow in 10th place.

The rank order of referenced authors remains practically the same when self-writtenarticles are excluded from the number of referencing articles—only Boocock would

change places with Kolb, with a difference of one referencing article. For comparison,

Table 6 also shows the number of referencing instances as well as the number of self-

referencing instances. As an example, Joe Wolfe has been referenced in 145 research

articles, appearing in 450 instances (i.e., around 3 of his articles have been referenced

in one referencing article). From these numbers 20 and 107 are self-referencing arti-

cles and instances, respectively.

Not surprising, most authors cite their own articles at least once in each article, a

pattern that is approximately constant with respect to the number of referencing arti-cles. More variation exists in the number of self-referencing instances, which ranges

from 2.3% to 35.4% of total referencing instances.

Table 5. Top-10 Affiliations of Authors of Research Articles (1970-2009)

Rank AffiliationNumber of

Research ArticlesNumber ofInstances

1 Oklahoma State University 26 292 University of Michigan 24 25

3 Rutgers University 20 20

3 University of Tulsa 20 20

5 Rochester Institute of Technology 16 16

6 Towson University 12 12

6 University of Windsor 12 16

6 Yale University 12 12

9 Georgia Institute of Technology 11 11

9 University of Southern California 11 11








Bragge et al. 877

Table 7 lists the most common journal or proceedings outlets of the references bro-

ken down by decades. Consistent with the findings of other research profiling studies(Bragge et al., 2010), the most commonly referenced journal is almost without excep-

tion the journal itself, as in this case. What is worth noticing is the astounding rise in

Figure 4. Distribution of number of references for research articles (1970-2009)

Table 6. Top-10 Referenced Authors of Research Articles (1970-2009)

Rank Referenced

AuthorNumber of

Referencing Articles

Number ofSelf-Referencing

Articles

Number ofReferencingInstances

Number ofSelf-Referencing

Instances

1 Wolfe, J. 145 20 (out of 23) 450 107

2 Greenblat, C. S. 99 4 (out of 5) 159 21

3 Keys, B. 96 4 (out of 5) 144 31

4 Crookall, D. 85 4 (out of 4) 130 14

5 Duke, R. D. 79 4 (out of 7) 166 58 6 Faria, A. J. 68 9 (out of 11) 147 42

7 Kolb, D. A. 57 4 (out of 4) 96 34

8 Boocock, S. S. 56 2 (out of 3) 86 2

9 Coleman, J. 53 1 (out of 1) 79 2

10 Biggs, W. D. 51 4 (out of 4) 91 26









the referencing for the conference proceedings of ABSEL ( Developments in Business

Simulation and Experiential Learning ) during the last two decades.

Figure 5 shows a graph of the cited years of the references. The earliest date back

to works such as Cournot’s (1838) Recherches sur les principes mathématiques de la

théorie des richesses. Most referenced works were published between 1966 and 2002,

peaking in 1975 and 1990, and dipping in 1983.

Table 8 presents the most common title phrases of the articles and their temporaldistribution. Natural to the scope of the journal, title phrases that include either simula-

tion or game/gaming dominate the list, appearing in 38% of the top-29 title phrases.

Among them, simulation games and computer simulation have decreased in usage

from the early decades, whereas business simulation has increased in the 1990s and

thereafter. The largest increase in the last decade has been with the word review, due

to 10 (out of more than 30) review articles already published in 2009 in the 40th

Anniversary Symposium (in which this article appears).

Table 9 presents the most common descriptors of the articles and their temporal

distribution. The descriptors are based on Sage Publications’ classification scheme.Inasmuch as the descriptors were not available for years 1970 and 2007-2009, we

divided the time horizon into six equal-length periods, from 1971 to 2006. The top two

descriptors have kept their ranks throughout the 36 years presented in the table.

Table 7. Top-10 Referenced Publications of Research Articles (1970-2009)

Rank

Number ofReferencing

Articles

Referenced

Publication

Number of Referencing Instances

1970-1979 1980-1989 1990-1999 2000-2009 1 470 S&G 142 351 519 1006

2 92 Management Science 40 54 51 30

3 86 American Behavioral

Scientist

53 29 20 10

4 74 Academy of

Management Journal

13 37 37 22

5 72 Developments in

Business Simulation

and Experiential

Learning (ABSELProceedings)

1 61 153 453

6 69 Academy of

Management Review

2 42 48 22

7 65 Decision Sciences 6 47 37 12

8 62 Journal of Business 24 33 20 12

9 60 Journal of Personality

and Social Psychology

36 34 39 36

10 56 Journal of Management 0 5 43 19








Bragge et al. 879

Descriptors such as management , organizational learning , management methods,

health services administration, business, and health planning have increased notice-

ably in frequency, whereas community development , industrial psychology, mathemat-

ical optimization, and discriminant analysis have declined in frequency.

Table 10 is a temporal breakdown of S&G’s top authors, affiliations, referenced

authors, referenced publications, title phrases, and descriptors. It summarizes the find-

ings of this section.

Analyzing the Relationships

We now analyze the relationships among S&G authors. More specifically, we examine

patterns of work division and knowledge creation that may differentiate top-publishing

authors from other prolific authors, and we determine the extent to which the research

articles of prolific authors are clustered. Our data suffice to answer three questions:

1. Among the prolific authors, do those who author more articles in S&G

engage in more work division than those who author fewer articles? Bywork division we mean authors dividing their work among themselves and

thereby raising the number of articles for which each receives publication

credit.

Figure 5. Cited years of references of research articles









If the higher number of publications by those who have authored the most articles

is due to work division, then those who have authored the most articles in S&G will

have engaged in more coauthorship than those who have authored fewer articles. The

alternative hypothesis is that highly published authors do not coauthor more than less published authors, so to the extent that they divide work among coauthors, they do not

do it more frequently than less published authors.

2. Among the prolific authors, do those who author more articles in S&G write

proportionately more articles that contribute incrementally to conventional

thinking, compared with articles that present new thinking, than those who

author fewer articles?

If those who have authored the most articles in S&G write proportionately morearticles that contribute incrementally to conventional thinking rather than articles that

present new thinking, then their articles will reference more of the widely referenced

S&G authors than those who have authored fewer articles. The alternative hypothesis

is that highly published authors do not reference widely referenced S&G authors more

frequently, so their articles should contain about as much new thinking as those of less

published authors.

3. Among the prolific authors, can clusters of authors be identified together with

each cluster’s exemplar, which is the author at the center of each cluster, so thatthose seeking to understand the totality of the work that has been published

in S&G may be able to identify quickly the dominant schools of thought by

studying first the articles authored by the exemplars?

The identification of clusters depends on the measure of similarity between items

and on the algorithm used to find the clusters given the selected measure. We use the

pattern of citations as our measure of similarity and affinity propagation (Frey &

Dueck, 2007, 2008) as the algorithm for finding clusters and their exemplars.

We obtained the answers to these questions by drawing from two S&G author database matrices: a matrix of coauthors and a matrix of authors by referenced authors. The

matrix of coauthors is an n-by-n matrix wherein the same n items, the authors, appear

in the rows as well as in the columns. The matrix of authors by referenced authors is

an n-by-m matrix wherein n items, the referencing authors, occupy the rows, and m

items, the referenced authors, occupy the columns. For both types of matrices, the

numbers appearing in the intersections of the rows and columns are the number of

times each row item is associated with each column item.

Matrix of Coauthors

For the matrix of coauthors, we selected authors who have authored or coauthored at

least four research articles in S&G between 1970 and 2009+ (including 8 OnlineFirst

articles). The collection amounts to 51 authors whose authored articles range from 4 to 23,







Bragge et al. 881

M = 6.16, SD = 3.69. Of this collection, we count 32 coauthor instances, each instance

being a case when one author’s name appears with another as a coauthor of an article.

To assess the extent of coauthorship, we define a coauthorship index (CAI) as the

number of coauthor instances divided by the number of articles authored. For example,

in the case of two authors both coauthoring two articles, each author’s CAI is 1(2 coauthor instances divided by 2 articles). Figure 6 is a plot of CAI by number of

articles. The Pearson correlation between the two is .016, p = .913, ns. Thus, highly

published authors do not achieve their higher numbers by dividing work more fre-

quently than less published authors.

Table 8. Top-29 Title Phrases of Research Articles (1970-2009)

Rank Number of

Articles Title Phrases 1970-1979 1980-1989 1990-1999 2000-2009

1 139 Simulation 24 23 47 38 2 46 Game 5 10 16 10

3 35 Gaming 4 4 15 11

4 34 Simulation Games 14 7 8 4

5 25 Learning 6 3 10 5

6 24 Simulation/Gaming 1 6 10 6

6 24 Effect 11 9 0 4

8 21 Use 5 5 8 3

9 19 Business Games/-ing 5 5 6 3

9 19 Review 0 2 3 1411 16 Computer Simulation 4 9 1 2

12 15 Business Simulation 0 0 9 6

13 13 Study 2 8 2 1

13 13 Development 3 3 2 5

15 12 Game/-ing Simulation 0 7 1 4

15 12 Role 2 1 3 6

17 11 Education 5 4 1 1

17 11 Experiential Learning 0 3 5 3

19 10 Practice 0 3 4 3

19 10 Research 3 3 1 3

21 9 Debriefing 0 1 4 2

21 9 Case Study 0 2 3 3

21 9 Theory 1 1 4 3

24 8 Simulation Model 4 1 1 1

24 8 Computerized BusinessSimulations

0 2 5 1

24 8 Assessment 0 1 2 5

27 7 Play/-ing 0 2 2 3

27 7 Computer 2 4 1 027 7 Attitude 2 4 1 0








882

T a b l e 9 . T o p - 3 9 D e s c r i p t o r s o f R e s e a r c h A r t i c l e s ( 1 9 7 1 - 2 0 0 6 )

R a n k

N u m b e r o f

A r t i c l e s

D e s c r i p t o r s

1 9 7 1 - 1 9 7 6

1 9 7 7 - 1 9 8 2

1 9 8 3 - 1 9 8 8

1 9 8 9 - 1 9 9 4

1 9 9 5 - 2 0 0 0

2 0 0 1 - 2 0 0 6

1

3 8 7

D e c i s i o n m a k i n g

5 8

7 3

5 7

7 3

7 7

4 9

2

3 8 1

M a n a g e m e n t g a m e s

2 8

7 1

4 7

7 5

9 0

7 0

3

2 3 0

P r o g r a m m i n g m a n a g e m e n t ( C o m p u t e r s )

2 4

2 4

2 8

4 3

7 4

3 7

4

1 5 1

M a n a g e m e n t s c i e n c e

1 5

6 2

1 2

2 1

5

3 6

5

1 4 0

B u s i n e s s e d u c a t i o n

6

1 7

1 7

3 2

4 6

2 2

6

1 2 0

P r o b l e m s o l v i n g

1 0

1 3

2 2

2 3

2 7

2 5

7

8 2

M a n a g e m e n t r e s e a r

c h

1 1

3 6

7

4

4

2 0

8

7 4

E d u c a t i o n a l a d m i n i s

t r a t i o n s o f t w a r e

9

1 2

6

1 7

1 4

1 6

9

5 7

H u m a n r e s o u r c e m a n a g e m e n t

8

7

7

1 2

1 1

1 2

1 0

5 3

O r g a n i z a t i o n a l b e h a v i o r

4

9

1 1

8

1 1

1 0

1 1

5 0

I n f o r m a t i o n s y s t e m s

1 4

2

8

8

1 2

6

1 2

4 5

M a n a g e m e n t

1

3

7

2

1 3

1 9

1 2

4 5

O r g a n i z a t i o n a l l e a r n i n g

0

0

3

1 5

1 5

1 2

1 4

4 4

O r g a n i z a t i o n a l r e s e a r c h

2

3

1 6

1 8

3

2

1 5

4 2

M a n a g e m e n t t r a i n i n

g

2

7

5

6

1 6

6

1 6

4 1

M a n a g e m e n t m e t h o

d s

1

1 0

1

0

0

2 9

1 7

3 9

P l a n n i n g

7

3

4

1 0

1 1

4

1 8

3 8

S c h o o l a d m i n i s t r a t i o n

4

1 3

4

2

1 2

3

1 9

3 7

C o n f l i c t m a n a g e m e n t

6

2

6

1 0

9

4

2 0

3 5

C o m m u n i t y d e v e l o p m e n t

9

1 0

5

3

6

2

2 1

3 4

S t r a t e g i c p l a n n i n g ( B u s i n e s s )

0

2

4

1 6

9

3

2 2

3 3

B u s i n e s s t e a c h e r s

8

1

7

2

1 0

5

2 3

3 1

I n d u s t r i a l p s y c h o l o g

y

5

1 0

6

6

3

1

2 4

2 9

S m a l l g r o u p s

1

2

8

1 0

5

3

2 5

2 7

C o m m u n i t y r e l a t i o n s

3

7

6

6

4

1

( c o n t i n u e d )








R a n k

N u m b e r o f

A r t i c l e s

D e s c r i p t o r s

1 9 7 1 - 1 9 7 6

1 9 7 7 - 1 9 8 2

1 9 8 3 - 1 9 8 8

1 9 8 9 - 1 9 9 4

1 9 9 5 - 2 0 0 0

2 0 0 1 - 2 0 0 6

2 6

2 6

M a t h e m a t i c a l o p t i m

i z a t i o n

1 1

4

5

0

4

2

2 7

2 5

H e a l t h s e r v i c e s a d m

i n i s t r a t i o n

0

1

2

5

1

1 6

2 8

2 3

B u s i n e s s

0

1

2

3

4

1 3

2 8

2 3

M a n a g e m e n t d e v e l o

p m e n t p r o g r a m s

0

1

4

9

8

1

2 8

2 3

O r g a n i z a t i o n a l c h a n

g e

1

1

3

3

9

6

2 8

2 3

E n t r e p r e n e u r s h i p

0

0

2

7

1 2

2

2 8

2 3

T e a m w o r k ( W o r k p l a c e )

1

0

3

8

7

4

3 3

2 2

I n f o r m a t i o n m a n a g e

m e n t

7

2

3

4

2

4

3 4

2 1

C r i s i s m a n a g e m e n t

2

2

1

3

5

8

3 5

2 0

D i s c r i m i n a n t a n a l y s i s

8

7

3

1

1

0

3 6

1 7

O r g a n i z a t i o n a l s t r u c t u r e

0

3

3

4

5

2

3 7

1 6

H e a l t h p l a n n i n g

0

1

2

2

1

1 0

3 8

1 5

L a b o r r e l a t i o n s

4

5

1

3

2

0

3 8

1 5

B u s i n e s s n e g o t i a t i o n s

0

2

2

0

7

4

T a b l e 9 . ( c o n t i n u e d )

883








884

T a b l e 1 0 .

S u m m a r y o f S & G R e s e a r c h A r t i c l e s O v e r F o u r D e c a d e s

1 9 7 0 - 1 9 7 9

1 9 8 0 - 1 9 8 9

1 9 9 0 - 1 9 9 9

2 0 0 0 - 2 0 0

9

N o . o

f a r t i c l e s

1 8 7

2 2 1

3 0 4

2 9 8

T o p a u t h o r

s

A l l e n , L . E . [ 4 ]

W o l f e , J .

[ 6 ]

J o n e s , K . [ 1 5 ]

C a n n o n , H . M . [ 7 ]

S h u b i k M . [ 4 ]

D u k e , R . D . [ 4 ]

W o l f e , J .

[ 1 3 ]

K l a b b e r s , J .

H . G . [ 7 ]

B r e w e r , G . D . [ 3 ]

L e d e r m a n , L . C . [ 3 ]

T h a t c h e r , D . C . [ 7 ]

B a r a c h , P .

[ 4 ]

D u k e s , R . L . [ 3 ]

N o r r i s , D . R . [ 3 ]

T h a v i k u l w a t , P .

[ 7 ]

F a r i a , A . J .

[ 4 ]

E n z l e , M . E . [ 3 ]

R e m u s , W . E . [ 3 ]

G o l d , S . C . [ 6 ]

G a s t a o S a l i e s , T . [ 4 ]

K i d d e r , S . J .

[ 3 ]

W i l l i a m s , R . H . [ 3 ]

F a r i a , A . J .

[ 5 ] ,

G o l d , S . C . [ 4 ]

L i v i n g s t o n , S . A . [ 3 ]

P r a y , T . F .

[ 5 ]

H a l l e c k , G . B . [ 4 ]

M i l l e r , L . D . [ 3 ]

Y e o , G . K . [ 5 ]

H i l l , J . L . [ 4 ]

N a y l o r , T . H . [ 3 ]

M a y e r , I .

S . [ 4 ]

T h a v i k u l w a t , P .

[ 4 ]

T o p a f f i l i a

t i o n s

J o h n s H o p k i n s U n i v e r s i t y

[ 8 ] R u t g e r s U n i v e r s i t y [ 8 ]

U n i v e r s i t y o f T u l s a [ 1 3 ]

O k l a h o m a S t a t e U n i v e r s i t y [ 2 2 ]

U n i v e r s i t y o f M i c h i g a n [ 8 ]

U n i v e r s i t y o f M i c h i g a n [ 7

]

R o c h e s t e r I n s t i t u t e o f

T e c h n o l o g y [ 8 ]

D e l f t U n i v e r s i t y o f T

e c h n o l o g y

[ 7 ]

Y a l e U n i v e r s i t y [ 7 ]

U n i v e r s i t y o f T u l s a [ 7 ]

T o w s o n U n i v e r s i t y [ 7 ]

W a y n e S t a t e U n i v e r

s i t y [ 7 ]

O h i o S t a t e U n i v e r s i t y [ 6 ]

U n i v e r s i t y o f M i n n e s o t a

[ 5 ]

G e o r g i a I n s t i t u t e o f


G e o r g i a I n s t i t u t e o f

T e c h n o l o g y

[ 5 ]

R u t g e r s U n i v e r s i t y [ 5 ]

A r i z o n a S t a t e U n i v e r s i t y

[ 3 ]

N a t i o n a l U n i v e r s i t y o f

S i n g a p o r e [ 5 ]

R o c h e s t e r I n s t i t u t e

o f


A u b u r n U n i v e r s i t y [ 3 ]

R u t g e r s U n i v e r s i t y [ 5 ]

C I R A D , F r a n c e [ 4 ]

I n t e r n a t i o n a l I n s t i t u t e f o r

A p p l i e d S y s t e m s A n a l y s i s [ 3 ]

U n i v e r s i t y o f M i c h i g a n [ 5 ]

T o w s o n U n i v e r s i t y [ 4 ]

N o r t h e r n I l l i n o i s U n i v e r s i t y [ 3 ] U n i v e r s i t y o f W i n d s o r [ 5 ]

U n i v e r s i t y o f B e r g e n [ 4 ]

O h i o S t a t e U n i v e r s i t y [ 3

]

G e o r g e M a s o n U n i v e r s i t y [ 4 ] U n i v e r s i t y o f C h i c a g o [ 4 ]

R i c e U n i v e r s i t y [ 3 ]

S o l e n t S i m u l a t i o n s [ 4 ]

U n i v e r s i t y o f M i c h i g

a n [ 4 ]









1 9 7 0 - 1 9 7 9

1 9 8 0 - 1 9 8 9

1 9 9 0 - 1 9 9 9

2 0 0 0 - 2 0 0

9

N o . o

f a r t i c l e s

1 8 7

2 2 1

3 0 4

2 9 8

R o c h e s t e r I n s t i t u t e o f


U n i v e r s i t y o f M a r y l a n

d [ 4 ]

U n i v e r s i t y o f W i n d s

o r [ 4 ]

U n i v e r s i t y o f H a i f a [ 3 ]

U n i v e r s i t y o f W e s t F l o r i d a [ 4 ]

U n i v e r s i t y o f H a w a i i [ 3 ]

U n i v e r s i t y o f M i s s o u r i –

C o l u m b i a [ 3 ]

U n i v e r s i t y o f U t r e c h t [ 3 ]

T o p r e f e r e n c e

d

a u t h o r s

C o l e m a n , J [ 3 4 ]

G r e e n b l a t , C . S . [ 3 7 ]

W o l f e , J .

[ 5 7 ]

C r o o k a l l , D . [ 5 6 ]

B o o c o c k , S . S . [ 3 2 ]

W o l f e , J .

[ 3 2 ]

K e y s , B . [ 4 8 ]

W o l f e , J .

[ 5 2 ]

C h e r r y h o l m e s , C . [ 2 5 ]

D u k e , R . D . [ 2 7 ]

F a r i a , A . J .

[ 3 1 ]

K o l b , D . A . [ 3 3 ]

G u e t z k o w , H . [ 2 1 ]

C o h e n , K . J [ 1 7 ]

C r o o k a l l , D . [ 2 8 ]

K l a b b e r s , J .

H . G . [ 3

1 ]

S h u b i k , M . [ 2 1 ]

K e y s , B . [ 1 7 ]

G r e e n b l a t , C . S . [ 2 8 ]

K e y s , B . [ 2 8 ]

S t o l l , C . S . [ 1 9 ]

D i l l , W . R . [ 1 6 ]

B i g g s , W . D . [ 2 2 ]

F a r i a , A . J .

[ 2 7 ]

F l e t c h e r , J . L . [ 1 6 ]

B o o c o c k , S . S . [ 1 5 ]

P r a y , T . F .

[ 2 2 ]

G r e e n b l a t , C . S . [ 2 7

]

R a s e r , J .

R . [ 1 6 ]

G r e e n l a w , P .

S [ 1 5 ]

T e a c h , R . D . [ 2 2 ]

J o n e s , K . [ 2 5 ]

L i v i n g s t o n , S . A . [ 1 5 ]

J a c k s o n , J .

R . [ 1 5 ]

D u k e , R . D . [ 1 9 ]

C a n n o n , H . M . [ 2 4 ]

S c h i l d , E . O . [ 1 5 ]

N o r r i s , D . R . [ 1 5 ]

F r i t z s c h e , D . J .

[ 1 8 ]

O x f o r d , R . [ 2 4 ]

G a m s o n , W . A . [ 1 4 ]

R e m u s , W . E . [ 1 5 ]

G o l d , S . C . [ 1 8 ]

S a u n d e r s , D . [ 2 3 ]

J o n e s , K . [ 1 8 ]

D u k e , R . D . [ 2 2 ]

S e n g e , P .

M . [ 2 2 ]


T a b l e 1 0 .


885








886

1 9 7 0 - 1 9 7 9

1 9 8 0 - 1 9 8 9

1 9 9 0 - 1 9 9 9

2 0 0 0 - 2 0 0

9

N o . o

f a r t i c l e s

1 8 7

2 2 1

3 0 4

2 9 8

T o p r e f e r e n c e

d

p u b l i c a t i o

n s S i m u l a t i o n & G a m i n g [ 5 7 ]

S i m u l a t i o n & G a m i n g [ 9 9 ]

S i m u l a t i o n & G a m i n g [ 1 3 8 ]

S i m u l a t i o n & G a m i n g

[ 1 7 6 ]

A m e r i c a n B e h a v i o r a l

S c i e n t i s t [ 3 7 ]

D e c i s i o n S c i e n c e s [ 2 7 ]

J o u r n a l o f M a n a g e m e n t [ 3 4 ]

D e v e l o p m e n t i n B u s i n

e s s

S i m u l a t i o n a n d E x p e r i e n t i a l

L e a r n i n g [ 3 3 ]

A m e r i c a n P o l i t i c a l S c i e n c e

R e v i e w [ 2 4 ]

A m e r i c a n B e h a v i o r a l

S c i e n t i s t [ 2 3 ]

M a n a g e m e n t S c i e n c e [ 3 3 ]

H a r v a r d B u s i n e s s R e v

i e w [ 2 1 ]

B e h a v i o r a l S c i e n c e [ 1 9 ]


A c a d e m y o f M a n a g e m e

n t

R e v i e w [ 2 9 ]

A m e r i c a n P s y c h o l o g i s t [ 2 0 ]


A c a d e m y o f M a n a g e m e n t

R e v i e w [ 2 2 ]

A c a d e m y o f M a n a g e m e

n t

J o u r n a l [ 2 6 ]

A c a d e m y o f M a n a g e m

e n t

J o u r n a l [ 1 9 ]

J o u r n a l o f C o n f l i c t

R e s o l u t i o n [ 1 6 ]

J o u r n a l o f B u s i n e s s [ 2 2 ]

D e v e l o p m e n t s i n B u s i n e s s

S i m u l a t i o n a n d E x p e r

i e n t i a l

L e a r n i n g [ 2 6 ]

J o u r n a l o f P e r s o n a l i t y

a n d S o c i a l

P s y c h o l o g y [ 1 9 ]

J o u r n a l o f B u s i n e s s [ 1 5 ]

A c a d e m y o f M a n a g e m e n t

J o u r n a l [ 2 0 ]

D e c i s i o n S c i e n c e s [ 2 4 ]

M a n a g e m e n t S c i e n c e

[ 1 8 ]

A m e r i c a n S o c i o l o g i c a l R e v i e

w

[ 1 4 ]

P s y c h o l o g i c a l B u l l e t i n [ 1 6 ]

H a r v a r d B u s i n e s s R e v i e

w [ 2 1 ]

J o u r n a l o f M a n a g e m e

n t [ 1 7 ]

S o c i o m e t r y [ 1 3 ]

B e h a v i o r a l S c i e n c e [ 1 5 ]

A d m i n i s t r a t i v e S c i e n c e

Q u a r t e r l y [ 2 0 ]

A c a d e m y o f M a n a g e m

e n t

R e v i e w [ 1 6 ]

J o u r n a l o f P e r s o n a l i t y a n d S o c i a l

P s y c h o l o g y [ 1 5 ]

S i m u l a t i o n / G a m e s f o r

L e a r n i n g [ 1 9 ]

E d u c a t i o n a l R e s e a r c h

e r [ 1 6 ]

S y s t e m D y n a m i c s R e v

i e w [ 1 6 ]


T a b l e 1 0 .









1 9 7 0 - 1 9 7 9

1 9 8 0 - 1 9 8 9

1 9 9 0 - 1 9 9 9

2 0 0 0 - 2 0 0

9

N o . o

f a r t i c l e s

1 8 7

2 2 1

3 0 4

2 9 8

T o p t i t l e

p h r a s e s

S i m u l a t i o n [ 2 4 ]

S i m u l a t i o n [ 2 3 ]

S i m u l a t i o n [ 4 7 ]

S i m u l a t i o n [ 3 8 ]

S i m u l a t i o n G a m e [ 1 4 ]

G a m e [ 1 0 ]

G a m e [ 1 6 ]

R e v i e w [ 1 4 ]

E f f e c t [ 1 1 ]

C o m p u t e r S i m u l a t i o n [ 9 ]

G a m i n g [ 1 5 ]

G a m i n g [ 1 1 ]

L e a r n i n g [ 6 ]

E f f e c t [ 9 ]

L e a r n i n g [ 1 0 ]

G a m e [ 1 0 ]

B u s i n e s s G a m e s / - i n g [ 5 ]

S t u d y [ 8 ]

S i m u l a t i o n / G a m i n g [ 1 0 ]

B u s i n e s s S i m u l a t i o n

[ 6 ]

E d u c a t i o n [ 5 ]

G a m e / - i n g S i m u l a t i o n [ 7 ]

B u s i n e s s S i m u l a t i o n [ 9 ]

R o l e [ 6 ]

G a m e [ 5 ]

S i m u l a t i o n G a m e s [ 7 ]

S i m u l a t i o n G a m e s [ 8 ]

S i m u l a t i o n / G a m i n g [ 6 ]

U s e [ 5 ]

U s e [ 8 ]

A s s e s s m e n t [ 5 ]

D e v e l o p m e n t [ 5 ]

L e a r n i n g [ 5 ]

L e s s o n [ 5 ]

S y m p o s i u m A r t i c l e [ 5 ]

T o p d e

s c r i p t o

r s a

D e c i s i o n m a k i n g [ 8 4 ]

D e c i s i o n m a k i n g [ 1 1 6 ]

M a n a g e m e n t g a m e s [ 1 3 2 ]

M a n a g e m e n t g a m e s

[ 8 6 ]

M a n a g e m e n t g a m e s [ 7 0 ]

M a n a g e m e n t g a m e s [ 9 3 ]

D e c i s i o n m a k i n g [ 1 2 6

]

D e c i s i o n m a k i n g [ 6 1 ]

M a n a g e m e n t s c i e n c e [ 4 8 ]

P r o g r a m m i n g m a n a g e m e

n t

( C o m p u t e r s ) [ 4 9 ]

P r o g r a m m i n g m a n a g e

m e n t

( C o m p u t e r s ) [ 9 7 ]

P r o g r a m m i n g m a n a g e m e n t

( C o m p u t e r s ) [ 5 2 ]

P r o g r a m m i n g m a n a g e m e n t

( C o m p u t e r s ) [ 3 2 ]

M a n a g e m e n t s c i e n c e [ 4 5

]

B u s i n e s s e d u c a t i o n [ 6

6 ]


M a n a g e m e n t r e s e a r c h [ 2 8 ]

B u s i n e s s e d u c a t i o n [ 3 6 ]

P r o b l e m s o l v i n g [ 4 4 ]

M a n a g e m e n t m e t h o

d s [ 2 9 ]


T a b l e 1 0 .


887








1 9 7 0 - 1 9 7 9

1 9 8 0 - 1 9 8 9

1 9 9 0 - 1 9 9 9

2 0 0 0 - 2 0 0

9

N o . o

f a r t i c

l e s

1 8 7

2 2 1

3 0 4

2 9 8

P r o b l e m s o l v i n g [ 3 2 ]

E d u c a t i o n a l a d m i n i s t r a t i o n

s o f t w a r e [ 3 1 ]

P r o b l e m s o l v i n g [ 2 9

]

M a n a g e m e n t r e s e a r c h [ 2

6 ]

H u m a n r e s o u r c e

m a n a g e m e n t [ 2 3 ]

B u s i n e s s e d u c a t i o n [ 2 8 ]

O r g a n i z a t i o n a l l e a r n i n

g [ 2 3 ]

M a n a g e m e n t [ 2 2 ]


M a n a g e m e n t r e s e a r c h [ 2 0 ]

O r g a n i z a t i o n a l l e a r n

i n g [ 1 8 ]

E d u c a t i o n a l a d m i n i s t r a t i o n

s o f t w a r e [ 1 6 ]

H e a l t h s e r v i c e s

a d m i n i s t r a t i o n [ 1 6

]

N o t e : N u m b

e r s i n b r a c k e t s a r e n u m b e r o f r e s e a r c h a r t i c l e s .

a . N o t e t h a t

S a g e h a s n o t p u b l i s h e d d e s c r i p t o r s i n 1 9 7 0 a n d f r o m 2 0 0 7 o n w a r d

s .

T a b l e 1 0 .


888







Bragge et al. 889

Matrix of Authors by Referenced Authors

For the matrix of authors by referenced authors, we selected the same 51 authors as

in the matrix of coauthors and matched them to the 75 highest referenced authors over

the same period (excluding the data from 8 OnlineFirst articles with no reference

information). The reference counts of the referenced authors range from 24 to 145, M = 39.05, SD = 20.00.

To assess the extent of reference, we define a reference index (RI) as the number of

referencing articles divided by the number of authored articles. So the author with 5

published articles who references one widely referenced author in 4 articles and

another widely referenced author in 3 articles has an RI of 1.4 (7 referencing articles

divided by 5 authored articles). Figure 7 is a plot of RI by number of articles. The

Pearson correlation between the two is .262, p = .06, ns. Thus, to the extent that refer-

encing highly referenced authors is a measure of conventional thinking, highly pub-

lished authors do not achieve their higher numbers by writing proportionately moreconventional-thinking articles than less published authors. The alternative hypothesis

that the articles of highly published authors contain about as much new thinking as

those of less published authors is supported.

Figure 6. Relationship between coauthorship index and number of articles by each author









To measure the similarities among authors by their pattern of references, we Pearson

correlated the frequency counts of the matrix of authors by referenced authors across

the reference authors to obtain a 51-by-51 correlation matrix of authors. We appliedthe affinity propagation algorithm (see the appendix) to the matrix to derive the mini-

mum and maximum number of clusters shown in Table 10, where publication rank

refers to the rank in number of articles authored by each exemplar. The authors fall

into a minimum of 7 clusters and a maximum of 11 clusters, centered around Faria,

Mayer, Gold, Hill, Kin, Gamson, and Thatcher in the first 7 clusters and also around

Klabbers, Hornaday, Thorngate, and Lederman in the additional 4 clusters of the

11-cluster set.

Finding how a population is clustered is an endeavor that depends on the pattern of

linkages among the members of the population and the purpose to be served by thefinding. One could find, for example, that a population has one cluster or that a popula-

tion has as many clusters as the number of its members. Neither finding is ever wrong,

but neither finding is ever useful. Utility requires that the number of clusters fall within

a useful range. To meet the utility requirement, the number of clusters desired can be

specified in advance, after which an algorithm is applied to assign each member to

each cluster, but this method might not coincide with the population’s pattern of link-

ages. The dilemma is resolved by affinity propagation, which can be set to search,

through an iterative algorithm, for a small or large number of clusters. The search

might fail, or it might succeed in finding cluster numbers that are not useful. In ourcase, the search found 7 clusters when the algorithm was set to search for a small num-

ber and 11 clusters when the algorithm was set to search for a large number.

Figure 7. Relationship between reference index and number of articles by each author








Bragge et al. 891

Thus, to the extent that the clusters identify schools of thought, we find between 7

and 11 schools of thought, each school centered on an exemplar, as listed in Table 11.

The articles written by the exemplars should be understood as the most typical of each

school and not necessarily the most forward thinking or the best written. Accordingly,

a researcher desiring a quick assessment of research articles in S&G could study onlythe articles written by the seven exemplars of the seven-cluster set. With time to spare,

the researcher could add the articles written by the four additional exemplars of the

11-cluster set.

A map of the similarities among authors based on their pattern of citations is shown

in Figure 8. That is, authors who commonly cite the same referenced authors are similar

to each other. The map was produced with VantagePoint Version 6.1 software, which

uses cosine distance, rather than Pearson correlations, as the measure of similarity.

Cosine distance scales the measurement from 0 to 1, eliminating negative values,

which makes cosine distance more suitable for mapping than Pearson correlations.The two measures are highly correlated, M = .976, SD = .013.

The vertical and horizontal axes of the map have no specific meaning, inasmuch as

the software simply reduces the 51-dimensional representation to two dimensions,

seeking in the process to maintain authors with a high degree of similarity in close

proximity to each other. Authors who are placed closer to each other generally are

more similar than those who are placed farther away; nonetheless, the presence or

absence of a line between authors and the thickness of the line correspond precisely to

similarity.

Two groups are evident on the map, the dense upper one and the sparse lower one.Differences between these two groups are tabulated in Table 12. To construct the last

two columns of this table, the 97 most referenced publications of the 51 authors were

Table 11. Clusters of Authors by Pattern of Citations

Cluster Size

Publication Rank Exemplar

Minimum Number

of Clusters

Maximum Number

of Clusters

6 Faria, Anthony J. 9 7

23 Mayer, Igor S. 10 7

4 Gold, Steven C. 7 6

30 Hill, Jonnie Lynn 6 6

20 Kin, Yeo Gee 7 5

35 Gamson, William A. 7 5

10 Thatcher, Donald C. 5 4

5 Klabbers, Jan H. G. 3

31 Hornaday, Robert W. 343 Thorngate, Warren 3

11 Lederman, Linda Costigan 2









Figure 8. Cross-correlation map of authors versus referenced authorsNote: The numbers in the nodes refer to exemplar authors who belong to the 7-cluster and/or11-cluster sets.








Bragge et al. 893

divided into two categories, business and others, based on the title of the publications.

Publication titles with business-related words, namely, business, decision sciences,

entrepreneurship, management , marketing , organizational behavior , and operations

management , were classified as business publications. We find that the dense group iscomposed predominantly of business researchers, inasmuch as 52.48% of their refer-

encing instances are to business publications, as compared with only 3.76% of the

referencing instances of the sparse group, χ 2(1, N = 2,837) = 481, p = .000.

The map of Figure 8 and the clusters of Table 11 are evidently valid. Although

derived by different algorithms, the two are consistent, considering that the dense group

contains fewer exemplars and the sparse group contains more exemplars. Three exem-

plars (Gold, Faria, and Kin) of the seven-cluster set are found in the dense group and

four exemplars (Hill, Mayer, Gamson, and Thatcher) are found in the sparse group. Of

the additional examplars from the 11-cluster set, one (Hornaday) is found in the densegroup and three (Klabbers, Lederman, and Thorngate) are found in the sparse group.

Discussion and Conclusion

We have depicted the articles appearing in S&G since its inauguration and compared

the patterns of knowledge creation and work division of top-publishing authors to

other authors. The research profiling method that we employed relies on the biblio-

metric information available from journal databases. This makes the approach differ-

ent from descriptive literature reviews that are based on reading the full texts of thearticles themselves. Accordingly, research profiling augments descriptive reviews but

does not replace them.

Applying the research profiling method, we find that the top S&G research-article

authors, each with more than 10 articles, are Joseph Wolfe, Ken Jones, Precha

Thavikulwat, Steven Gold, Jan Klabbers, and Anthony Faria, in that order. As to the

top research-article referenced author, Wolfe is by far the most referenced author, fol-

lowed by Cathy Greenblat, Bernard Keys, and David Crookall. Most of the referenced

works were published between 1966 and 2002, peaking in 1975 and 1990, and dipping

in 1983. On average, S&G research articles contain 22.6 references.The most referenced journal has been S&G itself. The reference instances to S&G

have almost doubled in the last two decades, from 519, in 1990-1999, to 1,006, in 2000-

2009. Other popular referenced periodicals include Management Science, American

Table 12. Differences Between Dense and Sparse Group

No. of Referencing Instances of 97 MostReferenced Publications

Group No. of Authors No. of Research Articles Business Publications Other Publications

Dense 20 145 1,154 1,045

Sparse 31 169 24 614









Behavioral Scientist , Academy of Management Journal , Developments in Business

Simulation and Experiential Learning (ABSEL conference proceedings), Academy

of Management Review, Decision Sciences, Journal of Business, and Journal of

Personality and Social Psychology. The ABSEL conference proceedings have increasedin popularity the most, being over three times more referenced in the last decade than

in the 1990s, 453 versus 153, indicating the growth of a very active research commu-

nity in business gaming. In the four decades of S&G publishing history, the references

to behavioral journals, such as the American Behavioral Scientist and Behavioral

Science, have clearly decreased. The majority of the research published in S&G comes

from U.S. universities. The only non-U.S. institution among the top-10 is the University

of Windsor, Canada.

The number of research articles and editorials are increasing, but the number of

reviews has dropped considerably in recent years. This decline in reviews might bedue to management issues (Barbara Steinwachs, who was in charge of S&G reviews

for many years, passed away), but it also might be due to the lowered value placed on

game/simulation reviews as compared with research articles, or with the increased dif-

ficulty in finding truly novel products that are worth of a review, or to publishers’

increasing reluctance in giving away copies of their products for review.

The article titles include often either the word simulation or game/gaming , inherent

to the journal’s scope. Business simulation has become a popular title phrase in the last

two decades, whereas simulation games and computer simulation have decreased in

usage from the early decades, depicting a change in the focus of research.Descriptors that have increased in prominence are management , organizational

learning , management methods, health services administration, business, and health

planning . Descriptors that have declined in prominence are community development ,

industrial psychology, mathematical optimization, and discriminant analysis.

By analyzing the relationships between highly published authors and their coau-

thors, and between highly published authors and the authors of the articles they refer-

ence, we found that the highly published authors have not achieved their higher numbers

by dividing work more frequently than less published authors and that their articles

contain about as much new thinking as those of less published authors. Moreover, wefind that the highly published authors fall into between 7 and 11 clusters, centered

around Faria, Mayer, Gold, Hill, Kin, Gamson, and Thatcher in the 7-cluster set and

also around Klabbers, Hornaday, Thorngate, and Lederman in the additional 4 clusters

of the 11-cluster set. These findings, together with the map of their relationships, are

thumbnail sketches of research articles in S&G since its inception 40 years ago.

We conclude by noting that our study has answered the who, what, where, and

when questions that are basic to the research profiling method. Moreover, we have

gone beyond these basic questions to examine the pattern of work division and knowl-

edge creation as evidenced through coauthorship and references. We have not, how-ever, compared these results to those of other journals and other disciplines. We believe

such comparisons will be a fruitful extension of this study.








Bragge et al. 895

Appendix

Affinity Propagation

Affinity propagation (Frey & Dueck, 2007, 2008) is an iterative algorithm for clus-

tering a collection of items based on a measure of similarity between items. It differs

from other clustering algorithms in that it does not require the user to specify the

number of clusters a priori. Rather, it finds that number based on the data and on

the user’s indicated preference for a smaller or larger number. The algorithm is suit-

able for our application, because S&G authors are not known to fall into any number

of clusters a priori.

The algorithm applies an ingenious logic that simulates how people become clus-

tered at a large gathering. At such gatherings, people move toward those they admiremore and away from those they admire less, so that after a suitable time, everyone is

found to be clustered, each cluster formed around a person who is known as that clus-

ter’s exemplar . At one extreme, if everyone views herself as extremely admirable as

compared with everyone else, then each person will admire only herself, each alone

will be her own exemplar, and the number of clusters will equal the size of the gather-

ing. At the other extreme, if everyone views herself as extremely unadmirable, then

only a minimum number of clusters will form, each cluster composed of those admir-

ing an exemplar who, although thinking of herself as unadmirable, cannot escape the

admiration of those surrounding her.Admiration corresponds with similarity. To obtain a range of clusters from the algo-

rithm, the user sets a self-similarity value that represents how much each item admires

itself. If the self-similarity value is set to the lowest of all the similarity values of the

items with respect to each other, then, provided the algorithm converges, the resulting

number of clusters is the minimum number.

Our measure of similarity is the correlation of authors by referenced authors, rang-

ing from −.34065 to .961561, Mdn = −.03717. To direct the algorithm to find the mini-

mum number of clusters of Table 11, we specified a self-similarity value that is the

smallest of the correlations. To direct the algorithm to find the maximum number ofclusters of that table, we specified a self-similarity value that is the median of the cor-

relations. In both cases, the algorithm converged after 11 iterations. We do not report

results for a self-similarity value higher than the median, because a larger number of

clusters does not serve the purpose of this study.

Acknowledgments

The authors are grateful to Anne Sunikka and Pekka Malo for their review and critic of this

article. Special thanks are due to Joe Wolfe for the generous guidance he provided when resolv-ing the inconsistencies in Sage Publication’s database and to David Crookall for his encouraging

and insightful comments throughout the writing process.









Declaration of Conflicting Interests

The author(s) declared no conflicts of interest with respect to the authorship and/or publication

of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research and/or author-

ship of this article:

The first author received funding for authorship of this article from the Jenny and Antti

Wihuri Foundation, which is gratefully acknowledged.

References

Boyack, K., Klavans, R., & Börner, K. (2005). Mapping the backbone of science. Scientomet-rics, 64, 351-374.

Bragge, J., Korhonen, P., Wallenius, H., & Wallenius, J. (2010). Bibliometric analysis of

multiple criteria decision making/multiattribute utility theory. In M. Ehrgott, B. Naujoks,

T. J. Stewart, & J. Wallenius (Eds.), Multiple criteria decision making for sustainable energy

and transportation systems (pp. 259-268). Berlin, Germany: Springer-Verlag.

Börner, K., Chen, C., & Boyack, K. W. (2003). Visualizing knowledge domains. Annual Review

of Information Science and Technology, 37 , 179-255.

Crookall, D. (2009). Evolving. Simulation & Gaming: An Interdisciplinary Journal , 40, 290-296.

Cournot, A. A. (1838). Recherches sur les principes mathématiques de la théorie des richesses.(Researches into the Mathematical Principles of the Theory of Wealth) Paris, France: L.

Hachette.

Culnan, M. J. (1986). The intellectual development of management information systems,

1972-1982: A co-citation analysis. Management Science, 32, 156-172.

Culnan, M. J. (1987). Mapping the intellectual structure of MIS, 1980-1985: A co-citation anal-

ysis. MIS Quarterly, 11, 341-353.

Faria, A. J., Hutchinson, D., & Wellington, W. J. (2009). Developments in business gaming:

A review of the past 40 years. Simulation & Gaming: An Interdisciplinary Journal , 40,

464-487.Frey, B. J., & Dueck, D. (2007). Clustering by passing messages between data points. Science,

315, 972-976.

Frey, B. J., & Dueck, D. (2008). Response to comment on “Clustering by passing messages

between data points.” Science, 319, 726d.

Hood, W. W., & Wilson, C. S. (2001). The literature of bibliometrics, scientometrics, and infor-

metrics. Scientometrics, 52, 291-314.

Guzzo, R. A., Jackson, S. E., & Katzell, R. A. (1987). Meta-analysis analysis. Research in Orga-

nizational Behavior , 9, 407-442.

King, W. R., & He, J. (2005). Understanding the role and methods of meta-analysis in IS research.Communications of the AIS , 16 , 665-686.

Meyer, M., Lorscheid, I., & Troitzsch, G. (2009). The development of social simulation as

reflected in the first ten years of JASSS: A citation and co-citation analysis. Journal of

Artificial Societies and Social Simulation, 12(4), Article 12. Retrieved from http://jasss.soc

.surrey.ac.uk/12/4/12/12.pdf








Bragge et al. 897

Porter, A. L., & Cunningham, S. W. (2005). Tech mining: Exploiting new technologies for

competitive advantage. Hoboken, NJ: Wiley.

Porter, A. L., Kongthon, A., & Lu, J. C. (2002). Research profiling: Improving the literature

review. Scientometrics, 53, 351-370.

Pritchard, A. (1969). Statistical bibliography or bibliometrics? Journal of Documentation, 25,348-349.

Raghuram, S., Tuertscher, P., & Garud, R. (2010). Mapping the field of virtual work: A cocita-

tion analysis. Information Systems Research, 21, 1-17.

SciMaps (2005). Places and spaces: Mapping science. School of Library and Information

Science, Indiana University. Retrieved from http://www.scimaps.org/

Simon, H. A. (1960). The new science of management decision. New York, NY: Harper & Row.

Small, H. (1999). Visualizing science by citation mapping. Journal of American Society of

Information Science, 50, 799-813.

Watts, R. J., & Porter, A. (2007). Mining conference proceedings for corporate technologyknowledge management. International Journal of Innovation and Technology Management ,

4, 103-119.

Webster, J., & Watson, R.T. (2002). Analyzing the past to prepare for the future: Writing a

literature review. MIS Quarterly, 26 , xiii-xxiii.

Yang, Y. Y., Akers, L., Klose, T., & Barcelon Yang, C. (2008). Text mining and visualization

tools –Impressions of emerging capabilities. World Patent Information, 30, 280-293.

Bios

Johanna Bragge (PhD, Helsinki School of Economics) is an assistant professor of informationsystems science at Aalto University School of Economics. Her research interests include

e-collaboration, service co-creation, digital marketing, and text-mining. Her research has been

published in IEEE Transactions on Professional Communication, Journal of the AIS , Group

Decision and Negotiation, Journal of Financial Services Marketing , and the European Journal

of Operational Research. Contact: [email protected].

Precha Thavikulwat (PhD, University of Minnesota) is a professor of management at Towson

University and a fellow and past president of the Association for Business Simulation and

Experiential Learning. His research interest is in the design of computerized business gamingsimulations, especially for the purpose of assessing business education. He is the author of four

computerized business gaming simulations: MANAGEMENT 500, CEO, DEAL, and GEO.

Contact: [email protected].

Juuso Töyli, (PhD, Turku School of Economics), (PhD, Helsinki University of Technology), is

acting professor at Turku School of Economics. His current research interests include business

simulation games, networking business, and logistics and financial performance. He is the

author of computerized business simulation game SIMBU and the coauthor of MOB. Contact:

[email protected].

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