What are the future prospects of Knowledge Management? An audit of the subject domain’s scholarly...

What are the future prospects of Knowledge Management? An audit of the subject domain’s scholarly publications and research

OB Onyancha

UNISA & UNIZUL

Introduction Using past events to forecast or predict future events is not new Various methods/approaches have been proposed (Vanston, 2003)

Extrapolation – the future will represent a logical extension of the past Pattern analysis – the future will reflect a replication of past events Goal analysis – the future will be determined by the beliefs of a collection of

individuals, organizations and institutions Counter-punching – the future will result from a series of events and actions that

are essentially unpredictable and to a large extent random Intuition – the future will be shaped by a complex mixture of inexorable driving

forces, random events, and the actions of key individuals and institutions Each of these methods consist of several techniques (e.g. trend

extrapolation; analog analysis; content analysis; impact analysis; scanning, monitoring and tracking, etc

Informetrics evaluates published literature/patents to predict future research trends

Growth in the amount of literature and citations as a means of telling the future status of a new concept, discipline, subject domain or an activity (e.g. collaboration) is not new either Price predicted in 1963 that single author papers would disappear in 1980

Stages of Growth of literature

Price in Gupta & Karisiddappa (2000) and Crane in Jacobs (2004) argue that literature undergoes four stages of growth: Preliminary period of growth: increments are small Exponential growth: literature doubles at regular

intervals Decline stage: rate of growth declines but annual

increments remain constant Decline stage: both rates of increase and absolute

increase decline and approaches zero

Models of growth of knowledge Growth of literature is assumed to reflect the growth

of knowledge Three models of growth of knowledge (Gupta &

Karisiddappa, 2000:323)1. Growth of knowledge is viewed as a cumulative progression

of new ideas, developing from antecedent ideas in a logical consequence

2. Growth of knowledge assumes that the origin of new ideas come not from the most recent developments, but from any previous developments in the history of the scientific speciality

3. Growth of knowledge is viewed as cumulated growth, intercepted with periods of discontinuity

Previous studies Chaudhry & Higgins (2001)

Investigated state of KM education Information systems – leading school (40% of Masters

programme schools) Business administration (35%) and Computer science (14%) Course title – KM, KM and decision systems, Intelligence

systems and KM, etc. Jacobs (2004) – described KM by analyzing 491

records published between 1993 to 2003 Identified most productive countries; Patterns of growth; Most

published authors, etc Onyancha (2005) – Used SABINET to conduct a study

on popularizing KM in HEIs in South Africa Sources, institutions, Growth of literature, inter-disciplinarity of

KM, KM researchers, etc

Purpose of study

To study the trend of KM publications in order to forecast the future prospects of KM (as a course, discipline and research area)

In other words, the study attempts to answer the question, what does the future hold for KM, particularly for LIS professionals?

At this stage, the study provides preliminary findings

Methods and materials

Trend analysis (TA) approach to study the KM literature TA is defined as a process whereby one uses movements in

past figures to predict future figures It involves the analysis of data to determine future

performance Used key concepts of knowledge and KM to download

data from Keywords, TS, Abstracts, TI Data was analyzed using Stikis, TI and Excel softwares Data was also subjected to multidimensional scaling Maps and social networks were generated using Pajek

and UCINET

Results and discussion

Trend for each K & KM concept

No Key term1981-1985

1986-1990

1991-1995

1996-2000

2001-2005

2006-2007

TOTAL

1 knowledge management 0 12 43 390 1735 608 2788

2 knowledge acquisition 16 99 689 724 702 231 2461

3 knowledge transfer 3 9 58 126 368 204 768

4 tacit knowledge 6 5 75 180 350 134 750

5 explicit knowledge 1 3 82 183 302 116 687

6 indigenous knowledge 5 2 40 127 233 98 505

7 knowledge creation 1 5 27 97 253 92 475

8 knowledge production 13 1 30 102 231 91 468

9 knowledge development 8 6 53 118 152 49 386

10 organizational knowledge 0 1 20 76 165 67 329

11 implicit knowledge 1 6 42 84 113 55 301

12 intellectual capital 1 2 8 109 131 39 290

13 corporate knowledge 0 1 5 20 35 8 69

0

200

400

600

800

1000

1200

1400

1600

1800

2000

1981-1985

1986-1990

1991-1995

1996-2000

2001-2005

2006-2007

Publication year

No

. o

f re

cord

s

knowledge management

knowledge acquisition

knowledge transfer

tacit knowledge

explicit knowledge

indigenous knowledge

knowledge creation

knowledge production

knowledge development

organizational knowledge

implicit knowledge

intellectual capital

corporate knowledge

Trend of K & KM concepts

Growth of literature

No. Variable1981-1985

1986-1990

1991-1995

1996-2000

2001-2005

2006-2007

1

Authors 83 259 2080 3823 8237 8533

Increase in number of authors - 176 1821 1743 4414 296

Percentage increase - 212.05 703.09 83.80 115.46 3.59

2

Countries 12 22 62 72 98 86

Increase in number of countries - 10 40 10 26 -12

Percentage increase - 83.33 181.82 16.13 36.11 -12.24

3

Document types 4 8 10 13 10 9

Increase in number of document types -

4 2 3 -3 -1

Percentage increase - 100.00 25.00 30.00 -23.08 -10.00

Growth of literature – cont’dNo. Variable

1981-1985

1986-1990

1991-1995

1996-2000

2001-2005

2006-2007

4

Institutions 50 130 845 1421 2565 1359

Increase in number of institutions - 80 715 576 1144 -1206


5

Language 2 5 7 10 14 8

Increase in number of languages - 3 2 3 4 -6


6

Sources 49 82 539 869 1402 771

Increase in number of sources - 33 457 330 533 -631


7

Subject categories 37 58 156 171 198 174

Increase in number of subject categories - 21 98 15 27 -24


Most productive subject categoryNo. Subject category No. of records %

1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 1405 15.22

%

2 MANAGEMENT 1370 14.84

%

3 COMPUTER SCIENCE, INFORMATION SYSTEMS 950 10.29

%

4 COMPUTER SCIENCE, THEORY & METHODS 752 8.15%

5 INFORMATION SCIENCE & LIBRARY SCIENCE 728 7.89%

6 OPERATIONS RESEARCH & MANAGEMENT SCIENCE 716 7.76%

7 BUSINESS 634 6.87%

8 ENGINEERING, ELECTRICAL & ELECTRONIC 483 5.23%

9 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 451 4.89%

10 ENGINEERING, INDUSTRIAL 370 4.01%

11 EDUCATION & EDUCATIONAL RESEARCH 346 3.75%

12 PSYCHOLOGY, MULTIDISCIPLINARY 285 3.09%

13 ENGINEERING, MULTIDISCIPLINARY 275 2.98%

14 PSYCHOLOGY, EXPERIMENTAL 259 2.81%

15 COMPUTER SCIENCE, SOFTWARE ENGINEERING 258 2.80%

16 COMPUTER SCIENCE, CYBERNETICS 255 2.76%

Growth of papers vs citations1981-1985

1996-1990

1991-1995

1996-2000

2001-2005

2006-2007 TOTAL

PAPERS Number of papers 57 170 1160 2230 4234 1378 9229

Change in no. of papers - 113 990 1070 2004 -2856 -

% change in no. of papers - 198.25 582.35 92.24 89.87 -67.45 -

Cumulative no. of papers 57 227 1387 3617 7851 9229 -

Change in cumulative no of papers - 170 1160 2230 4234 1378 -

% change in cumulative of papers - 298.25 511.01 160.78 117.06 17.55 -

Mean no of papers per year 11.40 34.00 232.00 446.00 846.80 918.67 341.81

CITATIONS Number of citations 70 452 2394 9654 27595 14454 54619

Change in no. of citations - 382 1942 7260 17941 -13141 -

% change in no. of citations - 545.71 429.65 303.26 185.84 -47.62 -

Cumulative no. of citations 70 522 2916 12570 40165 54619 -

Change in cumulative no. of citations - 452 2394 9654 27595 14454 -

% change in cumulative no. of citations - 645.71 458.62 331.07 219.53 35.99 -

Mean no. of citations per year 14.00 90.40 478.80 1930.80 5519.00 9636.00 2022.93

Average cites per paper 1.23 2.66 2.06 4.33 6.52 10.49 5.92

Growth of papers vs citations

y = 6.0791e1.3257x

y = 205.39x - 304.06

0

5000

10000

15000

20000

25000

30000

No

. of

pap

ers/

cita

tio

ns

-2000

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

Mea

n n

o. o

f p

aper

s/ci

tati

on

s

No. of papers 57 170 1160 2230 4234 1378

Number of citations 70 452 2394 9654 27595 14454

Mean no. of citations per year 14 90.4 478.8 1930.8 5519 9636

Mean no of papers per year 11.4 34 232 446 846.8 918.67

1981-1985 1996-1990 1991-1995 1996-2000 2001-2005 2006-2007

Growth of papers vs citations

Language of publicationNo. Rank

Language1981

-1985

1986-1990

1991-

1995

1996-2000

2001-

2005

2006-2007

TOTAL %

1 1ENGLISH

54 146 1097 20524074 1514 8937

96.84%

2 2 GERMAN 2 22 55 87 18 184 1.99%

3 3 FRENCH 2 13 17 14 4 50 0.54%

4 4 SPANISH 1 4 4 7 16 0.17%

5 5 JAPANESE 1 1 3 1 3 9 0.10%

6 6 RUSSIAN 1 1 2 4 8 0.09%

7 7 CZECH 4 1 5 0.05%

8 7 PORTUGUESE 1 3 1 5 0.05%

9 8 CROATIAN 2 2 4 0.04%

10 8 SLOVAK 1 1 2 4 0.04%

11 9 CHINESE 1 1 0.01%

12 9 DANISH 1 1 0.01%

13 9 ITALIAN 1 1 0.01%

14 9 NORWEGIAN 1 1 0.01%

15 9 RUMANIAN 1 1 0.01%

16 9 SWEDISH 1 1 0.01%

17 9 WELSH 1 1 0.01%

Core languages

Most productive countriesNo. Country

1981-1985

1986-1990

1991-1995

1996-2000

2001-2005

2006-2007

TOTAL %

1 USA 23 98 469 831 1306 475 3179 34.45%

2 ENGLAND 4 12 91 241 532 225 1101 11.93%

3 GERMANY 5 84 151 331 124 695 7.53%

4 CANADA 1 8 106 135 256 120 625 6.77%

5 AUSTRALIA 29 80 176 56 341 3.69%

6 FRANCE 1 3 57 86 141 43 330 3.58%

7 NETHERLANDS 1 5 39 87 123 47 301 3.26%

8 CHINA 6 23 167 79 275 2.98%

9 JAPAN 57 68 91 42 258 2.80%

10 ITALY 2 24 44 133 46 249 2.70%

11 TAIWAN 1 13 45 111 72 242 2.62%

12 SPAIN 1 13 35 123 59 231 2.50%

13 AUSTRIA 8 23 89 22 142 1.54%

14 SWEDEN 1 24 28 73 16 141 1.53%

15 SCOTLAND 1 16 27 79 18 140 1.52%

23 SOUTH AFRICA 1 6 14 50 22 93 1.01%

Social networks of key terms 1981-1995

Social networks of key terms 1996-2000

Conclusions Although KM is a relatively new concept, KM practices have

always existed All KM related concepts (e.g. processes and types of knowledge)

have shown exponential trend of growth KM as concept has the highest rate of growth indicating interest

from scholars Growth was reflected in growth of number of authors, countries,

document types, institutions, language, sources and subject categories

Pattern will continue – KM society/economy However, growth in the amount of literature and related citations

alone can not tell us the future prospects of a subject Validating studies need to be conducted (e.g. by use of expert

opinion, surveys, etc) Nevertheless, informetric studies provide substantive information

that can be used to determine the trend of a subject (e.g. hot topics, etc)

Conclusions Computer science, Management, Library and Information science, Business,

and Electrical & Electronic engineering are major contributors and utilizers of KM theory and research

On average, the number of citations have continued to increase exponentially while the number of papers’ growth can be said to follow a linear trend of growth

English language is the most commonly used to publish KM literature. Others – German, French, Spanish and Japanese Bias of database for English published literature English Language – Internationally spoken

Europe and the Americas are and will continue to be most productive countries KM theories are likely to originate from these countries Countries in Asia – Average performance – KM systems

There is no major changes in keyword usage in KM paper titles between 1981-1995 and 1996-200

We recommend usage of KM indexing keywords to monitor trends KM will continue to draw the attention of researchers as a viable research area

and discipline KM is fast moving from being a concept, to a course within a variety of

disciplines to a discipline on its own

Thanks for your attention

END

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