J. Software Engineering & Applications, 2010, 3, 580-587
doi:10.4236/jsea.2010.36067 Published Online June 2010 (http://www.SciRP.org/journal/jsea)
Copyright © 2010 SciRes. JSEA
The Exploratory Analysis on Knowledge Creation
Effective Factors in Software Requirement
Jiangping Wan1,2, Ruoting Wang1
1School of Business Administration, South China University of Technology, Guangzhou, China; 2Institute of Emerging Industrialization
Development, South China University of Technology, Guangzhou, China.
Email: scutwjp@126.com, mawangrt@gmail.com
Received March 16th, 2010; revised April 9th, 2010; accepted April 11th, 2010.
The knowledge creation effective factors were found in both necessary elements for stimulus of knowledge creation and
the key influencing factors of software project success. The research was carried with the specific successful practices of
Microsoft Corporation and William Johnson’s analysis of R & D project knowledge creation. The knowledge creation
effective factors in requirement development project are clarified through deeply interviewing the software enterprises in
Guangdong province as well as other corporate information departments. The effective factors are divided with R & D
project knowledge creation model in the view of organizational, team, personal and technical four levels through
literature research and interview in enterprises, and the empirical study was done with questionnaire and exploratory
Keywords: Software Requirement, Knowledge Creation, Project, Organization, Empirical Study
1. Introduction
The smooth development of software requirements needs
an efficient organization to support [1], this paper dis-
cusses the knowledge creation factors in software re-
quirements development process in the meta-level of the
software process with the instance of Microsoft corpora-
tion [2-4]. Software requirement development as a
knowledge creation process, Nonaka etc. have attributed
the knowledge effect factors to four reasons: intention,
autonomy, creative chaos, requisite variety. On this basis,
Krogh etc. re-emphasized the importance of friendly re-
lationship to build efficient “Ba” [5]. J. P. Wan etc. ana-
lyzed from knowledge management view, some of them
proposed a number of effective factors: experience in the
domain, knowledge gaps, user participation, administra-
tive support, personal capability, comprehensive training,
methodology and related technology and so on [6,7].
This paper is organized as follows: first knowledge
creation effective factors are illustrated and the effective
factors in the requirement development process are con-
cluded. With deeply interviewing the software enterprises
in Guangdong province as well as other corporate infor-
mation departments, the knowledge creation effective
factors in requirement development project are clarified,
finally the empirical study is done with questionnaire
survey and exploratory analysis.
2. Knowledge Creation Effective Factors
Nonaka attributed knowledge creation effective factors to
intention, self-management, creative chaos, redundancy
and requisite variety [8], and re-emphasizes the friendly
environment in the organization [5].
2.1 Intention
Nonaka indicated that the organization intention is the
most important criterion in judging the authenticity of
intent. If there is no organization intention, the organiza-
tion will not be able to judge the value of perceived in-
formation and creative knowledge, at the same time, the
organization intention must be affected by the organiza-
tional value. William Johnson considers that it should give
one intention for each project at last, and it is obviously
that if there is no intention, the next research will not
continue [9]. Software requirements development process
This research was supported by Key Project of Guangdong Province
Education Office (06JDXM63002), NSF of China (70471091), and
QualiPSo (IST- FP6-IP-034763)
The Exploratory Analysis on Knowledge Creation Effective Factors in Software Requirement Development581
is a knowledge creation process in nature [7]. For example,
the first is to establish a shared vision to enhance the
team’s sense of identity, belonging in the Microsoft cor-
poration’s successful rules [3].
2.2 Self-Management
It is that the members or the teams take actions volun-
tarily to improve the organization creativity. Autonomy
team refers to taking the team as operation mainstay vo-
luntarily. For example, William Johnson discovers that
personal autonomy is very important for knowledge cre-
ation with interviews [9]. It allows large teams to work
like small teams by dividing work into pieces, proceeding
in parallel but synchronizing continuously, stabilizing in
increments, and continuously finding and fixing problems
in Microsoft Corporation [10].
2.3 Creative Chaos
Nonaka etc. illustrate that turbulence and creative chaos
accelerate the interaction between the organization and
environment. Members will start to question the validity
of the basic attitudes. It will be opportunity to amend the
fundamental thinking and insight. It is obviously that
turbulence and creative chaos contribute to organizational
knowledge creation [5]. William Johnson discovered that
only in a few projects, turbulence and creative chaos
possess function which promotes knowledge creation, just
same as Nonaka’s description with R & D’s research
projects. In most projects, it is often closely linked with
the problem’s occurrence. There is no data illustrated that
the creative chaos and knowledge creation have a strong
correlation [9].
2.4 Redundancy
Redundancy usually refers to the repetition and share for
group members and the unnecessary information. It is a
kind of redundancy to adopt different technologies to
solve the same problem during requirement development
process. For example, it is an effective knowledge crea-
tion process to build a number of schemes and choose the
optimal with review.
2.5 Requisite Variety
William Johnson concluded that all projects regard the
requisite variety as a positive factor in the project knowl-
edge creation on the R & D project study [9]. Microsoft
Corporation emphasized the small teams, which should be
diversification and even in a role. There are usually many
different working ways and its members should have dif-
ferent job skills or experience levels in a project team [5].
2.6 Friendly Relationship
Krogh etc. considered that the friendly relationship can
remove the distrust, fear and dissatisfaction in the know-
ledge creation process, and allow team members to ex-
plore new markets, new customers, new products and new
manufacturing technologies in the unknown territory with
enough reassurance [5].
3. The Effective Factors in the Requirement
Development Process
The goal of software development is to exploit the high
quality software which meets the customers’ real requi-
rements timely within the budget. The success of the pro-
ject depends on good requirement management [11]. This
paper discusses the effect factors of requirement devel-
opment process in perspective of knowledge creation.
3.1 Domain Experiences
Cohen and Levinthal argued that if the organization had
more relevant knowledge or experiences, and its absorp-
tive capacity is better, it is a function of its past experi-
ences accumulation [12]. Y. H. Ke etc. analyzed the im-
portance of domain experience for system development,
and discovered that the system development experience
and deeply understanding of domain knowledge have a
positive effect on knowledge transfer [13]. Pete Sawyer
and Gerald Kotonya considered that one of the key re-
sources in software requirement acquisition is the domain
knowledge. Requirements engineers need to acquire ef-
fective knowledge on application domain. It can help
them to know the tacit knowledge what stakeholders can
not clearly illustrate and learn about the necessary balance
between the conflict requirements [14]. For example,
Microsoft’s team model advocates on the basis of deeply
understanding the client’s business requirements and
familiarly mastering related technologies to develop the
project and decision-making. Therefore, the project team
members should have the professional and deeply tech-
nology and business skills in themselves domains [15].
3.2 Knowledge Gap
It refers that the developer is lacking of business operation
knowledge, knowledge of technology, and understanding
of user business and software technology [16]. S. Alshawi
etc. argued that it is important to have the business and
technical knowledge for any enterprise [17].
3.3 User Participation
It is particularly important in information systems’ de-
velopment [18-20]. In Standish Group study, the most
reason of project “disagree” factor was the lack of user
participation, accounting for 13% in all failure projects.
All successful projects illustrated that the most important
factor was user participation, accounting for 16% of all
projects [21,22]. Standish Group enumerated the top ten
critical elements of software projects success with sur-
veying 8380 software projects, the lack of user involve-
Copyright © 2010 SciRes. JSEA
The Exploratory Analysis on Knowledge Creation Effective Factors in Software Requirement Development
ment is listed in the top ten reasons of software project
failure [23,24].
3.4 Administrative Support
For example, the Microsoft product development process
explicitly specifies that when the projects passing the
review and approval by higher managers, and the com-
pany will make sure the development progress is going
smoothly, and appropriate human and resources for de-
velopment will deploy through human resource depart-
ment and finance department [3,4].
3.5 Personal Capability
System analyst is a typical compound talent, and his
knowledge structure not only strides the social sciences
and natural sciences, but also is the perfect combination of
theory and practice. For example, Microsoft asked the
staff who participates the software development project
have good professionalism and excellent job skills. Staff
qualities include: personal quality, passion for product,
concerning customer feedback, having cooperation spirit
and so on [25].
3.6 Comprehensive Training
For example, Microsoft pays much attention to the de-
veloper’s re-improved process, including learning and
training and so on. The training ways are various, such as
professional skills training, many kinds of seminars,
training of product plan and development and so on. It
also pursues to learn from the past and current research
projects and products in system way [4].
3.7 Methodology
Today, many software organizations implement the best
industry practices as the software development method-
ology, such as the SW-CMM (Capability Maturity Model
For Software) has been promoted the Software Engi-
neering Institute (SEI) of Carnegie Mellon University in
United States since 1987 and so on [15,24].
3.8 Related Technology
Eriksson and Dickson argued that people share the exist-
ing knowledge and the new knowledge are created in
same time, and the IT infrastructure is one of the factors
impacting knowledge creation and share, including sup-
porting information circulation, integrating tools for
group problem-solving, such as Intranet, Extranet, video
conferencing etc. [26].
4. Interview in Enterprises
We interview some experienced requirement developers,
project managers, technical directors and other staffs of
the software enterprises in Guangzhou P. R. China for the
effect factors of software requirement development. The
results are summary as follows.
4.1 Positive Factors
Requirement developer generally plays by the veteran in a
team with abundant project experience. These skills in-
clude: 1) domain knowledge; 2) communication skill; 3)
analysis & arranging capability, comprehensive capability;
4) mastering a certain tool, specially the requirement
analysis tools.
It has great importance on the methods and techniques
of requirement development process in the software en-
terprise. First, it carries out the project generally according
to the project management standards. Second, it uses
prescriptive specification to develop requirement, e.g. the
standard template, the standard development tool and so
on. Finally, it will use variously interview methods, re-
cording methods and tools in the requirement develop-
ment process.
4.2 Uncertain Factors
Enterprises always hold uncertain attitude about auton-
omy. They considered that in the project management,
whether the team processes autonomy is related to the
project property. Employee must complete their work
following the requirement specification and the standard
format and submit the required report. However, they can
complete independently in really operation.
4.3 Negative Factors
Software companies generally oppose chaotic environ-
ment, in particularly they do not like working in a tense
environment. The creative chaos environment is not es-
tablished, and tense working environment usually causes
staffs turnoff.
5. The Classification on the Effective Factors
of Knowledge Creation in Software
Requirement Development
The knowledge creative factors in software requirement
development are classified into three areas through the
literature research and enterprise interviews (Table 1). 10
of which factors are positive, 3 are unable to determine
clearly, there are two negative factors.
6. Questionnaire Design and Collection
The quantitative sample survey is taken to test the hy-
potheses of knowledge creation effective factors in soft-
ware requirement development.
The questionnaire includes the following six areas: basic
information, organizational characteristic, personal chara-
cteristic, technical characteristic, knowledge creation and
requirement development characteristic relationship.
The first area is about the basic information, including
industry type, system user, system type, the number of
Copyright © 2010 SciRes. JSEA
The Exploratory Analysis on Knowledge Creation Effective Factors in Software Requirement Development
Copyright © 2010 SciRes. JSEA
system development team, the number of system requi-
rement development team and testee related role in order
to have a more clear understanding of the sample. The
second area is about organizational characteristic scale,
including 4 variables and 14 items. The third area is about
the team characteristic scales, including 6 variables and 16
items. The fourth area is about the personal characteristic
scale, including 3 variables and 8 items. The fifth area is
about the technical characteristic scales, including 2
variables and 7 items (Table 2). The sixth area is about the
knowledge creation and requirement development rela-
tionship characteristic scale, including 4 items.
Table 1. Classification on the effective factors
Relativity Level Effect factor Remark
Management support
Organization Friendly environment
Project intention
Requisite variety
User participation
Comprehensive training
Domain experience
Personal Personal capability
Technology Related technology
Organization Redundancy
Team Self-management
Personal Self-management
Organization Creative chaos
Negative Team Knowledge gap
Literature research and
enterprise interviews
on the effective factors’
classification is
basically same.
Table 2. Questionnaire detailed corresponding table
Level Variable factors Item References
Management support
Area one O1O3 Nonaka (2000), Johnson (2000), Standish Group (1994), Zhang Xiang-
hui (2005), Chen Honggang (2003), James Emery (2002)
Friendly environment
Area two O4O10 Nonaka (2000), Johnson (2000), Krogh (1994), Zhang Xianghui (2005),
Chen Honggang (2003)
Creative chaos
Area two O11O14 Nonaka (1995, 2000), Johnson (2000)
Area two O12O13 Nonaka (1995, 2000), Johnson (2000)
Project intention Area
three T1T2 Nonaka (1995, 2000), Johnson (2000), Zhang Xianghui (2005), Cheng
Honggang (2003)
Area three T3T5 Nonaka (1995, 2000), Johnson (2000), Zhang Xianghui (2005), Chen
Honggang (2003)
Requisite variety
Area three T6T7 Nonaka (1995, 2000), Johnson (2000), Zhang Xianghui (2005)
User participation
Area three T8T10 Standish Group (1994, 1995, 1999), Johnson (2000), Zhang Xianghui
(2005), Guinan (1998), Henri Barki (1994), Hirschheim (1994)
Comprehensive training Area three T11T13 Humphrey (2002), Constantine (1995), Chen Honggng (2003)
Knowledge gap
Area three T14T16 Alshawi (2003), Linda (2000), Ian McBriara (2003), Gilbert (1996)
Area four I1I2 Nonaka (1995, 2000), Johnson (2000), Chen Honggng (2003)
Domain experience
Area four I3I6 Cohen, Levinthal (1990), Ke Yihua (2005), Chen Honggang (2003)
Personal capability
Area four I7I8 Johnson (2000), Zhang Xianghui (2005), Chen Honggang (2003), Tian
Junguo (2003)
Technology Methodology
Area five Te1Te2 Johnson (2000), Zhang Xianghui (2005), Chen Honggang (2003)
Related technology
Area five Te3Te7 Johnson (2000), Zhang Xianghui (2005), Chen Honggang (2003), Ellen
Gottesdiener (1999), Eriksson, Dickson (2000, 2003)
4 15 45
The Exploratory Analysis on Knowledge Creation Effective Factors in Software Requirement Development
7. The Exploratory Analysis of Requirement
Development Effective Factors
7.1 Reclaiming Questionnaire
Questionnaire has surveyed during December 2006 to
January 2007 in Guangdong region, including Guangzhou
Ferryman Management Consulting Co., Ltd., Guangdong
Visionsky Information Technology Co., Ltd., Guangzhou
KeenFox Engineering Co., Ltd., Computer and Tech-
nologies Solution (Shenzhen) Co., Ltd., nearly 20 enter-
prises, issued totally 50 e-mails, and totally recovered 26s,
all are valid.
7.2 Characteristic of Sample
The highest proportion is the software industry, the num-
ber is 17, accounting for 65.4%; the rest of the industry
includes financial industry, service industry and other
industries accounted for 11.5%, 11.5% and 11.8% corre-
The products which belong to interviewee’s team are
generally provided to the external clients to use (sample
number 10, accounting for 38.5%), internal requirement
(sample number 8, accounting for 30.8%) and the com-
bination of the two (sample number 8, accounting for 30.8
%). The products which belong to the interviewee’s team,
mainly MIS (sample number 17, accounting for 26.6%)
and DSS (sample number 13, accounting for 20.3%),
others such as ERP, EC, KM, special products, common
products as well as other, accounting for 9.4%, 9.4%,
6.3%, 10.9%, 1.6% and 15.6% correspondingly.
The 51 persons and above (sample number 16) is do-
minated, in the software development team where the
interviewee is accounting for 38.5%; 1 to 10, 11 to 20, 21
to 50 are accounted for 26.9%, 23.1 % and 11.5% corre-
spondingly. The 4 to 5 persons is dominated in the re-
quirement development team, accounting for 42.3%,
while, 11 persons and above, 6 to 10, and less than 3, are
accounting for 26.9%, 19.2% and 11.5% correspondingly.
The main interviewees are team project management, the
sample number is 12, accounting for 36.2%; developer,
requirement person, designer, tester and others are ac-
counting for 26.9%, 7.7%, 3.8% and 7.7% correspond-
ingly. Software industry is dominated in the interviewee’s
enterprises, the main products is MIS and DSS. Inter-
viewee’s software development team usually are large, the
number of requirement team is 4 to 5 persons. Mainly
interviewees are project managers in order to make the
data more persuasive.
7.3 Analysis on Reliability and Validity
The Cronbach’s α value is used to determine internal con-
sistency because this paper is exploratory research and
items are limited. The reliability of every variable is more
than 0.350 after deleting items I3 and Te7, and reliability
can be basically acceptable (Table 3).
7.4 Statistical Analysis
7.4.1 Descriptive Statistics
The descriptive statistics is illustrated in the Table 4 ac-
cording to the variables in Table 2. The summary is in the
1) The average score of knowledge transformation &
requirement development is 4.4712 and indicates that
there is close relationship between knowledge transfor-
mation and requirement development, it is same as with
literature research and enterprise interview.
2) Personal capability, comprehensive training, friendly
environment, project intent, customer participation, do-
main experience and requisite variety and etc., score more
than 4 and have a higher acceptance.
3) Redundancy, creative chaos, team self-management,
individual self-management, methodology and technol-
ogy, score lower than 3.5, are basically same as the ex-
pected results.
7.4.2 One-Sample T Test
It judges one-sample T test which the test value is 3.5,
confidence interval is 95%. If the significant coefficient is
less than 0.05, and the upper and lower bounds are greater
than 0, indicating its value to more than 3.5 large (have
passed the examination); if a significant factor greater
than 0.05, or the upper and lower bounds are less than 0,
then its value is smaller than 3.5. It is illustrated in Table 5
that the items are passed the test except redundancy,
creative chaos, the team self-management, individual self-
management, methodology and technology.
Redundancy, creative chaos, team self-management,
individual self-management, methodology and technolo-
gy do not pass the test where the test value is 3.5. The
reverse scoring one-sample T test results is illustrated in
the Table 6 where the test value is 3. Only the individual
autonomy is significant, it specified that the individual
autonomy plays a negative effect on knowledge creation
of requirement development. The other variables do not
pass the test, they are unclear type. In addition, the
knowledge transfer and requirement development still
passing the test where test value 4, it illustrates in Table 7
that the relationship between the requirement develop-
ment and knowledge transfer is recognized highly.
8. Conclusions
It is illustrated in Table 8 that the management support,
friendly environment, intention, requisite variety, cus-
tomer participation, comprehensive training, knowledge
gap, domain experience and personal capability and so on
through the literature research, interview in enterprise and
questionnaire survey, The nine variables have the positive
effect on the knowledge creation of requirement devel-
opment, where the knowledge gap is measured by reducing
Copyright © 2010 SciRes. JSEA
The Exploratory Analysis on Knowledge Creation Effective Factors in Software Requirement Development585
Table 3. Reliability of variables
Variably Item number Cronbach’s α value Remove item Reference value
Management Support 3 0.710
Friendly environment 7 0.771
Redundancy 2 0.447
Creative chaos 2 0.683
Intention 2 0.410
Team self-management 3 0.532
Requisite variety 2 0.555
User participation 3 0.502
Comprehensive training 3 0.824
Knowledge gap 3 0.379
Personal self-management 2 0.703
Domain experience 2 0.552 I3
Personal capability 3 0.409
Methodology 2 0.627
Technology 4 0.469 Te7
Knowledge transfer& requirements development 4 0.914
Table 4. Descriptive statistics
N Minimum MaximumMean Std. Deviation
Management support 26 3.0000 5.0000 3.961538 .5360508
Friendly environment 26 3.4286 4.8571 4.131868 .3426739
Redundancy 26 2.0000 4.0000 3.096154 .6636148
Creative chaos 26 1.0000 5.0000 2.865385 .9225800
Intention 26 3.0000 5.0000 4.115385 .4540417
Team self-management 26 2.0000 4.3333 3.480769 .5931590
Requisite variety 26 3.5000 5.0000 4.019231 .3868015
User participation 26 3.6667 5.0000 4.423077 .4274752
Comprehensive training 26 3.3333 5.0000 4.192308 .5178852
Knowledge gap 26 3.0000 4.6667 3.987179 .4664835
Personal Self-management 26 1.0000 4.0000 2.500000 .7745967
Experience in the field 26 2.5000 5.0000 4.038462 .5463163
Personal capability 26 3.5000 5.0000 4.211538 .4043038
Methodology 26 2.5000 4.0000 3.403846 .4902903
Related technology 26 1.7500 4.0000 3.375000 .4962358
Knowledge transfer & requirements development 26 3.7500 5.0000 4.471154 .4707809
Valid Nlistwise 26
Table 5. Variable one-sample T test
Test Value = 3.5
95% Confidence Interval of the
t df Sig. (2-tailed) Mean Difference
Lower Upper
Management support 4.390 25 .000 .4615385 .245023 .678054
Friendly environment 9.402 25 .000 .6318681 .493459 .770277
Redundancy -3.103 25 .005 -.4038462 -.671886 -.135806
Creative chaos -3.507 25 .002 -.6346154 -1.007254 -.261977
Intention 6.911 25 .000 .6153846 .431993 .798776
Team self-management -.165 25 .870 -.0192308 -.258813 .220351
Requisite variety 6.845 25 .000 .5192308 .362998 .675463
User participation 11.011 25 .000 .9230769 .750416 1.095738
Comprehensive training 6.816 25 .000 .6923077 .483129 .901486
Knowledge gap 5.325 25 .000 .4871795 .298763 .675596
Personal self-management -6.583 25 .000 -1.0000000 -1.312866 -.687134
Domain experience 5.026 25 .000 .5384615 .317800 .759123
Personal capability 8.974 25 .000 .7115385 .548237 .874840
Methodology -1.000 25 .327 -.0961538 -.294186 .101879
Related technology -1.284 25 .211 -.1250000 -.325434 .075434
Copyright © 2010 SciRes. JSEA
The Exploratory Analysis on Knowledge Creation Effective Factors in Software Requirement Development
Table 6. Not pass the variable reverse scoring one-sample T test where the test value is 3.5
Test Value = 3.5
95% Confidence Interval of
the Difference
t df Sig. (2-tailed)Mean Difference
Lower Upper
Redundancy (reverse –.739 25 .467 –.0961538 –.364194 .171886
Creative chaos (re
verse .744 25 .464 .1346154 –.238023 .507254
Team Self-
managementreverse –4.133 25 .000 –.4807692 –.720351 –.241187
Personal Self-
managementreverse 3.291 25 .003 .5000000 .187134 .812866
Methodologyreverse –4.200 25 .000 –.4038462 –.601879 –.205814
Related technol
ogyreverse –3.853 25 .001 –.3750000 –.575434 –.174566
Table 7. Knowledge transfer and requirement development one-sample T test
Test Value = 3.5
95% Confidence Interval of the
t dfSig. (2-tailed)Mean Difference
Lower Upper
Knowledge transfer and requirement development 5.10325.000 .4711538 .281001 .661306
Table 8. The summarized relationship between variables
Correlation Level Effect factor Remark
Management support The same as with literature research and enterprise interview
Organization Friendly environment The same as with literature research and enterprise interview
Intention The same as with literature research and enterprise interview
Requisite variety The same as with literature research and enterprise interview
User participation The same as with literature research and enterprise interview
Comprehensive trainingThe same as with literature research and enterprise interview
Knowledge gap The same as with literature research and enterprise interview
Domain experience The same as with literature research and enterprise interview
Positive ()
Personal Personal capability The same as with literature research and enterprise interview
Redundancy The same as with literature research and enterprise interview
Organization Creative chaos Chaos is a demon for the software business (Larry • Constantine), but the creative
chaos has certain positive effect for enterprise management.
Team Self-management The same as with literature search and enterprise interview
Methodology Small-scale projects require little, but large-scale projects need.
Uncertain (U)
Technology Related technology Small-scale projects require little, but large-scale projects need.
Negative () Personal Self-management Requirement development projects generally obey the project management method,
have clear work plan and method.
knowledge gap and it is positive. Considering the litera-
ture research and interview in the enterprise, individual
independency is determined negative because it illustrates
significance in reverse scoring. The others, including
redundancy, creative chaos, team self-management,
methodology and technology, are unclear. It concludes
that the technology and the methodology are support
factors of project development and would be very useful
for large scale projects. On the contrary, redundancy,
creative chaos and team self-management should be
avoided as far as possible in the project, because it is
inconsistence with the goals of requirement development.
9. Acknowledgements
Thanks for helpful discussion with Mr. Huang Deyi,
Mr.Li Jiangzhang, Mr. Chen Zhening, Mr. Wang Shuwen,
Mr. Liu Bing, Brenda Huang, and Ms. Zhang Hui etc.
[1] X. M. Li, L. Y. Sun and Y. L. Wang, “Research on
Software Requirement Management Based on Knowledge
Management,” Management of Research and Deve-
lopment, Vol. 17, No. 2, February 2005, pp. 28-32, 39.
[2] Swebok, “Guide to Software the Software Engineering
Body of Knowledge,” 2004. http://www.swebok.org
[3] X. H. Zhang, “Software Development Process and Mana-
gement,” Tsinghua University Press, Beijing, 2005.
[4] M. A. Cusumano and R. W. Selby, “The Secrets of Micro-
soft,” Free Press, New York, 1995.
[5] G. von Krogh, K. Ichijo and I. Nonaka, “Enabling
Knowledge Creation: How to Unlock the Mystery of Tacit
Knowledge and Release the Power of Innovation,” Oxford
University Press, New York and Oxford, 2000.
[6] J. P. Wan, Q. J. Liu, D. J. Li and H. B. Xu, “Research on
Knowledge Transfer Influencing Factors in Software
Copyright © 2010 SciRes. JSEA
The Exploratory Analysis on Knowledge Creation Effective Factors in Software Requirement Development587
Process Improvement,” Journal of Software Engineering
and Applications, Vol. 3, No. 2, February 2010, pp. 134-
[7] J. P. Wan, H. Zhang, D. Wan and D. Y. Huang, “Research
on Knowledge Creation in Software Requirement Deve-
lopment,” Journal of Software Engineering and Applica-
tions, Vol. 3, No. 5, May 2010, pp. 487-494.
[8] I. Nonaka and H. Takeuchi, “The Knowledge Creating
Company,” Oxford University Press, New York, 1995.
[9] W. Johnson, “Technological Innovation and Knowledge
Creation: A Study of Enabling Condition and Processes of
Knowledge Creation in Collaborative R & D Project,”
Ph.D. Dissertation, York University, Toronto, 2000.
[10] H. G. Chen, et al., “The Science and Art of Software De-
velopment,” Electronic Industry Press, Beijing, 2002.
[11] Y. S. Zhang, “The Way of System Analyzer,” Electronic
Industry Press, Beijing, 2006.
[12] W. M. Cohen and D. Levinthal, “Absorptive Capacity: A
New Perspective on Learning and Innovation,” Adminis-
trative Science Quarterly, Vol. 35, No. 1, 1990, pp. 128-
[13] Y. H. Ke, “Research on Imparting Knowledge Transfer
across Team: Based Information Systems,” Master Thesis,
Information Management of Institute, National Sun
Yat-sen University, Taiwan, 2005.
[14] P. Sawyer and G. Kotonya, “Swebok: Software Require-
ments Engineering Knowledge Area Description Version
0.5,” IEEE and ACM Project on Software Engineering
Body of Knowledge, San Francisco, July 1999.
[15] J. P. Wan, “Research on Software Product Support Struc-
ture,” Journal of Software Engineering and Applications,
Vol. 2, No. 3, October 2009, pp.174-194.
[16] B. Jayatilaka, “The Role of Developer and User Know-
ledge Domains and Learning in Systems Development,”
AMCIS2000, 2000, pp.1323-1329.
[17] S. Alshawi and W. Al-Karaghouli, “Managing Know-
ledge in Business Requirements Identification,” Logis-
tics Information Management, Vol. 16, No. 5, 2003, pp.
[18] H. Barki and J. Hartwick, “User Participation, Conflict and
Conflict Resolution,” Information Systems Research, Vol.
5, No. 2, December 1994, pp. 422-440.
[19] P. J. Guinan, J. G. Cooprider and S. Faraj, “Enabling
Software Development Team Performance during Requi-
rement Definition: A Behavioral vs. Technical Approach,”
Information Systems Research, July 1998, pp. 101-125.
[20] R. Hirschheim and H. K. Heinz, “Realizing Emancipatory
Principles in Information Systems Development: The Case
for ETHICS,” Management Information Systems Quar-
terly, Vol. 18, No. 1, March 1994, pp. 83-109.
[21] Standish Group, “Chaos 1994,” The Standish Group
International, Massachusetts, 1994.
[22] Standish Group, “Chaos,” Standish Group Report, 1995.
[23] Standish Group, “Chaos: A Recipe to Success,” Standish
Group Report, 1999.
[24] W. S. Humphrey, “Managing the Software Process,”
Reading, Addison-Wesley, Massachusetts, 1989, pp. 19-
[25] L. L. Constantine, “Beyond Chaos: The Expert Edge in
Managing Software Development,” Addison-Wesley,
Boston, 2001.
[26] I. V. Eriksson and G. W. Dickson, “Knowledge Sharing in
High Technology Company,” American Conference on
Information System, Vol. 36, No. 2, 2000, pp. 1330-1335.
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