J. Software Engineering & Applications, 2010, 3: 134-140
doi:10.4236/jsea.2010.32017 Published Online February 2010 (http://www.SciRP.org/journal/jsea)
Research on Knowledge Transfer Influencing
Factors in Software Process Improvement
Jiangping Wan1,2, Qingjing Liu1, Dejie Li1, Hongbo Xu3,4
1School of Business Administration, South China University of Technology, Guangzhou, China; 2Institute of Emerging Industrializa-
tion Development, South China University of Technology, Guangzhou, China; 3School of Computer Science and Engineering, South
China University of Technology, Guangzhou, China; 4Guangzhou O-Engineer Information Technology Ltd, Guangzhou, China.
Received October 28th, 2009; revised November 17th, 2009; accepted November 25th, 2009.
ABSTRACT
Knowledge transfer model of software process improvement (SPI) and the conceptual framework of influencing factors
are established. The model includes five elements which are knowledge of transfer, sources of knowledge, recipients of
knowledge, relationship of transfer parties, and the environment of transfer. The conceptual framework includes ten key
factors which are ambiguity, systematism, transfer willingness, capacity of impartation, capacity of absorption, incen-
tive mechanism, culture, technical support, trust and knowledge distance. The research hypotheses is put forward. Em-
pirical study concludes that the trust relationship among SPI staffs has the greatest influence on knowledge transfer,
and organizational incentive mechanism can produce positive effect to knowledge transfer of SPI. Finally, some sug-
gestions are put forward to improve the knowledge transfer of SPI: establishing a rational incentive mechanism, exe-
cuting some necessary training to transfer parties and using software benchmarking.
Keywords: Software Process Improvement, Knowledge Transfer, Influence Factors, Pattern
The software process is the set of tools, method, and
practices we use to produce a software product. The
objectives of software process improvement (SPI) are
to process produce products according to plan while
simultaneously improving the organization’s capability
to produce better products [1]. The six basic principles of
SPI by Watts S. Humphrey are as follows: 1) Major
changes to the software process must start at the top; 2)
Ultimately, everyone must be involved; 3) Effective
change requires a goal and knowledge of the current
process; 4) Change is continuous; 5) Software process
changes will not be retained without conscious effort and
periodic reinforcement; 6) Software process improve-
ment requires investment [1]. Alfonso Fuggetta argues
that the scope of software improvement methods and
models should be widened in order to consider all the
different factors affecting software development activi-
ties. We should reuse the experiences gained in other
business domains and in organizational behavior research.
Statistics is not the only source of knowledge. We should
also appreciate the value of qualitative observations [2].
Wan Jiangping argues that managers should think deeply
into their think processes. The following issues in software
organization can be resolved with SPI: 1) The processes
and their principles for how to inherit and acquire others’
knowledge; 2) The processes and their principles for con-
version knowledge into their capability [3]. Literature 6
describes a repository of 400 process improvement ex-
periments and presents patterns that help organizations
plan their improvement initiatives [4].
1. Introduction
1.1 Organization Knowledge in Software Process
Improvement
Organizational knowledge creation is the process of
making available and amplifying knowledge created by
individuals as well as crystallizing and connecting it to
an organization’s knowledge system [5]. Software or-
ganization is a highly knowledge-intensive enterprise,
knowledge transfer is critical for software enterprise. It
is obvious that software process is also an organiza-
tional knowledge intensive learning process and needed
to be supported with knowledge management [6].
*This research was supported by Key Project of Guangdong Province
Education Office (06JDXM63002), Soft Science project of Guangdong
Province (2007B070900026), NSF of China (70471091), and QualiPSo
(IST-FP6-IP-034763).

Research on Knowledge Transfer Influencing Factors in Software Process Improvement 135
Sandra A. Slaughter and Laurie J. Kirsch conceptu-
alize knowledge transfer portfolios in terms of their
composition (the types of mechanisms used) and their
intensity (the frequency with which the mechanisms
are utilized). They hypothesize the influence of organ-
izational design decisions on the composition and inten-
sity of knowledge transfer portfolios for SPI. They then
posit how the composition and intensity of knowledge
transfer portfolios affect performance improvement.
Their findings indicate that a more intense portfolio of
knowledge transfer mechanisms is used when the source
and recipient are proximate, when they are in a hierar-
chical relationship, or when they work in different units
[7]. Literature 8 includes: 1) A knowledge management
framework for SPI; 2) An innovative knowledge model-
ing and control approaches; 3) Mining and retrieval ap-
proaches on the software process assets; 4) A knowledge
management for SPI.
1.2 Knowledge Transfer
Bloodgood considers knowledge transfer as knowledge
transfer and transmit among various organizations and
individual [9]. Argote considers enterprise knowledge
transfer as a process that one organization’s experiences
impact on other’s organizational action. It is that knowl-
edge change or change knowledge recipients’ behavior
[10]. Davenport considers knowledge transfer as unified
process which consists of both knowledge transfer proc-
ess and knowledge absorbs process [11]. The effective
knowledge transfer is that transfer knowledge is reserved
[12]. Ingram considers knowledge transfer as process
sharing knowledge in organization through various chan-
nels in order to make use of extant knowledge effectively
[13]. Dong-Gil Ko et al. consider knowledge transfer as
transmitting process in which knowledge transfer from
owners to recipients for their learning and application [14].
In our understanding, knowledge transfer includes
three aspects which are the process spreading from own-
ers to recipients, activities occurring under contextualiza-
tion and special goal. But the ultimate goal is to make the
knowledge of the owners be the recipients’ and narrow
the knowledge gap between owners and recipients so as
to promote the co-development of individuals and or-
ganizations. We define knowledge transfer as the process
making knowledge transferring from the source of
knowledge to recipients in contextualization.
1.3 Knowledge Transfer Model
The knowledge transfer model mainly includes process
model and factors model. The process model is a model
dividing knowledge transfer into different stages. The
representative process models are Nonaka knowledge
spiral model [15], Szulanski four stages model [12], and
Gilbert&Cordey-Hayes five steps model. While factors
model bases on factors in the process the knowledge
transfer [16]. The representative ones of it are the four
factors model invented by Jeffrey L. Cummings and
Bing-Sheng Teng which includes sources of the knowl-
edge, recipients of the knowledge, knowledge and con-
text and transfer framework invented by Vito Albino et
al. including transfer subject, context, content, and trans-
fer media. Jeffrey L. Cummings and Bing-Sheng Teng’s
factors model is applied in this study [17].
1.4 Knowledge Transfer Influencing Factors
Knowledge transfer influencing factors are in the fol-
lowing [18]: 1) Characteristics of knowledge transferred
include causal ambiguity and unprovability. 2) Charac-
teristics of source of knowledge include knowledge pro-
viders shortage of motivation to transfer knowledge and
unbelieving. The knowledge owners will not sharing
knowledge with others because they are afraid losing
knowledge possession, superiority complex, right and
status and so on, lack time to sharing knowledge with
others and couldn’t proper reward and return on knowl-
edge sharing. When the experts are not discovered and
believed, their suggestions may be rejected and more
challenged. 3) Characteristics of recipient of knowledge
include knowledge recipients’ both absorbing capability
and keeping capability. It is very important for the
knowledge recipient’s capability to absorb others’
knowledge and integrate into individual knowledge on
the condition he will accept the knowledge developed by
other. 4) Characteristics of context include barren organ-
izational context and arduous relationship. Both will im-
part on knowledge transfer.
2. Research Model and Hypotheses
The knowledge transfer model of SPI, which includes
five factors involving knowledge transferred, source of
knowledge, recipient of knowledge, relationship of two
parties and context. Besides is proposed, we consider
knowledge transfer of SPI as a process which includes
transmission, absorption and feedback (Figure 1).
Knowledge transferred between source of knowledge and
recipient of knowledge in the SPI must experience three
stages. The stage of transmission is to transmit knowl-
edge from source of knowledge to recipients of knowl-
edge. The stage of absorption is about processing, sorting,
and absorption process with their own mental model
when recipients receive new knowledge. In stage of
feedback, recipients of knowledge constantly communi-
cate and feedback with source of knowledge in the proc-
ess of absorption to master the knowledge transferred
by the owners of knowledge. Thus, based on knowledge
transfer model of SPI, we propose conceptual framework
of ten key influencing factors of knowledge transfer in
SPI. The influencing factors include ambiguity, systema-
tism, transfer willingness, capacity of impartation, capac-
ty of absorption, incentive mechanism, culture, technical i

Research on Knowledge Transfer Influencing Factors in Software Process Improvement 136
Figure 1. Knowledge transfer model of SPI
Figure 2. Conceptual framework of influencing factors
support, trust and knowledge distance (Figure 2). Then
we propose our research hypotheses based on knowl-
edge transfer model and conceptual framework of
knowledge transfer influencing factors (Table 1).
3. Research Design
3.1 Scale Design
We studied the variable indicators on a five-point
Likert-type scale. The questionnaire of pre-investigation
was divided into four parts. The first part was ques-
tionnaire direction, including questionnaire background
and introduction of basic information. The second part
was the basic information of respondents, including gen-
der, age and qualifications of respondents; the third part
was the main body of the questionnaire, mainly about the
issues of influence factors, including a total of 46 items.
The last part with regard to performance of knowledge
transfer involved a total of 4 items. After the completion
of the pre-prepared questionnaire, we pre-prepared ques-
tionnaire in a small number of target groups by e-mail.
We issued 10 questionnaires and six were received. In
the end, we received a total of 114 questionnaires, of
which eight were invalid, 106 were valid.
3.2 Data Collection
All the samples of our research are SPI staffs from Guang-
dong Software Organization who mainly concentrate in
Guangzhou, Shenzhen and Zhuhai. From the job level of
respondents, senior manager accounts for 8.5%, the percent
of project manager is 8.5%, while the general staff contrib-
utes the large percent of 66.0%. From the qualifications of
respondents, undergraduate accounts for about 74.5%,
graduate 25.5%. From the age of respondents, age ranged
from 24 to 30 accounts for the largest proportion of 67.9%,
age bellow 24 and above 30 are 14.2% and 17.9% respec-
tively. From the staff size of process improvement of in-
vestigated company, staff size bellow 10 comes up to
63.2%, staff size between 11 to 30 accounts for 17.9%, size
above 31 reaches 18.9%. It can clearly be seen that, the
samples have representative to some extent, and are suit-
able for the next phase of data analysis.

Research on Knowledge Transfer Influencing Factors in Software Process Improvement 137
Table 1. Research hypotheses
Items Hypotheses
H1: Ambiguity of knowledge transferred has correlation with performance of knowledge transfer
H1a: Ambiguity of knowledge transferred has negative correlation with satisfaction of knowledge transfer
Ambiguity
H1b: Ambiguity of knowledge transferred has negative correlation with frequency of knowledge transfer
H2: Systematism of knowledge transferred has correlation with performance of knowledge transfer
H2a: Systematism of knowledge transferred has negative correlation with satisfaction of knowledge transfer
Systematicness
H2b: Systematism of knowledge transferred has negative correlation with frequency of knowledge transfer
H3: Transfer Willingness of source of knowledge has correlation with performance of knowledge transfer
H3a: Transfer Willin
g
ness of source of knowled
g
e has
p
ositive correlation with satisfaction of
k
nowled
g
e
t
ransfe
r
Transfer Will-
ingness H3b: Transfer Willin
g
ness of source of knowled
g
e has
p
ositive correlation with fre
q
uenc
y
of
k
nowled
g
e
t
ransfe
r
H4: Capacity of Impartation of source of knowledge has correlation with performance of knowledge transfer
H4a: Ca
p
acit
y
of Im
p
artation of source of knowled
g
e has
p
ositive correlation with satisfaction of
k
nowled
g
e transfe
r
Capacity of Im-
partation H4b: Ca
p
acit
y
of Im
p
artation of source of knowled
g
e has
p
ositive correlation with fre
q
uenc
y
of
k
nowled
g
e
t
ransfe
r
H5: Capacity of Absorption of recipient of knowledge has correlation with performance of knowledge transfer
H5a: Ca
p
acit
y
of Absor
p
tion of reci
p
ient of knowled
g
e has
p
ositive correlation with satisfaction of
k
nowled
g
e
t
ransfe
r
Capacity of Ab-
sorption H5b: Ca
p
acit
y
of Absor
p
tion of reci
p
ient of knowled
g
e has
p
ositive correlation with fre
q
uenc
y
of
k
nowled
g
e
t
ransfe
r
H6: Incentive Mechanism of knowledge has correlation with performance of knowledge transfer
H6a: Incentive Mechanis
m
has
p
ositive correlation with satisfaction of
k
nowled
g
e
t
ransfe
r
Incentive
Mechanism H6b: Incentive Mechanis
m
has
p
ositive correlation with fre
q
uenc
y
of
k
nowled
g
e
t
ransfe
r
H7: Organizational culture has correlation with performance of knowledge transfer
H7a: Or
g
anizational culture has
p
ositive correlation with satisfaction of
k
nowled
g
e
t
ransfe
r
Culture H7b: Or
g
anizational culture has
p
ositive correlation with fre
q
uenc
y
of
k
nowled
g
e
t
ransfe
r
H8: Technical Support of knowledge transfer has correlation with performance of knowledge transfer
H8a: Technical Su
pp
ort of
k
nowled
g
e
t
ransfe
r
has
p
ositive correlation with satisfaction of
k
nowled
g
e
t
ransfe
r
Technical Sup-
port H8a: Technical Su
pp
ort of
k
nowled
g
e
t
ransfe
r
has
p
ositive correlation with fre
q
uenc
y
of
k
nowled
g
e
t
ransfe
r
H9: Trust relationship between transmitter and recipient of knowledge has correlation with performance of knowledge transfer
H9a: Trust relationship between transmitter and recipient of knowledge has positive correlation with satisfaction of
knowledge transfer
Trust
H9b:
T
rus
t
relationshi
p
between transmitter and reci
p
ient of knowled
g
e has
p
ositive correlation with fre
q
uenc
y
of
k
nowled
g
e
t
ransfe
r
H10: Knowledge Distance between transmitter and recipient of knowledge has curve correlation with performance of knowledge transfer
H10a: Knowledge Distance between transmitter and recipient of knowledge has curve correlation with staff’s satisfac-
tion of knowledge transfer When knowledge distance is short, the correlation appears negative; as it becomes moderate, the
correlation appears positive and when knowledge distance is considerable long, and the correlation becomes negative again.
Knowledge Dis-
tance H10b: Knowledge Distance between transmitter and recipient of knowledge has curve correlation with staff’s fre-
quency of knowledge transfer. When knowledge distance is short, the correlation appears negative; as it becomes moderate,
the correlation appears positive and when knowledge distance is considerable long, the correlation becomes negative again.
Control Variables H11: The individual’s working years, position and qualification have influence on performance of knowledge transfer
3.3 Result Analysis and Explanation
3.3.1 Statistical Result Analysis
In order to ensure the scientific nature of the proposition
certification, it is necessary to test the reliability and va-
lidity of the measure model. First, all variables’ Cron-
bach’s coefficient are significantly higher than the mini-
mum threshold 0.70, factor analysis and confirmatory
factor analysis are all met reference standards, so we can
judge it has internal validity. Second, for all indicators,
standardized loading factors are also higher than that of
the recommended minimum critical level 0.50, the statis-
tical value of Battelle is a much smaller than 0.01. All of
these indicate that all scales have highly convergent va-
lidity. The above shows that the research ha good exter-
nal validity. Integrated the test of reliability and validity,
the scales are reliable and effective which can be used to
verify model assumptions.
Correlation analysis was executed between knowledge
transfer performance and its influence factors by using
SPSS16.0 statistical software, and the result was pre-
sented in Table 2. The result concludes that culture has
no significant correlation to performance of knowledge
transfer. Capacity of impartation and incentive mecha-
nism has significantly positive correlation to perform-
ance of knowledge transfer at the significant level of
0.05. While ambiguity has significantly negative corre-
lation to performance of knowledge transfer at the sig-
nificant level of 0.01, the rest factors have significantly
positive correlation to performance of knowledge trans-
fer at the significant level of 0.01. To avoid multiple
co-linear problems, we defined values of knowledge

Research on Knowledge Transfer Influencing Factors in Software Process Improvement 138
transfer influence factors as independent variables, and
executed regression analysis by step regression method
when considering the causal relationship among knowl-
edge transfer performance and its influence factors. The
results are in Table 3.
From the overall regression effect with F=18.967, P=
0.000, regression equation has achieved a very signifi-
cant level which indicates better regression effect.
Meanwhile, variance expansion factor VIF had small
value and multiple co-linear problems were not obvious.
The adjusted determination coefficient is 0.339 indi-
cating that three indicators of knowledge transfer influ-
ence can explain 33.9% of the total variance. The coeffi-
cient values of constant and variables are all less than
0.05 suggesting that they have significant meaning.
Table 2. Correlation analysis between influencing factors
and performance of knowledge transfer
Performance of Knowl-
edge Transfer
Performance
Influencing Factors Correlation Sig.
L1 Ambiguity -.143* .000
L2 Systematism -.109** .000
L3 Transfer Willingness .375** .000
L4 Capacity of Impartation .285* .0016
L5 Capacity of Absorption .392** .000
L6 Incentive Mechanism .387** .009
L7 Culture 213 .159
L8 Trust .558** .000
Table 3. Overall effect parameter by step regression
Model R R2 Adjusted
R
2
Stan-
dard
Error
F Sig.
1 .512 .262 .255 .625 37.004.000
2 .560 .313 .300 .606 23.495.000
3 .598 .358 .339 .589 18.967.000
Figure 3. Influencing relationship of performance of
knowledge transfer in software process
In order to clear the influence factors and their direc-
tion of the performance of knowledge transfer in SPI, we
employed Figure 3 to express test results of Table 4
where the line indicated positive relationship and the
dotted line negative relationship. From Table 4 and Fig-
ure 3, we can conclude that the majority results of em-
pirical research are consistent with our hypotheses and
they are listed as follows: In the characteristics of
knowledge, the ambiguity impacting on the performance
of knowledge transfer mainly manifested on the satisfac-
tion of transfer. The more ambiguous the source of
knowledge is, the more time and energy will be spending
when we express the knowledge out from source of
knowledge. The systematization of knowledge impacting
on the performance of knowledge transfer mainly indi-
cates that it has a negative effect on the performance of
knowledge transfer. The higher the systematism of know-
ledge is, the more difficult for transmitter of knowledge to
express real meaning of the knowledge.
3.3.2 Empirical Result Analysis
According to the above statistical results, the test results
of our hypotheses were concluded in Table 4.
In the characteristics of source knowledge, the transfer
willingness of knowledge has significantly positive cor-
relation to the satisfaction and frequency of knowledge
transfer. In the characteristics of recipients of knowledge,
the recipients’ capacity of absorption has significantly
positive influence on the effect of knowledge transfer.
In the environmental factors of knowledge transfer in
SPI, the incentive mechanism has significantly positive
influence on the satisfaction and frequency of knowledge
transfer. The employees of the organization will retain
their knowledge because they are worried about losing
their authority when imparting their knowledge to others,
if the organization does not take certain incentives.
In the relationship between source of knowledge and
recipient of knowledge, trust relationship has the largest
impact on the performance of knowledge transfer of all
influence factors. This shows that trust relationship be-
tween source of knowledge and recipient of knowledge is
the most basic factor of knowledge transfer in SPI. The
curve relationship between knowledge distance and the
performance of knowledge transfer is not obvious.
4. Management Enlightment and Suggestion
In order to improve the staffs’ performance of SPI in
practice, the following three aspects are necessary.
1) Establishing a reasonable incentive mechanism.
Software organizations should build a reasonable in-
centive mechanism to enhance the transfer willingness
of source of knowledge and consciousness of recipients
of knowledge, it can also promote trust relationship be-
tween the source of knowledge and recipient of knowl-
edge. To enhance the transfer willingness of source of
knowledge, it is necessary to give corresponding material

Research on Knowledge Transfer Influencing Factors in Software Process Improvement 139
Table 4. Test results of hypotheses (a=10, b=11)
Hypothesis Results Hypothesis Results
H1 Support H6a Support
H1a Support H6b Support
H1b Not H7 Not Support
H2 Support H7a Not Support
H2a Support H7b Not Support
H2b Not H8 Support
H3 Support H8a Support
H3a Support H8b Support
H3b Support H9 Support
H4 Support H9a Support
H4a Support H9b Support
H4b Not HA Not Support
H5 Support HAa Not Support
H5a Support HAb Not Support
H5b Support HB Not Support
H6 Support
or mental compensation and think highly of achieve-
ment of source of knowledge. Similarly, recipient of
knowledge also need some encouragement to accept and
use new knowledge. It can make the two sides of knowl-
edge transfer participate actively by sharing their inter-
ests and therefore promote organizational SPI.
2) Carrying out necessary training for the both sides of
knowledge transfer. After solving the transfer willingness
of the two sides, the transfer capacity of source of
knowledge, capacity of absorption of recipient of
knowledge and distance between source of knowledge
and recipient of knowledge have greater influence on
knowledge transfer. Thus, software organizations need to
give corresponding training for both of the two parties.
They can employ external experts to train their staffs so
that the staffs’ capacity of imparting knowledge can gain
improvement.
3) Using software benchmarking [19]. In pursuit of a
capability model rating, software benchmarking (in our
understanding, the benchmarking is standard best
knowledge patterns, such as CMMI and SWEBOK [20],
etc.) would help its process improvement and assessment
effort. This benchmarking questionnaire can be grouped
into five categories: a) Philosophy of implementa-
tion—how each company achieved CMMI compliance in
terms of schedule, teams, and planning. b) Management
commitment—the strength of institutional support for the
process improvement effort. c) Cultural change and in-
stitutionalization—issues that arose regarding acceptance
of the new process philosophy. d) Definition of organiza-
tion—because the CMMI assessment is for specific or-
ganizations, these questions assessed the scope of their
effort (for example, a section, company, or corporation).
e) Objective evidence—the CMMI assessment process
requires objective evidence that the new process is being
followed, so these questions probed how each company
collected evidence.
5. Conclusions
In this study, the knowledge transfer model of SPI and the
conceptual framework of 10 key influence factors are es-
tablished. Then research hypotheses are put forward. Em-
pirical study concludes that the trust relationship among
SPI staffs has the greatest influence on knowledge transfer,
and organizational incentive mechanism can produce posi-
tive effect to knowledge transfer of SPI. We believed that
the research is helpful for SPI practitioners to improve
their performance of knowledge transfer.
6. Acknowledgements
Thanks for helpful discussion with Mr. Hou Yawen, Mr.
Zhou qiyang, Mr. Li Jiangzhang, Mr. Nihao, Mr. Zhou
Zhijun, and the hard work of my student Zeng
Yonghua and Zheng Chuwei.
REFERENCES
[1] W. S. Humphrey, “Managing the software process,”
Reading, Addison-Wesley, MA, 1989.
[2] Alfonso Fuggetta, “Software process: A roadmap,” Pro-
ceedings of the Conference on The Future of Software
Engineering, Limerick, Ireland, pp. 25–34, June 04–11,
2000.
[3] J. P. Wan and J. M. Yang, “Knowledge management in
SPI,” Application Research of Computer, Vol. 19, No. 5,
pp. 1–3, 2002.
[4] M. Blanco, P. Gutiérrez, and G. Satriani, “SPI patterns:
Learning from experience,” IEEE Software, Vol. 18, No.
3, pp. 28–35, 2001.
[5] I. Nonaka and G.g von Krogh, “Perspective-tacit knowl-
edge and knowledge conversion: Controversy and ad-
vancement in organizational knowledge creation theory,”
Organization Science, Vol. 20, No. 3, pp. 635–652, 2009.
[6] J. P. Wan, “Research on software product support struc-
ture,” Journal of Software Engineering and Applications,
Vol. 2, No. 3, pp. 174–194, 2009.
[7] S. A. Slaughter and L. J. Kirsch, “The effectiveness of
knowledge transfer portfolios in SPI: A field study,” In-
formation Systems Research, September 2006.
[8] X. G. Zhang, “Research on knowledge management
technology in SPI,” Chinese Academy of Sciences Doc-
toral Thesis, 2004.
[9] J. M. Bloodgood and W. D. Salisbury, “Understanding
the influence of organizational change strategies on in-
formation technology and knowledge management strate-
gies,” Decision Support Systems, Vol. 31, No. 1, pp.
55–69, 2001.
[10] L. Argote, “Organizational learning: Creating, retaining
and transfering knowledge,” Kluwer Academic Publishers,
pp. 143–189, 1999.
[11] T. H. Davenport and L. Prusak, “Working knowledge:

Research on Knowledge Transfer Influencing Factors in Software Process Improvement 140
How organization manage what they know,” Harvard
Business School Press, Boston, 1998.
[12] G. Szulanzki, “Exploring internal stickiness: Impediments
to the transfer of best practice with the firm,” Strategic
Management Journal, Vol. 17, pp. 27–43, 1996.
[13] L. Argote and P. Ingram, “Knowledge transfer: A basis
for competitive advantage in firms,” Organizational Be-
havior and Human Decision Processes, Vol. 82, No. 1, pp.
150–169, 2000.
[14] D.-G. Ko, L. J. Kirsch, and W. R. King, “Antecedents of
knowledge transfer from consultants to clients in enter-
prise system implementations,” Management Information
System Quarterly (Special Issue), Vol. 29, No. 1, pp. 59–
85, 2005.
[15] I. Nonaka and H. Takeuchi, “The knowledge creating
company,” Oxford University Press, New York, 1995.
[16] M. Gilbert and M. Cordey-Hayes, “Understanding the
process of knowledge transfer to achieve successful
technological innovation,” Technovation, Vol. 16, pp.
301–312, 1996.
[17] J. L. Cummings and B. S. Teng, “Transferring R & D
knowledge: The key factors affecting knowledge transfer
success,” Journal of Engineering and Technology Man-
agement, Vol. 20, pp. 39–68, 2003.
[18] M. Polanyi, “The study of man,” Routledge & Kegan,
London, Vol. 12, 1957.
[19] G. C. Thomas and H. R. Smith, “Using structured bench-
marking to fast-track CMM process improvement,” IEEE
Software, Vol. 18, No. 5, pp. 48–52, 2001.
[20] P. Bourque, R. Dupuis, A. Abran, J.W. Moore, and L.
Tripp, “The guide to the software engineering body of
knowledge,” IEEE Software, Vol. 16, No. 6, pp. 35–44,
1999.