E-ISSN 2039-2117
ISSN 2039-9340
Mediterranean Journal of Social Sciences
MCSER Publishing Rome-Italy
Vol 4 No 9
October 2013
Empirical Research on the Affecting Factors of
Knowledge Transfer Efficiency in CoPS R&D Project
Chu Yan-Feng
Assistant Professor, College of Economics and Management ,
Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
+86 13814021116,yanfengc@nuaa.edu.cn
Huang Xiao-Qiong
Mastrer degree, College of Economics and Management,
Nanjing University of Aeronautics and Astronautics,Nanjing 211106, China
Doi:10.5901/mjss.2013.v4n9p696
Abstract
Knowledge management is the core of CoPS R&D project management . How to enhance the knowledge transfer efficiency is
one key issue in the CoPS R & D alliances .The author firstly gave some assumptions and concept model about the influencing
factors of CoPS R&D project based on the existing research. With analyzing the survey date by SPSS and AMOS, knowledge
transfer willing and capacity, the tacit and complexity of the knowledge, knowledge receiver’s original knowledge accumulation,
leadership support degree, organization size, the level of technology and equipment and mutual distance ,all of these are the
main factors of knowledge transfer efficiency in CoPS R&D project. At the end of this paper, the author gave some advices on
how to promote the knowledge transfer effectiveness.
Keywords: Knowledge transfer influencing factors, Knowledge transfer efficiency, CoPS R&D project, Project management
1. Introduction
Knowledge management is the core of CoPS㸦complex product system R&D project management, after the modular
decomposition of the CoPS, there must be convergence interfaces between modules and this made there exist
communication and exchange of knowledge between project teams. Whether the union can achieve success knowledge
exchanging related to the mutual learning efficiency, besides, the league knowledge transfer effect also reflects the
Union learning effect. So, how to enhance the knowledge transfer efficiency is one key issue the CoPS R & D alliances
should consider.
Knowledge transfer is one process which contain two parts: the knowledge is imparted from the source to the
receiver and be absorbed, integrated, applied, innovated to achieve incremental knowledge. According to Szulansk,
Cumming and Teng, Albin, Simonin, CHEN zan-duo, Chen mei, CHEN fei-qiong and some others’ research, the main
factors of project teams’ mutual knowledge transfer contains the following ones: source and receiver’s knowledge
transfer abilities, the knowledge gaps between the source and receiver, knowledge characters(complexity, tacit,
systemic), knowledge transfer contexts(culture differences, the level of technical equipment, the leadership support). All
this factors effect each other and jointly influence the effectiveness of knowledge transfer. The purpose of this paper is to
find the main influencing factors and provide practical recommendations to improve the knowledge transfer effectiveness
between teams.
2. Assumptions and theoretical model
It can be from the timely efficiency, average efficiency and the changing of the receiver’s knowledge base to evaluate the
knowledge transfer effectiveness. A successful CoPS R&D project must meet the requirement on time and cost, the
project team should consider not only how to get enough knowledge but also the time and the cost. According to
previous analysis, this paper mainly analysis the affects of knowledge transfer capabilities, knowledge gap, knowledge
characteristics and the knowledge transfer context on the knowledge transfer effectiveness, the corresponding
assumptions shown in table 1.
696
Mediterranean Journal of Social Sciences
MCSER Publishing Rome-Italy
E-ISSN 2039-2117
ISSN 2039-9340
Vol 4 No 9
October 2013
Table 1. The original assumptions
Factors
Resources
Knowledge
transfer
Receiver
The
original
knowledge
accumulation
Tacit
The characters of
knowledge
Complexity
Systemic
The
level
technology
equipment
Knowledge
transfer context
of
and
The
level
of
leadership support
Size of receiver’s
organization
Mutual distance
Assumptions
H1㸸Resource’s willing do positive and significant impact on knowledge transfer
effectiveness
H2㸸Resource’s knowledge transfer abilities do positive and significant impact on
knowledge transfer effectiveness
H3㸸Receiver’s knowledge transfer abilities do positive and significant impact on
knowledge transfer effectiveness
H4㸸Receiver’s knowledge transfer willing do positive and significant impact on
knowledge transfer effectiveness
H5 㸸 Knowledge transfers’ original knowledge accumulation do positive and
significant impact on knowledge transfer effectiveness
H6㸸The tacit do negative impact on knowledge transfer effectiveness
H7㸸The Complexity do positive and significant impact on tacit
H8㸸The Complexity do positive and significant impact on the knowledge transfer
effectiveness
H9 㸸 The systemic do positive and significant impact on knowledge transfer
effectiveness
H10㸸The level of technical equipment do positive and significant impact on
resource’s knowledge transfer ability
H11㸸The level of technical equipment do positive and significant impact on
receiver’s knowledge transfer ability
H12㸸The level of technical equipment do positive and significant impact on
knowledge transfer effectiveness
H13 㸸 The level of leadership support do positive and significant impact on
knowledge transfer effectiveness
H14 㸸 Size of organization do positive and significant impact on receiver’s
knowledge transfer ability
H15 㸸 Size of organization do positive and significant impact on knowledge
transfer effectiveness
H16㸸Mutual distance do negative impact on knowledge transfer effectiveness
The relationship between the influencing factors can be used the structure model as the figure 1 to describe.
Figure 1. Knowledge transfer empirical model in CoPS R&D project
697
E-ISSN 2039-2117
ISSN 2039-9340
Mediterranean Journal of Social Sciences
MCSER Publishing Rome-Italy
Vol 4 No 9
October 2013
3. Empirical Analysis
According to the theoretical research and expert interviews, this paper designed a measuring scale (Likert5 level scale)
to survey the factors: 5—Strongly agree, 4— inclined to agree, 3 —agree, 2—do not agree, 1—strongly disagree. This
study has distributed 230 questionnaires, actual recovery is 182 copies, which 146 are valid questionnaires, the effective
rate is 63.5%. The value of sample overall Cronbach's Įis 0.797, reliability of each variable is above 0.7; the
questionnaire overall KMO value is 0.794, significance probability of Bartlett hemispheres test Ȥ2 is 0.000, significantly
less than 0.001, all the results support factor analysis .
3.1 Structural equation analysis
3.1.1 The establishment of model and confirmatory factor analysis
This paper firstly established the initial model by AMOS software and corrected the initial model by modified exponential.
The path coefficients and model fit indices are shown in Table 2.
Table 2. Path coefficients and fit index of correction model
No standardized
path coefficient
S.E.
Resource’s knowledge transfer ability<The level
.396
.113
of technology and equipment
Receiver’s knowledge transfer ability< The level
.428
.102
of technology and equipment
Receiver’s knowledge transfer ability <Size of
.189
.092
receiver ‘ organization
Tacit<---Systemic
.816
.110
Resource’s knowledge transfer willing < The level
.426
.092
of leadership support
Receiver’s knowledge transfer willing< The level
.385
.104
of leadership support
Knowledge transfer effectiveness < Receiver’s
.311
.078
knowledge transfer ability
Knowledge transfer effectiveness <Tacit
-.149
.050
Knowledge transfer effectiveness <Systemic
.014
.054
Knowledge transfer effectiveness <Mutual
-.195
.058
distance
Knowledge transfer effectiveness <Initial
.122
.056
knowledge accumulation
Knowledge transfer effectiveness <The level of
.091
.072
leader support
Knowledge transfer effectiveness < Resource’s
.398
.089
knowledge transfer willing
Knowledge transfer effectiveness< Receiver’s
.247
.068
knowledge transfer willing
Knowledge transfer effectiveness < Receiver’s
.132
.057
knowledge transfer ability
RMSEA=0.047 CMIN/OF=1.314 NFI=0.681 TLI=0.890 CFI=0.897 PNFI=0.637
CMIN=1162.662
Note: *** P<0.001㸹** P<0.01㸹* P<0.05
C.R.
P
standardized
path coefficient
3.498
***
.375
4.212
***
.484
2.058
*
.202
7.422
***
.849
4.608
***
.483
3.703
***
.383
3.971
***
.390
-2.972
.254
**
.799
-.252
.020
-3.388
***
-.303
2.193
*
.179
1.253
.210
.133
4.491
***
.515
3.644
***
.365
2.312
*
.199
The coefficients of skewness and kurtosis in this model are not greater than 1, the maximum value of cr is 1.76, that
means the data used in this model meeting the requirements of a normal distribution. No negative error variance exists in
model; The absolute value of the normalized coefficient is between 0.020 to 0.849, and not more than 0.95, no violated
698
E-ISSN 2039-2117
ISSN 2039-9340
Mediterranean Journal of Social Sciences
MCSER Publishing Rome-Italy
Vol 4 No 9
October 2013
estimated exists in the model, the results above shows this model is fit to do overall model fit test; after amending the
original model, it gets that PNFI= 0.637(significantly greater than the 0.5), Ȥ2 / d.f 㸦 Noffned Chi-square 㸧
=1.314(significantly less than the 3.0), PGF I=0.667, Default AIC㸦1465.349㸧is less than Saturated AIC㸦2068.000㸧
and Independence AIC㸦3822.028㸧, all this parameters passed the test, shows a better model parsimonious fit; both
RMSEA (0.047) and RMR(0.129) passed the test[12], absolute fit meets the requirement. All this results above show
satisfied fit, the model passed the overall fit test.
3.1.2 Analysis of the results
According to Table 2, the coefficient of “Systemic of knowledge” to “Knowledge transfer efficiency” is 0.014, P(0.799) is
larger than the standard value, do not support the original hypothesis H9,the remaining assumptions are reasonable. The
direct, indirect and total effectiveness to the knowledge transfer efficiency are respectively shown in table 3.
Table 3. Direct, indirect and total effectiveness to the knowledge transfer efficiency 㸦Unstandardized results㸧
Effect
Potential Variable
Resource’ knowledge transfer willing
Resource’ knowledge transfer ability
Receiver’s knowledge transfer willing
Receiver’s knowledge transfer ability
Original accumulation of knowledge
Tacit of knowledge
Systemic of knowledge
The level of technology and equipment
Organization size of receiver
The level of leadership support
Mutual distance
Direct effect
Indirect effect
Total effect
.398
.132
.247
.311
.122
-.149
—
—
—
.091
-.195
—
—
—
—
—
—
-.122
.185
.059
.334
—
.398
.132
.247
.311
.122
-.149
-.122
.185
.059
.425
-.195
According to the table 3, the knowledge transfer ability of resource, level of leadership support and knowledge transfer
ability of receiver do significant effect on the knowledge transfer effectiveness.
In fact, the knowledge transfer willing of sources determine whether the receiver will obtain desired amount of
knowledge from knowledge sources; the leadership support played a key role on the final amount of funds ,equipment
and the mutual communication; the knowledge transfer efficiency, the level of innovation was greatly decided by
receiver’s knowledge transfer ability.
4. Results
1) According to the survey and data analysis of the effect factors, the following are some conclusions and this
paper proposed some suggestions to improve knowledge transfer efficiency.The main factors in knowledge
transfer efficiency of CoPS R&D are the knowledge transfer willing and ability of resource and receiver, the
tacit ,systemic, complexity of knowledge, original knowledge accumulation of receiver, leadership support, the
level of technology and equipment, mutual distance.
2) The complexity of knowledge takes more difficulties to understand, absorption and integration the need
knowledge, greatly affects the knowledge transfer effectiveness. Accurately assess the complexity of
knowledge, an reasonable assessment on the knowledge recessive is an important part to improve the
knowledge transfer efficiency.
3) After determining the target knowledge resource, the first thing should do is to take various measures to gain
each other's trust, and not ignore the establishment of a variety of incentives to encourage internal members
to actively participate in knowledge transfer work.
4) The knowledge transfer support takes impact on knowledge transfer effectiveness mainly through the
transfers’ willing. Leadership support on the resources as funds, equipment can promote the knowledge
transfer efficiency, the establishment of mechanisms can greatly improve the transfer of enthusiasm.
699
E-ISSN 2039-2117
ISSN 2039-9340
Mediterranean Journal of Social Sciences
MCSER Publishing Rome-Italy
Vol 4 No 9
October 2013
5) In some technical communication, it often needs demonstration, exercise or an interim meeting, many related
equipments and some information technology are essential.
References
Lu Bing, Yue Liang, Lian Xiuwu. (2006 ).Research on knowledge transfer effect in an alliance . Science of science and management of
S&T , 8: 84-88.
Davenport T H, Prusak L. (2000).Working knowledge: How organizations manage what they know. Harvard Business Press.
Szulanski G. (2000).The process of knowledge transfer: A diachronic analysis of stickiness. Organizational behavior and human
decision processes, 82(1): 9-27.
Cummings J L, Teng B S. (2003).Transferring R&D knowledge: the key factors affecting knowledge transfer success. Journal of
Engineering and Technology Management, 20(1): 39-68.
Albino V, Garavelli A C, Schiuma G. (1998). Knowledge transfer and inter-firm relationships in industrial districts: the role of the leader
firm. Technovation,19(1): 53-63.
Simonin B L. (2004).An empirical investigation of the process of knowledge transfer in international strategic alliances. Journal of
International Business Studies, 35(5): 407-427.
Chen Zhanduo. (2008).Study on the Relationship between KM activities and NPD performance in CoPS . Dalian University of
Technology.
Chen Mei. (2008).Empirical Study on Influencing Factors of the Knowledge Transfer in PM . Tianjin Polytechnic University, 2008
Chen Feiqiong. (2001).Knowledge-transfer arrangement research in knowledge alliance of national enterprises with transnational
corporations Science research management,2: 66-73.
Yue Liang, LU Bing, LIAN Xiuwu. (2007). Analysis on the knowledge transfer inside an organization . Science research management, 6:
22-30.
Argote L, Ingram P. (2000).Knowledge transfer: A basis for competitive advantage in firms. Organizational behavior and human decision
processes, 82(1): 150-169.
Song Juan. (2011). A research on influencing factors of knowledge transfer behavior within technology alliance on the basis of
complexity theory . Central South University, 2011.
700