iBusiness, 2011, 3, 16-22
doi:10.4236/ib.2011.31003 Published Online March 2011 (http://www.SciRP.org/journal/ib)
The Effect of Knowledge Management on Product
Innovation - Evidence from the Chinese Software
Outsourcing Vendors*
Dong Yang
Department of Industrial Engineering, School of Economics and Management, XiDian University, Xi’an, China.
Received November 26th, 2010; revised December 16th, 2010; accepted December 20th, 2010.
ABSTRACT
The knowledge acquisition and exploitation are becoming more and more for local vendors in offshore outsourcing
cooperation. In the process of product innovation, internal knowledge sharing (IKS), external knowledge acquisition
(EKA) and their interactive relationship is discussed. Through the using of the method of 3SLS, the results of STATA
indicate that IKS and EKA positively complement for product innovation. Furthermore, the result of SPSS reveals that
knowledge exploitation mediates the (IKS) EKA and product innovation.
Keywords: Knowledge Management, Innovation, Outsourcing Cooperation
1. Introduction
Offshore outsourcing cooperation is becoming very
popular among local vendors not only as an effective tool
for market access but also as an effective means to learn
from foreign buyers. Cooperation often entails a transfer
of certain value-chain activities from foreign firms from
the developed economies to local firms in the emerging
economies such as China and India. In order to improve
their innovation, the local vendors hope to improve their
innovation through cooperation. In this case, knowledge
acquisition is an integral part of local vendors’ motive to
engage in internatio nal cooperation.
Despite of the growing importance of offshore out-
sourcing cooperation, there are some major limitations in
the extant literature. The prior research emphasizes the
importance of external knowledge acquisition, thereby
ignoring the increasing importance of effect of the inter-
nal relative knowledge on extern al knowledge in innova-
tion process, which leads to the inefficient innovation.
The impact of internal knowledge sharing and external
knowledge acquisition on product innovation has not
been studied in an integrated model [1]. Furthermore,
knowledge creation does not necessarily lead to per-
formance improvement or value creation, and the effi-
ciency of knowledge exploitation should impact the in-
novation and firms’ competitive advantages. Thus, it
should be very important for firms to understand the ef-
fect of relationship between the IKS (EKA) and exploita-
tion on the innovation so that they can improve the effect
of knowledge management on the product innovation
efficiently.
To fill these gaps, drawing on the perspectives of
knowledge management and absorptive capabilities, we
construct a theoretical framework to examine relation-
ships among IKS (EKA), exploitation and product inno-
vation from the view of Chinese vendors in outsourcing
cooperation. Empirically, using 172 samples collected
through face-to-face interviews of senior executives in
Chinese firms, we examine the interactive relationship
between IKS (EKA) and production, and test the medi-
ating effect of kno wl e d ge exp l oi ta t i on.
The paper proceeds as follows. First, by reviewing
current literatures on knowledge management and ab-
sorptive capacity, we present a conceptual model we
used to examine the knowledge assimilation and exploi-
tation processes. Next, we discuss the relationships be-
tween IKS (EKA) and exploitation processes and product
innovation, and present the hypotheses correspondingly.
Third, we describe the study method and the empirical
results, and finally we discuss our findings and offer
concluding comments.
* Supported by the Fundamental Research Funds for the Central Uni-
versities(72105557).

The Effect of Knowledge Management on Product Innovation - Evidence from the Chinese Software Outsourcing Vendors17
2. Theoretic Background and Hypothesis
Development
Knowledge management has been defined as the process
of identifying/creating, assimilating, and applying or-
ganizational knowledge to exploit new opportunities and
enhance organizational performance. To survive and keep
competitive advantage in turbulent environment, local
vendors must create know ledge or acq uire the know ledge
about the outsourcing business and the customer’s needs.
We define IKS as the process in which based on the rou-
tines, different employees and units share, process, inter-
pret and understand the knowledge. EKA refers to the
process in which local vendors process, interpret and
understand the knowledge from partners. IKS and EKA
are two important knowledge sources that can be impor-
tant source of innovation [2], and they play a key role in
overcoming resistance to innovations and in the reduc-
tion of uncertainty.
Absorptive capacity is a firm’s ability to utilize exter-
nally held knowledge thro ugh three sequential processes:
1) recognizing and understanding potentially valuable
new knowledge from the partners through exploratory
learning, 2) assimilating valuable new knowledge through
transformative learning, and 3) using the acquired knowl-
edge to create new knowledge and commercial outputs
through exploitative learning [3]. The incentive mecha-
nism, R&D intensity and customer’s knowledge within
the firms influence the efficiency and the effectiveness of
the firm’s absorptive capacity [4]. External environment
factors, such as outsourcing environment, knowledge
produced by partners, also influence on absorptive ca-
pacity. Absorptive capacity will influence on external
knowledge acquisition and exploitation. Absorptive ca-
pacity influence commercial outputs (products, services,
and patents) and knowledge outputs [5]. The success of
the commercial outputs and the new knowledge created
can influence the future investment in absorptive capac-
ity. IKS increases member interaction and facilitates the
free flow of knowledge. The process involves social in-
teractions among individuals using internal communica-
tion channels for knowledge transfer to arrive at a com-
mon understanding. Where organizational units hold
specialized knowledge, inter-unit linkages are the pri-
mary means of transferring the customer’s knowledge [6].
The higher level of internal knowledge is helpful for
firms to understand and acquire the customer’s knowl-
edge. External knowledge sources enable the firms to
develop need ed capabilities quick ly, leading to flexibility
and reducing costs. EKA also can help firms accumulate
relevant experiences and routines for knowledge sharing
and interpretation, thus which can promote the local
firm’s absorptive capabilities [7].
2.1. IKS and EKA as Complements
IKS emphasizes to build routines and norms, communi-
cate and share knowledge from different units, while
EKA pertains to availability of channels for securing
knowledge and to the possibility of understanding and
exploiting external knowledge. Furthermore, they inter-
act with each other as complements.
First, to create more value from IKS, firms should in-
tegrate the knowledge resources in which effectively
combine the knowledge [8]. IKS can be regarded as one
of integrative capabilities [9 ], and it helps firms build the
reputation in the outsourcing market, as the customers
(buyers) are more willing to collaborate with the firms
having a higher level of internal capabilities [8]. Thus,
the higher level of IKS is helpful for firms have more
access to the customer’s knowledge about technology
and marketing. Additionally, a higher leve l of IKS stands
for higher level of absorptive capacity, which helps firms
learn more from their partners and create more value
from the opportunities provided by their partners [3].
Thus, if an employee or unit cooperates closely with
other employees or units, norms of cooperation will be
established. These norms are reflected in the process of
EKA as well. Therefore, in the process of IKS, firms can
build good norms of knowledge transferring and sharing,
which also leads to easily acquire the knowledge from
partners.
Second, in the process of EKA from the customers,
firms can recognize and find opportunities, and learn the
routines about knowledge sharing and transferring from
the customer, which can enhance the capabilities of
knowledge management [6]. The capabilities enhance-
ment of the firm mitigates the obstacle to knowledge
sharing between different units inside the firm. Therefore,
through EKA, firms can reduce the costs of knowledge
coordination, and then IKS becomes more effective. In
order to be effective in external cooperation, organiza-
tions need well-functioning internal interfaces. The rou-
tines of external cooperation also directly influence the
efficiency and efficacy of the internal sharing vice versa.
Thus, in the process of EKA, firms can also promote the
IKS.
Furthermore, in the context of dynamic environment,
IKS are not enough for firms to implement innovation,
since they are very likely to be deficient of complemen-
tary knowledge. In order for firms to fully extract value
from their IKS, firms should have outsourcing coopera-
tion through which they can mobilize complementary
external knowledge and identify more rewarding oppor-
tunities. IKS can help the firms to use the complementary
external knowledge that can be obtained on the basis of
EKA. Lacking IKS, firms may have difficulty in gener-

The Effect of Knowledge Management on Product Innovation - Evidence from the Chinese Software Outsourcing Vendors 18
ating value from their EKA. Therefore, we suggest:
H1a: increases in IKS will enhance the level of EKA.
H1b: increases in EKA will enhance the level of IKS.
H1c: IKS and EKA will complement for product inno-
vation.
2.2. The Mediating Effect of Knowledge
Exploitation
The abundance o f knowledge resources do es not guaran-
tee that the firms will excel in, or practice, effective in-
novation, and efficient exploitation of knowledge is key
work of obtaining good performance. Especially, if lack-
ing the capabilities of knowledge exploitation, firms will
have obstacle in product innovation [10]. Only interpret-
ing and exploiting the knowledge, firms can promote
innovation.
Through efficiently exploiting knowledge, firms can
leverage and recombine the knowledge to pursue product
line extension or new product development. In the proc-
ess of the knowledge exploitation, firms can enhance
innovation capabilities, which through the process of bi-
sociation, help firms to develop new schema or chan ges to
existing process. Furthermore, firms can convert these
changes into innovation product [11]. After integrating
the knowledge, firms can fully and systematically apply
the knowledge of different units. Therefore, quickly in-
tegrating the knowledge from different units inside firm,
firm can reduce the cost of process. Comparing with the
other vendors, the firm can have the higher level of in-
novation. In the process of knowledge exploitation, firms
can also develop the new technological and market capa-
bilities. The development of functional capabilities can
promote R&D and understand the customer’s needs,
which is helpful for product innovation. Finally, through
effective knowledge exploitation, firms can improve their
flexibility in grasping the opportunities, which makes
firms have advantage in product innovation [12].
H2: The knowledge exploitation has a mediating effect
on knowledge assimilation and product innovation.
3. Methodology
3.1. Sample and Data Collection
To test the hypotheses, a questionnaire survey research
method was used to search responses from some soft-
ware firms with offshore outsourcing cooperation in the
Shaanxi and Shandong provinces of China. A total of
300 local firms involved in offshore outsourcing coop-
eration were selected from the list provided by the local
government agencies. We had three sampling criteria: the
firms had to be 1) at least engaging in offshore outsourc-
ing cooperation with 3 years; 2) at least 30 employees. A
total of 211 questionnaires were collected, and other 89
firms could not provide their information due to such
reasons as their mergers and acquisitions, business
changes, and turnover of top management team, among
others. Out of 211 returns, a total of 172 firms provided
complete data. The data was collected in the summer of
2009. Therefore, 172 enterprises had all the necessary
data. The effective rate was 57.73 percent (172 out of
300).
Two commonly raised issues concerning survey
methodology are non-response bias and common method
variance. Using t-tests, we compared the responding
firms with 89 non-responding firms as well as 39 firms
whose data could not be used with respect to the firm
attributes of firm size, ownership status, and sales based
on the public data from the Statistical Yearbook and the
Directory of Enterprises at the provincial level. None of
the t-statistics was significant. To further verify if our
sample was representative of the whole population, we
compared the sample’s firm size, ownership status and
sales to those of the national population using the infor-
mation from the China Statistical Yearbook at the na-
tional level. Again, none of the t-statistics was significant.
Also due to the relatively high responding rate, we did
expect any non-response bias in the data of this study.
3.2. Measurement
We consulted the extant literature to compile measure-
ment items. As noted some items were modified to re-
flect the specific context of the study. New questions
were developed based on a review of the inter-firm co-
operation literature. All constructs were measured by the
average of the responses, on a 7-point Likert scale.
IKS: Following the work of Chen and Huang (2007)
[13], we measure the IKS with five items. The items in-
clude: 1) corporate managers share the information about
customers with employees; 2) employees will transfer the
information of customers to managers; 3) different units
have stronger aspiration to learning each other; 4) em-
ployees can easily share the knowledge; and 5) firms
encourage employees from different units and hierarchy
to share knowledge; 6) encourage employees to partici-
pate in decision making; 7) encourage employees to learn
through teaching, guidance, and training.
EKA: Based primarily on a measurement instrument
created and validated by Lyles and Salk (1996) [14], we
adjusted their measures to fit our research better. Four
indicators used here are: through cooperation, we have
acquired much knowledge from partners, they are: 1)
new technological expertise; 2) new marketing expertise;
3) manufacturing process; and 4) experience in the en-
trance of new market.
Knowledge exploitation: following the work of Song
et al. (2005) [15] and Chen and Huang (2007) [13], we

The Effect of Knowledge Management on Product Innovation - Evidence from the Chinese Software Outsourcing Vendors 19
measure the knowledge exploitation with five items.
They are 1) a strong emphasis to put know-how, patents,
and new product designs into practice; 2) a strong ten-
dency to use the advanced technologies introduced into
firm; 3) a strong tendency to adopt the advanced man-
agement techniques introduced into firm; and 4) a strong
tendency to employ various experts introduced into firm;
5) a strong tendency to employ various experts intro-
duced into firm.
Product innovation: following the work of Danneels
and Kleinschmidt (2001) [16], we measure the product
innovation with fo ur ite ms. Four ind icator s u sed h ere are:
1) develop a large number of types of new product; 2)
improve the quality of the products; 3) expedite the in-
troduction of these new products to the market; 4) intro-
duce a large number of process technologies.
3.3. Reliability Analysis
Initially, an exploratory factor analysis (EFA) with the
SPSS was used to purify the original measures (total of
20 items), and the measures showed evidence of validity
and reliability once items with low loadings and high
cross loadings were eliminated. This process resulted in
the retention of 16 of the original 20 items. A confirma-
tory factor analysis (CFA) by means of LISREL 8.54
was also conducted to further validate the measures. All
items from the EFA remain in the final measurement
model, which demon st rates good fit.
Composite reliability assesses the inter-item consis-
tency, which was operationalized using the internal con-
sistency method that is estimated using Cronbach’s alpha.
Typically, reliability coefficients of 0.70 or higher are
considered adequate [17]. Therefore, an alpha value of
0.70 was considered as the cut-off value. In Table 1,
Cronbach’s alpha values of all factors were above 0.70.
These results suggest that the theoretical constructs ex-
hibit good psychometric properties.
3.4. Method of Analysis
Since the hypotheses involve mutual relationship be-
tween IKA and EKA, the relationship between inde-
pendent variables and dependent variables is blurred.
Thereby, we need to test two equations simultaneously.
First, we must test whether EKA is influenced by IKA.
Second, and simultaneously, we must test whether IKA
is influenced by EKA. Specifying and testing these two
equations independently would, of course, introduce sig-
nificantly biased estimates du e to the endogeneity of key
independent variables (IKA and EKA) in all equations
and due to common disturbances across equations.
In order to eliminate the effects on regression estima-
tion by endogenous variables, our econometric approach
is a simultaneous equation estimation using a three-stage
least squares method. This method uses generalized least
squares (GLS) on the basis of two stages least squares
(2SLS) to test equations simultaneously, then resolves
Table 1. Construct factors.
Factors Variables Loading alpha
Corporate manage rs share the inform ation about customers wi t h
employees 0.710
Employees will transfer the information of customers to man-
agers 0.709
Different units have stronger aspiration to learning from each
other 0.761
Employees can easily sh are the knowledge 0.807
Internal
Knowledge
Sharing
Firms encourage employees from different units and hierarchy
to share knowledge 0.732
0.762
new technological expertise 0.804
new marketing expertise 0.886
manufacturing process 0.892
External
Knowledge
Acquisition experience in the entrance of new market 0.805
0.882
a strong emphasis to put know-how, patents, and new pr o d u c t
designs into practice; 0.711
a strong tendency to u s e t h e advanced technologies introduced
into firm; 0.807
a strong tendency to adopt t h e advanced management tech-
niques introduced into firm; 0.781
Knowledge
Exploitation
a strong tendency to em ploy various expert s in t roduced into
firm; 0.771
0.829
develop a large number ty pes of new produ ct 0.829
improve the quality of the products 0.824
Product
Innovation expedite the introduction of th e se n ew products to the market 0.821
0.809

The Effect of Knowledge Management on Product Innovation - Evidence from the Chinese Software Outsourcing Vendors 20
the problem of relativity between endogenous variables
random disturbances (Stata, 1999). Stage 1 of this pro-
cedure can be thought of as producing instrumented val-
ues of all endogenous variables. These instrumented val
ues are essentially predicted values generated by the re-
gression of each endogenous variable on all exogenous
variables in the system. Stage 2 produces a consistent
estimate of the covariance matrix of the equation distur-
bances. Estimates are obtained from the residuals pro-
duced from a two-stage least squares estimation of each
structural equation. Finally, Stage 3 performs a GLS-type
estimation using the covariance matrix from Stage 2 and
with the instrumental values replacing all endogenous
right-hand side variables.
By correctly specifying this system of equations, we
can test for a pattern of complementarity between IKA
and EKA. If IKA positively affects EKA and EKA posi-
tively affects IKA, their complementarity is supported.
We adopt the software of STATA to demonstrate this
proposition.
In order to test the mediate relationship of knowledge
exploitation between IKS and EKA and product innova-
tion, we establish regression equation in which firms’
product innovation is dependent variable and IKS, EKA
and knowledge expl oi t at i on are depen dent variables. Then
we use SPSS to estimate the coefficients of equation.
3.5. Analysis and Results
The descriptive statistics in Table 2 show basic informa-
tion on each factor and correlations among these factors.
Table 3 and Table 4 present the results. In Table 3,
the critical test of the relationship, as complements be-
tween IKS and EKA, hinges on the sign and significance
of coefficients for IKS and EKA in the two equations.
Positive coefficients suggest a complementary relation-
ship in which IKS predicts greater EKA and greater EKA
predicts greater IKS. Consistent with our hypothesis of a
complementary relationship, we find that increases in the
level of IKS are associated with greater levels of EKA
(Hypothesis 1a, see Equation (1)) and that increases in
the level of EKA are associated with greater levels of
IKS (Hypothesis1b, see Equation (2)).
In Table 4, Model 1 and Model 4 are the base models
that include only the control variables. Adopting the
procedure r ecommend ed by Bar on and K enny (19 86) , we
tested the mediating effect of knowledge exploitation on
IKS，EKA and product innovation relationship. In the
first step, the dependent variable, product innovation,
was regressed on the independent variable of IKS and
EKA. As shown in Model 2, IKS and EKA have signifi-
Table 2. Descriptive Statistics and Pearson Correlation Matrix (N = 172).
Variables Mean S.D. 1 2 3 4 5
6
1.Firm size 3.3451.459 1
2 In industry the rate of new products is high 4.0831.532 0.148 1
3.Internal knowledge a ssimilation (I KS) 4. 59 80.828 0.082 0.301** 1
4.External knowledge a ssimilation (EKA) 3.5351.709 0.063 0.085* 0.171** 1
5.Knowledge exploitation 3.7521.123 0.092** 0.163** 0.308** 0.188** 1
6.Product innovation 4.4471.079 0.081 0.182** 0.277** 0.131** 0.564** 1
Table 3. Assessing the determinants and complementar ity of IKS and EKA.
Determinants of EKA Determinants of IKS
Variables Equation 1 Equation 2
Internal knowledge assimilation (IKS) 0.657*
External knowledge assimilation(EKA) 0.213***
Firm size 0.092 0.037
In industry the rate of new products is high 0.019 0.093**
Constant 1.251 1.301
N 172 172
Chi-square 124.341 183.626
F-value
P-value 0.000 0.000
R-square 0.133 0.158
+p<0.10; *p<0.05; **p<0.01; ***p<0.001

The Effect of Knowledge Management on Product Innovation - Evidence from the Chinese Software Outsourcing Vendors21
Table 4. Results of OLS Regression (N = 172).
Product Innovation Knowledge exploitation
Dependent variable Model 1 Model 2 Mo de l 3 Model 4 Model 5
Size 0.082** 0.077** 0.039 0.103** 0.097**
In industry the rate of new products is high 0.110*** 0.103* 0.062* 0.091*** 0.047
Internal knowledge assimilation (IKS) 0.285*** 0.006 0.347***
External knowledge assimilation (E KA) 0.201*** 0.003 0.336***
Knowledge exploitation 0.471***
R Square 0.061 0.115 0.347 0.103 0.155
F Value 13.235*** 15.846*** 49.123*** 14.003*** 20.133***
+p<0.10; *p<0.05; **p<0.01; ***p<0.001
cant positive effect on product innovation. Therefore, it
supports the Hypothesis1c. In the second step, the me-
diator of knowledge exploitation was regressed on the
independent variable of IKS and EKA. The result shown
in Model 5 indicates that IKS and EKA have a signifi-
cant positive impact on knowledge exploitation (β =
0.347, p < 0.001, β = 0.336, p < 0.001). The third step
was to examine the relationship between the mediator
and the dependent variable. The result shown in Model 3
indicates that knowledge exploitation has a significant
positive effect on product innovation (β = 0.471, p <
0.001). At the same time, the impact of IKS on product
innovation becomes not significant anymore (β = 0.006),
which indicates a full mediation effect. Similarly, knowl-
edge exploitation also has a full mediation effect on the
relationship between EKA and product innovation. Taken
in whole, hypothesis 2 is suppo rted.
4. Discussion
Based on knowledge management perspective, this paper
studies the relationship between IKS and EKA, knowl-
edge exploitation and product innovation. The comple-
ment relationship between IKS and EKA is discussed.
Furthermore, we also test the mediating effect of knowl-
edge exploitation on the relationship between IKS (EKA)
and product innovation.
Under turbulent environment and intern ational compe-
tition, to implement indigenous innovation strategy,
Chinese firms should search for technology and market
knowledge in time. However, the abundance of manu-
facturing resources does not guarantee that the firm will
excel in, or practice, effective manufacturing integration
(Hitt, Hoskisson, and Nixon, 1993). Chinese firms should
understand that knowledge creation and assimilation is
necessary for innovation, but not sufficient. In the proc-
ess of product innovation, firms should emphasize the
knowledge exploitation.
Second, IKS and EKA are important knowledge
source mode. Firms should encourage every employee
and unit to share the knowledge through necessary award.
Communication in different units can enhance the effi-
ciency of assimilation of the external knowledge. The
good relationship with suppliers, customers and universi-
ties will lead firms to have access to many skill and ex-
periences. Hence, EKA can enhance the firms’ absorp-
tive capacity.
5. Future Research
Like all empirical research, this study has some limita-
tions that should be addressed in future research. One
caution is that the results of the current study are the
context of software offshore outsourcing cooperation.
Although we believe that it is theoretically feasible to
extend this study to other contexts, the specific differ-
ences between China an d other transition economy coun-
tries restrict the adaptive capacity of our findings.
Therefore, the other useful extension would be to con-
duct this study in other transitional environments. More-
over, the cross-sectional data used in the study do not
allow for causal interpretation among the factors, al-
though we requested that the sample firms supply data
during the previous five-year period. Then, longitudinal
approach will be needed in future studies.
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