management capability in
business process outsourcing
A cluster analysis
School of Economics and Management, Wuhan University,
Wuhan, China, and
School of Medicine and Health Management,
Huazhong University of Science & Technology, Wuhan, China
Purpose â€“ The purpose of this paper is to investigate trends in the dimensions of low, medium, and high
knowledge management (KM) capability of business process outsourcing (BPO) firms. It also explores the
trends in BPO performance with different levels of KM capabilities of BPO firms. Moreover, the study
determines how firm characteristics, such as size, age, industry, and outsourcing age, affect KM capability.
Design/methodology/approach â€“ A survey was employed to collect data on managers from
605 firms. K-means cluster analysis was performed on the aggregate measures of the four KM
capability dimensions and BPO performance to reveal trends. Subsequently, MANOVA was used to
evaluate the effects of four firm characteristics on KM capability, and individual ANOVA tests were
performed to examine the specific differences among the four dimensions.
Findings â€“ Among the four dimensions of KM capability, knowledge application is the most
significant. Knowledge protection is the second highest in terms of expressing the profile for low KM
capability firms, but the lowest among the four dimensions of KM capability for medium and high
KM capability firms. Each dimension of KM capability affects BPO performance positively. Firm size,
age, industry, and outsourcing age differentially affect the dimensions of KM capability.
Originality/value â€“ This study presents a theoretical model of firm characteristics, KM
capability, and BPO performance. Through the model, ideas are offered: firms with high KM capability
significantly differ from those with low and medium KM capabilities; different firms exhibit different
KM capabilities; developing knowledge application capability should be the priority in managing BPO;
and improving KM capability is an effective means to enhance BPO performance.
Keywords Performance, Outsourcing, Quantitative techniques, Knowledge management
Paper type Research paper
Business process outsourcing (BPO) plays an increasingly significant role in service
outsourcing because of its high value added. The 2012 global service outsourcing
development report revealed that the BPO market size in 2011 alone was valued at
176 billion dollars. The compound annual growth rate of BPO market size has exceeded
that of information technology outsourcing (Zheng, 2013). However, the outcome
of BPO implementation has not been optimistic. According to a survey of 189 BPO
enterprise clients conducted by HfS Research, approximately 50 percent of the clients
Vol. 53 No. 1, 2015
Â© Emerald Group Publishing Limited
Received 13 April 2014
Revised 15 September 2014
Accepted 2 December 2014
The current issue and full text archive of this journal is available on Emerald Insight at:
This work was supported by grants from the National Natural Science Foundation of China
(Nos 71101060, 71471141, and 71201063). Shan Liu appreciates Zhaohua Deng to work as the
claimed that BPO failed to reduce the costs stipulated in the contract, offer knowledge
of specific industry processes, and provide talent that adds value beyond standard
operations (Fersht, 2014).
The proponents of knowledge management (KM) argue that the failure of BPO can
be attributed to the low KM capability of outsourcers based on the concept of BPO as a
process of creating, transferring, integrating, and using knowledge (Willcocks et al.,
2004; Blumenberg et al., 2009; Beverakis et al., 2009). Numerous studies have examined
the types and effectiveness of KM capability within an organization (Tanriverdi, 2005;
Wang et al., 2007), but minimal research (e.g. Christopher and Tanwar, 2012) has
attempted to categorize KM capability and investigate its effect in the context of BPO
in at least two organizations. Understanding the dimensions of KM capability and the
patterns or trends that these dimensions may follow in different types of organizations
is critical because managers can formulate appropriate KM strategies in BPO by taking
advantage of potentially high KM capability to improve BPO performance. In addition,
previous studies have presented contrasting findings on the relationship between
KM capability and firm performance. Although some studies have found that KM
capability affects firm performance directly and significantly (Tanriverdi, 2005), other
studies have determined that their relationship is insignificant and mediated by
innovation or organizational learning processes (Lee and Sukoco, 2007; Easterby-Smith
and Prieto, 2008). Investigating how KM capability dimensions change performance
is significant because understanding this issue enables managers to identify the most
effective KM capabilities in managing BPO. Therefore, the present study investigates
trends in KM capability dimensions across low, medium, and high KM capabilities of
BPO firms. Moreover, it explores the trends in BPO performance with different levels of
KM capabilities of BPO firms. Finally, this study determines the effects of firm
characteristics, such as size, age, industry, and outsourcing age, on organizational KM
capability in BPO. BPO performance relies on the extent to which the client integrates
and uses knowledge. Thus, we focus on the perspective of the clients.
BPO is a mechanism of designating business processes to a service provider that
possesses, manages, and administrates selected IT-intensive processes by adopting
predefined and measurable metrics (Gartner, 2013). The major area of BPO comprises
procurement, finance and accounting, training, human resource, and customer
relationship management. The two types of organizations involved in a BPO process
are the client and service provider (Malik et al., 2012). Clients should articulate their
requirements to allow the service provider to appropriately manage the outsourced
business processes (Mann et al., 2011). Clients should also acquire, integrate, and use
knowledge generated by the service provider to ensure that the BPO is well executed
and delivered with high performance (Narayanan et al., 2011). Therefore, greater
KM capability is likely to engender high BPO performance.
Literature defines KM capability as the ability to organize, shift, configure, and arrange
knowledge-based resources to achieve the goals of and gain business values from the
organization from a knowledge-based view (Kearns and Lederer, 2003; Chuang, 2004).
Among the different dimensions of KM capability that have been proposed, two
categorizations are noteworthy. On the one hand, Gold et al. (2001) categorized KM
capability from the infrastructure view and developed three dimensions of capabilities,
namely, technology, structure, and culture. These three variables can be measured by
37 items. Although this categorization was adopted by several studies, KM literature
provides no evidence with regard to its widespread use. On the other hand, KM capability
was categorized from the process view, a categorization that has been broadly accepted,
as proven by KM literature. For example, Tanriverdi (2005) categorized KM capability
into knowledge creation, transfer, integration, and leverage provision. Tseng and Lee
(2014) posited that KM capability consisted of knowledge transfer and integration. Gold
et al. (2001) also examined this variable and categorized it as knowledge acquisition,
conversion, application, and protection capabilities. Table I describes the four dimensions.
We adopted the categorization from the process view of Gold et al. (2001) because it can
accurately capture the nature of KM capability. Moreover, this categorization is widely
used as research basis.
Few attempts have been made to understand how KM capability dimensions vary
in different kinds of BPO firms. Although previous research has proposed a
BPO framework that integrates specific KM practices within each phase of BPO
(Mahmoodzadeh et al., 2009), KM capability has not been explicitly categorized and
investigated. The specification of KM capability enables managers to formulate an
appropriate strategy to manage knowledge in BPO. Managerial measures can be
derived by investigating the differences between BPO firms with high, medium, and
low KM capabilities. The nature of KM can be better understood through the insights
into the trends of KM capability. The current research addresses this issue by adopting
the instrument of Gold et al. (2001) to gather data from numerous BPO firms. High,
medium, and low KM capabilities were identified and used to determine whether
patterns across the categories and among the dimensions of KM capability exist.
In addition, evidence has shown that organizational performance can be enhanced
by KM capability, but the findings on the relationship between these two factors in
previous studies are contradictory (Alavi and Leidner, 2001; Lee and Sukoco, 2007).
Some researchers contend that KM capability affects effectiveness significantly and
directly. Alavi and Leidner (2001) surveyed 300 senior managers and found that
knowledge infrastructure and knowledge process capabilities have significant effects
on organizational effectiveness. Tanriverdi (2005) also empirically found that KM
capability enhances corporate performance based on the data from 1,000 of the 250
Fortune firms. However, other researchers point out that KM capability affects
performance indirectly. Innovation and agility can be mediators. Lee and Sukoco (2007)
found that innovation mediates the relationship between KM capability and effectiveness.
Easterby-Smith and Prieto (2008) proposed that KM capability affects organizational
effectiveness indirectly and through organizational learning process and dynamic
capability. Therefore, gaps exist among previous studies with regard to the relationship
between KM capability and performance. Further examination of this issue is necessary
because the process through which KM capability changes performance can be deeply
Ability to gain and accumulate knowledge inside and outside
Ability to organize, structure, coordinate, integrate, and distribute knowledge,
which enhances the usefulness of existing knowledge
Ability to store, retrieve, and use knowledge to enable the knowledge to be
accessed and employed by the organization
Ability to prevent knowledge within an organization from being inappropriately
or illegally used or stolen by other organizations
of KM capability
understood and the relative importance of each KM capability dimension can also be
investigated specifically. In this study, we investigate the trends of BPO performance with
the change of KM capability to understand the effectiveness of the latter.
Firm characteristics also influence the level of KM capability. In the present
research, four firm characteristics, namely, size, age, industry, and outsourcing age,
were examined. The former three are the basic characteristics of a firm, whereas the
latter one is a significant characteristic of BPO firms. These four factors were selected
despite the lack of empirical evidence to support this claim because previous studies
have suggested that these affect KM capability (Cui et al., 2005).
Firm size is the first characteristic that we examined. Cui et al. (2005) proposed that
firm size may affect KM capability because a large firm requires high KM capability to
enhance its organizational performance. We chose the number of employees to measure
firm size because it can appropriately reflect firm size (Lu and Ramamurthy, 2011).
Firm age is the second characteristic that we examined. Old and young firms exhibit
different organizational cultures and structures (Kale and Karaman, 2011) that can change
the KM process. Nevertheless, firm age affects KM capability both positively and
negatively. For instance, young firms may have low knowledge protection capability but
high knowledge acquisition capability, whereas old firms have high knowledge protection
capability but low knowledge acquisition capability. Young firms may not have complete
knowledge protection systems, but these easily accept new knowledge.
Firm industry is the third characteristic examined. Information intensity changes
across different industries (Mao et al., 2014). Thus, information intensity requires
different KM capabilities to process information.
Outsourcing age is the fourth characteristic examined. Firms with higher outsourcing
experience accumulate richer KM experience in BPO (Rustagi et al., 2008). Therefore, KM
capability varies according to the different outsourcing experiences of the firm.
In sum, better KM strategies to manage BPO can be proposed and developed by
understanding common patterns exhibited across KM capability dimensions and how
these characteristics affect each dimension of KM capability. The dimensions presented in
Table I are used in this study so that the following research questions can be addressed:
RQ1. Which trends in the dimensions of KM capability and BPO performance can
be detected across BPO firms with high, medium and low KM capabilities?
RQ2. How do firm size, age, industry, and outsourcing age affect the KM capability
of BPO firms?
3. Research methodology
3.1 Data collection
A survey was employed to gather data from a number of firms to address the research
questions. Managers from the client side who are in charge of BPO business in their
firms were identified as ideal subjects, because they were familiar with the situation
of BPO in their firms and were also in a good position to report KM capability. We
obtained a list of Chinese firms that experienced BPO as clients with the assistance of
an outsourcing association in China. Subsequently, we contacted the senior executives
in these firms to identify managers who are responsible for BPO management. These
managers were contacted to confirm their participation in the survey.
In 2013, we distributed approximately 1,000 questionnaires to the managers. The
managers were requested to evaluate the firm characteristics, KM capability, and BPO
performance of their firms. A total of 605 usable questionnaires were returned
after three months (60.5 percent response rate). The age of respondents ranged from
24 to 59 years (mean Â¼ 39.4). Previous experience of managing BPO ranged from two
to 26 years (mean Â¼ 12.2). The characteristics of the firms the managers worked for are
shown in Table II.
We examined external validity by evaluating non-response bias. A t-test was
performed on the latent variables between the questionnaires obtained in the early
and late stages of the study (Liu and Wang, 2014). The significant levels of
knowledge acquisition, conversion, application, protection, as well as BPO performance,
are pÂ¼ 0.36, 0.58, 0.27, 0.65, and 0.31, respectively. Therefore, no significant difference
exists between the two samples and non-response bias is also non-existent. We also
checked for common method bias by performing Harmonâ€™s single-factor test (Podsakoff
et al., 2003). Exploratory factor analysis was conducted with all latent variables. The results
show that no single factor in the samples accounts for the majority of the covariance
(maximum o24 percent). Thus, common method bias does not threaten our sample.
3.2 Constructs and measures
Each KM capability dimension includes multiple-item measures, as shown in
Appendix. These measures were adapted from Gold et al. (2001). The measures of BPO
performance were also included in the instrument. BPO performance is the success of
Index Range Number %
Number of employees â©½100 100 16.5
101-500 135 22.3
501-1,000 135 22.3
W1,000 235 38.8
Firm age (years) 1-5 90 14.9
6-10 85 14.0
11-15 95 15.7
16-20 45 7.4
20-25 90 14.9
26-30 75 12.4
More than 30 125 20.7
Firm industry Information technology 110 18.2
Business trading 50 8.3
Finance 50 8.3
Retailing 10 1.7
Manufacturing 125 20.7
Utilities 10 1.7
Agriculture 75 12.4
Engineering 15 2.5
Education and training 30 5.0
Others 130 21.5
Outsourcing age (years) 1-5 220 36.4
6-10 130 21.5
11-15 100 16.5
16-20 40 6.6
20-25 40 6.6
26-30 25 4.1
More than 30 50 8.3
BPO processes and outcomes that the firm undertakes. Four measures were adopted
from (2010), as shown in Appendix. Seven-point Likert scale that ranges from
â€œstrongly disagreeâ€ to â€œstrongly agreeâ€ was used to measure the items. In addition, firm
size was assessed through the number of employees in the firm (Table II). In particular,
respondents were requested to indicate the number of employees in their firms as â©½100,
101-500, 501-1,000, or W1,000. Seven levels, namely, 1-5, 6-10, 11-15, 16-20, 20-25, 26-30,
and W30 years, were used to measure firm age. Firm industry was identified using
ten classifications, namely, information technology, business trading, finance, retailing,
manufacturing, utilities, agriculture, engineering, education and training, and others.
Lastly, the outsourcing age of the firm was measured with the same seven levels used
to measure firm age.
3.3 Measurement evaluation
Measurement validity was assessed by employing several tests. We first evaluated
convergent validity and internal consistency. The constructs and their measurement
properties, as well as the number of measures of each construct, are shown in Table III.
The values of the average variance extracted (AVE) are all higher than 0.5 (Fornell and
Larcker, 1981). The reliability was checked by evaluating construct reliability (CR) and
Cronbachâ€™s Î± for the constructs of KM capability dimension and BPO performance. Table III
shows that the Cronbachâ€™s Î± and CRs of KM capability dimensions and performance
construct are W0.7, which indicates adequate reliability (Nunnally and Bernstein, 1994).
Factor loadings (FLs) of each scale are presented in Appendix. The FL of each item is
significant ( po0.000) and exceeds 0.7. Therefore, convergent validity has passed for the
constructs and measures. Afterward, discriminant validity was evaluated to check if the
construct measured by each scale is significantly different from the others (Liu, 2013).
Table IV shows that the correlation between each pair of latent variables is lower than the
square root of AVE. Thus, our model also passed this test. Lastly, we conducted
confirmatory factor analysis (CFA) on the five latent variables with AMOS 17.0 to check
Construct Number of items Mean SD Cronbachâ€™s Î± CR AVE
Knowledge acquisition 6 4.63 0.95 0.92 0.90 0.64
Knowledge conversion 5 4.80 0.92 0.91 0.89 0.62
Knowledge application 5 5.66 0.98 0.90 0.87 0.65
Knowledge protection 5 4.67 0.86 0.85 0.82 0.61
BPO performance 4 4.62 0.94 0.81 0.78 0.56
Knowledge acquisition 0.80
Knowledge conversion 0.33 0.79
Knowledge application 0.43 0.42 0.81
Knowledge protection 0.27 0.43 0.19 0.78
BPO performance 0.35 0.41 0.46 0.30 0.75
Note: The square root of AVE is reflected as italic numbers
whether good fit exists between the observed data and the measurement model (Yan and
Dooley, 2013). The CFA results indicate that Ï‡
2 is 859.56 and is significant ( pÂ¼ 0.000).
The degree of freedom (df) is 374. The ratio of Ï‡
2 to the df is 2.30, which is under the
threshold of 3. Moreover, the root mean square error of approximation is 0.048 and
the standardized root mean square residual is 0.0495, which are both lower than 0.08
(Hulland et al., 1996). In addition, the normed fit index is 0.91, the Tucker-Lewis fit index is
0.90, the comparative fit index is 0.92, and the incremental fit index (IFI) is 0.91, which are all
higher than 0.90 (Hulland et al., 1996). The aforementioned goodness-of-fit indices
demonstrate good fit between the data and the measurement model. Therefore, the overall
measurement model displays satisfactory measurement properties.
3.3 Cluster analysis
We performed K-means cluster analysis on the aggregate measures of four KM
capability dimensions. The clusters were subsequently obtained, representing that
the number of firms with low, medium, and high KM capabilities was 110, 275, and
4.1 Trends in the dimensions of KM capability across clusters
The cluster means for four dimensions of KM capability obtained by K-means cluster
analysis are shown in Table V. The higher the cluster means of each dimension, the
greater the level of KM capability. The profiles of firms with low, medium, and high
KM capabilities are shown as a star chart (Figure 1).
Results show potential trends in the dimensions of KM capability in BPO. First, the
mean level of the KM capability of each dension increases significantly as the cluster
KMC Low Medium High
Knowledge acquisition 2.38 4.60 5.79
Knowledge conversion 2.94 4.63 5.93
Knowledge application 4.03 5.39 6.80
Knowledge protection 3.23 4.43 5.70
Cluster means for
four KM capability
Star chart of
moves from low to medium level and from medium to high level. This observation has
an intuitive appeal and provides further empirical support for the validation of the KM
capability dimensions identified by Gold et al. (2001). Second, the use of seven-point
scale to indicate the aggregate level of each KM capability dimension reveals that on
average, the low KM capability cluster consists of firms with low KM capability
(between 2.38 and 3.23) along with three of the four KM capability dimensions. Firms
with low KM capability have moderate level of knowledge application capability as
indicated by the value of 4.03 that exceeds the midpoint of the seven-point scale.
However, the knowledge application capability of the medium KM capability cluster is
also higher (5.39) than those of other dimensions with values from 4.43 to 4.63.
Knowledge conversion and acquisition capabilities are the second and third highest
with respect to describing the profile of firms with medium KM capability. For the
cluster of firms with high KM capability, knowledge application capability remains
the most prominent dimension of KM capability. The overall results indicate that
knowledge application capability is the most significant among the four dimensions of
KM capability. Therefore, the effective application of knowledge is critical in managing
BPO. Interestingly, knowledge protection capability is the second highest in terms of
expressing the profile of firms with low KM capability, but the lowest among the four
dimensions of KM capability in firms with medium and high KM capabilities. Thus,
protecting knowledge is a necessary requirement although only minimal attention
should be paid to it in managing BPO.
We observe another trend in the relationship between the levels of KM capability
and BPO performance. This relationship is clearly presented in Table VI. As firms with
low KM capability move toward high KM capability, BPO performance significantly
increases. Firms with low KM capability exhibit low performance below three on the
seven-point scale. Moreover, firms with medium KM capability display medium
performance (4.38), and firms with high KM capability show high BPO performance
(5.76). Therefore, KM capability has a significant and positive effect on performance
4.2 Effect of firm size, age, industry, and outsourcing age on KM capability
We also examined the effect of firm size, age, industry, and outsourcing age
on KM capability to address the second research question. The mean values for firm
size for low, medium, and high KM capabilities are 3.94, 2.81, and 1.56, respectively.
Surprisingly, as firm size increases, the KM capability of the firm decreases
significantly. MANOVA was performed to evaluate the effects of firm size on the
four dimensions of KM capability. A significant level of the overall model was obtained
( p Â¼ 0.000 for Hotellingâ€™s and Pillaiâ€™s trace statistics). Four individual ANOVA
tests were conducted to investigate the specific differences among the four dimensions
of KM capability. The results show that three of the four dimensions of KM capability
(i.e. knowledge conversion, application, and protection) are significantly affected by
Cluster BPO performance
KM capability Low 2.96
KM capability and
firm size ( p Â¼ 0.002, 0.028, and 0.000), whereas the other dimension (knowledge
acquisition) is unaffected ( p Â¼ 0.092); larger firms may have a larger amount
of knowledge to manage, which makes converting, applying, and protecting
The relationship between firm age and KM capability dimensions was also
examined. The mean values of firm age for low, medium, and high KM capabilities
are 6.50, 5.43, and 2.22, respectively. Similar to firm size, the KM capability of
these firms decreases significantly as firm age increases. MANOVA was performed
to evaluate the effects of firm age on the four dimensions of KM capability.
The overall model is significant ( p Â¼ 0.000 for Hotellingâ€™s and Pillaiâ€™s trace
statistics) and the results of the four individual ANOVA tests also reveal
significant relationships ( p Â¼ 0.000, 0.000, 0.001, and 0.000). Old firms may
accumulate considerable knowledge, but may also lack an effective KM process to
utilize this knowledge.
The relationship between firm industry and KM capability dimensions was
subsequently examined. MANOVA analysis reveals that the model obtained a
significant level ( p Â¼ 0.000 for Hotellingâ€™s and Pillaiâ€™s trace statistics). The four
ANOVA tests show that all relationships are significant ( p Â¼ 0.000 for each dimension).
Therefore, as firm industry varies, the overall KM capability and all its four dimensions
also differ in BPO.
Finally, we examined the effect of outsourcing age on the KM capability
of firms. MANOVA analysis reveals that the model obtained a significant level
( p Â¼ 0.000 for Hotellingâ€™s and Pillaiâ€™s trace statistics). Nevertheless, when the
specific differences in the levels of KM capability were analyzed deeply by individual
ANOVA tests, the results suggest that the relationships of outsourcing
age with knowledge conversion and application are significant ( p Â¼ 0.028 and 0.000),
but the relationships of outsourcing age with knowledge acquisition and protection
are insignificant ( p Â¼ 0.283 and 0.218). Therefore, as firms gain richer outsourcing
experience, the knowledge conversion and application capabilities become higher in
relation to managing BPO.
4.3 Theoretical model of firm characteristics, KM capability, and performance
The above results indicate that a model of KM capability and BPO performance
can be proposed and developed (Figure 2). The results of cluster analysis indicate a
pattern where each dimension of KM capability move toward the same direction as
KM capability changes from low to high level. A positive relationship between KM
capability and BPO performance is also revealed. Further analysis indicates that firm
industry and age influence all four dimensions of KM capability, whereas firm size and
outsourcing age affect some specific dimensions of KM capability.
Model of firm
KM capability and
performance in BPO
5. Discussions and implications
5.1 Implications for research
This study is one of the first attempts to integrate KM capability, performance, and
firm characteristics in the context of BPO. A number of theoretical implications can be
obtained from our results.
First, the results have shown the cluster means of each dimension of KM capability
across firms with low, medium, and high KM capabilities. This can provide a basis for
the evaluation of a firmâ€™s KM capability when engaging BPO. If the KM capability of
the firm is low, the firm should strengthen its KM capability to effectively manage
BPO. Furthermore, we find that different KM capability dimensions exhibit various
levels across low, medium, and high KM capabilities. This observation indicates that
managing BPO requires different levels of KM capabilities. A unified KM practice is
unnecessary in managing BPO. This finding is consistent with the argument of
Mahmoodzadeh et al. (2009) that KM practices should differ across BPO processes.
Second, our results reveal the relative importance of each KM capability dimension
in managing BPO. In particular, high knowledge application capability is the most
important aspect in managing BPO, whereas knowledge protection capability appears
to receive minimal emphasis because its scores are the lowest in both medium and high
KM capabilities. Previous research has only compared the significance of KM
capability and other capabilities (Mao et al., 2014), but failed to focus on the specific
dimensions of KM capability. This finding adds new knowledge to literature because
no study has yet explicitly analyzed the importance of KM capability dimensions.
Third, the results reveal that each dimension of KM capability has a positive
effect on BPO performance. This finding provides additional evidence on the
significant effect of KM capability on performance. Moreover, it supports previous
result that KM capability is positively and directly associated with performance
(Tanriverdi, 2005). Therefore, improving KM capabilities is an effective means to
enhance BPO performance. Such capabilities can produce high competitiveness among
firms because other firms find them difficult to imitate.
Fourth, firm size, age, industry, and outsourcing age differentially affect the
dimensions of KM capability. Although some characteristics (e.g. firm age and
industry) significantly influence all four dimensions of KM capability, the relationship
between certain characteristics (e.g. outsourcing age) and KM capability dimensions
(e.g., knowledge protection) were insignificant. Moreover, the effect of different firm
characteristics on KM capabilities was identified as either positive or negative.
Therefore, the relationship between firm characteristics and KM capability dimensions
exhibits mixed results. Firm size has a negative effect on knowledge conversion,
application, and protection capabilities; meanwhile, it affects knowledge acquisition
insignificantly. This finding supports the proposition of Cui et al. (2005), who argued
that firm size significantly influences KM capability. However, not all KM capability
dimensions are affected by firm size (e.g. knowledge acquisition). Large firms are not
required to acquire excess new knowledge, but tend to create new knowledge based on
existing knowledge. Moreover, large firms also display lower KM capability. Therefore,
managers in large firms should recognize this disadvantage and focus on constructing
more effective KM capability to manage BPO. Firm age has a negative effect on each
dimension of KM capability. As old firms exhibit lower agility (Mao et al., 2014), which
is positively related to KM capability, these manifest deterioration of KM capability.
Each dimension of KM capability varies across industries. The intensity of knowledge
changes according to the different industry settings. Firms classified under industries
with high intensive knowledge should construct greater KM capability to deal with
knowledge effectively. Finally, outsourcing age positively affects knowledge conversion
and application capabilities. Higher outsourcing experience can strengthen the conversion
and application of knowledge in BPO. However, outsourcing age insignificantly affects
knowledge acquisition and protection. Therefore, firms with low outsourcing experience
should effectively leverage their knowledge acquisition and protection capabilities
because these two capabilities do not significantly differ between firms with young and old
5.2 Implications for practice
Several managerial implications can be drawn from our findings. First, given that
different levels of each KM capability dimension are required to manage BPO,
investments on their development should be unequal. Knowledge application
capability is the most important dimension for managing BPO. Thus, establishing
knowledge application capability should be prioritized. Knowledge conversion and
acquisition capabilities should also receive adequate investments and should be fairly
developed. However, managers should not overlook knowledge protection capability
because it still exhibits high cluster means in low KM capability. Managers must
selectively exchange information or knowledge with external providers, as well as
predefine scopes and rules to clarify which type of knowledge should be protected.
Second, the abilities to acquire, convert, apply, and protect knowledge are critical to
improve BPO performance. Therefore, managers can optimize KM structure; develop
the culture to acquire, convert, apply, and protect knowledge; and reward employees
who exhibit behaviors and activities that manage knowledge efficiently. Members of
organizations or groups should also positively and autonomously communicate with
one another, as well as acquire and process information from external providers,
partners, and markets. Third, different types of firms should develop various KM
strategies. Given that large and old firms display low KM capability in managing BPO,
they should develop an effective culture with rules and an enhanced infrastructure for
KM. They can also divide large groups into small teams such that information and
knowledge can be exchanged and used efficiently. Given that firms with young
outsourcing age exhibit low knowledge conversion and application capabilities, these
two KM capabilities should be considerably developed among them. Firms can
strengthen capabilities to integrate different types of knowledge and learn to
effectively accumulate and use such knowledge to address problems in BPO.
This study pioneered in revealing the trends of KM capability in BPO and in
developing a theoretical model of firm characteristics, KM capability and BPO
performance. Empirical evidence has shown that firms with high KM capability
significantly differ from those with low and medium KM capabilities. Knowledge
application is the most prominent capability of firms with low to high KM capabilities.
However, knowledge acquisition, conversion, application, and protection, affect BPO
performance positively and significantly. In addition, firm characteristics influence the
dimensions of KM capability.
7. Limitations and directions for future research
The present study has several limitations. First, KM capability is a complex variable,
and some of its aspects may not have been captured. Second, this study adopted
convenience samples. Therefore, generalizability of the results should be further
investigated. Third, four firm characteristics were selected because these have been
found to influence KM capability. However, other characteristics (e.g. firm structure)
may also affect the dimensions.
Future research can employ the theoretical model developed in the present study to
examine whether each dimension of KM capability complements or substitutes one
another in the process of affecting BPO performance. Such examination is particularly
significant in designing effective KM strategies and portfolios in BPO. In addition,
KM capability positively affects BPO performance. In this regard, understanding the
potential moderators that can change the relationship between KM capability and
performance is an interesting proposition. Finally, KM capability is affected not only by
firm characteristics, but also by other factors, such as resources. Therefore, future
research can explore more factors that can improve KM capability.
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Appendix. Constructs, measures, and FLs
In my firm, we have the ability to acquire knowledge about service providers (FL Â¼ 0.79).
In my firm, we are able to generate new knowledge from existing knowledge (FL Â¼ 0.86).
In my firm, we use feedback on projects to improve subsequent projects (FL Â¼ 0.87).
In my firm, we are able to distribute knowledge throughout the organization (FL Â¼ 0.91).
In my firm, we are able to exchange knowledge with service providers (FL Â¼ 0.94).
In my firm, we are able to acquire knowledge about new products/services within our industry
(FL Â¼ 0.86).
In my firm, we are able to apply knowledge learned from mistakes and experiences (FLÂ¼ 0.92).
In my firm, we are able to use knowledge to solve new problems (FL Â¼ 0.87).
In my firm, we have the ability to use knowledge in the development of new products/services
(FL Â¼ 0.88).
In my firm, we use knowledge to improve efficiency (FL Â¼ 0.86).
In my firm, we are able to quickly apply knowledge to critical competitive needs (FL Â¼ 0.89).
In my firm, we are able to convert knowledge into the design of new products/services (FLÂ¼ 0.94).
In my firm, we are able to transfer organizational knowledge to individuals (FL Â¼ 0.83).
In my firm, we have the ability to absorb knowledge from individuals and service providers
(FL Â¼ 0.89).
In my firm, we are able to integrate different sources and types of knowledge (FL Â¼ 0.87).
In my firm, we replace outdated knowledge (FL Â¼ 0.85).
In my firm, we are able to protect knowledge from inappropriate use inside and outside the
organization (FL Â¼ 0.81).
In my firm, we are able to protect knowledge from theft inside and outside the organization
(FL Â¼ 0.74).
In my firm, we have extensive policies and procedures for protecting secrets (FL Â¼ 0.83).
In my firm, we value and protect individualâ€™s knowledge (FL Â¼ 0.88).
In my firm, we often emphasize the importance of protecting knowledge (FL Â¼ 0.76).
In my firm, service providers perform contracted services dependably and accurately
(FL Â¼ 0.74).
In my firm, service providers are willing to help customers and provide prompt service
(FL Â¼ 0.83).
In my firm, service providers provide the processes, procedures, systems, and technologies that
make a service seamless (FL Â¼ 0.71).
In my firm, service providers leverage process knowledge to deliver a range of process
enhancements that go beyond the performance expectations of us and contracted service level
agreements (FL Â¼ 0.86).
About the authors
Dr Shan Liu is an Assistant Professor at Economics and Management School in the Wuhan
University. His research interests focus on mobile commerce and IT project management with
particular emphasis on software risk management. He has published more than ten refereed
publications including papers that have appeared or been accepted in Information Systems
Journal, European Journal of Information Systems, Management Decision, International Journal of
Project Management, Information Development and International Journal of Medical Informatics.
Dr Zhaohua Deng is an Associate Professor of Medical Information Management at the
Huazhong University of Science and Technology. Her research focusses on mobile business and
mobile health. Her research has appeared in Information Systems Journal, International Journal
of Information Management, International Journal of Medical Informatics, Electronic Markets,
International Journal of Mobile Communications, International Journal of Services Technology and
Management and International Journal of Information Technology and Management. Dr Zhaohua
Deng is the corresponding author and can be contacted at: [email protected]
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