Cited as: Chan, K.H., Chu, S.K.W., & Wu, W.W.Y. (in press). Exploring the correlation
between Knowledge Management maturity and Intellectual Capital efficiency in Mainland
Chinese listed companies. Journal of Information & Knowledge Management.
Target: Journal of Information & Knowledge Management
Title: Exploring the correlation between Knowledge Management maturity and Intellectual
Capital efficiency in Mainland Chinese listed companies
Authors:
Kin Hang Chan†
kinchan168hk@yahoo.com
Samuel Kai Wah Chu*
samchu@hku.hk
Wendy W.Y. Wu*
wendywu@hku.hk
* Faculty of Education, the University of Hong Kong, HKSAR
† Institute for China Business, School of Professional and Continuing Education, the
University of Hong Kong, HKSAR
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Abstract
In today’s knowledge-based society, discussion on intellectual capital (“IC”) has become
intertwined with knowledge management (“KM”). KM may be viewed as the activities and
processes to create and maximize IC. It may be possible to suggest that an organization`s
level of knowledge utilization is associated with its level of intellectual capital. The purpose
of this research was to explore whether there is an association between KM maturity level, as
a proxy of assessing the level of KM efficacy and IC utilization efficiency in companies listed
on CSI 100 (China Securities Index Co., Ltd.) in mainland China. A self-assessment of KM
maturity level, developed based on the KM self-assessment framework proposed by Collison
and Parcell, was used to gauge the knowledge utilization of an organization. The ICE
(intellectual capital efficiency coefficient), component of the Value Added Intellectual
Coefficient (VAIC™), was used to assess the efficiency of intellectual capital. Overall, 26
questionnaires were collected from the surveyed organizations to evaluate their level of KM,
which accounted for 25% of the sample. Finally, correlation analysis with SPSS was
performed to examine if there was a correlation between ICE and the maturity level of KM in
the sampled companies in mainland China. The results showed that the association between
the two variables was not statistically significant. In fact, no conclusive evidence was found
to support an association between efficiency of utilizing intellectual capital, and knowledge
management maturity score. The lack of an association may suggest that there may be other
intervening variables yet to be identified in the relationship between KM and IC. This study
is an attempt to explore the above assertion and to conduct empirical studies in studying their
applicability in China, one of the fastest growing economies in the world. While we are not
seeking to generalize the results, it may serve as a good reference for further studies in
examining the intricate 'relationship' between IC and KM, that is, linking a process view of
KM to the measurement of value creating intangibles of a corporation epitomized by IC.
Keywords:
Knowledge management, intellectual capital, VAIC™, ICE, value creation, KM maturity
model
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1. Introduction
There have been a large number of studies conducted in the fields of corporate knowledge
management (KM) and intellectual capital (IC) over the past couple decades (Sveiby, 1997;
Pulic, 1998; Bontis, 1999; Chen, Cheng and Hwang, 2005; Chan, 2009; Chu, Chan, Yu, Ng
and Wong, 2011a; Chu, Chan and Wu, 2011b; Ramezan, 2011). Knowledge-based resources
have emerged as an important factor of production in maintaining a company’s competitive
advantage, and have displaced traditional production inputs such as land and physical capital
in the classical economic models, especially in service-oriented industries (Kujansivu and
Lo¨nnqvist, 2007; Reinhardt, Bornemann, Pawlowsky and Schneider, 2001; Young, Su, Fang
and Fang, 2009). A more specific term, “IC”, has been taken up to refer to these “knowledgebased resources” as the fourth factor of production. In order to improve market
competitiveness, corporate leaders may benefit from assessing how well their companies
leverage intellectual capital, or viewed from the process-oriented perspective, how well a
company manages its knowledge. Many assessment tools have been developed to estimate
the value of intangible assets, and gauge the effectiveness of KM implementation. Two
approaches may be relevant to fulfilling such aims: assessing the KM maturity level of the
company, and estimating IC utilization efficiency of the company.
This study focuses on companies in mainland China as the country is undergoing massive
economic development. Mainland China, having embraced more market-oriented policies for
the past thirty years, has emerged as the world’s largest exporter in 2010 (Central Intelligence
Agency, 2011), contributing to 12% of the world’s exports in 2009, compared to 3% in 1995
(International Monetary Fund, 2011). How well business enterprises in mainland China
manage knowledge or utilize intellectual capital may be important factors in determining
their comparative advantage. Hence, it may be helpful for business managers to understand
the current level of KM in their organizations in order to improve their business
competitiveness. The purpose of this research is to explore the existence of a correlation
between KM maturity level and IC utilization efficiency in listed companies in mainland
China.
2. Literature Review
2.1. What is knowledge management
KM has been discussed widely in the literature. The review by Wiig (1997), regarded as one
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of the earliest reviews of KM developments, proposed that the objectives of KM is “to
maximize an enterprise’s knowledge-related effectiveness and returns from knowledge
assets” through “systematic, explicit and deliberate building, renewal and application of
knowledge” (p. 2). A more process-oriented view of KM was proposed by Rastogi (2000): “a
systematic and integrative process of coordinating organization-wide activities of acquiring,
creating, storing, sharing, diffusing, developing, and deploying knowledge by individuals and
groups in pursuit of major organizational goals” (p. 40).
2.2. What is intellectual capital
The term IC was first coined by the economist John Kenneth Galbraith in his letter dated
1969 (Sveiby, 2001). It was probably Thomas A. Stewart who pioneered the field of IC when
he wrote a Fortune article in 1991, putting IC in the context of gaining market competitive
advantage (Stewart, 1991). IC is the collective brainpower of an organization, which includes
information, practical technique, expertise, intellectual property, and everything members of
the organization know that can generate profit (Stewart, 1997). Bontis (1999) pointed out that
IC is an intangible organizational resource, and is commonly classified into human capital
(“HC”), structural capital (“SC”), and relationship capital (Sveiby, 1997; Saint Onge, 1996).
2.3. Relationship between IC and KM
Though Sveiby (2001) regards KM and IC as “two branches of the same tree”, IC has a
“value creation” focus while KM is on the operational level. Rastogi (2000) considers KM as
the foundation for successful leveraging of IC. Writing for an audience of knowledge
practitioners, Levinson (2007) of CIO magazine defines KM as “the process through which
organizations generate value from their intellectual and knowledge-based assets” (¶ 1).
Following this view, it may be easier to think of KM as a management practice to accumulate
IC in an organization. Some scholars viewed that KM initiatives can be gauged through their
impact on IC, which value can be assessed through quantitative methods. In fact, some
scholars have used IC and KM interchangeably. For instance, Bose (2004) categorizes the
widely discussed IC tools - Balanced Scorecard, EVA™, CIV, and Skandia Navigator as KM
metrics. The same is true for Kankanhalli and Tan (2005), which discusses the IC index,
Intangible Assets Monitor, EVA™, and the Balanced Scorecard as KM metrics. Ariely (2003)
argues that since knowledge contributes to HC, and managing knowledge contributes to SC,
“successful KM is in itself, part of the organization’s IC” (p. 4).
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If knowledge forms the basis of IC, as in Ramezan (2011), the maturity level of knowledge
management of an organization may be shown to be correlated with its efficiency of utilizing
intellectual capital. To test this empirically, the sampled companies in this study were
assessed on both their levels of knowledge management and their IC efficiency (through self
assessment of KM maturity level, and calculation of VAIC™, respectively). The two sets of
data were analyzed statistically to test for correlation. Such an empirical study on the
correlation between the two measurement metrics has not been found in the literature.
2.4. Importance of measuring value of intangible assets
Klein and Prusak (n.d., p. 67 as cited in Stewart, 1997) identifies IC as the “intellectual
material that has been formalized, captured, and leveraged” to create property by generating a
higher-valued asset, which is essential to company success. There is a common saying: “If it
can’t be measured, it can’t be managed”. In order to monitor such an asset, different
measurement metrics have been developed, e.g. IC-Index, Skandia Navigator, Balanced
Scorecard, Intangible Assets Monitor, VAIC, EVA, Tobin’s q, Calculated Intangible Value. A
detailed review can be found in Sveiby (2010).
Although the IC components are not explicitly presented on a company`s accounting balance
sheet, they have a significant influence on the company`s performance and overall business
achievement (Jelcic, 2007). Prior empirical studies have proved that VAIC (an assessment of
value-added efficiency of intellectual capital) is positively associated with company financial
performance indicators such as ROA and ROE (Chan, 2009; Chu et al., 2011a; Chu et al.,
2011b). Hence, understanding how effective companies are deploying intellectual capital may
help managers to improve their companies` financial performance.
Besides measuring the value of intangible assets, models have been developed to assess the
effectiveness of KM implementation, which is recognized as the process of building up of
intangible assets, which are used in creating value for the company.
There are many
variations of KM maturity models aiming to measure the effectiveness of KM. Details can be
found in the following section.
2.5. Methods of gauging KM effectiveness – KM Maturity Model
Kuriakose, Raj, Murty and Swaminathan (2010) argued that the KMMM is a structured
approach to implementing KM. KM maturity is defined as “the extent to which KM is
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explicitly defined, managed, controlled, and effected” (Pee and Kankanhalli, 2009, p. 81).
Various forms, structures and characteristics of KMMM have been developed, and a few of
them are summarized in Table 1.
Name of models and
the work in which it
was cited
Knowledge
Management
Framework Assessment
and Knowledge
Journey in
KPMG (2000)
Dimensions




People
Process
Content
Technology
Maturity stages
1.
2.
3.
4.
5.
KNMTM in Hsieh, Lin
and Lin (2009)



Process
Information
technology
Culture
1.
2.
3.
4.
5.
KM3 in Gallagher and
Hazlett (2000)



Knowledge
Management
Capability selfassessment Framework
in Collison and Parcell
(2004)


Infrastructure
Culture
Technology
KM strategy
Leadership
behaviors

Networking

Learning
before, during
and after

Capturing
knowledge
Table 1: Summary of the selected KMMMs
1.
2.
3.
4.
1.
2.
3.
4.
5.
Research
method(s)
Knowledge
chaotic
Knowledge aware
Knowledge
focused
Knowledge
managed
knowledge centric
Survey
Knowledge
chaotic stage
Knowledge
conscientious
stage
KM stage
KM advanced
stage
KM integration
stage
K-Aware
K-Managed
K-Enabled
K-Optimised
Level 1
Level 2
Level 3
Level 4
Level 5
In-depth
interview
Focus group
Questionnaire
Critical success
factors analysis
Fill in the self
assessment
individually, and
then conduct
focus group
discussion
KPMG (2000) delineated the four areas of KM as: people, process, content and technology.
Based on the implementation of organizational activities, the surveyed firm is placed in a
five-level model called the ‘Knowledge Journey’, which starts from the knowledge chaotic
level and progresses to the knowledge aware, knowledge focused, knowledge managed, and
knowledge centric levels.
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A knowledge navigator model (KNMTM) was put forward in Hsieh, Lin and Lin (2009) to
evaluate KM maturity. The model incorporates three target management objects: KM process,
information technology (IT) and culture. Through an in-depth focus group interview, and the
administering of a questionnaire, a weighted average or principal component score of the
maturity level of each of the three target management objects are formulated. The above
model emphasizes that technology, culture, process and people are the common dimensions
for measuring KM maturity level.
Gallagher and Hazlett (2000) proposed KM3 for organizations to self-assess their progress in
KM. This model aims at a balanced analysis of infrastructure, culture, and technology.
However, with this model there may be a complication if an organization is at different
maturity stages for the three components of KM development.
Collison and Parcell (2004) developed the KM capability self-assessment framework to
measure the KM maturity level of an organization. KM self-assessment is a strategic planning
and benchmarking tool that allows organizations to assess their KM maturity level based on
five competencies. As shown in Table 1, the framework developed by Collison and Parcell is
the most comprehensive (covering five distinct dimensions, compared to 3 dimensions in
KNMTM or KM3). This study adopts such a framework to develop a self-assessment tool to
measure the maturity level of listed companies in mainland China.
2.6. Assessing IC efficiency through VAIC™
There are many IC measurement methodologies in the literature (e.g. EVA™, CIV, Skandia
Navigator, etc.), as reviewed in Sveiby, 2010. However, intangibles are difficult to measure
(Ze´ghal & Maaloul, 2010), so only a few of these methods can empirically link the value of
IC to business performance. Among the different methodologies proposed in the literature,
the Value Added Intellectual Coefficient (VAIC™) methodology has been adopted widely in
empirical studies (Chan, 2009; Chen et al., 2005; Firer and Williams, 2003; Ze´ghal and
Maaloul, 2010) as a proxy for efficiency of IC in contributing to value-added. VAIC™ was
opted because the data required is easily accessible (since financial data can be easily found
in annual reports of listed companies). Also, the assessment is objective, and can be
compared between same-sector companies (Sveiby, 2010). Pulic (the founder of VAIC™)
defines VAIC™ as:
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VAIC™ = ICE + CEE
ICE = HCE + SCE
VAIC™ is an indicator of the overall value added efficiency of intellectual capital (ICE) and
physical capital employed (CEE). ICE is the sum of human capital efficiency (HCE) and
structural capital efficiency (SCE). VAIC™ methodology is used to measure the value
creation efficiency of a company. For a detailed discussion of the methodology, please refer
to a similar study on VAIC™ among Hong Kong listed companies (Chu et al., 2011b).
2.7. Research Gap
There have been a number of studies investigating KM maturity levels in organizations. For
example, Robinson, Carrillo, Anumba and Al-Ghassani (2005) focused on US companies,
while Salojärvi, Furu and Sveiby (2005) focused on Finnish small and medium-sized
enterprises. However, to the best of the authors’ knowledge, there has been hardly any
research on the maturity levels of listed companies in mainland China.
Furthermore, although there has been extensive theoretical discussion on how KM relates to
IC, it seems that no studies have established statistical evidence demonstrating the actual
relationship between IC performance and the KM maturity level. This correlation therefore
needs further investigation.
3. Research Methodology
This study consisted of 3 parts: (1) estimating the IC performance of the sampled companies;
(2) assessing the KM maturity level of the sampled companies using the KM self-assessment
framework; and (3) determining if a correlation existed between KM and IC. This study
focuses on companies listed on the Mainland Chinese stock market, which is composed of
two stock exchanges - Shanghai Stock Exchange (SSE) and Shenzhen Stock Exchange
(SZSE). Unlike the SSE Composite Index or the SZSE Component Index, the CSI 100 index
comprehensively reflects the price fluctuation and performance of the large and influential
companies in both Shanghai and Shenzhen securities market. Therefore constituent
companies in the CSI 100 index were chosen as samples of this study for their
representativeness of the PRC economy.
3.1. Data source – ICE and KM maturity scores
In part 1, financial data were gathered from the annual reports of the constituents of CSI 100
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from 2007-2009. ICE was assessed using the VAIC™ (value added intellectual coefficient)
methodology developed by Ante Pulic (Pulic, 2000). Detailed discussion of the calculation of
VAIC™ can be found in section 2.2 of Chu et al. (2011b). An average ICE over the 3 years
(2007-2009) for each company was calculated. In part 2, a questionnaire was developed from
the KM self-assessment framework (Collison and Parcell, 2004) to assess KM utilization
within the organization. All the companies from part 1 were invited to respond to the
questionnaire. At the end of the data collection period, 26 sets of questionnaires were
completed, which accounted for 25% of the response rate. An average score from the 25
closed end questions were computed for each respondent. In part 3, ICE and scores from
questionnaire on KM self-assessment were compiled for statistical analysis on correlation.
3.2. The instrument to assess KM
Part 2 of this study involved a questionnaire to assess the KM maturity levels of the sampled
organizations. The questionnaire was based on the KM self-assessment framework proposed
by Collison and Parcell (2004), and was translated into Simplified Chinese for ease of
administration to participants in mainland Chinese organizations. There were 25 questions in
total, which were separated into five dimensions (i.e. KM strategy, leadership behaviors,
networking, learning before, during and after, and capturing knowledge). Similar to the
KNMTM (Hsieh, Lin and Lin, 2009), a five-point Likert scale was used to gather interval data
concerning a company’s KM capability. Respondents were asked to indicate the degree to
which they agreed or disagreed with various statements, from “strongly disagree” to
“strongly agree” (i.e. strongly disagree, disagree, neutral, agree, and strongly agree). In
addition, if a respondent chose the choice “no opinion”, the scoring for that particular
question would be omitted. The five-point Likert scale was used to match the five maturity
levels. The sample mean, which summarized the collected data from the sample, was
calculated to determine the maturity level. For example, if the mean was 3, the maturity level
of that company was indicated to be 3.
3.3. Statistical model
Finally, the existence of a correlation between ICE and KM maturity level scores for the
sampled companies was determined using SPSS version 19. KM maturity level scores were
set to be an independent variable and ICE was a dependent variable. It was hypothesized that
a correlation existed between KM maturity level and IC utilization efficiency in the listed
companies in mainland China.
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4. Findings and Analysis
4.1. ICE as an indicator of IC utilization efficiency
The ICE of the surveyed companies in part 2 of the study was computed and listed in Table 2.
There are several reasons why some entries were left blank. Some of the companies had not
been constituents of CSI 100 in the earlier years; hence, their data in earlier years were not
included in this study. Also, as noted in Chu et al. (2011b), VAIC™ is invalid for companies
that have a negative value-added due to a negative book value of equity for the year. Hence
such invalid entries were removed from Table 2 before computing the mean VAIC™ for each
company. The average ICE ranged from 1.588 to 24.187.
ICE
Industry sector
2009
2008
2007
Avg.
KM
Maturity
Level Score
KM
dept?
A
Finance & insurance
4.351
4.375
5.129
4.618
3.24
N
B
Finance & insurance
5.234
4.618
4.768
4.873
3.60
N
C
Finance & insurance
3.977
0.528
3.673
2.726
4.60
Y
D
Finance & insurance
5.138
5.075
5.351
5.188
3.84
Y
E
Finance & insurance
4.671
4.615
3.925
4.404
4.54
N
F
Finance & insurance
3.499
0.958
-
2.229
3.04
N
G
Finance & insurance
4.626
4.435
-
4.531
4.28
N
H
Finance & insurance
4.346
4.069
4.346
4.254
3.57
N
1.578
1.538
1.572
1.563
4.24
Y
13.527
13.435
12.179
13.047
3.64
N
3.295
2.882
-
3.088
4.16
Y
4.981
3.739
5.766
4.829
2.96
N
4.473
4.888
6.648
5.336
3.20
N
2.445
3.808
4.775
3.676
3.44
N
4.679
5.138
8.916
6.244
2.12
N
3.999
5.467
7.242
5.569
3.12
N
Company
I
J
K
L
M
N
O
P
IT
Manufacturing (Food &
Beverage)
Manufacturing
(Machinery)
Manufacturing
(Machinery)
Manufacturing (Metals &
Non-metals)
Manufacturing (Metals &
Non-metals)
Manufacturing (Metals &
Non-metals)
Manufacturing (Metals &
Non-metals)
Q
Real estate
11.969
13.831
13.820
13.206
3.80
N
R
Real estate
34.105
15.409
23.046
24.187
3.63
N
S
Social Services
6.243
6.205
8.168
6.872
2.00
N
T
Transportation
3.255
0.890
4.197
2.781
4.96
Y
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U
Transportation
3.254
7.346
9.250
6.616
4.26
N
V
Transportation
2.039
0.971
2.917
1.976
3.36
N
W
Utilities
4.594
3.154
5.012
4.253
2.64
N
X
Utilities
12.263
16.434
23.092
17.263
4.82
N
Y
Wholesale & retail trade
4.059
3.829
4.199
4.029
3.36
N
Z
Transportation
2.101
1.074
-
1.588
3.18
Y
Table 2: Average VAIC™ (over the years 2007-2009) and KM maturity level of sampled
companies
4.2. Scores of KM maturity level assessment as a measure of KM utilization
In part 2, questionnaires were administered during the data collection period (from Jan 2011
to April 2011). 26 completed questionnaires were collected, equivalent to a response rate of
approximately 25%. The low response rate can be attributed to the controlled management
style in mainland Chinese organizations. Many of the respondents were reluctant to respond
and required management approval to participate in the survey. An overall score of KM
maturity level for each company was computed, and is shown in Table 2, along with the
respective VAIC™. Respondents were also asked whether their organization had a KM
department. It was found that 6 of the 26 surveyed companies had a KM department.
Companies which had a KM department were found to have higher KM maturity level scores
(average score of this group was 4.16). The average KM maturity level score for the group
without a KM department was 3.43. Hence, staff in companies with a KM department
generally perceived their organization to be at a higher maturity level in KM.
Mean SD*
Score
1. Knowledge Management Strategy
a The company has clearly identified intellectual assets.
3.67
1.05
b The company has embedded knowledge management (“KM”) into its
business strategy.
c The company has communicated a clear KM framework to its staff to
encourage learning and knowledge sharing.
d Most employees believe that sharing know-how is important to the success
of the organization.
e The company has implemented a set of KM tools to enable learning before,
during, and after.
2. Leadership Behaviors
3.50
0.83
3.60
1.12
3.64
0.91
3.80
1.08
a Company leaders recognize the link between KM and performance.
3.57
0.90
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b The company advocates the practice of knowledge sharing, and KM
activities are encouraged and rewarded.
c Managers set themselves as good examples of frequently conducting KM
activities.
d Managers offer the time and support to its staff on learning and knowledge
sharing.
e The company nurtures the right attitudes among the employees to facilitate
sharing and using others’ know-how.
3. Networking
3.88
1.03
3.42
0.83
3.56
1.00
3.58
0.86
a Employees seem to be rewarded for performing networking activities that
results in knowledge exchange.
b Networking on a needs basis helps employees know each other.
2.52
1.12
3.60
1.35
c The company has put in place technology to support networking, and they
are well utilized.
d Networks have a clear governance document, with clearly defined purpose,
roles and responsibilities.
e Networks meet regularly, and they are organized around business needs.
3.58
1.17
3.46
1.18
3.48
1.29
3.92
1.06
3.38
0.97
3.38
0.94
4.23
0.82
3.58
1.10
a Knowledge capturing occurs at the individual, team, and network level.
4.08
0.91
b Depositing and retrieving knowledge can be conducted at ease (e.g. it is
easy to locate accurate and up-to-date information from the Intranet).
c The company dedicates resources to collect and disseminate knowledge.
Relevant knowledge is pushed to you.
d The company promotes establishing communities of practice. Such
networks collect their subject’s knowledge in one place in a common
format.
e Following item “d” above, social networks act as guardians of their
knowledge (e.g. they regularly update the content to keep it current, and
validate the knowledge).
3.73
1.04
3.77
0.91
3.20
1.08
3.25
1.15
4. Learning before, during and after
a The company values knowledge, and require formal learning on some
occasions. Some business processes prompts for learning (e.g. the company
has established mentoring / apprenticeship programs).
b The company has implemented templates and guidelines, and established
common language to facilitate knowledge sharing.
c Not only the employees are engaged in learning, customers and partners
participate in review sessions as well.
d Employees are encouraged to capture the knowledge that they learn for
others to access.
e The company nurtures an atmosphere of learning before, during, and after.
5. Capturing Knowledge
Table 3: Score statistics of each question in the questionnaire. Questionnaire developed based
on the Knowledge Management Capability self-assessment framework (Collison and Parcell,
2004).
*Note: “SD” denotes standard deviation
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Score statistics for each question are shown in Table 3. A summary of the scores of the five
dimensions of the framework is illustrated in Table 4. The maturity level was ranked from
level 1 to level 5, where level 5 represented the highest maturity, and level 3 represented the
mid-point. The mean score of each of the five dimensions was found to exceed the mid-point.
Five dimensions of KM
Mean score
KM Strategy
3.64
Leadership Behaviours
3.60
Networking
3.33
Learning Before, During and After
3.70
Capturing Knowledge
3.61
Overall Performance
3.58
Table 4: Mean scores of the 5 dimensions of KM capability self-assessment framework in
mainland Chinese listed companies (N=26)
As networking was found to be a less ‘mature’ KM dimension in the surveyed companies,
mainland Chinese organizations may wish to nurture the concept of knowledge sharing
among employees to encourage the development of knowledge networks. Also, such
networking activities may be strengthened by the formation of communities of practice,
infrastructure development and technological support. Chinese businessmen are known to be
placing a great significance in relationship building and network (also known as ‘guanxi),
however, it had not been evident in some of the samples surveyed. This raises the question: is
measuring quanxi equivalent to measuring networking capability?
4.3. Correlation of scores of KM maturity level and ICE
Scores for KM maturity level and VAIC™ were collected for the same 26 companies.
Descriptive statistics were compiled through SPSS, and the results are shown in Table 5.
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
AVG_ICE
26
1.563
24.187 6.11331
5.234996
KM_Score
26
2.00
4.96 3.6000
.75303
Valid N (listwise) 26
Table 5: Descriptive statistics of KM scores and ICE
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A scatter plot does not reveal a strong relationship between scores of KM maturity level and
ICE, as shown in Figure 1.
Figure 1: Scatter plot between scores of KM maturity level and ICE
Statistical analysis revealed a correlation coefficient of 0.092, which does not support a
strong relationship between KM maturity level scores and ICE, as shown in Table 6. The
strength of linear relationship between KM and IC was extremely weak.
A previous study by Turner and Minonne (2010) claimed an interrelationship between IC and
KM. As reviewed earlier, many scholars have proposed KM as a foundation for IC. However,
our results contradicted the hypothesis of a coorelation between KM and IC.
Correlations
AVG_ICE
AVG_ICE Pearson Correlation
1
Sig. (2-tailed)
N
26
KM_Score Pearson Correlation
.092
Sig. (2-tailed)
.655
N
26
KM_Score
.092
.655
26
1
26
Table 6: Correlation statistics between KM maturity level scores and ICE
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4.4. Whether companies with KM departments had higher KM scores
Data on whether the surveyed company had a KM department were gathered. Six of the
companies had a KM department, whereas the rest (n=20) did not have a KM department.
The sample pool was divided into two groups based on whether the company had a KM
department, and the mean KM scores of the two groups are shown in Table 7. An independent
samples test as shown in Table 8 revealed that the two groups had statistically different mean
scores for KM maturity level.
Group Statistics
KM_Dept
KM_Score Yes
No
Std.
Deviation
.61688
.71812
N
Mean
6 4.1633
20 3.4310
Std. Error
Mean
.25184
.16058
Table 7: Companies with KM departments had a mean score of 4.16 in KM maturity level
while companies without KM departments had a mean score of 3.43.
Independent Samples Test
Levene's Test
t-test for Equality of Means
95% Confidence Interval
of the Difference
Equal variances
KM_Score
assumed
not assumed
F
.181
Sig.
.675
t
df
Sig. (2-
Mean
Std. Error
tailed)
Difference
Difference
Lower
Upper
2.253
24
.034
.73233
.32501
.06154
1.40313
2.452
9.479
.035
.73233
.29868
.06185
1.40282
Table 8: Result of Independent samples t-tests
Results of Levene’s test for equality of variances shows that equal variances were assumed.
Mean KM scores for the two groups (companies with KM departments vs. companies without
KM departments) were statistically different. Participants in companies with a KM
department gave a higher self-assessment score for KM maturity level than participants in
companies without a KM department.
5. Limitations
Part 2 of the study involved a questionnaire survey. Validity of the instrument developed by
Collison and Parcell (2004) has not been empirically tested in prior studies, therefore how
closely the instrument reflects the efficacy of KM remains uncertain, to an extent. Secondly,
the sample size was small, with only 26 companies willing to complete the questionnaires.
One representative from each company was invited to fill out the questionnaire. The results
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collected may have been affected by the prejudices and positions of the respondents. To
avoid such bias, sample size from each company should be enlarged to cover employees of
various grades. Employment grades of the respondents are shown in Table 9.
Employment grade
Frequency
Director
Manager
1
12
13
Executive
Table 9: Employment grade distribution of survey respondents
Furthermore, the questionnaire was designed with ease of administration in mind. Statements
in the questionnaire were phrased concisely to limit length, and KM concepts may not be
easily communicated to the layman under word limit constraints. Hence, participants lacking
a background in the discipline may not have been able to grasp the meaning of some of the
KM terminology used. Furthermore, the questionnaire was translated from English into
Simplified Chinese as that is the main language used in mainland China. This further
contributed to an additional layer of complexity. Therefore, it may be possible that there were
some misinterpretations of meanings. Moreover, the instrument is a perceptual survey
involving subjective judgment, which fluctuates from person to person. Hence, KM maturity
level scores may not closely reflect the reality. In the future, data accuracy may be enhanced
if a briefing about KM concepts can be conducted to participants before data collection.
With regards to using VAIC™ to gauge IC performance, the VAIC™ model was developed to
provide a simple-to-use and objective evaluation of IC efficiencies. Some scholars have
questioned the basic assumptions of the VAIC™ methodology and its validity (Ståhle, Ståhle
and Aho, 2011). There has been no clear justification of the mathematical formula used in the
derivation of the various components of VAIC™, and these components were found to be
interdependent (Kujansivu, 2006, Ståhle et al., 2011). As common in IC assessment
instruments, there has been a lack of solid verification of the effectiveness of VAIC™ as an
IC performance assessment tool.
6. Conclusion and further studies
In order to sustain market competitiveness in today’s fast-paced world, the results appear to
suggest that organizations in mainland China may need to strengthen their KM strategy to
harness more value from IC. As a starting point, organizations may explore the integration of
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KM practices into business strategy. KM is viewed to be critical in these companies’ value
creation processes.
It is believed that this is one of a few studies, if not the first, which attempts to explore the
status of knowledge management in mainland Chinese organizations in relation to the
utilization of IC. The results of this study may very well serve as a basis for baseline
comparison with future studies in the region. The study extends prior research by conducting
an exploratory investigation on the possible relationship between two completely different
parameters of intangible assets for companies in China. Although it is intuitive to suggest that
KM and IC are somehow related, empirically, a correlation between KM maturity level and
ICE was not substantiated in this study among companies in the PRC.
There may be differences between KM and IC efficiency. KM may be construed to be a set of
management intervention in a corporate environment in order to enhance competitiveness.
The outcome of positive KM intervention includes improvement in work processes, which
may lead to improved performance, however, these improvements may or may not be
realized in monetary terms (e.g. improved quality without increasing price of goods sold or a
decrease in labour cost). Indeed, the same may be said about KM maturity. The VAICTM
methodology uses value-added (outputs minus inputs) as the basis of its calculation.
Therefore, without an actual drop in human capital investment or an increase in output value
or an increase in value added, ICE will not improve. As a result, a high level of KM maturity
perceived by the survey participants may not suggest that first, monetary performance in a
firm may be enhanced; secondly, KM processes are greatly improved; and thirdly, it would
directly lead to a high efficiency in utilizing IC. Another possible reason of the lack of
correlation is that KM maturity level is a subjective qualitative assessment, whereas the
VAICTM methodology is a quantitative assessment. There may be in existence many other
intervening variables if we were to bring the two constructs together. This exploratory study
reveals the difficulty in testing the relationship between two different categories of
assessment tools, which represents two possible constructs of knowledge assets.
The authors attempt to highlight a process view on KM. If KM is viewed as a process within
an organization, then the results or output is corporate performance, and knowledge helps add
value to the company's products and services in each and every part of the value chain.
Besides the enjoyment of performance, IC is accumulated via human capital and structural
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capital, while HC can part with the company, SC stays and becomes a part of the
organizational knowledge of the memory bank.
It is this view that we are trying to put forward. Note however that the relationship between
KM processes and IC generation is complex. We are making an exploratory attempt to create
proxies for exploring their relationships. Time lag between KM maturity and IC accumulation
could be one of them, but we suspect that there may be many other intervening variables,
such as level of education, nature of industries, the awareness of knowledge, R & D spending
as so on. KMMM as noted in Collison and Parcell is just one of the many that we adopt from
a practitioners' viewpoint. As we found in this study, the relationship, if any, is complex and
require much further work to a) identify the variables b) establishing the right instruments
c) build a holistic model in exploring the relationship between KM and IC.
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