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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† [email protected]

Samuel Kai Wah Chu* [email protected]

Wendy W.Y. Wu* [email protected]

* 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

/tt/file_convert/5f80ee2f193df621dd32866e/document.doc 5/23/2023 1

mailto:[email protected]

mailto:[email protected]

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


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 “knowledge-

based 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


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).


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 /tt/file_convert/5f80ee2f193df621dd32866e/document.doc 5/23/2023 5

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

Dimensions Maturity stages Research method(s)

Knowledge Management Framework Assessment and Knowledge Journey inKPMG (2000)

People Process Content Technology

1. Knowledge chaotic

2. Knowledge aware3. Knowledge

focused4. Knowledge

managed5. knowledge centric

Survey

KNMTM in Hsieh, Lin and Lin (2009)

Process Information

technology Culture

1. Knowledge chaotic stage

2. Knowledge conscientious stage

3. KM stage4. KM advanced

stage 5. KM integration

stage

In-depth interviewFocus groupQuestionnaire

KM3 in Gallagher and Hazlett (2000)

Infrastructure Culture Technology

1. K-Aware2. K-Managed3. K-Enabled4. K-Optimised

Critical success factors analysis

Knowledge Management Capability self-assessment Framework in Collison and Parcell (2004)

KM strategy Leadership

behaviors Networking Learning

before, during and after

Capturing knowledge

1. Level 12. Level 23. Level 34. Level 45. Level 5

Fill in the self assessment individually, and then conduct focus group discussion

Table 1: Summary of the selected KMMMs

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.


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:/tt/file_convert/5f80ee2f193df621dd32866e/document.doc 5/23/2023 7

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 /tt/file_convert/5f80ee2f193df621dd32866e/document.doc 5/23/2023 8

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. /tt/file_convert/5f80ee2f193df621dd32866e/document.doc 5/23/2023 9

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

Company 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

I IT 1.578 1.538 1.572 1.563 4.24 Y

J Manufacturing (Food & Beverage) 13.527 13.435 12.179 13.047 3.64 N

K Manufacturing (Machinery) 3.295 2.882 - 3.088 4.16 Y

L Manufacturing (Machinery) 4.981 3.739 5.766 4.829 2.96 N

M Manufacturing (Metals & Non-metals) 4.473 4.888 6.648 5.336 3.20 N

N Manufacturing (Metals & Non-metals) 2.445 3.808 4.775 3.676 3.44 N

O Manufacturing (Metals & Non-metals) 4.679 5.138 8.916 6.244 2.12 N

P Manufacturing (Metals & Non-metals) 3.999 5.467 7.242 5.569 3.12 N

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

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 Score

SD*

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.

3.50 0.83

c The company has communicated a clear KM framework to its staff to encourage learning and knowledge sharing.

3.60 1.12

d Most employees believe that sharing know-how is important to the success of the organization.

3.64 0.91

e The company has implemented a set of KM tools to enable learning before, during, and after.

3.80 1.08

2. Leadership Behaviors

a Company leaders recognize the link between KM and performance. 3.57 0.90

b The company advocates the practice of knowledge sharing, and KM activities are encouraged and rewarded.

3.88 1.03


c Managers set themselves as good examples of frequently conducting KM activities.

3.42 0.83

d Managers offer the time and support to its staff on learning and knowledge sharing.

3.56 1.00

e The company nurtures the right attitudes among the employees to facilitate sharing and using others’ know-how.

3.58 0.86

3. Networking

a Employees seem to be rewarded for performing networking activities that results in knowledge exchange.

2.52 1.12

b Networking on a needs basis helps employees know each other. 3.60 1.35

c The company has put in place technology to support networking, and they are well utilized.

3.58 1.17

d Networks have a clear governance document, with clearly defined purpose, roles and responsibilities.

3.46 1.18

e Networks meet regularly, and they are organized around business needs. 3.48 1.29

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).

3.92 1.06

b The company has implemented templates and guidelines, and established common language to facilitate knowledge sharing.

3.38 0.97

c Not only the employees are engaged in learning, customers and partners participate in review sessions as well.

3.38 0.94

d Employees are encouraged to capture the knowledge that they learn for others to access.

4.23 0.82

e The company nurtures an atmosphere of learning before, during, and after. 3.58 1.10

5. Capturing Knowledge

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).

3.73 1.04

c The company dedicates resources to collect and disseminate knowledge. Relevant knowledge is pushed to you.

3.77 0.91

d The company promotes establishing communities of practice. Such networks collect their subject’s knowledge in one place in a common format.

3.20 1.08

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).

3.25 1.15

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

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 scoreKM Strategy 3.64

Leadership Behaviours 3.60Networking 3.33

Learning Before, During and After 3.70

Capturing Knowledge 3.61Overall 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 StatisticsN Minimum Maximum Mean Std. Deviation

AVG_ICE 26 1.563 24.187 6.11331 5.234996KM_Score 26 2.00 4.96 3.6000 .75303Valid N (listwise) 26Table 5: Descriptive statistics of KM scores and ICE


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.

CorrelationsAVG_ICE KM_Score

AVG_ICE Pearson Correlation 1 .092Sig. (2-tailed) .655N 26 26

KM_Score Pearson Correlation .092 1Sig. (2-tailed) .655N 26 26

Table 6: Correlation statistics between KM maturity level scores and ICE


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 N MeanStd.

DeviationStd. Error

MeanKM_Score Yes 6 4.1633 .61688 .25184

No 20 3.4310 .71812 .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

Equal variances

Levene's Test t-test for Equality of Means

F Sig. t df

Sig. (2-

tailed)

Mean

Difference

Std. Error

Difference

95% Confidence Interval

of the Difference

Lower Upper

KM_Score assumed .181 .675 2.253 24 .034 .73233 .32501 .06154 1.40313

not assumed 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


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 1

Manager 12

Executive 13Table 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


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 /tt/file_convert/5f80ee2f193df621dd32866e/document.doc 5/23/2023 17

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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