Target: Journal of Information & Knowledge Management

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†

Samuel Kai Wah Chu*

Wendy W.Y. Wu*

* 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

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.

1. Literature Review
2. 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 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.

1. Knowledge chaotic
2. Knowledge aware
3. Knowledge focused
4. Knowledge managed
5. knowledge centric

1. Knowledge chaotic stage
2. Knowledge conscientious stage
3. KM stage
4. KM advanced stage
5. KM integration stage

1. K-Aware
2. K-Managed
3. K-Enabled
4. K-Optimised

1. Level 1
2. Level 2
3. Level 3
4. Level 4
5. Level 5

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:

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.

1. 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 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.

1. Findings and Analysis
2. 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.

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