2011-49
The role of network boundary information
in intellectual capital measures
Tadanori Yosano Eric Bjurström Ulf Johanson

The role of network boundary information in intellectual capital measures

Authors
Tadanori Yosano1, Eric Bjurström2and Ulf Johanson2

Abstract
In this paper, we will explore the role of network information in intellectual capital
measures, focusing specifically on data from lender-borrower relationships.

Our

analysis found as follows: First, Japanese lenders who judge the creditworthiness offirms, place a great deal of importance on network boundary information in comparison
to human resources and technology. Second, while Japanese lenders confront difficulties
in accessing and evaluating a firm’ human resources and technology, they comprehend
that both resources are essential performance generators for firms. Third, lender
perceptions show that both human resources and technologies are highly correlated to
the larger network information. How do we interpret these local practices to help
explain the surrounding societal context?

This paper provides three possible

interpretations as follows: First, Western philosophical and scientific perspective leads
us to postulate that lenders are efficient, rational decision makers.

Second, the New

Institutionalism concludes that lenders make decisions in accordance with social norm
determinants, rather than for personal optimization. Third, a pragmatic interpretation of
the phenomenal use of network information from the more traditional philosophicalawareness and rejection of the perfect measurement idea can be as follows: Lenders
make decisions based on their daily practice and reflection on firms’ human resources
and technology within the socially shared context.

Key words
Intellectual capital, Network, Lender-borrower relationship, New institutional theory,
Micro-macro synthesis
1

Corresponding author. Graduate School of Business Administration of KobeUniversity, 2-1,
Rokko-daicho, Nada-ku, Kobe, 657-8501, Japan. Tel,: +81-78-803-6989; fax: +81-78-803-6989.

E-mail address: yosano@kobe-u.ac.jp (T. Yosano)2
School of Sustainable Development of Society and Technology at Märlardalen
University, Sweden.
1

1.

PurposeBefore the 1980’s, the Japanese lender-borrower relationship lending was characterized
by longevity.

This would change after the middle of the 1980’s when Japan would

enter into a bubble economy or boom economy.

While lenders depended on theeconomic value of land holdings and real estate to determine trustworthiness whenlending money to borrowers, this philosophy will be a myth as land and real estatevalues keep appreciating.
real estate collapse.

The bubble economy turned in a similar fashion as the U.S.

After this Japanese financial, land value, and real estate crash in

the early 1990’s, Japan was forced to shift toward another style of securing lending
relationships and building trustworthiness. This new method relies more on “hard,” orfinancial, information and lending is now based on finance theory consistent with theWestern philo-scientific perspective.

The shift led to two market reactions:competition between lenders, and 2) low profitability.

1) high

The lenders’ heavy reliance on

financial figures limited the advantages between lenders, and high competition meantthat not all firms would be able to survive. In efforts to counteract this market reaction,
the Japanese Financial Service Agency (FSA) introduced an action program that was
intended to help all lenders survive the harsh market conditions.

The “Program forstrengthening Relationship Banking Function,” introduced in March 2003, enhanced thelender’s ability to judge a borrower’s trustworthiness using “soft,” or intellectual capital(IC), information (Yosano and Nakaoka, 2011a).

While the FSA works to strengthen

lending relationships, the Ministry of Economy, Trade, and Industry (METI), on theother hand, focuses on IC information. In Japan, the METI strives to strengthen
information transference between firms and stakeholders.

IC information includes the corporate strategy, technology, intellectual property, humanresources, and networks.

All of these factors are seen as promising engines for lenders,

and the efforts of the METI to promote the utilization and transference of these factors
is actually supportive of the FSA’s relationship lending action program (c.f. Yosano andNakaoka, 2011a).

Hard information generally involves a corporate financial trackrecord and historically accumulated resources. However, the company is alive and2under constant change, therefore, the company should act on real social circumstances
according to their corporate strategy. Intellectual capital is able to transform the actual
corporate figures into a realistic picture of the borrower’s trustworthiness.

However,

we cannot access IC information directly, because they are naturally, by definition,
intangible. However, if lenders want to judge a corporation’s true trustworthiness,then they should manage to find a way to evaluate the realistic picture. METI’s
initiative3 can motivate us to find the way to judge the actual corporate trustworthinessusing IC information as studied in this paper.

We are motivated to see the realistic image of the corporation’s business by studying
their intellectual capital. With thanks to the METI initiative, we were able to accesssurvey questionnaire data, and analyze how lenders use IC information to create
trustworthiness with the borrower.

We can hypothesize that lenders rely on network

information to create trustworthiness.

Our goal is to clarify how IC information isused by lenders judging a borrower’s trustworthiness, and provide evidence that
supports our hypothetical issue: “How network information helps to create
trustworthiness with lenders?”
In this paper, we address the importance transferring IC information, especially within
networks, to mitigate the information asymmetries between the firms and the lenders.
We interpret the actual lenders judging practices by analyzing survey research that was
conducted by METI in the 2008 fiscal year.

3

The METI encouraged small businesses to disclose supplemental, non-financial information that
could be used to determine the potential for growth and/or sustainability, and to eliminate
information barriers for raising funds (Holland and Johanson, 2003). Following this example, the“Organization for Small and Medium Enterprises and Regional Innovation, Japan” (SMRJ), who isan affiliated association of METI, issued The Manual for SME Intellectual Capital Reporting inMarch 2007. This manual focuses on the specific concerns of small businesses. Non-financial
information could further help convince lenders of the small businesses’ trustworthiness when they
provided IC Reports. This trend is a complete contrast from the “Guideline for disclosing Intellectual
Assets Based Management” (GIABM) that focuses on big businesses as illustrated in detail by
Sumita (2008) and Johanson et al. (2009). In fact, the number of small business disclosures has
increased from thirteen in the 2006 fiscal year to sixty in the 2008 fiscal year. While sixty is a small
number, it still reflects a dramatic increase over a two year period.
3

2.

The lender’s perception of IC information

In order for lenders to survive the inter-competitive market crippled from the severe
economic recession of the late 1990s in Japan, lenders have been given a strong
incentive to use “hard,” or financial, based lending technologies. However, “hard”
information that is based on lending technologies drive lender to face low profitability,
because of the difficulties with differential lending technologies. “Hard” information
gives lenders equal opportunity to borrower information and increases the market
competition. In this setting, the FSA introduced the action program in 2003. The action
program encourages lenders to use “soft,” or IC, information based lending technologies(the FSA refers to this as “relationship banking”). Meanwhile, the METI initiative has
supported this action program. The METI introduced in rapid succession both the“Intellectual Property Outline” in 2002 that focuses on the corporate technology andintellectual property (e.g. Johanson et al., 2006),and the “Guideline for disclosing
Intellectual Assets Based Management” (GIABM) in 2005 which

focuses on the

broader corporate IC (e.g. Sumita, 2008; Johanson et al., 2009). Lending technologies
expanded from “hard” information based traditional lending technologies, which wereprimarily underwritten based on strong financial ratios, to “soft” information based
lending technologies that involve screening and underwriting policies/procedures,contract structuring, and monitoring strategies/mechanisms with IC information (Uchida
et al., 2008). In this setting, we can review the literature with regard to lending
technologies, and trace how the current practice with lending technologies has prevailed
and the key role of IC information has played in reducing information asymmetries
between firms and lenders (e.g. Holland and Johanson, 2003).

This is especially true

for small and medium-sized businesses (SMEs), because SMEs tend to face moredifficulties when obtaining a working amount of capital and investment capital due tothe credibility of their financial information.

In other words, their financial

4information has been regarded as “opaque.”
The challenge of establishing efficient lending technologies for SME’s is becoming
increasingly not only a practical issue, but also a matter of finding a relevant paradigm
that is able to theoretically explain the needs for such technologies.

It is also an issue

to reconcile the contradictory findings on how credit decisions are achieved in themultifaceted lending industry. In the aftermath of bank crises and global recessions, theefficiency of lending technologies is becoming crucial, especially in relation to SME’s
which are often seen as promising engines for growth. However, lending to SME’s istypically associated with high levels of uncertainty and scarce track records of success(Berger and Udell, 2006). Conventional thought holds that small, niche lenders have an
advantage in relation to big banks through their greater capacity to process “soft”
information, and are more able to deliver relationship lending (Uchida et al., 2007).
However, big lenders are increasingly showing interest toward SMEs, and continuing todevelop tools and services beyond relationship lending (Torre et al., 2010).
While the debate within economics has framed the problem as a large vs. small lenders
problem, later findings suggest a more nuanced picture. Berger et al. (2005) found thatlarge lenders who lend over a greater distance interact more impersonally with theirborrowers, and have greater difficulties in collecting and making use of “soft”
information.

Hence, they are less willing to lend to informationally “difficult” credits.

Instead, they tend to lend primarily to larger firms with consistent accounting records.
However, Berger et al. (2005) points to the possibility that

this tendency does notnecessarily have to do with the bank size, but, rather, with finding an appropriate levelof decentralization. They argued that the long-standing debate within economics about
the boundary of the firm should be complemented with issues about the shortcomings oflarge organizations that create a greater distance between the information gathering andthe decision-making authority of bank clerks.
As an extension of this former research, we may focus on what actually gets organizedwithin larger lenders, such as the information and judgments with SME’s. Berger et al.

5(2005) argued that more attention should be directed toward issues with the nature of
the firm and its business rather than on the formalization of information. According tothis view, the solution for large lenders may lie in the possibility of decentralizing theiroperations to facilitate efficient processing of “soft’” information and increase
relationship lending. Berger and Black (2010) suggested that “soft” vs. “hard,” in
addition to “small” vs. “large” distinctions, should be nuanced by extending the present
paradigm with analyses of various combinations of small and large banks and firms anda broader and more specified range of lending technologies. With this extension, they
were able to find results that go with the grain with the dominant paradigm, such as
suggestions that relationship lending is indeed a strength of small banks, but especiallyin relation to large firms. They also remarked that some lending technologies, such as
the soft information technology used in judgment lending, are largely ignored by thepresent dominating paradigm.

Additionally, the Japanese economy is facing similar challenges. Discussing thechallenges of Japanese regional banks, Choe (2007) called for the need to improve thelending technology by reducing screening costs and political pressure to achieve a more
efficient allocation of financial resources and avoid suboptimal investment projects with
regional SMEs or supporting non-performing SMEs alive. Uchida et al. (2008) also
confirm in the Japanese context that the advantage small lenders have in relationshiplending to SMEs matters, but that large lenders are increasingly lending to SMEs. Overthe last decade, the METI has also taken a number of initiatives to improve the market
communication through the use of IC reports, first for big firms, but more recently also
for SMEs. The outcome of these initiatives has been controversial, indicating someinterest but also to some extent skepticism against attempts to create “hard”
measurements for “soft” issues.

In the intersection between accounting and finance theory, the character of the market
and its ways of organizing exchange, addresses long-standing issues with the borders of
the organization and whether a market may be characterized by network relations rather
than fierce and anonymous competition. For example, the term relationship lending may
indicate the point of the Uppsala school that many market relations stabilize into6

repetitive interaction, forming patterns of interaction resembling an organization morethan a market. In a similar perspective, SME’s, while not formally integrated into biggerorganizations, are typically part of a bigger context of other relations with customers,
suppliers, service firms and other complementaries. Given these complexities beyond
simple contingency-theory approaches, we may ask how credit-decisions are made “inthe wild” (c.f. cognition in the wild) given all the contextual influences of concrete
situations. Therefore, lending technologies may include more than the “soft” and “hard”
distinction, and embrace more techniques to reduce complexity rather than quantifying
information about SME’s. Furthermore, the same figures may be used differently in
different situations and by different actors.
In section 3, we analyze how lenders use IC information to create trustworthiness withthe borrower. Through empirical evidence we will support our hypothesis that lenders
rely on network information in order to determine a businesses trustworthiness. We
adopted the factor analysis in order to visualize the latent IC measures such as thecorporate strategy, technology, intellectual property, human resources, and networks.
We hope to show how lenders try to interpret corporate figures into a realistic picture of
the borrower’s trustworthiness.

3. The case study: Developing a Visual Representation of Intellectual Capital

The METI set up a research committee that focuses on "investigating and researchingcredit technologies that utilize evaluation corporate technologies.*" in the 2008 fiscal
year. The METI committee involved survey research from 429 lenders (4 Mega banks,
85 Tier I and II Regional banks, 247 cooperative banks, 82 Credit Unions, and 11 otherfinancial institutions).

Before sending questionnaires to lending institutions,committee members first discussed which IC items are representative of corporate
technologies involving intellectual properties, networks, organizations, as well as
corporate technological strategies. This list of IC items was in reference to interview
surveys from 6 regional and cooperative banks conducted by the SMRJ in late 2007
(Yosano and Koga, 2008, Yosano and Nakaoka, 2011).

3.1 Sample and Methodology
7Our study focuses on the lenders’ development of trustworthiness by utilizing IC
information, and therefore, our study sample only involves 4 Mega Banks, 85 Tier I andII regional banks, and 247 cooperative banks. It excludes the 82 Credit Unions and 11
other financial institutions used in the METI survey. Our sample size totaled 336
lenders.

Table 1 shows the utilization level of IC information for creating trustworthiness during
the lending process. 1 = "No usage level," 2 = "Weak Usage level," 3 = "Medium Usagelevel," 4 = "Strong Usage level," 5 = "Extremely Strong Usage level."

The MEasuRing Intangibles To Understand and improve innovation Management(MERITUM) coalition introduced intangible concepts as the following: human capital,
structural capital, and relational capital. In March 2002, the Japanese government
introduced propaganda to “strengthen industrial competitiveness and promote
intellectual property policies,” in order to coincide with a technologically driven
economy.
technologies

Our survey data from METI 2008, also focuses on how Japaneserelatewith

networks,

includingcustomers,

suppliers,

government/municipal offices, and R&D affiliates. Therefore, it was logical for us toisolate the technology factor apart from the structural capital factor.

We further

separated the corporate strategy from structural capital, because it is by definition a
corporation’s business policies for the future and the decisive way for a corporation tocontinue through the changing market.

Hence, we updated the MERITUM intangible

structural capital term into three factors: corporate strategy, technology, andorganizational structure.

We also updated the definition of relational capital toencompass the network factor as a boundary influence rather than an internal concept.
These four concepts, corporate strategy, technology, organizational structure, andnetworks, are then considered along with human resource capital to total 5 IC factors
used in our study.

We show our updated intangible factor concepts in Figure 1.

Therefore, we combined 5 corporate strategy IC items, shown in Table 1 and found on
the Likert scale that measures credit decider attitudes toward usage, into one corporate

8

strategy factor (cronbach alpha4 = 0.9007). 9 organizational structure IC items werecombined into one organizational structure factor (cronbach alpha = 0.8776), 3
employee IC items into one human resource factor (cronbach alpha = 0.9751), 23
technological and intellectual property IC items into one technology factor (cronbachalpha = 0.9740), and lastly, 15 network IC items into one network factor (cronbachalpha = 0.9452).

All of these factors are shown in Table 1.

[Table 1 Insert Here]

3.2 Methodologies and factor analysis results
We conducted factor analysis in order to extract the primary, latent factor present ineach category. The corporate strategy factor has a primary factor whose eigenvalue5 is2.41299 and to a 0.8043 proportion6. The organizational structure factor has a primaryfactor whose eigenvalue is 5.04378 to a 0.5604 proportion. The human resource factor
has a primary factor whose eigenvalue is 2.41299 to a 0.8043 proportion. The
technology factor has a primary factor whose eigenvalue is 9.63748 to a 0.7413
proportion. Lastly, the network factor has a primary factor whose eigenvalue is 8.57670
to a 0.5718 proportion.

[Figure 1 Insert Here]Internally, within the corporation, the correlation between the organizational structure
factor and the human resource factor is 0.6199*** 7 , the correlation between the4

Cronbach alpha is a coefficient for reliability. It is commonly used to measure theinternal consistency or reliability of a psychometric test score for a sample of examinees.
Here, for example, 0.9007 means that over 90% of the examinees in our sample, who
are representative of lenders, consider our corporate strategy category, containing 5 IC
items, as a primary factor.
5
Eigenvalue measures the variance between all the variables that are accounted forwithin a factor.
6
Proportion shows the degree to which a primary factor variance can explain thevariance of all the individual variables. Here, for example, the primary corporate
strategy factor’s variance explains over 80% of all five IC items’ variance.
7*** denotes 1% significance level for the rejection of the null hypothesis: theorganizational structure factor is equal to the human resource factor.
9organizational structure and the technology factor is 0.8022***, the correlation between
the human resources factor and the technology factor is 0.6674***.

Between the corporation and its boundary networks, such as suppliers, customers, R&D
co-developers, and government and municipal offices, the correlation between theorganizational structure factor and the network factor is 0.6261***.

The correlation

between the human resource factor and the network factor is 0.5103***, and thecorrelation between the technology factor and the network factor is 0.6655***.

We are able to address that the latent factors within the corporation, especially for
SMEs, are highly correlated with the boundary network factor (1% significance level).
We are able to interpret this Japanese lender practical phenomena from the threefollowing philo-sociological view points.

Last, we combined three primary factors: the organizational structure factor, the humanresources factor, and the technology factor into one primary internal corporate factor.
We found that this comprehensive factor has a 2.40409 eigenvalue to a 0.8014
proportion.
Foucault (1980) presented that the corporate phenomenon is created by “conditions ofpossibility.”

He suggests that we can map or predict conditional events in the future,meanwhile, in actuality corporations have resources, such as human resources, anorganizational structure, technologies, and boundary organizational networks. These
present-day resources help drive corporations into the future, but how they are used isdetermined by the corporate strategy.

“Past and future are living in the present.

Whatever humanbeings do in the present is decisively influenced by the past and by
the future...the future is not something that will come later,
independently of our will.

There are several possible futures andone of them as to be made (Markovic, 1974, pp. 10-11).”10

If the corporate strategy is flawed, then the corporation is at risk for bankruptcy.

Onthe other hand, if the corporate strategy is successful, then the corporation will haveglorious future prospects.

This delicate relationship between the corporate strategy

and corporate resources will actually determine whether or not the corporation will beable to fully utilize their corporate resources toward the most promising future.
Therefore, we would like to sketch the relationship between the corporate strategy andinternal corporate resources in addition to boundary organizations or networks (thedetails of this analysis is shown in Figure 1).
The correlation between the corporate strategy and the boundary network factor is0.6175***, and the correlation between the corporate strategy and the primary internal
corporate factor is 0.8123***. The internal corporate and boundary factors are highly
correlated with the corporate strategy factor (1% significance level). With this data, weare able to suggest that lending practices definitely notice the relationship between thecorporate strategy and the internal corporate and boundary network latent factors.
Therefore, the corporation needs the strategic viewpoint that the internal and boundary
factors should produce in order to enhance the strategic corporate business purpose.

4.

Conclusion

After updating the MERITUM intangible concept terminology, we were able to analyze
our hypothetical concept that the corporate strategy determines whether or not internal
resources, human resources, the organizational structure, technology, and boundary
networks are utilized fully.

This phenomenon was also supported by “investigating

and researching credit technologies that utilize corporate evaluation technology.” Our
updated concepts support not only our hypothetical concept, but our data analysis of thesurveys provided by the METI 2008 investigation also supports our hypothetical issue
that network information helps to create trustworthiness with lenders.

Our empirical findings are as follows: First, Japanese lenders who judge thecreditworthiness of firms, place a great deal of importance on network boundary
information in comparison to human resources (human capital) and technology(structural capital). Second, while Japanese lenders confront difficulties in accessing
11and evaluating a firm’ human resources and technology, they comprehend that bothresources are essential performance generators for firms. Third, lender perceptions showthat both human resources and technologies are highly correlated to the larger network
information. How do we interpret these local practices from various perspectives inorder to further our understanding of the effects of intellectual capital at large? How do
we analyze this local phenomenon to help explain the surrounding societal context?

Why do lenders primarily focus on network information? If we interpret this local
phenomenon from a dominant Western philosophical and scientific perspective, that is,
in a rationalistic manner, the functional reasoning leads us to postulate that lenders are
efficient, rational decision makers. Therefore, we can further hypothesize that lenders
use networks rationally as a proxy for human resources and technology, in order to
optimize their ability to make a fully educated judgment about the borrowers’
trustworthiness. Lenders are constantly trying to overcome obstacles through a rationalmanner in order to maximize their investment profitability. Lenders are not only
evaluating human resources and technology independently, but they are also using other“market” partner impressions of the target firm’s capabilities and trustworthiness.

If we interpret this phenomenon from the Western critical perspective, that is, in the
New Institutionalism (e.g. Powell and DiMaggio, 1991), adopted in the 1980s, we canconclude the following: Lenders make decisions in accordance with social norm
determinants, rather than for personal optimization. In other words, lenders makejudgments through the social dialectic between individuals creating a larger social
influence of habit. Why do lenders focus primarily on network information? We proposethat lenders rely heavily on the macro structure of their social context, because,
ironically, the social context trains lenders to distrust human resources andtechnological measures, and rather, use network information as a lens into thecredibility of a prospective firm. However, the construction and reconstruction of othermeasures creates continuous dialectic of measurement problems.

Another possible pragmatic interpretation of the phenomenal use of network
information from the more traditional philosophical awareness and rejection of the12perfect measurement idea from before the 1990s can be as follows: Lenders makedecisions based on their daily practice and reflection on firms’ human resources andtechnology within the socially shared context. The numerical controls, calculations, andprobabilities, used as basic techniques, are not understood as exact depictions or
predictions, but rather as a rough, yet practical approximation for the moment.
Observing borrower human resources and technology through networks produces theclosest approximation of the lenders’ socially shared context: the measurements aretreated as a part of the social context and reality that counts for their business. The
ultimate concern for lenders is the practical effects of their approximations, and the pros
and cons of their decision making. Lenders simply try to make their business workwithin the local social and cultural context, and therefore, they treat the client’s
perceptions as the most valid reality for consideration.
[2011.12.2 1066]

13

Reference

Abbott, A. (1988) The System of Professions – An Essay on the Division of Expert
Labor-, The University of Chicago Press, Chicago and London.
Aoki, M., and H. Patrick (1994) The Japanese Main Bank System - Its Relevance forDeveloping and Transforming Economies -, Oxford University Press, London.
Berger, A. N., and L. K. Black (2010) “Bank Size, Lending Technologies, and Small
Business Finance,” Journal of Banking & Finance, Vol.35, No.3, pp.724-735.
, A. M. Cowan, and W. S. Frame (2011) “The Surprising Use of Credit
Scoring in Small Business Lending by Community Banks and the Attendant
Effects on Credit Availability, Risk, and Profitability,” Journal of Financial
Services Research, Vol.39, No.1, pp.1-17.
, N. H. Miller, M. A. Petersen, R. G. Rajand, and J. C. Stein (2005)“Does Function Follow Organizational Form? Evidence from the Lending
Practices of Large and Small Banks,” Journal of Financial Economic, Vol.76,
pp.237–269.
, and G. F. Udell (2002) "Small Business Credit Availability and
Relationship Lending: The Importance of Bank Organizational Structure," The
Economic Journal, Vol.112, No.477, pp.F32-F53.
, and G. F. Udell (2006) “A more Complete Conceptual Framework for
SME Finance,”Journal of Banking & Finance,Vol.30, NO.11, pp.2945-2966.
Berger, G. P., F. Li, and M. H. F. Wong (2005) “The Impact of Sarbanes-Oxley on
Cross-listed Companies,” Working Paper, University of Chicago Graduate Schoolof Business.
Bjurstrom, E. (2011, forthcoming) “Minding the Contexts of Mindfulness in Quality
Management, ” International Journal of Quality & Reliability Management.
Choe, C. (2007) “The Political Economy of SMA Financing and Japan's Regional Bank
Problem,” Pacific-Basic Finance Journal, Vol.15, No.4, pp.353-367.
Degryse, H., M. Kim, and S. Ongena (2009) Microeconometrics of Banking: Methods,
Applications, and Results, Oxford University Press, London.
Dumay, J. C.(2009) “Intellectual Capital Measurement: A Critical Approach,”Journal ofIntellectual Capital, Vol. 10, No. 2, pp.190-210.
14

, (2009) “Reflective Discourse about Intellectual Capital: Research andPractice,” Journal of Intellectual Capital, Vol.10, No.4, pp.489-503.
Edvinsson, L., and M. S. Malone (1997) Intellectual Capital - Realizing yourCompany’s True Value by Finding Its Hidden Roots, Harper Business, New York.
Financial Services Agency (FSA), “Action Program for Strengthening RelationshipBankingFunction”(announcedinMarch,

2003),

availableat:

http://www.fsa.go.jp/news/newse/e20020821-1.html
Foucault, M. (1980) Power/Knowledge: Selected Interviews and Other Writings 19721977, Gordon,C. (Ed.) Harvester Press, Brighton.
Freidson, E. (1986) Professional Powers - A Study of the Institutionalization of Formal
Knowledge, The University of Chicago Press, Chicago and London.
Freixas, X., and J. C. Rochet (2008) Microeconomics of Banking: Second Edition, The
MIT Press, London.
Giddens, A. (2004) Sociolory 4th edition, Polity Press, London.
Hamel, G., and C. K. Prahalad (1994) Competing for the Future, Harvard Business
School Press, Boston.
Hand, J. R. M., and B. Lev (2003) Intangible Assets.Values, Measures, and Risks,
Oxford University Press, London.
Holland, J., J. Henningsson, U. Johanson, C. Koga, and S. Sakakibara (2008)“Japanese Financial Institutions and their Use of Company Intangibles
Information in their Corporate Investment Decisions,”Work in progress.
, and U. Johanson (2003) “Value Relevant Information on Corporate
Intangibles - Creation, Use and Barriers in Capital Markets - between a Rock and aHard Place,” Journal of Intellectual Capital, Vol.4, No.4, pp.465-486.
Johanson, U., C. Koga, R. Almqvist, and M. Skoog (2009) “Breaking Taboos,” The
Journal of Intellecyual Capital, Vol.10, No.4, pp.520-538.
, M. Martensson, and M. Skoog (2001), “Mobilising Change by Means of
the Management Control of Intangibles,” Accounting, Organisation and Society,Vol.26, Nos.7- 8, pp.715-33.
, C. Koga, M. Skoog and J. Henningsson (2006) “The Japanese
Government's Intellectual Capital Reporting Guidelines - What are the Challenges
for Firms and Capital Market Actors?,” Journal of Intellectual Capital, Vol.7, No.
15

4, pp.474-491.
Knorr, D. K., R. Krohn, and R. Whitley (1980) The Social Process of Scientific
Investigation, D. Reidel Publishing Company, Boston.
Markovic,M. (1974) From Affluence to Praxis, Michigan University Press, Ann Arbor,MI.
MEasuRing Intangibles To Understand and improve innovation Management(MERITUM) (2002) Guidelines for Managing and Reporting on Intangibles,
Meritum.
Ministry of Economy, Trade and Industry (METI) (2002) Intellectual Property Policy
Outline, METI.
Ministry of Economy, Trade and Industry (METI) (2005) Guidelines for Disclosure ofIntellectual Assets Based Management, METI.
Mouritsen, J., H. T. Larsen, and P. N. D. Bukh (2001) “Intellectual Capital and the‘Capable Firm’: Narrating, Visualising and Numbering for Managing Knowledge,”
Accounting, Organizations and Society, Vol.26, pp.735-762.
, (2006) “Problematising Intellectual Capital Research: Ostensive Versus
Performative IC,” Accounting, Auditing & Accountability Journal, Vol.19, No.6,
pp.820-841.
, H. T. Larsen, and P. N. Bukh (2005) “Dealing with the Knowledge
Economy: Intellectual Capital Versus Balanced Scorecard,”Journal of Intellectual
Capital, Vol.6, No.1, pp.8-27.
, P. N. Bukh, H. T. Larsen, and M. R. Johansen (2002) “Developing andManaging Knowledge through Intellectual Capital Statements,” Journal ofIntellectual Capital, Vol.3, No.1, pp.10-29.
, H. T. Larsen, P. N. Bukh, and M. R. Johansen (2001) “Reading anIntellectual

CapitalStatement:

Describing

and

Prescribing

Knowledge

Management Strategies,” Journal of Intellectual Capital, Vol.2, No.44, pp.359-383.
Murphy, A. N., R. Rorty, and J. P. Murphy (1990) Pragmatism: From Pierce toDavidson, Westview Press, Boulder.
Nielsen, C., R. Roslender, and P. N. Bukh (2008) “Intellectual Capital Reporting: Can a
Strategy Perspective Solve Accounting Problems?,” in Knowledge Ecology in
Global Business, edited by M. D. Lytras, The American College of Greece, Greece.
16

Nonaka, I., and H. Takeuchi (1994) The Knowledge-Creating Company, Oxford
University Press, London.
Powell, W. W., and P. J. Dimaggio,eds. (1991) The New Institutionalism in
Organizational Analysis,The University of Chicago Press, Chicago and London.
Ritter, L. S. and W. L. Silber, G. F. Udell (1974) Principles of Money, Banking, andFinancial Markets, Prentice Hall, New Jersey.
Roos, J. G., N. C. Dragonetti, and L. E. Dvinsson (1997) Intellectual Capital,
Macmillan Business, London.
Rorty, R. (1991) Objectivity, Relativism, and Truth, Cambridge University Press,
London.
Small and Medium Enterprises and Regional Innovation (SMRJ) (2007) The Manualfor SME Intellectual Capital Reporting, SMRJ.
Stehr, N. (1994) Knowledge Societies, SAGE Publications.
Sumita, T. (2008) “Intellectual Assets based Management for Innovation Lessons from
Experiences in Japan,” Journal of Intellectual Capital, Vol.9, No.2, pp.206-227.
Thakor, A. V., and A. W. A, Boot (2008) Handbook of Financial Intermediation andBanking, Elsevier Science, North-Holland.
Torre, A. De la, M. S. M. Peria, and S. L. Schmukler (2010) “Bank Involvement with
SMEs: Beyond Relationship Lending,” Journal of Banking & Finance, Vol.34,
pp.2280–2293.
Uchida, H., G. F. Udell, and W. Watanabe (2008) “Bank Size and Lending Relationships
in Japan,” Jounal of the Japanese and International Economies, Vol. 22, pp.242267.
Weick, K.E. and Putnam, T. (2006) “Organizing for Mindfulness: Eastern Wisdom andWestern Knowledge,”Journal of Management Inquiry, Vol.15, No.3, pp.275-287.
Woolgar, S. (1988) Knowledge and Reflexivity-New Frontiers in the Sociology ofKnowledge, SAGE Publications.
Yosano, T., and C. Koga (2008) “Influence of Intellectual Capital Information on Credit
Risk Rating Process/Criterion and Credit Conditions - Survey Analysis of Japanese
Financial Institutions,” Proceedings of 4th Work Shop on Visualising, Measuringand Managing Intangibles and Intellectual Capital.
, and T. Nakaoka (2011a) “Soft Information Management Effects on Lending
17

Credit Terms in Japan,” Discussion paper, No.2011-41, Kobe University.
, and T. Nakaoka (2011b) “The Roles of Relationship Lending and Utilizationof Soft Information on Bank Performance in Competitive Local Markets.”
Discussion paper, No.2011-41, Kobe University.

18

Table 1

Corporate Strategy
2.84

Mean Value

Standard Deviation

Whether the firms make the most of
their uniqueness and specialties intechnology when outlining their businessplan.

3.16710.7416

Whether the firms utilize their intellectual
properties strategically.

2.55940.7254

Whether a complete switch of
products/services is needed with the
adoption of technologies.

2.86960.7415

Clarify and improve corporate revenue
by utilizing technologies and intellectual
property.

2.58600.6950Management
technology.

3.25290.7578Cronbach Alpha = 0.9007
2.77780.7486

Brand power or profit margin, based on
the high level of technology.

2.8596

0.7834Training program for
department employees.

2.38010.6947

Technology conformity to the market
demands.

2.9912

0.7914The use of the quality management
system (ISO, etc).

2.93290.75212.28740.7232The construction of security systems toprevent technology leaks (The security
management, etc).

2.13130.7115

Actual results of the public grant and/orawards.

2.88050.7800

Incentive system for an invention(salary,
bonus,
andpersonnel
evaluation, etc).

2.18480.7619comprehensionofCompletion of R&D equipment.

Systematized,

and/ortechnology

visualized

Organization Stracture technologicalknow-how(The
2.56
construction of a database, the spreadof an employee training manual, etc).

Cronbach Alpha = 0.8776

Human Resource
2.52

The expertise and experience level ofsenior workers and the rank and file in
the engineering and/or strategic planningdepartment.

2.6531

0.7831The explicit and implicit knowledge,
morale, and motivation of senior andjunior workers in the engineering and/or
planning department.

2.47970.80792.5652
Cronbach Alpha =0.9751

0.7209Qualified employees.

19

Table 1 (continue)
Undefended technological know-how(business secret).

2.40520.8177The superiority over other same
products and/or technological areas.

3.05540.8549The level of barriers for new entry.

2.62100.8106

The product life cycle (The period
which the product produces therevenues).

2.61810.7854

Innovation of core technologies.

2.65500.87202.6696

0.84182.68620.8633The possibility of expanding theapplication of the core technology andbusiness model.

2.52050.8343Time frame when the product/service(which is based on the technologies)
will find its market.

2.53100.8148The possibility of product/service
commercialization which is based on thecore technology and business model.

2.41640.8382

Whether the core technology is easily
copied or imitated.

2.36950.8251

Whether the core technology is heavily
dependent on the specific qualified
expertise.

2.37240.8184

The relationship between the coretechnology
andsurrounding
technologies.

2.50290.8001

The number of patents.

2.76680.8260The number of annual applicants and/or
legislative patents.

2.32650.7636

The actual results of licensees.

2.33430.7608The economic value of the patents.

2.32750.7529The patent portfolio for the product.

2.15590.7179Whether the patent is core or close tocore.

2.09090.7158Whether the core patent needs
application for the surrounding patents.

2.04970.7022

Whether the patent is easily copied or
imitated.

2.11990.7145

Whether the patent is heavily dependent
on the specific qualifying expertise.

2.16670.7133The economic value of the unused
patents.

2.01750.7344The pricetechnologies.

Technilogies
2.40

and

Intellectual Propeties

superiority

The functional
technologies.

ofsuperiority

core
of theCronbach Alpha = 0.9751

20

Table 1 (continue)

Networks
3.12

The relationship with retailers (retailsellers and wholesalers).

3.62500.8128Whether the firm has already captured
the target market.

3.75940.8195

Small customer base (Whether the firm
relies exclusively on a small number
customers).

3.35360.8227

Whether the relationship with existingcustomers is regular or irregular.

3.39420.8325

Coordination with the developers andengineering firms.

2.64810.7855

Coordination with the product/service
design firms.

2.64810.7742

Coordination with the manufacturingfirms.

2.61580.7085

The relationship with suppliers.

2.72810.7990Collaboration regarding research within
the firms.

2.78430.7610Collaboration regarding distribution
within the firms.

2.73100.7644

Collaboration regarding research with
universities and public (government)
research institutes.

3.05800.7717The support system of specialists, suchas lawyers, patent agencies, andconsultant engineers.

3.83530.9035

The relationship with government andmunicipal offices.

2.85130.9450

The relationship with other lenders.

3.66760.8761

The relationship with equity holders.

3.69280.8481

Cronbach Alpha = 0.9452

21

Figure 1

Corporate
Strategy

Corr=Correlation

Corr=0.6175
Corr=0.8123

Corr=0.5103

Corr=0.6199

Corr=0.6261

Human
Resources

Organization
Structure

Corr=0.6674

Technologies

Corr=0.8022
Corr=0.6655

Networks : Boundary Organizations

22