asset management framewrok

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Intangible asset managementframework: an empirical

evidenceChaichan Chareonsuk and Chuvej Chansa-ngavej

School of Management, Shinawatra University (SIU International),Bangkok, Thailand

Abstract

Purpose The purpose of this paper is to explore the interrelationships of intangible assets tobusiness performance. The paper reports an empirical evidence for the impact of three elements ofintangible assets: learning and growth, internal business process, and external structure on the business

performance of the firm. The linkages between intangible asset elements and business performance areinvestigated in companies of various business sizes, business sectors and establishment ages.

Design/methodology/approach The proposed model was adapted from the balanced scorecardstrategy map. The primary data for analyzing and investigating the interrelationships betweenintangible assets and business performance were gathered by subjective opinion survey questionnaire.In all, 3,084 questionnaires were distributed to the top management. The numbers of qualifiedresponses were 304 and the data were analyzed using the structural equation modeling technique.

Findings The commonly assumed causal relationships are confirmed, i.e. the element of learningand growth has influence on internal business process, the element of internal process has effect onexternal structure, and the element of external structure, in turn, has effect on business performance.

Originality/value Following the research findings, top management in companies of different sizes,business sectors, and establishment ages should understand the nature of interrelationships andrecognize the importance of intangible assets. These findings will enable top management to realize the

impact of intangible asset elements on business performance so that long-term strategies for effectiveintangible asset management may be emphasized for sustainable competitive advantage of the firm.

KeywordsThailand, Intangible assets, Balanced scorecard, Business performance,Management strategy

Paper typeResearch paper

IntroductionTraditionally, profit and loss figures in the balance sheet and annual financial reportsare used as the main financial performance indicators for the management actionspreviously taken monitoring and crafting short-term strategies. Although theinvestment of tangible assets such as equipment, machinery, building, etc. is alsorecorded in the balance sheet, this simple accounting record is no longer sufficient in theknowledge-based economy. This is because such a bookkeeping account provides nolinkage with long-term strategies to compete with global competitors and survive indynamic economic. Prior to the knowledge era, business lived in the world of tangibles,which worked well with the traditional accounting practices. However, things aredifferent in todays world of intangibles. The focus on tangible assets in the industrialage has shifted to intangible assets in the knowledge age. Toffler and Toffler (1990)proposed knowledge as the key success factor in the present competition. With therealization of this paradigm shift, issues concerning intangible assets are now more

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0263-5577.htm

IMDS110,7

1094

Received 6 January 2010Revised 2 March 2010Accepted 16 June 2010

Industrial Management & Data

Systems

Vol. 110 No. 7, 2010

pp. 1094-1112

q Emerald Group Publishing Limited

0263-5577

DOI 10.1108/02635571011069121


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widely researched and practiced. Intangible assets are of increasing importance forthe corporate value-creation processes of all kinds of organizations. To be sustainable,companies need to understand and able to manage intangible factors, includingorganizational learning and growth, internal process, and external structure.

This study follows up on the intangible asset management framework proposed byChareonsuk and Chansa-ngavej (2008), in which the balanced scorecard strategy map(Kaplan and Norton, 2004) is chosen as a model to depict how strategy links intangibleassets to value-creating processes. The reasons for choosing balanced scorecard as thestage to build the intangible asset management framework are as follows: first, balancedscorecard is a practical approach to measure the intangible assets that has been widelyused in a variety of organizations over the past two decades. Second, through thestrategy map concept, balanced scorecard provides the linkage relationship betweenintangible assets and business performance including the interrelationship betweenintangible assets elements:

. learning and growth affect internal process;

.

internal process affects external structure; and. external structure affects business performance.

There are two key areas of expected outcomes of the study. First, the impact ofintangible assets on business performance is expected to be empirically established. Inparticular, the interrelationships between learning and growth, internal process, andexternal structure would be identified and analyzed. This is so that the detailsunderlying the relationships can be implemented in practice.

Second, it is expected that the effects of business size, business sector, andestablishment age on the causal links between intangible assets and businessperformance would be established. As there are various types of firms business (serviceand non-service), sizes of business (large and small and medium enterprise SME),

establishment age in the industry, this study would provide the pattern ofinterrelationships between intangible assets and business performance in eachbusiness characteristic.

Given the expected outcomes, the expected academic contributions of the presentstudy would be to encourage similar studies to establish the causal links betweenintangible assets and business performance in other types of economies. The studywould also provide the foundation for the field of intangible asset management.

For business practitioners, top management will benefit from the understanding ofinterrelationship and the realization of the importance of intangible assets (learning andgrowth, internal business process, and external structure) and business performance.With the clearer understanding, proper budget allocation and intangible assetsmanagement will be more properly focused and controlled to increase sustainable

competitive advantage. The intangible assets are the strategic key to a sustainablecompetitive advantage and future economic profit.

Literature reviewThere have been a large number of studies in intangible assets during the last twodecades. Intangible assets are involved in the customers, external structure, humanresources, and internal process. The intangible assets are defined as non-financialassets without physical substance that are held for use in the production or supply

Intangible assetmanagement

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of goods or services or for rental to others, or for administrative purpose (Epsteinand Mirza, 2005). Intangible assets may be considered very much part of the balancedscorecard. Following are the review of intangible assets in balanced scorecard byKaplan and Norton (1992) and the review of intangible asset monitor by Sveiby (1997).

By adapting the categories developed by Hall (1993), Sveiby (1997), Shaikh (2004) andRoos et al. (1997) reviewed and classified the intangible assets into a framework ofinternal structure, external structure, and employee competence as shown in Table I.

There are interrelationship linkages among learning and growth, internal process,customer, and financial performance. Olve et al. (1999) reported that Halifax applied thebalanced scorecard in business and found the relationship that if we have the right staff,we will get doing the right thing and customers will be delighted. When the customersare delighted, we will in turn get more business. There are many functions involved inaligning the intangible assets with strategies. Each function has to build its ownintangible assets in order to deliver and convert them to financial performance.Chareonsuk and Chansa-ngavej (2006) found that most of the companies in the StockExchange of Thailand have functional organization structure. The intangible assets are

linked to performance.Chareonsuk and Chansa-ngavej (2008) proposed the framework for intangible

assets management in business and industrial organization. The framework refines thestrategy map concept in the balanced scorecard approach for use in intangible assetsmanagement. Intangible assets belong to different functional departments. To sustainlong-term growth, intangible assets must be carefully monitored and properlynurtured by the organization. Intangible assets are believed to depend not only on thetype of functional departments but also on the type of industries.

Balanced scorecard can comprise all activities needed to implement the strategy.Most of the previous empirical studies use the subjective opinion survey questionnaireto find the interrelationship while there are only two previous studies during last fiveyears, namely Chin et al. (2005) and Cheng et al. (2008), that gather the availablefinancial data.

Bontis (1998) applied a subjective opinion survey questionnaire that used aseven-point Likert-type scale. The so-called intellectual capital questionnaire hadearlier been successfully administered in Canada. Bontis et al. (2000) re-administered thesurvey in Malaysia for 107 respondents in both service and non-service sectors toinvestigate the interrelationships between intangible assets and business performance.

Intangible assets element Intangible assets indicators

External structure Customer satisfaction

Customer loyaltyBrandsInternal process Process improvemen

InnovaInforma

Learning and growth KnowledgeKnow-howEmployee competenceEngagement

Table I.Framework of intangibleassets indicators

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They found positive relationships among intangible assets elements and businessperformance. In a follow-up study using a five-point Likert scale, Moon and Kym (2006)confirmed the interrelationship of intangible assets proposed by Bontis et al. (2000).The five-point Likert scale has also been successfully used by McKeen et al.(2006) and

Rose et al. (2008). Lin and Tang (2009) employed a questionnaire survey for theevaluation of intangible assets, which would help business in appraising corporate valueratios.

In addition to financial data, subjective opinions have often been used as primarydata in questionnaire survey in this type of studies. Chenet al.(2005) gathered the datafrom 4,254 registered companies in Taiwan and analyzed the relationship betweenintangible assets and business performance. Chen et al. (2005) use financial andaccounting quantitative data and Bontis et al. (2000) and Bontis and Serenko (2009)employ a subjective opinion survey questionnaire. Both of the latter studies reach thesame conclusion that intangible assets have a positive relationship with businessperformance.

The present study employs a qualitative survey of subjective opinions as measuredby a Likert-type scale.

Measuring intangible assets using balanced scorecard strategy map modelKaplan and Norton (2004) developed the balanced scorecard strategy map to explainthe interrelations of learning and growth, internal process, and customer perspective.It is the concept of interrelationships that separates the balanced scorecard from otherperformance management systems. The elements in the balanced scorecard should belinked together in a series of interrelationships to tell the organizations strategic story.

There have been no previous empirical studies investigating the entire framework ofthe balanced scorecard strategy map. Only part of the framework have been studied toestablish the relationships of some intangible assets elements with business

performance. For example, Bontis et al. (2000), Carmeli and Tishler (2005) and Wangand Chang (2005) study the interrelationships of learning and growth to businessperformance through customer and internal process. However, interrelationshipsamong sub-elements of intangible assets, and interrelationships between intangibleassets and business performance have not been investigated in the above-mentionedstudies.

The balanced scorecard strategy map framework, as delineated by Chareonsuk andChansa-ngavej (2008), is used in the present study to explore:

. sub-elements of each intangible assets element as shown in Table I; and

. the interrelationships among intangible assets elements.

As mentioned earlier, previous studies have no concrete structure of interrelationshipamong intangible assets elements to business performance. Thus, the main hypothesesH1-H3in this study are defined in terms of the relationships between intangible assetsand business performance as follows:

H1. Learning and growth is positively related to internal process.

H2. Internal process is positively related to external structure.

H3. External structure is positively related to business performance.


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Learning and growth is an intangible assets element. There have been several previousempirical studies, such as Huselid and Becker (1997), Hittet al.(2001) and Liu and Tsai(2007), establishing that learning and growth has direct positive relationship tobusiness performance. Research hypothesis H4 is used for testing the relationship

between learning and growth and business performance. The research hypothesistesting model is shown in Figure 1.

Three different company characteristics are tested in this study:

. Business sector. It is interesting to find out if intangible assets are differentlyemphasized in service versus non-service sectors. Among the often cited reasonsare service businesses cannot be standardized to the same extent as non-servicebusinesses, have a heavier dependence on staff, and greater potential for qualityvariability.

. Size of business. The size of business is another factor of concerns in describing theinterrelationship between intangible assets indicators and business performance.The small-business sector is very important for the overall economic growth.

Simpsonet al.(2004) and Alasadi and Abdelrahim (2008) studied the key successfactors, training, education, and knowledge of small business. Bhaskaran (2006)mentioned that the incremental innovation of SMEs can increase thecompetitiveness against large businesses. Thus, the SME sector is an importantsector in world economy.

. Establishment age. The establishment age is the last factor for testing. Huergoand Jaumandreu (2004) investigated the impact of establishment age onproductivity growth and business performance. Thus, establishment age is a

Figure 1.Structural model result:overall

t-value*: 4.61

coefficient: 0.51 Support

Support

Support

t-value*: 1.86

coefficient: 0.19

H4

Know-how

Note:*p 0.05

Knowledge Competency Engagement

Processimprovement

InnovationInformationtechnology

Learning and growth

Internal process

Customer

satisfaction

Customer

loyaltyBrand

External structure

Financial Sales Customer

Business performance

H1

H2

H3

t-value*: 17.62

coefficient: 0.89

t-value*:14.77

coefficient: 0.88

No support

Goodness-of-fit

261= 183,p 0.001

CFI = 0.982

NNFI = 0.978

RMSEA = 0.08

AOSR = 0.029

R2 = 0.45

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likely factor of concerns in establishing the relationship between intangibleassets and business performance.

Research design and methodology of studyThe purpose of the present research is to explore the interrelationships of intangibleassets to business performance. Therefore, the appropriate subjects to complete thequestionnaires should ideally be the senior manager or at least highly experiencedofficers. The 304 qualified questionnaires that passed the screening procedure allsatisfied this criterion. Clear and simple words were used to ensure that the questionscould be easily understood by top management. About 70 percent of the questions ineach intangible assets element, especially the parts dealing with learning and growthand business performance, are constructed from Bontis (1998). Some questions areadapted from Roos et al. (1997), Olve et al. (1999) and Hinshaw (2005). The questionsabout external structure are modified from Hinshaw (2005) and Bontis (1998).To complete the questionnaires, the questions related to internal process and other

intangible assets sub-elements in learning and growth and external structure areadapted from Olveet al.(1999) and Rooset al.(1997). In the questionnaire survey form,the questions are grouped into learning and growth, internal process, external structure,and business performance sections. A five-point Likert scale is used ranging from (1)low/disagree to (5) high/strongly agree. The survey instrument has been pre-testedto the top management of 20 companies in different types and sizes of business. As aresult of the pre-test, a few wordings and sequence in the questionnaire were improvedand ready for actual distribution to the industry.

The preliminary stage of data analysis involves an examination of descriptivestatistics and frequency distribution of each variable included in this study in order toinitially explore the survey findings and to inspect whether there is a variation in theresponses. The next stage is to assess the reliability and validity of the measures

through item-total correlation, Cronbachs coefficient a, and confirmatory factoranalysis. The final stage is to test the research hypotheses through structural equationmodeling. This method enables all the complex interrelationships included in theframework to be examined at the same time. The preliminary data analysis andreliability testing were performed by using SPSS 14.0. The confirmatory factor analysesand structural equation modeling were tested by using EQS 6.0 (Bentler, 1995; Byrne,2006; Pearl, 2009).

Reliability assessment resultsA preliminary step involving item-total correlation analysis and Cronbachs atestingis performed to identify items that do not belong to the domain of a specific constructor dimension. The questionnaire elements consisting of business performance and

intangible assets indicators and the number of questionnaire items in each element areshown in Table I. All of the questions in each intangible assets element are found to beconsistent and reliable. Among the items in the business performance section, only onequestion is found to be not reliable (item-total consistency equal 0.3 which is less thanthe set criterion of 0.4). This question on the rate of goods return was thereforeremoved. The final result of Cronbachs a for each questionnaire element is higher than0.70. Therefore, on the basis of these preliminary analyses, each item contributes wellto the model and is statistically reliable.


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Validity assessment resultsThere are two types of validity assessment, convergent and discriminant validity.

(a) Convergent validity results. The convergent validity is to analyze the degree towhich the scale positively correlates with other measures of the same construct. Two

convergent validity testings, convergent validity testing for intangible assets andconvergence validity testing for business performance, are conducted as shown inTables II and III. As already listed in Table I, there are four sub-elements in learningand growth, three sub-elements in internal process, three sub-elements in externalstructure. The convergent validity testing for intangible assets comprises tensub-elements that belong to the three intangible assets elements: external structure,internal process, and learning and growth. The results are shown in Table II.

As shown in Table II, the lowest standardized loading was 0.82 and lowest t-valuewas 17.03. These values are higher than the minimum acceptable levels. All tenintangible assets indicators are found to satisfy convergent validity test. The resultsshow that know-how, knowledge, competency, and engagement belong to learning andgrowth elements. Process improvement, innovation, and information technology

belong to internal process. Customer satisfaction, customer loyalty, and brand belongto external structure.

Table III shows the convergent validity result for business performance. Theconvergent validity testing for business performance consists of first-order andsecond-order factors. There are three financial performance indicators, four salesperformance indicators and seven customer performance indicators. Totally, 14indicators of business performance are used which combined into three second-orderfactors, namely financial performance, sales performance, and customer-fulfillmentperformance.

As shown in Table III, the lowest standardized loading was 0.63 and the lowestt-value was 9.43. These values are higher than the minimum acceptable levels. Thus,financial, sales, and customer-fulfillment performances are positively correlated inbusiness performance.

Intangible assets indicator Standardized loadinga

Learning and growthKnow-how 0.82b

Knowledge 0.90 (17.56)Competency 0.90 (17.33)Engagement 0.89 (17.03)

Internal processProcess improvement 0.89b

Innovation 0.87 (19.20)Information 0.91 (20.87)

External structureCustomer satisfaction 0.92

b

Customer loyalty 0.88 (20.73)Brand 0.86 (19.33)

Notes: Goodness-of-fit statistics: x232 107:4, p , 0.001; CFI 0.99; NNFI 0.98; RMSEA 0.08;AOSR 0.03;

at-values from the unstandardized solution are in parentheses; bfixed parameter

Table II.Convergent validityresults for intangibleassets

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(b) Discriminant validity results. The other validity assessment analysis is discriminantvalidity analysis. The discriminant validity analysis is to analyze the degree that each of

the construct variables, learning and growth, internal process, external structure, andbusiness performance, is different from other construct variables. The SEM results of thediscriminant validity analysis for each pair of construct variables are shown in Table IV.

As shown in Table IV, in all cases, the pairs of elements were not collinear, whichprovide the evidence of discriminant validity. It was found that for each pair of elements,the difference ofx2-values between constrainedand unconstrained models is consistentlygreater than the minimum acceptable criterion of 3.841 atp , 0.05. In addition, for eachpair of elements, the value of the correlation plus twice the standard error is found to beless than one. Thus, bothx2-difference tests and confidence intervals assessment provideevidence of discriminant validity among the study element. That is to say, each of the fourstudy elements: learning and growth, internal process, external structure, and businessperformance are found to belong to different grouping from other elements.

Hypotheses testing resultsThe results of the overall analysis for all the 304 qualified respondents regardless ofbusiness sector, establishment age, and size of business. The hypotheses testing isconducted based on the diagram as drawn by using EQS program for SEM analysis.

Figure 1 shows the goodness-of-fit statistics on the right-hand side. The hypothesestesting results for the four hypotheses are shown along with thet-values and the factorloading coefficients.

Standardized loadinga

First-order factorFinancial performance

Profitability 0.89b

Profit growth 0.91 (20.05)Profit margin 0.90 (19.72)

Sales performanceSales volume 0.81

b

Sales growth 0.87 (15.62)Market share 0.89 (16.45)Market share growth 0.91 (16.68)

Customer performanceCustomer satisfaction 0.78

b

Customer retention 0.71 (10.96)New customer 0.63 (9.43)New product 0.66 (9.95)

Brand recognition 0.69 (10.54)Customer service 0.69 (10.53)Overall response 0.78 (12.09)

Second-order factorFinancial performance 0.77b

Sales performance 0.91 (8.89)Customer performance 0.77 (8.57)

Notes:Goodness-of-fit statistics; x274 266:97,p , 0.001; CFI 0.96; NNFI 0.95; RMSEA 0.09;AOSR 0.05;

at-values from the unstandardized solution are in parentheses; bfixed parameter

Table III.Convergent validityresults for business

performance


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All the goodness-of-fit statistic results are found to meet the generally accepted criteria,comparative fit index (CFI) 0.982 exceeds 0.9, non-normed fit index (NNFI) 0.978exceeds 0.9. The root mean square error of approximation (RMSEA) is equal to 0.08and absolute off-diagonal standardized residual (AOSR) is found to be small. With thelarge sample size of 304 respondents, the ratio of x2 and degree of freedom is 3.0,which is again acceptable according to the generally accepted criterion. Thus, thegoodness-of-fit statistics confirmed the proposed hypotheses model. It is found that allthe hypotheses are supported at p # 0.05 with the exception of the relationshipbetween learning and growth and business performance (H4). It is noted that the H4isrejected because of the low t-value 1.86.

This structural equation model solution produced an R

2

-value of 0.45 whichsuggests that the propose model structure explains 45 percent of the variance inbusiness performance. This compares favorably with such past studies as Cabrita andVaz (2006) (0.44), Wang and Chang (2005) (0.43), and Bontis et al. (2000) (0.37).

Based on the 304 respondents, all hypotheses are supported except the relationshipbetween learning and growth and business performance (H4). It is also found thatthe hypotheses testing results of both non-service and service business sectors confirmthe pattern established in the case of overall business.

Grouping the responses by business size, SME and large, the following results arefound:

. All the four hypotheses in the case of SME are supported, including therelationship between learning and growth and business performance. An

explanation for this result could be that SME organizations tend to be lean,efficient, and have relatively simple command line.

. The hypotheses testing results of large businesses also confirm the pattern ofhypotheses testing results for the case of overall businesses.

When the responses are analyzed by establishment age, it is found that there isa positive relationship between learning and growth and business performance forthe case of young businesses. However, there is no relationship between external

ElementLearning and

growthInternalprocess

Externalstructure

Businessperformance

Learning and growth

Internal process 0.88 (0.02)a

141.51b

63.21c

External structure 0.79 (0.03) 0.88 (0.02)105.71 85.1254.72 30.23

Businessperformance

0.55 (0.06) 0.55 (0.05) 0.67 (0.05)

198.99 139.26 158.8671.90 37.43 44.64

Notes:Entries below the diagonal show; acoefficients, reflecting correlations among constructs andstandard error in the parentheses;

bdifference in x

2from fixed ( 1.00) model and free (estimated)

model; and cx

2for free model

Table IV.Results of thediscriminant validityanalysis

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structure and business performance. The reason might be because there is a simplecommand line in such organizations and they are still in process of establishing thecustomer relations and developing customer loyalty as well as brand awareness.Again, the hypotheses testing results of the remaining cases, middle-age and old

businesses, also confirm the pattern of hypotheses test result for the overall businesses.

Testing with relevant intangible assets modelsThe previous section explores the full interrelationship of three intangible assetselements and business performance by using the concept of balanced scorecardstrategy map. In addition, there have been past studies models that explored variousaspects of the relationships between intangible assets and business performance, asshown in Figure 2.

To clarify the models of other interrelationships that may exist between intangibleassets and business performance, as shown in the above past studies, four speculativemodels of interrelationships are therefore tested for possible fits as shown below.

Model I: conceptual framework of the direct impact of intangible assets to businessperformance. The first model assumes that learning and growth, internal process, andexternal are positively related to business performance as shown in Figure 3.

From the goodness-of-fit statistic result, the model does not properly fit because theratio ofx2and degree of freedom is higher than 3, which is more than the general accepted

Figure 2.Past studies models

Human

capital

Customer

capital

Structural

capital

Business

performance

Bontis et al.

(2000)

Method PLC

H1 Support

H2 Support

H3 No support (service)

Support (non-service)

H4 Support

Moon & Kim

(2006)

AMOS

Support

Support

Support

N/A

H1

H2

H3

H4

Human

capital

Process

capital

Innovation

capital

Business

performance

Support

Customer

capital

Support

SupportNo

support

Support

Wang & Chang (2005)


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criteria. Thus, this is not a good model of intangible assets and business performance.Moreover, the R2 is rather low compared to the overall hypotheses testing model in

Figure 1. Thus, this model is rejected and no further discussion will be made.

Model II: conceptual framework of direct and indirect impact of learning and growth,

internal process and external structure to business performance. Not only is there the

possibility of sequential impact learning and growth, internal process, external structure

to business performance, but also there is the possibility direct impact of learning and

growth, internal process to business performance as shown in Figure 4.

From Figure 4, it is found that all the goodness-of-fit statistics satisfy the generally

accepted criteria. It is found that:

Figure 3.Structural model resultsof the direct impact ofintangible assets tobusiness performance

Internal process External structure


Learning and growth

t-value: 0.66*

coefficient: 0.042

t-value: 6.65*

coefficient: 0.51

t-value: 3.68*

coefficient: 0.25 Support

SupportNo support

Goodness-of-fit

258= 649,p 0.001

CFI = 0.92

NNFI = .089

RMSEA = 0.18

AOSR = 0.38

R2= 0.32

Note:*p 0.05

Figure 4.Structural model resultsof direct and indirectimpact of learning andgrowth, internal processand external structure tobusiness performance

Internal process

External structure


Learning and growth

t-value: >1.96*

coefficient: 0.60

t-value:16.28*

coefficient: 0.88

t-value: 16.59*

coefficient: 0.88

Support

Support

Support

No

support

t-value: 0.97*

coefficient: 0.18

No

support

Goodness-of-fit

256= 195,p 0.001

CFI = 0.98

NNFI = 0.97

RMSEA = 0.08

AOSR = 0.039

R2= 0.44

t-value: 1.78*

coefficient: 0.27

Note:*p 0.05

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. there is no direct relationship between learning and growth and businessperformance; and

. no direct relationship between internal process and business performance.

The result confirms the interrelationships between intangible assets elements andbusiness performance in Figure 1.

Model III: conceptual framework of the indirect impact of learning and growth tobusiness performance. There is a possibility to have indirect interrelationship oflearning and growth to business performance through internal process and externalstructure as shown in Figure 5.

It is found that the model does not satisfy the goodness-of-fit statistic criteria. Theratio of x2 and degree of freedom is relatively higher compared to the generallyaccepted criteria and RMSEA is 0.11 is out of range of the accepted criterion of0.05-0.08. This model is rejected and no further discussion will be made.

Model IV: conceptual framework of indirect impact of learning and growth andexternal structure to business performance. This model is very close to the model thathas been studied by Bontiset al.(2000). There is indirect impact of learning and growthto business performance as shown in Figure 6.

Figure 6 shows the hypotheses testing result. The goodness-of-fit statistics arefound to satisfy the generally accepted criteria. The R2 is 0.35, lower than theR2 of theresearch model in Figure 1, which is 0.45. This means that the model in Figure 1 isbetter and has a stronger explanatory power than the model in Figure 6.

Model V: conceptual framework of indirect impact of learning and growth andinterrelation of internal process and external structure. This model is close to the modelstudied by Cabrita and Vaz (2006). The model is shown in Figure 7.

It is found that all the goodness-of-fit statistics satisfy the generally accepted criteria.There is no direct relation of learning and growth to external structure in this empirical

Figure 5.Structural model result of

the indirect impact oflearning and growth to

business performance



Learning and growth

t-value: 1.22*

coefficient: 0.12

t-value: 5.27*

coefficient: 0.56

t-value: >1.96*coefficient: 0.90

SupportNo support

Support Support

Goodness-of-fit

257= 248,p 0.001

CFI = 0.97

NNFI = 0.96

RMSEA = 0.11

AOSR = 0.05

R2= 0.42

t-value: 12.88*coefficient: 0.82

Note:*p 0.05


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study, while there was positive relationship in the study of Cabrita and Vaz (2006).The model in Figure 7 also confirms the interrelationship of intangible assets elementsand business performance.

Empirical results summaryTests with the relevant intangible assets models find that there are no models thatperform as well in terms of goodness-of-fit statistics as the main research hypothesismodel in Figure 1. In particular, Models I and III are rejected because they do not satisfy

Figure 6.Structural model result ofindirect impact of learningand growth to businessperformance with impact

of external structure tointernal process



Learning and growth

t-value: 8.75*

coefficient: 0.59

t-value: 4.15*

coefficient: 0.5

t-value: >1.96*

coefficient: 0.48

Support

Support

Support Support

Goodness-of-fit

257= 190,p 0.001

CFI = 0.98

NNFI = 0.97

RMSEA = 0.088

AOSR = 0.056

R2= 0.35

t-value: >1.96*

coefficient: 0.79

Note:*p 0.05

Figure 7.Structural model ofindirect impact of learningand growth andinterrelationship ofinternal process andexternal structure withdirect impact of externalstructure, and internalprocess to businessperformance



Learning and growth

t-value: 0.7*

coefficient: 0.1

t-value: 6.84*

coefficient: 0.71

t-value: 16.2*

coefficient: 0.87

Support

No Support

SupportNo

support

t-value: 3.5*

coefficient: 0.54

Support

Goodness-of-fit

256= 170,p 0.001

CFI = 0.98

NNFI = 0.98

RMSEA = 0.08

AOSR = 0.10

R2= 0.40

t-value: 1.86*

coefficient: 0.18

Note:*p 0.05

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goodness-of-fit criteria. Models II and V confirm the same result as the proposed modelin Figure 1, while Model IV has a weaker explanatory power than the proposed model.

Thus, both the main research hypothesis model and the tests with the relevantintangible assets models confirm the interrelationship between learning and growth,

internal process, external structure and business performance, including theinterrelationships between the three intangible assets elements and business performance.

Conclusion and recommendationsIntangible assets are ubiquitous and could be found in all functions and at all levels oforganizations. Not only that, they play important roles in any value chains in business.There are three fundamental intangible assets elements, namely learning and growth,internal process, and external structure. These intangible assets elements were initiallyintroduced by Kaplan and Norton (1992) and Sveiby (1997). Kaplan and Norton (1992)described the concept of intangible assets in the context of balanced scorecard (1992)and strategic mapping (2004). This empirical study confirms the interrelationshipsbetween learning and growth, internal process, external structure and business

performance, including the interrelationships between the intangible assets elementsand business performance.

All the intangible assets indicators in each intangible assets element, learning andgrowth, internal process, and external structure, are found to be positively correlatedwith other intangible assets indicators in the same element. Moreover, each of the fourstudy elements: learning and growth, internal process, external structure, and businessperformance are found to belong to different grouping from other elements.

Based on the 304 respondents, all hypotheses are supported except the relationshipbetween learning and growth and business performance (H4). Both, the main researchhypothesis model and the tests with relevant intangible assets models confirm theinterrelationship between learning and growth, internal process, external structure andbusiness performance, as well as the interrelationships between the intangible assets

elements:. learning and growth has a positive relationship with internal process;. internal process has a positive relationship with external structure; and

. external structure has a positive relationship with business performance.

The investment in human capital, i.e. knowledge, know-how, employee competenceand engagement, creates the continuous learning, and growth in the organization. Whenemployees have more experience and knowledge, they can create efficient internalprocesses, (process improvement, innovation, and information technology). Thisconfirms the finding of Wang and Chang (2005) study. The internal process serves thecustomers in the end and leads to customer satisfaction. The profit and revenue are

naturally the final outcomes of this causal chain.

Recommendations and managerial implicationsSeveral interesting observations may be made based on the grouping of the responsesby business size, establishment age, and business sector, as follows:

Grouping the responses by business size SME and large:

. large businesses, all the hypothesis test results for the large businesses are foundto follow the same patterns of overall analysis in Figure 8;


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. SME businesses, all the four hypotheses of SME are supported in this caseincludingH4, the interrelationships between learning and growth and businessperformance.

The support found in the case of SME businesses could perhaps be explained in termsof more direct chain of command in SME businesses relative to the more complex

organization in large businesses. That is to say, when an SME business improves itshuman resources by way of training, employee engagement, knowledge managementand so on, the effects are more directly felt in its business performance. This resultconfirms the work of Alasadi and Abdelrahim (2008). They found that learning andgrowth is positively related to successful business performance. SME organizationstend to be lean, efficient, and have relatively simple command line.

Grouping the responses by establishment age:

. old businesses (more than ten years), all the hypothesis test results are found tofollow the same pattern of overall analysis in Figure 8; and

. the case of young businesses (less than ten years), there is no relationshipbetween external structure and business performance.

The reason might be because they are still in the process of establishing the customerrelations and developing customer loyalty as well as brand awareness. Also,interrelationships are found between learning and growth and business performance.The reason might be due to the simple line of command in such an organization.

Grouping the responses by the type of business sectors, hypotheses testing resultsof service sector follow the same pattern as non-service business sector and overallbusiness sectors. Therefore, it could be concluded that no relationship was found

Figure 8.Final structural model ofthis research Know-how Knowledge Competency Engagement

InnovationInformation

technology

Learning and growth

Internal process

Customer

satisfaction

Customer

loyaltyBrands

External structure

Financial Sales Customer


Process

improvement

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between learning and growth and business performance no matter what type ofbusiness sectors.

Intangible assets are the strategic key for long-term profit prospect and sustainablecompetitive advantage. Financial profit alone could not guarantee the long-term

survival of companies. To be sustainable, companies need to understand and be ableto manage intangible assets, including organizational learning and growth, internalprocess and external structure. Management that aspires for sustainable businessgrowth and industrial leadership in the twenty-first century has to focus on superiormanagement skills and knowledge under limited resources. Management has to ensurethat intangible assets are identified, monitored, built, and leveraged. Some guidelinesfor improving business performance in each intangible asset are discussed below:

. Learning and growth is the capacity of employees to act in a wide variety ofsituations. Human resource is the most valuable asset of the company in the highlycompetitive market. It is the one asset that creates uniqueness to the company anddifferentiates the company from the competitors. Knowledge is one element in the

learning and growth perspective. Hiring the best candidate for the company,sharing knowledge, and on-the-job training are guidelines for improvingemployee knowledge. To constantly upgrading skills with effective training roadmap is necessary to enlarge the employee capacity. When the business isexpanded, there might be the communication gap between management andemployees. Frequent communication and constant consultation with all levels arethe factors to increase teamwork and move the company forward in the samedirection.

. Internal process includes process improvement, innovation, and informationtechnology. The global market is widely open and highly competitive.The products often have to comply with the existing international standards

otherwise the products may not be qualified to enter international market. Gainingcustomers input on new product ideas is the foundation for creating new productsand supporting customers needs. Information technology is necessary to supportthe information flows of internal and external communication. When the companyimproves the internal process, the outcomes are directly reaped by customers.

. External structure encompasses brands, customer satisfaction, and customerloyalty. Customers are invaluable and the key success assets in business.Continuously in touch with the customers to get to know and fulfill their demandsand receive their feedbacks helps improve the products and internal process.When the company understands the market and customers, the company knowshow to keep the customers for sustainable long-term relationship. Brandconstitutes valuable intangible assets. Frequent and sustained brand building

and communication with customers to be familiar with their perception andexpectation are strategic guidelines.

When companies have crystallized long-term strategies as set out in the abovediscussion, they would have good management and monitoring tools in hand. Creatinga culture of measurement-driven intangible assets and business performance helps topmanagement understand how to drive the greater return from intangible assetsinvestment.


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Limitations and directions for future researchesThe present empirical study has several limitations that should be mentioned for thesake of future researchers. First, this research was conducted in the business firms inThailand. Similar researches should be conducted in relatively more developed

economies, e.g. Japan, Hong Kong, Singapore, etc. which have similar culture and valueas those of Thailand. Benchmarking of results among different countries andeconomies is highly recommended for future researches. In addition, similar researchshould be conducted in other developing countries, e.g. the Philippines, Vietnam, etc.for result comparison.

Second, the survey questionnaire in this research uses subjective rating scale (1-5) foranalyzing the interrelationships between intangible assets and business performance.The study results might be subjected to the wordings used in the questions and therespondents attitude. The financial accounting and other quantitative data might be analternative measure in establishing the interrelationship between intangible assets andbusiness performance at a specific period or timing. However, it should be noted thatdata of intangible assets are difficult to obtain in accounting or company reports.

Third, the intangible assets elements in this study are conducted in internalintangible assets elements, i.e. learning and growth, internal process and customer,within an organization. There might be other intangible assets elements which havenot been covered such as social and environmental responsibility. There are alsointangible assets elements external to the organization that could be considered,e.g. those related to government relationship and supply network. When all intangibleassets elements are properly identified and managed, the company will then achievelong-term sustainable growth under dynamic environment.

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About the authorsChaichan Chareonsuk received his PhD degree from School of Management, ShinawatraUniversity (SIU International) in 2009. He is an executive at Srithai Superware PCL.

Chuvej Chansa-ngavej is the Provost and PhD (Management) Program Director, SIUInternational. Prior to joining SIU in 2004, he taught at Department of Industrial Engineering,

Chulalongkorn University for 26 years. Chuvej Chansa-ngavej is the corresponding author andcan be contacted at: [email protected]

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