impact of intangible assets on corporate financial performance

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316 Copyright © 2016. Vandana Publications. All Rights Reserved.

Volume-6, Issue-1, January-February-2016

International Journal of Engineering and Management Research

Page Number: 316-333

Impact of Intangible Assets on Corporate Financial Performance

Dr. N. Nagaraja1, Vinay N2 1Associate Professor, Department of Studies in Commerce, University of Mysore, INDIA

2

Research Scholar, University of Mysore, INDIA

ABSTRACT

The purpose of this study was to determine whether a firm’s higher intangible asset value leads to a better financial performance. Specifically, this study explored relationships between intangible indicators, such as Tobin q, market-to-book ratio, and EVA, and financial indicators, such as the return on asset (ROA), return on equity (ROE), return on invested capital (ROIC) and net income. The study also determined relationships between firms’ intellectual property, such as the number of patents, trademarks, and copyrights the firms own, and their intangible indicators. Keywords--- Indicators, Industry, Market over

I. INTRODUCTION

The purpose of this study was to determine whether a firm’s higher intangible asset value leads to a better financial performance. Specifically, this study explored relationships between intangible indicators, such as Tobin q, market-to-book ratio, and EVA, and financial indicators, such as the return on asset (ROA), return on equity (ROE), return on invested capital (ROIC) and net income. The study also determined relationships between firms’ intellectual property, such as the number of patents, trademarks, and copyrights the firms own, and their intangible indicators.

The results presented in chapter 4 successfully answered the research questions and confirmed positive relationships between firms’ financial performance indicators and their intangible indicators. A firm with more

patents, trademarks, and copyrights has higher size-based intangible asset values, such as EVA, market-over-book, and intangible values, and in turn has a better financial performance as measured in net income.

The study was also designed to test whether relationships between firms’ intangible assets and their financial performance are different for different industries. The data collection covered three industries: brick-and-mortar, high-tech and service. The discussions here focused on comparisons of the study results across three industries and implications from such comparisons. This section first examines hypothesis tests, followed by Pearson’s correlations, and ends with linear regression results.

II. HYPOTHESIS TEST

Table 17 summarized the percentage of rejection of hypothesis tests. Although twenty-one hypotheses were tested on the data of all three industries, the test results were different. Only one null hypothesis was rejected for the brick-and-mortar industry. On the other hand, six null hypotheses were rejected for the service industry. The brick-and-mortar industry has more established relationships of its financial performance indicators and its intangible indicators than other two industries. At overall level of three industries combined, the study rejected nineteen of twenty-one null hypotheses. Ninety percent of null hypotheses rejection rate strongly confirms that there is a relationship between firms’ financial performance and their intangibles.

Table 17. Summary of Hypotheses Test Results



Pearson’s Correlation Table 18 summarizes Pearson’s correlation

coefficients. It shows that for all three industries, correlations among the size-based variables are stronger than those among the ratio-based variables. On the other hand, there are very weak correlations between a ratio-based variable and a size-based variable. Moreover, compared to the other two intangible indicators (the intangible value and intellectual property), EVA and

market-over-book value tend to have much closer correlations with net income, indicating a very good net income predictability from EVA and market-over-book value. In other words, the reliability of using EVA or market-over-book value for predicting the firms’ net income is very high. As expected, there is a very close correlation between EVA and market-over-book value for all three industries.

Table 18. Summary of Correlation Coefficients

The reasonably consistent correlations were

observed between market-to-book ratio or Tobin q and ratio-based financial indicators such as ROE, ROIC, and ROA for all three industries. Compared to the brick-and-mortar and high-tech industries, service industry shows closer correlation between market-to-book ratio and ratio-based financial indicators, i.e., ROE, ROIC, and ROA. Unexpectedly, there was a negative correlation between intangible-to-total asset ratio and ratio-based financial performance indicators of ROE, ROIC, and ROA, which requires further studies.

The correlations between intellectual property and ratio-based intangible indicators such as market-to-book ratio, Tobin q, and intangible-to-total asset ratio presented mixed results. Correlations are all positive for the brick-and-mortar industry, but all negative for the high-tech industry. For the service industry, the correlation with

intangible-to-total-asset ratio is positive, but the correlation with market-to-book ratio or Tobin q is negative. The coefficients for all correlations between intellectual property and ratio-based intangible indicators are small, indicating a relatively poor predictability of the ratio-based intangible indicators by the intellectual property. Regression Coefficients

The slope B of a line obtained from a regression is called the regression coefficient, representing the slope of a linear relationship. It represents rate of change in a dependent variable as a function of change in an independent variable. For the same kind of linear regression equations for different industries in this study, the regression coefficient is a measure of the degree of impact of an independent variable on a dependent variable. Table 19 summarized the regression coefficients of different linear equations of the different industries.



In general, the results for all three industries

followed the same trend. There are consistently positive relationships between size-based variables. The slopes of these linear relations for all three industries are reasonably similar. Based on the magnitude of the value of slope B,

the intellectual property impacts on size-based dependent variables, such as net income, EVA, and market-over-book, appears stronger for high-tech industry than those for other two industries, indicating an importance of intellectual property to the high-tech industry.

Table 19. Summary of the Regression Coefficients (B)

No positive impacts of intangible-to-total-asset

ratio on the ratio-based financial performance indicators (ROE, ROIC, and ROA) was observed. On the other hand, a positive impact of market-to-book ratio on ROA for service industry existed. Small values of the regression coefficients for relationships between ratio-based variables and size-based variables indicate a minimal impact of firms’ intellectual property on their intangible-to-total asset ratio, market-to-book ratio, and Tobin q for all three industries under study.

III. LINEAR REGRESSION RELATIONSHIPS

This section explains discusses linear regression

relationships that link to the firms’ financial performance indicators such as net income and the returns on equity, invested capital and asset. To facilitate the discussions, structural diagrams were built to show the different levels of variables that eventually link to the firms’ key financial performance indicators.

Net income. Figures 10, 11, 12 and 13 are the structural linear relationships of netincome for brick-and-mortar, high-tech and service industries, and the overall of three industries respectively. In this structure, there are three levels of relationships, which eventually link to net income. The bottom level shows relationships between size-based intangible indicators (i.e., market-over-book and intangible value) and intellectual property. The middle level demonstrates relationships between EVA and size-based intangible indicators (i.e., market-over-book value, intellectual property, and intangible value). The top level links relationships between net income and size-based intangible indicators (i.e., intellectual property, market-over-book value, EVA, and intangible value).



Figure 10. Structural results of Brick & Mortar Industry: Net income

Structural Results – High - Tech Industry : Net Income

Figure 11. Structural results of high-tech industry: Net income

Structural Results – Service Industry: Net Income

Figure 12. Structural results of service industry: Net income

Structural Results – The Overall: Net Income



Figure 13. Structural results of the overall: Net income

Based on R-square value, links at the bottom level

of tend to be weaker than those at the middle level, with the top link being the strongest. The predictability order of firms’ net income by their intangible indicators is, EVA, market-over-book value, than intangible value and intellectual property. This conclusion is generally consistent for all three industries and the overall of three industries. Similarity in calculating EVA and net income explains a very good correlation between EVA and net income.

Of three independent variables related to EVA at the middle level in the structure, the market-over-book value has the strongest relationship with EVA, with intangible value being the next and intellectual property being the least. This conclusion is also consistent across all three industries and the overall of three industries.

Furthermore, compared to the other two industries, high-tech industry has a much larger value of R-square for relationships between intellectual property and other size-based dependent variables. The results imply that for high-tech industry, the intellectual property plays a significant role in a firm’s business performance and it can reasonably predict the size-based financial and intangible indicators, such as net income, EVA, market-over-book value, and intangible values. With importance of

intellectual properties to high-tech industry, the results of this study is understandable.

Return on equity. Figures 14, 15, 16 and 17 are the structural linear relationshipsof the return on equity (ROE) for brick-and-mortar, high-tech and service industries, and the overall of three industries respectively. In this structure, there are two levels of linear relationships that eventually link to the return on the equity. The lower level shows the relationship between ratio-based intangible indicators (market-to-book ratio, Tobin q, and intangible-to-total-asset ratio) and intellectual property. The upper level demonstrates relationships between return on equity and ratio-based intangible indicators (market-to-book ratio, Tobin q, and intangible-to-total-asset ratio).

There were no established relationships at the lower level for both high-tech and service industries because the AVONA failed to reject the null hypotheses associated with lower level relationships. For the brick-and-mortar industry, a small value of R-square at the lower level suggested very weak relationships between intangible indicators (market-to-book ratio, Tobin q, and intangible-to-total-asset ratio) and intellectual property. The implication is the intellectual property cannot be used for predicting ratio-based intangible values.



Figure 14. Structural results of brick-and-mortar industry: ROE

Figure 15. Structural Results of High-Tech Industry: ROE



Figure 16. Structural results of service industry: ROE

Figure 17. Structural results of the overall: ROE

At the upper level, reasonably good positive linear

relationships exist between ROE and market-to-book ratio or Tobin q across all three industries. However, the relationship between ROE and intangible-to-total-asset ratio was not established because of the failure in rejections of the null hypothesis on these two variables. Although relationships exist between ROE and intangible-to-total-asset ratio for both brick-and-mortar and high-tech

industry, the sign for regression coefficients (the slope B) suggests a negative relationship, that is, a higher intangible-to-total-asset ratio would lead to a lower ROE. This contrasts with the expectation and the current theory about intangible assets. Unlike other financial data, the intangible values reported in company’s financial statements were not well defined. Different companies might have different definitions of intangibles, which could



lead to inconsistency in reported data. This insinuates potential foundation for part of the reasons contributing to the observed results. A further study is definitely required to further explore this phenomena. Therefore, it is not recommended to use the intangible-to-total-asset ratio to predict the ROE.

Return on invested capital.Figures 17, 18, 19 and 20 are structural linearrelationships of return on invested capital (ROIC) for brick-and-mortar, high-tech

and service industries, and the overall of three industries respectively. The structures are very similar to those shown in Figures 16 - 19. They also have two levels of linear relationships which eventually link to the return on invested capital (ROIC). The lower level has exactly the same relationships as shown in Figures 14 - 17. Therefore, the analyses and conclusions on it are the same as those discussed in the above ROE section.

Figure 18. Structural results of brick-and-mortar industry: ROIC

Figure 19. Structural results of High-Tech Industry: ROIC



Figure 20. Structural results of service industry: ROIC

Figure 21. Structural results of the overall: ROIC

The upper level demonstrates relationships

between return on invested capital and ratio-based intangible indicators (market-to-book ratio, Tobin q, and intangible-to-total-asset ratio). Although positive linear relationships exist between ROIC and market-to-book ratio

or Tobin q across all three industries, based on the value of R-squared such relationships in general tend to be weaker as compared to their relations to ROE, especially for both brick-and-mortar and high-tech industries. Similar to the ROE results the sign of regression coefficients (the slope



B) suggests a negative relationship between ROIC and intangible-to-total-asset ratio, which requires further study to understand the reasons. Again, it is not recommended to use the intangible-to-total-asset ratio to predict ROIC.

Return on asset. Figures 22, 23, 24 and 25 show very similar structuralrelationships of the return on asset (ROA) for brick-and-mortar, high-tech and service industries, and the overall of three industries respectively. Again, the lower level has exactly the same relationships as

shown in Figures 16 - 23. The upper level shows a positive linear relationship between ROA and Tobin q consistently across all three industries and the overall of three industries combined. However, based on the hypothesis test results, there are no established relationships between ROA and market-to-book ratio for either brick-and-mortar or high-tech industry and between ROA and intangible-to-total-asset ratio for either service industry or the overall of three industries.

Figure 22. Structural results of brick-and-mortar industry: ROA

Figure 23. Structural results of high-tech industry: ROA



Figure 24. Structural results of service industry: ROA

Figure 25. Structural results of the overall: ROA

There are a total of 12 upper level relationships

for all three industries and the overall of three industries. Only eight upper level established relations of ROA to the ratio-based intangible indicators as compared to eleven established relations of ROE or ROIC. A general

implication is ROA is less closely related to the firms’ ratio-based intangible value than ROE and ROIC. Validity, Reliability and Predictability

Three most important aspects of statistical regression analyses are validity, reliability and



predictability. Validity refers to the agreement between value of a measurement and its true value. It is quantified by comparing measurements with values that are as close to the true values as possible. Poor validity degrades the precision of a single measurement, and reduces ability to characterize relationships between variables (Creswell, 2009).

This study was to explore relationships between firms’ intangibles and their financial performance. Compared to intangible properties, intangible resources are much more dynamic and present more difficulty in quantifying their true values. As shown in Figure 1, intangibles are largely classified into two categories: property and resources. However, considering the data accessibility and scope limitation, this study focused on the intellectual property part of intangible assets and their relations to the firms’ financial performance. Treating intellectual property alone as an intangible asset without considering contributions from intangible resources would adversely affect the study validity to a certain extent. Relatively low correlation coefficients associated with intellectual property as shown in Table 24 has indirectly proven this. Further studies on valuation of intangible resources, such as human resources, relational resources, and organizational systems, are required to consider the inclusion of intangible resources in the intangible valuation and improve the validity of an intangible study.

Reliability refers to reproducibility of a measurement. It implies data consistency and stability over time. Reliability is quantified simply by taking several measurements of the same subjects. Using the existing financial data does not allow a research instrument design for a direct Cronbach’s alpha type of data reliability test as the companies with different intangible assets are expected to have different financial performances. However, reliability is a form of correlation (Creswell, 2009). The

values of R-squared were used to indirectly test the reliability of the study results.

The concepts of reliability and predictability are closely related as the term “reliable” means dependable, predictable. A high reliability generally leads to high predictability. In this study, no attempt was made to distinguish predictability from reliability, with both being measured by the R-square values indirectly.

As an important measure in the regression analyses, a value of R-square is commonly used to check how well data fits the linear relationship. Figure 28 plotted values of R-square for different established regression relationships for all three industries and the overall of three industries. It shows the distribution of the R-square values for brick-and-mortar industry concentrates either at higher or lower ends. The relationships are either very strong or very weak for brick-and-mortar industry. On the other hand, high-tech and service industries have more even distribution of the R-squared values. This means that there are some relationships which are reasonably strong (with a value of the R-square between 0.25 and 0.75).

This study categorized the regression relationships into four groups: very strong with a value of the R-squared >0.75, strong with a value of the R-squared = 0.5 – 0.75, weak with a value of the R-squared = 0.25 – 0.5 and very weak with a value of the R-squared <0.25. Table 26 shows different ranges of the R-squared for three industries. Although only one of 21 null hypotheses was rejected for the brick-and-mortar industry, which allows for establishing 20 linear relationships. Of 20 linear relations, 80% of them are very weak. On the other hand, the high-tech industry had 30% of established relationships either strong or very strong, which confirm the high-tech industry has stronger relationships between its financial performance indicators and intangible values.

Table 20. Different Ranges of R-squared



As the R-square value measures the degree to which the linear models developed in this study can describe the relationship between two variables, it is used as an indicator of the reliability of a linear relationship. A point system was established to quantify comparisons, a relationship with a value of R-square >0.75 was assigned for four points; with a value of the R-squared = 0.5 – 0.75 for three points; with a value of R-squared = 0.25 – 0.5 for two points; and with a value of R-squared <0.25 for one point. If there was no relationship established, no point was assigned.

Table 21 shows reliability points for different relationships. The relationships for net income versus EVA, net income versus market-over-book value, and EVA versus market-over-book value have four points for all three industries, indicating a very good predictability

and reliability. On the other hand, the relationships of ROE versus intangible-to-asset ratio, ROA versus intangible-to-asset ratio, and market-to-book ratio, intangible-to-asset ratio and Tobin q versus intellectual property for all three industries have low points of either one or zero, suggesting a poor predictability and reliability. An interesting observation is that although the brick-and-mortar industry has the largest number of established regression equations among three industries, it has the lowest total points. Instead, the high-tech industry has the highest points. Logically, high-tech companies largely depend on how well they develop and utilize their intangible assets for their current business performance and future growth. This is why there is generally higher reliability for a high-tech company to use intangible indicators to predict its financial performance, compared to the other two industries.

Table 21. Reliability and Predictability for Different Relationships



Overall, the order of independent variables for net income predictability is EVA, market-over-book value, intangible value, and intellectual property. Similarly, the order of independent variables for EVA predictability is market-over-book value, intangible value, and intellectual property. Furthermore, the order of independent variables for ROE, ROIC and ROA predictability tends to be Tobin q, market-to-book ratio, and intangible-to-total-asset ratio. However, the reliability of using the size-based variable intellectual property for predicting the ratio-based intangible variables, such as Tobin q, market-to-book ratio, and intangible-to-total-asset ratio, is very low.

IV. RESULT COMPARISONS WITH

PREVIOUS STUDIES

Although there have recently been many studies on the intangible valuation and many methods were presented in the literature, only limited previous studies have quantitatively assessed the intangible values relative to the financial performance of the firms (Erickson & McCall, 2008; Fama & French, 1992, 1995; Landsman & Shapiro, 1995; Lehn & Makhija, 1996; Said, HassabElnaby, & Nowlin, 2008; Sellers-Rubio, Nicolau-Gonzálbez, & Mas-Ruiz, 2007). Many researchers have embarked upon a similar line of inquiry recently (Basu & Waymire, 2008; Chang, 2007; Eris, 2008;

Freund, Trahan, & Vasudevan, 2007; Gerpott, Thomas, & Hoffmann, 2008; Holsapple & Wu, 2008; Low, 2009; Pradosh, 2009; Shih, 2009; Sonnier, Carson, &

Carson, 2007; Sriram, 2008). However, few researchers have more specifically explored the relationships between intangible indicators (market-to-book ratio, Tobin q, intangible-to-total-asset ratio, EVA and the number of patents, trademarks and copyright) and financial performance indicators (ROA, ROE, ROIC and net income) systematically based on the financial data from several industries as conducted in this study. As this study is not a replication of an earlier study, a direct comparison of the study results is impossible. Furthermore, lack of details on data collection, treatment and calculations in previous studies prevented any apple-to-apple comparisons. Here, the results and the implications from different previous studies were compared to this study on a piece-by-piece basis to see the potential consistency in the study results. Market-To-Book Ratio

Due to its readily data availability and simplicity in calculation, the market-to-book ratio has been used as an indicator of the intangibles by many researchers (e.g., Fama & French, 1992, 1995; Gerpott et al, 2008; Hall, 1992, 1993, 2001; Hirschey, 1982; Ulrich & Smallwood, 2003). Some researchers attempted to simplify their Tobin q calculations and ended at market-to-book ratio, but they still labeled as Tobin q (e.g.,rickson & McCall, 2008; Erickson & Rothberg, 2008).

This study showed a positive correlation between intellectual property and market-to-book ratio with Pearson coefficient of 0.100 (Figure 11). Hirschey (1982) conducted an earlier study and explored impact of R & D expenditure on the intangible capital. The results suggested a positive correlation between R&D expenditure and market-to-book ratio with Pearson coefficient of 0.317, indicating the consistency with the results of this study. Tobin q

In this study, the average value of Tobin q for 47 IT companies was 2.22. In the study, Tobin q was used as a measure of firm performance which can provide a way of capturing its true value to a firm. The possible explanation could be that IT industry has built their intangible asset over years, especially after IT industry consolidation in the recent years.

An earlier study conducted back in 1995 in US by Landsman and Shapiro (1995) showed a statistically significant relationships between Tobin q and ROIC or ROA, which is in line with the results of this study. However, the more recent study by Said, HassabElnaby, and Nowlin (2008) observed the insignificance of the correlation between ROA and Tobin q ratio. They attributed such observations to an inadequacy of ROA as a measure of economic return or possible measurement errors in ROA and Tobin q.

Several studies were conducted on impacts of a firm’s R&D, patents, and other intellectual properties on Tobin q (e.g., Deng, 2000; Sellers-Rubio, Nicolau-Gonzálbez, & Mas-Ruiz, 2007). Deng (2000) observed that a high-tech company has a higher Tobin q than a traditional company, which is consistent with the results of this study with averaged Tobin q of 2.3 for high-tech industry versus 1.5 for brick-and-mortar industry (Table 6). Sellers-Rubio, Nicolau-Gonzálbez and Mas-Ruiz (2007) studied the economic value of patent protection and rivalry in the Spanish electrical sector by investigating the relationship between Tobin q and number of patents applied or granted. Their results showed a negative impact of own patents on the company value (Tobin q). This is indirectly consistent with the results of this study, which revealed that there is very small negative correlation between the intellectual property and their Tobin q for high-tech industry (Figure 9). Others

The fact that EVA calculation is very similar to the net income calculation has encouraged many companies to use EVA as their business performance indicators (Bontis, et al., 1999; Chen & Dodd, 1997). About half of Canadian firms have adopted EVA for their value-based management (Athanassakos, 2007). A larger percentage of firms (up to 84%) were reported to use EVA at the corporate level in the U. S. (Ryan and Trahan, 1999).

This study has observed a close relationship between net income and EVA. Although some studies indicated no evidence that strong EVA corporations



significantly outperform the market (e.g., Palliam, 2006), many other recent studies (e.g., Alkhalialeh, 2008; Athanassakos 2007; Baum, Sarver, & Strickland, 2004; Erasmus, 2008; Holland, 2006; Huang & Wang, 2008; Ferguson, Rentzler, & Yu, 2005; Sharma, Teng, Hui, & Tan, 2007) showes a positive correlation between EVA and firms’ financial performance. Another study conducted by Ferguson, Rentzler, and Yu (2005) showed some evidence that EVA adopters experience increased profitability relative to their peers following adoption. Athanassakos (2007) also suggested that companies that use EVA seem to have had better stock price performance in the 1990s than those that did not use EVA. A positive correlation between market adjusted return and EVA was also observed in a more recent study (Erasmus, 2008). Sharma, Teng, Hui, and Tan (2007) demonstrated that EVA does not only serve as a good proxy as a valuation of intellectual capital, but can also be further used as an objective measure for knowledge management initiatives.

In summary, a positive relationship between firms’ intangible value and their financial performance observed in this study is within many people’s expectations. With very limited previous study results for direct comparison, the conclusions and implications of this study are largely consistent with the earlier studies.

V. APPLICATIONS OF STUDY RESULTS TO BUSINESS WORLD

Most people, whether investors or business

managers, have recognized the importance of intangibles by now. That is why many organizations have invested significant money and efforts in the intangible assets, such as employees training, organizational learning, knowledge management, research and development, brand-name building and reputation maintaining. However, many organizations struggle to turn this recognition into a better management decision. Business managers have difficulties in linking the intangibles to their business “bottom-line.” This study has established the relationships between the intangible assets a firm has and its financial performance indicators. Business managers of companies large or small could benefit from the study results to make better and more informed decisions based on the assets that have gone previously unseen or not properly recognized in a formal way.

A very strong relationship was observed between a firm’s market-over-book value and its net income. An investor could closely monitor trends in the change of a firm’s market-over-book value to make an investment decision. Another example is based on the relationships between a high-tech firm’s intellectual property and its size-based financial performance indicators. A high-tech business manager can justify a research and development project by using these relationships to estimate the potential benefits of the project.

For organizations that are not formally engaged in intangible valuation, this study results provide a good start-point for their efficient intangible management. As this study used the financial data readily available for any publicly traded companies, a company can easily retrieve its own financial data and follow the same methodology as presented in this study to come up with its own relationships between its intangible indicators and financial performance indicators. By doing this, the manager establishes a baseline for future improvements in intangible utilizations and makes intangible more visible in the company’s asset portfolio.

A word of caution to the business managers is that when applying the study results, they should understand the potential limitations of this study, as mentioned in the previous section. The relationships established in this study can help business managers make an informed business decision by linking their intangible investment and future potential benefits. However, such relationships cannot and should not replace managers’ own good business practices, due diligence and sound judgments.

VI. CONCLUSIONS The study results have successfully answered the

research questions and confirmed positive relationships between firms’ intellectual property and their intangible asset value. In addition, positive relationships between firms’ intangible asset values and their financial performance were also confirmed. As anticipated, a firm with more patents, trademarks and copyrights generally has a higher intangible asset value and in turn has a better financial performance. In overall, the study rejected 4 of 5 null hypotheses. An overall 90% of rejections on the null hypotheses strongly confirm that there is a relationship between firms’ financial performance and their intangibles.

In general, the results are very consistent across all three industries studied. Some minor differences were observed. While the brick-and-mortar industry had the most null hypotheses passed the tests, the service industry had the least, the high-tech industry had the strongest correlation of its financial performance with its intangible asset. In other words, there is generally a higher reliability for a high-tech company to use intangible indictors to predict its financial performance, compared to the other two industries.

For all three industries, the correlations among the size-based variables are stronger than those among ratio-based variables, and very weak correlations exist between a ratio-based variable and a size-based variable. Both Tobin q and market-to-book ratio have a reasonably close relationship with ROE, ROIC, and to a less extent with ROA. In general, ROA is less closely related to the firms’ ratio-based intangible value than ROE and ROIC.

On surface, it seems that compared to the other two industries, the brick-and-mortar industry has more



close relationships between its financial performance indicators and its intangible value indicators. However, more detailed analyses revealed high-tech industry have the strongest relationships between its financial performance indicators and its intangible values.

Overall, the order of independent variables for net income predictability is EVA, market-over-book, intangible value, and then intellectual property. Similarly, the order of independent variables for EVA predictability is market-over-book, intangible value, and then intellectual property. Furthermore, the order of independent variables for ROE, ROIC and ROA predictability tends to be Tobin q, market-to-book ratio, and then intangible-to-total-asset ratio. However, the reliability of using the size-based variable intellectual property to predict the ratio-based intangible variables, such as Tobin q, market-to-book ratio, and intangible-to-total-asset ratio, is very low.

With very limited previous study results for direct comparison, the conclusions and implications of this study are largely consistent with the earlier studies. There are potential applications of study results to business world. Suggestions

The empirical research design selected by this study allowed for limited generalization of the study results. A key strength of this method is that it is more feasible than doing a randomized experiment or using a quasi-experimental design in terms of time and resources. However, one of key weaknesses is the difficulty in realizing whether or not any other variables had an effect on the study results. Obviously, the other variables related to intangible value in this study are intangible resources. As shown in Figure 1, intangibles are largely classified into two categories: property and resources. This study mainly focused on the intellectual property part of intangible assets and their relations to the firms’ financial performance. To improve the validity of intangible study, it is recommended to conduct more studies on valuation of intangible resources, such as human resources, relational resources, and organizational systems.

This study has the following limitations, which would adversely affect the generalization of the study results:

1. Limited sectors: The data from only nine sectors were used in this study. The results cannot be generalized beyond the sectors used in the study because of instrument limitations.

2. Limited demography: The data used in this study was limited to the India companies. The scholar recommends future studies involving

the data of international companies and from more industries to determine if the results of this study are further generalizable.

The data in this study revealed some interesting issues. Most of the data supported the researcher’s hypotheses and theoretical framework in the field, but some did not. For example, there were unexpected negative

correlations between intangible-to-total asset ratio and ratio-based financial performance indicators: ROE, ROIC and ROA, which leaves much doubt about any validity of using intangible-to-total-asset ratio as intangible indicator. This phenomenon calls for further studies. For now it is not recommended to use the intangible-to-total-asset ratio to predict any ratio-based financial performance indicators, such as ROE, ROIC and ROA.

In this study, intellectual property of patents, trademark and copyrights were treated equally for simplicity. However, in reality they are different and should be weighted differently (Smith, 2007). People would generally expect that patents would be weighted heavier than trademarks and copyrights as the potential benefits from the patents are generally considered higher than the trademarks and copyrights. Such considerations should be taken into the future studies.

Although this study has concluded that EVA, market-over-book value, Tobin q, and market-to-book ratio are reasonably good indication of firms’ intangible value and can be used for predicting the firms’ financial performance, using the market-to-book ratio, Tobin q and economic value added (EVA) as an approximation to the intangible values is a simplification of complicated intangible valuation. This study could be expanded into developing an intangible valuation method that has more sound theoretical foundations. Furthermore, future studies may consider other components of intangibles, such as employees’ training and development, global alliances, corporate culture, and etc.

Currently, many companies start to report intangibles in their financial statement. However, there is an urgent need for a common definition, transparency in data sources and consistency in data treatment. A clear definition of intangibles and development of new tools to value the intangibles, as well as a better understanding of effects of intangibles on a firm’s business strategy are required.

Other questions have arisen as a result of this work that would lead to morestudies in this area, such as:

1. Why where there positive correlations between sized-based variables and intangible value, but negative correlations between ratio-based variables and intangible-to-total asset ratio (refer to Table 24)?

2. How would one quantify the value of organizational knowledge and what is the relationship between firms’ knowledge value and their financial performance?

3. How would one incorporate all learning obtained from the previous studies on the intangibles into the current accounting systems to reflect the importance of intangibles and really look into different ways of improving the current accounting practices?

Final Conclusions



The study results have successfully answered the research questions and confirmed positive relationships between firms’ intellectual property and their intangible asset value. In addition, positive relationships between firms’ intangible asset values and their financial performance were also confirmed. As anticipated, a firm with more patents, trademarks and copyrights generally has a higher intangible asset value and in turn has a better financial performance. In overall, the study rejected 4 of 5 null hypotheses. An overall 90% of rejections on the null hypotheses strongly confirm that there is a relationship between firms’ financial performance and their intangibles.

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