empirical evidence from india

1. Prepared By:
Dr. Khushboo Aggarwal
Pt. Mohan Lal S.D. College for Women,
Department of Commerce and Business Management
Gurdaspur

2.  Since the inception of the economic policy of 1990,
Indian firms are facing tough competition. To remain
globally competitive in this contemporary era, the
companies are now focusing on Intangible Assets.
 The companies investing in intangible assets are able to
compete with their rivals (Canibano et al., 1999) as they
tend to become more productive and efficient (OECD,
2008).
 Intangible assets also enhance the market value of the
firm (Mishra and Jhunjhunwala, 2009) and thus provide
stable and profitable earning stream (Alres, 2007).

3.  The review of literature suggests that much of the
work has been conducted in the developed countries as
in Australia (Bosworth and Rogers, 1998), Canada
(Cumming and Macintosh, 2000), USA (Harmantzis and
Tanguturi, 2005 and Singh and Faircloth, 2005), UK (El-
Bannay, 2008), Japan (Al-Twaijry, 2009 and Arikawa,
2011) etc.) than in the developing countries as Malaysia
(Zainol et al., 2008), Korea (Chen, 2010) and Taiwan (Lu
et al., 2010).
 Only a handful of studies have been found with specific
reference to India as Kumar (1997); Siddharthan
(1998); Kumar and Saqib (1996); Pradhan (2003); Kumar
and Aggarwal (2005); Mishra (2007) and Parameshwaran
(2010) etc.

4.  Studies have defined intangible assets in
fragments. Some have taken them only as R&D
(Bhagat and Welch, 1995; Kumar and Saqib,
1996; Kumar and Aggarwal, 2005; Singh and
Faircloth, 2005; Lai et al., 2015) and others
only as intellectual capital (El- Bannay, 2008).
 With respect to India, all the studies have
evaluated determinants of intangible assets in
terms of R&D only.
 Thus, the present study endeavors to study
these inadequacies with reference to India.

5.  Sample, Time period and Data Source
The study is based on a sample of 346
companies selected from BT- 500 (Business
Today, November 11, 2012).
The data for a period of twelve years i.e. 2000-
2001 to 2011-2012 is collected through
secondary sources.

6.  Technique Used
The factors affecting the investment in intangible
assets have been examined using the Dynamic Panel
Generalized Method of Moments (GMM).
Dynamics are often modeled by including a lag of the
dependent variable on the right hand side of the
regression equation. Therefore, the panel data
regression in case of Dynamic Panel Data becomes as
Yit = γYi,t-1 + X’itβ+ ui+εit (i= 1,2…., n) and (t=1,2….T)
Where,
γ is a scalar, X’it is 1xK and β is Kx1.
ui is the individual effect of the ith unit, either fixed or
random effects.

7.  The Intangible Asset Monitor Method
developed by Karl- Erik Sveiby (1997) is used
for measuring intangible assets.
The Invisible Balance Sheet

8. Variables Measurement Expected Relationship
Firm Size Log of market capitalization +
(H1)
Profitability Profits after taxes/ net
sales
+
(H2)
Leverage Debt/ Equity -
(H3)
Age of a firm Observation year-Year of
incorporation
+
(H4)
Capital Intensity Fixed assets/total assets -
(H5)
Market Share Company's sales/industry
sales
+
(H6)
Export Intensity Total exports/ total sales +
(H7)

9. Arellano-Bond Dynamic Panel Data estimation Coefficient
Intangible Assets
(t-1)
0.1098* (5.86)
Intangible Assets
(t-2)
0.120999 (0.90)
Size 0.000711* (4.24)
ROS 0.010345** (2.07)
Leverage -0.012523 (-0.94)
Age -0.000127 (-0.75)
Capital Intensity -0.014124 (-0.73)
Market Share 0.522323* (3.05)
Export Intensity 0.000113 (0.59)
Food and Beverages -0.01382 (-1.12)
Textile -0.013139 (-1.1)
Chemicals -0.009987 (-0.91)
Drugs & Pharmaceutical -0.007741 (-0.98)

10. Arellano-Bond Dynamic Panel Data
estimation
Coefficient
Rubber and Plastic Products -0.008** (-1.98)
Other non- metallic mineral products -0.015947** (-1.91)
Metals -0.022373*** (-1.84)
Electrical Equipment -0.00959 (-0.69)
Machinery and Equipment -0.015395 (-0.25)
Automotive -0.013941 (-0.75)

11. Variables Expected Relationship Actual Relationship
Firm Size +
(H1)
+
(Accepted)
Profitability +
(H2)
+
(Accepted)
Leverage -
(H3)
-
(Accepted)
Age of a firm +
(H4)
-
(Rejected)
Capital Intensity -
(H5)
-
(Accepted)
Market Share +
(H6)
+
(Accepted)
Export Intensity +
(H7)
+
(Accepted)

12.  The present study unravels the factors determining the
investment in intangible assets by applying the Dynamic Panel
Data Regression.
 The results reveal that firm size, profitability, market share
and export intensity are positively and significantly affecting
the investment in intangibles. Leverage, age and capital
intensity are negatively influencing the same.
 The study also throws light on the dynamic nature of
intangible assets and shows that the past investment in
intangible assets is related to current investment in
intangible assets as the lags of the intangible assets are
found to be positive and significant at 1% level of
significance.

13.  The results strongly recommend that corporate managers should increase
their investment in intangible assets as their business grows in size and
they tend to grab huge market share.
 The economies of large scale business should further be capitalized by
improving upon R&D, innovation and novelty. The older companies too have
to make deliberate efforts through effective Customer Relationship
Management (CRM) and Supply Chain Management (SCM) to generate
goodwill, earn good reputation and build a brand name for themselves.
 Though age denotes the number of years a business has been operating in
the arena, but these days it is measured by companies’ intellectual capital.
Hence endeavors should be made to invest in training and development of
employees so that organization remains updated even while it matures in
years.
 The researchers are hopeful that the current results would improve
managerial insight into the significance of investment in intangible assets.
However, the results would gain more meaning if the impact of investment
in intangible assets is also evaluated on the basis of its performance.