INTRODUCTION:

Regional governments around the world hope to become significant players in the world biotechnology industry through their support for local clusters. (Gilding, Michael, 2008). Vaidyanathan, Geetha 2008 observed that there are several upcoming biotech parks in different Indian states with support from the respective state governments. The government of India continues to play a important role in establishing technology parks. The present technology transfer policies and mechanisms are weak and need to be restructured. They advised the government to revise the existing promotional measures for R and D and FDI policies also need to be tailored to encourage technology transfers and capability building (Agarwal, S. P., Gupta, et al, 2007).

A study by Nyerhovwo J. Tonukari, 2004, concluded that Biotechnology is now one of the hot areas driving the stock markets as well as a frontier of knowledge and job creation. Just as the provision of research grants is a major issue, entrepreneurship and financing for biotechnology companies are high on government policy and educational agenda.

Biotechnology is being entrenched in developing countries with the establishment of a strong research base and entrepreneurial culture. Developing countries’ scientists who summon enough courage to take part in these ventures will become part of the business elite of the future. Finally, any country that can assist its scientists and entrepreneurs in successful biotechnology start-ups will enjoy economic growth.

A study by the U.K. Department of Trade and Industry (1999) observes that economic activity in the biotechnology industry tends to be heavily concentrated. For example, biotechnology industries in the United States are located in two main areas: California and the northeastern seaboard from Massachusetts to North Carolina. Likewise, major pharmaceutical industries in the United Kingdom are located in Oxford and Cambridge. Prevezer (1997) concluded that the main reason for geographic concentration of biotechnology industries is their dependence on the academic research carried out in particular locations. In the same vein, additional studies have shown that the positive externalities of academic research, particularly research on biotechnology, tend to be local (Jaffe et al, 1993; Audretsch and Stephan, 1996). The literature on innovation and interactive learning has tended to emphasize the importance of local networks, inter-firm collaboration and knowledge flows as the principal source of technological dynamism (Gertler, M. S et al 2005).

Currently, globalization reflects the continuing expansion and mutual integration of market frontiers, and is an irreversible trend for the economic development in the whole world at the turn of the millennium (Shangquan, 2000). The biotechnology industry is going through a process of assimilate massive amounts of information and intellectual property produced so far, how to profit from it and how to gain public acceptance. With the knowledge brought by initiatives such as the Human Genome Project and the advent and diffusion of technologies such as PCR, PAGE, NMR and various forms of high throughput screening, unending streams of new companies are entering the domain of biotechnology companies. Every time a new scientific advance and area of scientific endeavor emerges such as pharmacogenomics, nanomics, metabolomics or proteomics, many new companies come into existence to capitalize on or commercialize the intellectual property. So the advances in the industry stem from a close combination of technology and science. Several reasons make it very likely that the biotech industry is going to maintain its momentum. The entrepreneurial, market-driven RandD model works for healthcare innovation which accounts for more than 95%of the biotech industry revenues (Deloitte Touche Tohmatsu, 2003).

The State Government of Andhra Pradesh, India has declared an area of about 900 square kilometres in Ranga Reddy and Medak Districts in Andhra Pradesh, India as the Genome Valley in which biotech activities are being encouraged and promoted. Based on the industrial analysis of the Biotech sector and inputs from the industry experts, research organizations and academic experts, the Government of Andhra Pradesh has short-listed Diagnostics, Therapeutics, Industrial Biotechnology, Inputs to the industry (hardware suppliers, Instrumentation and Chemicals), Agricultural Biotechnology, Pharmaco genomics, Bioinformatics, Marine, Forest, Environment Biotechnologies and Aqua Culture as thrust areas in Biotechnology. The above spectrum of policies and areas earmarked for the development of biotechnology justifies the interdisciplinary approach taken by the State Government. Realizing the utility of this industry, the Government policy aims to achieve the for the promotion of biotech units in the State.

1. The State Government wants the private sector to play an active role in developing the biotechnology industry in the state.

2. The government would act as a facilitator and a catalyst in the development process.

3. To take up a detailed inventory of the bio-resources in the State with the help of Universities, research bodies, NGOs and private agencies.

4. To promote conservation of bio-diversity and sustainable exploitation of bio-resources.

5. To develop high quality infrastructure with the required support services for manufacturing units by setting up specialized biotech parks in various parts of the State.

6. To facilitate the flow of venture capital funds and bank credit to biotech companies.

Objectives of the present study:

To understand the Biotech Investment Ready Climate at Genome Valley in Andhra Pradesh the following objectives are proposed for the purpose of the study:

1. To understand the importance for R and D labs for development of biotech industry.

2. To study the transport facilities required for better development of biotech industry.

3. To understand perception of entrepreneurs regarding the security for the industry from political issues.

4. To understand whether the entrepreneurs maintain green name.

5. To know the quality cost per product.

6. To understand whether anticipated activities are taking place as per the vision of the Government.

Hypotheses:

To study the above objectives the following hypotheses were framed and data collected the test the hypotheses.

H_a1 : There is a significant association between R and D and better development of biotech industry. Accepted.

H_a2 : There is a significant association between transport facilities and development of biotech industry at genome valley. Accepted.

H_a 3 : Biotech industry at genome valley is secure from political environment. Accepted.

H_a4 : Entrepreneurs maintain green name as promised earlier- Rejected.

H_a5 : Quality cost per product is highly satisfactory by entrepreneurs. Rejected.

Research Methodology:

The study was conducted during December 2009 – February 2010 at various Biotech parks in genome valley situated in and around GHMC, Hyderabad. The respondents include Scientists of different R and D organizations like ICRISAT, CCBM, NIN, IICT etc.. , Research scholars in various Central Universities who are working under government funded biotech projects and senior employees of Biotech companies like Dr Reddy’s, Shantha biotech etc. The total sample size for this study is 173. Scientists 118, senior employees of the cadre of vice presidents and chairmen of biotech companies 24, 31 senior research scholars. Primary data to understand biotech investment ready climate at genome valley in Andhra Pradesh is collected through a questionnaire to measure the opinion of the respondents with reference to RandD Testing, Transport, Security, Green Name, Quality Cost, Vision, Anticipated Activities. After collecting the primary data, the interpretation was done by using SPSS 18.0. Relevant statistical tools are used to test the hypotheses.

Discussion of Results:

Frequencies

Test Statistics
	R and D Testing
Chi-square	26.369^a
df	3
Asymp. Sig.	.000

a. 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 46.8.

Inference: The obtained chi-square value is equal to 26.369 at 3 degrees of freedom, the significance value is less than 0.05, suggest that there is a significant importance for R and D labs for better development of biotech industry.

Chi-Square Test

Test Statistics
	Transport
Chi-square	13.854^a
df	3
Asymp. Sig.	.003

a. 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 46.3.

Inference: The obtained chi-square value is equal to 13.589 at 3 degrees of freedom, the significance value is less than 0.05, suggest that transport facilities are highly required for better development of biotech industry at genome valley.

Chi-Square Test

Test Statistics
	Security
Chi-square	82.270^a
df	4
Asymp. Sig.	.000

a. 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 37.0.

Inference: The obtained chi-square value is equal to 82.270 at 4 degrees of freedom, the significance value is less than 0.05, suggest that presently the entrepreneurs feels that they are secure from political issues.

Chi-Square Test

Test Statistics
	Green Name
Chi-square	10.360^a
df	3
Asymp. Sig.	.016

a. 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 44.5.

Inference: The obtained chi-square value is equal to 10.360 at 3 degrees of freedom, the significance value is greater than 0.05, which indicate that the entrepreneurs fail to maintain green name in their organizations as they promise before starting their units.

Test Statistics
	Quality Cost
Chi-square	58.890^a
df	3
Asymp. Sig.	.017

a. 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 45.3.

Inference: The obtained chi-square value is equal to 58.890 at 3 degrees of freedom, the significance value is greater than 0.05, which indicate that the availability of quality cost per product is highly unsatisfactory to the entrepreneurs.

Correlation	R and D Testing	Security	Quality Cost	Vision	Green Name	Anticipated Activities
R and D Testing Pearson Correlation Sig. (2-tailed) N	1	0.457^**	0.571^**	0.412^**	0.379^**	0.316^**
		.000	.000	.000	.000	.000
	187	184	180	174	175	172
Security Pearson Correlation Sig. (2-tailed) N	0.457^**	1	0.514^**	0.201^*	0.408^**	0.374^**
	0.000		0.000	0.000	0.000	0.000
	184	185	179	173	173	171
Quality Cost Pearson Correlation Sig. (2-tailed) N	.571^**	.514^**	1	.505^**	.450^**	.553^**
	.000	.000		.000	.000	.000
	180	179	181	172	172	170
Vision Pearson Correlation Sig. (2-tailed) N	0.412^**	0.401^**	-505^**	1	0.691^**	0.600^**
	0.000	0.000	0.000		0.000	0.000
	174	173	172	178	176	173
Green Name Pearson Correlation Sig. (2-tailed) N	0.379^**	0.408^**	0.450^**	0.691^**	1	0.468^**
	0.000	0.000	0.000	0.000		0.000
	175	173	172	176	178	173
Anticipated Activities Pearson Correlation Sig. (2-tailed) N	0.316^**	0.374^**	0.553^**	0.600^**	0.468^**	1
	0.000	0.000	0.000	0.000	0.000
	172	171	170	173	173	175

** Correlation is significant at the 0.01 level (2-tailed)

Inference: Based on the above correlation table R and D highly correlates with quality cost and poorly correlates with anticipated activities. The security also highly correlates with same quality cost and partially with vision. Surprisingly quality cost highly correlates with all activities indicating that to achieve quality we are supposed to maintain all factors effectively with reference to tested elements. Vision, surprisingly negatively correlates with quality cost which is an indication that words have no meaning until they are put into action. Anticipated activities partially correlate with all elements.

Regression:

Model

R Square

Adjusted R Square

Std. Error of the Estimate

Durbin-Watson

0.885^a

0.710

0.453

0.770

1.969

a. Predictors: (Constant), 26.Green Name, 20.RandD Testing, 24.Anticipated Activities, 21.Transport, 25.Vision

b. Dependent Variable: 23.Quality Cost

ANOVA^b

Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	83.609	5	16.722	28.175	.001^a
	Residual	94.367	159	.594
	Total	177.976	164

a. Predictors: (Constant), 26.Green Name, 20.RandD Testing, 24.Anticipated Activities, 21.Transport, 25.Vision

b. Dependent Variable: 23.Quality Cost

Coefficients^a

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
Model		B	Std. Error	Beta	t	Sig.
1	(Constant)	0.178	0.170		1.043	0.298
	R and D Testing	0.360	0.076	0.354	4.712	0.000
	Transport	0.040	0.074	0.043	0.548	0.584
	Anticipated Activities	-0.325	0.072	0.339	4.498	0.000
	Vision	0.048	0.080	0.053	0.596	0.552
	Green Name	0.081	0.072	0.091	1.123	0.263

a. Dependent Variable: 23.Quality Cost

Quality Cost=.178+.360(R and D Testing) +.040 (Transport)- 0353(Anticipated activities) +.048

(Vision)+.081(Green Name).

Before we look at the equation however we need to look at the statistical significance of their model and the R²value, the analysis of variance (ANOVA) table which are given above. The last column indicates 0.01, meaning the model is statically significant at 99% confidence level. Where the R Square value .0710 is statistically treated as significant for the above model. The above equation for tested elements is to achieve quality cost effectively, where all elements are supporting positively except anticipated activity which is a negative contribution.

Factor Analysis:

KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.		.796
Bartlett's Test of Sphericity	Approx. Chi-Square	372.222
	df	15
	Sig.	.000

Total Variance Explained

Component		Initial Eigen values			Extraction Sums of Squared Loadings
Component		Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
dimension	1	3.364	56.061	56.061	3.364	56.061	56.061
	2	.760	12.672	68.733
	3	.633	10.545	79.278
	4	.559	9.320	88.598
	5	.427	7.120	95.718
	6	.257	4.282	100.000

Extraction Method: Principal Component Analysis.

Component Matrix^a

	Component
	1
R and D Testing	0.742
Transport	0.808
Security	0.747
Quality Cost	0.786
Anticipated Activities	0.711
Green Name	0.692

Extraction Method: Principal Component Analysis.

a. 1 components extracted.

Inference: Before we proceed for factor analysis first the researcher tested the eligibility by KMO- Bartlett's test where KMO value is 0.796 which is statistically significant for further analysis of factors and Bartlett's value is less than .005 which provoke researcher to do factor analysis.

The analysis is surprisingly very simple. The Initial Eigen values extracted only 1 factor and remaining all factors with very near values which indicate all the tested factors are most influencing factors for better biotech cluster and climate.

CONCLUSION:

From the about results we can conclude that the hypotheses are accepted / rejected as follows:

H_a1 : There is a significant association between R and D and better development of biotech industry. Accepted.

H_a2 : There is a significant association between transport facilities and development of biotech industry at genome valley. Accepted.

H_a 3 : Biotech industry at genome valley is secure from political environment. Accepted.

H_a4 : Entrepreneurs maintain green name as promised earlier- Rejected.

H_a5 : Quality cost per product is highly satisfactory by entrepreneurs. Rejected.

Biotechnology being cost intensive requires huge funds for creation of adequate RandD and manufacturing facilities. Indian industry focused initially on the development of diagnostic kits and reagents because it is faster and relatively cheaper to bring such products into the market which ensures quick returns on the investments. The biotechnology industry is seen as one of the most globalized, given the broad application of its products in human health, agriculture and industrials. It is also an industry dominated by SMEs in most countries, as the industry is immature in all countries but the US. It would be expected then that these biotechnology SMEs themselves would be global in their focus on clustered development of the biotech parks.

REFERENCES:

1. Agarwal, S. P.; Gupta, Ashwani; Dayal, R. Technology Transfer Perspectives in Globalising India (Drugs and Pharmaceuticals and Biotechnology) Journal of Technology Transfer, August 2007, v. 32, iss. 4, pp. 397-423.

2. Audretsch, D. and P. Stephan. (1996) “Company Scientist Locational Links: The case of Biotechnology”. American Economic Review, Vol. 86, No. 3. 641-652.

3. Deloitte Touche Tohmatsu (2003), Borderless biotechnology. New York: Deloitte Co.

4. Gertler, M. S.; Levitte, Y. M. Local Nodes in Global Networks: The Geography of Knowledge Flows in Biotechnology Innovation, Industry and Innovation, December 2005, v. 12, iss. 4, pp. 487-507.

5. Gilding, Michael, 'The Tyranny of Distance': Biotechnology Networks and Clusters in the Antipodes, Research Policy, July 2008, v. 37, iss. 6-7, pp. 1132-44.

6. Jaffe, A.B., M. Trajtenberg and R. Henderson. 1993. “Geographic Location of Knowledge Spillovers as Evidenced by Patent Citations”. Quarterly Journal of Economics, Vol. 63, No. 3. 577-598.

7. Nyerhovwo J. Tonukari, Fostering biotechnology entrepreneurship in developing countries, African Journal of Biotechnology Vol. 3 (6), pp. 299-301, June 2004.

8. Prevezer, Martha. 1997. “The Dynamics of Industrial Clustering in Biotechnology”. Small Business Economics, Vol 9, No. 3. 255-271.

9. Serageldin I (1999). Biotechnology and food security in the 21^st century. Science 285: 387-389.

10. Shangquan, G. 2000. Economic globalization: Trends, risks and risk prevention. Peking: China Research Society for Restructuring the Economic System.

11. Vaidyanathan, Geetha, Technology Parks in a Developing Country: The Case of India Journal of Technology Transfer, June 2008, v. 33, issue. 3, pp. 285-99.

Received on 20.01.2011 Accepted on 27.01.2011

Asian J. Management 2(1): Jan. – Mar. 2011 page 20-24