Technological Innovations and Firm Performance of ... - ASPIRE

ASCI Journal of Management 41(1): 109–122 Copyright © 2011 Administrative Staff College of India

M H Bala Subrahmanya*

Technological Innovations and Firm Performance of Manufacturing SMEs: Determinants and Outcomes

Abstract This paper is an attempt to probe how entrepreneurship and firm level factors promote technological innovations and thereby facilitate economic performance of Small & Medium Enterprises (SMEs) in the auto components, electronics and machine tool sectors of Bangalore. The study is carried out based on primary data gathered from 157 SMEs for a period of five years (2001/02–2005/06) and by means of step-wise regression analysis. It throws light on how entrepreneurship and other firm level factors influence innovation and how entrepreneurship, firm level factors, factor inputs and innovations determine economic performance of SMEs. Innovative SMEs largely comprise technically qualified entrepreneurs, exclusive design office, and carry out innovations with external support. Together they determine the innovation performance of SMEs in terms of innovation sales. Innovation sales and factor inputs enable entrepreneurs, particularly of younger firms, to achieve better economic performance in the form of higher growth of sales turnover. Introduction Technological innovations are a necessary precondition for a knowledge-oriented business which promote not only the economic competitiveness of the whole country, but also the welfare of each entrepreneur and the society (Ciemleja and Lace, 2008). Among firms of different sizes, Small and Medium Enterprises (SMEs) across industries and economies have the unrealized innovation potential (Chaminade and Van-Lauridsen, 2006; The World Bank, 2010). This is attributed to their inherent characteristics such as flexibility, better adaptability and receptivity, effective internal communication, simple organizational structure, etc. (Ussman, et al, 2001). Although SMEs typically face considerable resource constraints, they are often successful innovators (Rosenbusch, et al, forthcoming). The success of small firm innovation would invariably revolve around the personality of the entrepreneur/s and characteristics of the firm. Methodology Considering the above, this paper attempts to understand how entrepreneurship and firm level factors promote innovation and thereby facilitate economic performance of SMEs. This question has been probed in the context of auto components, electronics and machine tool *

Professor in Economics, Department of Management Studies, Indian Institute of Science, BANGALORE-560012. [email protected]

110

ASCI Journal of Management 41(1) September 2011

sectors in the city of Bangalore covering 157 SMEs. Primary data were gathered using a structured questionnaire for a period of five years: 2001/02–2005/06 on quantitative variables such as number of people employed, investment and sales turnover, apart from data on background of entrepreneurs, firm characteristics, nature of innovations and innovation sales. The study uses step-wise regression to determine how entrepreneurship and other firm level factors influence innovation and how firm level factors and innovation performance determine economic performance over a period of time. The paper is structured in five sections. Section 2 deals with literature review and identification of research gaps, section 3 presents the conceptual framework of the study and section 4 discusses the analysis and results of the study. Section 5 presents conclusions and brings out policy implications of the study. Firm Level Factors, Innovation Performance and Economic Performance of SMEs Technological innovation is a key factor in a firm’s competitiveness. It is essential for firms wanting to develop and maintain a competitive advantage and/or gain entry into new markets (Becheikh, et al, 2006). SMEs are generally more flexible, adapt themselves better, and are better placed to develop and implement new ideas. These qualities along with their simple organizational structure, their low risk and receptivity are, in fact, essential features facilitating them to be innovative (Harrison and Watson, 1998). Therefore, it is appropriate to examine the internal factors closely which might influence SME innovation capability, either with or without external support. Several empirical studies with reference to diverse economies have identified various internal factors that would influence SMEs to obtain external support and innovate. According to Radas and Bozic (2009) innovative performance of SMEs is influenced by internal factors involving characteristics and policies of SMEs and external factors of opportunities that SMEs can seize from its environment. Important internal factors for SMEs to develop their innovative capabilities are owners’ technical education and prior work experience, technical skills of the workforce, and investment in R&D and training (Romijn and Albaladejo, 2002). Researchers found the background and role of entrepreneurs crucial for SME innovations. This observation is upheld in different countries and different industries. Ciemleja and Lace (2008) found that innovative activity of the enterprise is directly connected with the educational level of managers. Unskilled management of SMEs reduce its innovative capability. Technical education background and work experience of entrepreneurs in the same industry generate necessary capability and thereby facilitate innovations of small firms (Bala Subrahmanya, 2007). Academic experience of the CEO, among others, is an important determinant of innovation performance of SMEs in the bio-technology industry of South Korea (Kang and Lee, 2008). Science or engineering education qualification of entrepreneurs and designers as a percentage of total work-force are some of the important internal factors which influenced innovation capability of SMEs in China (Tie-jun and Jin, 2006). This brings out that it is not only the technically qualified owner/manager but also greater proportion of technical workforce in total work-force which matters for a firm’s innovation capability. Another internal factor that would determine innovation capability is the presence of an exclusive design office. A design office is generally more oriented towards the development

Subrahmanya

Technological Innovations and Firm Performance

111

and improvement of existing products than radical innovation. Firms which are endowed with this office rely less on informal knowledge (Bougrain and Haudeville, 2002). Most innovative small firms are involved in extensive and diverse links with a variety of external sources of knowledge and expertise (Freel, 2000). Acquiring knowledge and skills through external collaboration has become an efficient way towards the success of innovations of SMEs (Kaminski, et al, 2008). Among firms, new ventures benefit more from innovation than aged and established SMEs. Considering the trade-off between flexibility and specialization of resources as companies mature, the flexibility of new firms might be more beneficial for innovation success than specialization of assets found in established firms. As such the flexibility of new firms might enable them to adapt to changing environments or induce rapid industry changes themselves (Rosenbusch, et al, forthcoming). Marques and Ferreira (2009) reveal that firm level factors such as quality of entrepreneurship, life cycle, age and size of firms along with external partnerships or cooperation have a significant influence on a firm’s innovative capacity and this in turn has an influence on firm’s sales turnover. SMEs which develop innovative capability, with or without external support, achieve either cost effective quality improved versions of existing products or altogether new products. This is a desirable outcome, as it enables them to gain and maintain technological advantage over competitors (Lee, 1998). If they succeed, they would realize a greater share of such innovated products in their total sales. As a result many such innovative SMEs might achieve a higher growth of sales turnover. However, not many empirical studies on SME innovations, have focused on probing the relationship between innovation and firm performance (Hoffman, et al, 1998). A major difficulty in observing the effect of innovation on growth is that it may take a firm a long time to convert increases in economically valuable knowledge (i.e. innovation) into economic performance. Roper (1997), focusing on product innovations of SMEs in Germany, the UK and Ireland, revealed that innovated products significantly contributed to the sales growth of innovative SMEs, which grew faster than non-innovative SMEs. He found a strong association between innovation and sales growth. Engel, et al (2004), similar to Roper, found that sales turnover of innovative SMEs grew faster than that of non-innovative SMEs in the craft dominated industries of Germany. They ascertained a significant relationship between the share of innovative sales and sales turnover change of firms. Coad and Rao (2008) probed the relationship between innovation and sales growth for incumbent firms in the high-tech sectors of the US. Their findings revealed that a firm, on average, might experience only a modest growth and may grow for a number of reasons that may or may not be related to innovativeness. But innovation is of crucial importance for a handful of ‘superstar’ fastgrowth firms. It is with this backdrop that the present study attempts to probe the relationship between entrepreneurship, firm level factors and innovation performance. It also explores the relationship between innovation performance and economic performance of SMEs in Bangalore. Conceptual Framework The relationship of variables such as entrepreneurship, firm level factors, innovation performance, factor inputs and firm performance is described in Fig 1. In an SME, the entrepreneur/s or founder/s lead and drive the organization. Therefore, whether an SME will

112


innovate or not would depend on the nature of entrepreneurship. The nature of entrepreneurship can be defined in terms of age of firm or entrepreneur/s, educational background, nature of firm organization, firm size and objective of firm origin. In addition, it is entrepreneur/founder who decides on the composition of labour force, obtaining external support, presence of a design centre, frequency of innovations and nature of innovations. Thus, nature of entrepreneurship and firm level factors together would have a profound influence on firm level innovation performance. Along with the growth of factor inputs, nature of entrepreneurship, firm level factors and innovation performance will have an impact on firm’s economic performance. The extent of influence of these factors on firm performance would, however, vary from firm to firm or from sector to sector. Figure 1: Innovation Performance and Economic Performance of SMEs

Analysis and Results The descriptive statistics of the variables of 157 SMEs relating to firm level characteristics, innovation features, firm level economic variables and sector specific firms are presented in Table 1. The Bangalore SMEs vary not only in terms of firm level characteristics including that of entrepreneurship and growth of factor inputs but also in terms of innovation features including innovation sales, and firm performance indicator, ie, sales growth. Table 2 presents correlation coefficients between these variables for the whole sample (all the coefficients which are >+/- 0.15 are statistically significant at 0.05 level). What is significant to note is that there is a relatively strong positive correlation between innovation sales (IS) and sales growth (SG). The relationship between the objective of setting up a firm (FO) and sales growth as well as between technically qualified entrepreneur (TE) and sales growth are positive, though relatively low. All these positive relationships are statistically significant. There is a statistically significant negative relationship between the age of firms (FA) and sales growth. The relationships between sales growth and the remaining variables are not statistically significant. The objective of setting up a firm (FO) has a moderate positive relationship with innovation

Subrahmanya


113

sales. Similarly, external support for innovations (SIIE) and technical entrepreneur have statistically significant positive relationships with innovation sales. Technical entrepreneur has a significant positive relationship with the objective of setting up a firm. Most of the remaining relationships lack statistical significance. Given the moderately strong positive relationship (+0.54) between innovation sales and sales growth of SMEs, it would be appropriate to examine the firm-wise trends of both these variables. Fig 2 presents the firm-wise trends of innovation sales and sales growth of SMEs, which broadly indicate the close relationship between the two. Table 1: Description Statistics of Variables Variable

Mean

Standard Deviation

Minimum Maximum

19.80398

17.57078

-25.05228

91.54932

11.9172

9.096323

0

42

Firm Age (FA)

15.45223

8.801548

5

49

Firm Objective (FO)

3.127389

1.269596

1

5

Tech Entrepreneur (TEd)*

.7643312

.4257742

0

1

Skilled Labour % (SLP)

46.93127

19.13271

10

94

.343949

.4765444

0

1

Firm Organization (FORd)*

.8025478

.3993503

0

1

Innovations’ Frequency (FI)

2.681529

1.423422

1

5

Product Innovations (PTId)*

.7600637

.2560711

0

1

Process Innovations (PSId)*

.3057325

.4621913

0

1

Product & Process Innovations (PTSId)*

.6242038

.4858776

0

1

Internal Source for Innovations (SIId)*

.6624204

.4743976

0

1

External Support for Innovations (SIIEd)*

.3375796

.4743976

0

1

Labor Intensity (LI)

.6637211

.674602

.0125

4

Employment Growth (EG)

13.90822

18.04497

-27.40205

104.5312

Capital Growth (KG)

22.25829

19.09339

-65.00365

111.4743

Auto Components (Ad)*

.3949045

.4903944

0

1

Electronics (Ed)*

.3184713

.4673742

0

1

Machine Tools (Md)*

.2866242

.4536314

0

1

Sales Growth (SG) Innovation Sales % (IS)

Design Office (DOd)*

*Dummy variables

N 157

-0.2322

0.3074

-0.0325

0.2230

0.0422

-0.1220 -0.1158

0.1164

0.0109

-0.0517 -0.1651

0.0739

0.0810

-0.0278 -0.0737

-0.0589

FA

FO

FI

TEd

DOd

SLP

SIIEd

PTSId

FORd

LI

Ad

Ed

Md

1.0000

FO

* n = 157 df = 155

-0.0926

0.0340

0.0716

0.0010

0.0757

-0.1271

0.0054

-0.0399

0.0474

0.1079

-0.0467

1.0000

SLP

-0.1303 -0.0335

0.1069

0.0599

1.0000

DOd

0.1924

1.0000

SIIEd

1.0000

PTSId

0.0185

0.0572

0.1673

-0.0167

0.0533

0.0748

0.1917

0.0619

0.2272

1.0000

LI

0.0987

0.2579

-0.0575 -0.0876

0.1491

1.0000

FORd

-0.5522

1.0000

Ad

-0.0439 -0.1546 -0.2643 -0.0394 -0.1710 -0.5121

-0.0714 -0.0920 -0.0175 -0.0254

0.0188

0.0608

-0.0115 -0.0787 -0.0593 -0.0858 -0.1536

0.0339

0.1108

0.0436

0.0861

1.0000

TEd

-0.0238 -0.0342 -0.1914

0.0465

0.2244

0.2077

-0.0057 -0.0698

0.1708

0.2575

1.0000

FI

-0.0343 -0.0749 -0.1655

0.0411

-0.0075

-0.0569

0.2772

0.0655

0.0737

0.0541

0.1369

-0.0381

-0.0115 -0.1264

0.1640

1.0000

FA

Note: For description of variables, see Table 1

0.0182

0.0534

0.0072

0.0176

0.2591

0.2077

0.2432

0.0737

0.3612

0.0189

0.5413

IS

1.0000

1.0000

IS

SG

SG

Table 2: Correlation between Variables

-04333

1.0000

Ed

1.0000

Md

114 ASCI Journal of Management 41(1) September 2011

Subrahmanya


115

To begin with, it is appropriate to examine how entrepreneurship and firm level factors influence innovation performance of SMEs in terms of innovated products as a percentage of total sales. To ascertain the influence of these variables, a step-wise regression analysis was carried out with the following regression function: IS=f(FA, FO, SLP, TEd, FORd, DOd, FI, PTId, PSId, SIIEd, LI, Ad, Ed) ——— (1) Where IS = percentage of innovated products in total sales during 2001/02- 2005/06 FA = Firm’s age in years (as of 2005/06). It was assumed that an older firm with experience might be able to innovate better compared to younger ones. FO = Objective of setting up the firm. The objectives have been ranked from 1 to 5. This has been ranked as follows: Those which came up exclusively to enjoy government sponsored benefits have been given the lowest rank (1), those which came up only as a source of employment (2), those which came up to serve large enterprises’ demand (3), those which came up to exploit market opportunities (4), and those which came up exclusively to implement innovative ideas have been given the highest rank (5). Those which have come up exclusively to implement innovative ideas or to exploit market opportunities might innovate much more than those which have come up as a means of employment or just to enjoy government sponsored concessions and benefits. Figure 2: Innovation Performance and Economic Performance of SMEs

SLP = Percentage of skilled labour in total employees. It is believed that higher the percentage of skilled labour in a firm, more beneficial for it to implement innovations. TEd = Dummy variable to distinguish the Owner/Managing Partner/CEO who is technically qualified (1) in the form of a Diploma, Degree (BE or equivalent/ME or equivalent/Ph.d) from those who are non-technically qualified (0). Other things remaining the same, technically qualified chiefs will be able to carry out innovations relatively better than non-technically qualified chiefs.

116


FORd = Dummy variable to distinguish Proprietorship (1) from partnership and private limited companies (0). An individual proprietor who has better control and can keep close watch on the enterprise and communicate effectively within is expected to be in a better position to carry out innovations compared to partners and private limited companies. DOd = Dummy variable for the presence of an exclusive Design Office (1) in the enterprise, (0) otherwise. For a high-tech MSME, product design plays an important role in introducing a new or improving an existing product. Therefore, presence of an exclusive design office will help in the process of innovations. FI = Frequency of innovations. The frequency is ranked from 1 to 5 as follows: those which carry out innovations once in 13 months or more are ranked the lowest (1), those which carry out innovations once in 10 to 12 months (2), those which carry out once in 7 to 9 months (3), those which carry out innovations once in 4 to 6 months (4), and those which carry out innovations in one to three months are given the highest rank (5). Other things remaining the same, if a firm carries out innovations more frequently, it will be able to achieve more innovation outcomes. PTId = Dummy variable for those which carry out only product innovations (1) and (0) for others. PSId = Dummy variable for those which carry out only process innovations (1) and (0) for others. The above two dummy variables will distinguish them from those which carry out both product and process innovations together. SIIEd = Dummy variable for those which carry out innovations by combining internal capabilities with external support (1), and (0) otherwise. The above dummy variable will distinguish them from those which carry out innovations exclusively with internal capabilities. It is often argued that MSMEs in general have resource constraints and, therefore, they will be better off to undertake innovations with external support. LI = Labour intensity which is measured in terms of value added per unit of labour and is used as a control variable for firm size. Ad = Dummy variable to distinguish Auto Component MSMEs (1) from Electronics and Machine Tool MSMEs (0). Ed = Dummy variable to distinguish Electronic MSMEs (1) from Auto Component and Machine Tool MSMEs (0). To ascertain the influence of independent variables on innovation performance, we use stepwise backward elimination regression with innovation sales percentage (2001/02-2005/ 06) as the dependent variable along with the independent variables explained above. The analysis is done by using Stata 11 software and the regression coefficients with t values, F value and R square are reported below. Similarly, to examine the influence of innovation performance on sales growth performance, sales growth (2001/02–2005/06) is taken as the

Subrahmanya


117

dependent variable along with independent variables used for the previous analysis, employment growth (%), investment growth (%) and innovation sales percentage (average for 2001/02–2005/06) to perform stepwise backward elimination regression analysis (using Stata 11). The advantage in using backward elimination regression is that it starts with a regression equation including all the independent variables, and then deletes independent variables that do not contribute significantly. Sequential search methods which include backward elimination regression among others, offers a perfect solution to researchers because it results in a model with maximum predictive power with only those variables that contribute in a statistically significant amount (Hair, et al, 2007). The results of stepwise backward elimination regression analysis are given in Table 3. The model is statistically significant as indicated by the F value and it explained more than a quarter of the total variation in innovation performance as reflected in the R2 value. This may be considered satisfactory because the independent variables are mostly qualitative variables and they did not include exclusively two crucial explanatory variables of innovation, namely, personnel devoted and capital expenditure incurred on innovations (since these data were not gathered). There is no problem of multicollinearity between the independent variables. The stepwise elimination resulted in the removal of sector dummies, age of firms, dummies for product innovations and product & process innovations, and frequency of innovations indicating that they did not influence firm level innovation performance. The regression model contains six specific factors which influenced innovation performance. First is the objective of firm origin. Those firms which came up either to implement innovative ideas or to exploit market opportunities have achieved innovation sales much more than those which came up either to enjoy government benefits or as a means of employment. Second, those firms which have carried out innovations with external support have excelled in innovation performance compared to those which have relied only on internal efforts. Third, those with technically qualified entrepreneurs have performed more innovatively than the rest. Fourth, those which have an exclusive design office have achieved higher innovation sales compared to those which did not have any exclusive design office. Finally, a higher share of skilled labour proved counter productive for innovations as it has a negative influence on innovations. The nature of firm organization did not make any difference to innovations since the coefficient is significant only at 0.15 level. Over all these results indicate that firm level technological capability as reflected in the technical background of entrepreneur, presence of an exclusive design office, and objective of firm origin and external support play a crucial role in innovation outcomes. After the examination of factors which influenced SME innovation performance, the study ascertained the role of innovation performance along with firm level factors and growth in factor inputs of labour and capital in firm’s sales growth performance with the following regression equation: SG = f (IS, FA, FO, SLP, TEd, FORd, DOd, FI, PTId, PSId, SIIEd, LI, EG, KG, Ad, Ed) ———— (2)

118


Table 3: Influence of Entrepreneurship & Firm Level Factors on Innovation Stepwise Regression Results Dependent variable = IS Number of observations = 157 F (6, 150) = 8.64; Prob > F = 0.00 R- square = 0.26 Variable

Coefficient

t

FO SIIE TEd SLP DOd FORd Constant

2.16 4.39 2.86 -0.06 2.78 -2.35 5.33

4.04 3.18 1.82 -1.73 1.65 -1.45 1.79

p>% t % 0.000 0.002 0.071 0.086 0.100 0.150 0.075

The results of stepwise backward elimination regression analysis are given in Table 4. The multiple regression model is statistically significant and it explained 56% of the variations in sales growth performance. The problem of multicollinearity did not exist between the explanatory variables. The stepwise backward elimination regression eliminated dummy for nature of firm organization, dummy for external support of innovations, percentage of skilled labour, dummy for product and process innovations, dummy for exclusive design office, dummy for technical qualification of CEO, and dummies for industry sectors since these variables did not have sufficient influence on sales growth. The stepwise regression model clearly reveals the importance of innovation performance for sales growth performance of SMEs. In addition what matters is the objective of firm origin. Those firms which came up to implement innovative ideas or to exploit market opportunities grow faster than those which came up to enjoy government benefits or as a means of employment. In addition, both employment and investment growths encourage the sales growth of firms. Further, the control variable (labour intensity) has a statistically significant positive influence on sales growth. What is significant is that age of firms has a negative influence indicating that younger firms grow faster. Similarly, firms which engage in more frequent innovations suffer in growth compared to those which carry out innovations less frequently. This could be because more frequent innovations might divert the precious scarce resources of the firm away from production operations, thereby affecting growth compared to those which do not seek such diversions due to infrequent innovations.

Subrahmanya


119

Table 4: Influence of Innovation, Factor Inputs & Firm Level Factors on Firm Performance: Stepwise Regression Results Dependent variable = SG Number of observations = 157 F (7, 149) = 8.64 R square = 56 ”! 0.56 Variable

Coefficient

t

IS EG KG FA FO FI LI Constant

0.59 0.36 0.23 -0.22 1.58 -1.82 2.31 4.44

4.86 6.00 4.13 -1.92 1.90 -2.61 1.61 1.15

p>% t % 0.000 0.000 0.000 0.057 0.060 0.010 0.109 0.251

Conclusion We found that an innovative entrepreneur is more often technically qualified and he strategically builds up internal firm capability by setting up an exclusive design office inhouse. In addition, he attracts and supplements external support for undertaking innovations and bringing out innovated products as part of total firm sales. Innovation sales and factor inputs enable entrepreneurs, particularly of younger firms and those who have formed startups with the objective of implementing their innovative ideas or exploiting market opportunities, to achieve better firm performance in the form of higher growth of sales turnover. Such innovators carry out innovations judiciously and infrequently. Given this, policy support to encourage innovative start-ups and enable SMEs to access external support would go a long way in enhancing their ability to achieve technological innovations and such innovative firms with growth of factor inputs would be able to achieve better economic performance in the form of higher sales growth. These findings have significant policy relevance. Innovation literature argues that the central concern of developing countries is the absorption and adaptation of the backlog of existing knowledge with the long-term goal of gaining technological independence by developing their own path of technological development (path-creating catching-up) (Godhino and Fagerberg, 2005). Our findings support the view that there is ample scope for promoting learning and knowledge absorption by MSMEs in India through extending policy support to their innovative activities. The study has found that innovation performance of firms strongly depends on the synergies and external linkages in the local environment. This assumes added significance due to other finding, namely proprietorship form of organizations lacks the ability to perform as innovatively as partnership or private limited form of organizations. Further, interaction among firms, governments, and research institutes provide feedback

120


mechanisms that involve learning, which determines the level of innovative activities (Rosenberg, 1982; Lundvall, 1992). Learning within the context of innovation process consists of interaction that reflects the strength of the interrelationships among different sectors, institutions, and agents, including firms, training institutes and universities. Bangalore, which is considered one of the global hubs of technological innovation, ideally would give ample scope for nurturing these interactive processes. The same may hold good for other cities and even MSME clusters located elsewhere in the country. This is because clusters provide a favorable environment for innovation and technology diffusion (The World Bank, 2010). Therefore forming an exclusive “Innovation Policy” to focus on “entrepreneurial firms” and “their linkages” with external organizations such as R&D establishments, Institutes/Universities and Large enterprises, apart from promoting MSME networking, would facilitate the heralding of a new innovative culture for the promotion of competitiveness of firms and industries. In this context, Micro, Small and Medium Enterprises Development Institutes (MSMEDIs) may be revamped as the nodal agency for promoting linkages and networks revolving around MSMEs for aiding and promoting their innovations. The next step should be to apply the same strategy with respect to District Industries Centres (DICs) across the country.

References Bala Subrahmanya, M. H. 2007. “The Process of Technological Innovations in Small Enterprises: The Indian Way”. International Journal of Technology Management, Special Issue on “The Process of Innovation: The Asian Way”, 39 (3/4), 396–411. Becheikh, N., R. Landry and N. Amara. 2006. “Lessons from Innovation Empirical Studies in the Manufacturing Sector: A Systematic Review of the Literature from 1993–2003”. Technovation, 26(5/6), 644–664. Bougrain F., and B. Haudeville. 2002. “Innovation, collaboration and SMEs internal research capacities”. Research Policy, 31, 735–747. Chaminade, C., and J. Van-Lauridsen. 2006. “Innovation Policies for Asian SMEs: An Innovation System Perspective”, (in Yeung, H. W. (Ed), Handbook of Research on Asian Studies) 381–408, Edward-Elgar, London. Ciemleja, G., and N. Lace. 2008. “The factors determining innovation-based attitude of Latvian SMEs towards sustainability”. paper presented at 5th International Conference on Business and Management, 16-17 May, Vilnius, Lithuania. Coad, A., and R. Rao. 2008. “Innovation and Firm Growth in High-Tech Sectors: A Quantile Regression Approach”. Research Policy, 37 (4), 633–648. Freel, M. 2000. “External linkages and product innovation in small manufacturing firms”. Entrepreneurship and Regional development, 2, 245–266. Godhino, M., and J. Fagerberg. 2005. “Innovation and Catching Up”, (in The Oxford Handbook of Innovation, edited by Fagerberg J., D. Mowery and R. R. Nelson.) OUP, London.

Subrahmanya


121

Harrison, N. J. and T. Watson. 1998. “The Focus for Innovation, (in Small and Medium Service Enterprises, Conference Proceedings of 27th Annual Meeting of the Western Decision Sciences Institute, 7–11, Reno, NV) Hair, Black, Babin, Anderson and Tatham. 2007. Multivariate Data Analysis, Dorling Kindersley (India) Private Limited, New Delhi. Hoffman, K, et al. 1998.”Small Firms, R&D, Technology and Innovation in the UK: A Literature Review”. Technovation, 18 (1), 39–55. Kaminski, P.C. et al. 2008. “Knowledge Transfer in Product Development Processes: A Case Study in Small and Medium Enterprises of the metal-mechanic sector from Sao Paulo, Brazil”. Technovation, 29, 589–606. Kang, K. N., and Y . S .Lee 2008. “What affects the innovation performance of SMEs in the biotechnology industry? An empirical study on Korean biotech SMEs”, Biotechnology Letters, 30, 00 1699–1704. Lee, J. 1998. “Small firms’ innovation in two technological settings”. Research Policy, 24, 391–401. Lundvall, B. A. 1992. National Systems of Innovation: Towards a Theory of Innovation and Interactive Learning, Pinter, London. Marques, C. S. and J. Ferreira. 2009. “SME Innovative Capacity, Competitive Advantage and Performance in a ‘Traditional’ Industrial Region of Portugal”. Journal of Technology Management & Innovation, 4(4), 53–68. Maurer, I ., and M. Ebers. 2006. “Dynamics of social capital and their performance implications: lessons from biotechnology start-ups”. Administrative Science Quarterly, 51, 262–292. Radas, S., and L. Bozic. 2009. “The antecedents of SME innovativeness in an emerging transition economy”. Technovation, 29, 438–450. Romijn, H., and M. Albaladego. 2002. “Determinants of innovation capability in small electronics and software firms in southeast England”. Research Policy, 31, 1053–1067. Roper, S. 1997. “Product Innovation and Small Business Growth: A Comparison of the Strategies of German, UK and Irish Companies”. Small Business Economics, 9, 523–537. Rosenberg, N . 1982. Inside the Black Box: Technology and Economics. Cambridge University Press, Cambridge. Rosenbusch, N, J . Brinckmann and A . Bausch. (forthcoming). “Is Innovation always beneficial? A meta-analysis of the relationship between innovation and performance of SMEs”. Journal of Business Venturing (2010), doi:10.1016/j.jbusvent.2009.12.002. Sen, F .K., and W. G. Egelhoff. 2000. “Innovative capabilities of a firm and the use of technical alliances”. IEEE Transactions on Engineering Management, 47(2), 174–183.

122


Tie-jun, C and C. Jin . 2006. “Determinants of Innovation Capability in Small and Medium Enterprises: An Empirical Analysis from China”. Paper presented at IEEE International Conference on Engineering Management, Proceedings, I, 283–286. The World Bank. 2010. Innovation Policy: A Guide for Developing Countries, World Bank, Washington, D C. Ussman, A, et al. 2001. “SMEs and Innovation: Perceived Barriers and Behavioural Patterns”. The International Journal of Entrepreneurship and Innovation, 2(2), 111-–18.

Acknowledgement This paper forms part of the Research Project on The Influence of Technological Innovations on the Growth of Manufacturing SMEs sponsored by Department of Science and Technology (DST), Government of India, New Delhi. The author is grateful to DST for this sponsorship. An earlier version of this paper was presented at the 12th International Conference of Society for Global Business and Economic Development (SGBED) held in Singapore Management University, Singapore during 21–23 July 2011. The usual disclaimers apply.