Innovation and growth of engineering SMEs in Bangalore: Why do only some innovate and only some grow faster?

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Innovation and growth of engineering SMEs in Bangalore: Why[1_TD$IF] do only some innovate and only some grow faster? M.[1_TD$IF]H. [1_TD$IF]Bala[2_TD$IF] Subrahmanya * Department of Management Studies, Indian Institute of Science, Bangalore 560012, India

A R T I C L E I N F O

A B S T R A C T

[3_TD$IF]JEL classification: L5 O3

This paper probes two research questions by ascertaining the factors which distinguish (i) innovative SMEs from those which are not, and (ii) SMEs which experienced a higher sales growth from those which experienced a lower sales growth, with reference to 197 engineering industry SMEs in Bangalore city. The differentiating factors between innovative and non-innovative SMEs brought out that SMEs must have ‘‘own resources and capabilities’’ in the form of internal strength and definite internal strategy if they have to innovate successfully. Younger and smaller firms which are ‘‘entrepreneurial’’ in nature and which are innovative contributed to higher sales growth of SMEs compared to older and larger firms which are ‘‘salary-substitute firms’’ in nature and which are not innovative. ß 2015 Elsevier B.V. All rights reserved.

Keywords: Innovations SMEs Growth Bangalore India

Introduction Small and Medium Enterprises (SMEs) are significant contributors to employment generation, economic growth and economic dynamics of both developing and developed economies. One of the most important means through which SMEs are able to make these contributions is their capability to realise innovations (Keizer et al., 2002). Among firms of different sizes, SMEs are generally more flexible, adapt themselves better, close to their customers and are better placed to develop and implement new ideas (Laforet and [4_TD$IF]Tann, 2006; Madrid-Guijarro et al., 2009). These qualities along

* Tel.: +91 8022933266; fax: +91 8023604534. E-mail address: [email protected]. http://dx.doi.org/10.1016/j.jengtecman.2015.05.001 0923-4748/ß 2015 Elsevier B.V. All rights reserved.

Please cite this article in press as: Bala, M.H., Innovation and growth of engineering SMEs in Bangalore: Why do only some innovate and only some grow faster?. J. Eng. Technol. Manage. (2015), http://dx.doi.org/10.1016/j.jengtecman.2015.05.001

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with their simple organisational structure, their low risk and receptivity are in fact essential features facilitating them to be innovative (The World Bank, 2010). The ability of SMEs to innovate assumes significance because innovation is widely recognised as a key factor in the competitiveness of nations, regions and firms (Madrid-Guijarro et al., 2009; Hong and Jung, 2012). Technological innovation has the potential to spur growth of individual enterprises at the micro level and give a new dimension to industry growth at the macro level. Among firms of different sizes, SMEs including start-ups, across industries and economies have the unrealised innovation potential (Chaminade and Van-Lauridsen, 2006). Small firms that successfully embrace innovation increase their chances of survival and growth (Cefis and Marsili, 2006). Innovation is of crucial importance for fast growing SMEs (Coad and Rao, 2008). SMEs that carry out product innovations will achieve greater growth rates than those that do not (Sole and Capelleras, 2011). Considering the above, there are two pertinent issues concerning SME innovation: (i) What factors differentiate innovative SMEs from non-innovative SMEs? (ii) Does innovation differentiate growth of SMEs? This paper is an attempt to answer these two questions, in the context of engineering industry SMEs in Bangalore, Karnataka state of India. There is relatively less research conducted in the context of emerging economies like India to throw light on these two issues and therefore the present study assumes significance. Karnataka is one amongst the industrially better developed states in the country (Government of Karnataka, 2009). Further, Bangalore occupies a unique position in India as it is known internationally as India’s ‘‘high-tech city’’ (Department of Industries and Commerce website, 2013). Bangalore is considered one of the 46 ‘‘global hubs of technological innovation’’ (UNDP, 2001) and it is one of the globally known technology cities in the world (Rogers et al., 2001). Bangalore has many nationally renowned educational and research institutions and R&D centres of Multinational Corporations (MNCs) (DST, 2010). Bangalore (urban district) accounted for the highest annual registration of SMEs (indicating that start-ups are emerging on a rapid scale here) and therefore highest share of SMEs among all the districts of Karnataka (Directorate of Industries & Commerce website, 2013). Therefore, undertaking a study covering SMEs of Bangalore is justified to throw ample light on the two research issues. The remaining sections of the paper are organised as follows. Section ‘SMEs and technological innovations: definitions of concepts’ comprises definitions of concepts, and Section ‘Research setting: review of literature’ describes the research setting of the paper based on review of literature pertaining to the core theme, and presents the conceptual framework developed on the identified research gaps. The objectives, scope, data sources and methods of analysis are elaborated in Section ‘Objectives, scope and method of analysis’. The objectives are analysed and inferences are drawn in Section ‘Factors distinguishing innovative and non-innovative SMEs and their growth: results and discussion’ and Section ‘Inferences and conclusions’ presents the conclusions and policy implications. SMEs and technological innovations: definitions of concepts SMEs in this paper have been defined in terms of investment in plant and machinery only. Accordingly, SMEs include manufacturing enterprises in the auto components, electronics and machine tool industries having original investment in plant and machinery up to Rs. 100 million as of 2006/07. This is in line with the definition of Micro, Small and Medium Enterprises Development Act, 2006 of Government of India (Ministry of MSMEs website, 2013). Innovation in this paper refers to only technological innovations. The most widely accepted definition of technological innovation is that of Organization for Economic Cooperation and Development (OECD, 1997). A technological product innovation is the implementation/commercialisation of a product with improved performance characteristics such as to deliver objectively new or improved services to the consumer. A technological process innovation is the implementation/ adoption of new or significantly improved production or delivery methods. It may involve changes in equipment, human resources, working methods or a combination of these. Please cite this article in press as: Bala, M.H., Innovation and growth of engineering SMEs in Bangalore: Why do only some innovate and only some grow faster?. J. Eng. Technol. Manage. (2015), http://dx.doi.org/10.1016/j.jengtecman.2015.05.001

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It is important to note that technological innovation is an intangible asset and is therefore difficult to measure. However, varied attempts have been made by researchers to measure it in tangible ways. It is commonly measured by either input indicators such as R&D personnel and R&D expenditure or output indicators such as patent or total factor productivity, both of which have advantages as well as disadvantages. But in terms of technological innovation achievements, the most commonly used indicator is patent statistics, which are widely used as the indicators of innovation of individual firms, regions and countries (Hong and Jung, 2012). In the context of a developing country, technological innovations are defined as the process by which firms master and implement the design and production of goods and services that are new to them irrespective of whether they are new to their competitors, their customers or the world (Mytelka, 2000). In general, technological innovation can be understood as the introduction of a new product/process or improvement of an existing product or process by a firm in an economy, irrespective of whether it has been used elsewhere before. This definition assumes significance because of the diverse constraints within which a developing country enterprise has to function to perform technological innovation. Further, overall the ‘patenting culture’ is low among innovative SMEs, as has been observed internationally (Soete and Freeman, 1997). As observed in an earlier study (Bala Subrahmanya et al., 2001), patenting culture was virtually absent in Karnataka state of India, including Bangalore. In view of the above, in this paper a technologically innovative firm is defined as one which has claimed that it has been carrying out product/process innovations either continuously or sporadically and has been able to sell and therefore identify innovated products as part of its sales turnover (during each of the years from 2001/02 to 2005/06, for which quantitative data were gathered). These products need not be new to the industry or economy but suffice if it is new to the firm itself. These products cannot be more than three years old in any particular year. Non-innovative firms are those which have declared that they do not carry out any product/process innovation. Thus the present study has adopted a broader definition of technological innovation. Research setting: review of literature As the present study is an attempt to throw light on two research questions, empirical literature has been reviewed under two broad heads, namely, (i) factors influencing the innovations of SMEs, and (ii) role of innovations in the growth of SMEs. Subsequently, a conceptual framework is developed covering the key variables focusing on the two research questions. Factors influencing the innovations of SMEs Though among firms of different sizes, SMEs have some unique advantages to carry out technological innovations, not all SMEs carry out innovations. In fact, literature covering factors influencing SMEs to innovate focuses on resource-based view, among others. The resource-based view broadly reveals that a firm’s resources and capabilities would largely determine whether it would undertake innovation or not (Hadjimanolis, 2000). These resources are defined as those tangible and intangible assets that are tied semi-permanently to the firm (Maijoor and van Witteloostuijn, 1996). They are physical, human, technological or reputational. Resources include skills, R&D, capital investment and liquidity (Love and Roper, 2013). Thus, a firm’s resources and capabilities, as revealed by different researchers, would be reflected in firm size, firm age, entrepreneur’s educational qualification, proportion of skilled labour, presence or absence of an exclusive design centre, objective of firm origin, firm strategy, and ownership structure, etc. (Becheikh et al., 2006). A firm’s dynamic capabilities have been defined as the firm’s ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments (Sole and Capelleras, [5_TD$IF]2011). This would involve collaboration with suppliers, relationship with customers, contributions by professional consultants, technical support obtained from technology resource centres, financial assistance obtained from financial institutions due to government policy, etc. (Keizer et al., 2002; Laforet and Tann, 2006; Freel, 2012). Innovative firms maintain and make much more extensive use of linkages with external cooperation partners more frequently than non-innovative firms (Barnett and Storey, 2000; Thorgren Please cite this article in press as: Bala, M.H., Innovation and growth of engineering SMEs in Bangalore: Why do only some innovate and only some grow faster?. J. Eng. Technol. Manage. (2015), http://dx.doi.org/10.1016/j.jengtecman.2015.05.001

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et al., 2009; Zeng et al., 2010; Sternberg, 2012). Thus there are internal as well as external variables which determine SME innovations (Keizer et al., 2002; Kang and Lee, 2008; Freel, 2012). These variables can act either as prompting factors or alternatively as hurdles for undertaking innovations. However, all these empirical studies are confined to either industrialised or Newly Industrialised Countries. Considering this, we are keen to ascertain which of these variables significantly differentiate innovative SMEs from non-innovative SMEs in the context of an emerging economy. Though emerging economy SMEs might have more constraints to innovate, their internal/firm level capability would still matter to collaborate with external agents to carry out innovations. Therefore, we hypothesise as follows. H1. Firms with better internal capability will be able to innovate, relative to firms with lesser internal capability. Role of innovation in the growth of SMEs The second important issue is the role of innovation in promoting firm performance and growth. According to Roberts and Amit (2003), successful innovation activity helps to establish a more positive competitive position leading to a competitive advantage and, consequently, an improved firm performance. Innovative capability is a crucial factor which drives sustainable competitive advantage in today’s changing markets, where continuous development of new products is a key to survival, growth and profitability (Verhees and Meulenberg, 2004). Pett and Wolff (2009) based on their empirical study indicated that product innovations are positively associated with firm growth. If innovative SMEs are successful in their innovations and are able to realise innovated products as a larger proportion of their sales, particularly in the medium to long run, such SMEs will be able to grow in terms of sales turnover. Small firms that successfully pursue innovation as a core business strategy increase productivity, growth potential, and likelihood of survival compared to non-innovative firms (Madrid-Guijarro et al., 2009). Therefore, growth of SMEs is likely to be positively related to a strategy based on continuous improvement, innovation and change (Maranto-Vargas and Gomez-Tagle, 2007). Freel (2000) in the context of England found that innovators are likely to grow more than noninnovators. He found that innovators accounted for a larger proportion of those firms which may be termed ‘‘Super Growth’’ and a smaller proportion of ‘‘declining firms’’. In the context of Germany, Engel et al. (2004), found that sales turnover of innovative firms grew faster than that of noninnovative firms. They detected a significant relationship between the share of innovative sales and sales turnover change of firms. Coad and Rao (2008) have explicitly probed the relationship between innovation and growth of SMEs in the context of high-tech sectors in the USA. They ascertained that a firm, on average, might experience only modest growth and may grow for a number of reasons that may or may not be related to innovativeness. But using a quantile regression approach, they observed that innovation is of crucial importance for a handful of ‘superstar’ fast-growth firms. Holzl (2009) covering SMEs of 16 EU countries concluded that high-growth SMEs are only more innovative than other SMEs in countries close to the technological frontier. For the other country groups, the results are not statistically significant. This implies that high-growth SMEs derive much of their drive from the exploitation of comparative advantage that they acquired through innovation in a superior technology environment. Marques and Ferreira (2009) explored the factors that contribute to the building of a firm’s innovative capacity and assessed the way in which this contributes to improvements in the firm’s performance, in the context of SMEs in the Beira Interior region of Portugal. They examined how (i) firm characteristics (firm’s age, size, labour force training levels, sector of activity and phase of its life cycle); (ii) entrepreneurial characteristics (age and quality of entrepreneurship); and (iii) external relationships of SMEs (partnerships and cooperation agreements with other firms/institutions; and the extent of openness to the external environment, as measured by the importance of its export activities) would influence firms’ innovative capacity. They ascertained that the most influential factors are (i) entrepreneurship, (ii) life cycle, (iii) establishment of partnerships and cooperation agreements with other firms/institutions, (iv) age of the firm, and (v) the size of the firm. They further found that greater a firm’s innovative capacity the better is its performance. Czarnitzki and Delanote Please cite this article in press as: Bala, M.H., Innovation and growth of engineering SMEs in Bangalore: Why do only some innovate and only some grow faster?. J. Eng. Technol. Manage. (2015), http://dx.doi.org/10.1016/j.jengtecman.2015.05.001

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(2012) in the context of Belgium found out that young innovative firms grew more than other firms. Their results revealed that the combination of factors – age, size and R&D intensity seems to be crucial for the superior growth of firms. But all these empirical studies are confined to developed countries. Given the above, we would like to explore whether innovation makes any difference to the growth of SMEs in an emerging economy, with a focus on Bangalore. Therefore, we hypothesise as follows. H2. Firms which are innovative but younger and smaller are likely to register a higher growth compared to firms which are non-innovative, older and larger. Factors which differentiate innovative and non-innovative SMEs and their growth: a conceptual framework We propose to examine the two core research questions in the context of ‘‘resource based view’’ of innovations in SMEs. Innovation would call for some internal resources and technical capability. Those firms which do not have adequate internal resources and technical capability might supplement them with external interactions and support. Firms which are relatively well-established are likely to have more resources and such firms are likely to be older and larger in size. Therefore, firm age and firm size (in terms of sales turnover) are two important factors to be considered. The objective of firm origin can also have an influence on a firm to undertake or not to undertake innovations. To be specific, firms which have come up exclusively as a means of employment or have come up to enjoy official benefits and concessions are unlikely to innovate whereas a firm which has come up to exploit identified market opportunities or due to innovative ideas of entrepreneurs are likely to engage in innovations. Further, a partnership firm more than an individual proprietor, might have better resources, financial as well as technical. Therefore, the nature of firm organisation is another important factor which can influence a firm towards innovation. But the technical capability of the firm will be reflected in the educational qualification of the entrepreneur/s, and proportion of skilled labour in the total employees of the firm. Technically qualified entrepreneurs and firms with a higher proportion of skilled employees will be better-off to undertake innovations than otherwise. In addition, the existence of an exclusive design centre might prompt a firm and facilitate the undertaking of innovations. Thus, in total, firm age, firm size, objective of firm origin, nature of firm organisation, educational qualification of entrepreneur/s, proportion of skilled employees, and presence/absence of an exclusive design centre are the internal factors which can prompt or prevent a firm from undertaking technological innovations. Those firms which do or do not have adequate internal resources might supplement them with external support and interaction. These interactions might take place relating to products or processes either sporadically or on a continuous basis with either customers or suppliers or large firms or universities/institutions or technology resource centres. Therefore, external interaction can be another decisive factor for undertaking innovations. The presence or absence of above factors, together, can act as either prompters or barriers for SMEs to undertake technological innovations. The presence of these factors together with innovation can contribute to the growth of SMEs and therefore differentiate higher growth firms from lower growth firms. This relationship between the identified variables is presented schematically in the form of a conceptual model in Fig. 1. Objectives, scope and method of analysis As mentioned above, the study has two specific objectives.  To ascertain the factors which differentiate innovative SMEs from non-innovative SMEs.  To probe the factors with or without innovation, which differentiate the sales growth of SMEs. This study is confined to manufacturing SMEs in the auto component, electronics and machine tool industry sectors of Bangalore city in India. We choose manufacturing sector because manufacturing SMEs accounted for more than 67% of the total SMEs in the country (DCMSME, 2011). In India, Bangalore region is industrially more developed with a relatively high concentration of engineering Please cite this article in press as: Bala, M.H., Innovation and growth of engineering SMEs in Bangalore: Why do only some innovate and only some grow faster?. J. Eng. Technol. Manage. (2015), http://dx.doi.org/10.1016/j.jengtecman.2015.05.001

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Internal Factors 1. Firm size 2. Firm age 3. Objective of firm origin 4. Firm organization 5. Entrepreneur’s qualification 6. Skilled labour 7. Design centre

Undertake Innovations

Higher Growth of SMEs

Do not undertake Innovations

Lower growth of SMEs

Promoters or Barriers

External Interaction 1. Relationship with suppliers, 2. Collaboration with customers, 3. Help from consultants, 4. Support from Technology Resource Centres, etc.

Fig. 1. Model of determinants of innovation and growth of SMEs.

and electronics industries. Many industrial estates located across the city comprise a significant number manufacturing SMEs encompassing auto component, electronics and machine tool sectors, among others (Government of Karnataka, 2009). Therefore, this study in Bangalore is expected to throw sufficient light on the two core research issues. We developed a semi-structured questionnaire containing about 60 questions/items covering both quantitative and qualitative variables covering employment, investment, sales turnover, percentage of innovated products in total sales, age of firms, age and education of entrepreneur/manager, objective of firm origin, nature of firm organisation, percentage of skilled labour, presence/absence of an exclusive design centre, presence/absence of external support, etc. The validity and reliability of the questionnaire was ensured based on the knowledge and experience of the author, discussions held with industry experts and representatives of SME associations. Further, based on a pilot study (conducted during November and December 2006) covering about 10 enterprises each in the three sectors, we did an item analysis for the questions excluding those which are (i) opinions on policies, (ii) dichotomous questions, and (iii) descriptive questions, which yielded a Cronbach’s a (alpha) of 0.653. In the absence of an official database, we relied on the databases of SME associations like Karnataka Small Scale Industries Association, Bangalore and Peenya Industries Association, Bangalore, among others. Accordingly, with the validated questionnaire, we approached about 150 SMEs in each of the three sectors across Bangalore city and gathered primary data from 72 auto component SMEs, 67 electronic SMEs and 75 machine tool SMEs. The regions covered within Bangalore city included Please cite this article in press as: Bala, M.H., Innovation and growth of engineering SMEs in Bangalore: Why do only some innovate and only some grow faster?. J. Eng. Technol. Manage. (2015), http://dx.doi.org/10.1016/j.jengtecman.2015.05.001

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Peenya industrial estate, Bommasandra industrial estate, Electronic city, Koramangala, Kumbalgod industrial area, Rajajinagar industrial estate, Kamakshipalya industrial estate, Veerasandra industrial estate, and Whitefield industrial area. Only those SMEs which have come up prior to 2001/02 were covered by the study. The quantitative data were gathered for a period of five years from 2001/02 to 2005/06. Data collection was done during January–December 2007. It is important to mention here why we gathered quantitative data for five long years. 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 (Coad, 2007). We have assumed that five years is a period good enough to examine the impact of innovation on growth and therefore gathered quantitative data accordingly. Of the 214 filled out questionnaires, we have chosen 197 SMEs which had given answers to all the required questions for the present analysis. The 197 SMEs were classified into two groups: innovative and non-innovative as follows: (i) SMEs, which declared that they are innovating and which individually had identified a minimum share of 5% of innovated products in their total sales turnover, are classified as innovative firms, and (ii) SMEs which declared that they are not innovating and did not account for any innovated product share in their total sales turnover, are classified as noninnovative firms. Accordingly, we identified 141 innovative SMEs and 56 non-innovative SMEs. The primary data gathered from these SMEs formed the basis of our analysis. Dependent variables There are two dependent variables in the analysis: 1. To distinguish innovative firms from non-innovative firms, a binary dependent variable (ID) is used for step-wise logistic regression analysis. SMEs, which are innovative and which have indicated innovated products as a percentage of their total sales, are coded as 1 to distinguish them as innovative from those which are not innovative and therefore did not have innovated products as part of their total sales (coded as 0). 2. Sales growth performance (SG) is measured in terms of compound average rate of growth of sales turnover of SMEs for 2001/02–2005/06, at constant (2001/02) prices, covering both innovative and non-innovative firms. Using the data on Small Scale Industry production, which are given at current prices as well as at 2001/02 prices from 2001/02 onwards by the Ministry of Micro, Small and Medium Enterprises, Government of India, we derived the output deflator for 2005/06. Using this output deflator, we converted the value of 2005/06 sales at current prices into value at constant (2001/02) prices. Thereafter, we calculated the compound average rate of growth of sales for 2001/02 to 2005/06. Step-wise multiple regression analysis is done to ascertain the influence of factors on firm (sales) growth.

Independent variables (i) (ii) (iii) (iv)

Firm age (FA): expressed in number of years since inception till 2001/02. Firm size (FS1): measured in terms of sales turnover as of 2001/02. Firm size (FS2): measured in terms of sales turnover as of 2005/06 Objective of firm origin (FO): used a dummy variable which assumed the value of 0 for those which came up as a means of employment for the entrepreneur or those which have come up to enjoy government sponsored benefits and concessions, to differentiate from firms which have come up due to identified market opportunities or innovative ideas of entrepreneurs which are assigned the value of 1. (v) Firm organisation (FOR): used a dummy variable of 1 for proprietorship firms and 0 for partnership firms. The SMEs covered for the study comprised only these two forms of organisation. (vi) Entrepreneur’s qualification (TD): used a dummy variable (1) for those who are technically qualified (diploma/degree holders in engineering) and (0) for others. Please cite this article in press as: Bala, M.H., Innovation and growth of engineering SMEs in Bangalore: Why do only some innovate and only some grow faster?. J. Eng. Technol. Manage. (2015), http://dx.doi.org/10.1016/j.jengtecman.2015.05.001

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(vii) Skilled labour (SL): expressed as a percentage of skilled labour in total employees. (viii) Exclusive design centre (DO): used a dummy variable for the presence of an exclusive design centre (1) to differentiate from those which did not have any exclusive design centre (0). (ix) External interaction (EI): expressed as a dummy variable of 1 for those which have indulged in external interaction with suppliers or customers or consultants or technology resource centres (either sporadically or on a sustained basis), and 0 for those which did not have any external interaction. (x) Control variable: two dummy variables (AD for auto components and ED for electronics) are used to distinguish the three industry sectors, namely, auto components, electronics and machine tools. Method of analysis We used stepwise backward elimination logistic regression analysis to examine what factors differentiate innovative SMEs from non-innovative SMEs. The dependent variable is binary (1 for innovative SMEs and 0 for non-innovative SMEs) and the independent variables are firm size, firm age, objectives of firm origin, nature of firm organisation, age of CEO, technical qualification of CEO, percentage of skilled labour, presence or absence of an exclusive design office. In addition, control (dummy) variables to distinguish industry sectors as explained above are used. We reported logistic regression coefficients (along with their z values, LR chi2 and Pseudo R2) (estimated by Stata 11 software) to indicate the influence or otherwise of independent variables. A positive coefficient of an independent variable would indicate its positive influence on SMEs to innovate whereas a negative coefficient implies otherwise. To find out whether firm size distinguishes the two groups after the observed period of innovation (2001/02–2005/06) as compared to the beginning of the observed period, we have carried out logistic regression analysis by adopting firm size of 2001/02 (FS1) and firm size of 2005/06 (FS2), alternatively. Accordingly, the first logistic regression equation used for the analysis is as follows: Ln ½ p=ð1  pÞ ¼ b0 þ b1FA þ b2FS1 þ b3FO þ b4FOR þ b5TD þ b6EI þ b7SL þ b8DO þ b9AD þ b10ED where p represents the probability of an event (innovation), b0 is the y-intercept, and each independent variable’s association with the outcome (log odds) is indicated by the coefficients b1 to b10. In effect, we are trying to model the probability that an event (carrying out innovation) is a result of a linear combination of variables as indicated in the equation above. In this model, we have used sales turnover as of 2001/02 (FS1) to represent the firm size. Subsequently, we have used the second logistic regression equation where sales turnover of 2005/06 (FS2) is alternatively used to represent the firm size. The second equation is as follows: Ln ½ p=ð1  pÞ ¼ b0 þ b1FA þ b2FS2 þ b3FO þ b4FOR þ b5TD þ b6EI þ b7SL þ b8DO þ b9AD þ b10ED To ascertain the influence of independent variables on firm growth, we used stepwise backward elimination multiple regression with the independent variables explained above, where firm size is represented by sales turnover as of 2001/02 (FS1). The analysis is done by using Stata 11 software and we reported the regression coefficients with t values, F value and R square. To examine the influence of innovation on firm growth, sales turnover growth (2001/02–2005/06) is taken as the dependent variable along with independent variables used for the previous analysis. To know whether innovation makes any difference to the growth of firms, we have carried out multiple regression analysis by measuring innovation variable in two different ways: (i) a dummy variable (ID) is used for innovative SMEs (1) to distinguish them from non-innovative SMEs (0), and (ii) innovation sales (IS) (as a percentage of total sales turnover) is alternatively used to represent the innovative SMEs and to differentiate them from the non-innovative SMEs. Accordingly, the first Please cite this article in press as: Bala, M.H., Innovation and growth of engineering SMEs in Bangalore: Why do only some innovate and only some grow faster?. J. Eng. Technol. Manage. (2015), http://dx.doi.org/10.1016/j.jengtecman.2015.05.001

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multiple regression model (where ID is one of the explanatory variables) used for the analysis is as follows: SG ¼ b0 þ b1FA þ b2FO þ b3FS1 þ b4FOR þ b5TD þ b6EI þ b7DO þ b8ID þ b9SL þ b10AD þ b11ED Here SG represents growth of sales turnover of SMEs (during 2001/02–2005/06), b0 is the y-intercept, and b1 to b10 are coefficients of the independent variables. These coefficients represent the levels of influence that they have on the dependent variable. Subsequently, we have used the second multiple regression model where innovation sales (IS) (as a percentage of total sales turnover) is used to represent the innovative SMEs and to differentiate them from the non-innovative SMEs. The objective is to examine how important is innovations sales to the growth of sales turnover of SMEs. We have used average innovation sales of innovative SMEs during 2001/02–2004/05 as one of the explanatory variables. The second multiple regression model used for the analysis is as follows: SG ¼ b0 þ b1FA þ b2FO þ b3FS1 þ b4FOR þ b5TD þ b6EI þ b7DO þ b8IS þ b9SL þ b10AD þ b11ED The advantage in using backward elimination logistic/multiple regression is that it starts with a logistic/multiple regression equation including all the independent variables, and then deletes independent variables that do not contribute significantly. Sequential search methods which include backward elimination logistic/multiple regression among others, offer 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 to explain the dependent variable (Hair et al., 2007). Though stepwise modelling has several disadvantages (Whittingham et al., 2006), it is useful, particularly in exploratory research as a screening device. Since the present study is exploratory in nature, we feel it is appropriate for the proposed analysis. Factors distinguishing innovative and non-innovative SMEs and their growth: results and discussion The results are presented and discussed under three subsections. The descriptive statistics and correlation coefficients between the variables are described in Section ‘Basic features of innovative and non-innovative SMEs’. The results of stepwise logistic regression analysis are presented and discussed in Section ‘Factors differentiating innovative and non-innovative SMEs’ whereas the results of stepwise multiple regression analysis are examined in Section ‘Role of innovation in the growth of SMEs’. Basic features of innovative and non-innovative SMEs The descriptive statistics of variables used for the study, comprising mean, standard deviation, minimum and maximum values, are presented in Table 1. Firm age (FA) varied from a minimum of five years to a maximum of 49 years with a mean of about 10 years, implying that majority firms are younger by age. It is important to note that both minimum and maximum sales turnovers and mean sales turnover of SMEs have increased in 2005/06 (FS2) compared to 2001/02 (FS1). In the same way, their standard deviation which was much higher than their mean in 2001/02, increased much more than their mean by 2005/06, implying the skewed sales growth of firms during the period (2001/02– 2005/06). This is also reflected in the mean, standard deviation, minimum and maximum of sales growth (SG). In fact, some firms have experienced negative sales growth during the period. The share of skilled labour (SL) in total labour force varied from 0 to as high as 94% and the percentage of revenue generated from the sale of innovated products (IS) in total sales turnover ranged from 0 to 42. All the remaining variables are dummy variables indicating objective of firm origin (FO), nature of firm Please cite this article in press as: Bala, M.H., Innovation and growth of engineering SMEs in Bangalore: Why do only some innovate and only some grow faster?. J. Eng. Technol. Manage. (2015), http://dx.doi.org/10.1016/j.jengtecman.2015.05.001

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Table 1 Descriptive statistics of variables. Variable

Mean

Standard deviation

Minimum

Maximum

N

Firm Age (FA) Firm Size of 2001/02 (FS1) Firm Size of 2005/06 (FS2) Sales Growth (SG) Firm Origin Objective (FOa) Firm Organisation (FORa) Technical Education (TDa) Skilled Labour Percentage (SL) Design Centre (DOa) External Interaction (EIa) Innovation Dummy (IDa) Auto Components Dummy (ADa) Electronics Industry Dummy (EDa) Machine Tools Dummy (MDa)

16.33 134.03 246.90 16.92 0.37 0.82 0.76 46.77 0.31 0.54 0.47 0.32 0.32 0.38

9.69 292.21 557.53 18.25 0.48 0.38 0.43 19.91 0.46 0.50 0.50 0.47 0.47 0.49

5 1.5 5.0484 29.12 0 0 0 0 0 0 0 0 0 0

49 2500 6058.08 91.55 1 1 1 94 1 1 1 1 1 1

197

a

Dummy variables.

organisation (FOR), technical education (TD) of the owner/entrepreneur, presence of an exclusive design centre (DO), external interaction (EI), innovation dummy (ID), and three industry sector dummies (AD, ED and MD, respectively). The relationships between the different variables in terms of correlation coefficients are presented in Table 2. All correlation coefficients which are 0.14 are statistically significant. Firm size – FS2 (Sales turnover of 2005/06), but not firm size – FS1 (Sales turnover of 2001/02), has a statistically significant positive relationship with sales growth (during 2001/02–2005/06), innovation dummy (ID) and innovation sales (IS). Sales growth (SG) has a statistically significant negative relationship with firm age (FA) whereas a moderately high positive relationship with innovation dummy, innovation sales and objective of firm origin (FO). Firm age has a statistically significant negative relationship with firm organisation (FOR). Objective of firm origin has a statistically significant positive relationship with innovation dummy, innovation sales, presence of design office (DO), and external support (EI). Firm organisation has a statistically significant negative relationship with innovation dummy and innovation sales. Technical qualification (TD) of entrepreneur/manager has a significant positive relationship with innovation dummy and innovation sales. Presence of design office has a significant positive relationship with innovation dummy and innovation sales. Among the industry dummy variables, only auto components sector dummy (AD) has a significant positive relationship with innovation dummy and innovation sales whereas machine tools sector dummy (MD) has a significant negative

Table 2 Correlation between variables. Variable

FS1

FS2

SG

FA

FO

Firm Size of 2001/02 (FS1) 1.00 Firm Size of 2005/06 (FS2) 0.85 1.00 Sales Growth (SG) 0.11 0.16 1.00 Firm Age (FA) 0.12 0.03 0.27 1.00 Firm Origin Objective (FOa) 0.08 0.11 0.34 0.09 1.00 Firm Organisation (FORa) 0.11 0.07 0.06 0.14 0.08 Technical Education (TDa) 0.10 0.12 0.13 0.02 0.13 Skilled Labour (SL) 0.02 0.05 0.10 0.01 0.02 a Design Centre (DO ) 0.15 0.12 0.10 0.09 0.24 External Interaction (EIa) 0.07 0.08 0.07 0.14 0.16 a Innovation Dummy (ID ) 0.13 0.21 0.46 0.07 0.33 Auto Components (ADa) 0.00 0.00 0.16 0.06 0.03 Electronics Dummy (EDa) 0.09 0.05 0.06 0.05 0.01 Machine Tools (MDa) 0.08 0.06 0.13 0.00 0.06 a

FOR

TD

SL

DO

EI

ID

AD

ED

1.00 0.04 1.00 0.08 0.08 1.00 0.06 0.09 0.08 1.00 0.02 0.11 0.04 0.03 1.00 0.22 0.15 0.11 0.25 0.08 1.00 0.07 0.03 0.07 0.05 0.01 0.21 1.00 0.09 0.07 0.09 0.11 0.00 0.10 0.48 1.00 0.01 0.02 0.01 0.02 0.03 0.16 0.43 0.54

Dummy variables.

Please cite this article in press as: Bala, M.H., Innovation and growth of engineering SMEs in Bangalore: Why do only some innovate and only some grow faster?. J. Eng. Technol. Manage. (2015), http://dx.doi.org/10.1016/j.jengtecman.2015.05.001

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relationship with innovation dummy and innovation sales. Percentage of skilled labour (SL) does not have any significant relationship with any other variable. Factors differentiating innovative and non-innovative SMEs To explore the factors which distinguish innovative SMEs from non-innovative SMEs, we have carried out stepwise logistic regression analysis. Table 3 presents the results of stepwise logistic regression analysis for distinguishing innovative SMEs from non-innovative SMEs. The stepwise (backward elimination) logistic regression model I is statistically significant and it adequately explains the factors which distinguish innovative SMEs from non-innovative SMEs. The model retained six variables, namely, firm age (FA), firm origin objective (FO), firm organisation (FOR), presence of an exclusive design office (DO), skilled labour percentage (SL), and auto component industry dummy (AD). Thus it eliminated the remaining four variables, namely, dummy variable for electronics industry (ED), external support/interaction of SMEs (EI), technical qualification of the entrepreneur (TD), and firm size – FS1 (sales turnover of 2001/02). The results imply that among the firm level variables, firm age (FA), objective of firm origin (FO), firm organisation (FOR), presence of an exclusive design office (DO), and skilled labour percentage (SL) significantly differentiated innovative SMEs from non-innovative SMEs. A description on each of these variables is in order. Firms which are younger are innovators as against older firms. Younger firms are under pressure to establish them in the market and therefore are likely to innovate as a means of business survival and growth. Objective of firm origin (FO) clearly distinguishes innovators and noninnovators. To elaborate, among firms, salary-substitute firms (which come up as a means of employment) are not innovators whereas entrepreneurial firms (firms which come up due to identified market opportunities or innovative ideas of entrepreneurs) are innovators. Firm organisation (FOR) differentiates innovators and non-innovators because partnership firms which are innovators are better-off compared to proprietorship firms, particularly for technical resources and decision-making. In addition, clearly firms with the presence of an exclusive design office (DO) are Table 3 Factors distinguishing innovative and non-innovative SMEs: results of stepwise logistic regression analysis. Logistic Regression Model I

Logistic Regression Model II

Stepwise, pr(.10): logit ID FS1 FA FO FOR TD SL DO EI AD ED p = 0.9854  0.1000 removing Electronics sector dummy (ED) p = 0.7273  0.1000 removing External interaction dummy (EI) p = 0.1771  0.1000 removing Technical education dummy (TD) p = 0.1173  0.1000 removing Firm size of 2001/02 (FS1) Number of observations = 197 LR chi2 (6) = 53.82 Prob > chi2 = 0.0000 Log likelihood = 109.33233 Pseudo R2 = 0.1975

Stepwise, pr(.10): logit ID FS2 FA FO FOR TD SL DO EI AD ED p = 0.8411  0.1000 removing Electronics sector dummy (ED) p = 0.7410  0.1000 removing External interaction dummy (EI) p = 0.2194  0.1000 removing Technical education dummy (TD)

Innovation Dummy (ID) Firm age (FA) Firm origin objective (FO) Firm organisation dummy (FOR) Auto component dummy (AD) Skilled labour % (SL) Design Centre dummy (DO) Constant

Coefficient **

0.035 (1.93) 1.39 (3.92)*** 1.51 (3.13)*** 1.02 (2.81)*** 0.019 (2.22)** 0.982 (.266)*** 1.482 (2.03)**

Number of observations = 197 LR chi2 (7) = 60.55 Prob > chi2 = 0.0000 Log likelihood = 105.96724

Pseudo R2 = 0.2222

Innovation Dummy (ID)

Coefficient

Firm size of 2005/06 (FS2) Firm age (FA) Firm origin objective (FO) Firm organisation dummy (FOR) Auto component dummy (AD) Skilled labour % (SL) Design Centre dummy (DO) Constant

0.001 (2.01)** 0.038 (2.01)** 1.321 (3.63)*** 1.514 (3.07)*** 1.03 (2.83)*** 0.019 (2.10)** 0.891 (2.36)** 1.302 (1.75)*

Note: Figures in parentheses are z values. * Significant at 0.10 level. ** Significant at 0.05 level. *** Significant at 0.01 level.

Please cite this article in press as: Bala, M.H., Innovation and growth of engineering SMEs in Bangalore: Why do only some innovate and only some grow faster?. J. Eng. Technol. Manage. (2015), http://dx.doi.org/10.1016/j.jengtecman.2015.05.001

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innovators whereas those who do not have are non-innovators. The above results together indicate that if SMEs, irrespective of their size, are in the partnership form and have emerged due to either perceived market opportunities or innovative ideas and have set up exclusive design centres, then they are likely to engage in technological innovations in their early years for an early ‘‘settling down’’. However the presence of a larger proportion of skilled labour (SL) is not conducive to make a firm innovator. This is because firms irrespective of size, age, objective, organisation and sector have absorbed a larger proportion of skilled labour in Bangalore. This is because retaining skilled labour for a longer duration is a major challenge for SMEs in Bangalore. The common grouse among these entrepreneurs (as we have learnt from our interactions with these SME entrepreneurs) is that unskilled labourers join them for getting trained over a couple of years. Thus, many labourers use SMEs as the ‘‘training centres’’ and once they are fully equipped with the ‘‘required skills’’, they quit SMEs and join large enterprises including MNCs located in the city. As a result, the rate of labour turnover is high and therefore, whether SMEs innovate or not, in general, recruit a larger proportion of skilled labour-force than required. Further, even in innovative SMEs, such trained labourers hardly make a difference to innovation due to their short stint in SMEs. Among sectors, auto component SMEs (AD) are more innovators compared to SMEs of electronics and machine tools. This is because auto component SMEs are much better integrated with the large firms (in the automobile industry of the city) than electronic and machine tool SMEs. These large firms provide support of different kinds including technical assistance, to their subcontractors which have prompted them to undertake innovations (Kumar and Bala Subrahmanya, 2010). Further, a description on the variables removed by the model is in order. Technical qualification of entrepreneurs did not matter because we have found SMEs with technically qualified entrepreneurs who run their enterprises without carrying out innovations. They focus on customer demand and meet customer expectations with little product/process modifications. They indulge in regular or sporadic external interactions, particularly with customers. That is why external interaction did not differentiate innovative from non-innovative SMEs. Among the sectors, innovative SMEs did not get differentiated from non-innovative SMEs in the electronic industry, and finally firm size – FS1 did not matter for SME innovations. This implied that firm resources as reflected in firm size did not matter for SME innovations in Bangalore. This result can be understood better from other variables which differentiated innovative and non-innovative SMEs. We are also keen to ascertain whether firm size along with other explanatory variables, differentiates innovative and non-innovative SMEs after the observed period of innovation. Accordingly, we carried our logistic regression analysis using FS2 (sales turnover of 2005/06) instead of FS1, as one of the explanatory variables in the second equation. The stepwise logistic regression analysis II results are also presented in Table 3. The model eliminated only three variables, which were eliminated by the previous model as well, namely, ED, EI and TD. However, firm size (FS2) positively differentiated innovative SMEs from non-innovative SMEs (along with all the other statistically significant variables of the previous model). This would mean that the SMEs which registered a higher sales growth over the period 2001/02–2005/06 would have innovated unlike the SMEs which registered a lower sales growth during the same period. Overall, our results support the proposed hypothesis that firms with better internal capability undertake innovations compared those which have lesser internal capability. Role of innovation in the growth of SMEs Once we ascertained the factors which differentiated innovative from non-innovative SMEs, we examined what factors explicitly contributed to the growth of SMEs over a period of time. We have taken growth of sales turnover (SG) of SMEs during 2001/02–2005/06 as the dependent variable and all the explanatory variables of the previous analysis (firm size is represented by FS1) as well as innovation dummy variable (ID) to distinguish innovative from non-innovative SMEs, for the stepwise (backward elimination) multiple regression analysis. We used FS1 (sales turnover of 2001/02) because we are keen to explore whether firm size mattered for firm growth during the period 2001/02–2005/06. The results of stepwise backward elimination multiple regression analysis along with VIF values of independent variables are given in Table 4. The regression model I is statistically significant as Please cite this article in press as: Bala, M.H., Innovation and growth of engineering SMEs in Bangalore: Why do only some innovate and only some grow faster?. J. Eng. Technol. Manage. (2015), http://dx.doi.org/10.1016/j.jengtecman.2015.05.001

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Table 4 Factors influencing sales growth of SMEs: results of stepwise multiple regression analysis. Multiple Regression Model I

Multiple Regression Model II

Stepwise, pr(.10): regress SG FS1 FA FO FOR TD SL DO EI ID AD ED p = 0.9677  0.1000 removing Design Centre dummy (DO)

Stepwise, pr(.10): regress SG FS1 FA FO FOR TD SL DO EI IS AD ED p = 0.8326  0.1000 removing Design Centre dummy (DO) p = 0.7848  0.1000 removing Firm organisation dummy (FOR) p = 0.7488  0.1000 removing Electronics sector dummy (ED) p = 0.7400  0.1000 removing External interaction dummy (EI) p = 0.6323  0.1000 removing Technical education dummy (TD) p = 0.3145  0.1000 removing Auto component dummy (AD) p = 0.3076  0.1000 removing Skilled labour % (SL) Number of observations = 197 F(4, 192) = 34.50 Prob > F = 0.0000 R-squared = 0.4182 Adjusted R-squared = 0.4061 Root MSE = 14.06

p = 0.7330  0.1000 removing Firm organisation dummy (FOR) p = 0.6771  0.1000 removing External interaction dummy (EI) p = 0.5207  0.1000 removing Electronics sector dummy (ED) p = 0.3358  0.1000 removing Technical education dummy (TD) p = 0.3079  0.1000 removing Skilled labour % (SL) p = 0.2932  0.1000 removing Auto component dummy (AD) Number of observations = 197 F(4, 192) = 25.23 Prob > F = 0.0000 R-squared = 0.3445 Adjusted R-squared = 0.3309 Root MSE = 14.93 Sales Growth (SG)

Coefficient

t

Sales Growth (SG)

Coefficient

t

Firm size of 2001/02 (FS1) Firm age (FA) Firm origin objective (FO) Innovation dummy (ID) Constant

0.009 0.482 9.626 13.638 16.053

2.50** 4.29*** 4.10*** 5.97*** 6.69***

Firm size of 2001/02 (FS1) Firm age (FA) Firm origin objective (FO) Innovation sales % (IS) Constant

0.008 0.447 7.478 0.931 13.82

2.56*** 4.21*** 3.32*** 8.03*** 6.02***

Variable

VIF

1/VIF

Variable

VIF

1/VIF

Firm origin objective (FO) Innovation dummy (ID) Firm age (FA) Firm size of 2001/02 (FS1) Mean VIF

1.15 1.14 1.04 1.04 1.09

0.870041 0.879312 0.964266 0.964996

Innovation Sales (IS) Firm origin objective (FO) Firm age (FA) Firm size of 2001/02 (FS1) Mean VIF

1.18 1.18 1.04 1.03 1.11

0.844677 0.848312 0.960121 0.972821

Note: ** Significant at 0.05 level. *** Significant at 0.01 level.

indicated by the F value and it explained nearly one-third of the total variation in sales growth performance as reflected in the adjusted R squared value. This can be considered satisfactory because the independent variables are mostly qualitative variables and they did not include two crucial explanatory variables of sales growth, namely, employment growth and investment growth. There is no problem of multicollinearity between the statistically significant explanatory variables as indicated by their VIF values (Table 4). The stepwise backward elimination regression model retained only four of the explanatory variables, namely, firm size (FS1), firm age (FA), objective of firm origin (FO), and innovation dummy (ID) but eliminated both of the sector dummies (AD and ED), skilled labour proportion (SL), technical education of entrepreneurs (TD), external interaction (EI), presence of an exclusive design office (DO), and nature of firm organisation (FOR). Firm size (as of 2001/02) influenced sales growth negatively and so is firm age implying that smaller and younger firms grew faster than larger and older firms. The objective of firm origin influenced firm growth positively in the sense that entrepreneurial firms grew faster than salary-substitute firms. Finally, innovation influenced firm growth positively indicating that innovative firms registered a higher sales growth relative to that of non-innovative firms. This result substantiates the inference that we derived from the previous logistic regression analyses. Firm Please cite this article in press as: Bala, M.H., Innovation and growth of engineering SMEs in Bangalore: Why do only some innovate and only some grow faster?. J. Eng. Technol. Manage. (2015), http://dx.doi.org/10.1016/j.jengtecman.2015.05.001

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organisation, exclusive design centre, external interactions, technical qualification of entrepreneur/s, proportion of skilled labour, and industry sectors did not matter for sales growth. The results of the second stepwise backward elimination multiple regression analysis along with VIF values of independent variables are also given in Table 4. The model is statistically significant and it has a higher explanatory power (>40%) compared to the previous model. The results are similar to the previous model: it retained only four of the independent variables as the statistically significant explanatory variables and innovation sales (as a percentage of sales turnover) (instead of innovation dummy) is one of the explanatory variables contributing to the growth of sales turnover of SMEs. This result re-emphasises the role and contribution of innovation to the sales growth of SMEs. The above results indicate clearly that primarily firm level resources and capabilities distinguish innovative from non-innovative SMEs as well as higher-growth from lower-growth SMEs. These firm level resources and capabilities reflect internal strength and internal strategy as far as innovations are concerned whereas they reflect internal strategy and innovations as far as firm growth is concerned. Internal strength, in turn, is reflected in firm organisation (FOR) and presence of an exclusive design office (DO) whereas internal strategy is reflected in firm age (FA) and objective of firm origin (FO). A firm should have internal strength and it should strategise its resources for undertaking successful innovations. Similarly, a firm must have a strategy for long-term growth and with innovations it will be able to achieve its strategic objective of sales growth. The question is why younger and smaller innovative SMEs grow faster than older and larger noninnovative SMEs? The answer perhaps lies in ‘population ecology’ or ‘organisational ecology’ perspective as argued by Coad (2007). The basic theoretical prediction pertaining to the growth of firms is that firms require resources which are specific to niches, and these niches have a particular ‘carrying capacity’. If a firm has discovered a new niche with a rich resource pool, then this firm will be able to grow without hindrance. The number of firms in the niche will also grow, due to entry of new organisations. If the population grows to a level where the niche’s resource is saturated, then competition between firms will limit the growth rates of firms (Coad, 2007). To ascertain whether this argument holds well in the context of Bangalore, we approached some of the SMEs which experienced above average sales growth during 2001/02–2005/06. Interaction with these SME entrepreneurs revealed that: (i) they have entered ‘niche product’ areas, which enabled them to achieve a high rate of sales turnover in the initial years of their entry into the market. However, soon the initial advantage gained through the entry into a niche area saturated resulting in a lower rate of sales growth thereafter. (ii) Competitors emerged for their products in the later years of their entry into the market. Both explained why younger and smaller innovative SMEs grew faster compared to older and larger non-innovative SMEs. Thus, the empirical results substantiate our second hypothesis that younger and smaller innovative firms grow faster than older and larger sized non-innovative firms. Inferences and conclusions We have probed two research questions in this paper: - What are the factors which differentiate innovative SMEs from non-innovative SMEs? - What factors, including or excluding innovation, differentiate sales growth of SMEs? These research questions are probed in the context of resource based view for innovation with reference to 197 manufacturing SMEs covering auto components, electronics and machine tool industries in Bangalore city of India. Of these 141 are innovative SMEs and 56 are non-innovative SMEs. These SMEs varied in terms of firm size, firm age, firm organisation, objectives of firm origin, educational qualifications of entrepreneurs/managers, and percentages of skilled labour, presence/ absence of an exclusive design office, external interactions and industry sectors. But innovative SMEs got differentiated from non-innovative SMEs fairly clearly. Similarly, differentiating factors of sales growth of SMEs are clearly identified where the role of innovation is ascertained. Our results brought out clearly that innovation aids firm growth but firm size does not make a difference to innovation. Thus, this paper has two important contributions: (1) how are innovative SMEs different from noninnovative SMEs? (2) what is the role of innovation in differentiating the sales growth of SMEs? This Please cite this article in press as: Bala, M.H., Innovation and growth of engineering SMEs in Bangalore: Why do only some innovate and only some grow faster?. J. Eng. Technol. Manage. (2015), http://dx.doi.org/10.1016/j.jengtecman.2015.05.001

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Innovative SMEs

Higher Growth of SMEs - Younger by age - Entrepreneurial by nature - Partnership firms by organization - Have an exclusive design centre within

Smaller by Size

Non-Innovative SMEs

Lower Growth of SMEs

- Older by age - Salary-Substitute in nature - Proprietorship firms by organization - No exclusive design centre within

Larger by Size Fig. 2. Distinguishing features of innovative and non-innovative SMEs, and growth of SMEs.

paper provides empirical answers to both of these questions in the Indian context. In the light of the obtained answers, the distinguishing factors between innovative and non-innovative SMEs on the one hand, and between higher growth and lower growth SMEs, on the other, are depicted in Fig. 2. The differentiating factors between innovative and non-innovative SMEs brought out clearly that SMEs must have ‘‘own resources and capabilities’’ in the form of internal strength and definite internal strategy if they have to be successfully innovative. This internal strength is exhibited in firm ownership structure in the form of partnership firms and having an exclusive design office within the firm. Conversely, firms which did not innovate are individual proprietorships, and they did not have any exclusive design centres. Firm’s internal strategy is reflected in firm age and objective of firm origin. Younger SMEs which are entrepreneurial firms are innovative whereas older SMEs which are salary-substitute firms are not innovative. These results emphasise that SME innovation promotion programmes should focus more on those firms which are younger, have better talent pool (in terms of ownership) and have some internal technical strength. Further, such SMEs should be preferably entrepreneurial in nature. If these characteristics of SMEs are ensured, they can be successfully encouraged to engage themselves in innovations. The second and more important finding is with respect to the role of innovation in firm growth. Younger and smaller firms which are entrepreneurial in nature and which are innovative contributed to higher sales growth of SMEs. Conversely older and larger firms which are salary-substitute firms in Please cite this article in press as: Bala, M.H., Innovation and growth of engineering SMEs in Bangalore: Why do only some innovate and only some grow faster?. J. Eng. Technol. Manage. (2015), http://dx.doi.org/10.1016/j.jengtecman.2015.05.001

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nature and which are not innovative have experienced relatively a lower rate of sales growth. This result brings out that there is ample justification for making policy efforts to promote innovation among younger and entrepreneurial SMEs, to enable them to emerge competitive and grow over a period of time. It is important to understand that it is neither feasible nor desirable to make every SME innovative, given the limited resources devoted for SME promotion in the country. Therefore there is a need for ‘‘targeted policy’’ efforts through a ‘‘selective approach’’ for promoting innovation and growth among SMEs. This paper has made some contributions to innovation literature as well. While SMEs occupy considerable importance in developing economies including emerging economies, due to their significant contributions in terms of employment, production and exports (Edinburgh Group, 2014), their innovation dimensions, and contribution either to their own economic performance or to national economies, are not adequately revealed. This paper has thrown light on what kind of SMEs are able to carry out innovations and how significant are these innovations for their economic performance, in terms of their growth over a period of time. This study can trigger further empirical research focusing on other emerging economies/developing countries, and facilitate ‘appropriate innovation promotion policies’ for SMEs in these regions. Finally, a note on the limitations of the study is in order. Since stepwise modelling (which has the limitations of ‘bias in parameter estimation’ and ‘an in appropriate focus on a single best model’, among others) is used for the analysis of both the research objectives, the conclusions have to be treated with caution for its wider acceptability, in terms of reliability and validity. Acknowledgement This article has been developed based on primary data gathered for the Research Project titled, Influence of Technological innovations on the Growth of Manufacturing SMEs, sponsored by the Department of Science & Technology (DST), Government of India. The interpretations and conclusions contained in this article are solely attributable to the author and nobody else. The sponsorship obtained from DST is gratefully acknowledged, with usual disclaimers. References Bala Subrahmanya, M.H., Mathirajan, M., Balachandra, P., Srinivasan, M.N., 2001. R&D in Small Scale Industries in Karnataka, Research Project Report. Department of Science and Technology, Government of India, New Delhi. Barnett, E., Storey, J., 2000. Manager’s accounts of innovation processes in small and medium-sized enterprises. J. Small Bus. Enterprise Dev. 7, 315–324. Becheikh, N., Landry, R., Amara, N., 2006. Lessons from innovation empirical studies in the manufacturing sector: a systematic review of the literature from 1993–2003. Technovation 26, 644–664. Cefis, E., Marsili, O., 2006. Survivor: the role of innovation in firm’s survival. Res. Policy 35, 626–641. Chaminade, C., Van-Lauridsen, J., 2006. Innovation policies for Asian SMEs: an innovation system perspective. In: Yeung, H. (Ed.), Handbook of Research on Asian Studies. Edward Elgar, London. Coad, A., 2007. Firm Growth: A Survey, Working Paper No. 0703 Max Planck Institute of Economics, Jena, Germany. Coad, A., Rao, R., 2008. Innovation and firm growth in high-tech sectors: a quantile regression approach. Res. Policy 37 (4), 633–648. 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Please cite this article in press as: Bala, M.H., Innovation and growth of engineering SMEs in Bangalore: Why do only some innovate and only some grow faster?. J. Eng. Technol. Manage. (2015), http://dx.doi.org/10.1016/j.jengtecman.2015.05.001