1. INTRODUCTION:

The basis for today’s business are business networks within and between organizations (Connell et al., 2014). When a group of firms cooperate with each other in order to achieve collective efficiency, overcome common problems beyond their individual reach and penetrate and conquer marketsthen such cooperation is called business networks among firms (Ceglie and Dini, 1999; UNIDO, 2001).

Business networks imply inter-connections with input suppliers, industry associations, R&D organizations, trading agencies and relevant government and inter-government bodies (Das, 2008). Business network can be defined as a multi-facet concept where different firms display different degree of involvement (Lei and Huang, 2014). Web of overlappingand dense ties where rapid diffusion of knowledge takes place is known as network of relationships among firms (Exposito-Langa et al., 2015).

Clustering has been the age old phenomenon in India. Clusters have been in existence in India for centuries and are known for their products at the national and international level (Singh, 2010). Clusters are geographic concentration of interconnected companies and institutions in a particular field which includes actors such as suppliers, customers, manufacturers, government and other institutions such as standards-setting agencies, universities, think tanks, and trade associations (Porter, 1998). Clusters occur in many types of industries either large or small and at several geographical levels like nations, states, or cities, this occurrence decreases the appropriateness in the definition of cluster (Porter, 2000). With respect to micro, small and medium enterprises (MSMEs), a cluster is a sectoral and geographical concentration of MSMEs facing similar threats and opportunities and producing a similar range of goods or services (UNIDO, 2006; Das et al., 2007). Firms present in a region and dedicated to a particular product is defined as industrial cluster (Niu et al., 2008).

A particular element which characterises a cluster and allow enterprises to create value and competitive advantage is the interconnectedness or the linkages between the firms (Connell et al., 2014). Networks with government and other supportive institutions and inter-firm collaboration are firm’s innovativeness determinants (Niu, 2010b). Marketing performanceand long-term competitiveness of firms can be improved by engaging in strong vertical inter-firm relationship in a cluster (Lamprinopoulou and Tregear (2011), Martinez et al., (2012)). Barriers such as global competition, technological obsolescence, and investment shortages can be overcome by MSMEs in India by adopting networking approach (IBEF, 2013). There exist a positive and strong relationship between proximity to companies and firm performance (Singh and Shrivastava (2013), Lei and Huang (2014)). Firms’ relationship with business service providers plays an important role in firm innovativeness (Exposito-Langa et al., 2015). Degree of clusters’ intra and extra linkages drives innovation performance of a firm (Chandrashekar and Hillemane, 2018).

Despite this significance of business networks formed by firms in a cluster, there are some research gaps which remain relatively unexplored. Zhao etal., (2010) suggested that there exist a need to express how firms in cluster interact with other firms. Structure of networks is different in different clusters (Martinez et al., 2012). Difference between the kind of relationship among firms and the stakeholders in a cluster need to be highlighted (Lamprinopoulou and Tregear, 2011).Therefore, there exists a need to focus and provide a better understanding of different types of business networks which a firm forms with the stakeholders in a cluster, i.e. in what way firms interact with various stakeholders. In addition to this, studies conducted on auto-component sector of India are related to the following areas: analysis of different strategies adopted by firms in the Indian auto component sector to become competitive (Singh et al., 2007), creation and categorisation of knowledge (Pillania, 2008), measuring performance and leanness (Saranga, 2009; Singh, Garg, and Sharma, 2010), study of strategic technology management practices adopted by firms operating in the auto component industry of India (Sahoo et al., 2011), determining determinants of competitiveness (Joshi et al., 2013), factors contributing to efficient inventory management (Saranga et al., 2015). Given the lack of literature on types of business networks formed by firms with the other participants and its impact on firm performance present in an auto-component cluster, this study tries to answer these research gaps. Based on the above research gaps, this study aims to answer the following two research question .i.e. first, what are the different types of business networks which can be observed between firms and stakeholders in a cluster and second, to explain the relationship between these business networks and the performance of firms.This paper consists of 6 sections. Section 1 is the introduction. Section 2 consists of literature review related to clusters, business networks in a cluster and firm performance. Section 3 and 4 are about objectives and research methodology. Section 5 discusses the results of the study. Lastly section 6 covers the conclusion part of the studyalong with theoretical and practical implications, limitations of this study and some suggestions for future research.

2. LITERATURE REVIEW:

2.1 Cluster Concept:

The underlying concept of cluster dates back to 1890 in the work of Alfred Marshal. Alfred Marshall is among the first who examined the clustering phenomenon in industrial organizations. Alfred Marshall in 1920 explained why particular specialized industry concentrates in particular locality through industrial districts. As per Marshall industrial districts were the concentration of specialized industries of similar kind in a particular locality. Pouder and St. John (1996) explained geographic clustering of firms in the same industry through hot spot which they defined as regional clusters of firms competing in the same industry which, as a group grow more rapidly than other firms in sales and employment levels. However, Michael Porter was the one who gave relevance to clustering of firms or cluster concept. Michael Porter, a leading authority on competitiveness of regions and nations and on company strategy introduced the term industry cluster in his book The Competitive Advantage of Nations in 1990. Later various other scholars and organizations worked in this area (Baptista and Swann, 1998; Morosini, 2004; UNIDO, 2006; Das et al., 2007; Planning Commission, 2012). Baptista and Swann (1998) defined geographic cluster as an intense collection of related companies located in a small geographical area. Morosini (2004) defined industrial cluster as a socioeconomic entity characterized by a social community of people and a population of economic agents localized in close proximity in a specific geographic region. In India, a Cluster is defined as a geographically proximate group of interconnected firms and associated institutions which share common markets, technologies, worker skill needs in a particular field and which are also often linked by buyer-seller relationship (Planning commission, 2012). Industry clusters are geographic agglomerations of enterprises which are specialized in one or more related industries (Giuliani, 2013). According to Fundeanu and Badele (2014) competitive and innovative industry which favours the emergence of new form of competitive advantages in the form of partnerships between businesses, research institutions, universities and states is called cluster.

2.2 Firm’s Business Networks:

Iacobucci and Hopkins (1992) define business networks as pattern of relationships that tie large number of actors together. Business networks are sets of interactive and connected relationships among firms (Hakansson and Johanson, 1993). Business networks are defined as structures of exchange relationships among individuals, business actors, firms (Halinen and Tornroos, 1998). Business networks consist of independent firms coordinating their activities and resources and working together toward common goals (Johnston et al, 1999). Business networks are co-ordinated and integrated set of economic and non-economic relations embedded among business firms (Keeble and Wilkinson, 1999). In small businesses, business networks are long-term contacts between small business owners and external persons or organizations in order to obtain information (Premaratne, 2002). Business networks are relationships that create connections between two or more independent entities (Das and Teng, 2002). Business networks in its most basic form represent a set of actors linked by a set of social relationships (Nooy et al., 2005; Hakansson and Ford, 2002). Business networks are established in an open and unplanned form from local interactions, with market, social and institutional relationships coexisting within the cluster (Giuliani, 2007). Business networks are interactive relationships or linkages that individuals have, or may seek to develop with others (Hampton et al., 2009). Anderson, Dodd and Jack (2010) defined business networks as a socially constructed strategic alliance for instituting change, helping companies to grow and create their future. Rietveldt and Goedegebuure (2014) define business networks as relationships that are linked together by exchange transactions. Business networks can also be broadly described as interactive relationships that individuals, businesses or any other entities have with others (Desta, 2015).

2.3 Stakeholders in Clusters:

Table 1 show some previous studies which have talked about different set of players or stakeholders present in a cluster with whom firm form relationship.

Table 1: Summary of Studies on Stakeholders

Stakeholders

Author (s)

Banks and other financial institutions/financial services, Other supporting institutions / existence of local support institutions, Suppliers of inputs, Providers of Business services/local consulting, marketing and distribution services, Local customers and Foreign customers, Universities/public and private automation related institutions, Training and R&D institutes, Central and local government/ Government agencies, Competitors, Upstream firms, Downstream firms, Professional associations

Barkley and Henry (1997), Boari (2001), Das (2005), Fensterseifer and Rastoin (2013), Fundeanu and Badele (2014),Giuliani (2013), Gutierrez-Martinez et al. (2015), He and Rayman-Bacchus (2010),Hoffmann et al. (2014), Hsu et al. (2014), Lai et al. (2014), Lei and Huang (2014), Li et al.( 2015), Morosini (2004), Niu (2010a), Porter (1998, 2000), Prajapati and Biswas (2011), Tambunan (2009),

2.4 Types of Business Networks Formed by Firms in a Cluster

Different types of business networks are formed by firms in a cluster. Table 2 shows the summary of literature on business networks formed by firm in a cluster.

Table 2: Summary of literature on business networks formed in a cluster

Type of Business networks	Author (s)
Horizontal networks which are formed among small and medium enterprises (SMEs) and vertical networks which are among SMEs and larger enterprises.	UNIDO (2001)
Technological alliance and Joint R&Dcollaboration	Yamawaki (2002)
Managerial network where the first one is network of informal ties among managers and an institutional network which is network of formal ties between the firms.	Bell (2005)
Knowledge and business networks where knowledge network are the network which link firms through the transfer of innovation related knowledge and business networks as set of relationships established by technical professionals while meeting or interacting with other firms on various business issues.	Giuliani (2007)
Information networks and knowledge networks where information networks are the networks which involve free available generic information flow between the firms and knowledge networks as the networks which are intentionally formed by the firms and which involve specific problem-solving knowledge.	Morrison and Rabellotti (2009)
External and internal networks, where internal networks defined as links among firms inside the cluster and external networks as such networks which involve firm’s relations with institutions such as banks, government, university, research institutes, state government owned companies, business association and local associations.	Tambunan (2009)
Supportive and competitive where supportive network consist of NGOs, designers, banks, and government; and competitive network shows the extent of competition with the rival firms in the form of copying designs, poaching of employees, price competition, and hiding information.	Prajapati and Biswas (2011)
Localised and external networks where localised network were formed within a cluster by the firms and second, external networks involve networks with firms outside the cluster.	Li et al., (2015)
Technical and business network	Balland, Belso-Martinez, and Morrison (2016)

The above literature on business networks in clusters explains the different types of business networks formed by firms with cluster participants. However these studies do not explain the different types of relationship which could be present within a network formed by the firm with a particular cluster participant. Also we did not find a literature which covered all the cluster participants present in a cluster. Thus this study is an attempt to answer these research gaps.

2.5 Firm Performance in clusters:

Many scholars have worked around the notion of business performance and its measurement. Measuring performance is important because it inform the firms about the correctness of business decisions taken (Prajapati and Saswata, 2011). Performance measurement involves more than just financial measures (O’Regan and Ghobadian, 2004). There is no agreement in the existing literature on how to measure performance of a firm and scholars have used variety of measures (Wiklund et al., 2009). Generally cluster consist of small and medium enterprises. i.e. small firms, where such firms use self report surveys as a favoured approach. Smaller firms have less sophisticated accounting practices and their internal records are not easily available for outside research (Singh et al., 2010). It is difficult to measure performance of small firms which are not listed since there are no reporting requirements which makes it difficult to obtain reliable information (Watson, 2007). According to Simpson et al., (2012) three most common approaches to measure firm performance on the basis of self reported data: firstly assessing firm performance in broadly defined categories; secondly, subjective measure of owner’s satisfaction with firm satisfaction; and lastly subjective measures of performance relative to competitors. Dobbs and Hamilton (2007) explained two approaches to study small business performance. i.e. stochastic and deterministic approach. According to them stochastic approach explains that there are many factors which affect performance however there is no dominant factor among them and deterministic approach involve identification of stable set of explanatory variables which explain major proportion of explained variation in firm performance. Singh et al., (2007, 2010) measured firm performance in Indian auto-component sector through two measures. i.e. subjective and objective. Subjective measure includes comparing firm performance to national standards and objective measure includes measuring performance on measures like market share, profitability, and export. Atalay et al., (2013) measured firm performance in Turkish automotive supplier industry through subjective measures like customer satisfaction and profit margin which were measured on the basis of perceived performance relative to competitors. Nurcahyo and Wibowo (2015) measured firm performance of automotive companies in Indonesia with help of two variables namely manufacturing performances and business performances. Performance of small firms can be measured in various ways and the use of multiple measures is likely to provide a more complete picture of firm performance (Dobbs and Hamilton, 2007). Table 3 shows list of determinants used for measuring firm performance in clusters by various scholars.

Table 3: Summary of list of determinants used for measuring firm performance in clusters

Author	Measures used
Baptista and Swann (1998)	Measured innovation performance through secondary data from innovation database by measuring total number of innovations produced by the enterprises.
Bell (2005)	Measured firm’s innovativeness along three dimensions: introducing new products, introducing new services, and adopting new technologies by asking experts.
Narayana (2007)	Measured economic performance of clustered small scale enterprises through collecting secondary data on three indicators at sector level first, output/capital ratio as measured by value of gross output per unit value of fixed assets; second, output/labour ratio as measured by value of gross output per employment; and third, labour/capital ratio as measured by employment per unit value of fixed assets.
Lerch et al., (2008)	Measured firm-level innovative performance through variables like patent frequency, number of developed prototypes, and publications in photonics clusters of Germany.
Morrison and Rabellotti (2009)	Measured firms’ economic performance through collecting data on total sales, exports, main destination markets.
Beaudry and Swann (2009)	Used level of employment as a variable for measuring firm growth.
Wennberg and Lindqvist (2010)	Measured firm performance through four variables: Vat payments, survival, higher employment, and wages per employee in context of subjective measurement
Zhao et al., (2010)	Measured firm innovation performance from the perspective of comparison with other firms with the help of three variables: patents (applied or authorized) or copyrights, new products or new services, and revenue or profit.
He and Rayman-Bacchus (2010)	Measured firm innovative performance by asking managers of enterprises along three dimensions: design new products, develop new processes, and developing markets which include creating new and different relations and use of internet
Lamprinopoulou and Tregear (2011)	Measured marketing performance through mental consumer measures of performance, such as perceived differentiation of end products, strong image/reputation and consumer preference and the level of transformation involved in the end product, specifically, whether the product was processed or fresh in agrifood cluster.
(Prajapati and Biswas, 2011)	Measured firm performance through sales growth, product quality, new product development, customer service, new design development etc. compared to other enterprises
Casanueva et al., (2013)	Measured firm innovativeness through product innovation and process innovation by asking experts and firms.
Singh and Shrivastava (2013)	Calculated performance of firms in rice mill cluster through quality performance indicators like quality of goods and processes, quality of relationships with customers, and quality of relationships with suppliers, visibility of enterprise in the cluster, and sales and profit
Hsu et al., (2014)	Measured firm performance through asking firms about their potential improvement in context to company turnover; company business profits; company operating costs can be reduced; company profitability can improve; company technology can improve; company innovation and development capability can improve; and company business competitiveness can improve.
Lei and Huang (2014)	Measured competitive advantage of a firm through innovativeness in product design and access to new technologies, quality parameter and flexibility, accuracy in delivery and customer service
Lai et al., (2014)	Measured firm innovative performance through market performance (market share, profit, customer demand, and customer satisfaction) and product performance (product innovation and its benefits)
Li et al., (2015)	Measured firm business performance through market performance variables like sales growth, market share growth, and profitability.
Exposito-Langa et al., (2015)	Measured innovative performance of a firm by measuring the degree to which a firm dedicates its product portfolio to technical textiles which implies new products development.
Resbeut and Gugler (2016)	Used employment growth as a performance measure

3. OBJECTIVES OF THE STUDY:

· To explore different types of business networks formed by firms with the stakeholders present in a cluster.

· To find out the relationship between business networks with different cluster stakeholders and performance of firms.

4. RESEARCH METHODOLOGY:

4.1 Research context:

The auto-component cluster of Gurgaon was chosen as the research setting for this study. The reason for studying auto-component industry is its capability of being the driver of economic growth and its prominent place which it occupies in India’s industrial development. It is one of the fastest growing industries in the country and produces a comprehensive range of components demanded by the automobile companies across the world. It is one of the high economic importance sectors. The industry caters to some of the big names in the global automobile industry and has a distinct global competitive advantage in terms of cost and quality. The reasons for selecting this particular auto-component cluster stemmed from the fact that this cluster has maximum number of firms including micro, small and medium enterprises operating in this cluster and also the fact that the automotive industry is the biggest industry in the state of Haryana, which ranks first in India in the production of motorcycles, passenger cars and tractors. The auto-components industry of India is present in three major regions. First, western region which include Mumbai, Pune, Nashik, and Aurangabad, second, southern region which include Chennai, Bangalore, and Hosur, and third, northern region which include Delhi, and Gurgaon. In addition to this, the eastern region which include Jamshedpur and Kolkata also consist of many firms involve in auto-components manufacturing. Due to the presence of large number of micro, small, and medium enterprises and unorganised units, the auto components industry in India is present in the form of clusters. Major auto component clusters of India are Chennai Auto Components, Pune Auto Components, Gurgaon Auto Components, Jamshedpur Auto Components and Meerut Auto Components (Cluster Observatory of India, 2016).

4.2 RESEARCH METHOD:

To answer the first research question, this study uses an exploratory research design which makes use of survey questionnaire conducted with managers and owners of firms operating in the cluster at their offices. 100 questionnaires were filled from the owners and managers of firms operating in Gurgaon cluster. Due to the lack of appropriate measures of business networks formed with particular stakeholders by the firms in a cluster, a questionnaire based on appropriate literature was developed. The scale items were refined based on reviews of experts working in auto-component industry, owner/ manager of firms, and also reviews of academicians to refine the questionnaire wording, identifying and removing misleading questions, and to improve the overall presentation of the survey instrument. The wordings of the questions in the questionnaire were made very simple. Respondents in the survey indicated their responses to questions / items through using seven point Likert scale, with 7 as strongly agree, 6 as agree, 5 as slightly agree,4 as neutral, 3 as slightly disagree, 2 as disagree, and 1 as strongly disagree.

To answer the second research question, this study uses a descriptive research design which makes use of survey research method to examine the relationship among business networks and firm performance by collecting data through survey questionnaire developed earlier.

4.3 Sample:

Firms operating in Gurgaon auto-component cluster are included to form a sampling frame. The sampling frame consisting of firms operating in Gurgaon auto component cluster was made with the help of database of associations like Gurgaon Udyog Association, Industrial Development Association, Gurgaon Industrial Association, Manesar Industries Welfare Association, and Gurgaon Chamber of Commerce and Industry. The sampling elements or the respondents were the managers and owners who have knowledge and experience of auto-component sector. We used convenience sampling technique for this study.

4.4 Data analysis tool:

For research question first, this study has used exploratory factor analysis using SPSS for analyzing the data since the survey questionnaire was self- developed. Exploratory factor analysis was carried out to ensure the rightness of newly created questionnaire and to extract mutually independent common factors from multiple relevant variables which were names as different business networks among firms and cluster stakeholders. For second research question, multiple regression analysiswas used to analyse the data.

4.5 Proposed Research Hypotheses:

Research scholars have supported the idea that the more the firm is embedded in a cluster higher will be its innovativeness and market performance (Giuliani, 2013; Porter, 1998). Networking firms were more likely than non-networking firms to engage in new product development, information sharing in marketing, enhanced profitability and competitiveness, and technological upgrading (Barkley and Henry, 1997). Bell (2005) investigated the relationship among clusters, networks, and firm innovativeness in an industrial cluster of Toronto and found that managerial network centrality enhances the firm innovativeness. Lerch et al., (2008) concluded through their empirical research on photonics clusters of Germany that network integration among firms is strongly related to firm-level innovative performance. Firms in cluster work together for enhancing their competitiveness and networks formed by firms in clusters carry potential advantage for firms’ innovation and competitive advantage (Niu et al., 2008). Inter-firm collaborations are important determinants for firm innovativeness (Niu, 2010b). Firms within a cluster pursue partnerships and such partnerships are key determinant of cooperation and innovation, promoting learning, and competitiveness (He and Rayman-Bacchus, 2010). Stronger inter-firm interaction leads to high level of operational efficiency (Oprime et al., 2010). Network openness and strength are the building blocks of competitive advantage in clusters (Casanueva et al., 2010). Stronger the relation between firms within a cluster, greater would be the marketing performance of firm (Lamprinopoulou and Tregear, 2011). Competitive networks are positively associated with subjective performance of enterprise in handloom and handicraft clusters since competition among firms increases the product quality and enhance innovative activities which positively affect performance (Prajapati and Biswas, 2011). Martinez et al., (2012) suggested that inter-firm linkages help in maintaining long term competitiveness of firms. The inter-firm networks stimulated by the MSME clusters have helped these MSMEs to move up the value chain and gain competitiveness (IBEF, 2013). There exist a positive and strong relationship between firm performance and the factors such as proximity to companies and business environment (Singh and Shrivastava, 2013). Competitive advantage of firms will be enhanced through the development of relationship among firms (Lei and Huang, 2014). Connell, Kriz, and Thorpe (2014) conducted a research to examine how knowledge sharing is facilitated in industry clusters of Dubai and Australia and concluded that ability of a firm to connect effectively with other firms is a key to support knowledge sharing among the firms and to promote innovation in a cluster. They also suggested that if firms in clusters lack strong networks among them then it can lead to lack of knowledge sharing among the firms.

Proposed Hypothesis: Firm networks with suppliers have a positive impact on firms’ performance

Proposed Hypothesis: Firm networks with buyers have a positive impact on firms’ performance

Resbeut and Gugler (2016) conducted a research on the precision industry located in Switzerland and concluded that such industries which are located in nearby or in regions with a strong cluster environment experience higher employment growth rates which is one of the measure of performance. Institutions like academic institutions and research institutions bridge the ties between firms outside of clusters and cluster firms (Meyer-Stamer, 1998) and also help the cluster firms in acquiring external resources to help in their business growth (Belso-Martınez, 2006). The interventions by government agencies in a cluster facilitate local collaborations among the firms and also upgrade firm capabilities (McDonald et al. 2006; Sellitto and Burgess 2005). Tambunan (2009) concluded that networks with traders, foreign tourist, and trading houses are important for performance of export oriented SME clusters in Indonesia. Clusters helps in network formation with supporting institutions which further lead to market development, product development and increase in sales (FMC, 2006). Networks with supportive institutions and government are important determinants for firm innovativeness (Niu, 2010b). Localised networks and external networks of firms in a cluster directly influence firm market performance of firms in wine clusters of Australia (Li et al., 2015). Through building network with institutions like academic institutions and research institutions play an important role in promoting development of the region (Tiffin and Kunc, 2011). Gutierrez-Martinez et al. (2015) conducted a research on IT clusters of Mexico and concluded that presence of supporting institutions like institutions for collaboration plays an important role in the performance of the IT cluster.

Proposed Hypothesis: Firm networks with government agencies have a positive impact on firms’ performance

Proposed Hypothesis: Firm networks with educational institutes have a positive impact on firms’ performance

Proposed Hypothesis: Firm networks with local associations have a positive impact on firms’ performance

5. RESULTS:

5.1 Exploring different types of business networks between a firm and cluster stakeholders:

A questionnaire based on appropriate literature was developed and an exploratory factor analysis was carried out to ensure the rightness of newly created questionnaire and to extract mutually independent common factors from multiple relevant variables which were names as different business networks among firms and cluster stakeholders. Reliability and content validity of the questionnaire was determined and exploratory factor analysis was carried out individually for each stakeholder.

Reliability: The reliability is commonly defined as the degree of consistency of a measure. The most general method of reliability estimation is the internal consistency method. The internal consistency of a set of measurement items refers tothe degree to which items in the set are homogeneous (Singh and Shrivastava, 2013). Internal consistencywas assessed using Cronbach’s alpha (Cronbach, 1951). Tables 3, 4, 5 and 6 illustrate that the values of Cronbach’s alpha coefficients more than 0.80 for all business networks identified which indicates the questionnaire has good reliability.

Content validity: A measure has content validity if there is general agreementamong the subjects and researchers that the instrument has measurement itemsthat cover all aspects of the variable being measured (Singh and Shrivastava, 2013). Content validity dependson how well the researchers created measurement items to cover the content domainof the variable being measured (Nunnally, 1967). Content validity is not evaluatednumerically rather it is subjectively judged by the researchers. In this study the developed instrument have content validity since selection of measurement itemswere based on an exhaustive review of literature and detailed reviews of owner and managers of firms, academicians, and experts working in auto-component industry, which indicated that the content of each factor is well represented by the variables.

5.1.1 Exploratory Factor Analysis for business networks with buyers:

Prior to applying exploratory factor analysis, the study first calculated KMO value. The KMO value was 0.860 suggesting that data was suitable for factor analysis. The factor analysis results suggested four factors for business networks with buyers, with a cumulative explanatory variation of 88.15 percent. The result of factor analysis is shown in Table 4. It can be seen from Table 4 that Factor I contains six questions, Factor II contains five questions, Factor III contains four questions and Factor IV contains three questions. The factors extracted in factor analysis were named as Information Network, Technological Collaborative network, Resource sharing network, and Training network.

Table 4: EFA for buyers

Business network	Factor loading	Accumulated explained variance (%)	Cronbach’s Alpha value
Information Network		30.162	0.973
Your firm exchange information related to markets with your buyers	0.917
Your firm share specific technical information with your buyers	0.884
Employees of your firm can obtain data required for work from databases of your buyers	0.924
In order to solve work problem employees of your firm usually communicate with workers of your buyers	0.875
Your firm exchanges information about new equipments with your buyers	0.857
Your firm take advice from your buyers	0.883
Technological Collaborative network		54.460	0.955
Your firm is engaged in Joint R&D with your buyers	0.895
Your firm jointly introduces new products with support of your buyers	0.879
Your firm work on new patent(s) with support of your buyers	0.878
Your firm uses technologies developed by your buyers	0.842
For purchase of specific equipments your firm collaborates with your buyers	0.922
Resource sharing network		74.970	0.950
Your firm shares infrastructure resources (land) with your buyers	0.889
Your firm shares incubation center with your buyers	0.862
To solve a technical issue your firm pools human resources with your buyers	0.843
Your firm jointly use raw materials with your buyers	0.897
Training network		88.157	0.942
Your firm gets help in comprehensive training of your employees from your buyers	0.834
Your firm carries out joint entrepreneurship programs with your buyers	0.803
To organise workshops on skills enhancement your firm set up consortia with your buyers	0.747

5.1.2 EFA for business networks with educational institutes:

Prior to applying factor analysis, the study first calculated KMO value. The KMO value was 0.822 suggesting that data was suitable for factor analysis. The factor analysis results suggested only one factor for business networks with educational institutes, with a cumulative explanatory variation of 89.902 percent. The result of factor analysis is shown in Table 5. The factor extracted in factor analysis was named as Recruitment and Training network.

Table 5: EFA for Educational institutes

Business network	Factor loading	Accumulated explained variance (%)	Cronbach’s Alpha value
Recruitment and Training network		89.902	0.967
Your firm easily obtains talented individual from educational institutes	0.957
Your firm gets help in comprehensive training of your employees from educational institutes	0.967
Employees of your firm get opportunity to learn technical skills from members of educational institutes	0.931
Your firm carries out joint entrepreneurship programs with educational institutes	0.933
To organise workshops on skills enhancement your firm set up consortia with educational institutes	0.952

5.1.3 EFA for business networks with suppliers: Prior to applying factor analysis, the study first calculated KMO value. The KMO value was 0.911 suggesting that data was suitable for factor analysis. The factor analysis results suggested only one factor for business networks with suppliers, with a cumulative explanatory variation of 86.741 percent. The result of factor analysis is shown in Table 6. The factor extracted in factor analysis was named as Information network.

Table 6: EFA for suppliers

Name of Business network	Factor loading	Accumulated explained variance (%)	Cronbach’s Alpha value
Information network		86.741	0.966
Your firm exchange information related to markets with your suppliers	0.933
Your firm share specific technical knowledge with your suppliers	0.952
Your firm take advice from your suppliers	0.913
Employees of your firm can obtain data required for work from databases of your suppliers	0.918
In order to solve work problem, employees of your firm usually communicate with workers of your suppliers	0.939
Your firm exchanges information about new equipments with your suppliers	0.931

5.1.4 EFA for business networks with government agencies and local associations:

Prior to applying factor analysis, the study first calculated KMO value. The KMO value was 0.897 suggesting that data was suitable for factor analysis. The factor analysis results suggested two factors for business networks with government agencies and local associations, with a cumulative explanatory variation of 84.889 percent. The result of factor analysis is shown in Table 6. It can be seen from Table 7 that Factor I contains four questions and Factor II contains six questions. The factors extracted in factor analysis were named as Informational & training network and Marketing & Development network.

Table 7: EFA for government agencies and local associations

	Factor loading	Accumulated explained variance (%)	Cronbach’s Alpha value
Informational and training network		48.448	0.957
Your firm exchange information related to markets with government agencies and local associations	0.941
Your firm acquires information about trade events, meetings, and seminars or other types of events from government agencies and local associations	0.897
To organise workshops on skills enhancement your firm set up consortia with government agencies and local associations	0.934
Your firm carries out joint entrepreneurship programs with government agencies and local associations	0.911
Marketing and Development network		84.889	0.952
Your firm get support of government agencies and local associations for entering into a new market	0.889
For branding and promotion of products, your firm jointly participates in industrial fairs with government agencies and local associations	0.915
In getting quality certification your firm get support from government agencies and local associations	0.874
Your firm get assistance in ITR (Income Tax return) filling from government agencies and local associations	0.863
To get subsidies your firm get support of government agencies and local associations	0.887
In making more use of internet and e-commerce your firm get support of government agencies and local associations	0.837

5.1.5 Exploratory factor analysis for firm performance:

Since the scale items were newly created thus exploratory factor analysis was carried out to ensure the rightness of newly created questionnaire for measuring firm performance. Respondents were requested to access their firm performance within the cluster using seven point Likert scale, with 7 as strongly agree, 6 as agree, 5 as slightly agree,4 as neutral, 3 as slightly disagree, 2 as disagree, and 1 as strongly disagree. As was expected, the factor analysis results suggested two factors for firm performance, with a cumulative explanatory variation of 87.625 percent. The result of factor analysis is shown in Table 8. It can be seen from Table 8 that Factor I contains eight questions and Factor II contains seven questions.

Table 8: EFA for firm performance

	Factor loading	Accumulated explained variance (%)	Cronbach’s Alpha value
Market performance		46.855	0.978
Business networks have helped in increasing the sales of your firm over the years	0.939
Business networks have helped in increasing the profit rate of your firm year by year	0.956
Business networks have helped in increasing the level of employment in the firm over the years	0.901
Business networks have helped the firm in increasing the customer demand and satisfaction over the years	0.918
Business networks have helped the firm in increasing its market share continuously	0.948
Business networks have helped in increasing the export of the company continuously	0.916
Business networks have helped in decreasing the manufacturing cost for the firm over the years	0.891
Business networks have helped in increasing the wages per employee in the firm over the years	0.936
Innovative Performance		87.625	0.972
Business networks have improved the quality of products of firm over the years	0.915
Business networks have helped the firm in adopting new technologies on continuous basis	0.928
Business networks have helped the firm on introducing new product design	0.896
Business networks have helped the firm on increasing the number of patents or copyrights	0.924
Business networks have helped in increasing the R&D capability of the firm over the years	0.921
Business networks have helped in enhancing the process innovation of the firm over the years	0.914
Business networks have helped the firm in increased use of e-commerce	0.922

Construct validity refers to the degree to which a good representation of the measures can be made from the operationalisations in a study to the theoretical constructs on which those operationalisations were based and the most widely two adopted subcategories of construct validity are convergent validity and discriminant validity (Anderson and Gerbing 1988; Holmes-Smith 2013). Convergent validity is the extent to which the scale correlates positively with other measurements of the same construct and Discriminant validity is the extent to which a measure does not correlate with other constructs from which it is supposed to differ (Malhotra and Birks, 2006). Convergent and discriminant validity were measured by using principle component factor analysis in this study. Convergent validity is demonstrated if the items load strongly (more than 0.50) on their associated factors and discriminant validity is achieved when each item loads more strongly on its associated factor than on any other factor (Grandon and Pearson, 2003). Factor analysis tables 3, 4, 5, 6, and 7 illustrate that all items loaded more strongly on their associated factors than on other factors. Thus, there is evidence to support convergent and discriminant validity in this study.

5.2 To identify the relationship between business networks and firm performance

Before applying multiple regression analysis, we have discussed the constructs and the developed hypotheses.

5.2.1 Constructs used in the study:

The survey questionnaire captures the following constructs.

Dependent Variables:

Firm performance:

The firm performance includes market performance and innovative performance. The scale items for measuring firm performance in a cluster have been created by studying previous studies like Lerch et al., (2008), Bell (2005), Li et al., (2015), Lai et al., (2014), Hsu et al., (2014), Singh and Shrivastava (2013), Casanueva et al., (2013), Prajapati and Biswas (2011), and He and Rayman-Bacchus (2010). The scale consisted of 15 items out of which 8 items covering sales, profit rate, level of employment, customer demand, market share, export, wages, manufacturing cost used for measuring market performance of firms and 7 items covering quality of products, technology, product design, patents, R&D capability used for measuring innovative performance.

Independent Variables:

Following independent variables were identified after exploratory factor analysis.

Information Network with buyers:

This network involves of flow of information either technical or informal between firm and its buyers. In this network, firms take advice from their buyers, exchange information related to markets and new equipments with buyers, share specific technical knowledge. In this network, firms engage with their buyers for utilizing their databases and also utilizing their employees as a source of technical information and for solving any identified issue. A total of six items were included in the questionnaire for measuring this construct.

Technological Collaborative network with buyers: This network involves firms working jointly with their buyers for new innovations. Firms use technologies developed by their buyers, jointly introduces new products with support of their buyers, engaged in Joint R&D with their buyers, and also jointly work on new patent(s). A total of five items were included in the questionnaire for measuring this construct.

Resource sharing network with buyers:

The main focus of this network is sharing resources like land and incubation centres for increasing production of firm. This network involves joint usage of raw materials by firms with their buyers. In such network there is easy mobility of workforce between firms and its buyers. Firms pools human resources from their buyers in order to solve a technical issue. A total of four items were included in the questionnaire for measuring this construct.

Training network with buyers:

The main focus of this network is working jointly for increasing skills of employees. This network involves firms in cluster join hands with their buyers so that employees of their firm get opportunity to learn technical skills from members of buyers, to set up consortia with buyers in order to organise workshops on skills enhancement, getting help in comprehensive training of their employees, and to carry out joint entrepreneurship programs with the buyers. A total of three items were included in the questionnaire for measuring this construct.

Information network with suppliers:

This network involves flow of information either technical or informal between firm and its suppliers. In this network firms take advice from their suppliers, exchange information related to markets and new equipments with suppliers, share specific technical knowledge. In this network firms engage with their suppliers for utilizing their databases and also utilizing their employees as a source of technical information and for solving any identified issue. A total of six items were included in the questionnaire for measuring this construct.

Informational and training network with Government agencies and local associations:

This network is slightly different from the informational networks formed by firms with their buyers and suppliers since this network involves more flow of informal information between firms and local associations and Government agencies. In this network firms exchange information related to markets, and utilise the relationship with government agencies and local associations for acquiring information about trade events, meetings, and seminars or other types of events. Apart from exchange of informal information, this network also involve setting up of consortia between firms and local associations in order to organise workshops on skills enhancement and they also carry out joint entrepreneurship programs. A total of four items were included in the questionnaire for measuring this construct.

Marketing and Development network with Government agencies and local associations:

The main focus of this network is working jointly for increasing the market of firms. In this network firms take help of government agencies and local associations for marketing their products, entering new market. In this network firms jointly participates in industrial fairs to promote its brand. Apart from marketing aspect, this network also involves the support or the assistance which a firm get from the government agencies and local associations for their development. In this network firms get assistance in ITR (Income Tax return) filling, support in getting quality certification, support in making more use of internet and e-commerce, and support in getting subsidies. A total of six items were included in the questionnaire for measuring this construct.

Recruitment and Training network with educational institutes:

This network is quite similar to training network formed by firms with their buyers. In this network educational institutes join hands with the firms so that employees of the firms get opportunity to learn technical skills, also set up consortia with the firms in order to organise workshops on skills enhancement and comprehensive training of the employees. In addition to this, this network involves recruitment aspect where firms easily obtains talented individual from these educational institutes by going there for recruitment of new employees. A total of five items were included in the questionnaire for measuring this construct.

5.2.2 Hypotheses developed for the study:

In case of networks with government agencies and local associations:

H1 Marketing and Development network with Government agencies and local associations is positively related to firm performance

H2 Informational and training network with Government agencies and local associations is positively related to firm performance

In case of networks with educational institutes:

H3 Recruitment and Training network with educational institutes is positively related to firm performance

In case of networks with suppliers:

H4 Information network with suppliers is positively related to firm performance

In case of networks with buyers:

H5 Information Network with buyers is positively related to firm performance

H6 Technological Collaborative network with buyers is positively related to firm performance

H7 Resource sharing network with buyers is positively related to firm performance

H8 Training network with buyers is positively related to firm performance

5.2.3 Hypotheses testing:

We used multiple regression analysis for testing our hypotheses developed. Hypothesis 1 and 2 addressed the relationship between business networks and firm performance where such networks are formed with Government agencies and local associations. For H1 and H2, the dependent variables were market performance and innovative performance and the independent variables were marketing and development network and informational and training network. The results in Table 9 provide support for these two hypotheses. As indicated in Table 9, informational and training network predicts market performance, explaining 94 percent of the variance in this measure of performance, while marketing and development network is not significant. Conversely, marketing and development network predicts innovative performance, explaining 39 percent of variance, while informational and training network is not significant.

Table 9: Regression analysis table for business networks with Government agencies and local associations

Independent variables	Dependent variables
	Market performance	Innovative performance
	Beta value	Beta value
Constant	-0.195	8.357
Marketing and Development network with Government agencies and local associations	0.039 (0.148)	-0.603 (0.000)
Informational and training network with Government agencies and local associations	0.953 (0.000)	-0.067 (0.447)
p value	0.000	0.000
F value	832.382	33.493
R ²	0.944	0.406
Adjusted R ²	0.943	0.394
D-W	2.023	2.316

Hypothesis 3 and 4 addressed the relationship between firm performance and business networks formed by firm with educational institutes and suppliers. For H3 and H4, the dependent variables were market performance and innovative performance, the two measures of firm performance and the independent variables were recruitment and training network with educational institutes and information network with suppliers. The results in Table 10 provide support for theses hypotheses. As indicated in Table 10, information network with suppliers predicts market performance, explaining 94 percent of the variance in this measure of performance, while recruitment and training network with educational institutes is not significant. However, in context of innovative performance, both the networks .i.e. recruitment and training network with educational institutes and information network with suppliers were significant and predicts innovative performance, explaining only 16 percent of variance.

Table 10: Regression analysis table for business networks with educational institutes and suppliers

Independent variables	Dependent variables
	Market performance	Innovative performance
	Beta value	Beta value
Constant	-0.92	5.695
Recruitment and Training network with educational institutes	0.333 (0.264)	3.035 (0.009)
Information network with suppliers	0.640 (0.033)	-3.372 (0.004)
p value	0.000	0.000
F value	824.777	10.785
R ²	0.944	0.180
Adjusted R ²	0.943	0.164
D-W	2.004	2.629

Hypothesis 5, 6, 7, and 8 addressed the relationship between firm performance and business networks formed by firm with buyers. The dependent variables were market performance and innovative performance, the two measures of firm performance and the independent variables were information network, technological collaborative network, resource sharing network, and training network with buyers. As indicated in Table 11, technological collaborative network and training network with buyers predicts market performance, explaining 94 percent of the variance, while information network and resource sharing network were not significant. However, in context of innovative performance, only resource sharing network was significant and predicts innovative performance, explaining 98 percent of variance.

Table 11: Regression analysis table for business networks with buyers

Independent variables	Dependent variables
	Market performance	Innovative performance
	Beta value	Beta value
Constant	-0.626	-0.274
Information Network with buyers	-.018 (0.551)	-0.001 (0.936)
Technological Collaborative network with buyers	0.946 (0.000)	0.0.18 (0.167)
Resource sharing network with buyers	0.029 (0.359)	0.999 (0.000)
Training network with buyers	0.094 (0.009)	0.000 (0.990)
p value	0.000	0.000
F value	417.804	1973
R ²	0.946	0.988
Adjusted R ²	0.943	0.987
D-W	1.973	1.909

Table 12 shows some key findings of this study.

6. CONCLUSION AND DISCUSSION:

This study contributed to the existing literature of business networks formed by firms with the stakeholders present in a cluster. The primary objective of this paper was to explore different types of business networks a firm can form with the stakeholders in Gurgaon auto component cluster. In this context the study has identified four types of business networks between a firm and its buyers. They are informational network, technological collaborative network, resource sharing network, and training network. Even the owners of many firms told us that “…working with big buyers in the auto-component industry has helped their firm in making their business easier due to the reputation brought by working with such respectable companies.” In case of business networks with the suppliers, the study has identified only one type of business network. i.e. informational network. Similarly with educational institutes, the study found only one type of business network. i.e. Recruitment and training network. Lastly with government agencies and local associations, the study found two types of business networks. i.e. Marketing and Development network and Informational and training network. However with respect to network with other stakeholders like competitors, financial institutes, and research institutes, the study shows some contrasting results. The study suggests that there is no such cooperation or linkage between a firm and these stakeholders. i.e. the linkages between them remain largely unfilled. There is no dissemination of information, no sharing of resources, and no collaboration. Possible reason for this conclusion could be the lack of accessibility towards the services provided by such research institutes. The second main objective of this study was to determine that how these business networks impact the performance of firms operating in that cluster. The study clearly brings forth the importance of business networks to educate the managers of firms on different aspects of cluster.The investigation of impact of different types of business networks which a firm has with the stakeholders of cluster on firm performance is of particular interest because it will offer new insights regarding how these firms form networks which could be used to complement the firm business performance. According to researchers, more the firm is embedded. i.e. more the business networks it forms in a cluster, higher will be its innovativeness and market performance (Porter, 1998; Giuliani, 2013). The findings of this study confirmed that the firms in an industrial cluster achieve better market and innovative performance due to the formation of business networks with the stakeholders. This study support the argument that mere geographic proximity to a cluster by itself will not strengthen a firm’s competitive advantages, but instead focus on better opportunities to develop strong network relationships with other firms in the same cluster. Our first hypothesis about relationship of firm performance with marketing and development network of firm formed with government agencies and local associations was partially supported. However our second hypothesis which was about informational and training network with government agencies and local associations was not supported. Our hypotheses, third and fourth which were about business networks formed with educational institutes and suppliers were partially supported. Similarly hypotheses like sixth and seventh related to technological collaboration and resource sharing business networks with buyers were also supported however fifth and eight hypotheses which were related to information network and training network with buyers were not supported. Thus majority of business networks which a firm formed with the cluster stakeholders are significantly and positively related to firm performance. The study provides following theoretical contributions to the existing literature. First, we have tried to answer the research gap of lack of literature on types of business networks formed by firms with the stakeholders present in an auto component industrial cluster. Secondly, this study has enriched the understanding of business networks which could be present in an auto component cluster between a firm and other cluster stakeholders. Third, this study has supported the existing fact that firms do not have access to networks with all cluster stakeholders just by being present in a cluster. As it was found in the study that the firms of Gurgaon auto-component cluster are aware of the fact that they are part of a cluster, however still most of them had business networks only with their buyers and government agencies, not with other stakeholders like educational institutes, competitors, and research institutes. The study also has some practical implications for Gurgaon auto-component cluster. Gurgaon auto-component cluster is one of the cluster where diagnostic study and soft interventions under micro and small enterprise – cluster development programme (MSE CDP) have been carried out however being part of MSE CDP we got such results which suggest that networking with stakeholders like research institutes, educational institutions, competitors, and suppliers has still not been that much developed. Since networks with supportive institutions and government are important determinants for firm innovativeness (Niu, 2010b). Thus emphasis should be given to networks with all cluster stakeholders. Since the study has suggested that the majority of business networks which a firm formed with the cluster stakeholders are significantly and positively related to firm performance. Thus a firm situated in an auto-component cluster must make efficient use of cluster stakeholders’ presence and form business networks with the stakeholders. More business networks a firm forms with the stakeholders present in an auto-component cluster better are its market and innovative performance. Weak linkages between firm and cluster stakeholders will isolate cluster firms and will affect their performance. Thus based on our findings we suggest that the policy makers should focus more on the networking aspect with stakeholders in a cluster and include this aspect in diagnostic study report and soft interventions part of MSE CDP for all auto-component clusters. We also suggest that firms operating in Gurgoan auto-component cluster should focus more on networks with all cluster stakeholders especially with research institutes, educational institutes, and competitors for better management of their firms as it is evident that by simply moving to the cluster does not lead to better firm performance, instead it is formation of business networks with the stakeholders which assist a firm in making more informed decisions. The findings from this research will add academic value in the context of expanding knowledge in relation to the impact of business networks on firm performance and will also contribute in filling gaps within the existing literature related to types of business networks formed by firms with the cluster stakeholders. The findings would also provide deeper insight into the future development of auto-component clusters in India.The study has been limited to only one cluster thus it might not be appropriate to generalize the findings. Further research in this area needs to be done by taking other clusters in order to generalize the findings.

7. REFERENCES:

1. Anderson, A.R., Dodd, S.D. and Jack, S. (2010) ‘Network practices and entrepreneurial growth’, Scandinavian Journal of Management, Vol. 26, No. 2, pp.121–133.

2. Anderson, J.C. and Gerbing, D.W. (1988) ‘Structural equation modeling in practice: a review and recommended two-step approach’, Psychological Bulletin, Vol. 103, No. 3, pp.411–423.

3. Atalay, M., Anafarta, N., and Sarvan, F. (2013). The relationship between innovation and firm performance: An empirical evidence from Turkish automotive supplier industry. 2nd International Conference on Leadership, Technology and Innovation Management (226 – 235). Antalya: Elsevier

4. Balland, P.A., Belso-Martínez, J.A. and Morrison, A. (2016) ‘The dynamics of technical and business knowledge networks in industrial clusters: embeddedness, status or proximity?’, Economic Geography, Vol. 92, No. 1, pp.35–60.

5. Baptista, R. and Swann, P. (1998) ‘Do firms in clusters innovate more?’, Research Policy, Vol. 27, No. 5, pp.525–540.

6. Barkley, D. L., and Henry, M. S. (1997). Rural Industrial Development: To Cluster or Not to Cluster? Review of Agricultural Economics, 19(2), 308–325.

7. Beaudry, C., & Swann, G. M. P. (2009). Firm growth in industrial clusters of the United Kingdom. Small Business Economics, 32(4), 409–424.

8. Bell, G.G. (2005) ‘Clusters, networks, and firm innovativeness’, Strategic Management Journal, Vol. 26, No. 3, pp.287–295.

9. Belso-Martınez, J. A. (2006). Why are some Spanish manufacturing firms internationalizing rapidly? The role of business and institutional international networks. Entrepreneurship and Regional Development, 18(3), 207–226.

10. Boari, C. (2001). Industrial Clusters, Focal Firms, and Economic Dynamism: A Perspective from Italy, Perspective, 24.

11. Casanueva, C., Castro, I., and Galán, J. L. (2013). Informational Networks and Innovation in Mature Industrial Clusters, Journal of Business Research, 66(5), 603–613.

12. Ceglie, G. and Dini, M. (1999) SME Cluster and Network Development in Developing Countries: the Experience of UNIDO, UNIDO, Vienna.

13. Chandrashekar, D. and Hillemane, B.S.M. (2018) ‘Absorptive capacity, cluster linkages, and innovation: an evidence from Bengaluru high-tech manufacturing cluster’, Journal ofManufacturing Technology Management, Vol. 29, No. 1, pp.121–148.

14. Cluster Observatory of India (2016) Cluster Observatory [online] http://www.clusterobservatory.in/ clustermap.php (accessed 20 August 2016).

15. Connell, J., Kriz, A. and Thorpe, M. (2014) ‘Industry clusters: an antidote for knowledge sharing and collaborative innovation?’, Journal of Knowledge Management, Vol. 18, No. 1, pp.137–151.

16. Cronbach, L. (1951) ‘Coefficient alpha and the internal structure of tests’, Psychometrika, Vol. 16, No. 3, pp.297–334.

17. Das, K. (2005). Industrial Clustering in India: Local Dynamics and the Global Debate, In Indian IndustrialClusters (pp. 1–19).

18. Das, K. (2008) Fostering Competitive Clusters in Asia: Towards an Inclusive Policy Perspective, IDE-JETRO, Japan.

19. Das, K., Gulati, M. and Sarkar, T. (2007) Policy and Status Paper on Cluster Development in India, Foundation for MSME Clusters, New Delhi.

20. Das, T. K., & Teng, B.-S. (2002). Alliance Constellations : A Social Exchange Perspective. Academy of Management Review, 27(3), 445–456.

21. Desta, N. (2015) Networking as a Growth Initiative for Small and Medium Enterprises in South Africa, University of the Free State, Bloemfontein.

22. Dobbs, M., & Hamilton, R. T. T. (2007). Small business growth: recent evidence and new directions. International Journal of Entrepreneurial Behavior & Research, 13(5), 296–322.

23. Expósito-Langa, M., Tomás-Miquel, J-V. and Xavier Molina-Morales, F. (2015) ‘Innovation in clusters: exploration capacity, networking intensity and external resources’, Journal of Organizational Change Management, Vol. 28, No. 1, pp. 26-42

24. Fensterseifer, J. E., and Rastoin, J.-L. (2013). Cluster Resources and Competitive Advantage: A Typology of Potentially Strategic Wine Cluster Resources, International Journal of Wine Business Research, 25(4), 267–284.

25. FMC. (2006). Working together works: cluster case studies.

26. Fundeanu, D.D. and Badele, C.S. (2014) ‘The impact of regional innovative clusters on competitiveness’, Procedia – Social and Behavioral Sciences, Vol. 124, No. 1, pp.405–414.

27. Giuliani, E. (2007) ‘The selective nature of knowledge networks in clusters: evidence from the wine industry’, Journal of Economic Geography, Vol. 7, No. 2, pp.139–168.

28. Giuliani, E. (2013) ‘Clusters, networks and firms’ product success: an empirical study’, Management Decision, Vol. 51, No. 6, pp.1135–1160.

29. Grandon, E. and Pearson, J. (2003) ‘Strategic value and adoption of electronic commerce: an empirical study of Chilean small and medium businesses’, Journal of Global Information Technology Management, Vol. 6, No. 3, pp.22–43.

30. Gutierrez-Martinez, I., Duhamel, F., Luna-Reyes, L., Picazo-Vela, S., and Huerta-Carvajal, M. (2015). The role of joint actions in the performance of IT clusters in Mexico, Competitiveness Review, 25 (2), 156-178

31. Hakansson, H., & Ford, D. (2002). How should companies interact in business networks? Journal of Business Research, 55(2), 133–139.

32. Hakansson, H. and Johanson, J. (1993) ‘Industrial functions of business relationships’, Advances in International Marketing, Vol. 5, No. 13, pp.13–29.

33. Halinen, A. and Tornroos, J-Å. (1998) ‘The role of embeddedness in the evolution of business networks’, Scandinavian Journal of Management, Vol. 14, No. 3, pp.187–205.

34. Hampton, A., Cooper, S., & McGowan, P. (2009). Female Entrepreneurial Networks and Networking Activity in Technology Based Ventures. International Small Business Journal, 27(2), 193–214.

35. He, Z., and Rayman-Bacchus, L. (2010). Cluster Network and Innovation under Transitional Economies: An Empirical Study of the Shaxi Garment Cluster, Chinese Management Studies, 4(4), 360–384.

36. Hoffmann, V. E., Lopes, G. S. C., and Medeiros, J. J. (2014). Knowledge Transfer among the Small Businesses of a Brazilian Cluster, Journal of Business Research, 67(5), 856–864.

37. Holmes-Smith, P. (2013) Structural Equation Modelling (USING AMOS), Australian Consortium for Social and Political Research Incorporated (ACSPRI), S. R. E. a. M. Services Perth.

38. Hsu, M.-S., Lai, Y.-L., and Lin, F.-J. (2014). The Impact of Industrial Clusters on Human Resource and Firms Performance, Journal of Modelling in Management, 9(2), 141.

39. Iacobucci, D. and Hopkins, N. (1992) ‘Modeling dyadic interactions and networks in marketing’, Journal of Marketing Research, Vol. 29, No. 1, pp.5–17.

40. IBEF (2013) MSMEs and the Growing Role of Industrial Clusters, Indian Brand Equity Foundation.

41. Johnston, W.J., Lewin, J.E. and Spekman, R.E. (1999) ‘International industrial marketing interactions’, Journal of Business Research, Vol. 46, No. 3, pp.259–271.

42. Joshi, D., Nepal, B., Rathore, A. and Sharma, D. (2013) ‘On supply chain competitiveness of Indian automotive component manufacturing industry’, Intern. Journal of Production Economics, Vol. 143, No. 1, pp.151–161.

43. Keeble, D., & Wilkinson, F. (1999). Collective Learning and Knowledge Development in the Evolution of Regional Clusters of High Technology SMEs in Europe. Regional Studies, 33(4), 295–303.

44. Lai, Y., Hsu, M., Lin, F., Chen, Y., and Lin, Y. (2014). The Effects of Industry Cluster Knowledge Management on Innovation Performance, Journal of Business Research, 67(5), 734–739.

45. Lamprinopoulou, C. and Tregear, A. (2011) ‘Inter-firm relations in SME clusters and the link to marketing performance’, Journal of Business & Industrial Marketing, Vol. 26, No. 6, pp.421–429.

46. Lei, H-S. and Huang, C-H. (2014) ‘Geographic clustering, network relationships and competitive two industrial clusters in Taiwan’, Management Decision, Vol. 52, No. 5, pp.852–871.

47. Lerch, F., Provan, K., & Sydow, J. (2008). Network Integration in Regional Clusters and Firm Innovation – A Comparison of Measures. In Academy of Management Annual Meeting (pp. 1–37). Anaheim, California.

48. Li, H., de Zubielqui, G.C. and O’Connor, A. (2015) ‘Entrepreneurial networking capacity of cluster firms: a social network perspective on how shared resources enhance firm performance’, Small Business Economics, Vol. 45, No. 3, pp.523–541.

49. Malhotra, N.K. and Birks, D.F. (2006) Marketing Research: an Applied Approach, Pearson Publication, UK.

50. Marshall, A. (1920). Principles of Economics, London: Macmillan and Co., Ltd.

51. Martínez, A., Belso-Martínez, J.A. and Más-Verdú, F. (2012) ‘Industrial clusters in Mexico and Spain: comparing inter-organizational structures within context of change’, Journal ofOrganizational Change Management, Vol. 25, No. 5, pp.657–681.

52. McDonald, F., Tsagdis, D., & Huang, Q. (2006). The development of industrial clusters and public policy. Entrepreneurship and Regional Development, 18(6), 525–542.

53. Meyer-Stamer, J. (1998). Path dependence in regional development: Persistence and change in three industrial clusters in Santa Catarina, Brazil. World Development, 26(8), 1495–1511.

54. Morosini, P. (2004) ‘Industrial clusters, knowledge integration and performance’, World Development, Vol. 32, No. 2, pp.305–326.

55. Morrison, A. and Rabellotti, R. (2009) ‘Knowledge and information networks in an Italian wine cluster’, European Planning Studies, Vol. 17, No. 7, pp.983–1006.

56. Narayana, M. R. (2007). Economic size and performance of dispersed and clustered small scale enterprises in India: Recent evidence and implications. International Journal of Social Economics, 34(9), 599–611.

57. Niu, K.-H. (2010a). Industrial Cluster Involvement and Organizational Adaptation: An Empirical Study in International Industrial Clusters, Competitiveness Review: An International Business Journal, 20(5), 395–406.

58. Niu, K-H. (2010b) ‘Organizational trust and knowledge obtaining in industrial clusters’, Journal of Knowledge Management, Vol. 14, No. 1, pp.141–155.

59. Niu, K-H., Miles, G. and Lee, C-S. (2008) ‘Strategic development of network clusters: a study of high technology regional development and global competitiveness’, Competitiveness Review: An International Business Journal, Vol. 18, No. 3, pp.176–191.

60. Nooy, W. De, Mrvar, A., & Batagelj, V. (2005). Exploratory Social Network Analysis with Pajek (Google eBook). Cambridge University Press (Vol. 53).

61. Nunnally, J.C. (1967) Psychometric Theory, McGraw Hill, New York, NY.

62. Nurcahyo, R., and Wibowo, A. (2015). Manufacturing Capability, Manufacturing Strategy and Performance. 12th Global Conference on Sustainable Manufacturing ( 653-657). Berlin: Elsevier.

63. Oprime, P. C., Tristão, H. M., & Pimenta, M. L. (2011). Relationships, cooperation and development in a Brazilian industrial cluster. International Journal of Productivity and Performance Management, 60(2), 115–131.

64. O’Regan, N., & Ghobadian, A. (2004). Short- and long-term performance in manufacturing SMEs: Different targets, different drivers. International Journal of Productivity and Performance Management, 53(5), 405–424.

65. Pillania, R. (2008) ‘Creation and categorization of knowledge in automotive components SMEs in India’, Management Decision, Vol. 46, No. 10, pp.1452–1464.

66. Planning Commission, G.O.I (2012) Report of the Steering Committee on Handicrafts and Handlooms, Government of India, New Delhi.

67. Porter, M.E. (1998) ‘Clusters and the new economics of competition’, Harvard Business Review, Vol. 11, No. 11, pp.77–90.

68. Porter, M. E. (2000). Location, Competition, and Economic Development: Local Clusters in a Global Economy, Economic Development Quarterly, 14(1), 15–34.

69. Pouder, R., and St. John, C. H. (1996). Hot Spots and Blind Spots: Geographical Clusters of Firms and Innovation, The Academy of Management Review, 21(4), 1192–1225.

70. Prajapati, K. and Biswas, S.N. (2011) ‘Effect of entrepreneur network and entrepreneur self-efficacy on subjective performance: a study of handicraft and handloom cluster’, Journal of Entrepreneurship, Vol. 20, No. 2, pp.227–247.

71. Premaratne, S.P. (2002) Entrepreneurial Networks and Small Business Development: the Case of Small Enterprises in Sri Lanka. Network, Maastricht School of Management, Netherland.

72. Resbeut, M., & Gugler, P. (2016). Impact of clusters on regional economic performance: A methodological investigation and application in the case of the precision goods sector in Switzerland. Competitiveness Review, 26 (2), 188-209.

73. Rietveldt, L. and Goedegebuure, R. (2014) The Influence of Network Relationships on the Internationalization Process of SMEs a Multiple Case-Study of Ethiopian SMEs, Maastricht School of Management, Netherland.

74. Sahoo, T., Banwet, D.K. and Momaya, K. (2011) ‘Strategic technology management in the auto component industry in India a case study of select organizations’, Journal of Advances inManagement Research, Vol. 8, No. 1, pp.9–29.

75. Saranga, H. (2009) ‘The Indian auto component industry – estimation of operational efficiency and its determinants using DEA’, European Journal of Operational Research, Vol. 196, No. 2, pp.707–718.

76. Saranga, H., Mukherji, A. and Shah, J. (2015) ‘Inventory trends in emerging market supply chains: evidence from the Indian automotive industry’, IIMB Management Review, Vol. 27, No. 1, pp.6–18.

77. Sellitto, C.,&Burgess, S. (2005). A government-funded internet portal as a promoter of regional cluster relationships: A case study from the Australian wine industry. Environment and Planning C: Government and Policy, 23(6), 851–866.

78. Simpson, M., Padmore, J., & Newman, N. (2012). Towards a new model of success and performance in SMEs. International Journal of Entrepreneurial Behaviour & Research, 18(3), 264–285.

79. Singh, A. (2010). Clusters in India. New Delhi: Foundation for MSME Clusters.

80. Singh, A.K. and Shrivastava, R.L. (2013) ‘Critical success factors of rice mills located in a cluster’, International Journal of Productivity and Performance Management, Vol. 62, No. 6, pp.616–633.

81. Singh, B., Garg, S. and Sharma, S. (2010) ‘Development of index for measuring leanness: study of an Indian auto component industry’, Measuring Business Excellence, Vol. 14, No. 2, pp.46–53.

82. Singh, R., Garg, S. and Deshmukh, S. (2007) ‘Strategy development for competitiveness: a study on Indian auto component sector’, International Journal of Productivity and Performance Management, Vol. 56, No. 4, pp.285–304.

83. Tambunan, T. (2009) ‘Export-oriented small and medium industry clusters in Indonesia’, Journal of Enterprising Communities: People and Places in the Global Economy, Vol. 3, No. 3, pp.25–28.

84. Tiffin, S., & Kunc, M. (2011). Measuring the roles universities play in regional innovation systems: A comparative study between Chilean and Canadian natural resource-based regions. Science and Public Policy, 38(1), 55–66.

85. UNIDO (2001) Development of Clusters and Networks of SMEs, UNIDO.

86. UNIDO (2006) Making Clusters Work, UNIDO Methodology.

87. Watson, J. (2007). Modeling the relationship between networking and firm performance. Journal of Business Venturing, 22(6), 852–874.

88. Wennberg, K., & Lindqvist, G. (2010). The effect of clusters on the survival and performance of new firms. Small Business Economics, 34(3), 221–241.

89. Wiklund, J., & Shepherd, D. (2005). Entrepreneurial orientation and small business performance: A configurational approach. Journal of Business Venturing, 20(1), 71–91.

90. Yamawaki, H. (2002) ‘The evolution and structure of industrial clusters in Japan’, Small Business Economics, Vol. 18, Nos. 1–3, pp.121–140.

91. Zhao, Y., Zhou, W., Hüsig, S. and Vanhaverbeke, W. (2010) ‘Environment, network interactions and innovation performance of industrial clusters: evidences from Germany, the Netherlands and China’, Journal of Science and Technology Policy in China, Vol. 1, No. 3, pp.210–233.

Received on 07.01.2019 Modified on 20.01.2019

Res. J. Humanities and Social Sciences. 2019; 10(2): 449-464.

DOI: 10.5958/2321-5828.2019.00076.7

Hypothesis	Type of Network	Impact on Market performance	Impact on Innovative performance	Conclusion
H 1	Marketing and Development network with Government agencies and local associations	Insignificant	Negatively related	Not supported
H 2	Informational and training network with Government agencies and local associations	Positively related	Insignificant	Partially supported
H 3	Recruitment and Training network with educational institutes	Insignificant	Positively related	Partially supported
H 4	Information network with suppliers	Positively related	Negatively related	Partially supported
H 5	Information Network with buyers	Insignificant	Insignificant	Not supported
H 6	Technological Collaborative network with buyers	Positively related	Insignificant	Partially supported
H 7	Resource sharing network with buyers	Insignificant	Positively related	Partially supported
H 8	Training network with buyers	Positively related	Insignificant	Partially supported