Social Network Analysis of Supply Chain of the Manufacturing Firms in India
Abstract
Existing literature on social networks has focused primarily on linkages between social networks and supply chains. These studies show that there exists a significant relationship between network measures and various firm level indicators. The present paper is an attempt to study the influence of social networks of Indian manufacturing firms on individual firm’s performance. The study apply network concepts to both hard types of ties (e.g. materials and money flows) and soft types of ties (e.g. friendships and information sharing), as both are crucial in the supply chain context.
There is social glue that binds the networks and there is something more than utility or profit maximization
- – Christopher and J�ttner
The primary objective of business alliance is to enhance the competitive capabilities of firms by identifying right partners which provide resource complementarities thereby ensuring sustainable development of the firm. Over time, economies of scale become important and organizations tend to develop infrastructures that serve the needs of each of the alliances, conceiving new relationships in the network. Today, multi-party alliances have become a reality where multiple synergies are derived from cooperation in R&D, product development, value creation, marketing and outsourcing of activities. The resultant is a network consisting of one-one, one-many, many-one, or/and many-many inter-firm alliance that works towards a mutual goal. Although, each individual organization may develop links with more than one partner, studies have shown that each of these links is largely independent of the others.
A network is a structure consisting of actors (such as individuals, groups, organizations) which are direct/indirect partners of a firm, and are connected through different kinds of ties or relations (both business and non-business). Relations defined by the linkages among units are a fundamental component of network theories. The literature[1] regarding the emergence of networks in organizational research includes: (1) theories of self-interest (such as social capital theory and transaction cost economics), (2) theories of mutual self-interest and collective action, (3) exchange and dependency theories (which include social exchange, resource dependency, and network organizational forms), (4) contagion theories, (5) theories on social information processing, (6) institutional theory, (7) structural theory of action, (8) cognitive theories (on semantic networks, knowledge structures, cognitive social structures, cognitive consistency), (9) theories of homophile (social comparison theory, social identity theory), (10) theories of proximity (physical and electronic propinquity), (11) uncertainty reduction and contingency theories, (12) social support theories, and (13) evolutionary theories.
Social network theory views social relationships in terms of nodes and ties. Nodes are the individual actors or agents within the networks, and ties are the relationships such as interdependency, friendship, kinship, common interest, financial exchange, knowledge sharing etc. between the actors. The network can also be used to determine the social capital (the value that an individual gets from the social network) of individual actors. Social networks are now being used to examine how firms interact with each other, characterizing the many informal connections that link executives together, as well as associations and connections between individual employees at different firms[2]. These networks provide ways for firms to share information, deter competition, and even collude in setting up of prices or policies. Social networks and their management are crucial for the smooth functioning of the supply chain. Bernardes and Fensterseifer (2004) in their study found that nature of social relations in a network decides the success of technology initiatives in a supply chain. Handfield and Batchel (2001) stated that a suitable relationship structure among supply chain partners can improve supply chain responsiveness. Elmuti (2002) showed that organizations that started supply network management projects have earned benefits like improved customer service quality, improved delivery, reliability and efficient human resource deployment.
Business networks primarily deal with supplier-buyer interactions and the impact of such relationships on various partners in the network. Hakansson and Johanson (1992) describe a business network as ARA-model (Activities, Resources and Actors). Hinterhuber and Levin (1994) found out that there can be four types of social networks namely Internal, Vertical, Horizontal and Diagonal. Grandori and Soda (1995) define supply networks in three forms: (1) Social networks – these include equity-based personal networks, industrial relationships and centralized arrangements such as subcontracting; (2) Bureaucratic networks – these include trade associations and consortia, which are formalized through contractual agreements; and (3) Proprietary networks – these include joint ventures and capital ventures; also, crossholdings and other networks, which are controlled through propriety rights. Hines (1994) defines the development of networking, beginning from the conventional win-lose relationship of competition, and ending with the win-win relationship of collaboration.
The network influence is classified into relational and structural effects. While the relational effect is due to the characteristics or nature of the tie (such as strong or weak ties), the structural effect is by virtue of the position of a firm relative to other actors in the network. Studies have considered various relational aspects of inter-firm linkages such as strength of association diversity of ties etc. these relational characteristics are said to have influence on firm level indicators such as financial performance, cost, choice of technology, project completion time, knowledge creation and sharing. The structural effects of inter-firm network on a firm are captured by various measures of firms’ position (such as centrality, density, structural holes etc.) in the network. These characteristics have their influence on firm performance, innovation, creation and termination of ties, competitive and technical ability etc.
Although, there has been a lot of study examining the social relationships in certain substantive areas such as in marketing, supply chain, logistics etc. there has been very little work done on examining the buyer-supplier relationships particularly in the manufacturing industry. One useful approach for examining the informal social interactions in logistics and supply chain of a typical manufacturing industry is social network analysis (SNA). From a managerial perspective, SNA is a powerful tool that allows managers to assess their individual position in the network as well as help to map influential partners. Social network analysis is a powerful methodology for describing and analyzing the interrelationships of nodes (represented by the actors) within a network[3]. At the inter-organizational level, SNA has examined the interrelationships of organizations within horizontal and vertical networks. Unlike the traditional multivariate analyses which focus on an actor (usually, the individual or the organization) as the unit of analysis, the SNA methodology focuses on relationships among actors as the unit of analysis. Most of the existing inter-organizational SNA research has focused in the area of strategic management, where researchers have examined the interrelationships among organizations in a variety of social networks, which includes supplier relationships, interlocking directorates and horizontal alliances (Mishra, 2008). Gulati (1995)[4] notes that social networks can promote trust and reduce transaction costs by enhancing the ability of firms to gather information about each other, and thereby reducing the likelihood of opportunistic behavior. Dyer (1996) examined the buyer-supplier alliances in the automobile industry and provided empirical evidence that the creation of valuable and non-imitable specialized assets in combination with other firms can improve a firm’s performance.
Borgatti and Li (2009) classified ties among actors (such as firms) in any network, along the same four dimensions as ties among individuals. The continuous relations are of two types (a) based on similarities such as co-location (e.g. physical distance), co-membership (e.g. same boards) or shared attributes (e.g. same race); and (b) based on social relations such as kinship, other roles (e.g. being boss or friend) or influence by cognitive /affective attitudes (e.g. likes and dislikes). While the discrete relations can be distinguished as interaction types (that is social in nature) and flow types (such as information exchange). Emerson (1962) defines power within a dependency framework, where the power of an individual A, over another individual B, is equal to the degree to which B is dependent on A. Actors who are centrally located within a network are more likely to control valuable information and thus more likely to have greater influence (Brass 1984).
[1] http://lrs.ed.uiuc.edu/TSE-portal/analysis/social-network-analysis/
[2] http://www.istheory.yorku.ca/socialnetworktheory.htm
[3] http://www.allbusiness.com/company-activities-management/product-management/8898104-1.html
[4] Gulati R (1995), “Does Familiarity Breed Trust? The Implications of Repeated Ties for Contractual Choices,” Academy of Management Journal, Vol. 35, No. 1, pp. 85-112.