Using Social Network Theory to Explain Performance ...

Proceedings of 8th Global Business and Finance Research Conference 26 - 28 October 2017, Howard Civil Service International House, Taipei, Taiwan ISBN: 978-1-925488-48-7

Using Social Network Theory to Explain Performance in Value Chains Arthur Mapanga1, Collins Miruka2 and Nehemiah Mavetera1 The ability of value chains toproduce and deliver required products and services to the consumers depends on continuousimprovements on the efficiency and effectiveness of these systems. Value chain efficiency and effectiveness improvements require the use of appropriate methods to ensuresuccess. However, such methods can only be effectively exploited if the characteristics of the value chains are included in the framework of understanding. Value chains are complex, fragmented and continuously changing systems comprising numerous role players with different and often misaligned business objectives. Currently, there is no convergence in literature on how the framework of understanding should look like to fully inform the improvement strategies in value chains.Social network theory has increasingly become influential in explaining other fields. This study presents a discussion of how social network theory explains the performance in value chains. The study was done through a literature review and some documentary evidence gathering from the cotton value chain in Zimbabwe. Thematic analysis captured some key aspects closely related to social network theory with a positive effect on the performance of value chains.

JEL Codes: L14, M11 and P13

1. Introduction The ability of value chains to produce and deliver required products and services to the consumers depends on continuous improvements on systemic efficiency and effectiveness. Value chain efficiency and effectiveness improvements require the use of appropriate methods to ensure success.However, such methods can be effectively exploited only if all the inherent characteristics of the value chains are included in the framework of understanding. Value chains are complex, fragmented and continuously changing systems comprising numerous role players with different and often misaligned business objectives. Currently, there seems to be no convergence in literature on how the framework of understanding should look like to fully inform the improvement strategies in value chains. Despite the use of the so called “grand theories” in prior studies, achieving productive relationships within value chains continues to be a fundamental challenge to managers around the world (Christopher, 2016; Ross, 2013; Kramer and Porter, 2011). In this study, we employ social network theory to build a framework of understanding.Social network theory has increasingly become influential in explaining other fields such as sociology, psychology and more recently the management field (Ellison and Boyd, 2013). We then use this framework of understanding to solve performance problems in the cotton value chain in a developing country. Apart from this introductory section, the paper is organized as follows: First we discuss the theoretical context of social network theory, as developed in diverse fields ranging from social psychology to mathematics. In doing so, we examine several threads of network studies to reveal its core tenets. Next, we focus on how the social network theory can be applied to value chains. This culminates in the framework of understanding. The subsequent section provides the theoretical reflections about social network theory. ___________________________________________________________________ 1

North-West University; South Africa and 2Sol Plaatje University, South Africa

Proceedings of 8th Global Business and Finance Research Conference 26 - 28 October 2017, Howard Civil Service International House, Taipei, Taiwan ISBN: 978-1-925488-48-7 2. Theoretical Framework Since the turn of the 21st Century, managershave been preoccupied with finding solutions to deal with complexities in value chains. The proliferation of information and communication technologies along with the reduction of logistical costs are creditedfor the intensified interactions and interrelationships observed in today’s business space. While, this has been beneficial, more complications and complexities are increasingly impacting on business performance. Traditionally, managers have always relied reductionist approaches to deal with such complexities in value chains. However, most reductionist approaches while effective in the short run, have failed to sustain competitiveness in today’s businesses because today’s firms are inherently embedded in a web of network structures (Staber, 2001; Yang and Liu, 2012; Möller, 2010; Lin and Lo, 2015).In such an environment, reliance on relation-specific assets, knowledge exchange mechanisms and resource combinations have without doubt become the only avenues available to the contemporary manager in dealing with the inherent complexity in value chains (Granovetter, 1985; Williamson, 1985; Krause et al., 2006; Ketchener and Hult, 2006; Borgatti and Li, 2009). Implied here, is the necessity of instruments that inspire and facilitate collaborative behaviour of all the stakeholders in the value chain (Christopher, 2016; Gattorna, 2016). In this study, we propose that social network theory is best positioned to achieve these demands. Indeed, the social network theory has become the language in all sciences including business management (Kilduff and Brass, 2010; Edvardsson, Tronvoll and Gruber, 2011). In business management, social network has come to symbolize the basis of future approaches (Wasserman and Faust, 1994; Grant, 2016). The subsequent section dissects the core tenets of the social network theory. 2.1 Social Network Theory Social network theory has its origins in Kurt Lewin (1936)’swork which brought to the fore the importance of relationships in value creation in complex organizations such as value chain (Rodriguez-Rodriguez& Leon, 2016).Literature (Carter et al., 2015; Dinh et al., 2014 Norton et al., 2015) applauds social network theory’s contribution to the understanding of the variables in multifacetedphenomena. The objective of this section is to provide a snapshot of the fundamental concepts and perspectives in social network theory. Typically, the social network theory relates to the interactionism paradigm in explaining societal dynamics (Granovetter, 1982; Geels, 2010; Marin and Wellman, 2011) with much of the focus placed on the social structure by representing a system of social relations in organizations (Marin and Wellman, 2011). This is in recognition that actor motivations, mind-sets and demographics have an impact on the performance of organizations. In addition, the theory explains the patterns of the interdependence of the diverse relations (Leinhardt, 1997), the resultant behaviour of the individual (Parker and Collins, 2010) and lastly the impact of the entities’ qualities on the patterns of relationships (Phelps, Heidl and Wadhwa, 2012). Accordingly social networks can be understood in terms actors (nodes) and their relationships (Scott, 2012; Borgatti and Foster, 2003; Borgatti and Halgin, 2011; Kilduff and Brass, 2011; Burt, Kilduff and Tasselli, 2013; Berkowitz, 2013; Scott,2017),) or ties (Costa et al., 2013; Shaikh and Jiaxin, 2006; Huang et al., 2014) that can be information ties, formal ties, affective ties, material or work flow ties, proximity ties, and cognitive ties (Marin & Wellman, 2011).

Proceedings of 8th Global Business and Finance Research Conference 26 - 28 October 2017, Howard Civil Service International House, Taipei, Taiwan ISBN: 978-1-925488-48-7 Given the above, it is evident that social network theories can elaborate the fundamental structural positions and characteristics of both the node and network levels (Rodriguez-Rodriguez and Leon, 2016). In so doing, the theories allow the visualization of the characteristics of the nodes and networks. As well, the theories can be used to predict the nature of future relationships (Kim, Choi, Yan and Dooley, 2011). At the node level, emphasis has generally been placed on individual position and characteristics (Zhu et al., 2010). According to Hu (2013) the social network theory can also be employed to analyze the importance of any node within the network through defining the degree, betweenness and closeness centrality, reachability and connectivity and preferences. From the point of view of the network, Huang (2015) find the social network theories important in showing the characteristic differences among different networks. In the same vein, Lin and Lo (2015) find SNT being able to elucidate the potential, density and diversity of the networks. The three aspects have important implications on the performance of any network. We find the above discussion quite instructive in the sense that it proves that social network theories can be used to reveal the relationships among and between the participants in particular groups. Furthermore, as argued in Turetken and Sharda (2007), the application of network theories brings out vast amounts of information, especially with regard to the measures of the degree centrality, degree of intermediation (Betweenness centrality) and proximity (closeness). We examine further these network measures in order to motivate the application of social network theories in understanding interorganizational dynamics. 2.1.1 Degree of Centrality Centralization, as a characteristic of a network, has been linked to the efficiency of a network as an exchange system. Centrality has been related with the occupation of a core position by a firm in a network of inter-organizational relationships (Osman, 2015). According to Rodriguez-Rodriguez and Leon (2016), the degree of centrality denotes the total number sum of direct ties that an actor (or node) has.This measure is thus important as it reveals the most connected actor in the group and therefore the most important.The view of centrality in social network theories is that the inherent ties are important conduits for exchanging information, goods and services among organizations. Hence assuming a central position in an exchange system may result in influence and satisfaction. Rodriguez-Rodriguez and Leon (2016) propound that an actor or node with a high degree of centrality has direct connections with many other network nodes. This implies therefore that the node is much more influential, that is, has more control and power over all others (Sandru, 2012) and therefore is able to perform hub tasks within the group(Zhu et al., 2010). An active or prominent actor in the network has many ties thus has access to diverse sources of resources and information making it more efficient than the rest. This issue makes others more dependent of it for information and other resources. This can explain the importance of social network theories in explaining the competitiveness position of firms embedded in a network. 2.1.2 Degree of Intermediation The degree of intermediation relates to the measure of betweeness centrality, a concept that measures the degree to which a node in a network is positioned between two or more nodes (Wasserman and Faust, 1994; Scott, 2015).This means the index of the degree of intermediation denotes the extent to which an actor is

Proceedings of 8th Global Business and Finance Research Conference 26 - 28 October 2017, Howard Civil Service International House, Taipei, Taiwan ISBN: 978-1-925488-48-7 located in a bridging position between actors of a network (Rodriguez-Rodriguez and Leon, 2016). Osman (2015) find those actors occupying the brokerage position in a network to have the power and control of the flow of information and knowledge among the network actors. This leads Rodriguez-Rodriguez and Leon (2016) to conclude that knowledge sharing loss occurs when the intermediary actors are removed between the two isolated actors in the network. Accordingly, actions to nurture the existence of intermediaries should be implemented and monitored.. However, as a cutpoint in the shortest path connecting two other nodes, an intermediary actor’s control of the flow of information or the exchange of resources might increase the transaction costs for other nodes as they usually charge a fee or brokerage commission for transaction services rendered. 2.1.3 The Degree of Closeness or Proximity The closeness or proximity measure in groups signifies the closeness of a particular node from the rest of the group nodes. For Rodriguez-Rodriguez and Leon (2016), this is a sign of a node’s ability to reach others within the group without having to depend on intermediaries.This gives the actor the ability to quickly interact and communicate with other nodes them without going through many intermediaries. As such, minimizing the number of intermediary nodes should assure maximum independence and maximum efficiency in the exchange process in the network. However, Granovetter find relatively superficial ties to be much more useful in terms of information access since they are more likely to link the actors to those they have no direct connections.

3. Social Network Theories and the Performance of Value Chains The spectrum we have described so fardemonstrates the complexities of value chains from a social perspective (Granovetter, 1982; Borgatti & Halgin; 2011). It is evident, therefore, that embeddedness as described by social network theories has implications for the performance of the value chains (Granovetter, 1982; Memon &. Wiil, 2014).Indeed, we observed earlier on that value chains are complex, fragmented and continuously changing systems whose many role players often have different and misaligned business objectives. To succeed, value chains thus rely on high levels of socio-emotional support and trust among the heterogeneous elements (Memon &. Wiil, 2014). Stronger ties among the value chain elements should allow the ability by individual actors to access and acquire varied or exclusive input resources for greater performance (Axelsson and Easton, 2016). As well, measures such as actor degree centrality, betweenness centrality, betweenness centrality, reciprocity and transitivity should givethe diverse value chain actors the ability to gainvaluable information and knowledge within the network of actors defining the value chains (Bosse & Phillips, 2016). Given the foregoing, it is evident that social network theories can be applicable in attempts to build a framework of understanding with regard to the performance of value chains. A number of studies have since urged the use of social network theories in such attempts. For instance, the reflections of Trienekens (2011) on Slotte‐ Kock & Coviello (2010)’s point of view that economic behaviour is intrinsically embedded behaviour in networks of on-going social relations suggest the applicability of social network theories in informing the performance of value chains. For Trienekens (2011) this embeddedness of firms in complex horizontal, vertical and support connections allows the use of the much needed inputs and services

Proceedings of 8th Global Business and Finance Research Conference 26 - 28 October 2017, Howard Civil Service International House, Taipei, Taiwan ISBN: 978-1-925488-48-7 from other organizations leading to the success of business undertakings. Similarly, Medlin & Törnroos (2015) argue that embeddedness and social networks reverberate in many management and organizational studies. Furthermore, Brass (2011) highlights the prevalence of diverse social network theories such as selfinterest, social exchange or dependency, mutual or collective interest, cognitive theories, and homophily in the analysis of performance variables of a value chain. Bosse & Phillips (2016) further find that in their collectivity, social network theories indicate the observed rational self-interest (Fujita & Thisse, 2013; Hardin, 2015) in capitalist business undertaking. It is also interesting to note that even earlier scholars such as Coleman (1988)’s had already seen the value of network ties in maximizing personal preferences and desires. Recently, Misztal (2013) found that most entities seek to create ties as an investment to accumulate “social capital”, that is, the “sum of the resources, actual or virtual, that accrue to an individual or group by virtue of possessing a durable network of institutionalized relationships of mutual acquaintance and recognition” (Bourdieu, 2011:84). Implied here is the notion of equating self-interest and the use of social capital (Burt; 1992, 1997; Myers & Nelson, 2010) in order create opportunities of profitmaking. Thus, for Porter, Hills, Pfitzer, Patscheke, & Hawkins (2011), actors in value chains actors receive return on their investments by “brokering” knowledge and information flows with those they are indirectly connected. When it comes to social dynamics in value chains, we see scholars such as Huggins, Johnston, & Thompson (2012) magnifying the role of inter-firm relationships on performance improvement. Similarly, Scott & Davis (2015) and Robbins et al. (2013) stress the relevance of external group ties on competitiveness and performance of value chains. According to Daft (2012) such external group ties are pertinent to reduce organizational conflict as they thwart free-rider behaviour (Scott & Davis, 2015; Foster, Borgatti, & Jones, 2011; Granovetter, 1985). Furthermore, network theories of mutual interest (Bandura, 2008) and collective action (Ostrom, 2014) are well indicated in organizational studies. Literature (Hardin, 2015) amplify that mutual interests and coordinated action are positively associated with collective as opposed to private ownership or the production of pertinent infrastructure and intellectual property. According to Emerson (1972a, 1972b) sustaining well-established network relations, gives individuals or groups in value chains the ability to mobilize the valued resources. Enduring interdependencies (Sørensen & Torfing, 2016) imply highly performance value chains. However, Dunning (2014) argues that positioning the network theory within the gambit of value chain is problematic ostensibly because of the entailed levels of analysis and the static nature of social network analysis. Baggio (2011) cites the single view of network ties provided by the social network theory. In addition, Eisingerich, Bell & Tracey( 2010) highlight the inability of the social network theory to apportion whether network data results from prior to group outputs (and received feedback),or are a consequence of the ties themselves. Conclusively, Ross (2016) demonstrated the intrinsic network structure in value chains suggesting that individual businesses in value chains depend on established connections to collectively produce goods and services for the market. For Nielsen (2010) linkages underlined by the various contracts by value chain actors constitute exchange relations. However, focusing on the social relations to the exclusion of other pertinent factors in the value chain detracts its applicability in a holistic depiction of the functioning of value chains. Other theories are thus needed to augment this contribution to the understanding of the value chain.

Proceedings of 8th Global Business and Finance Research Conference 26 - 28 October 2017, Howard Civil Service International House, Taipei, Taiwan ISBN: 978-1-925488-48-7 However, the issue of social capital as described by social network theories cannot be discounted in an attempt to build a framework of understanding of the performance in value chain. The essence of the social capital concept is social relations, through these relations actors in value chains can fulfil their goal. It is possible from a social network theory standpoint to highlight the keytenets of maintaining social ties in value chains. Looking at the measures discussed earlier on we are able to offer three propositions, namely,promotionof innovation capabilities, increment of the task orientation level and facilitating of both internal and external communication. Osman (2015) argues that, among the firms that are embedded in the centralized upstream supply network; some will obtain more relational capital compared to other firms as a result of this embeddedness. Thus, the level of relational capital influence will depend upon the network structural positions of the firms in both formal and informal inter-firm relations (Molina‐ Morales and Martínez‐ Fernández, 2010). In this research, we posit that firm embeddedness based on this network structural position implies a firm level of relational capital outcomes in the value chain structure. The enactment and use of social ties in value chains boosts the innovation capability of each actor and the value chain as a whole. Firstly, social ties ensure that a common vision for the value chain can be established and thus increase value chain cohesion. Drawing from the social network theories, the leadership in the value chain employs its social ties to distribute and disseminate an image of what the value chain is and what it should be. Consequently, other actors become aware of what is expected of them if they are to remain in the defined value chain. In line with the established shared vision of the value chain, the value chain actors come to align their roles with those of their partners in order to improve their strengths and minimize vulnerabilities in light of the new value chain ties. Secondly, the application of social theories, intensifies the level of task orientation in the value chain. Once each actor becomes aware of its position in the value chain, it can adapt its own internal processes and structures in order to increase its performance and also that of the value chain. At this unit of analysis, it is possible to see an intensification of process innovations that should assist in improving decisionmaking and teamwork. Thirdly, social network theories facilitate internal and external communication and knowledge sharing. Internally, social network theories support organizational learning as well as the process of intergenerational learning since knowledge is shared among the social ties. In the process, social network theories offer the necessary framework for designing internal value chain knowledge maps (which shows who knows what).

4.

Application of SNT

To illustrate the potential of social network theory as a helpful framework of understanding the performance of value chains, we use the cotton value chain in Zimbabwe to draw important implications. We proposed that a social network analysis on the value chain can bring a cogent understanding of its performance parameters.However, there still remains a question:To what extent? The creation, implementation and maintenance of a social network within the cotton value chain in a developing country is examined next to see how the application of social network theories will contribute to the understanding of the performance in value chains.

Proceedings of 8th Global Business and Finance Research Conference 26 - 28 October 2017, Howard Civil Service International House, Taipei, Taiwan ISBN: 978-1-925488-48-7 Cotton value chains in Southern Africa generally and in Zimbabwe in particular are in dire need of transformation to create the jobs needed by the growing young populations (Mujeyi, 2013). Cotton represents an important source of income for millions of people in this part of the world (Hove, 2014). Yet, cotton chains suffer major inefficiencies that constrain sustainability (Mujeyi, 2013). A complex problem set of interdependent policy, institutional, social and technical challenges is acknowledged but no consistent solutions at the value chain level have been forthcoming from all involved in the chains. To disentangle the interdependent problems constraining performance in the Zimbabwean cotton value chains, this study adopts the social network theories as discussed earlier on in this study to show how cotton actors (either in private, public or non-profit organizations) can combine the available resources to improve performance in the cotton value chains. As a mapping tool, the social network theory lens brings a framework of understanding that contributes to the development of cotton value chains in developing countries. As already noted, SNT presupposes that fundamental to value creation are relationships among actors within and outside the chain (Peppard and Rylander, 2006). The focal point of the value chain is the end product and the chain is designed around the activities required to produce it (Stavrulaki and Davis, 2010). However, from a social network lens, relationships define the value creation in value chains (Barney, 2014;Capaldo, 2007; Wassmer, 2010). By combining value chain and social network analysis, Lazzarini et al. (2001) introduce “netchain analysis” to assess how tangible and intangible resources are exchanged and pooled along product chains. We start by arguing that the challenges faced by actors in the cotton value chain in Zimbabwe have to do with intangible dimensions of the value chain. The serious capacity underutilization in the cotton value chain in Zimbabwe from cotton farming to industrial sectors (Mujeyi, 2013; Hove, 2014) is a result of weaknesses in social contest under which the cotton value chain is embedded. We examine how social network theory metrics can be used to show avenues for improving the cotton value chain in Zimbabwe. 4.1 Degree of Centrality The cotton value chain in Zimbabwe has of let experienced unprecedented levels of deindustrialization (Mujeyi, 2013; Hove, 2014). As such, lead firms that should develop stronger ties with role players have since divested out of the value chain. In the absence of central lead firms, many of the cotton value chain remain isolated. This isolation in reality has meant that exchange of vital information has remained insignificant to support higher levels of performance. Before, this demise, the cotton value chain used to boast of numerous lead firms along the composite segments, as strategic centers for value chain coordination. Without lead firms performing the hub tasks along the cotton value chain, many of the chain’s actors are starved of vital resources and information to achieve effectiveness and efficiency in the value chain. Without dominant firms to build preferential knowledge networks with other role players in the cotton value chain in Zimbabwe, it is difficult for gain innovative practices which should drive the performance of the value chain. Furthermore lead firms in cotton value chain could have occupied dominant positions in value chains due to their financial strength, buying power, control of key technology or market access which would have benefitted all other nodes in the chain. As well, lead firms in the cotton value chain could be providing important products or support to the actors they buy from or sell to, as part of their commercial relationships with them in the cotton value chain. Indeed, lead firms in the cotton

Proceedings of 8th Global Business and Finance Research Conference 26 - 28 October 2017, Howard Civil Service International House, Taipei, Taiwan ISBN: 978-1-925488-48-7 value chain should command a dominant market position such that their activities and investments produce benefits for themselves and other firms in the value chain. Currently, market mechanisms that should provide information along the cotton value chain are not working because of the high expenses involved in trying to reach the other value chain actors. Lead firms’ investments in the cotton value chain would be providing the necessary points of leverage for other cotton value chain participants. Since, most of the benefits would have accrued to them, lead firms in the cotton value chain would be more incentivized to broaden the opportunity for coordinating the value chain outside the price mechanism, thus supporting the entire chain to minimize transaction costs. In addition, the relationships between lead firms and other firms (either mutual or predatory) would then have result in improved industry competitiveness.The conclusion derived from the foregoing suggests that if all if all firms in cotton value chain could act like lead s firms, then the entire value chain better would be better off since the quality of coordination in the cotton value chain in Zimbabwe could be better than the current status. 4.2 Degree of Intermediation The role of intermediaries in value chain activities is widely accepted in literature (See Christopher, 2016; Monczka, Handfield, Giunipero and Patterson, 2015; Fernie and Sparks, 2014; Prause and Solesvik, 2011; Zhang, 2007; Kaplinsky and Morris, 2001). Admittedly, there are more opportunities and circumstances in the cotton value chain where a group of actors draw benefits from using third party agents as intermediaries. The presence of intermediaries in the cotton value chain can refine the market entry conditions as well as the upgrading capacity for individual actors. Given the above, intermediaries in the cotton value chain should ensure that there is some kind of coordination and arbitration between the different role players. Different categories of intermediaries are evident in the cotton value chain in Zimbabwe. Some are transactional and some are informational. However, due to the lack of lead firms to disseminate vital information to all the non-lead firms in cotton value chain, the resultant information asymmetry in the chain, most of the intermediaries present in the cotton value chain are seen to act opportunistically. The effect is the nonperformance of the entire cotton value chain due to uncertainty and often mismatch between supply and demand of the cotton products. 4.3 The Degree of Closeness or Proximity The concept of homophily is also relevant in the cotton value chain in Zimbabwe. The absence of collective action to minimize costs associated with the acquisition of inputs, market information, new technologies and the exploitation of market opportunities is evident in the cotton chain. Also collective action regimes in the cotton value chain give impetus to the actors’ ability to build winning coalitions necessary for informal coordination across institutional boundaries. Furthermore, since collective regimes are more interested in amassing the “power to” (capacity to act), rather than “power over” others (social control), cooperation would be ensured in the cotton value chain. The same mechanisms may also be used to secure participation in coalition to govern the value chains. The problem of free-riders in cooperative efforts limit collective action within the cotton value chain in Zimbabwe. By implication, the presence of limited collective action negatively affects the performance of the cotton value chain. All of the above present evidence of the lack of trust to enhance homophily, that is the tendency for cotton value chain participants

Proceedings of 8th Global Business and Finance Research Conference 26 - 28 October 2017, Howard Civil Service International House, Taipei, Taiwan ISBN: 978-1-925488-48-7 to bond andcooperate. Table 1.1 below presents the framework of understanding derived from the social network theories to explain performance in value chains. Table 1.1: The Framework of understanding Elements of social network

The presence of trusting culture

The presence of intermediaries

Effects on value chain performance

The presence of trust reduces the costs of coordination in value chains due to decline in the need for contract specifications. As well it improves cooperation due to homophily that encourages clustering in value chains. Trust decreases free-rider risks leading to the enlargement of the scope of coordination beyond price transactions in the value chains. Intermediaries minimize the costs of coordination and broaden the scope for coordinating beyond price transaction due to specialized management of coordination tasks. They link isolated nodes in the value chain so that they participate fully. Lead firms generate positive external effects for firms in their network, mainly by encouraging innovation and promoting internationalization.

The presence of lead business firms

Quality of regimes

Lead business firms leverage firms in the value chain by investing in training, education, infrastructure, innovation and regimes for collective action thus leading to improved performance in the value chain Quality collective action regimes add to the performance in value chains through encouraging cooperation among the participants.

Conclusions This paper had the objective of introducing SNT as a tool to explain the performance in the value chains. The case of the cotton value chain in Zimbabwe provided d an illustration of how SNT works to inform the performance of value chains. It was shown that the three metrics of SNT can be used to build a framework of understanding with regard to the performance in value chains.The interpretation of the SNT sheds insights on how business actors recombine the resources available in the cotton value chain to deal with the identified challenges and thus create value. In this endeavour, SNT used to assess, among the others, indicators such as the degree of centrality, degree of intermediation and lastly the degree of closeness in the value chains and their contribution to performance thereof. Moreover, SNT can be applied to give a “snapshot” that captures the dynamics of the value chain systems. As well, in the form discussed in this paper, SNT allows the identification of opportunities for policy makers to encourage effective relationships and complementarities that can stimulate performance in value chains. As demonstrated in the cotton value chain in Zimbabwe, SNT has the potential of becoming a useful lens for researchers seeking to understand and change value chains.

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