Dynamics of Brokerage Positions in Clusters: Evidence from the Spanish Foodstuffs Industry

[EN] Shifting away from traditional approaches orientated towards the analysis of the benefits associated with brokerage, this paper provides valuable insights into the dynamics of this network position and the opportunities to innovate that it provides. Using fine grain micro data collected in a foodstuff Spanish cluster, the evolution of different brokerage profiles is analyzed in depth. It was particularly evident how firm-level characteristics (status, former mediating experience and external openness) and their interactions may generate changes in the different brokerage roles over a period of time. The findings of this work partially validate expectations based on the network dynamics approaches. Status and previous mediating experience facilitate the creation of partnerships, fostering brokerage. Conversely, interaction effects demote brokerage activity at the intra-cluster level, suggesting the selective nature of brokers¿ relational behavior.Financial support from the Spanish Ministry of Economy and Competitiveness (ECO2015-67122-R) is gratefully acknowledged.Belso-Martínez, JA.; Expósito-Langa, M.; Mas-Verdú, F.; Molina-Morales, F. (2017). Dynamics of Brokerage Positions in Clusters: Evidence from the Spanish Foodstuffs Industry. Sustainability. 9(2):1-18. https://doi.org/10.3390/su9020290S1189

Studies on Interorganizational Networks: The Case of Two Regional Clusters in Norway

The overall purpose of this dissertation is to study interorganizational networks. Firms are open systems and simultaneously embedded in interorganizational networks of various kinds. Interorganizational networks consist of a group of organizations and relations between these organizations, reflecting the allocation and flow of resources among network members. Conceivably, network structures largely affect involved firms’ different behaviors. Nevertheless, such knowledge is insufficient without knowing how interorganizational networks emerge and develop into a specific structure. Using a sociometric structural approach, this dissertation contributes to two related topics: (1) the influence of network properties on firms’ behaviors (Articles 1 and 2) and (2) the dynamics of network structures (Article 3). A firm’s position in a network has implications for its opportunities and constraints (Brass et al., 2004). The first two empirical articles focus on the influence of network structures on firms’ behaviors. In Article 1, I demonstrate how firms adapt exploration strategies according to network properties. Management research has alluded to environmental and organizational antecedents for firms’ exploration. I complement this knowledge by applying a network perspective to explain how a firm may adjust its exploration strategy based on its position within the interorganizational network. I particularly focus on two network constructs: closeness centrality and local cohesion. Closeness centrality captures a firm’s distance to network knowledge and resources, and local cohesion shows the connection between a focal firm’s alters. The findings show positive impacts of closeness centrality and local cohesion on exploration strategy, and local cohesion has a more significant impact. I offer insights into antecedents of exploration by underscoring the network drivers. In Article 2, I study firms’ prosocial behavior in dyads within a broader network context. Research on relationship marketing has traditionally focused on dyadic properties to explain behaviors within dyads. This article adds to this body of research by investigating network Abstract The overall purpose of this dissertation is to study interorganizational networks. Firms are open systems and simultaneously embedded in interorganizational networks of various kinds. Interorganizational networks consist of a group of organizations and relations between these organizations, reflecting the allocation and flow of resources among network members. Conceivably, network structures largely affect involved firms' different behaviors. Nevertheless, such knowledge is insufficient without knowing how interorganizational networks emerge and develop into a specific structure. Using a sociometric structural approach, this dissertation contributes to two related topics: (l) the influence of network properties on firms' behaviors (Articles l and 2) and (2) the dynamics of network structures (Article 3). A firm's position in a network has implications for its opportunities and constraints (Brass et al., 2004). The first two empirical articles focus on the influence of network structures on firms' behaviors. In Article l, I demonstrate how firms adapt exploration strategies according to network properties. Management research has alluded to environmental and organizational antecedents for firms' exploration. I complement this knowledge by applying a network perspective to explain how a firm may adjust its exploration strategy based on its position within the interorganizational network. I particularly focus on two network constructs: closeness centrality and local cohesion. Closeness centrality captures a firm's distance to network knowledge and resources, and local cohesion shows the connection between a focal firm's alters. The findings show positive impacts of closeness centrality and local cohesion on exploration strategy, and local cohesion has a more significant impact. I offer insights into antecedents of exploration by underscoring the network drivers. In Article 2, I study firms' prosocial behavior in dyads within a broader network context. Research on relationship marketing has traditionally focused on dyadic properties to explain behaviors within dyads. This article adds to this body of research by investigating network 111 iv level antecedents of prosocial behaviors in dyadic relations. Prosocial behavior refers to a firm’s beneficial actions toward another firm beyond formal requirements. Since a contract is normally incomplete, such behavior is desirable in business relationships. Our findings show that in-degree centrality (i.e., the number of ties received from other network members) has an inverted U-shaped relationship with a focal firm’s prosocial behavior. Besides, triadic embeddedness (i.e., the number of common third parties) is likely to facilitate prosocial behavior between involved parties, regardless of firms’ in-degree centrality. This study shows the need to consider the dyadic relationship in a wider network context. While Articles 1 and 2 implicitly assume network properties are static, Article 3 contributes to knowledge of network development in the interorganizational setting. Sociologists and management scholars provide explanations mainly for dyadic tie formation, such as alliance formation and joint ventures. Limited is known about system-level structural dynamics. Specifically, I focus on two system-level properties: small-world and scale-free networks. Small-world networks are characterized by dense local clustering and short path length between actors. Scale-free networks are centralized with a small portion of central actors spanning the structure and take a skewed degree distribution. Some empirical networks demonstrate both properties simultaneously, yet few studies have aimed to discuss the dynamics and interrelation of these properties. In article 3, I retrospectively visualize the annual structures of two empirical networks to show how small-world and scale-free properties together explain the development patterns. The results show that the small-world and scale free properties have an inversed dynamic pattern, and the scale-free structure may be less common in the interorganizational setting. Altogether, this study adds to the understanding of the dynamics and development of interorganizational networks in terms of small-world and scale-free structures. level antecedents of prosocial behaviors in dyadic relations. Prosocial behavior refers to a firm's beneficial actions toward another firm beyond formal requirements. Since a contract is normally incomplete, such behavior is desirable in business relationships. Our findings show that in-degree centrality (i.e., the number of ties received from other network members) has an inverted U-shaped relationship with a focal firm's prosocial behavior. Besides, triadic embeddedness (i.e., the number of common third parties) is likely to facilitate prosocial behavior between involved parties, regardless of firms' in-degree centrality. This study shows the need to consider the dyadic relationship in a wider network context. While Articles l and 2 implicitly assume network properties are static, Article 3 contributes to knowledge of network development in the interorganizational setting. Sociologists and management scholars provide explanations mainly for dyadic tie formation, such as alliance formation and joint ventures. Limited is known about system-level structural dynamics. Specifically, I focus on two system-level properties: small-world and scale-free networks. Small-world networks are characterized by dense local clustering and short path length between actors. Scale-free networks are centralized with a small portion of central actors spanning the structure and take a skewed degree distribution. Some empirical networks demonstrate both properties simultaneously, yet few studies have aimed to discuss the dynamics and interrelation of these properties. In article 3, I retrospectively visualize the annual structures of two empirical networks to show how small-world and scale-free properties together explain the development patterns. The results show that the small-world and scale free properties have an inversed dynamic pattern, and the scale-free structure may be less common in the interorganizational setting. Altogether, this study adds to the understanding of the dynamics and development of interorganizational networks in terms of small-world and scale-free structures. lV v Contextually, I investigate two regional industry networks in western Norway, focusing on the media industry and fintech. Overall, this dissertation provides an in-depth analysis of these two interorganizational networks by focusing on multiple levels and aspects of a network and adds to the current literature on management, relationship marketing, and network dynamics. Moreover, this dissertation combines network data and survey data for hypotheses testing in Articles 1 and 2, which is unique and increases the validity of the findings. I also present key findings, discuss the implications and limitations of this work, and suggest future research directions

Characterizing Nodes and Edges in Dynamic Attributed Networks: A Social-based Approach

How to characterize nodes and edges in dynamic attributed networks based on social aspects? We address this problem by exploring the strength of the ties between actors and their associated attributes over time, thus capturing the social roles of the actors and the meaning of their dynamic interactions in different social network scenarios. For this, we apply social concepts to promote a better understanding of the underlying complexity that involves actors and their social motivations. More specifically, we explore the notion of social capital given by the strategic positioning of a particular actor in a social structure by means of the concepts of brokerage, the ability of creating bridges with diversified patterns, and closure, the ability of aggregating nodes with similar patterns. As a result, we unveil the differences of social interactions in distinct academic coauthorship networks and questions \& answers communities. We also statistically validate our social definitions considering the importance of the nodes and edges in a social structure by means of network properties.Comment: 11 pages, 5 figure

Understanding cluster dynamics in evolutionary economic geography : essays on the structure of networks and clusters life style

L’objectif principal de cette thèse est d’étudier l’évolution des clusters. La littérature concernant les clusters s’est longuement intéressée aux raisons de leur existence ainsi qu’à la manière dont ils favorisent l’innovation, la productivité et la croissance. Nous étudions comment ces effets durent dans le temps, poursuivant l’objectif d’identifier les déterminants de la performance dynamique des clusters. Il s’agit, ainsi, d’expliquer pourquoi certains clusters déclinent tandis que d’autres continuent à fonctionner grâce à un renouveau constant. Cette thèse adopte une approche des clusters par les réseaux. Nous défendons l’idée que les structures de réseau hétérogènes des clusters démontrent des capacités différentes à s’associer ou à se dissocier des cycles industriels/technologiques au bon moment. Ainsi, nous identifions les propriétés de structure du réseau qui favorisent la performance dynamique des clusters ou la résilience des clusters. Nous appuyons nos développements théoriques sur des regards empiriques dans deux contextes bien différents. D’une part, nous étudions les structures des clusters de l’industrie de la téléphonie mobile en Europe. D’autre part, nous analysons la structure des relations entre les producteurs de fromage d’Aculco (Mexique). Le résultat principal de ce travail montre que la hiérarchie et la disassortativité des réseaux, ainsi que les interactions entre des réseaux de natures différentes (multiplexité), influencent la capacité des clusters à éviter les lock-in négatifs, conduisant à leur déclin, et favorisent le lock-out pour la survie du cluster, c’est-à-dire la prolongation de leur vie.The main objective of this thesis is to study clusters’ evolution. The literature on clusters has widely studied why clusters exist and how they favor innovation, productivity and growth. Our concern is to study how these effects hold over time. Therefore, we aim at identifying the determinants of dynamic performance of clusters to explain why some clusters decline while others keep working by continuous renewal. To do so, this thesis approaches clusters from a network perspective. We contend that clusters with heterogeneous network structures exhibit different capacities to associate and dissociate cluster’s evolution and industrial/technological cycle at the right moment. Thus, we identify the properties of network structures that favor dynamic performance of clusters or cluster resilience. We support our theoretical developments with empirical insights in two different contexts. On the one hand, we study the structure of clusters in the European mobile phone industry. On the other hand, we analyze the structure of relations between cheese producers in Aculco (Mexico). The main result of this work is that network hierarchy, network disassortativity and the interplay between different networks (multiplexity) influence the capacity of clusters to avoid negative lock-in leading to cluster failure, and favor lock-out to enhance cluster continuation, i.e. extending the life of the cluster

Edge manipulation techniques for complex networks with applications to communicability and triadic closure.

Complex networks are ubiquitous in our everyday life and can be used to model a wide variety of phenomena. For this reason, they have captured the interest of researchers from a wide variety of fields. In this work, we describe how to tackle two problems that have their focus on the edges of networks. Our first goal is to develop mathematically inferred, efficient methods based on some newly introduced edge centrality measures for the manipulation of links in a network. We want to make a small number of changes to the edges in order to tune its overall ability to exchange information according to certain goals. Specifically, we consider the problem of adding a few links in order to increase as much as possible this ability and that of selecting a given number of connections to be removed from the graph in order to penalize it as little as possible. Techniques to tackle these problems are developed for both undirected and directed networks. Concerning the directed case, we further discuss how to approximate certain quantities that are used to measure the importance of edges. Secondly, we consider the problem of understanding the mechanism underlying triadic closure in networks and we describe how communicability distance functions play a role in this process. Extensive numerical tests are presented to validate our approaches

Understanding cluster dynamics in evolutionary economic geography : essays on the structure of networks and clusters life style

L’objectif principal de cette thèse est d’étudier l’évolution des clusters. La littérature concernant les clusters s’est longuement intéressée aux raisons de leur existence ainsi qu’à la manière dont ils favorisent l’innovation, la productivité et la croissance. Nous étudions comment ces effets durent dans le temps, poursuivant l’objectif d’identifier les déterminants de la performance dynamique des clusters. Il s’agit, ainsi, d’expliquer pourquoi certains clusters déclinent tandis que d’autres continuent à fonctionner grâce à un renouveau constant. Cette thèse adopte une approche des clusters par les réseaux. Nous défendons l’idée que les structures de réseau hétérogènes des clusters démontrent des capacités différentes à s’associer ou à se dissocier des cycles industriels/technologiques au bon moment. Ainsi, nous identifions les propriétés de structure du réseau qui favorisent la performance dynamique des clusters ou la résilience des clusters. Nous appuyons nos développements théoriques sur des regards empiriques dans deux contextes bien différents. D’une part, nous étudions les structures des clusters de l’industrie de la téléphonie mobile en Europe. D’autre part, nous analysons la structure des relations entre les producteurs de fromage d’Aculco (Mexique). Le résultat principal de ce travail montre que la hiérarchie et la disassortativité des réseaux, ainsi que les interactions entre des réseaux de natures différentes (multiplexité), influencent la capacité des clusters à éviter les lock-in négatifs, conduisant à leur déclin, et favorisent le lock-out pour la survie du cluster, c’est-à-dire la prolongation de leur vie.The main objective of this thesis is to study clusters’ evolution. The literature on clusters has widely studied why clusters exist and how they favor innovation, productivity and growth. Our concern is to study how these effects hold over time. Therefore, we aim at identifying the determinants of dynamic performance of clusters to explain why some clusters decline while others keep working by continuous renewal. To do so, this thesis approaches clusters from a network perspective. We contend that clusters with heterogeneous network structures exhibit different capacities to associate and dissociate cluster’s evolution and industrial/technological cycle at the right moment. Thus, we identify the properties of network structures that favor dynamic performance of clusters or cluster resilience. We support our theoretical developments with empirical insights in two different contexts. On the one hand, we study the structure of clusters in the European mobile phone industry. On the other hand, we analyze the structure of relations between cheese producers in Aculco (Mexico). The main result of this work is that network hierarchy, network disassortativity and the interplay between different networks (multiplexity) influence the capacity of clusters to avoid negative lock-in leading to cluster failure, and favor lock-out to enhance cluster continuation, i.e. extending the life of the cluster

Edge manipulation techniques for complex networks with applications to communicability and triadic closure.

Complex networks are ubiquitous in our everyday life and can be used to model a wide variety of phenomena. For this reason, they have captured the interest of researchers from a wide variety of fields. In this work, we describe how to tackle two problems that have their focus on the edges of networks. Our first goal is to develop mathematically inferred, efficient methods based on some newly introduced edge centrality measures for the manipulation of links in a network. We want to make a small number of changes to the edges in order to tune its overall ability to exchange information according to certain goals. Specifically, we consider the problem of adding a few links in order to increase as much as possible this ability and that of selecting a given number of connections to be removed from the graph in order to penalize it as little as possible. Techniques to tackle these problems are developed for both undirected and directed networks. Concerning the directed case, we further discuss how to approximate certain quantities that are used to measure the importance of edges. Secondly, we consider the problem of understanding the mechanism underlying triadic closure in networks and we describe how communicability distance functions play a role in this process. Extensive numerical tests are presented to validate our approaches

Can policy forums overcome echo chamber effects by enabling policy learning? : Evidence from the Irish climate change policy network

Research has repeatedly shown that individuals and organisations tend to obtain information from others whose beliefs are similar to their own, forming “echo chambers” with their network ties. Echo chambers are potentially harmful for evidence-based policymaking as they can hinder policy learning and consensus building. Policy forums could help alleviate the effects of echo chambers if organisations with different views were to participate and to use the opportunities that forums provide to learn from those outside their networks. Applying exponential random graph models on survey data of the Irish climate change policy network, we find that policy actors do indeed tend to obtain policy advice from those whose beliefs are similar to their own. We also find that actors tend not to obtain policy advice from the those that they encounter at policy forums, suggesting forums are not enabling policy learning.Peer reviewe