Search results

Boundary-spanning exploration through establishing alliances is an effective strategy to explore technologies beyond local search in innovating firms. The purpose of this paper is to argue that it is useful to make a distinction in boundary-spanning exploration between what a firm learns from its alliance partners (explorative learning from partners (ELP)) and what it learns from other organisations (explorative learning from non-partners (ELN)).

The authors contend that alliances play a role in both types of exploration. More specifically, the authors discern three types of alliances (inside ties, clique-spanning ties and outside ties) based on their role vis-à-vis existing alliance cliques. Clique members are highly embedded, and breaking out of the cliques through clique-spanning and outside alliances is crucial to improving explorative learning. Thereafter, the authors claim that clique-spanning ties and outside ties have a different effect on ELN and ELP.

The empirical analysis of the “application specific integrated circuits” industry indicates that inside ties have negligible effects on both types of explorative learning. Clique-spanning ties have a positive effect on ELP, but not on ELN. The reverse is true for outside ties. The results show that research on explorative learning should devote greater attention to the various roles alliance partners and types of alliances play in advancing technological exploration.

The literature only emphasises the learning from partners, focussing mainly on accessing their technology. In sum, alliance partners play different roles in exploration, and their network position influences the role they are able to play.

Social media platforms play a key role in information propagation and there is a need to study the same. This study aims to explore the impact of the number of close communities (represented by cliques), the size of these close communities and its impact on information virality.

This study identified 6,786 users from over 11 million tweets for analysis using sentiment mining and network science methods. Inferential analysis has also been established by introducing multiple regression analysis and path analysis.

Sentiments of content did not have a significant impact on the information virality. However, there exists a stagewise development relationship between communities of close friends, user reputation and information propagation through virality.

This paper contributes to the theory by introducing a stagewise progression model for influencers to manage and develop their social networks.

There is a gap in the existing literature on the role of the number and size of cliques on information propagation and virality. This study attempts to address this gap.

We examined factors contributing to the formation of a communications clique among deans representing a group of AACSB accredited colleges of business. We considered whether several variables which appeared to be related to clique status could be used to predict clique membership. We found some support for the idea that several factors play a role in determining group membership, but only agreement in opinion had a significant effect. Implications and suggestions for future research are discussed.

The aim of this paper is to segment the US food industry market through a network representation of the market.

A tangible technique is implemented to study the structural properties of food industry market. A systematic procedure is described to interpret the US food industry sector market data as a graph, which provides the framework under which the market is studied. The maximum cliques and independent sets are found in the market graph, which provides an efficient way for clustering the financial instruments representing food industry. A statistical analysis on the degree distribution of food industry market graph is also performed to study the properties of the market graph.

The maximum cliques provided a classification of stocks with similar behaviour. Market graphs were empirically shown to follow the power‐law model. The statistical analysis performed on the food industry market graph corroborated with this observation.

This research is an extension of the work by Boginski, Butenko and Pardalos as an application to the food industry. The study helps in efficient segmentation of the food industry market and provides more insights into the market structure.

Social media such as Facebook thrive with the arrival of Web 2.0. This chapter merges traditional social network analysis (SNA) with symbolic interactionism (SI) to create a hybrid method of SNA to allow researchers to study the sociability found in Facebook. The discussion begins with identifying a common ground of SNA and SI, found in Georg Simmel’s work, and then develops methodological procedures to locate cliques in Facebook networks. A visualization technique is also suggested to further single out the social forms found in Facebook.

We introduce a multi-level model of the dependence of interfirm network topologies on the distribution and commonality of information in a network and the information strategies pursued by its member firms. Network topology, information properties of the network, and firm-level action within the network form dynamic, recursive, cross-level relationships – information properties in the network determine firm-level action, which in turn impacts the network topology and information properties. We derive predictions about the kinds of information strategies that firms are likely to adopt and succeed with in different information regimes, and about the kinds and short- and long-run dynamics of network topologies expected under different information regimes. Our model sheds new light on network topologies as a dependent variable that can be explained by network-level information regimes and firm-level information strategies.

Interorganizational partner selection decisions are plagued with uncertainty. When making partnering decisions, firms strive to answer two questions: does the prospective partner have resources which can be used to generate value in the relationship; and will the partner be willing to actively share these resources and cooperate in good faith? Answers to these questions help reduce three types of uncertainty – partner capability uncertainty, partner competitiveness uncertainty and partner reliability uncertainty. For a relationship to benefit both partners, they have to possess complimentary resources of comparable quality, avoid explicit competition as well as be willing to engage in the cooperative behaviors within the confines of their relationship. In this paper, we examine the importance of prospective partners’ characteristics (differences in size, status and specialization) as well as their network characteristics (existence of a common partner and membership in the same clique) to the formation and longevity of their social relationships, as these characteristics reduce firms’ value generation and partner reliability uncertainty.

The purpose of this study is to offer a theoretical and practical explanation for the nature and reasons for inter-organizational knowledge sharing across an informal clique of competing five-star hotels in the Saudi Arabian religious tourism and hospitality industry.

The methodology is an adapted form of the grounded theory approach deploying a four-stage research design using qualitative interviews with key players in the industry to inform the analysis of the knowledge sharing approaches.

The findings illustrate the features of the knowledge sharing approaches across the five-star hotels studied. In particular, the findings highlight the existence of a cooperative-competitive tension in the relationships and knowledge sharing between the hotels. This illustrates the existence of a tacit strategy that cooperation can lead to long-term benefits for the competitor hotels.

The study is unique in its focus on the cooperative-competitive tension of five-star hotels in the Saudi Arabian religious tourism and hospitality industry and on this influence on the inter-organizational knowledge sharing across hotels within an oligopolistic market structure. The study also has value in using elements of oligopoly theory and of game theory, particularly, the prisoner’s dilemma, in explaining how inter-organizational knowledge sharing occurs within this market context.

The purpose of this study is to determine whether women entrepreneurs are satisfied with belonging to a women’s network, as this issue is crucial for network performance and legitimacy.

The authors tested the hypotheses on a sample of 127 French women entrepreneurs who belonged to women’s networks using multiple regression analysis.

The authors showed that these women entrepreneurs were satisfied when they developed strong ties and when cliques in the network were limited. Education had a negative effect: the higher the educational level, the less satisfaction with their networks the women reported.

The sample was small and composed only of women entrepreneurs who were members of women’s networks and not women who had left them.

The survey findings suggest ways that managers can optimize network satisfaction to keep current members while continuing to add new ones: create an environment with no cliques where members can develop strong ties. This means connecting members with similar values or status and common interests, while making sure that cliques do not develop.

To the authors’ knowledge, satisfaction with professional women’s networks has never been studied. The authors’ highlight the role of strong ties in these networks and identify the contingent effect of cliques.