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How do ego-networks evolve? How does such evolution affect firms’ innovation output? This chapter uses a longitudinal sample of firms in the biotechnology industry to address these questions. We use social network theory to develop a model of the structure and dynamics of firms’ interorganizational research collaboration ego-networks. Using novel longitudinal methods, this chapter demonstrates how research collaboration ego-networks in the biotechnology industry change over time and how this evolution affects focal firms’ subsequent innovative output. The model is tested on a sample of 482 biotechnology firms over a span of 17 years (1990–2006). The results indicate the significant impacts of ego-network size, ego-network growth, and the inclusion of new members in the ego-network on the innovation output of biotechnology firms. Our results also suggest that enlarging ego-networks by adding new and diverse members presents significant management challenges.

Many studies have investigated how the structure of the collaborative networks of researchers influences the nature of their work, and its outcome. Co-authorship networks (CANs) have been widely looked at as proxies that can help bring understanding to the structure of research collaborative ties. The purpose of this paper is to provide a framework for describing what influences the formation of different research collaboration patterns.

The authors use social network analysis (SNA) to analyze the co-authorship ego networks of the ten most central authors in 24 years of papers (703 papers and 1,118 authors) published in the Proceedings of CASCON, a computer science conference. In order to understand what lead to the formation of the different CANs the authors examined, the authors conducted semi-structured interviews with these authors.

Based on this examination, the authors propose a typology that differentiates three styles of co-authorship: matchmaking, brokerage, and teamwork. The authors also provide quantitative SNA-based measures that can help place researchers’ CAN into one of these proposed categories. Given that many different network measures can describe the collaborative network structure of researchers, the authors believe it is important to identify specific network structures that would be meaningful when studying research collaboration. The proposed typology can offer guidance in choosing the appropriate measures for studying research collaboration.

The results presented in this paper highlight the value of combining SNA analysis with interviews when studying CAN. Moreover, the results show how co-authorship styles can be used to understand the mechanisms leading to the formation of collaborative ties among researchers. The authors discuss several potential implications of these findings for the study of research collaborations.

This paper aims to investigate the extent professionals from the vocational sector are networked individuals. The authors explore how professionals use their personal networks to engage in a wide variety of learning activities and examine what social mechanisms influence professionals’ agency to form personal informal learning networks.

This study applied a mixed-method approach to data collection. Social network data were gathered among school professionals working in the vocational sector. Ego-network analysis was performed. A total of 24 in-depth, semi-structured, qualitative interviews were analyzed.

This study found that networked individualism is not represented to its full potential in the vocational sector. However, it is important to form informal learning ties with different stakeholders because all types of informal learning ties serve different learning purposes. The extent to which social mechanisms (i.e. proximity, trust, level of expertise and homophily) influence professionals’ agency to form informal learning ties differs depending on the stakeholder with whom the informal learning ties are formed.

This study excludes the investigation of social mechanisms that shape learning through more impersonal virtual learning resources, such as social media or expert forums. Moreover, the authors only included individual- and dyadic-level social mechanisms.

By investigating the social mechanisms that shape informal learning ties, this study provides insights how professionals can be stimulated to build rich personal learning networks in the vocational sector.

The authors extend earlier research with in-depth information on the different types of learning activities professionals engage in in their personal learning networks with different stakeholders. The ego-network perspective reveals how different social mechanisms influence professionals’ agency to shape informal learning networks with different stakeholders.

Prefabricated construction (PC) will play a vital role in the transformation and upgrading of the construction industry in the future. However, high capital cost is currently one of the biggest obstacles to the application and promotion of PC in China. Clarifying the factors that affect the PC cost from the perspectives of stakeholders and exploring key cost control paths help to achieve effective cost management, but few studies have paid enough attention to this. Therefore, this research aims to explore the critical cost influencing factors (CIFs) and critical stakeholders of PC based on stakeholder theories and propose corresponding strategies for different stakeholders to reduce the cost of PC.

Based on the stakeholder theory and social network theory, literature review and two rounds of expert interviews were used to obtain the stakeholder-associated CIFs and their mutual effects, then the consistency of the data was tested. After that, social network analysis was applied to identify the critical CIFs, critical interaction and key stakeholders in PC cost control and mine the influence conduction paths between CIFs.

The results reveal that the cognition and attitude of developer and relevant standards and codes are the most critical CIFs while the government, developer and contractor are crucial to the cost control of PC. The findings further suggest that measures should be taken to reduce the transaction costs of the developer, and the contractor ought to efficiently apply information technology. Moreover, the collaborative work between designer and manufacturer can avoid unnecessary cost consumption.

This research combines stakeholder management and cost management in PC for the first time and explores the effective cost control paths. The research results can contribute to clarifying the key points of cost management for different stakeholders and improving the cost performance of PC projects.

A particular feature that makes foresight powerful is its capability to learn from past trends to help guide decision‐making for future policy. However, in studying both past and future trends, network perspectives are often missing. Since networks are capable of revealing the structure that underpins relationships between stakeholders, key issues and actions in the past, they are powerful to help envisage the future. The purpose of this paper is to propose a methodological framework to incorporate network analysis in foresight.

The paper develops a generic framework to incorporate network analysis into foresight's five stages. Trends identified by respondents of the Big Picture Survey are used to demonstrate how we operationalize this framework.

A network perspective can enrich foresight analysis in that it helps reveal structural linkages between trends and thus can better identify emerging future issues, both of which are critical in foresight.

The inclusion of network analysis can shed light on the process of understanding complex data and assist in building a model based on links and relationships. Network analysis can reveal otherwise unobservable structural features of the data and can help boundary setting discussions in foresight.

Network concepts and measures could usefully enrich the interpretation of foresight data for further analysis, or plausible scenarios.

Network analysis offers a new way of looking at the foresight data by disentangling complicated issue webs. As shaping the future becomes more essential because of the complexity of science, technology and society interrelationships, the incorporation of network perspectives in foresight might be one of the ways to propel future studies.

The purpose of this paper is to present a Big Data solution as a methodological approach to the automated collection, cleaning, collation, and mapping of multimodal, longitudinal data sets from social media. The paper constructs social information landscapes (SIL).

The research presented here adopts a Big Data methodological approach for mapping user-generated contents in social media. The methodology and algorithms presented are generic, and can be applied to diverse types of social media or user-generated contents involving user interactions, such as within blogs, comments in product pages, and other forms of media, so long as a formal data structure proposed here can be constructed.

The limited presentation of the sequential nature of content listings within social media and Web 2.0 pages, as viewed on web browsers or on mobile devices, do not necessarily reveal nor make obvious an unknown nature of the medium; that every participant, from content producers, to consumers, to followers and subscribers, including the contents they produce or subscribed to, are intrinsically connected in a hidden but massive network. Such networks when mapped, could be quantitatively analysed using social network analysis (e.g. centralities), and the semantics and sentiments could equally reveal valuable information with appropriate analytics. Yet that which is difficult is the traditional approach of collecting, cleaning, collating, and mapping such data sets into a sufficiently large sample of data that could yield important insights into the community structure and the directional, and polarity of interaction on diverse topics. This research solves this particular strand of problem.

The automated mapping of extremely large networks involving hundreds of thousands to millions of nodes, encapsulating high resolution and contextual information, over a long period of time could possibly assist in the proving or even disproving of theories. The goal of this paper is to demonstrate the feasibility of using automated approaches for acquiring massive, connected data sets for academic inquiry in the social sciences.

The methods presented in this paper, together with the Big Data architecture can assist individuals and institutions with a limited budget, with practical approaches in constructing SIL. The software-hardware integrated architecture uses open source software, furthermore, the SIL mapping algorithms are easy to implement.

The majority of research in the literature uses traditional approaches for collecting social networks data. Traditional approaches can be slow and tedious; they do not yield adequate sample size to be of significant value for research. Whilst traditional approaches collect only a small percentage of data, the original methods presented here are able to collect and collate entire data sets in social media due to the automated and scalable mapping techniques.

Hate speech in recent times has become a troubling development. It has different meanings to different people in different cultures. The anonymity and ubiquity of the social media provides a breeding ground for hate speech and makes combating it seems like a lost battle. However, what may constitute a hate speech in a cultural or religious neutral society may not be perceived as such in a polarized multi-cultural and multi-religious society like Nigeria. Defining hate speech, therefore, may be contextual. Hate speech in Nigeria may be perceived along ethnic, religious and political boundaries. The purpose of this paper is to check for the presence of hate speech in social media platforms like Twitter, and to what degree is hate speech permissible, if available? It also intends to find out what monitoring mechanisms the social media platforms like Facebook and Twitter have put in place to combat hate speech. Lexalytics is a term coined by the authors from the words lexical analytics for the purpose of opinion mining unstructured texts like tweets.

This research developed a Python software called polarized opinions sentiment analyzer (POSA), adopting an ego social network analytics technique in which an individual’s behavior is mined and described. POSA uses a customized Python N-Gram dictionary of local context-based terms that may be considered as hate terms. It then applied the Twitter API to stream tweets from popular and trending Nigerian Twitter handles in politics, ethnicity, religion, social activism, racism, etc., and filtered the tweets against the custom dictionary using unsupervised classification of the texts as either positive or negative sentiments. The outcome is visualized using tables, pie charts and word clouds. A similar implementation was also carried out using R-Studio codes and both results are compared and a t-test was applied to determine if there was a significant difference in the results. The research methodology can be classified as both qualitative and quantitative. Qualitative in terms of data classification, and quantitative in terms of being able to identify the results as either negative or positive from the computation of text to vector.

The findings from two sets of experiments on POSA and R are as follows: in the first experiment, the POSA software found that the Twitter handles analyzed contained between 33 and 55 percent hate contents, while the R results show hate contents ranging from 38 to 62 percent. Performing a t-test on both positive and negative scores for both POSA and R-studio, results reveal p-values of 0.389 and 0.289, respectively, on an α value of 0.05, implying that there is no significant difference in the results from POSA and R. During the second experiment performed on 11 local handles with 1,207 tweets, the authors deduce as follows: that the percentage of hate contents classified by POSA is 40 percent, while the percentage of hate contents classified by R is 51 percent. That the accuracy of hate speech classification predicted by POSA is 87 percent, while free speech is 86 percent. And the accuracy of hate speech classification predicted by R is 65 percent, while free speech is 74 percent. This study reveals that neither Twitter nor Facebook has an automated monitoring system for hate speech, and no benchmark is set to decide the level of hate contents allowed in a text. The monitoring is rather done by humans whose assessment is usually subjective and sometimes inconsistent.

This study establishes the fact that hate speech is on the increase on social media. It also shows that hate mongers can actually be pinned down, with the contents of their messages. The POSA system can be used as a plug-in by Twitter to detect and stop hate speech on its platform. The study was limited to public Twitter handles only. N-grams are effective features for word-sense disambiguation, but when using N-grams, the feature vector could take on enormous proportions and in turn increasing sparsity of the feature vectors.

The findings of this study show that if urgent measures are not taken to combat hate speech there could be dare consequences, especially in highly polarized societies that are always heated up along religious and ethnic sentiments. On daily basis tempers are flaring in the social media over comments made by participants. This study has also demonstrated that it is possible to implement a technology that can track and terminate hate speech in a micro-blog like Twitter. This can also be extended to other social media platforms.

This study will help to promote a more positive society, ensuring the social media is positively utilized to the benefit of mankind.

The findings can be used by social media companies to monitor user behaviors, and pin hate crimes to specific persons. Governments and law enforcement bodies can also use the POSA application to track down hate peddlers.

Previous studies examined the effect of inter-organizational collaboration relationships on organizational innovation. However, most focused on the configuration of the network from the static network perspective, and few examined the influence of network structure stability on an organization's exploratory innovation from the ego-network perspective. This study addresses this research gap by focusing on ego-network stability and its effect on an organization's exploratory innovation.

The empirical setting is the smartphone collaboration network from 2004 to 2017. We selected one-site schemes and panel data of patents from the Derwent Innovation Database. A negative binomial model with fixed effects was used to test our hypotheses.

The regression results show that an organization's ego-network stability has an inverted-U-shaped relationship with its exploratory innovation. Global cohesion of the focal organization's knowledge network moderates the process in such a way that when it is at a high level, an organization's exploratory innovation can benefit more from a moderate level of ego-network stability. However, local cohesion moderates in such a way that, at a low level, an organization's exploratory innovation can benefit more from a moderate level of ego-network stability.

This study highlights the importance of ego-network stability and its effect on the focal organization's exploratory innovation. It contributes to the literature on the relationship between ego-network stability and exploratory innovation by investigating the moderating role of global cohesion and local cohesion in knowledge networks.

The sharp increase in interest in social networks among marketing scholars and practitioners has coincided with the rapid proliferation of social networks among broader populations. Considering the substantial body of research that has emerged, it is an opportune time to reflect on the state of social network research (SNR) in marketing. Therefore, this chapter reviews recent marketing research, organized according to substantive areas of interest, followed by a discussion of critical dimensions of SNR for researchers, including network actor characteristics, modes, boundaries, impacts, and mechanisms, as well as the relevant level of analysis. By documenting how SNR can inform marketing decisions and influence marketing outcomes, this study also establishes recommendations for research to advance the state of SNR in marketing. A 2 × 2 classification schema reveals four categories that might guide scholars' choices of research designs, theories, constructs, and measures for SNR.

The purpose of this article is to compare design choices and assess the structural complexity of six manufacturing supply chains (SCs) of the Brazilian wind turbine industry.

The research method is quantitative modeling. This study adopts the social network perspective to provide a broad set of network metrics for comparative analysis and characterization of the structural configuration and complexity of SCs. Transaction costs and the risk of disruption supported the metrics employed in the study. Network size, network density, core-size and centralization metrics stem from transaction costs, whereas constraint and betweenness centrality stem from risk of disruption.

The main conclusion is that, in the Brazilian wind manufacturing industry, increasing the SC structural complexity by adding redundant ties to minimize disruption risks, even implying higher transaction costs, increases the capacity to win orders.

Only the Brazilian wind turbine industry was studied. Therefore, findings are not general, but specific, to the case.

Managers and practitioners of the Brazilian wind turbine industry should focus on increasing the complexity of their SCs, even if it increases transaction costs, to ensure due dates compliance in orders.

To the best of the available knowledge, there is no commonly accepted or shared measurement for SC complexity, and this study proposed an alternative approach to bridge this research gap, the structural perspective of social networks. Traditional measures were complemented by new metrics, and the power of the application of social network analysis to SC investigations was empirically demonstrated in different levels of analysis.