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This chapter explains how to design and operationalize a knowledge network analysis. The authors walk through a nine-step methodology that addresses each stage of the process. The nine-step process is the result of an in-depth review of the theoretical and applied literature. The authors explain how and why each step contributes to the quality and goodness of the analysis. The risks of skipping or sub-optimizing steps are explained. The step-by-step process highlights the dependence of a knowledge network analysis on data sources. The authors explain the importance of identifying, collecting, and curating sources.

Most of us have experienced the “small world” phenomenon; you meet a stranger while traveling or waiting in a queue, for example, and begin to discuss where you are from, what type of work you do, or why you are at this certain location. Surprisingly, you and the stranger discover you both know the same person, possibly from your hometown, academic department, or children's school. You both remark “what a small world” and then go on your way. Small worlds are just one example of social networks, or how individuals know one another. Social scientists have been interested in this phenomenon since the 1930s and have developed network analysis tools to gain an understanding of how social networks are formed and evolve. These methods have improved significantly over the past 15 years and may provide an informative lens through which to investigate international entrepreneurship (IE).

Design-based research (DBR) involves multiple iterations, and innovations are needed in analytical methods for understanding how learners experience a learning experience in ways that both embrace the complexity of learning and allow for data-driven changes to the design of the learning experience between iterations. The purpose of this paper is to propose a method of crafting design moves in DBR using network analysis.

This paper introduces learning experience network analysis (LENA) to allow researchers to investigate the multiple interdependencies between aspects of learner experiences, and to craft design moves that leverage the relationships between struggles, what worked and experiences aligned with principles from theory.

The use of network analysis is a promising method of crafting data-driven design changes between iterations in DBR. The LENA process developed by the authors may serve as inspiration for other researchers to develop even more powerful methodological innovations.

LENA may provide design-based researchers with a new approach to analyzing learner experiences and crafting data-driven design moves in a way that honors the complexity of learning.

LENA may provide novice design-based researchers with a structured and easy-to-use method of crafting design moves informed by patterns emergent in the data.

To the best of the authors’ knowledge, this paper is the first to propose a method for using network analysis of qualitative learning experience data for DBR.

Network orchestration has received widespread attention from scholars engaged in network-relationship governance research. This study aims to explore progress in network orchestration research and future prospects.

In this study, 105 articles related to network orchestration from the Web of Science Core Collection Database for the period 2006–2022 are collected as sample data, and bibliometric relationship maps of network orchestration research are visually presented using the CiteSpace software.

First, this study presents distributions of articles, methods, journals, authors, institutions and countries/regions in network orchestration research. Second, based on a reference co-citation analysis, three articles are identified as the key literature in network orchestration research. Based on a keyword co-occurrence analysis, the hotspots are found to include studies related to overall network and individual levels. A cluster analysis reveals six themes: innovation ecosystem, sustainable development, product development, sourcing, hub firm and innovation network. From a burst detection analysis, three frontiers emerge: organisation, collaboration and governance. Finally, some future research directions are proposed and important issues are raised.

This study is the first to reveal the progress of network orchestration research using quantitative bibliometric methods, and it provides insights to scholars and offers practitioners with a better understanding of network orchestration.

This study proposes a novel method utilising a speech-word pair bipartite network to examine the correlation structure between members of parliament (MPs) in the context of the UK- China relationship.

We construct MP-word pair bipartite networks based on the co-occurrence relationship between MPs and words in their speech content. These networks are then mapped into monopartite MPs correlation networks. Additionally, the study calculates correlation network indicators and identifies MP communities and factions to determine the characteristics of MPs and their interrelation in the UK-China relationship. This includes insights into the distribution of key MPs, their correlation structure and the evolution and development trends of MP factions.

Analysis of the parliamentary speeches on China-related affairs in the British Parliament from 2011 to 2020 reveals that the distribution and interrelationship of MPs engaged in UK-China affairs are centralised and discrete, with a few core MPs playing an integral role in the UK-China relationship. Among them, MPs such as Lord Ahmad of Wimbledon, David Cameron, Lord Hunt of Chesterton and Lord Howell of Guildford formed factions with significant differences; however, the continuity of their evolution exhibits unstableness. The core MP factions, such as those led by Lord Ahmad of Wimbledon and David Cameron, have achieved a level of maturity and exert significant influence.

The research has several limitations that warrant acknowledgement. First, we mapped the MP-word pair bipartite network into the MP correlation network for analysis without directly analysing the structure of MPs based on the bipartite network. In future studies, we aim to explore various types of analysis based on the proposed bipartite networks to provide more comprehensive and accurate references for studying UK-China relations. In addition, we seek to incorporate semantic-level analyses, such as sentiment analysis of MPs, into the MP-word -pair bipartite networks for in-depth analysis. Second, the interpretations of MP structures in the UK-China relationship in this study are limited. Consequently, expertise in UK-China relations should be incorporated to enhance the study and provide more practical recommendations.

Firstly, the findings can contribute to an objective understanding of the characteristics and connotations of UK-China relations, thereby informing adjustments of focus accordingly. The identification of the main factions in the UK-China relationship emphasises the imperative for governments to pay greater attention to these MPs’ speeches and social relationships. Secondly, examining the evolution and development of MP factions aids in identifying a country’s diplomatic focus during different periods. This can assist governments in responding promptly to relevant issues and contribute to the formulation of effective foreign policies.

First, this study expands the research methodology of parliamentary debates analysis in previous studies. To the best of our knowledge, we are the first to study the UK-China relationship through the MP-word-pair bipartite network. This outcome inspires future researchers to apply various knowledge networks in the LIS field to elucidate deeper characteristics and connotations of UK-China relations. Second, this study provides a novel perspective for UK-China relationship analysis, which deepens the research object from keywords to MPs. This finding may offer important implications for researchers to further study the role of MPs in the UK-China relationship.

This study proposes a novel scheme for analysing the correlation structure between MPs based on bipartite networks. This approach offers insights into the development and evolving dynamics of MPs.

University libraries have traditionally been the primary caretaker of scholarly resources. However, as electronic modes of information delivery replace print materials, expectations of academic libraries have evolved rapidly. In this environment, academic libraries need to be adaptable organizations. Librarianship, though, is deeply rooted in strong values and beliefs which inherently limit receptivity to change and innovation, but these constraints are not absolute. Social network research indicates that professional advice networks play a significant role in how one thinks about and performs work and that individual perspectives are broadened when diverse input is received. Based on social network analysis methods, this study explored the relationship between individual receptivity to innovation and the composition of a person's professional advice network through a purposive sample of academic librarians in Illinois. The group completed a survey that explored two dimensions: (1) the nature of relationships within their professional advice network and (2) the individual's personal receptivity to innovation. Analysis of the nature of relationships within the professional advice networks was based on a combination of quantitative and qualitative techniques, in contrast to the analysis of the respondents’ receptivity to innovation which was based on quantitative measures. Based on the information from the 440 respondents, the results of this research indicate that there is a relationship between the size of the professional advice networks and individual's receptivity to innovation, but additional aspects of the professional advice network may play a role in an individual's overall receptivity to innovation.

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 review the post‐1996 literature of information science and other disciplines for the application of social network theory and social network analysis to research that provides an understanding of information environments.

The literature review involved a content analysis of 373 articles retrieved from five electronic journal databases offering broad disciplinary coverage, and a selection of nine peer‐reviewed electronic access journals in information science. Each database was limited to academic or peer reviewed journals and searched using two query phrases: social network theory (SNT) and social network analysis (SNA).

The paper demonstrates the growth of interest by information science and other disciplines in research that applies social network theory and utilizes social network analysis, indicating what research approaches and major focus trends differentiate the disciplines.

The search phrases overlook articles using social networks as the only key phrase for indexing. However, the intention was to examine the application of a theoretical concept and specific methodology, so the terms used were appropriate for this purpose.

The paper identifies opportunities to apply social network theory and social network analysis to the study of the exchange of information resources.

The paper demonstrates that information science could advance valuable contributions to an understanding of information behavior using social network theory and social network analysis as a vehicle to connect with a significant body of existing research in other disciplines.

This article introduces a method for assessing network dynamics over time. It integrates a qualitative approach to data collection with a bifocal approach to data analysis, i.e. where data are interpreted with two lenses: qualitative and quantitative.

The dynamics of an entrepreneurial firm's network are analyzed by combining: content and event analysis of case data and network maps with the use of UCINET 6, a software package developed for social network analysis.

In illustrating the bifocal approach, steps related to data collection, preparation and analysis are discussed. The findings show how the bifocal approach captures change in both a network's structure and its interactions, through a firm's life‐stages.

The primary limitation of the approach is that reliance on UCINET 6 statistics oversimplifies network analysis. Thus, optimal use of the approach is best achieved when the structural patterns generated by UCINET 6 are balanced by qualitative analysis of the interactional dimensions of the network on a longitudinal basis. Future research opportunities include cross‐network analysis and examination of the networks of lead entrepreneurs in comparison with one another, over time.

The bifocal approach allows examination of network power shifts and identification of opportunities for strategic action and relationship management.

This article shows that the application of the bifocal approach facilitates a more meaningful analysis of networks than does a purely qualitative approach. It allows for time‐based examination of whole systems of organizations and scrutiny of dimensions pertaining to both network structure and the interactions and relationships between individual actors.

Network analysis is a well consolidated research area in several disciplines. Within management and organizational studies, network scholars consolidated a set of research practices that allowed ease of data collection, high inter case comparability, establishment of nomological laws and commitment to social capital motivation. This paper aims to elicit the criticism it has received and highlight the unsettled lacunae.

This paper sheds light on Network Analysis’s breakthroughs, while showing how its scholars innovated by responding to critics, and identifying outstanding debates.

The paper identifies and discusses three streams of criticism that are still outstanding: the role of human agency, the meaning of social ties and the treatment of temporality.

This paper brings to fore current debates within the Network Analysis community, highlighting areas where future studies might contribute.