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The purpose of this study is to examine the extent to which dynamic network analysis (DNA), text mining and natural language processing (NLP) are helpful research tools in identifying the key actors in a complex international crisis. The study uses these tools to identify the key actors in the Syrian crisis as a case study to validate the proposed algorithm.

To achieve its main purpose, the study uses a collection of three methodologies, namely, DNA, text mining and NLP.

The results of the analysis show four key actors in the Syrian crisis, namely, Russia, the USA, Turkey and China. The results also reveal changes in their powerful positions from 2012 to 2016, which matches the changes that occurred in the real world. The matching between the findings of the proposed algorithm and the real world events that happened in Syria validate our proposed algorithm and proves that the algorithm can be used in identifying the key actors in complex international crises.

The importance of the study lies in two main points. It proposes a new algorithm that mixes NLP, network extraction from textual unstructured data and DNA to understand and monitor changes occurring in a complex international crisis. It applies the proposed algorithm on the Syrian crisis as a case study to identify the key actors and hence validate the proposed algorithm.

The purpose of this paper is to provide examples of evaluating value‐creating networks and to address the organizational issues and challenges of a network orientation.

Value network analysis was first developed in 1993 and was adapted in 1997 for intangible asset management. It has been applied from shopfloor work groups to business webs and economic regions. It draws from a theory base of living systems, knowledge management, complexity theory, and intangible asset management.

The paper provides an overview of a value network analysis method and examples and insights from its practical application.

The paper does not provide a detailed comparative analysis with social network analysis, but rather looks forward to where interest in social networks may evolve into continuing concentration on value‐creating networks.

Value network analysis provides an opportunity to overcome the “split” in business management practices, where human interactions and relationships reside in one world of models and practices, and business processes and transactions reside in another. The engineering approaches of the last two decades have focused on driving out variation, with the unanticipated consequence of stifling organizational agility and innovation. The more human‐centric orientation of the value network perspective brings these two worlds together in a powerful, simple, and pragmatic way to model business activities.

The paper augments and expands the growing application of social or organizational network analysis by pointing to a next generation of analysis and analytics that can support organizational effectiveness. The value network analysis method fills a gap between network theory and practical application for managers, executives, analysts, and researchers.

There is growing dissatisfaction with the static, reductionist, socially insensitive and unimaginative methods used in construction management research. The present paper challenges the emerging view that methods are strictly associated with philosophies, and in particular, that quantitative methods are at odds with interpretative aspirations. It does so by providing a practical example of social network analysis, a quantitative method which is sympathetic to these aspirations. The example is set within a crisis management context, and illustrates the dangers of using qualitative or quantitative methods in isolation. The present paper concludes by questioning the association of quantitative methods with causality and the production of universal models, and argues that both quantitative and qualitative methods have a role to play in understanding the complexity of people's changing social roles, positions and behaviours within construction organizations.

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.

The explosion of internet-generated content, coupled with methodologies such as sentiment analysis, present exciting opportunities for marketers to generate market intelligence on consumer attitudes and brand opinions. The purpose of this paper is to review the marketing literature on online sentiment analysis and examines the application of sentiment analysis from three main perspectives: the unit of analysis, sampling design and methods used in sentiment detection and statistical analysis.

The paper reviews the prior literature on the application of online sentiment analysis published in marketing journals over the period 2008-2016.

The findings highlight the uniqueness of online sentiment analysis in action-oriented marketing research and examine the technical, practical and ethical challenges faced by researchers.

The paper discusses the application of sentiment analysis in marketing research and offers recommendations to address the challenges researchers confront in using this technique.

This study provides academics and practitioners with a comprehensive review of the application of online sentiment analysis within the marketing discipline. The paper focuses attention on the limitations surrounding the utilization of this technique and provides suggestions for mitigating these challenges.

In recent times, the growing use of electronic word of mouth (eWOM) has attracted consumers, organizations and marketers alike. The objective of this study is to summarize and compare the current mass of eWOM research published in leading hospitality and tourism journals with research published in the other fields of both business and management.

This study uses multiple bibliometric analysis methods, including citation, co-citation, keyword and co-word analysis. It compares various assessments of eWOM research published in 399 selected business publications and 398 selected hospitality/tourism publications (ABDC A and above and ABS 3 and above) between 2003 and 2021.

The co-citation analysis identified three thematic areas under each of the domains, i.e. in the hospitality/tourism field, the three themes included eWOM and behavior; eWOM and social media; and eWOM as a marketing tool. Similarly, under the business field (encompasses remaining business and management subdisciplines), the three themes are eWOM and sales, eWOM quality and attributes; and eWOM, information and consumer. Additionally, the word and co-word analysis mapped the comparative evolution of research in these two fields. The study advocates more research focusing on less researched platforms using diverse data, recommender systems adoption and application of eWOM in the business to business (B2B) context.

This study summarizes the overall theoretical and conceptual structure of eWOM research in both business and hospitality/tourism fields; based upon which, several recommendations for future research are proposed.

By comparing the developments in the specialized hospitality/tourism sector with broader management literature using multiple, complementary techniques, this study brings out important insights for hospitality/tourism researchers.

This paper seeks to propose a method of discovering uncommercialized research fronts by comparing scientific papers and patents. A comparative study was performed to measure the semantic similarity between academic papers and patents in order to discover research fronts that do not correspond to any patents.

The authors compared structures of citation networks of scientific publications with those of patents by citation analysis and measured the similarity between sets of academic papers and sets of patents by natural language processing. After the documents (papers/patents) in each layer were categorized by a citation‐based method, the authors compared three semantic similarity measurements between a set of academic papers and a set of patents: Jaccard coefficient, cosine similarity of term frequency‐inverse document frequency (tfidf) vector, and cosine similarity of log‐tfidf vector. A case study was performed in solar cells.

As a result, the cosine similarity of tfidf was found to be the best way of discovering corresponding relationships.

This proposed approach makes it possible to obtain candidates of unexplored research fronts, where academic researches exist but patents do not. This methodology can be immediately applied to support the decision making of R&D investment by both R&D managers in companies and policy makers in government.

This paper enables comparison of scientific outcomes and patents in more detail by citation analysis and natural language processing than previous studies which just count the direct linkage from patents to papers.

The purpose of this study is to reveal the similarities and differences between the manufacturing and service industries in their prioritization of technologies and public research and development (R&D) roles, along with the complementation of properties of technology and public R&D role in the context of Fourth Industrial Revolution.

Two rounds of Delphi surveys were designed to meet the purpose of this study, which used rigorous triangulation techniques. The Delphi method was combined with the brainstorming method in the first-round Delphi survey, while the second-round Delphi survey focused on experts’ judgments. Finally, language network analysis was performed on the properties of technology and public R&D roles to complement the data analyses regarding prioritization.

This study identifies different prioritizations of five similar key technologies in each industry, so that it can note different technological impacts to the two industries in the Fourth Industrial Revolution. Smart factory technology is the first priority in the manufacturing industry, whereas artificial intelligence is the first priority in the service industry. The properties of the three common technologies: artificial intelligence, big data and Internet of things in both industries are summarized in hyper-intelligence on hyper-connectivity. Moreover, it is found that different technological priorities in the service and manufacturing industries require different approaches to public R&D roles, while public R&D roles cover market failure, system failure and government failure. The highest priority public R&D role for the service industry is the emphasis of non-R&D roles. Public R&D role to solve dy-functions, focus basic technologies and support challenging areas of R&D is prioritized at the highest for the manufacturing industry.

This study of the different prioritizations of technologies in the manufacturing and service industries offers practical lessons for executive officers, managers and policy-makers. They, by noting the different technological impacts in the manufacturing and service industries, can prepare for current actions and establish the priority of technology for R&D influencing the future paths of their industries in the context of the Fourth Industrial Revolution. While managers in the service industry should pay greater attention to the technological content of hyper-intelligence and hyper-connectivity, managers in the manufacturing industry should consider smart factory and robot technology.

This paper seeks to investigate the impact of e‐mail on individuals and organizations and to provide a framework that frames e‐mail management as a complex and multi‐stranded issue.

The paper employs a multi‐dimensional appraisal of e‐mail use in organisations and introduces a management framework as a practical tool to enact change and organisational learning. The approach uses Lee's five central concepts for understanding hermeneutics: distanciation; autonomization; appropriation; social construction; and enactment, applying these to the problem of e‐mail overload.

This paper contributes to the theoretical understanding of e‐mail use in organisations, and develops a practitioner toolkit for enacting change in e‐mail use. This work uses hermeneutics and an interpretive framework to investigate the impact of e‐mail on organisations, employing concepts from Ricoeur, Gadamer, Habermas, and Klein.

Future research could evaluate the effectiveness of e‐mail performance improvement programmes using a mix of research methods including surveys of e‐mail attitudes and analysis of e‐mail readability and language analysis.

The paper introduces an e‐mail management framework that is a practical tool that can be used to enact change and organisational learning

Implementation could lead to improved communication; improved visibility of the informal organizational network, knowledge sharing, and action network; business process improvement; improved knowledge management; and increased employee morale.

The paper shows how actors may choose to enact emancipation from e‐mail oppression by taking deliberate action to reconstruct the environment in which they exist in an inquiry‐change cycle of organisational learning by implementing an e‐mail management framework.

This paper aims to contribute to the emerging body of research on firestorms, specifically on the inflammatory user-generated content (UGC) created in response to brand transgressions. By analyzing and segmenting UGC created and shared in the wake of three different events, the authors identify which type of inflammatory message is most likely to be widely shared; thus, contributing to a possible online firestorm.

Tweets were collected involving brand transgressions in the retail, fast food and technology space from varying timeframe and diverse media coverage. Then, the tweets were coded for message intention and analyzed with linguistics software to determine the message characteristics and framing. A two-step cluster analysis identified three types of UGC.

The authors found that message dimensions and the framing of tweets in the context of brand transgressions differed in characteristics, sentiment, call to action and the extent to which the messages were shared. The findings contradict traditional negative word-of-mouth studies involving idiosyncratic service and product failure. During online brand firestorms, rational activism messages with a call to action, generated in response to a firm’s transgression or “sparks,” have a higher likelihood of being shared (virality).

This research provides novel insights into UGC created after brand transgressions. Different types of messages created after these events vary in the extent that they “fan the flames” of the transgression. A message typology and flowchart are provided to assist managers in identifying and responding to three message types: ash, sparks and embers.