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Based on theories related to coopetition, the purpose of this paper is to examine the patterns of business-to-business digital referrals inscribed in businesses’ digital content.

A complete industry-wise digital data set is formed by extracting digital referrals in all the content pages. The authors outline how digital referrals are strategically used among peer businesses in the peer-to-peer digital network and in the augmented digital network, taking into consideration geographical framing and physical distance.

The authors reveal how geographical framing and physical distance influence peer-to-peer referral patterns in the digital space. Quite counter-intuitively, businesses are more likely to give digital referrals for peers residing in the same region, as well as for peers located in closer proximity. Further, results from the augmented digital network show that peer businesses in closer proximity exhibit greater strategic similarity in their digital referring strategy.

The findings extend the understanding of business-to-business coopetition to the digital space and suggest that geographical framing and physical distance can induce reciprocated relationships between peers by offering each other digital referrals.

The findings shed light on the formation of a business-to-business digital coopetition strategy using digital referral marketing.

This study highlights the impact of digital referrals in business-to-business relationship management, especially in the digital coopetition context.

In this chapter we investigate the ownership and control of UK firms using contemporary methods from computational graph theory. Specifically, we analyze a ‘small-world’ model of ownership and control. A small-world is a network whose actors are linked by a short chain of acquaintances (short path lengths), but at the same time have a strongly overlapping circle of friends (high clustering). We simulate a set of corporate worlds using an ensemble of random graphs introduced by Chung and Lu (2002a, 2002b). We find that the corporate governance network structures analyzed here are more clustered (‘clubby’) than would be predicted by the random-graph model. Path lengths, though, are generally not shorter than expected. In addition, we investigate the role of financial institutions: potentially important conduits creating connectivity in corporate networks. We find such institutions give rise to systematically different network topologies.

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.

Bipartite networks (e.g., software developers linked to open-source projects) are common in settings studied by organization scholars. But the structure underlying bipartite networks tends to be overlooked. Commonly, two modes are reduced to one mode for analysis, causing loss of information. We review techniques for projecting 2-modes onto 1-mode and discuss 2-mode measures of clustering. We also address the potential for 2-mode theory development concerning (a) how change in one mode influences change in the other, (b) the question of two types of agency, and (c) how diversity in one mode is a substitute for diversity in the other mode.

By adopting network analytic techniques, this paper aims to examine interlocking directorates among firms operating in the hospitality services sector in seven major Italian tourism destinations.

The authors collected information for all the hotel corporations whose headquarters are located in the seven top Italian destinations: Florence, Milan, Naples, Rimini, Rome, Turin and Venice. Data come from the Analisi Informatizzata delle Aziende Italiane database by Bureau Van Dijk and were used to build a network where the nodes are board members (people) and corporations (hotels) and the links represent the membership of individuals in the boards. From this, with a one-mode projection, the authors obtain two networks: people and corporations. The overall networks’ structures are analysed by assessing their connectivity characteristics.

The findings indicate a relatively low number of interlocks that signals a high degree of fragmentation, showing that the interconnections (both within and between destinations) are scarce. This suggests that in absence of formalized cooperation arrangements, corporations might collaborate informally.

This work extends previous research on complexity in business settings, focusing specifically on service companies whose output depends on multiple interactions and helps clarifying coopetition practices of hospitality service firms. Policymaking perspectives are discussed as well as managerial viewpoints.

Not many studies of the interlocking directorates in the hospitality domain exist. This paper uses network analysis for a better understanding of the cooperative practices and the formal social structures of the Italian hospitality industry and derives a series of implications important for both researchers and practitioners while also looking at potential future studies.

This study aims to determine how the most sustainable brands in Italy and Spain developed communication and awareness-raising actions during the COVID-19 pandemic and whether these companies were truly involved in raising public awareness of the pandemic. The authors specifically focus on sustainable companies, as health communication is considered to be an important corporate social responsibility task.

To answer these questions, the authors have used data on the social media activity (Twitter and Instagram) of these brands in Spain and Italy, extracting the posts and associated hashtags that each of them has published throughout the pandemic to be processed using social network analysis and visualization techniques.

The detailed analysis of both the levels of activity and the content of the messages provides interesting insights into the communication models of the companies and the influence of factors such as time, country and the specific social media platform used.

The authors analyze the communication of the most sustainable businesses on social media during the pandemic, adopting a highly innovative approach. The particular originality of this study lies in the parallel analysis of two different countries that were simultaneously shaken by the pandemic in very similar circumstances. This study also presents a novel use of graphical representation tools in terms of companies’ behavior for health communication on social media.

El objetivo de esta investigación es determinar cómo las marcas reconocidas como las más sostenibles en Italia y España han desarrollado acciones de comunicación durante la pandemia del Covid-19 y si estas empresas se han involucrado realmente en la sensibilización pública de la pandemia. Nos centramos específicamente en las empresas sostenibles dado que la Comunicación en Salud se considera una tarea importante de responsabilidad social corporativa.

Para responder a estas preguntas, hemos utilizado datos de actividad en medios sociales (Twitter e Instagram) de estas marcas en España e Italia, extrayendo los posts y hashtags asociados que cada una ha publicado a lo largo de la pandemia para procesarlos usando técnicas de análisis y visualización de redes sociales.

El análisis detallado tanto de los niveles de actividad como del contenido de los mensajes permite obtener conclusiones interesantes sobre los modelos de comunicación de las empresas y la influencia de factores como el tiempo, el país y el medio social concreto utilizado.

Analizamos la comunicación realizada por las compañías más sostenibles en medios sociales durante la pandemia, adoptando un enfoque muy innovador. La particularidad de este estudio radica en el análisis paralelo de dos países diferentes que fueron sacudidos simultáneamente por la pandemia en circunstancias muy similares. Este estudio también conlleva un uso novedoso de herramientas de representación gráfica en términos del comportamiento de las empresas para Comunicación en Salud en medios sociales.

本研究的目的是确定在意大利和西班牙被认为是最可持续发展的品牌在Covid-19大流行期间是如何开展传播行动的, 以及这些公司是否真正参与了提高公众对该大流行病的认识。我们特别关注可持续发展的公司, 因为健康传播被认为是企业社会责任的一项重要任务。

为了回答这些问题, 我们使用了这些品牌在西班牙和意大利的社交媒体活动数据（Twitter和Instagram）, 提取了每个品牌在整个大流行期间发布的帖子和相关标签, 并使用社交网络分析和可视化技术进行处理。

通过对活动水平和信息内容的详细分析, 可以得出关于这些公司的传播模式以及诸如时间、国家和所使用的特定社交媒体等因素的影响的有趣结论。

我们分析了最可持续的公司在大流行期间在社交媒体上进行的传播, 采取了一种非常创新的方法。这项研究的特殊性在于对两个不同国家的平行分析, 这两个国家在非常相似的情况下同时受到大流行病的冲击。这项研究还包括在社交媒体上对公司健康传播行为的图形表示工具的新颖使用。

This chapter discusses the empirical application of a class of strategic network formation models, using the approach to identification introduced by de Paula, Richards-Shubik, and Tamer (2018). The author emphasizes the interplay between model specification and computational complexity, and suggests tactics to make empirically realistic models become tractable. Two detailed examples, on friendship networks and coauthorship networks, are used to illustrate these issues and to demonstrate the performance of the approach with both simulation and empirical evidence. Also, the author presents extensions to the estimation method, which expand the potential range of applications, and which provide statistical inference with minimal computational burden.

This study aims to investigate the content of hotel brands’ mission statements (MSs) and their relationship with selected attributes of hotel brands.

Content analysis of hotel brands’ MSs was used to detect the MSs’ key words, which were further processed by methods of social network analysis, complemented by clustering techniques and correspondence analysis on the generalized aggregated lexical tables, a special type of correspondence analysis.

Hotel brands operating in luxurious markets more often emphasize experiences than those in midscale markets. Furthermore, hotel brands with longer traditions and those with a large number of controlled rooms communicate words in their MSs that represent a rather traditional approach to hospitality. Younger hotel brands with fewer controlled rooms chose words that indicate a more commercially oriented approach. Finally, cluster analysis revealed four dimensions of hotel brands’ MSs, instead of the nine most typically used in mission statement component models.

Understanding the frequencies and networks of keywords, and their relationship with hotel brand attributes, will help create more focussed MSs. This will strengthen hotel brands, raise their revenues and subsequently increase company performance.

The analysis provides valuable insight into MSs in the specific tourism context of hotel brands. The authors have achieved this with the use of a wide range of advanced network analytic methods. These insights can guide hotel brands to better position themselves in the competitive tourism accommodation market.

MicroRNAs (miRNAs) have been proved to be a significant type of non-coding RNAs related to various human diseases. This paper aims to identify the potential miRNA–disease interactions.

A computational framework, MDIRM is presented to predict miRNAs-disease interactions. Unlike traditional approaches, the miRNA function similarity is calculated by miRNA–disease interactions. The k-mean method is further used to cluster miRNA similarity network. For miRNAs in the same cluster, their similarities are enhanced, as the miRNAs from the same cluster may be reliable. Further, the potential miRNA–disease association is predicted by using recommend method.

To evaluate the performance of our model, the fivefold cross validation is implemented to compare with two state-of-the-art methods. The experimental results indicate that MDIRM achieves an AUC of 0.926, which outperforms other methods.

This paper proposes a novel computational method for miRNA–disease interaction prediction based on recommend method. Identifying the relationship between miRNAs and diseases not only helps us better understand the disease occurrence and mechanism through the perspective of miRNA but also promotes disease diagnosis and treatment.

With the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application recommendation approaches based on user attributes and behaviors have achieved notable effectiveness, they overlook the diffusion patterns and interdependencies of topic-specific mobile applications among user groups. mobile applications among user groups. This paper aims to capture the diffusion patterns and interdependencies of mobile applications among user groups. To achieve this, a topic-aware neural network-based mobile application recommendation method, referred to as TN-MR, is proposed.

In this method, first, the user representations are enhanced by introducing a topic-aware attention layer, which captures both the topic context and the diffusion history context. Second, it exploits a time-decay mechanism to simulate changes in user interest. Multitopic user representations are aggregated by the time decay module to output the user representations of cascading representations under multiple topics. Finally, user scores that are likely to download the mobile application are predicted and ranked.

Experimental comparisons and analyses were conducted on the actual 360App data set, and the results demonstrate that the effectiveness of mobile application recommendations can be significantly improved by using TN-MR.

In this paper, the authors propose a mobile application recommendation method based on topic-aware attention networks. By capturing the diffusion patterns and dependencies of mobile applications, it effectively assists users in selecting their applications of interest from thousands of options, significantly improving the accuracy of mobile application recommendations.