Search results

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.

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 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.

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.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

This study aims to examine how differences in the strength of interpersonal ties affect the social structure of organisational family and non-family relationships and their implications for work-related interactions.

This study used a quantitative approach. The hypotheses were tested using multi-group analysis in PLS-SEM as implemented in WarpPLS Version.

The results show that both family and non-family organisational members are inclined to ask from others whom they previously have given information, implying that reciprocity in work-related interactions in the workplace is present at the dyad level. Furthermore, the existing robust strength of ties among family employees facilitate a three-way relationship where each member is responsible for the quality of work-related interactions between other members. This means that triadic communication is only present within family networks. While, the absence of strong interpersonal ties within non-family network fuels the popularity effect, where non-family employees who are perceived to be knowledgeable tend to be approached by others for work-related information.

This study brings to the fore a nuanced perspective that complements our current understanding of the implications of social relationships within family and non-family employee groups on work-related interactions in the workplace. It provides clues on how family and non-family employees identify with the firm through their informal relational embeddedness towards work-related interactions within the organisation.

This paper aims to study the issues of adaptive fuzzy control for a category of switched under-actuated systems with input nonlinearities and external disturbances.

A control scheme based on sliding mode surface with a hierarchical structure is introduced to enhance the responsiveness and robustness of the studied systems. An equivalent control and switching control rules are co-designed in a hierarchical sliding mode control (HSMC) framework to ensure that the system state reaches a given sliding surface and remains sliding on the surface, finally stabilizing at the equilibrium point. Besides, the input nonlinearities consist of non-symmetric saturation and dead-zone, which are estimated by an unknown bounded function and a known affine function.

Based on fuzzy logic systems and the hierarchical sliding mode control method, an adaptive fuzzy control method for uncertain switched under-actuated systems is put forward.

The “cause and effect” problems often existing in conventional backstepping designs can be prevented. Furthermore, the presented adaptive laws can eliminate the influence of external disturbances and approximation errors. Besides, in contrast to arbitrary switching strategies, the authors consider a switching rule with average dwell time, which resolves control problems that cannot be resolved with arbitrary switching signals and reduces conservatism.

Professional touching behavior (PTB), defined as intentional touching behavior that occurs between organizational members and that falls within the boundaries of appropriateness and professionalism in the workplace, is prevalent in organizations. Scholars from multiple disciplines, including human resources researchers, have acknowledged the importance of physical contact for facilitating interpersonal communication and relationship-building. However, PTB may not only elicit positive reactions from those who receive it but also negative reactions as well, with implications for social dynamics in organizations. PTB can, on the one hand, fulfill employees’ desires for interpersonal connection; at the same time, such physical contact at work can represent a threat to employees’ health. To explain the nature and implications of these divergent effects of receiving PTB, the authors draw upon sociometer theory and behavioral immune system (BIS) theory to model the emotional, cognitive, and physiological processes via which, and the conditions under which, receiving such behavior will result in socially functional responses and prompt subsequent prosocial behavior, and when PTB will be perceived as a health risk and prompt withdrawal behavior. The theoretical framework of this chapter expands our conceptual understanding of the consequences of interpersonal physical contact at work and has important human resources management (HRM) implications for organizational managers.