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This study aims to explore the connection between travel content consumption in social networks and social comparison, envy as well as travel intention. It analyzes whether tourists’ travel intention is affected by travel content consumption in social networks, and more importantly, whether social comparison and envy play a mediating role in this process.

Data was collected through intercept in four popular tourist spots in Guangzhou and Zhuhai in South China. A self-administered questionnaire was used. A total of 400 participants were recruited, and 291 valid questionnaires were obtained. Bias-corrected nonparametric percentile bootstrap mediation variable test method was used to test hypotheses.

The study yielded three results. First, travel content consumption in the social networks positively influences travel intention. Second, travel content consumption in social networks indirectly affects travel intention through social comparison and envy. Third, the control variables, such as gender, age, education and income, mainly affect envy.

This study constructs a theoretical framework of stimulus–cognitive appraisal–emotion–behavioral responses. To the best of the authors’ knowledge, it is the first study to reveal that the internal psychological mechanism of travel content consumption affects travel intention. It also discloses that envy of seemingly negative emotions can encourage positive behaviors in certain situations.

International comparison studies provide a useful opportunity in logistics to benchmark logistics practices. Given this, it is perhaps surprising that there do not appear to be any benchmarking studies comparing performance between the developed and developing world. The paper aims to address this shortcoming, focusing on fast moving consumer goods (FMCG) distribution networks from the UK and South Africa as comparator countries.

A two‐stage study has been undertaken. Firstly, a focus group approach was undertaken to identify the main causes of supply chain uncertainty affecting freight transport operations in FMCG distribution. In the second phase, four case studies (two in the UK and two in South Africa) were undertaken to examine how logistics is managed and the impact of uncertainty.

In the focus groups undertaken in both countries, the uncertainty clusters found were very similar, if not the same. However, when looking into the findings from the case studies, the extra miles generated due to uncertainty within the supply chains studied was greater in the South African distribution networks studied than in their UK counterparts. Furthermore, the UK distribution networks studied seem to be more effective in terms of uncertainty preparedness and responsiveness than their South African counterparts.

The paper has identified significant differences in terms of uncertainty preparedness and responsiveness within the distribution networks studied from the UK and South Africa. Nevertheless, more case studies need to be run in both countries, and in different sectors, to verify those differences.

This paper aims to provide a network study of the structural and dynamical characteristics of tourism flows in Europe from 1995 to 2012.

Travels in Europe were studied by following the network science research paradigm and by focusing on the whole network of intra-European tourism destinations. Network analysis was used to map and reveal the pattern of connections between states as shaped by bilateral tourism flows. Data were provided by the United Nations World Tourism Organization, and the data were integrated with tourism data available from national statistical offices of the individual countries, when necessary.

For 2012, results obtained from the UNWTO record-based network were compared to geo-located Twitter data as a proxy of human mobility patterns. The present analysis provides evidence of a shift towards an increased homogeneity in the travelling preferences of European tourists, an acquired attitude of visitors to travel shorter distances and a tendency of mobility patterns to merge. Finally, the comparison between UNWTO and Twitter data shows a different spatial distribution of visitors. These results provide a useful insight for policymakers involved in tourism planning.

The contribution of this study is threefold. First, to the best of the authors’ knowledge, this is the only study that focuses on the bilateral tourism flows between all countries falling, geographically or politically, under the definition of Europe. Second, evidence is provided of a shift towards a greater homogeneity in the travelling preferences of European tourists. Lastly, for the first time, this study provides a comparison between topological structure and bilateral mobility patterns of tourism flows, based on two different data-recording methods.

This paper aims to propose a classification method for steel strip surface defects based on a mixed attention mechanism to achieve fast and accurate classification performance. The traditional method of classifying surface defects of hot-rolled steel strips has the problems of low recognition accuracy and low efficiency in the industrial complex production environment.

The authors selected min–max scaling comparison method to filter the training results of multiple network models on the steel strip surface defect data set. Then, the best comprehensive performance model EfficientNet-B0 was refined. Based on this, the authors proposed two mixed attention addition methods, which include squeeze-excitation spatial mixed module and multilayer mixed attention mechanism (MMAM) module, respectively.

With these two methods, the authors achieved 96.72% and 97.70% recognition accuracy on the steel strip data set after data augmentation for adapting to the complex production environment, respectively. Using the transfer learning method, the EfficientNet-B0 based on MMAM obtained 100% recognition accuracy.

This study not only focuses on improving the recognition accuracy of the network model itself but also considers other performance indicators of the network, which are rarely considered by many researchers. The authors further improve the intelligent production technique and address this issue. Both methods proposed in this paper can be applied to embedded equipment, which can effectively improve steel strip factory production efficiency and reduce material and time loss.

The governance of information technology outsourcing (ITO) has been identified as an essential determinant for ITO success. Prior studies have shed light on effective governance structures in different organizational contexts. This study aims to advance this prior knowledge by exploring how interpersonal networks, as an important aspect of such context, reflect and influence ITO governance.

A single case study was conducted from a vendor’s perspective in an ITO dyad. Social network analysis was leveraged to reveal the interpersonal networks, with whole-network analysis on 24 team members in an ITO vendor company. In addition, open-ended interviews with six selected team members were utilized to identify the perceived governance structure.

The findings of this study suggest certain features of interpersonal networks, i.e. network density and cross-network comparison, can reflect governance structure in multiple aspects. Meanwhile, the authors also argue that interpersonal networks can influence the form of governance structure.

As a single case study, the context of the research site cannot be ignored in the inference of findings. To increase the confidence for further generalization, future empirical studies are needed especially in contrasting sites, such as ITO relations based on network governance.

This study associates intra-organizational characteristics of the vendor to the inter-organizational governance structure of the ITO relationship. It also provides an innovative methodology for both researchers and practitioners to assess ITO governance structure.

The purpose of this study is to examine the importance of human capital and professional networks for women’s and men’s appointment to the boards of directors of public companies. The study provides an in-depth analysis of how human capital and professional networks contribute to women’s as compared with men’s odds of corporate board membership.

The study analyzes the human capital and professional networks of 494 male and female corporate outside (non-executive) directors appointed between 2005 and 2010 to the boards of US public companies listed in the Standard & Poor’s 500 index. Human capital was measured as director age, education and professional experience (function and role). Professional network variables measured included composition of professional network, network centrality, constraint and cohesion.

The study’s findings reveal that the characteristics that impact the appointment of women as outside directors to public company boards differ from those of men. Relative to men, certain professions such as government relations and education improve the odds of appointment of women to corporate boards, while age lowers women’s odds. The number of network ties and the degree of network cohesion were also significant in predicting the likelihood of female board appointment to public corporations relative to men’s odds. The final model was able to predict female board membership correctly only in 28 per cent of the cases, while male board membership was predicted in 89 per cent of the cases, suggesting that factors other than human capital and professional networks (e.g. their gender) impact women’s appointment to corporate boards.

To the authors ' knowledge, this study is the first to comprehensively examine the professional network components of female and male directors along with their human capital in the analysis of their prospects for board appointment. The conceptualization of professional networks as well the depth of quantitative analysis of the network components of the study advance the extant literature on the composition of corporate boards.

Thin cracks on the surface, such as those found in nuclear power plant concrete structures, are difficult to identify because they tend to be thin. This paper aims to design a novel segmentation network, called U-shaped contextual aggregation network (UCAN), for better recognition of weak cracks.

UCAN uses dilated convolutional layers with exponentially changing dilation rates to extract additional contextual features of thin cracks while preserving resolution. Furthermore, this paper has developed a topology-based loss function, called ℓ_cl Dice, which enhances the crack segmentation’s connectivity.

This paper generated five data sets with varying crack widths to evaluate the performance of multiple algorithms. The results show that the UCAN network proposed in this study achieves the highest F1-Score on thinner cracks. Additionally, training the UCAN network with the ℓ_cl Dice improves the F1-Scores compared to using the cross-entropy function alone. These findings demonstrate the effectiveness of the UCAN network and the value of incorporating the ℓ_cl Dice in crack segmentation tasks.

In this paper, an exponentially dilated convolutional layer is constructed to replace the commonly used pooling layer to improve the model receptive field. To address the challenge of preserving fracture connectivity segmentation, this paper introduces ℓ_cl Dice. This design enables UCAN to extract more contextual features while maintaining resolution, thus improving the crack segmentation performance. The proposed method is evaluated using extensive experiments where the results demonstrate the effectiveness of the algorithm.

The use of consumer credit by Chinese citizens has risen rapidly in the Internet era. The purpose of this paper is to predict a mechanism for credit consumption through Internet usage, with social comparison and materialism as mediators. Four types of Internet usage (social use, entertainment use, informational use, and online shopping) were identified to investigate whether different types of Internet usage influence credit consumption differently and whether the influencing mechanisms vary.

A structured online survey involving 558 valid responses from Chinese college students was completed, with structural equation modeling being applied to analyze the collected data.

Among the four types of Internet activities, online shopping was found to be the most significant predictor of credit consumption; results show that it influences credit consumption through two indirect pathways: materialism and a combination of social comparison and materialism. Social use was found to only affect credit consumption through materialism. In contrast, the influences of both informational use and entertainment use on credit consumption were insignificant.

By testing the concurrent mediating effects of social comparison and materialism, this study broadens our understanding of how Internet usage and credit consumption are connected. While most studies empirically test overall Internet usage and focus on direct relationships, we identify four types of Internet activities and demonstrate the mechanisms by which different types of Internet usage influence credit consumption, and how consumption varies based on Internet activity.

This chapter discusses the emerging network science approach to the study of complex adaptive systems and applies tools derived from statistical physics to the analysis of tourism destinations. The authors provide a brief history of network science and the characteristics of a network as well as different models such as small world and scale free networks, and dynamic properties such as resilience and information diffusion. The Italian resort island of Elba is used as a case study allowing comparison of the communication network of tourist organizations and the virtual network formed by the websites of these organizations. The study compares the parameters of these networks to networks from the literature and to randomly created networks. The analyses include computer simulations to assess the dynamic properties of these networks. The results indicate that the Elba tourism network has a low degree of collaboration between members. These findings provide a quantitative measure of network performance. In general, the application of network science to the study of social systems offers opportunities for better management of tourism destinations and complex social systems.