On the Co-authorship network analysis in the Process Mining research Community: A social network analysis perspective

HabibAgahi, Mohammad Reza; Kermani, Mehrdad Agha Mohammad Ali; Maghsoudi, Mehrdad

doi:10.1016/j.eswa.2022.117853

Cited by 19 publications

(13 citation statements)

References 73 publications

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“…Eigenvector centrality: an extension of degree centrality that measures the impact of hashtags in a network. Eigenvector centrality is determined based on the assumption that connections to hashtags with high-degree centrality values have a greater effect than connections to hashtags with similar or lower-degree centrality values [ 79 ]. A high eigenvector centrality value indicates that a hashtag is linked to a large number of hashtags with high-degree centrality values.…”

Section: Methodsmentioning

confidence: 99%

Cultured Meat on the Social Network Twitter: Clean, Future and Sustainable Meats

Pilařová

Stanislavská

Pilař

et al. 2022

Foods

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The rapid development of technologies for cultured meat production has led to new challenges for producers regarding appropriate communication with future customers in order to deliver products to a viable market. Communication analysis of social media enables the identification of the key characteristics of the monitored topic, as well as the main areas of communication by individual users based on active digital footprints. This study aimed to identify the key characteristics of cultured meat based on communication analysis of the social network Twitter. Communication analysis was performed based on 36,356 Tweets posted by 4128 individual users. This analysis identified the following main communicated characteristics: clean meat, future meat, and sustainable meat. Latent Dittrich allocation identified five communication topics: (1) clean and sustainable products, (2) comparisons with plant-based protein and the impact on agribusiness, (3) positive environmental aspects, (4) cultured meat as an alternative protein, and (5) the regulation of cultured meat.

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Section: Methodsmentioning

confidence: 99%

Cultured Meat on the Social Network Twitter: Clean, Future and Sustainable Meats

Pilařová

Stanislavská

Pilař

et al. 2022

Foods

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“…This index reflects the closeness of the association between network nodes. The greater the network density, the closer the connection between network nodes, and the stronger the ability to influence each other (Reza HabibAgahi et al, 2022).…”

Section: Social Network Analysis Methodsmentioning

confidence: 99%

Understanding the structure and determinants of economic linkage network: The case of three major city clusters in Yangtze River Economic belt

Zhang

Sheng

2023

Front. Environ. Sci.

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The economic linkages between cities within the Yangtze River Economic Belt city cluster are important for precisely implementing the Yangtze River Economic Belt strategy. Accordingly, this study used the revised gravity model, social network method, and spatial Durbin model to analyze the economic linkage strength, spatial-temporal differentiation, and driving factors of the three major city cluster in the Yangtze River Economic Belt from 2006–2020. The results confirm that the Chengdu-Chongqing urban agglomeration has formed a network structure with Chengdu and Chongqing as the two poles; the urban agglomeration in the middle reaches of the Yangtze River has formed a point-axis development pattern of Wuhan, Changsha and Nanchang. The Yangtze River Delta urban agglomeration formed a multi-center radiation and sub-network development pattern. Additionally, the characteristics of network structure significantly affected the flow of factors, the level of economic development was the basis for strengthening communication and cooperation between cities. Meanwhile, the advanced industrial structures played an important role in promoting industrial linkages, and human capital exerts an obvious spatial spillover effect. Therefore, these three major urban agglomerations should implement differentiated development strategies and enhance the synergy of their linkage networks, thus promoting high-quality development.

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Section: Hypotheses Developmentmentioning

confidence: 99%

“…Under the framework of the RBV theory, network centrality represents the capacity of resources available for enterprises, which includes three dimensions: degree centrality, betweenness centrality and closeness centrality (Reza HabibAgahi et al, 2022;Zaefarian et al, 2022;Chakravarty et al, 2020). Specifically, degree centrality determines the degree to which a node occupies the center position, i.e.…”

Section: Social Activities Network Centrality and Corporate Credit Be...mentioning

confidence: 99%

How social activities affect corporate credit behavior? – the mediating role of network centrality

Chen

2023

IMDS

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PurposeSocial information is crucial to credit ratings and can improve the accuracy of the traditional credit assessment model. Drawing on the resource-based view (RBV) and social capital theory (SCT), this research explores the relationships between corporate social activities, network centrality and corporate credit behavior.Design/methodology/approachThe authors used social network analysis (SNA) and regression analysis to analyze the data collected from 14,544 enterprises on the Alibaba platform.FindingsThe results indicate that among the four types of social activities, the number of corporate questions and posts shows a positive relationship with credit behavior; while the number of corporate comments has negative relationship with credit behavior. Further, degree and betweenness centralities mediate the relationship between the number of corporate questions, posts and comments with credit behavior.Originality/valueThis study contributes to the literature on non-financial factors (soft information) by exploring the social behavioral factors related to corporate credit. In addition, this study offers a new theoretical lens and reasonable explanations for investigating the relationship between corporate social activities, network centrality and credit behavior from the perspective of the resource-based view, while most studies are predictive and methodological. Moreover, this study provides new insights for platforms to evaluate enterprise credit and for managers to improve credit behavior.

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On the Co-authorship network analysis in the Process Mining research Community: A social network analysis perspective

Cited by 19 publications

References 73 publications

Cultured Meat on the Social Network Twitter: Clean, Future and Sustainable Meats

Cultured Meat on the Social Network Twitter: Clean, Future and Sustainable Meats

Understanding the structure and determinants of economic linkage network: The case of three major city clusters in Yangtze River Economic belt

How social activities affect corporate credit behavior? – the mediating role of network centrality

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