Search results

This study aims to identify the characteristics of the maritime shipping network in Northeast Asia as well as compare the level of port connectivity among these container ports in the region. In addition, this study analyses the change in role and position of 20 ports in the region by clustering these ports based on connectivity index and container throughput and route index.

This study employs Social Network Analysis (SNA) to delineate the international connectivity of major container ports in Northeast Asia. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to identify each port's connectivity index and container throughput index, and the resulting indexes are employed as the basis to cluster 20 major ports by fuzzy C-mean (FCM).

The results revealed that Northeast Asia is a highly connected maritime shipping network with the domination of Shanghai, Shenzhen, Hong Kong and Busan. Furthermore, both container throughput and connectivity in almost all container ports in the region have decreased significantly due to the coronavirus disease 2019 (COVID-19) pandemic. The rapid growth of Shenzhen and Ningbo has allowed them to join Cluster 1 with Shanghai while maintaining high connectivity, yet decreasing container throughput has pushed Busan down to Cluster 2.

The originality of this study is to combine indexes of SNA into connectivity index reflecting characteristics of the maritime shipping network in Northeast Asia and categorize 20 major ports by FCM.

This study explores the levels of Facebook engagement of the two largest Europe-based shipping lines, Maersk and Mediterranean Shipping Company (MSC), to discover the marketing orientation of the topics advertised and to ascertain whether they tend to be about brand recognition, new transport services, or value propositions for stakeholders.

The Facebook posts of Maersk and MSC were analysed using social media text mining and social network analysis (SNA); in- and out-degree centrality analysis was performed to determine the key terms in their posts. NetMiner software was used to collect the respective data on Maersk and MSC. The inquiry period was set between May 2020 and February 2021.

The results indicated a divergence in their post contents, with higher engagement and a wider, more active follower base for MSC than for Maersk. Maersk primarily posts about logistics services and supply chain solutions. MSC communicates about new and large container vessels. Both companies seek greater brand recognition and information sharing through social media.

These results can be used by the stakeholders to evaluate whether Maersk and MSC truly deliver on their respective value propositions communicated online through their social media engagement. It can also help Maersk and MSC gauge the level of effectiveness of their communication with stakeholders and modify their digital engagement strategy accordingly.

Understanding the supply network of construction materials used to construct shelters in refugee camps, or during the reconstruction of communities, is important as it can reveal the intricate links between different stakeholders and the volumes and speeds of material flows to the end-user. Using social network analysis (SNA) enables another dimension to be analysed – the role of commonalities. This is likely to be particularly important when attempting to replace vernacular materials with higher-performing alternatives or when encouraging the use of non-vernacular methods. This paper aims to analyse the supply networks of four different disaster-relief situations.

Data were collected from interviews with 272 displaced (or formally displaced) families in Afghanistan, Bangladesh, Nepal and Turkey, often in difficult conditions.

The results show that the form of the supply networks was highly influenced by the nature/cause of the initial displacement, the geographical location, the local availability of materials and the degree of support/advice given by aid agencies and or governments. In addition, it was found that SNA could be used to indicate which strategies might work in a particular context and which might not, thereby potentially speeding up the delivery of novel solutions.

This study represents the first attempt in theorising and empirically investigating supply networks using SNA in a post-disaster reconstruction context. It is suggested that future studies might map the up-stream supply chain to include manufacturers and higher-order, out of country, suppliers. This would provide a complete picture of the origins of all materials and components in the supply network.

This is original research, and it aims to produce new knowledge.

The research approach is based on the concept that a failure event is rarely random and is often generated by a chain of previous events connected by a sort of domino effect. Thus, the purpose of this study is the optimal selection of the components to predictively maintain on the basis of their failure probability, under budget and time constraints.

Assets maintenance is a major challenge for any process industry. Thanks to the development of Big Data Analytics techniques and tools, data produced by such systems can be analyzed in order to predict their behavior. Considering the asset as a social system composed of several interacting components, in this work, a framework is developed to identify the relationships between component failures and to avoid them through the predictive replacement of critical ones: such relationships are identified through the Association Rule Mining (ARM), while their interaction is studied through the Social Network Analysis (SNA).

A case example of a process industry is presented to explain and test the proposed model and to discuss its applicability. The proposed framework provides an approach to expand upon previous work in the areas of prediction of fault events and monitoring strategy of critical components.

The novel combined adoption of ARM and SNA is proposed to identify the hidden interaction among events and to define the nature of such interactions and communities of nodes in order to analyze local and global paths and define the most influential entities.

The purpose of this paper is to use the practical application of tools provided by social network theory for the detection of potential influencers from the point of view of marketing within online communities. It proposes a method to detect significant actors based on centrality metrics.

A matrix is proposed for the classification of the individuals that integrate a social network based on the combination of eigenvector centrality and betweenness centrality. The model is tested on a Facebook fan page for a sporting event. NodeXL is used to extract and analyze information. Semantic analysis and agent-based simulation are used to test the model.

The proposed model is effective in detecting actors with the potential to efficiently spread a message in relation to the rest of the community, which is achieved from their position within the network. Social network analysis (SNA) and the proposed model, in particular, are useful to detect subgroups of components with particular characteristics that are not evident from other analysis methods.

This paper approaches the application of SNA to online social communities from an empirical and experimental perspective. Its originality lies in combining information from two individual metrics to understand the phenomenon of influence. Online social networks are gaining relevance and the literature that exists in relation to this subject is still fragmented and incipient. This paper contributes to a better understanding of this phenomenon of networks and the development of better tools to manage it through the proposal of a novel method.

This study aims to identify the gaps in current policy and propose a viable framework for policy improvement regarding people-centered tsunami early warning chain in Padang City. The objectives are: to describe the gaps and flaws in the current policy regarding local tsunami early warning chain, to identify potential actors to be involved in the tsunami early warning chain and to assess the roles and capacity of actors, and their potential for involvement in early warning.

This study is an exploratory study using social network analysis (SNA) on regulations and other legal documents, and primary data sources from a focus group discussion and semi-structured interviews.

The study found that the existed regulation lacks extension nodes to relay warnings to the populations at risk, often referred to as “the last mile.” Moreover, receiving warning information from both formal and informal sources is important to mobilize people evacuation more effectively during an emergency. The study found that mosque communities and disaster preparedness leaders are the potential actors who should be involved in the local early warning chain.

The research findings were presented as a recommendation to Padang City Government and have been legalized as the new tsunami early warning chain procedure in the Padang City Mayor Regulation 19/2018.

This research investigated local tsunami early warning dissemination in Padang City using SNA. The study demonstrates a close collaboration between researchers, practitioners and the community.

This chapter examines the structure and sentiment of the Twitter response to Nathan Broad's naming as the originator of an image-based sexual abuse incident following the 2017 Australian Football League Grand Final. Employing Social Network Analysis to visualize the hierarchy of Twitter users responding to the incident and Applied Thematic Analysis to trace the diffusion of differing streams of sentiment within this hierarchy, we produced a representation of participatory social media engagement in the context of image-based sexual abuse. Following two streams of findings, a model of social media user engagement was established that hierarchized the interplay between institutional and personal Twitter users. In this model, it was observed that the Broad incident generated sympathetic and compassionate discourses among an articulated network of social media users. This sentiment gradually diffused to institutional Twitter users – or Reference accounts – through the process of intermedia agenda-setting, whereby the narrative of terrestrial media accounts was altered by personal Twitter users over time.

According to the Project Management Institute, 70% of projects fail globally. The causes of project failure in many instances can be identified as non-technical or behavioral in nature arising from interactions between participants. These intangible risks can emerge in any project setting but especially in project settings having diversity of cultures, customs, beliefs and traditions of various companies or countries. This paper provides an objective framework to address these intangible risks.

This paper presents a structured approach to identify, assess and manage intangible risks to enhance a project team’s ability to meet its objectives. The authors propose a user-friendly framework, Intangible Risk Assessment Methodology for Projects (IRAMP), to address these risks and the factors that cause them. Meta-network (e.g., a network of networks) simulation and established social network analysis (SNA) measures provide a quantitative assessment and ranking of causal events and their influence on the intangible behavior centric risks.

The proposed IRAMP and meta-network approach were utilized to examine the project delivery process of an international energy firm. Data were gathered using structured interviews, surveys and project team workshops. The use of the IRAMP to highlight intangible risk areas underpinned by the SNA measures led to changes in the company’s organizational structure to enhance project delivery effectiveness.

This work extends the existing project risk management literature by providing a novel objective approach to identify and quantify behavior centric intangible risks and the conditions that cause them to emerge.

This article provides a snapshot of several innovative and underused methodological approaches employed by scholars from across public management.

A comparative review of methodological approaches to public management research is used.

The authors find evidence of scholars applying numerous novel methodological approaches to study social science phenomena, including agent-based modeling, nonparametic approaches, social network analysis, Granger equations and techniques for correcting selection bias.

The review does not cover all of the innovative methodological approaches used in social science. However, the methodological techniques showcased offer promise for advancing public management research, whether used as primary applications or as one of multiple methods (triangulation) to test the validity of research findings and/or to more comprehensively understand the phenomena being studied.

The techniques reviewed include example applications to aid researchers who may wish to apply them in their own research. The novel tools and methods already in use by public management researchers contradict the perception that public management methods have lagged behind other social science disciplines.