1,721,011 research outputs found
Resilience and Vulnerability of Spatial Economic Networks
No full textThis Special Issue (SI) brings ‘under one roof’ the work of scholars dedicated to the resilience and vulnerability of systems. Three main strands knit the different contributions together, and constitute the rationale for their selection in this volume: a) resilience/vulnerability; b) dynamic (complex) networks; and c) space-economy. In particular this SI provides a synthetic perspective on the various interpretations of resilience and vulnerability in spatial economic networks, given the different objectives and landscapes, with special attention to the understanding of the effects generated by disruptions of spatial economic systems. Consequently, the selection of articles in this SI offers novel theoretical and empirical insights into the complex dynamics of economic and spatial networks, using new, systematic data sources and employing cutting-edge network analysis and spatial econometric techniques. Within this framework, the chosen contributions analyse transport networks and economic networks – at various spatial scales – with the view to identifying the critical factors that lead to resilient and vulnerable outcomes
Guest editorial: Resilience of Networks
No full textIn recent years, the complex dynamics of local as well as global transport networks have attracted ever increasing attention due to reports of their multiple failures and deleterious impacts, which have often occurred during disruptive events.
Not only have hazards recently increased in complexity and intensity but their impacts are also no longer confined within the local boundaries of specific transport operations, and instead more widely affect global connections and accessibility.
The above mentioned merge of characteristics within transport systems has compelled scholars and practitioners to place great emphasis on exploring transport systems from different perspectives of resilience and vulnerability. Research findings from the analyses on the various dimensions of transport resilience and vulnerability now allow us to better understand the similarities and differences in systems, but more importantly, they have opened the way for researchers to relate concepts of resilience and vulnerability with those of interdependency and connectivity/accessibility.
From this stand point, it seems necessary to reflect on the relevance of transport network resilience and vulnerability by exploring definitions, interpretations and applications from different methodological/empirical angles and perspectives. This is the platform which has paved the way to this Special Issue (SI)
Measuring the scope of inter-firm agreements in the container shipping industry: an empirical assessment
No full textIn container shipping industry inter-firm agreements are becoming progressively popular as ship-owners share their slot capacity with commercial partners in order to have fully loaded container ships and reduce financial risk.
This manuscript focuses on the cooperative agreements among shipping firms, i.e., vessel sharing and slot charter agreements within consortia and strategic alliances. Through a quantitative approach based on network and OLS
regression analysis, we scrutinise the propensity to cooperate, the geographic extent and ‘leveraging effect’ generated by this commercial practise on the
container-shipping industry. Results show that carriers, usually regarded as independent, are instead fairly cooperative, especially when involved in trade
lanes originating from the Far East. Finally, we show that carriers increase their commercial objectives by leveraging the operated fleet capacity. We conclude with some implications for managers and practitioners as well as a discussion on limitations and future extensions of this study
A machine learning approach to support decision in insider trading detection
Full text linkIdentifying market abuse activity from data on investors' trading activity is very challenging both for the data volume and for the low signal to noise ratio. Here we propose two complementary unsupervised machine learning methods to support market surveillance aimed at identifying potential insider trading activities. The first one uses clustering to identify, in the vicinity of a price sensitive event such as a takeover bid, discontinuities in the trading activity of an investor with respect to her own past trading history and on the present trading activity of her peers. The second unsupervised approach aims at identifying (small) groups of investors that act coherently around price sensitive events, pointing to potential insider rings, i.e. a group of synchronised traders displaying strong directional trading in rewarding position in a period before the price sensitive event. As a case study, we apply our methods to investor resolved data of Italian stocks around takeover bids
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
An analysis of shipping agreements: The Cooperative Container Network
No full textThe recent economic downturn has intensified the need for cooperation among carriers in the container shipping industry. Indeed, carriers join inter-firm networks for several reasons such as achieving economies of scale, scope, and the search for new markets. In this paper we apply network analysis and construct the Cooperative Container Network in order to study how shipping companies integrate and coordinate their activities and to investigate the topology and hierarchical structure of inter-carrier relationships. Our data set is comprised of 65 carriers that provide 603 container services. The results indicate that the Cooperative Container Network (CCN) belongs to the family of small world networks. This finding suggests that the most cooperative companies are small-to-medium-size carriers that engage in commercial agreements in order to reduce costs and, when in partnership with larger carriers, these cooperative companies are able to compete, especially against the largest carriers. However shipping companies with high capacity engage in cooperation with other carriers by merely looking for local partners in order to increase their local and specialized market penetration
- …
