Vienna University of Economics and Business
Elektronische Publikationen der Wirtschaftsuniversität WienNot a member yet
4204 research outputs found
Sort by
Logistics
Full text linkSports logistics represents the intersection between the field of (sport) event management and logistics management and refers to efficient and effective logistics activities behind any sports event. The relatively new concept of sports logistics can be regarded as an emerging research stream and focuses on four sports logistics pillars: first, venue logistics management, e.g. ticketing, security and hospitality, second, fan and spectator logistics management, e.g. existing and to be developed infrastructure and transportation systems, third, athletes logistics management, e.g. travel, coaching and management entourage); and, fourth, equipment logistics, e.g. logistics services and transport of required goods. So far, logistics research has received only little attention, however, given that sport event managers increasingly rely on more complex and sophisticated logistics management practices, it will assume new importance in the future of sports event planning and execution
Key Marketing Challenges for Micro and Small Sized Non–Profit Organisations: A Study of Australian Third Sector Firms
Full text linkThis chapter offers exploratory evidence on critical marketing issues impacting small to medium (SME) sized non–profit firms. Extant literature advances that utilising marketing platforms and services – including digital marketing channels – offers several prospects for non–profit business practitioners to interact and communicate with customers, clients, stakeholders, and the wider public [1,2]. Whilst recent research [3,4] clearly highlights that the potential uptake of modern marketing approaches by non–profits, like social media applications, can help drive new marketing possibilities within the third sector, understanding how non–profits develop and deploy their marketing activities most effectively appears to be of critical importance to organisations competing in this space. Drawing on the knowledge and experiences of 115 non–profit managers in Australia, this study identified four prominent marketing themes/issues: fundraising and other monetary related pressures; donor acquisition and relationship management; a ‘lag behind’ mentality in using new marketing platforms like social media; and a perceived lack of expertise in the area of marketing. The results in this chapter affirm the need for a greater adoption of a marketing approach in non–profits firms, particularly within small and micro non–profit enterprises
Assessing the cascading impacts of natural disasters in a multi-layer behavioral network framework
Full text linkNatural disasters negatively impact regions and exacerbate socioeconomic vulnerabilities. While the direct impacts of natural disasters are well understood, the channels through which these shocks spread to non-affected regions, still represents an open research question. In this paper we propose modelling socioeconomic systems as spatially-explicit, multi-layer behavioral networks, where the interplay of supply-side production, and demand-side consumption decisions, can help us understand how climate shocks cascade. We apply this modelling framework to analyze the spatial-temporal evolution of vulnerability following a negative food-production shock in one part of an agriculture-dependent economy. Simulation results show that vulnerability is cyclical, and its distribution critically depends on the network density and distance from the epicenter of the shock. We also introduce a new multi-layer measure, the Vulnerability Rank (VRank), which synthesizes various location-level risks into a single index. This framework can help design policies, aimed to better understand, effectively respond, and build resilience to natural disasters. This is particularly important for poorer regions, where response time is critical and financial resources are limited
Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions
Full text linkGeospatial artificial intelligence (GeoAI) has emerged as a subfield of GIScience that
uses artificial intelligence approaches and machine learning techniques for geographic
knowledge discovery. The non-regularity of data structures has recently led to different variants of graph neural networks in the field of computer science, with graph
convolutional neural networks being one of the most prominent that operate on non-
euclidean structured data where the numbers of nodes connections vary and the nodes
are unordered. These networks use graph convolution - commonly known as filters or kernels - in place of general matrix multiplication in at least one of their layers. This
paper suggests spatial regression graph convolutional neural networks (SRGCNNs) as a deep learning paradigm that is capable of handling a wide range of geographical
tasks where multivariate spatial data needs modeling and prediction. The feasibility
of SRGCNNs lies in the feature propagation mechanisms, the spatial locality nature, and a semi-supervised training strategy. In the experiments, this paper demonstrates
the operation of SRGCNNs with social media check-in data in Beijing and house price data in San Diego. The results indicate that a well-trained SRGCNN model is capable of learning from samples and performing reasonable predictions for unobserved locations. The paper also presents the effectiveness of incorporating the idea of geographically weighted regression for handling heterogeneity between locations in the model approach. Compared to conventional spatial regression approaches, SRGCNN-based models tend to generate much more accurate and stable results, especially when the sampling ratio is low. This study offers to bridge the methodological gap between graph deep learning and spatial regression analytics. The proposed idea serves as an
example to illustrate how spatial analytics can be combined with state-of-the-art deep learning models, and to enlighten future research at the front of GeoAI.Series: Working Papers in Regional Scienc
The global carbon footprint of Austria's consumption of agricultural (food and non-food) products
Full text linkAgricultural production is one of the largest contributors to global greenhouse gas emissions. High-income countries like Austria source large quantities of feed, food and nonfood crops abroad thereby outsourcing emissions. Understanding global supply chains and geographical patterns of the trade with agricultural products is crucial for taking on responsibility for consumption-based emissions arising in other world regions. This study investigates
Austria’s carbon footprint capturing all emissions from global agriculture associated with the consumption of food and non-food products. The analysis gives detailed insights into the contribution of various products and product categories, countries and regions, and carbon emitting processes across global supply chains, while comprehensively capturing all products consumed in Austria including their upstream emissions. The results show that while emission sources vary considerably for different consumption products, animal-based products account for the major part of emissions across the source regions. About 64% of Austrian emissions related to Austria’s carbon footprint of food products occur outside Austrian borders. Most emissions origin in Austria itself (36%), the rest of Europe (22%) and
Asia (19%) and Latin America (14%). More than two thirds of emissions are related to the consumption of meat and other animal-based products. The results show the importance of
consumption patterns, especially of meat and other animal products, for the Austrian footprint, which implies a great reduction potential through alternative diets and indicates
clear limitations for emission mitigation strategies that instead focus on production efficiency.Series: Ecological Economic Paper
Estimating water input in the mining industry in Brazil: A methodological proposal in a data-scarce context
Full text linkMetal mining plays a significant role in the Brazilian economy since its foundation as an overseas colony. The rapid increase in ore extraction brings along pressures on the country’s water resources, as mining is a particularly water-intensive activity. However, site-specific data on water input and management are scarce. We propose a methodology for estimating water input in mining at a high geographical resolution. We focus on the three key metals mined in Brazil: iron, aluminum (i.e. bauxite ore), and copper, and derive water input coefficients for all mines from governmental and corporate sources as well as from the literature. We estimate that overall, the sum of the water inputs estimated for Brazilian bauxite, copper, and iron ore mining decreased by 15% from an average of 506.5±62.4 hm3 in 2014 to an average of 408.4±67.2 hm3 in 2017. The regions where most water was appropriated were Northern (Pará state) and Southeast (Minas Gerais) for iron, Northern (Pará) for aluminum, and Northern (Pará) and Central West (Goiás) for copper. We show that there are still significant consistency and data availability gaps, and that further work is still necessary to improve site-specific reporting and open access to data collected by public institutions
The political science of Covid‐19: An introduction
Full text linkPolitical decisions, constellations, and behaviors exert a large influence of the dynamics of the severe acute respiratory syndrome coronavirus 2 (Sars-CoV-2) pandemic. Politics influences the choice of containment policies and the compliance with these policies—and therefore ultimately the epidemiological situation in each country, state, district, or even neighborhood. This introduction puts the articles collected in this special issue into the broader perspective of the social science literature on Covid-19
COVID-19 and the pursuit of supply chain resilience: reactions and "lessons learned" from logistics service providers (LSPs)
Full text linkPurpose – The purpose of this paper is to provide new insights into the reactions and lessons learned with regard to the COVID-19 pandemic in terms of how logistics service providers (LSPs) managed to maintain supply chains resilience and what focus areas have been changed to keep operations functional and uphold financial stability.
Design/methodology/approach – Based on data-gathering techniques in interpretive research this study collected primary data via semistructured interviews, interviewing informants from selected LSPs that operate on a global scale.
Findings – The results show that LSPs have built their reactions and actions to the COVID-19 outbreak around five main themes: “create revenue streams,” “enhance operational transport flexibility,” “enforce digitalization and data management,” “optimize logistics infrastructure” and “optimize personnel capacity.” These pillars build the foundation to LSP resilience that enables supply chains to stay resilient during an external shock of high impact and low probability.
Originality/value – The results of this study provide insights into how LSPs have managed the downsides and found innovative ways to overcome operational and financial challenges during the COVID-19 outbreak. As one of the first studies that specially focuses on the role of LSPs during the COVID-19 pandemic, this study categorizes the LSPs’ reactions and provides a “lessons learned” framework from a managerial perspective. From a theoretical perspective, this paper discusses the strategic role of LSPs in supply chain management and thereby extends current supply chain literature with a focus on LSP resilience
Identifying Groups of Determinants in Bayesian Model Averaging Using Dirichlet Process Clustering
Full text linkModel uncertainty is a pervasive problem in regression applications. Bayesian model averaging (BMA) takes model uncertainty into account and identifies robust determinants. However, it requires the specification of suitable model priors. Mixture model priors are appealing because they explicitly account for different groups of covariates as robust determinants. Specific Dirichlet process clustering (DPC) model priors are proposed; their correspondence to the binomial model prior derived and methods to perform the BMA analysis including a DPC postprocessing procedure to identify groups of determinants are outlined. The application of these model priors is demonstrated in a simulation exercise and in an empirical analysis of cross-country economic growth data. The BMA analysis is performed using the Markov chain Monte Carlo model composition sampler to obtain samples from the posterior of the model specifications. Results are compared with those obtained under a beta-binomial and a collinearity-adjusted dilution model prior
Knowledge Graphs
Full text linkIn this article, we provide a comprehensive introduction to knowledge graphs, which have recently garnered
significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic,
large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based
data models, as well as languages used to query and validate knowledge graphs. We explain how knowledge
can be represented and extracted using a combination of deductive and inductive techniques. We conclude
with high-level future research directions for knowledge graphs