5,922 research outputs found

    Mobile Identity Protection: The Moderation Role of Self-Efficacy

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    The rapid growth of mobile applications and the associated increased dependency on digital identity raises the growing risk of identity theft and related fraud. Hence, protecting identity in a mobile environment is a problem. This study develops a model that examines the role of identity protection self-efficacy in increasing users’ motivation intentions to achieve actual mobile identity protection. Our research found that self-efficacy significantly affects the relationship between users’ perceived threat appraisal and their motivational intentions for identity protection. The relation between mobile users’ protection, motivational intentions, and actual mobile identity protection actions was also found to be significant. Additionally, the findings revealed the considerable impact of awareness in fully mediating between self-efficacy and actual identity protection. The model and its hypotheses are empirically tested through a survey of 383 mobile users, and the findings are validated through a panel of experts, thus confirming the impact of self-efficacy on an individual’s identity protection in the mobile context

    Robustness, Heterogeneity and Structure Capturing for Graph Representation Learning and its Application

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    Graph neural networks (GNNs) are potent methods for graph representation learn- ing (GRL), which extract knowledge from complicated (graph) structured data in various real-world scenarios. However, GRL still faces many challenges. Firstly GNN-based node classification may deteriorate substantially by overlooking the pos- sibility of noisy data in graph structures, as models wrongly process the relation among nodes in the input graphs as the ground truth. Secondly, nodes and edges have different types in the real-world and it is essential to capture this heterogeneity in graph representation learning. Next, relations among nodes are not restricted to pairwise relations and it is necessary to capture the complex relations accordingly. Finally, the absence of structural encodings, such as positional information, deterio- rates the performance of GNNs. This thesis proposes novel methods to address the aforementioned problems: 1. Bayesian Graph Attention Network (BGAT): Developed for situations with scarce data, this method addresses the influence of spurious edges. Incor- porating Bayesian principles into the graph attention mechanism enhances robustness, leading to competitive performance against benchmarks (Chapter 3). 2. Neighbour Contrastive Heterogeneous Graph Attention Network (NC-HGAT): By enhancing a cutting-edge self-supervised heterogeneous graph neural net- work model (HGAT) with neighbour contrastive learning, this method ad- dresses heterogeneity and uncertainty simultaneously. Extra attention to edge relations in heterogeneous graphs also aids in subsequent classification tasks (Chapter 4). 3. A novel ensemble learning framework is introduced for predicting stock price movements. It adeptly captures both group-level and pairwise relations, lead- ing to notable advancements over the existing state-of-the-art. The integration of hypergraph and graph models, coupled with the utilisation of auxiliary data via GNNs before recurrent neural network (RNN), provides a deeper under- standing of long-term dependencies between similar entities in multivariate time series analysis (Chapter 5). 4. A novel framework for graph structure learning is introduced, segmenting graphs into distinct patches. By harnessing the capabilities of transformers and integrating other position encoding techniques, this approach robustly capture intricate structural information within a graph. This results in a more comprehensive understanding of its underlying patterns (Chapter 6)

    Strategy Tripod Perspective on the Determinants of Airline Efficiency in A Global Context: An Application of DEA and Tobit Analysis

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    The airline industry is vital to contemporary civilization since it is a key player in the globalization process: linking regions, fostering global commerce, promoting tourism and aiding economic and social progress. However, there has been little study on the link between the operational environment and airline efficiency. Investigating the amalgamation of institutions, organisations and strategic decisions is critical to understanding how airlines operate efficiently. This research aims to employ the strategy tripod perspective to investigate the efficiency of a global airline sample using a non-parametric linear programming method (data envelopment analysis [DEA]). Using a Tobit regression, the bootstrapped DEA efficiency change scores are further regressed to determine the drivers of efficiency. The strategy tripod is employed to assess the impact of institutions, industry and resources on airline efficiency. Institutions are measured by global indices of destination attractiveness; industry, including competition, jet fuel and business model; and finally, resources, such as the number of full-time employees, alliances, ownership and connectivity. The first part of the study uses panel data from 35 major airlines, collected from their annual reports for the period 2011 to 2018, and country attractiveness indices from global indicators. The second part of the research involves a qualitative data collection approach and semi-structured interviews with experts in the field to evaluate the impact of COVID-19 on the first part’s significant findings. The main findings reveal that airlines operate at a highly competitive level regardless of their competition intensity or origin. Furthermore, the unpredictability of the environment complicates airline operations. The efficiency drivers of an airline are partially determined by its type of business model, its degree of cooperation and how fuel cost is managed. Trade openness has a negative influence on airline efficiency. COVID-19 has toppled the airline industry, forcing airlines to reconsider their business model and continuously increase cooperation. Human resources, sustainability and alternative fuel sources are critical to airline survival. Finally, this study provides some evidence for the practicality of the strategy tripod and hints at the need for a broader approach in the study of international strategies

    The value and structuring role of web APIs in digital innovation ecosystems: the case of the online travel ecosystem

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    Interfaces play a key role in facilitating the integration of external sources of innovation and structuring ecosystems. They have been conceptualized as design rules that ensure the interoperability of independently produced modules, with important strategic value for lead firms to attract and control access to complementary assets in platform ecosystems. While meaningful, these theorizations do not fully capture the value and structuring role of web APIs in digital innovation ecosystems. We show this with an empirical study of the online travel ecosystem in the 26 years (1995–2021) after the first Online Travel Agencies (OTAs) were launched. Our findings reveal that web APIs foster a dynamic digital innovation ecosystem with a distributed networked structure in which multiple actors design and use them. We provide evidence of an ecosystem where decentralized interfaces enable decentralized governance and where interfaces establish not only cooperative relationships, but also competitive ones. Instead of locking in complementors, web APIs enable the integration of capabilities from multiple organizations for the co-production of services and products, by interfacing their information systems. Web APIs are important sources of value creation and capture, increasingly being used to offer or sell services, constituting important sources of revenue

    From abuse to trust and back again

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    oai:westminsterresearch.westminster.ac.uk:w7qv

    Essays on monetary policy and financial stability

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    Doutoramento em EconomiaBy focusing on the relationship between financial stability and monetary policy for the cases of Chile, Colombia, Japan, Portugal and the UK, this thesis aims to add to the existing literature on the fundamental issue of the relationship between financial stability and monetary policy, a traditional topic that gained importance in the aftermath of the GFC as Central Banks lowered policy rates in an effort to rescue their economies. As the zero-lower bound loomed and the reach of traditional monetary policy narrowed, policy makers realised that alternative frameworks were needed and hence, macroprudential policy measures aimed at targeting the financial system as a whole were introduced. The second chapter looks at the relationship between monetary policy and financial stability, which has gained importance in recent years as Central Bank policy rates neared the zero-lower bound. We use an SVAR model to study the impact of monetary policy shocks on three proxies for financial stability as well as a proxy for economic growth. Monetary policy is represented by policy rates for the EMEs and shadow rates for the AEs in our chapter. Our main results show that monetary policy may be used to correct asset mispricing, to control fluctuations in the real business cycle and also to tame credit cycles in the majority of cases. Our results also show that for the majority of cases, in line with theory, local currencies appreciate following a positive monetary policy shock. Monetary policy intervention may indeed be successful in contributing to or achieving financial stability. However, the results show that monetary policy may not have the ability to maintain or re-establish financial stability in all cases. Alternative policy choices such as macroprudential policy tool frameworks which are aimed at targeting the financial system as a whole may be implemented as a means of fortifying the economy. The third chapter looks at the institutional setting of the countries in question, the independence of the Central Bank, the political environment and the impact of these factors on financial Abstract stability. I substantiate the literature review discussion with a brief empirical analysis of the effect of Central Bank Independence on credit growth using an existing database created by Romelli (2018). The empirical results show that there is a positive relationship between credit growth and the level of Central Bank Independence (CBI) due to the positive and statistically significant coefficient on the interaction term between growth in domestic credit to the private sector and the level of CBI. When considering domestic credit by deposit money banks and other financial institutions, the interaction term is positive and statistically significant for the case of the UK for the third regression equation. A number of robustness checks show that the coefficient is positive and statistically significant for a number of cases when implementing a variety of estimation methods. Fluctuations in credit growth are larger for higher levels of CBI and hence, in periods of financial instability or ultimately financial crises, CBI would be reined back in an effort to re-establish financial stability. Based on the empirical results, and in an effort to slow down surging credit supply and to maintain financial stability, policy makers and governmental authorities should attempt to decrease the level of CBI when the economy shows signs of overheating and credit supply continues to increase. The fourth chapter looks at the interaction between macroprudential policy and financial stability. The unexpected interconnectedness of the global economy and the economic blight that occurred as a result of this, recapitulated the need to implement an alternative policy framework aimed at targeting the financial system as a whole and hence, targeting the maintenance of financial stability. In this chapter, an index of domestic macroprudential policy tools is constructed and the effectiveness of these tools in controlling credit growth, managing GDP growth and stabilising inflation growth is studied using a dynamic panel data model for the period between 2000 and 2017. The empirical analysis includes two panels namely an EU panel of 27 countries and a Latin American panel of 7 countries, the chapter also looks at a case study of Japan, Portugal and the UK. Our main results find that a tighter macroprudential policy tool stance leads to a decrease in both credit growth and GDP growth while, a tighter macroprudential policy tool stance results in higher inflation in the majority of cases. Further, we find that capital openness plays a more important role in the case of Latin America, this may be due to the region’s dependence on foreign capital flows and exchange rate movements. Lastly, we find that, in times of higher perceived market volatility, GDP growth tends to be higher and inflation growth tends to be lower in the EU. In the other cases, higher levels of perceived market volatility result in higher inflation, higher credit growth and lower GDP Abstract growth. This is in line with expectations as an increase in perceived market volatility is met with an increased flow of assets into safer markets such as the EU. This thesis establishes a relationship between financial stability and monetary policy by studying the response of Chile, Colombia, Japan, Portugal and the UK in the aftermath of the GFC as Central Banks lowered policy rates in an effort to rescue their economies. In short, the results of the work conducted in this thesis may be summarised as follows. Our results show that monetary policy contributes to the achievement of financial stability. Still, monetary policy alone is not sufficient and should be reinforced by less traditional policy choices such as macroprudential policy tools. Secondly, we find that the level of CBI should be reined in in times of surging credit supply in an effort to maintain financial stability. Finally, we conclude that macroprudential policy tools play an important role in the achievement of financial stability. These tools should complement traditional monetary policy frameworks and should be adapted for each region.info:eu-repo/semantics/publishedVersio

    Google search intensity, mortgage default and house prices in regional residential markets

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    The internet provides a new way for households to access relevant information, while their online search behaviour may also contain information for their concerns and intentions, or even be used to predict real economic activity. This thesis explores the use of Google search data to predict mortgage default and regional house price dynamics in an empirical macroeconomic framework. The thesis is composed of three independent empirical studies. The first study examines the dynamic interdependence between mortgage default and house price across different housing market segments, i.e., top-tier vs. bottom-tier houses and recourse states vs. non-recourse states, based on a Panel VAR model. In particular, this study uses the Mortgage Default Risk Index (MDRI) proposed by Chauvet et al. (2016). It captures the intensity of Google search for keywords and phrases such as “mortgage foreclosure” or “foreclosure help” and measures the potential default risk of households. It is shown that shocks to house price returns have a significantly stronger effect on actual foreclosures in non-recourse states than in recourse states. The results suggest that borrowers are financially sophisticated and strategic as they are less likely to default in recourse states. Additionally, the MDRI has a stronger negative impact on top-tier home price returns, while the foreclosure rate of homes more pronouncedly decreases bottom-tier home price returns. These findings hold for the entire sample and recourse states. However, in non-recourse states, the impacts of the MDRI and the HF on bottom- and top-tier house price returns are about the same. The second study examines the impact of house prices on the foreclosure rates in the local housing market and explores whether the MDRI helps predict future house prices and foreclosures. In particular, this study uses an error correction framework to capture both the long-run equilibrium fundamental component of house prices as well as the short-run dynamics of house prices, including the component of bubbles. It is found that the MDRI shows a negative impact on both components of house prices but, more importantly, a negative impact on foreclosure rates. Furthermore, it is shown that foreclosure rates are negatively affected by the fundamental component of house prices but are not sensitive to their bubble component. This study sheds new light on the predictive power of household sentiment derived from Google searches on prices and foreclosure rates in local housing markets. The third study recognizes that, by searching online, households are transmitting information to and simultaneously receiving information from the Google Search engine. While they might divulge information about their financial concerns or vulnerability, they are also gathering information and learning through their search behaviour. This chapter aims to examine the comprehensive impact of the disclosure and information-learning effects of online searches on mortgage default. To that end, based on the assumption of different pre-existing knowledge of households, this study defines two kinds of Google search activities of households, i.e., naïve and sophisticated searches, and practically performed by aggregating the search activities for different query terms. It is found that sophisticated search activity has a positive impact on mortgage delinquency but a negative impact on foreclosure starts, while naïve search activity only positively affects foreclosure starts. The results suggest that the Google search activity of households is a combination of information disclosure and information-learning processes. Furthermore, borrowers are more likely to learn from sophisticated online searches, and they can use the information to avoid foreclosure starts

    AN ANALYSIS OF IF THE ADVANCED PLACEMENT: WORLD HISTORY MODERN READING IS EFFECTIVE TEACHER PROFESSIONAL DEVELOPMENT

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    This study explored if the AP World History: Modern Reading is effective teacher professional development and if it impacts teachers and students. Desimone’s Core Conceptual Framework served as the foundation for which to evaluate professional development. Adult learning theory served as the theoretical framework. The researcher used a mixed methods explanatory sequential design. A survey was completed by 83 AP World History: Modern high school teachers who had attended the AP World History: Modern on-site Reading. The survey provided data on teacher perceptions of the Reading as well as the perceived impacts on student learning. The researcher then conducted a focus group discussion with eight participants to study teacher perceptions and the impact on student learning in greater depth. Quantitative and qualitative results were integrated in order to gain a deeper understanding. The researcher found that attending the AP World History: Modern on-site Reading is beneficial teacher professional development. The structure of the in-person Reading allows for collaboration, engages participants and is relevant to the attendees’ classroom practice. As a result of attending the Reading, teachers perceive increases in their knowledge and understanding of exam requirements, and they perceive increased levels of confidence in their abilities to instruct students and assess student learning. Teachers perceive that attending the AP World History: Modern Reading improved their students’ scores and contributed to growth in student learning

    Low- and high-resource opinion summarization

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    Customer reviews play a vital role in the online purchasing decisions we make. The reviews express user opinions that are useful for setting realistic expectations and uncovering important details about products. However, some products receive hundreds or even thousands of reviews, making them time-consuming to read. Moreover, many reviews contain uninformative content, such as irrelevant personal experiences. Automatic summarization offers an alternative – short text summaries capturing the essential information expressed in reviews. Automatically produced summaries can reflect overall or particular opinions and be tailored to user preferences. Besides being presented on major e-commerce platforms, home assistants can also vocalize them. This approach can improve user satisfaction by assisting in making faster and better decisions. Modern summarization approaches are based on neural networks, often requiring thousands of annotated samples for training. However, human-written summaries for products are expensive to produce because annotators need to read many reviews. This has led to annotated data scarcity where only a few datasets are available. Data scarcity is the central theme of our works, and we propose a number of approaches to alleviate the problem. The thesis consists of two parts where we discuss low- and high-resource data settings. In the first part, we propose self-supervised learning methods applied to customer reviews and few-shot methods for learning from small annotated datasets. Customer reviews without summaries are available in large quantities, contain a breadth of in-domain specifics, and provide a powerful training signal. We show that reviews can be used for learning summarizers via a self-supervised objective. Further, we address two main challenges associated with learning from small annotated datasets. First, large models rapidly overfit on small datasets leading to poor generalization. Second, it is not possible to learn a wide range of in-domain specifics (e.g., product aspects and usage) from a handful of gold samples. This leads to subtle semantic mistakes in generated summaries, such as ‘great dead on arrival battery.’ We address the first challenge by explicitly modeling summary properties (e.g., content coverage and sentiment alignment). Furthermore, we leverage small modules – adapters – that are more robust to overfitting. As we show, despite their size, these modules can be used to store in-domain knowledge to reduce semantic mistakes. Lastly, we propose a simple method for learning personalized summarizers based on aspects, such as ‘price,’ ‘battery life,’ and ‘resolution.’ This task is harder to learn, and we present a few-shot method for training a query-based summarizer on small annotated datasets. In the second part, we focus on the high-resource setting and present a large dataset with summaries collected from various online resources. The dataset has more than 33,000 humanwritten summaries, where each is linked up to thousands of reviews. This, however, makes it challenging to apply an ‘expensive’ deep encoder due to memory and computational costs. To address this problem, we propose selecting small subsets of informative reviews. Only these subsets are encoded by the deep encoder and subsequently summarized. We show that the selector and summarizer can be trained end-to-end via amortized inference and policy gradient methods

    Hydrogen-bonding receptors for anion recovery in a capacitive deionisation system

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    Receptors are ubiquitous throughout nature and are found heavily within biological systems. This has led to synthetic supramolecular chemists to modify or develop analogous mimics of these receptors with high affinity and specificity for a range of target compounds, for potential commercial use. One group of particular interest are receptors that function through the formation of hydrogen bonds to the guest species. This class of receptor has been shown to have a range of different structural geometries and binding motifs, that allow for the sequestration of a number of different species. In the context of this work, anionic hydrogen-bonding receptors, specifically for ‘phosphate’- in most cases dihydrogenphosphate- and bicarbonate are of interest. Phosphate is an integral part of the DNA backbone, however a organophosphorus containing compounds also comprise a large group of chemical weapons which can have a devasting impact on the bodies ability to function. Chemical weapon compounds, such as sarin and Novichok, are based on the functionalisation of a central phosphate core which can be biotransformed into a highly potent active species within the body. Phosphate is also an essential component of plant fertilizers and is used on a huge scale in order to maintain global food security. However, phosphate loss as a consequence of agricultural run-off leads to reduced availability of essential minerals as well as large scale eutrophication. One such method that could be utilised for the recovery of phosphate is electrochemical capacitive deionisation. The principle and idea of capacitive deionisation has been around since the late 1960’s to early 1970’s and has been shown to be a suitable method for the desalination of low-to-medium salinity input streams. The purpose of the work within this thesis was to modify and synthesise receptors that could be covalently attached to porous carbon electrodes and impart selectivity to a capacitive deionisation system. In Chapter 1, the importance of ‘phosphate’, biologically and commercially is addressed before an in depth look at some of the phosphate specific hydrogen bonding receptors that have been reported in the literature. The design of a successful hydrogen bonding receptor relies on the correct orientation of the binding motifs and the range of structural scaffolds have been shown to be useable. Following this, the electrochemical principles of capacitive deionisation and its suitability for the recovery of phosphate are detailed, including some examples of capacitive deionisation set-ups and the overall processes involved. Chapter 2 details the theory of the techniques used throughout this thesis, which include, but not limited to, 1H and 13C NMR for the structural elucidation of the synthesised receptors and cyclic voltammetry which was used for the attachment of organic groups to an electrode. The historical and theoretical background established in Chapters 1 and 2 will lead into the work undertaken in Chapters 3-5. Chapter 3 focusses on the first of three hydrogen bonding receptors synthesised. Building upon previous work within the field, two neutral indole-based receptors were modified to include two different potential attachment points for the electrode- a carboxylic acid and an alkyne. Following the successful synthesis of the alkyne-based receptor, 1H NMR titrations were used to confirm the affinity of the new receptor for dihydrogenphosphate. Chapter 4 introduces the second anion of interest, bicarbonate. The underlying principles for hydrogen bonding are the same for bicarbonate, as in phosphate, however a different receptor was synthesised. The carbazole receptor synthesised contained free amine groups that were proposed to act as points of attachment to an already surface bound organic spacer group. 1H NMR titrations are once again used to determine the affinity of the receptor for the bicarbonate anion. Finally, Chapter 5 introduces the second of the dihydrogenphosphate-specific receptors, this time based on the amino acid leucine. UVVis titrations with a number of different anions were used to determine the affinity of the receptor. Within this chapter, methods for the attachment of organic groups are detailed including the electroreduction of 4-nitrobenzene diazonium and the direct oxidation of the alkyne
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