Implications of Perceived Utility on Individual Choice and Preferences: A New Framework for Designing Recommender System

Consumer psychology and consumer behaviour has constantly been a field of interest for the researchers. Volumes of researches are available in this area, still new theories and concepts keep emerging in response to change in context and environment. Consumer psychology has large cross disciplinary implications. In this paper, a relationship between consumer psychology and recommender system has been explained and how implementing a consumer psychology model can improve recommender system performance. This paper also gives first level analysis of various filters that can be developed along with the design of recommender system in order to generate a more refined and relevant choice sets. There are attraction effects observed among different items and the attractiveness of a product is co- dependent on attractiveness of other options available in a choice set. In the present paper, we have explored the utility of defender model in the design of recommender systems. Different effects like decoy and asymmetric dominance are als

Meeting their potential: the role of education and technology in overcoming disadvantage and disaffection in young people

This report is a review of literature, policy and reported practice, exploring the potential of technology to mitigate disaffection and disadvantage in education and raise attainment of those young people who are under-achieving in school or other educational settings

Motivating for behavioral change through smart nudging. Evaluating digital representations of psychological effects

This thesis aims to study psychological effects and how to represent them digitally within a smart nudging system. A smart nudging system creates personalized digital nudges that are highly relevant to the user's context. How the system presents the nudges and what psychological effects are used is critical to influencing the user towards the nudging goal. The goal of the thesis is to find, implement and evaluate what effects can be used with a smart nudging system and if some of the effects are better suited for digital nudging. A design for applying effects towards a goal of being more physical active is provided, and a subset of the effects are implemented from this design. An evaluation of the implementation and the experiences showed that most of the effects are helpful for a smart nudging system. However, some of the effects are both difficult to solve and less useful. Difficulty refers to how hard it is to create and use the effect in a nudge, and usefulness refers to how well it can be combined with other effects and if it can be used individually as part of a nudge

Information Delivery, User Decision Approach, and Choice Environment: Examining the Effectiveness of Non-Compensatory and Customization-based Online Decision Support.

Decision support research has largely focused on the mechanics of tool design, with less attention paid to the way the alternatives are presented to the user - that is, the format of the output, how the decision tool design can play a role in it, and the output content (characteristics). Furthermore, little research has examined specific decision contexts and user’s cognitive aspects pertinent to the choice task, and their role during an online purchase. This study addresses these issues by investigating the impact of output format and content of a non-compensatory (NC) tool and a customization-based tool on user’s decision quality in the context of a health insurance purchase. It also examines the moderating role of context (perceived risk) and user’s decision approach (price heuristics) – both salient in a health plan choice. Drawing from risk perception, decoy effect, price order effect, and options framing, this research carries out 2 studies: 2x3x2 full factorial between subjects experiments. Study 1 examines the effect of NC Descending (price High-Low) choice sets with asymmetrically dominated alternatives, while Study 2 examines NC Descending, NC Ascending, and customization-based tools. Both studies also investigate the roles of perceived risk (high vs low), and user’s decision approach (price heuristics-driven strong vs weak). Results of Study 1 demonstrate that output content characterized by price anchoring differentially affects user’s decision quality. These dynamics change for users under different levels of perceived risk and with disparate decision approaches. Study 2 indicate that by subjecting the user to reference dependence, usage of NC Descending tool can have a negative impact on decision quality (highest price paid), and usage of NC Ascending and Financial tool have a positive impact (lower price paid). Usage of a customization-based tool, as per the design delineated here, mitigates the negative impact of NC Descending, and further lowers, the influence of NC Ascending tools, by enforcing cost-utility analysis, adopting base-level reference point, and enabling more flexible item composition. The study contributes to: a) information systems, by uncovering detailed dynamics of the interactions between information delivery and the user; and b) boundaries of reference dependence, thus, loss aversion

The impact of online reviews on consumer evaluations and decision making: an analysis of review volume and user-generated photos

This thesis investigates the impact of online social influence on consumer behaviour, specifically within the context of online reviews. It examines how review volume and user-generated photos affect consumer evaluations and decision-making. In Chapter 2, I introduce a novel phenomenon, the N-effect, which explores how opinion volume influences the content of online evaluations. I find that as the number of opinions increases, the content becomes more emotional and less analytical. In Chapter 3, I investigate the role of user-generated photos in shaping purchase intentions. This research demonstrates that photos can enhance review helpfulness, even when they lack diagnostic information. This effect is driven by the confidence signalled by the reviewer when posting a review with a photo, which is later assimilated by readers, leading to increased perceived helpfulness and purchase likelihood. This thesis makes several theoretical and practical contributions to the literature on human interaction with technology. Theoretically, it expands our understanding of online social influence by examining the dynamics of online opinion expression and content. I contribute to the literature on group size by demonstrating how responsibility may be lost in online contexts. Furthermore, the findings provide insights into the social influence of photos on viewers and the role of pseudo-evidence in shaping beliefs and attitudes. From a practical standpoint, this research offers valuable insights for online platform managers and marketers on interpreting and using consumer-written reviews. Overall, this thesis contributes to the existing literature on online social influence and provides insights for businesses to improve communication and interpretation with consumers by better understanding and leveraging online reviews and opinions.Open Acces

Entwicklung einer Methodik zur Unterstützung der Entscheidungsfindung auf der Grundlage heuristischer Entscheidungen in der Produktentwicklung und im Innovationsmanagement = Developing a method to support decision making based on heuristic decisions in product development and inno-vation management

Die Entscheidungsfindung ist ein wichtiger Faktor, der die Produktentwicklung so steuert, dass sie auf dem Markt erfolgreich ist oder scheitert. Die meisten Entschei-dungen beruhen auf den verfügbaren Informationen, Berechnungen und Analysen verschiedener systematischer Methoden. Diese Methoden können jedoch keine definitiv richtige und gute Entscheidung liefern, insbesondere unter Unsicherheit und begrenzten Informationen. Die Entscheidungsfindung mit einfachen Strategien und mentalen Prozessen, die heuristische Entscheidungen genannt werden, wird dann automatisch angewendet, um eine Lösung zu finden. Obwohl Heuristiken in vielen Situationen hilfreich sind, können sie zu Entscheidungsverzerrungen führen. Diese Forschung zielt darauf ab, das Auftreten Entscheidungsverzerrungen bei der Verwendung von Heuristiken zur Entscheidungsfindung zu untersuchen. Dies zu verstehen und sich dessen bewusst zu sein, ist wichtig für die Verbesserung der Entscheidungseffizienz. Darüber hinaus wurden die De-Biasing Techniken entwi-ckelt, um kognitive Probleme zu lösen und Entscheidungsfehler zu reduzieren, die durch Entscheidungsverzerrungen verursacht werden. Informationen aus der Literatur und empirischen Studien zeigen viele Erscheinungen von heuristischen Entscheidungen und Verzerrungen während der Entscheidungs-findung in verschiedenen Aktivitäten der Produktentwicklung - insbesondere bei der Priorisierung von Alternativen und der Auswahl der Lösung. Methoden zum Umgang mit den Entscheidungsverzerrungen bei der Entscheidungsfindung in der Produkt-entwicklung wurden dann durch die Modifizierung verfügbarer Techniken aus anderen Bereichen entwickelt und zur Behandlung der kognitiven Vorurteile bei der Entscheidungsfindung in der Produktentwicklung angewandt. Es wurden vier Arten von De-Biasing Techniken entwickelt und in einem Rahmenwerk vorgeschlagen, die in Experimenten unter simulierten und realen Situationen angewandt und ausgewer-tet wurden. Die Ergebnisse zeigen, dass Entscheidungsverzerrungen mit verschiedenen Entzer-rungstechniken auf der Grundlage der Ziele und Ableitung von Vorurteilen behandelt werden kann. Diese Techniken können mit einer Art von Verzerrung umgehen, aber auch zu einer anderen Art von Verzerrung führen. Darüber hinaus gibt es keine Antwort darauf, dass Entscheidungen mit oder ohne Verzerrungen eine korrekte Lösung bieten können. Daher ist die geeignetste Methode zur Behandlung von Entscheidungsverzerrungen in der Produktentwicklung, das Bewusstsein der Ent-scheidungsträger für heuristische Entscheidungen und Verzerrungen zu schärfen. Somit kann der Entscheidungsträger entscheiden, die Entscheidungen und Lösun-gen zu akzeptieren oder abzulehnen

TOWARDS RELIABLE CIRCUMVENTION OF INTERNET CENSORSHIP

The Internet plays a crucial role in today\u27s social and political movements by facilitating the free circulation of speech, information, and ideas; democracy and human rights throughout the world critically depend on preserving and bolstering the Internet\u27s openness. Consequently, repressive regimes, totalitarian governments, and corrupt corporations regulate, monitor, and restrict the access to the Internet, which is broadly known as Internet \emph{censorship}. Most countries are improving the internet infrastructures, as a result they can implement more advanced censoring techniques. Also with the advancements in the application of machine learning techniques for network traffic analysis have enabled the more sophisticated Internet censorship. In this thesis, We take a close look at the main pillars of internet censorship, we will introduce new defense and attacks in the internet censorship literature. Internet censorship techniques investigate users’ communications and they can decide to interrupt a connection to prevent a user from communicating with a specific entity. Traffic analysis is one of the main techniques used to infer information from internet communications. One of the major challenges to traffic analysis mechanisms is scaling the techniques to today\u27s exploding volumes of network traffic, i.e., they impose high storage, communications, and computation overheads. We aim at addressing this scalability issue by introducing a new direction for traffic analysis, which we call \emph{compressive traffic analysis}. Moreover, we show that, unfortunately, traffic analysis attacks can be conducted on Anonymity systems with drastically higher accuracies than before by leveraging emerging learning mechanisms. We particularly design a system, called \deepcorr, that outperforms the state-of-the-art by significant margins in correlating network connections. \deepcorr leverages an advanced deep learning architecture to \emph{learn} a flow correlation function tailored to complex networks. Also to be able to analyze the weakness of such approaches we show that an adversary can defeat deep neural network based traffic analysis techniques by applying statistically undetectable \emph{adversarial perturbations} on the patterns of live network traffic. We also design techniques to circumvent internet censorship. Decoy routing is an emerging approach for censorship circumvention in which circumvention is implemented with help from a number of volunteer Internet autonomous systems, called decoy ASes. We propose a new architecture for decoy routing that, by design, is significantly stronger to rerouting attacks compared to \emph{all} previous designs. Unlike previous designs, our new architecture operates decoy routers only on the downstream traffic of the censored users; therefore we call it \emph{downstream-only} decoy routing. As we demonstrate through Internet-scale BGP simulations, downstream-only decoy routing offers significantly stronger resistance to rerouting attacks, which is intuitively because a (censoring) ISP has much less control on the downstream BGP routes of its traffic. Then, we propose to use game theoretic approaches to model the arms races between the censors and the censorship circumvention tools. This will allow us to analyze the effect of different parameters or censoring behaviors on the performance of censorship circumvention tools. We apply our methods on two fundamental problems in internet censorship. Finally, to bring our ideas to practice, we designed a new censorship circumvention tool called \name. \name aims at increasing the collateral damage of censorship by employing a ``mass\u27\u27 of normal Internet users, from both censored and uncensored areas, to serve as circumvention proxies

e-Nudging Justice: The Role of Digital Choice Architecture in Online Courts

Justice systems around the world are launching online courts and tribunals as a means to improve their efficiency, increase access to justice, and ameliorate the quality of proceedings. These online courts and tribunals are publicly administered judicial online dispute resolution (ODR) systems that enable litigants, lawyers, judges and court personnel to complete all litigation related activities, from filing through final disposition, on a dedicated digital platform. Online courts are envisioned as a promising response to many challenges that civil justice systems face, including those stemming from voluminous case filings, procedural complexity, limited accessibility, high costs of litigation, and the ubiquity of settlements and non-trial adjudication. The guiding premise of judicial ODR systems is that information technology (IT) and innovative procedural design can improve the accessibility, efficiency and effectiveness of courts. Specifically, most online courts are designed to improve access to justice for self-represented litigants (SRLs), who access courts unassisted by lawyers. As such, they respond to calls to redesign civil courts for the typical litigant and process, and specifically, for the skills and needs of SRLs

CLASSIFYING AND RESPONDING TO NETWORK INTRUSIONS

Intrusion detection systems (IDS) have been widely adopted within the IT community, as passive monitoring tools that report security related problems to system administrators. However, the increasing number and evolving complexity of attacks, along with the growth and complexity of networking infrastructures, has led to overwhelming numbers of IDS alerts, which allow significantly smaller timeframe for a human to respond. The need for automated response is therefore very much evident. However, the adoption of such approaches has been constrained by practical limitations and administrators' consequent mistrust of systems' abilities to issue appropriate responses. The thesis presents a thorough analysis of the problem of intrusions, and identifies false alarms as the main obstacle to the adoption of automated response. A critical examination of existing automated response systems is provided, along with a discussion of why a new solution is needed. The thesis determines that, while the detection capabilities remain imperfect, the problem of false alarms cannot be eliminated. Automated response technology must take this into account, and instead focus upon avoiding the disruption of legitimate users and services in such scenarios. The overall aim of the research has therefore been to enhance the automated response process, by considering the context of an attack, and investigate and evaluate a means of making intelligent response decisions. The realisation of this objective has included the formulation of a response-oriented taxonomy of intrusions, which is used as a basis to systematically study intrusions and understand the threats detected by an IDS. From this foundation, a novel Flexible Automated and Intelligent Responder (FAIR) architecture has been designed, as the basis from which flexible and escalating levels of response are offered, according to the context of an attack. The thesis describes the design and operation of the architecture, focusing upon the contextual factors influencing the response process, and the way they are measured and assessed to formulate response decisions. The architecture is underpinned by the use of response policies which provide a means to reflect the changing needs and characteristics of organisations. The main concepts of the new architecture were validated via a proof-of-concept prototype system. A series of test scenarios were used to demonstrate how the context of an attack can influence the response decisions, and how the response policies can be customised and used to enable intelligent decisions. This helped to prove that the concept of flexible automated response is indeed viable, and that the research has provided a suitable contribution to knowledge in this important domain