Using explainable food swaps to nudge users towards more sustainable products in grocery websites

The growing concern for climate issues has prompted both consumers and the grocery retail industry to prioritize environmental sustainability. This thesis aims to examine the effectiveness of nudging users towards more sustainable food options in an online grocery store using food swaps. Further, the study utilizes different motivational explanations accompanying the swaps to investigate their impact on swap acceptance and perceived understand- ing. A mockup supermarket interface was created, and screenshots were uploaded to an online survey tool, where participants (N=202) were assigned to one of four conditions (baseline, health, sustainability, or money). Results indicate that motivational framing did not significantly influence swap acceptance. However, perceived understandability was significant in affecting swap acceptance, with the sustainability framing being better understood. Participants were more likely to swap when the cost of the alternative product increased, suggesting other factors influenced consumer behavior. Finally, perceived similarity between the original and alternative product significantly affected the swap acceptance and perceived similarity, where meat swaps showed a strong positive, statistical significance. This thesis provided novel work within the field of encouraging more sustainable products in online grocery shopping services, which can further be expanded by implementing sustainable food swaps in a recommender system.Masteroppgave i informasjonsvitenskapINFO390MASV-INF

Salience in decision-making: a neuroeconomic analysis

Attention and the closely related concept of salience play an important role in the complex process of human decision-making. In 2012, Bordalo et al. (2012a) proposed a theory on human decision-making that is based on salience. They suggest that salience differences within a decision problem may explain many decision biases. Concerning decisions under risk, Bordalo and colleagues developed a formula to calculate salience differences that are shaped by bottom-up processes. These salience differences have been experimentally investigated. Reaction times in a dot-probe task served as indicator of attentional biases. Data revealed a significant salience effect after a lottery exposure duration of 150 ms. This supports the salience concept proposed by Bordalo et al. (2012a) and suggests an early attentional orienting towards salient payoffs. In order to further differentiate attentional processes involved in the salience effect EEG has been recorded. Different ERP-components may indicate attentional biases at different stages of attentional processing and give a hint at more detailed reasons behind the salience effect. All investigated components, namely, P1, N1, P3a and P3b, showed no significant salience differences. Part III presents a further experiment that was devoted to nudges. These interventions often work by altering the salience within a decision problem or by directing the attention to the decision task itself. Since these interventions influence decisions at least partly on an unconscious level, nudges are subject to criticism. The experiment aimed at investigating the effect of transparent information accompanying the nudges on their efficacy. In line with previous research adding information on the nudge itself, on its purpose and the combination of both had no significant effect on the efficacy of the nudge, even though this additional information again alters salience ratios within the decision problem

IMPROVING COLLABORATIVE FILTERING RECOMMENDER BY USING MULTI-CRITERIA RATING AND IMPLICIT SOCIAL NETWORKS TO RECOMMEND RESEARCH PAPERS

Research paper recommender systems (RSs) aim to alleviate the information overload of researchers by suggesting relevant and useful papers. The collaborative filtering in the area of recommending research papers can benefit by using richer user feedback data through multi-criteria rating, and by integrating richer social network data into the recommender algorithm. Existing approaches using collaborative filtering or hybrid approaches typically allow only one rating criterion (overall liking) for users to evaluate papers. We conducted a qualitative study using focus group to explore the most important criteria for rating research papers that can be used to control the paper recommendation by enabling users to set the weight for each criterion. We investigated also the effect of using different rating criteria on the user interface design and how the user can control the weight of the criteria. We followed that by a quantitative study using a questionnaire to validate our findings from the focus group and to find if the chosen criteria are domain independent. Combining social network information with collaborative filtering recommendation algorithms has successfully reduced some of the drawbacks of collaborative filtering and increased the accuracy of recommendations. All existing recommendation approaches that combine social network information with collaborative filtering in this domain have used explicit social relations that are initiated by users (e.g. “friendship”, “following”). The results have shown that the recommendations produced using explicit social relations cannot compete with traditional collaborative filtering and suffer from the low user coverage. We argue that the available data in social bookmarking Web sites can be exploited to connect similar users using implicit social connections based on their bookmarking behavior. We explore the implicit social relations between users in social bookmarking Web sites (such as CiteULike and Mendeley), and propose three different implicit social networks to recommend relevant papers to users: readership, co-readership and tag-based implicit social networks. First, for each network, we tested the interest similarities of users who are connected using the proposed implicit social networks and compare them with the interest similarities using two explicit social networks: co-authorship and friendship. We found that the readership implicit social network connects users with more similarities than users who are connected using co-authorship and friendship explicit social networks. Then, we compare the recommendation using three different recommendation approaches and implicit social network alone with the recommendation using implicit and explicit social network. We found that fusing recommendation from implicit and explicit social networks can increase the prediction accuracy, and user coverage. The trade-off between the prediction accuracy and diversity was also studied with different social distances between users. The results showed that the diversity of the recommended list increases with the increase of social distance. To summarize, the main contributions of this dissertation to the area of research paper recommendation are two-fold. It is the first to explore the use of multi-criteria rating for research papers. Secondly, it proposes and evaluates a novel approach to improve collaborative filtering in both prediction accuracy (performance) and user coverage and diversity (nonperformance measures) in social bookmarking systems for sharing research papers, by defining and exploiting several implicit social networks from usage data that is widely available

Influential Factors In Consumer\u27s Adoption Of Innovative Products

This dissertation addresses the challenges involved with the process of diffusion of innovations in the contexts of innovative educational materials and technological innovations. Chapters 2 and 3 discuss building and using Online Brand Communities (OBCs) to disseminate innovative math educational materials. OBCs are known to be important platforms where consumers can communicate with the brand as well as other consumers. Through the effective use of these platforms, brands could accelerate the process of diffusion of their innovations. However, OBCs will not survive if consumers do not get engaged and participate in these communities. The purpose of this section of the dissertation is to investigate how customer engagement can be increased in social media based Online Brand Communities (OBCs) so that these communities could be effectively used as platforms for disseminating innovations. Different hypotheses are suggested based on the consumer engagement literature and well-known organizational and psychological theories. These hypotheses are then tested in different studies in order to better understand the drivers of customer engagement behavior. Since one of the important factors that can impact the success of OBCs is the size of the communities, chapter 3 discusses Referral Reward Programs (RRPs) as a means for growing the OBC size. In this chapter, different hypotheses are proposed based on well-known psychological theories. These hypotheses are then tested in 3 different research studies to understand the impact of different rewards on customers’ likelihood to participate in the referral programs. The next section of this dissertation which is presented in chapter 5 uses the context of technological innovations, particularly Augmented Reality Smart Glasses (ARSGs). The purpose of this chapter is to understand the factors that would impact consumer’s decision to adopt a particular type of ARSGs: Microsoft HoloLens. The results of the studies in this dissertation have important theoretical and managerial implications in the areas of customer engagement in OBCs, Word-of-Mouth marketing, and consumer’s adoption of innovations

Geoinformatics in Citizen Science

The book features contributions that report original research in the theoretical, technological, and social aspects of geoinformation methods, as applied to supporting citizen science. Specifically, the book focuses on the technological aspects of the field and their application toward the recruitment of volunteers and the collection, management, and analysis of geotagged information to support volunteer involvement in scientific projects. Internationally renowned research groups share research in three areas: First, the key methods of geoinformatics within citizen science initiatives to support scientists in discovering new knowledge in specific application domains or in performing relevant activities, such as reliable geodata filtering, management, analysis, synthesis, sharing, and visualization; second, the critical aspects of citizen science initiatives that call for emerging or novel approaches of geoinformatics to acquire and handle geoinformation; and third, novel geoinformatics research that could serve in support of citizen science

Dimensions of participation. Experiences, lessons and tips from agricultural research practitioners in Sub-Saharan Africa

This book is about participation in agricultural research. It documents the experience of practitioners in implementing agricultural research projects in which participation has been a central issue. This experience is documented through case studies, and through summaries of the authors' experience. Reference is made to other literature on aspects of participation, both specific and general. The case studies give first hand accounts of the challenges and successes involved in using participatory approaches in agricultural research projects undertaking technology development and adaptation. Written by practitioners, the case studies cover many practical aspects of design and implementation that are not covered in more academic and conceptual writing on this subject, or in general manuals on how to undertake participatory agricultural research. The existing books, manuals and guidelines adequately outline the key principles and approaches in participatory agricultural research (e.g. Okali et al., 1994; Van Veldhuizen et al., 1997; Sutherland, 1998). This book is different from most others on participatory agricultural research in the following respects: it organizes and compares case-study experiences within topical chapters, rather than having case studies written as separate chapters; it embraces a wider view of participation- in addition to interaction between farmers and researchers, this view includes participation both within project teams and between the project team and other stakeholders in the agricultural research process; it is not a training manual detailing what to do, when to do it and how; however, lessons and tips are provided for the topics covered; it is rooted in project experiences rather than in development discourse, and does not advocate a particular participatory research philosophy, or claim to break new ground in terms of participatory concepts and methods. The aim of the book is to stimulate learning, primarily by presenting examples of how a range of projects handled various components of the participatory research process. These examples are given within a broader discussion of the typical challenges and issues faced by projects and practitioners when using participatory approaches to develop and adapt agricultural technology. Drawing on the case studies and other experiences, some lessons, strategies and tips are outlined in relation to particular topics within participatory agricultural research

Eco-friendly Naturalistic Vehicular Sensing and Driving Behaviour Profiling

PhD ThesisInternet of Things (IoT) technologies are spurring of serious games that support training directly in the field. This PhD implements field user performance evaluators usable in reality-enhanced serious games (RESGs) for promoting fuel-efficient driving. This work proposes two modules – that have been implemented by processing information related to fuel-efficient driving – to be employed as real-time virtual sensors in RESGS. The first module estimates and assesses instantly fuel consumption, where I compared the performance of three configured machine learning algorithms, support vector regression, random forest and artificial neural networks. The experiments show that the algorithms have similar performance and random forest slightly outperforms the others. The second module provides instant recommendations using fuzzy logic when inefficient driving patterns are detected. For the game design, I resorted to the on-board diagnostics II standard interface to diagnostic circulating information on vehicular buses for a wide diffusion of a game, avoiding sticking to manufacturer proprietary solutions. The approach has been implemented and tested with data from the enviroCar server site. The data is not calibrated for a specific car model and is recorded in different driving environments, which made the work challenging and robust for real-world conditions. The proposed approach to virtual sensor design is general and thus applicable to various application domains other than fuel-efficient driving. An important word of caution concerns users’ privacy, as the modules rely on sensitive data, and provide information that by no means should be misused

Building Blocks for IoT Analytics Internet-of-Things Analytics

Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI)

Technology and Service Assessment Tools in Healthcare

The role of clinical engineers is rapidly changing and the economic constraints have pulled them towards new responsibilities to manage. Particularly, the assessment of health technologies has covered one of the most important areas among clinical engineers\u2019 duties. Different techniques and methodologies for technology assessment and improvement are available in the literature and they are currently in use within hospitals and healthcare facilities. However, scientific research and practical needs seem to be misaligned, causing misuse of scientific results due to the lack of tools easy-to-use from practical perspective. This thesis aims at integrating methodologies, even derived from different sectors, for providing standardized and versatile tools that overcome the current issues, providing healthcare facility with a path to follow for choosing the best methodology to be used in diverse situations. Different case studies are presented, in order to cover the wide range of possibilities within health technology assessment (HTA). Particularly, technology assessment was performed on medical devices using both Hospital-Based HTA for an existing technology and horizon scanning for designing an innovative solution. Then the assessment was extended to hospital services, with particular attention to clinical engineering services, using Multi-Criteria Decision Analysis. Process improvement methodologies were also considered and applied to sterilization service that was also studied and assessed integrating the classical HTA approach with Multi-Criteria Decision Analysis. These studies allowed to identify a path useful from practical perspective and based on scientific approach aimed at helping healthcare professionals and clinical engineers to choose the best methodology in accordance to specific constraints and needs of particular situations