Effect of Values and Technology Use on Exercise: Implications for Personalized Behavior Change Interventions

Technology has recently been recruited in the war against the ongoing obesity crisis; however, the adoption of Health & Fitness applications for regular exercise is a struggle. In this study, we present a unique demographically representative dataset of 15k US residents that combines technology use logs with surveys on moral views, human values, and emotional contagion. Combining these data, we provide a holistic view of individuals to model their physical exercise behavior. First, we show which values determine the adoption of Health & Fitness mobile applications, finding that users who prioritize the value of purity and de-emphasize values of conformity, hedonism, and security are more likely to use such apps. Further, we achieve a weighted AUROC of .673 in predicting whether individual exercises, and we also show that the application usage data allows for substantially better classification performance (.608) compared to using basic demographics (.513) or internet browsing data (.546). We also find a strong link of exercise to respondent socioeconomic status, as well as the value of happiness. Using these insights, we propose actionable design guidelines for persuasive technologies targeting health behavior modification

The Role of the Mangement Sciences in Research on Personalization

We present a review of research studies that deal with personalization. We synthesize current knowledge about these areas, and identify issues that we envision will be of interest to researchers working in the management sciences. We take an interdisciplinary approach that spans the areas of economics, marketing, information technology, and operations. We present an overarching framework for personalization that allows us to identify key players in the personalization process, as well as, the key stages of personalization. The framework enables us to examine the strategic role of personalization in the interactions between a firm and other key players in the firm's value system. We review extant literature in the strategic behavior of firms, and discuss opportunities for analytical and empirical research in this regard. Next, we examine how a firm can learn a customer's preferences, which is one of the key components of the personalization process. We use a utility-based approach to formalize such preference functions, and to understand how these preference functions could be learnt based on a customer's interactions with a firm. We identify well-established techniques in management sciences that can be gainfully employed in future research on personalization.CRM, Persoanlization, Marketing, e-commerce,

Post-Click Conversion Rate Predictive Model on E-commerce Recommender System

This paper discusses about how different features influence customers’ decision on their online purchase after click behavior. The dataset is gathered from real-world traffic log of the recommender system in e-commerce. Logistic Regression and Extreme Gradient Boosting are used as main machine learning approaches for predictive analysis and modeling. In this study, features from users’ profile, shops’ profile and context are tested to see to what extent they may exert influence on customers’ purchase intention. Based on the experiment results, this paper also proposes some possible improvement for e-commerce platform in personalized recommendation in order to increase conversions and discusses about potential approaches to improve conversion rate prediction performance.Master of Science in Information Scienc

Portuguese online searching and buying behavior for personal lifestyle products and services

The Internet has revolutionized the way we communicate and relate with each other. E-commerce, has emerged as a naturally consequence of a new and exciting reality where everything can be accessed within the distance of a click. From products to services, or even ideas, all sorts of things can now be exchanged online. This online world, much different from the “real world” encompasses its specific characteristics, opportunities and challenges that represent an interesting, and at the same time complex, object of study. In the past few years, e-Commerce sales have been growing considerably, and Portugal has not distanced itself from this trend. However, despite the growing adoption of online commerce, very few studies have approached this subject taking into consideration the specificities of the Portuguese reality. Additionally, not only e-Commerce has its own determinants and defining elements, but also these elements change regarding the specific product category we are considering. Personal Lifestyle products, in this case, are directly associated with individual taste and preferences, in sum, with their lifestyle, and therefore represent a bigger challenge for online vendors, when satisfying its customer’s needs. This research consisted of an exploratory and quantitative study, combined with literature review on the topic online search and buying behavior, which allowed setting the ground for further statistical investigation.A Internet revolucionou a forma como os indivíduos comunicam e interagem. O e-Commerce surgiu como uma consequência natural desta nova e estimulante realidade onde tudo está à distância de um click. Desde produtos a serviços, passando por ideias, tudo pode ser trocado online. Este novo mundo online, bastante diferente do mundo real, possui as suas próprias características, oportunidades e desafios, que representam um objecto de estudo interessante e complexo. Nos últimos anos, as vendas online têm vindo a crescer consideravelmente, e Portugal tem acompanhado esta tendência. No entanto, apesar do crescimento significativo, a verdade é que muito poucos estudos têm abordado este tema, considerando as características específicas do país. A juntar aos elementos determinantes do e-Commerce, é necessário, ainda, considerar os elementos específicos da categoria de produtos considerada. Os produtos de estilo de vida pessoal, neste caso, estão diretamente associados ao gosto e preferências individuais, em suma, ao estilo de vida de cada individuo, representando um desafio acrescido para os vendedores online, que procuram satisfazer as necessidades de cada consumidor. Este estudo consiste num estudo exploratório e num estudo quantitativo, aliado à revisão de literatura e sobre o tópico que ajudou a preparar o caminho para a consequente análise estatística

The Impact of Social Presence and User Experience on Gender Sensitive E-Tail Websites

Internet has come afar, from connecting computers to connecting people. Since its early days, the use of Internet has evolved tremendously. People use the Internet today in a variety of different ways, including communicating with friends, family, co-workers and performing activities like paying bills and shopping. With the increase in electronic retailing (e-Tailing), attracting and retaining customers has become the most important part of running a successful business. However, the online shopping experience may be viewed as lacking human warmth and sociability as it is more impersonal, anonymous, automated and generally devoid of face-to face interactions. Thus, understanding how to create electronic loyalty (e-Loyalty) by retaining existing customers in online environments is a complex process. To maintain e-Loyalty, e-Tailing sites should provide customized user experience. Men and women have been known to have different perception of online shopping. Women tend to be less satisfied because of lack of human connection in online shopping environment. To date, how social presence (interpreting human warmth and human presence electronically) affects e-Loyalty and adoption of e-Tailing across genders has been relatively underexplored. A research on influence of gender towards social presence features in e-Tailing websites could contribute to our understanding of gender preferences in online environments, allowing researchers to predict and measure differences among user interfaces, and guide the design of customized interfaces customized for gender sensitive e-Tailing websites. In this research, we developed a research model based on extensive literature review. We developed a survey instrument to measure predictability of the model and used t-tests, principal component analysis and linear and multiple regression analysis to analyze and validate the model. We conducted an extensive survey of social presence and user experience design features, and synthesized survey response with the above methodologies using SPSS. The study revealed social presence and user experience factors that positively affect gender experience in development of e-Loyalty. Based on the analysis of survey responses, we conclude that gender plays an important role in determining the state of social presence and user experience for e-Tailing websites to create e-Loyalty among customers

The Mechanisms of Interpersonal Privacy in Social Networking Websites: A Study of Subconscious Processes, Social Network Analysis, and Fear of Social Exclusion

With increasing usage of Social networking sites like Facebook there is a need to study privacy. Previous research has placed more emphasis on outcome-oriented contexts, such as e-commerce sites. In process-oriented contexts, like Facebook, privacy has become a source of conflict for users. The majority of architectural privacy (e.g. privacy policies, website mechanisms) enables the relationship between a user and business, focusing on the institutional privacy concern and trust; however, architectural privacy mechanisms that enables relationships between and among users is lacking. This leaves users the responsibility to manage privacy for their interpersonal relationships. This research focuses on the following question: How does privacy influence the sharing of personal information in interpersonal relationships on Social networking sites? The management of the sharing of personal information is explained using the Need to Belong theory, psychological contract, and approach-avoidance motivation theory. Individuals\u27 desire to interact Socially and engage in relationships where respect for personal information is implied leads to overcoming concerns over privacy. Three essays address the question of interest. Essay 1 explains that this drive is motivated by a fear of Social exclusion from Social transactions and interpersonal relationships and does not rely on the institutional relationship between a user and the Social media website. Essay 2 uses a Social network analysis lens to describe how the multiplexity of relationships and Social influences (both of the network and the self) influence Social interaction and the sharing of personal information. Essay 3 focuses on explaining how individuals\u27 disposition toward subconscious processes of approach or avoidance motivation influence decisions to share and not share personal information. The implication of these studies is that privacy in a process-oriented context--like Facebook--involves different attitudes and beliefs centered on interpersonal relationships rather than institutional ones

A new technique for intelligent web personal recommendation

Personal recommendation systems nowadays are very important in web applications because of the available huge volume of information on the World Wide Web, and the necessity to save users’ time, and provide appropriate desired information, knowledge, items, etc. The most popular recommendation systems are collaborative filtering systems, which suffer from certain problems such as cold-start, privacy, user identification, and scalability. In this thesis, we suggest a new method to solve the cold start problem taking into consideration the privacy issue. The method is shown to perform very well in comparison with alternative methods, while having better properties regarding user privacy. The cold start problem covers the situation when recommendation systems have not sufficient information about a new user’s preferences (the user cold start problem), as well as the case of newly added items to the system (the item cold start problem), in which case the system will not be able to provide recommendations. Some systems use users’ demographical data as a basis for generating recommendations in such cases (e.g. the Triadic Aspect method), but this solves only the user cold start problem and enforces user’s privacy. Some systems use users’ ’stereotypes’ to generate recommendations, but stereotypes often do not reflect the actual preferences of individual users. While some other systems use user’s ’filterbots’ by injecting pseudo users or bots into the system and consider these as existing ones, but this leads to poor accuracy. We propose the active node method, that uses previous and recent users’ browsing targets and browsing patterns to infer preferences and generate recommendations (node recommendations, in which a single suggestion is given, and batch recommendations, in which a set of possible target nodes are shown to the user at once). We compare the active node method with three alternative methods (Triadic Aspect Method, Naïve Filterbots Method, and MediaScout Stereotype Method), and we used a dataset collected from online web news to generate recommendations based on our method and based on the three alternative methods. We calculated the levels of novelty, coverage, and precision in these experiments, and we found that our method achieves higher levels of novelty in batch recommendation while achieving higher levels of coverage and precision in node recommendations comparing to these alternative methods. Further, we develop a variant of the active node method that incorporates semantic structure elements. A further experimental evaluation with real data and users showed that semantic node recommendation with the active node method achieved higher levels of novelty than nonsemantic node recommendation, and semantic-batch recommendation achieved higher levels of coverage and precision than non-semantic batch recommendation