Incorporating the ‘Theory of Planned Behavior’ into personalized healthcare behavior change research: a call to action

The ‘Theory of Planned Behavior’ (TPB) has been tested and validated in the scientific literature across multiple disciplines and is arguably the most widely accepted theory among behavior change academics. Despite this widespread acceptability, the TPB has yet to be incorporated into personalized healthcare behavior change research. Several prominent personalized healthcare researchers suggest that personalizing healthcare recommendations have a positive impact on changes in lifestyle habits. However, research in this area has demonstrated conflicting findings. We provide a scientific and theoretical basis to support a proposed expansion of the TPB to include personalization, and call to action-personalized healthcare behavior change researchers to test this expansion. Specific recommendations for study design are included

Empirical Research on the Impact of Personalized Recommendation Diversity

Personalized recommendation has important implications in raising online shopping efficiency and increasing product sales. There has been wide interest in finding ways to provide more efficient personalized recommendations. Most existing studies focus on how to improve the accuracy of the recommendation algorithms, or are more concerned on ways to increase consumer satisfaction. Unlike these studies, our study focuses on the process of decision-making, using long tail theory as a basis, to reveal the mechanisms involved in consumers’ adoption of recommendations. This paper analyzes the effect of personalized recommendations from two angles: product sales and ratings, and tries to point out differences in consumer preferences between mainstream products and niche products, high rating products and low rating products, search products and experience products. The study verifies that consumers demand diversity in the recommended content, and also provides suggestions on how to better plan and operate a personalized recommendation system

Information Entropy Theory Based Recognition of the Validity of Contextual Information of Restaurants: An Empirical Study

Contextual information plays a key role in personalized recommendations. However, not all contextual information plays a positive role in personalized recommendations. Therefore, it is critical to identify the effective contextual information to realize personalized recommendations. This study aims to develop a set of feasible context importance calculation methods that can identify effective contextual information in different application scenarios. The information entropy of each contextual dimension is calculated, and the validity of the context compared according to the magnitude of its entropy is determined based on the informational entropy theory. Subsequently, this approach is applied to hotel and catering service data to determine the valid context in the dining domain. The experimental results indicate that location, work-rest condition, weather, mood and companionship considerably influence consumers’ behaviour and decisions in a catering environment, and the user preference in such contexts should be carefully considered

Have You Thought About This? Trust and Responsibility in News Personalization

Personalization is increasingly becoming a familiar concept, regardless of industry. This thesis explores how personalized news platforms can be designed, while fulfilling the news media’s responsibility to society and maintaining the users’ trust. The study is based on the historical development of the news media, and how it has been affected by technology. Further, it is based on research on personalization, its benefits and risks, and users' attitude towards personalization in the news media. To understand users’ behavior and needs, a combination of quantitative and qualitative methods like questionnaires and interviews have been used. Based on these insights, three prototypes of a news platform using different personalization tactics were developed. The prototypes were evaluated through user tests with nine participants in total. The third prototype was the final product and was based on the insights and testing of the other prototypes. Inspired by the work of developing the prototypes, along with heuristic evaluations of the three prototypes and basic theory on personalization and the news media, this thesis presents five recommendations which can be used when designing personalized news platforms. The results from the research indicates that users are positive to personalized news platforms, but for the experience to be optimal, there are some recommendations that will be worth following.Masteroppgave i medie- og interaksjonsdesignMIX350MASV-MI

Making 'The Daily Me': Technology, economics and habit in the mainstream assimilation of personalized news

The mechanisms of personalization deployed by news websites are resulting in an increasing number of editorial decisions being taken by computer algorithms — many of which are under the control of external companies — and by end users. Despite its prevalence, personalization has yet to be addressed fully by the journalism studies literature. This study defines personalization as a distinct form of interactivity and classifies its explicit and implicit forms. Using this taxonomy, it surveys the use of personalization at 11 national news websites in the UK and USA. Research interviews bring a qualitative dimension to the analysis, acknowledging the influence that institutional contexts and journalists’ attitudes have on the adoption of technology. The study shows how: personalization informs debates on news consumption, content diversity, and the economic context for journalism; and how it challenges the continuing relevance of established theories of journalistic gate-keeping

Toward an mHealth Intervention for Smoking Cessation

The prevalence of tobacco dependence in the United States (US) remains alarming. Invariably, smoke-related health problems are the leading preventable causes of death in the US. Research has shown that a culturally tailored cessation counseling program can help reduce smoking and other tobacco usage. In this paper, we present a mobile health (mHealth) solution that leverages the Short Message Service (SMS) or text messaging feature of mobile devices to motivate behavior change among tobacco users. Our approach implements the Theory of Planned Behavior (TPB) and a phase-based framework. We make contributions to improving previous mHealth intervention approaches by delivering personalized and evidence-based motivational SMS messages to participants. Our proposed solution implements machine learning algorithms that take the participant\u27s demographic profile and previous smoking behavior into account. We discuss our preliminary evaluation of the system against a couple of pseudo-scenarios and our observation of the system\u27s performance