The Intersection of Persuasive System Design and Personalization in Mobile Health: Statistical Evaluation

Background: Persuasive technology is an umbrella term that encompasses software (eg, mobile apps) or hardware (eg, smartwatches) designed to influence users to perform preferable behavior once or on a long-term basis. Considering the ubiquitous nature of mobile devices across all socioeconomic groups, user behavior modification thrives under the personalized care that persuasive technology can offer. However, there is no guidance for developing personalized persuasive technologies based on the psychological characteristics of users. Objective: This study examined the role that psychological characteristics play in interpreted mobile health (mHealth) screen perceived persuasiveness. In addition, this study aims to explore how users’ psychological characteristics drive the perceived persuasiveness of digital health technologies in an effort to assist developers and researchers of digital health technologies by creating more engaging solutions. Methods: An experiment was designed to evaluate how psychological characteristics (self-efficacy, health consciousness, health motivation, and the Big Five personality traits) affect the perceived persuasiveness of digital health technologies, using the persuasive system design framework. Participants (n=262) were recruited by Qualtrics International, Inc, using the web-based survey system of the XM Research Service. This experiment involved a survey-based design with a series of 25 mHealth app screens that featured the use of persuasive principles, with a focus on physical activity. Exploratory factor analysis and linear regression were used to evaluate the multifaceted needs of digital health users based on their psychological characteristics. Results: The results imply that an individual user’s psychological characteristics (self-efficacy, health consciousness, health motivation, and extraversion) affect interpreted mHealth screen perceived persuasiveness, and combinations of persuasive principles and psychological characteristics lead to greater perceived persuasiveness. The F test (ie, ANOVA) for model 1 was significant (F9,6540=191.806; PR2 of 0.208, indicating that the demographic variables explained 20.8% of the variance in perceived persuasiveness. Gender was a significant predictor, with women having higher perceived persuasiveness (P=.008) relative to men. Age was a significant predictor of perceived persuasiveness with individuals aged 40 to 59 years (PPF13,6536=341.035; PR2 of 0.403, indicating that the demographic variables self-efficacy, health consciousness, health motivation, and extraversion together explained 40.3% of the variance in perceived persuasiveness. Conclusions: This study evaluates the role that psychological characteristics play in interpreted mHealth screen perceived persuasiveness. Findings indicate that self-efficacy, health consciousness, health motivation, extraversion, gender, age, and education significantly influence the perceived persuasiveness of digital health technologies. Moreover, this study showed that varying combinations of psychological characteristics and demographic variables affected the perceived persuasiveness of the primary persuasive technology category

A Privacy Impact Assessment Method for Organizations Implementing IoT for Occupational Health and Safety

Internet of Things (IoT) technologies are increasingly being integrated into occupational health and safety (OHS) practices; however, their adoption raises significant privacy concerns. The General Data Protection Regulation (GDPR) has established the requirement for organizations to conduct Privacy Impact Assessments (PIAs) prior to processing personal data, emphasizing the need for privacy safeguards in the workplace. Despite this, the GDPR provisions related to the IoT, particularly in the area of OHS, lack clarity and specificity. This research aims to bridge this gap by proposing a tailored method for conducting PIAs in the OHS context, with a particular focus on addressing the how to aspect of the assessment process. The proposed method integrates insights from domain experts, relevant literature sources, and GDPR regulations, ultimately leading to the development of an online PIA tool

Human Computer Interaction and Emerging Technologies

The INTERACT Conferences are an important platform for researchers and practitioners in the field of human-computer interaction (HCI) to showcase their work. They are organised biennially by the International Federation for Information Processing (IFIP) Technical Committee on Human–Computer Interaction (IFIP TC13), an international committee of 30 member national societies and nine Working Groups. INTERACT is truly international in its spirit and has attracted researchers from several countries and cultures. With an emphasis on inclusiveness, it works to lower the barriers that prevent people in developing countries from participating in conferences. As a multidisciplinary field, HCI requires interaction and discussion among diverse people with different interests and backgrounds. The 17th IFIP TC13 International Conference on Human-Computer Interaction (INTERACT 2019) took place during 2-6 September 2019 in Paphos, Cyprus. The conference was held at the Coral Beach Hotel Resort, and was co-sponsored by the Cyprus University of Technology and Tallinn University, in cooperation with ACM and ACM SIGCHI. This volume contains the Adjunct Proceedings to the 17th INTERACT Conference, comprising a series of selected papers from workshops, the Student Design Consortium and the Doctoral Consortium. The volume follows the INTERACT conference tradition of submitting adjunct papers after the main publication deadline, to be published by a University Press with a connection to the conference itself. In this case, both the Adjunct Proceedings Chair of the conference, Dr Usashi Chatterjee, and the lead Editor of this volume, Dr Fernando Loizides, work at Cardiff University which is the home of Cardiff University Press

A User-Based Look at Visualization Tools for Environmental Data and Suggestions for Improvement - An Inventory among City Planners in Gothenburg

With a growing interest in environmental data and the need to consider various environmental factors earlier in the planning processes, it becomes more important to disseminate this type of information to different target groups in a comprehensible way. To support easier decision making, many cities and municipalities are increasingly using digital city models where it is possible to integrate different types of information based on simulation and visualization of future scenarios. Such tools have high potential, but the visual representation of data still needs to be developed. In this paper, we investigate how professionals within urban planning currently use visualization to communicate environmental data, and what their needs are regarding tools and visual representation. We discuss challenges for representing environmental data in urban development processes, with the aim of contributing to a better understanding of these issues. We base our investigation on a literature study, an inventorying survey and a focus group discussion with professionals within urban planning. This study provides an end-user perspective among urban planners and valuable insights on tool usage and visualization. Results show that applications used for environmental visualization still can be improved regarding, e.g., user friendliness and information handling, which may increase their efficiency

The Multi Business Model Innovation Approach

It is argued in most academic literature that Business Model (BM) is a general model for how any business runs or should be run, it is the "blueprint of the business". Conversely we argue that no business has just one BM, one model on which it runs all its business or intends to run its business. In other words the BM can be used for "as-is" and the "to-be" businesses. However our research, in contrast to the other BM frameworks, indicates that businesses have more BMs - both "as-is" and "to-be" BMs - the multi business model approach. This was already theoretically indicated by Markides and Charitou in 2004, and again in the Casadesus-Masanell and Ricart model of 2010, but sadly no one in the BM community has followed up on this since then. It could have made a breakthrough in our understanding of BMs, Business Model Innovation (BMI) and Strategic BMI. The Multi Business Model Innovation Approach addresses the concerns in the BM community and in BMI practice to just focus on the ideation and conceptualization of BMs. "BM canvassing", innovating BM building blocks or BM dimensions when carrying out BMI, so-called "blind business model innovation", is not sufficient to run and understand a business today. BMs and BMI must address all the different levels in a business. All BMs are objects to BMI and should be used to maximize the performance and sustainability of the business. The core business and all levels BMs, such as BM dimension components, BM dimensions, BM portfolio, and Business Model Ecosystem (BMES), should all be considered for BMI. The book addresses and documents a gap in BM research and the BM community - but also proposes a generic definition and language of a BM and BMI layers. The significance and importance of this work is related to significant and unexplored possibilities that BMI offers today, and can offer tomorrow. When we thoroughly understand all levels, dimensions and components of the business and its business models, and we are able to communicate, work and innovate with business models at all levels together, then a next step in BM and BMI research and practice can be taken. It is proposed that any BMs are related to seven dimensions- value proposition, user and/or customer, value chain functions (internal), competence, network, relations and value formulae. It is further proposed that seven different levels of a BMI from the most detailed level - the BM dimension component - to the BM dimension, BM, BM portfolio, business, and the vertical and horizontal business model ecosystem layer - and these can be objects to BMI. Conceptually, the Business Model Cube was formed using the seven dimensions which could be used both in a 2D and a 3D version