Theory-Based Problem Formulation and Ideation in mHealth Analysis and Recommendations

This article reports on an investigation into how to improve problem formulation and ideation in Design Science Research (DSR) within the mHealth domain. A Systematic Literature Review of problem formulation in published mHealth DSR papers found that problem formulation is often only weakly performed, with shortcomings in stakeholder analysis, patient-centricity, clinical input, use of kernel theory, and problem analysis. The study proposes using Coloured Cognitive Mapping for DSR (CCM4DSR) as a tool to improve problem formulation in mHealth DSR. A case study using CCM4DSR found that using CCM4DSR provided a more comprehensive problem formulation and analysis, highlighting aspects that, until CCM4DSR was used, weren’t apparent to the research team and which served as a better basis for mHealth feature ideation.

Nonparametric inference of stochastic differential equations based on the relative entropy rate

The information detection of complex systems from data is currently undergoing a revolution,driven by the emergence of big data and machine learning methodology. Discovering governingequations and quantifying dynamical properties of complex systems are among central challenges. Inthis work, we devise a nonparametric approach to learn the relative entropy rate from observationsof stochastic differential equations with different drift functions. The estimator corresponding tothe relative entropy rate then is presented via the Gaussian process kernel theory. Meanwhile, thisapproach enables to extract the governing equations. We illustrate our approach in several examples.Numerical experiments show the proposed approach performs well for rational drift functions, notonly polynomial drift functions.

A Procustes's Procedure for Obtaining Divergences

Divergences have become a very useful tool for measuring similarity (or dissimilarity) between probability distributions. Depending on the field of application a more appropriate measure may be necessary. In this paper we introduce a family of divergences we call gamma-divergences. They are based on the convexity property of the functions that generate them. We demonstrate that these divergences verify all the usually required properties, and we extend them to weighted probability distribution. We investigate their properties in the context of kernel theory. Finally, we apply our findings to the analysis of simulated and real time series.

Finding Useful Solutions in Online Knowledge Communities: A Theory-Driven Design and Multilevel Analysis

In this study, we utilize a kernel theory of knowledge adoption model and propose a novel text analytic framework to classify the usefulness of solutions in online knowledge communities. The study combines multiple disciplines (behavioral, empirical, design science, and technical) to tackle an important and relevant business problem: how to effectively manage an online knowledge repository and identify useful solutions. Our framework can be implemented in online knowledge communities to improve users’ experience of searching for useful knowledge. The proposed framework has the potential to guide the development of customer-facing chatbots, which understand human-language questions and return helpful answers immediately.