This study furthers game-based learning for circular business model innovation (CBMI), the complex, dynamic process of designing business models according to the circular economy principles. The study explores how game-play in an educational setting affects learning progress on the level of business model elements and from the perspective of six learning categories. We experimented with two student groups using our game education package Re-Organise. All students first studied a reader and a game role description and then filled out a circular business model canvas and a learning reflection. The first group, i.e., the game group, updated the canvas and the reflection in an interactive tutorial after gameplay. The control group submitted their updated canvas and reflection directly after the interactive tutorial without playing the game. The results were analyzed using text-mining and qualitative methods such as word co-occurrence and sentiment polarity. The game group created richer business models (using more waste processing technologies) and reflections with stronger sentiments toward the learning experience. Our detailed study results (i.e., per business model element and learning category) enhance understanding of game-based learning for circular business model innovation while providing directions for improving serious games and accompanying educational packages.
MULTIFILE
By way of a case study on the regulatory role of owners and managers of brothels and rented rooms for prostitution, this study focuses on the strategies deployed by a municipality to govern these intermediaries. The analysis is based on a typology of responsibilization distinguishing between who the responsible should govern (themselves or others) and forms of power (repressive or facilitative). The regulator concomitantly renders these entrepreneurs responsible for their own possible criminal conduct (self-governing) and empowers them to keep out traffickers and pimps and to control sex-workers (others-governing). Moreover, the municipality applies both repressive and facilitative power. Although the responsibilization strategy succeeds in having entrepreneurs govern themselves, it also unintentionally undermines sex-workers’ independence and favors the largest entrepreneurs. Our study enriches the R(egulator)I(ntermediary)T(arget) model by showing how varied and contentious the interactions between regulators and involuntary intermediaries are and by demonstrating the power game that the responsibilization strategy entails
Anxiety among pregnant women can significantly impact their overall well-being. However, the development of data-driven HCI interventions for this demographic is often hindered by data scarcity and collection challenges. In this study, we leverage the Empatica E4 wristband to gather physiological data from pregnant women in both resting and relaxed states. Additionally, we collect subjective reports on their anxiety levels. We integrate features from signals including Blood Volume Pulse (BVP), Skin Temperature (SKT), and Inter-Beat Interval (IBI). Employing a Support Vector Machine (SVM) algorithm, we construct a model capable of evaluating anxiety levels in pregnant women. Our model attains an emotion recognition accuracy of 69.3%, marking achievements in HCI technology tailored for this specific user group. Furthermore, we introduce conceptual ideas for biofeedback on maternal emotions and its interactive mechanism, shedding light on improved monitoring and timely intervention strategies to enhance the emotional health of pregnant women.
