This study explores a resource-based perspective on subjective wellbeing in educational contexts by introducing the concept of happiness capital. Building on the educational and learning capital approach, we adapted existing measures to assess how personal and environmental resources contribute to students’ wellbeing. In a sample of 1,528 Egyptian secondary school students, perceived happiness capital significantly predicted life satisfaction, positive and negative emotions across all age groups. These findings suggest that the same resource structures supporting learning can foster emotional flourishing. We briefly discuss implications for resource-based models of wellbeing in education and propose directions for future research.

 

• Vor dem Hintergrund sinkender Studierendenzahlen und des Fachkräftemangels in den technischen Berufen in Deutschland, rekrutieren die Hochschulen in Bayern zunehmend Studierende aus dem Ausland. Dabei stoßen die angebotenen internationalen Studiengänge besonders bei Studierenden aus Indien auf großes Interesse. Andersartige Kompetenzprofile aus dem außereuropäischen Bachelorstudium in Kombination mit sehr hohen Erwartungen der Studierenden an ein Masterstudium in Deutschland führen zu enormen Herausforderungen bei allen Beteiligten. Förderlich für den erfolgreichen Studienverlauf ist es, Maßnahmen zur Gruppendurchmischung zwischen ausländischen und deutschen Studierenden wie internationale Postersessions und gemeinsame Studienangebote zu implementieren. Darüber hinaus ist es erforderlich, Regeln der guten wissenschaftlichen Praxis klar zu kommunizieren und deren Befolgung einzufordern. Nicht zuletzt ist die Stärkung der Sprachkompetenz ein Schlüssel für ein erfolgreiches Studium.

Sport-tracking applications can enhance physical activity but may also induce stress through social comparison. This study examines how social comparison features influence user experience (UX), focusing on motivation, stress, and mental well-being. A survey of 101 German sport-tracking app users assessed intrinsic and extrinsic motivation, perceived stress, self-efficacy, enjoyment, social comparison orientation, and UX. Results reveal that higher social comparison consistently lowers intrinsic motivation, raises extrinsic motivation, and increases stress levels, while positive UX boosts intrinsic motivation and enjoyment and indirectly buffers stress. Notably, app designs prioritizing mastery and personal progress over competitive rankings foster autonomy, competence, and relatedness, supporting mental health and long-term adherence. These findings highlight the ambivalent effects of social features and underscore the need for sport-tracking apps to minimize harmful comparisons and promote self-determined motivation through supportive, user-friendly features. Practical implications include designing for sustainable engagement and well-being rather than exclusive competition.

Emotion AI is transforming how emotional dynamics are integrated into consumer research and product design. By detecting and interpreting emotional cues from facial expressions, voice, and behaviour, it enables emotionally intelligent systems that move beyond conventional user interaction models. This chapter examines Emotion AI as both a methodological tool and a design principle, offering deeper insights into user sentiment and supporting the creation of hedonic experiences – what we term digital empathy. Grounded in established emotion theories and recent technological advances, the chapter explores cross-sector applications and reflects on the ethical implications surrounding the use of emotional data.

Background

The Freiburg Mindfulness Inventory (FMI) in its short form is one of the most frequently used instruments in research to measure self-attributed mindfulness levels. Despite the widespread use, there is no calibration sample available until now. Thus, we decided to calibrate it in a representative German sample as norm values are of relevance for clinicians and researchers alike. In addition, we tried to replicate the instruments’ psychometric properties.

Methods

A sample of 1,012 respondents was recruited from a commercial online panel, approximating a representative sample of the German population. Participants filled in the FMI-14 (short from). We performed psychometric analyses and calculated a Confirmatory Factor Analysis (CFA). We also computed a measurement invariance analysis and a LASSO regression to identify population variables that predict FMI scores. To gauge external validity of the instrument, we also presented the FMI together with four items of the Patient Health Questionnaire (PHQ-4).

Results

We obtained the best psychometric properties with a revised version of the FMI-13R, where the only negatively coded item was removed due to lack of fit. McDonald’s Omega was found to be 0.88 and the mean item-scale intercorrelation was 0.36. The construct is unidimensional, with two highly correlated subconstructs, Presence and Acceptance (r = 0.93), which can be separated for conceptual reasons if desired. The CFA supported these models. Age and gender stratified norm scores were calculated and reported. Regression analyses and partial correlation analyses showed negative correlation of FMI-13R scores with measures of anxiety and depression, as expected, and thereby support our previous findings that the instrument is valid and that the Acceptance component of mindfulness is the one that is associated with positive effects on health-related parameters.

Conclusion

The FMI-13R is a psychometrically sound and valid instrument for the assessment of mindfulness. Norm scores can now be used by clinicians and researchers to classify individuals or samples into a range of values compared to the German population.

In additive manufacturing, the optimisation of process parameters to simultaneously enhance both mechanical and geometrical properties remains a significant challenge. This study presents a comparative analysis between two distinct approaches to multi-response optimisation: a hybrid method combining Artificial Neural Networks with Pattern Search Algorithm, and the traditional Response Surface Methodology. Both methods were applied to optimise the process parameters of Fused Filament Fabrication using Polylactic Acid. The hybrid ANN + PSA model was used to predict process outputs and optimise printing parameters, while RSM employed a statistical approach to identify optimal parameter combinations through designed experiments. Results show that ANN + PSA achieved better outcomes with a maximum tensile strength of 61.88 MPa and dimensional deviations within 3%, compared to RSM's tensile strength of 53.05 MPa and deviations up to 4%. The ANN + PSA model demonstrated higher predictive accuracy with an R² of 94.34% during training and 91.34% during evaluation, versus RSM’s R² of 90.5% for tensile strength prediction. Additionally, ANN + PSA consistently required fewer experimental trials due to its integration with the Pattern Search Algorithm, improving computational efficiency. The findings demonstrate that ANN + PSA is computationally efficient for scenarios with fewer experimental trials, while RSM offers a more detailed understanding of interaction effects. The comparative insights from this study contribute to enhanced multi-response optimisation strategies in additive manufacturing.

·       Hyperpersonalisierung und Extended Reality (XR) verändern die Customer Journey

·       KI-gestützte Personalisierung gestaltet Markenkommunikation und Kundenerlebnisse emotionaler und interaktiver

·       Extended Reality (XR) eröffnen neue Dimensionen immersiver Markenerlebnisse

·       Immersive Consumer Experience bindet Konsumenten stärker an Marken und erleichtern die Kaufentscheidung