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JAQM Volume 20, Issue 1-4 - December 30, 2025
Contents
Innovation in Design Thinking from a Romanian Entrepreneural perspective
Alina MATEI
This study explored the underlying mechanisms of empathy and motivation in innovation in design thinking from an entrepreneurial perspective. We built an explanatory model to examine the effect of empathy on motivation and innovation and the mediating effects of four aspects of motivation. In the study, 247 entrepreneurs participated in research centered on design thinking activities that lasted from November 2024 to September 2025, and then completed a questionnaire measuring their perceptions of empathy, motivation, and innovation at the end of the design activities. PLS SEM was used to analyze the collected data. The results showed that empathy positively predicted innovation. In addition, empathy had a significant impact on innovation through the effect of intrinsic motivation, but not through that of extrinsic motivation. Among the four aspects of motivation, attention, pertinence, and trust each strengthened the association between empathy and innovation. However, fulfillment had a negative effect on innovation and a non-significant mediating effect. These findings increase our understanding of the internal mechanisms of design thinking and innovation. Some practical implications of empathy and motivation in innovation are also discussed.
Metaheuristics for renewable energy residential communities detection.
Giacomo DI TOLLO,
Graziella PACELLI,
Oliver TOTH,
Salvatore VERGINE
Metaheuristics are high-level heuristic strategies that guide the behaviour of subordinated heuristics to solve optimization problems. This paper proposes a population-based metaheuristic approach for the detection of cost-efficient renewable-based residential energy communities. The problem is formulated as an optimization model minimizing total operational costs through coor-dinated photovoltaic generation and energy storage systems. Particle Swarm Optimization (PSO) is employed to efficiently explore the large combinatorial space of candidate communities. Results on a residential case study show that PSO achieves near-optimal solutions comparable to Monte Carlo-based benchmarks, while reducing computational time.