Despite the increasing environmental awareness, sustainable products are not routinely adopted, revealing a persistent attitude–behavior gap. While this gap has been widely studied through psychological and behavioral approaches, less attention has been given to how product design attributes shape users’ perceptions and influence their interaction with products. Therefore, this article aims to explore the literature on the role of design attributes in this context, analyzing how product design attributes and sustainability cues affect user experience and product-related decisions. Based on a systematic literature review process, 52 articles were extracted from the SCOPUS database. These articles were classified and analyzed according to their main objectives. Based on the findings, attribute centrality (i.e., whether a product feature is perceived as central or peripheral to the product’s identity) and product category (utilitarian or hedonic) are relevant to shape environmental attitudes and behavior. Specifically, sustainability cues tend to be perceived more negatively in utilitarian products, where functionality is prioritized, and more positively in hedonic products, where they may enhance emotional value, especially when perceived as central attributes. Therefore, these characteristics must be strategically integrated in product design to reduce the perceived trade-off between sustainability and performance. The review conclusions highlight the need for further investigation to identify how specific product design attributes resulting from the application of ecodesign principles trigger sustainability perception.

This research examines occupants’ ability to detect lighting differences as a function of proximity to the illuminated area. By understanding how proximity influences light difference detection, energy-saving lighting design techniques can be developed that do not negatively impact the appearance of architectural interiors. The experiment examined vertical surface illumination, hypothesizing that vertical illuminance difference detection thresholds would increase with greater spatial separation from the observer, regardless of gaze conditions. Illuminance was selected as the accepted metric for assessing lighting, aligning with commonly used standards, such as the European Standard and the Australian/New Zealand Standard. Eighty participants viewed a 10.0 m × 2.4 m vertical wall with five sections using a five-alternative forced choice method to identify the dimmer section. Eight experimental conditions manipulated participant position and gaze, with each subject completing 10 trials for 20 lighting conditions. Participants’ ability to detect lighting differences was very poor for wall end portions, regardless of position or gaze. Results suggest vertical illuminance in temporarily unoccupied areas can be reduced by at least 10% without affecting perceived illumination quality. Greater reductions of 25% can be achieved in room corners. These findings provide a foundation for future research into illuminance optimization across all surfaces within architectural spaces.

Creative thinking is a crucial step in the design ideation process, where analogical reasoning plays a vital role in expanding the design concept space. The emergence of Generative AI has brought a significant revolution in co-creative systems, with a growing number of studies on Design-by-Analogy support tools. However, there is a lack of studies investigating the creative performance of Large Language Model (LLM)-generated analogical content and benchmarking of language models in creative tasks such as design ideation. Through this study, we aim to (i) investigate the effect of creativity heuristics by leveraging LLMs to generate analogical stimuli for novice designers in ideation tasks and (ii) evaluate and benchmark language models across analogical creative tasks. We developed a support tool based on the proposed conceptual framework and validated it by conducting controlled ideation experiments with 24 undergraduate design students. Groups assisted with the support tool generated higher-rated ideas, thus validating the proposed framework and the effectiveness of analogical reasoning for augmenting creative output with LLMs. Benchmarking of the models revealed significant differences in the creative performance of analogies across various language models, suggesting that future studies should focus on evaluating language models across creative, subjective tasks.

Design creativity is an inherently complex and recursive cognitive process involving nonlinear transitions between distinct cognitive states. This experimental neurocognitive study provides empirical support for theoretical nonlinear and recursive models of design creativity by examining neurocognitive processes across design creativity cognitive states, including idea generation (IDG), idea evolution (IDE), rating process (IDR), and rest mode (RST). EEG signals were recorded during loosely controlled design creativity tasks, and 13 well-established features were extracted from recurrence quantification analysis (RQA). A feature selection pipeline identified the most significant features for distinguishing between the cognitive states. Statistical analyses of the features provided deeper insights into brain dynamics and confirmed the significance of the selected features, supported by EEG topography maps. The findings revealed distinct and complex recursive dynamics across cognitive states, primarily involving the frontal, parietal and central regions, offering novel insights complementary to prior EEG studies. We also classified the cognitive states using the selected significant features through six classification models: k-Nearest Neighbor, Support Vector Machine, Naïve Bayes, Multi-Layer Perceptron, Linear Discriminant Analysis and Random Forest. To ensure robust evaluation, we applied three cross-validation strategies – hold-out, k-fold and one-subject-out – and combined the classifiers using majority voting fusion. Classification results (10-fold cross-validation) demonstrated high performance, with an average accuracy (96.23%), kappa (93.56%), recall (96.58%), precision (98.08%), F1-score (97.29%) and specificity (98.43%). The study provides findings that are consistent with theoretical expectations. Consistent with theoretical expectations, the findings deepen understanding of recursive and nonlinear neural dynamics in design creativity cognition and guide future research.

In recent decades, design creativity and design theory have made great progress in terms of understanding and supporting the logic of engineering design for breakthrough and disruptive innovation. Design for transition relies on these new methods, but it also requires the capacity to be creative to facilitate more effective preservation – whether in terms of natural resources, biodiversity, energy, ways of life or other factors. Design for transition calls for a type of engineering design that is not Schumpeterian, not a ‘creative destruction’, but rather a design that manages creative preservation, creativity for better preservation and preservation for improved creativity. In the first section, we clarify the notion of creative preservation for transition; in the second section, we show how creative preservation can be addressed by recent advances in design theory, namely, C-K/Topos. Finally, in the conclusion, we demonstrate the implications of C-K/Topos for the management of the unknowns of transitions and the underlying logic of creative preservation.

Designing complex products increasingly requires integrative methodologies that address the rising challenges of multi-disciplinary complexity and functional inter-dependencies. This article proposes a conceptual design framework that combines the abstractional design method (ADM) with a novel inter-coupling index (ICX) to model and manage inter-component dependencies within cyber-physical vehicle (CPV) systems. The ADM provides a unified object-based representation of system components through functional and attribute abstraction, facilitating shared understanding across disciplines. The ICX quantitatively captures the degree of inter-dependency among system elements, offering a new metric for evaluating design complexity. A case study of a CPV acceleration module demonstrates how indirect coupling and cascading failure risks can be identified and mitigated in the early design process. The methodology supports the decomposition and synthesis of design architectures while preserving functional intent and reducing system vulnerability. This research contributes a transferable and scalable approach to conceptual system design in multi-disciplinary domains.

The success of modern product design often relies on the thoughtful selection of next-generation technologies. However, common systems engineering methodologies tend to treat new technologies as risks to be minimized rather than as opportunities to enhance system capabilities. To bridge this gap, this study presents a new framework called PoLaRis for comprehensive technology infusion concepts assessment based on three parameters: Leap Potential, Learning and Risk. The introduction of Learning as a decision-making criterion complements Risk and Leap Potential, embedding an organizational learning perspective that values the knowledge gained through technology infusion. These three main parameters can be evaluated through expert feedback or a numerical approach. In the numerical approach, rooted in DSM analysis, Risk is quantified based on the maturity of the technology components and a system integration risk metric, while Learning is estimated from the structural complexity of the architectural changes. Leap Potential is quantified using the Technology Leap Potential (TLP) metric, which captures a technology’s contribution to product value from the user’s perspective and applies to both incremental and disruptive innovations. Two case studies were conducted to evaluate three smartwatch concepts featuring an AI power-saving chip and innovative stress detection methods. The first case study relied on 11 expert evaluations, while the second applied the numerical approach. The results showed alignment between expert and numerical assessments, indicating the internal consistency between the selected mathematical measures and expert opinions. Taken together, the Leap–Learning–Risk profiles visualize each option’s benefits and trade-offs, facilitating comparison and informed decision making.

The evaluation of idea sets for design solutions using Shah et al.’s criteria of quality, quantity, novelty and variety can help design teams understand the thoroughness of their ideation work and can help design researchers compare the performance of different ideation methods. However, existing methods for aggregating these metrics to obtain total set scores for quality, quantity, novelty and variety are problematic. The present paper proposes axioms for the desired behavior of aggregation functions for quality, quantity, variety and novelty, then defines functions that meet the axioms. These axioms are intended to ensure that scoring methods reflect best practices in ideation and appropriately reward preferred ideation behavior, such as promoting the contribution of all ideas. Further, this paper provides operational definitions for quality, novelty and quantity evaluations of ideas and draws from previous methods to provide expedient scoring methods of individual ideas. Evaluation mechanics are presented that allow repeatable evaluation of idea sets containing thousands of ideas. Software tools are provided to automatically calculate the aggregation functions for ideas evaluated according to the mechanics of this paper. Finally, a method for evaluating both the variety of complete sets of ideas and the contributions of individual ideas to the overall set variety is proposed. The evaluation of variety is sufficiently defined that it can be automatically evaluated for any genealogy tree of ideas. The operational definitions for evaluating quality, novelty and quantity are suitable for adoption in artificial intelligence tools to allow automated evaluation of idea sets for these quantities.