The evolving landscape of engineering is shaped by trends such as digitalization, sustainability, and globalization. While these trends impact product development, their direct effects on engineers remain underexplored. This study investigates how current trends shape engineering work environments and identifies key factors that enable engineers to thrive. Using a mixed-method approach, we conducted qualitative interviews and a quantitative survey with 122 engineers across industries. Our findings reveal that trends influence collaboration, autonomy, stakeholder involvement, and digital tool integration. The results emphasize the need for human-centered approaches, such as New Work, to balance flexibility and structure. The insights contribute to designing adaptive engineering environments that foster resilience, well-being, and innovation.

This study investigates the relationship between MBTI personality type diversity and team performance in the first-year “Introduction to Design” course at SUTD. Analysis reveals a statistically significant yet weak correlation between greater MBTI diversity and higher final project grades. Additionally, teams with more introverted (I) and intuitive (N) members tended to perform better, consistent with research linking introverts to deep reflection and structured decision-making, and intuitives to creative problem-solving and future-oriented thinking. Result also shows that teams with INTJ and ISTP members performed better, while those with ISFP members showed lower performance. While these findings suggest personality composition influences team performances, measured in terms of grades, further research is needed to establish causation and underlying mechanisms.

Engineering design is inherently a collaborative process that requires active engagement and effective communication. Project-based Learning (PBL) is increasingly recognized for fostering these essential skills. However, instructors face challenges in objectively monitoring interactions and providing process-oriented feedback, particularly in large-scale settings where free-riders and disengaged participants affect team dynamics. This study introduces a generative AI approach to deliver real-time, scalable, and empathetic feedback that enhances team collaboration. Findings highlight the potential of AI-driven systems to improve student engagement and learning outcomes, though limitations remain in providing context-specific advice. A secure framework for AI integration in collaborative learning environments is also proposed.

We explore the role of aesthetic interaction in controlling music playback control and its influence on user experience. Three music playback control designs of different aesthetic interactions were developed and prototyped. An experiment was conducted to measure how their experiences varied regarding aesthetic interaction. Participant responses were then gathered through PrEmo that measured the influence on emotions and user experience, Results indicate how each aesthetic interaction evoked particular emotions and experiences. The aesthetic interaction of music playback control was shown to influence the participants’ appraisal of their music-listening experiences significantly. Findings contribute to a better understanding of how aesthetic interaction in the music listening experience implicates the user’s affective response.

This paper provides a design solution to the existing problem of using eye trackers for large screens. Traditional eye trackers are limited to commercial and smaller-sized screens. However, as larger screens become increasingly popular and essential for various tasks, their impact needs further investigation in user performance and behavioral studies. This work introduces a design approach for adjustable guide rail system to make moving an eye tracker along with the user's head position possible. The testing results showcase robust, accurate and functions under varying real-world conditions, making it ideal for Human-Computer Interaction and User Experience Research. The Guide Rail design employed by this system is easy to manufacture and incorporates 3D printed parts making it easily reproducible and open for customization.

Food production systems are shaped by external factors, such as social events and economic shifts, which influence and are influenced by labour dynamics—e.g., workforce availability—and human factors—e.g., worker skills. Using a systems approach, this paper explores how labour shortages impacting worker teams—such as in terms of mixture of availability, skills, and human behaviours—affect production and quality. UK apple harvesting is chosen as a case study due to its reliance on skilled seasonal migrant workers. Findings highlight the need for strategies such as upskilling local workers, enhancing training programmes, and adopting new technologies to mitigate labour shortages and enable high-performance collaborative worker groups.

The rise of mindfulness apps has integrated these tools into daily life, but concerns arise about preserving traditional practices and the ethical use of manipulative dark patterns that undermine user autonomy. This study examines the impact of dark patterns on user perceptions, engagement, and trust in mindfulness apps using expert reviews, surveys, journaling, and interviews. Three apps—Calm, Headspace, and Insight Timer—were analyzed for dark patterns, with participants documenting their experiences and perceptions. The findings underscore the need for ethical design practices to enhance trust and informed decision-making while highlighting the influence of dark patterns on user behavior and experience.

Digital products and applications are rapidly evolving, offering immense potential to drive social change and encourage sustainable behaviors. This raises a critical question: how can we effectively design these products to support and inspire sustainable practices? This paper presents a literature review of design for sustainable behavior (DfSB) strategies across various digital and physical product-service systems in engineering design and human-computer interaction. The review examines DfSB intervention trends over the last decade, highlighting the increasing diversity of technological interventions, and categorizes the design methods employed in these technologies. These categories identify opportunities where future DfSB interventions can be applied and illustrate how the unique affordances of digital vs physical technologies can be effectively used to support sustainable practices.

The increasing number of accidents involving electric vehicles (EVs) and pedestrians underscores the need of enhancing pedestrian safety. Autonomous vehicles (AVs), which have been introduced to mitigate traffic injuries caused by human error, still miss pedestrian trust due to the absence of a human driver. To improve pedestrian perceptions, EVs and AVs must integrate communication interfaces. This study administers two questionnaires to assess pedestrians’ emotional responses when crossing in front of EVs and AVs, and their preferences of modes of interaction. Vehicles’ ability to communicate their intentions through visual signals results crucial for pedestrians. Finally, findings regarding signals effectiveness when interacting with EVs and AVs allow for guidelines to emerge for the design of EAVs interfaces, offering valuable insights for the development of such vehicles.

This study examines how second-hand clothing (SHC) store design influences consumer perceptions and purchase behaviour. Based on findings from two studies (study 1: survey, n=268; study 2: experimental, n=90), design strategies were developed informed by Norman’s Emotional Design model. Results show that fresh and floral scents enhance hygiene perception and purchase intention, while attractive visual presentation improves purchase intention, willingness to pay, and quality and hygiene perceptions. While visceral design addresses some consumer concerns, strategies at all three levels are needed to fully improve the SHC shopping experience as they address consumer concerns, build trust, and encourage sustainable purchasing behaviour.

Design activities rely on external representations to offload cognitive effort and communicate ideas. These representations, ranging from sketches to virtual reality (VR), influence cognitive processes and perceptual outcomes. This study investigates the impact of different media representations on brain activity by comparing neural responses to design representations in VR and desktop monitor conditions. Utilizing brain network analyses derived from EEG signals in alpha, beta, gamma, and theta bands, results demonstrate that VR elicits greater cognitive integration and sensory engagement. These patterns suggest that VR facilitates holistic evaluations, while desktop representations support precision-focused tasks. These findings provide actionable guidance for optimizing design media selection based on cognitive objectives and contribute to the emerging design neurocognition field.

To support the transition towards a circular economy in hospitals, this qualitative study aimed at understanding how the adoption of reusable surgical gown can be facilitated. It investigates design features that enhance usability and promote sustained (re)use. A wearing test identified difficulties in wearing reusable gowns. Data collection included observations of 34 surgeries and a survey completed by 73 respondents. Thematic analysis revealed opportunities to improve usability, such as optimising packaging to speed up donning, a wider neck opening to reduce discomfort, and incorporating ‘tearable’ closures to simplify doffing. Innovation strategies relevant to the users involve thermal regulation, monitoring gown performance, and including reusable gowns in custom procedure tray packaging. These findings are discussed in relation to design adjustments and value-chain partners.

CAD tasks require engineering designers to manage cognitive, perceptual, and motor demands while solving complex design problems. Understanding the relationship between workload (WL) and CAD performance is essential for improving design outcomes and processes. However, this relationship, particularly under varying task complexities, remains insufficiently explored. This study investigates WL-performance relationships in two CAD modelling tasks of differing complexity. WL was measured with NASA TLX, including its individual components. CAD performance was evaluated and described through outcomes and processes using multiple metrics. The results revealed significant monotonic relationships between WL and performance, with stronger correlations in the high-complexity task.

Organizational capability is key to achieving strategic goals and adaptability. This study applies the TASKS framework to evaluate taskload, affect, skills, knowledge, and stress using a questionnaire developed through the Environment-Based Design (EBD) methodology. A structured perception-centered evaluation was conducted to assess employees’ perceptions of organizational alignment, with middle managers’ responses serving as a reference. Findings emphasize the need for better communication, leadership engagement, and goal clarity to enhance transformation readiness. The TASKS framework’s perception-centered evaluation assesses organizational capability and identifies role-based misalignments. Future research will expand the framework’s application to validate its effectiveness and refine strategies for enhancing organizational capability.

Assumption-making is a critical cognitive process in design, where incomplete information is ever-present. Understanding how assumptions are formed, maintained, and adapted can offer key insights into decision-making. While theoretical explorations of assumptions exist, empirical research remains limited. This pilot study investigates how varying temporal constraints influence assumption-making while solving ill-structured problems. The challenge lies in isolating the temporal and cognitive factors at play. The early insights reveal that task ambiguity, contextual framing, and time constraints play significant roles in shaping responses, highlighting the dual nature of assumption-making as both adaptable and resistant to change. The insights highlight the importance of strategic task design that balances ambiguity and structure to deepen our understanding of assumption-making.

Design decision-making under competition is a critical challenge in real-world engineering design. These challenges are compounded by bounded rationality, where cognitive limitations and imperfect information influence decision-making strategies. To address these issues, we develop a game-theoretic research platform to investigate team-based design under competition. This platform abstracts and simulates real-world competitive design scenarios through controlled experiments. It features a user-friendly interface to collect behavioral data, which supports the analysis of team and individual strategies. Additionally, we validated the platform through a pilot study, demonstrating its ability to capture realistic design features and generate meaningful insights into competitive design behaviors.

This study investigates the elements influencing consumer behavior in the proper disposal of e-waste to advance management practices and circularity. Anchored in Sustainable Behavior Theory and the SHIFT framework, it analyzes secondary data from 51 Brazilian e-waste management companies through document analysis. Findings reveal diverse strategies addressing behavioral barriers and gaps in consumer engagement, informing the Circular Behavior Integration Framework (CBIF). The CBIF provides actionable insights for aligning consumer behavior with reverse logistics systems, advancing material circularity. This study contributes to theory by integrating behavioral dimensions with circular economy principles and offers practical guidance for policymakers and practitioners.

The study investigates the cognitive aspects of aesthetic taste, which is a subjective quality linked to individuals’ ability to make superior aesthetic judgments. It explores how evaluation modes during product choice decision-making relate to aesthetic taste. We defined taste through two dimensions: expertise (professional experience) and acumen (consumption experiences). By comparing research participants in a consumer study across these dimensions, we analyzed decision-making patterns using both quantitative and qualitative methods. Our results show that participants with low aesthetic taste (across both dimensions) express their product choice in terms of product attributes they dislike. We also find that the expression of personal preferences is associated with low aesthetic taste for the expertise dimension but is associated with high aesthetic taste for the acumen dimension.

Structured reflection can initiate learning, increase team performance and support engineering teams in adapting their engineering design activities or methods. Engineering teams with limited reflection experience use reflection often not effectively. Therefore, additional support in the implementation of reflection, guiding and structuring reflection and in providing goal-related reflection guiding questions is needed. To improve the quality of reflection and enable engineering teams to reflect, a chatbot-supported reflection concept to assist engineers is proposed in this contribution. For this purpose, the potential and challenges of existing chatbot approaches are analyzed and classified. Based on the reflection process and tools from preliminary work, use cases and an initial architectural reflection chatbot concept are developed and presented in this paper.

Gracia de Luna conducted experiments with an HMD virtual environment in which human subjects were presented with surprise distractions. His collected data for head, dominant hand, and non-dominant hand included 6 DOF human subject trajectories. This paper examines this data from 57 human subject responses to those surprise virtual environment distractions using statistical trajectory clustering algorithms. The data is organized and processed with a Dynamic Time Warping (DTW) algorithm and then analyzed using the Density Based Spatial Clustering (DBSCAN) algorithm. The K-means method was used to determine the appropriate number of clusters. Chi Squared goodness of fit was used to determine statistical significance. For five of the data sets, a p value of less than 0.05 was found. These five data sets were found to have a limited relationship to the measured variables.