Parisa Jourabchi Amirkhizi

As artificial intelligence (AI) continues to reshape creative industries, design management is facing critical transformation. Despite growing interest in human-AI collaboration, current literature lacks a comprehensive and structured understanding of how socio-technical, cognitive, ethical, and organizational factors interact in design environments. Most existing studies either focus on isolated aspects such as algorithmic efficiency or ethical guidelines, while overlooking dynamic interplay among cultural, participatory, and managerial dimensions essential to human-centered innovation. Addressing this gap, the present study proposes and validates an interpretive structural model that maps the hierarchical interrelations among twelve key dimensions of AI-human collaboration in design management, grounded in the principles of Industry 5.0. The novelty of this research lies in its dual-layered methodology. It integrates interpretive structural modeling and MICMAC analysis with real-world validation through a case study involving a multidisciplinary design team. The results not only reveal foundational drivers such as Human-AI Co-Creation Culture and Human-Centered Value Integration but also highlight the dynamic roles of Explainability, Agile Management, and Organizational Readiness. Notably, the model was refined based on empirical insights, introducing new relational pathways and feedback loops that enhance its practical applicability. The findings demonstrate that successful AI-human collaboration requires a layered ecosystem of trust, transparency, cultural alignment, and adaptive capacity. This study contributes a validated, actionable framework that guides both researchers and practitioners in implementing ethical, effective, and scalable AI-assisted design strategies. By bridging conceptual modeling and field-based validation, it offers a significant step forward in operationalizing the values of human-centered design in AI-integrated environments.

Design management in Industry 5.0 faces a persistent gap due to fragmented conceptualizations that treat AI, human creativity, and machine capabilities as largely separate elements, limiting their effective integration within complex socio-technical systems. Addressing this gap, the present study develops and empirically validates a Design Management Model grounded in human-centered, ethical, and sustainability-oriented principles, framing design management as an adaptive and relational system rather than a linear or technology-driven process. Departing from the automation-oriented logic of Industry 4.0, the study adopts an augmented cognition perspective in which AI functions as a collaborative partner supporting, rather than replacing, human judgment. A sequential mixed-methods approach was employed, integrating systematic literature review, qualitative content analysis, expert evaluation, and Structural Equation Modeling (SEM) based on data from 316 participants, followed by empirical examination in two service-oriented case contexts, namely tourism/hospitality and healthcare services. The findings identify and validate six interrelated domains and demonstrate that Human–AI–Machine Synergy plays a central role in shaping design outcomes. More specifically, the results show that effective design management in Industry 5.0 depends on the coordinated interaction between cognitive processes, technological infrastructures, and organizational strategies, rather than on isolated technological advancement. Empirical applications further illustrate how the model supports ethically guided AI integration, enhances adaptive decision-making, and improves experience-oriented innovation across different contexts. By providing a validated structural framework that connects previously disjointed elements, this study contributes a clearer operational understanding of how human–AI collaboration can be embedded within design management practices in Industry 5.0.

As manufacturing transitions from Industry 4.0 to Industry 5.0, a critical challenge emerges in integrating Generative Artificial Intelligence (GAI) into adaptive social manufacturing to achieve sustainability goals. This transition reflects a paradigmatic shift from a technology-centric model focused on automation and efficiency toward a more holistic framework that embeds human-centricity and environmental responsibility into industrial systems. Whereas Industry 4.0 emphasizes digital innovation and productivity, Industry 5.0 seeks to align technological advancement with broader ecological and societal objectives. Despite advancements in automation and digitalization, existing frameworks lack a structured approach to leveraging GAI for environmental, social, and economic sustainability. This study explores the transformative role of GAI in adaptive social manufacturing, addressing the gap in the existing frameworks. Employing a multi-method research design, including content analysis, expert-driven validation, and system dynamics modeling, the study identifies nine key sustainability dimensions of Industry 5.0 and maps them to 17 GAI functions. The findings reveal that GAI significantly enhances adaptive social manufacturing by optimizing resource efficiency, promoting inclusivity, and supporting ethical governance. System dynamics analysis highlights the complex interdependencies between GAI-driven functions and sustainability outcomes, underscoring the need to balance technological innovation with human values. The research provides a novel framework for industries seeking to implement GAI in sustainable production systems, bridging theoretical insights with practical applications. Additionally, it offers actionable strategies to address challenges such as workforce adaptation, ethical AI governance, and adoption barriers, ultimately facilitating the transition toward Industry 5.0’s sustainability goals.

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This study examines the use of injection moulding to evaluate mechanical properties in plastic products, such as shear and residual stresses. Key process variables like melt temperature, mould temperature, hold pressure duration, and pure hold duration are meticulously chosen for study. A full factorial experiment design is utilised to determine the best settings. These variables notably influence the end product’s physical and mechanical properties. Computational techniques, like the finite element method, are used to analyse behaviours based on varied input parameters. A CAD model of a dashboard part is incorporated into a finite element analysis to measure shear and residual stresses. Four specific parameters from the injection moulding process are subjected to an in-depth experimental design. It is worth noting that the injection moulding process does not incorporate a type-2 fuzzy neural network (T2FNN). However, in this particular investigation, T2FNN was employed to replicate the mechanical stress model associated with dashboard injection moulding. Its purpose was to estimate shear and residual stress levels. Additionally, the multi-objective genetic algorithm (MOGA) was utilised to extract the most optimal parameters for the injection moulding process, aiming to minimise shear and residual stress and thereby increase the resistance of the final product. The proposed model was developed and implemented using MATLAB software. A Pareto front was derived from the MOGA by employing the T2FNN within the process, identifying fourteen optimal solutions.

Based on the provided text, the paper appears to be well-researched, pulling from various disciplines and cultures. It’s crucial that the research supporting the claims is current, representative of diverse viewpoints, and accurately interpreted. SU...