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Mass customization has yet to fulfill its original purpose as established by marketing researchers to become an alternative to mass production, largely due to its inability to achieve mass market levels of efficiency. The purpose of this study is to survey consumer's perceptions, willingness and capabilities of participating in a mass customization system, and understand the implications of its findings related to an alternative production system, generative customization.

After an extensive literature review of mass customization, consumer behavior, complex adaptive systems and generative design, a survey was conducted across US and Swedish consumers relative to their willingness and perceptions regarding mass customization, with hypotheses based upon extant research standards.

The survey results found that consumers are ambivalent toward mass customization in mass markets, and a conceptual alternative (generative customization) appears to achieve, at least conceptually, the necessary objectives relative to product design conceptualization and fulfillment that mass customization cannot achieve.

A lack of significant findings from extant research regarding consumer perceptions and tolerances regarding mass customization in mass markets is a limitation to this study. The discussion of a new concept (generative customization) as a viable alternative to mass customization as a result of the survey findings needs to be validated empirically in future research.

The paper empirically validates a definition of mass customization as a complementary rather than an alternative to mass production. It also introduces and develops the concept of generative customization as viable alternative to mass production, albeit one that must be empirically validated in future research.

The purpose of this paper is to find optimal solutions for conceptual design automation, which can be integrated with Building Information Modelling (BIM) support for construction automation. Problems relating ostensibly to failures in computational support for the conceptual design stage are well-documented in extant literature. These failures are multifarious and significant, with several deficiencies being acknowledged in the Architecture, Engineering, and Construction (AEC) industry. Whilst acknowledging this, extant literature has highlighted the importance of computational design in the AEC industry; and failures in this area include the need to strengthen the congruent links and support mechanisms in order to exploit the opportunities presented by new computational design methods. Given this, it is postulated that the application of generative design could enhance the design experience by assisting designers with the iterative generation of alternatives and parameterisation (change management) processes. Moreover, as BIM applications are increasingly providing comprehensive support for modelling and management, then additional synergies could be examined for further exploitation.

This paper focusses on the potential for developing an interactive BIM environment that purposefully adopts generative design as a method of computational design for the early design stages. This research facilitates the automation of the conceptual architectural design process, using BIM as the central conduit for enhancing the integration of the whole building design process (including design interfaces). This approach is designed to improve designers’ cognition and collaboration during the conceptual architectural design process.

This paper evaluates the existing methods and decision support mechanisms, and it introduces the potential of combining different concepts into a single environment (generative design/BIM).

This research is novel, in that it critically appraises virtual generative workspaces using BIM as the central conduit. The outcome and intervention of this research forms a theoretical basis for the development of a “proof of concept” prototype, which actively engages generative design into a single dynamic BIM environment to support the early conceptual design process.

This study aimed to propose a performance-oriented approach of automatically generative design and optimization of hospital building layouts in consideration of public health emergency, which intended to conduct reasonable layout design of hospital building to meet different performance requirements for both high efficiency during normal periods and low risk in the pandemic.

The research design follows a sequential mixed methodology. First, key points and parameters of hospital building layout design (HBLD) are analyzed. Then, to meet the requirements of high efficiency and low risk, adjacent preference score and infection risk coefficient are constructed as constraints. On this basis, automatic generative design is conducted to generate building layout schemes. Finally, multi-objective deviation analysis is carried out to obtain the optimal scheme of hospital building layouts.

Automatic generative design of building layouts that integrates adjacent preferences and infection risks enables hospitals to achieve rapid transitions between normal (high efficiency) and pandemic (low risk) periods, which can effectively respond to public health emergencies. The proposed approach has been verified in an actual project, which can help systematically explore the solution for better decision-making.

The form of building layouts is limited to rectangles, and future work can explore conducting irregular layouts into optimization for the framework of generative design.

The contribution of this paper is the developed approach that can quickly and effectively generate more hospital layout alternatives satisfying high operational efficiency and low infection risk by formulating space design rules, which is of great significance in response to public health emergency.

The purpose of this paper is to address the problem in research of a lack of a holistic conceptual framework related to incremental and discontinuous innovation. Too often, the term “innovation” is used without an understanding of how it can be applied in a product design and supply chain fulfillment system. In this study, the goal is to develop a holistic “innovation continuum” to treat innovation as a complex adaptive system (CAS).

A comprehensive literature review was conducted, and from it, four propositions and a conceptual framework were developed.

The authors determined that an “innovation continuum” can be established through the use of a complex adaptive system. Two, incremental innovation is determined to possess adaptive qualities, and can be enhanced through the use of collaboration and evolutionary algorithms. Three, discontinuous innovation is established as a creative process enabled through expert designers, and improved through the use of generative design. Next, a supply chain system for incremental innovation can be assisted by the use of adaptive systems, but it is not proven that a generative customization system (for discontinuous innovation) can be improved through the use of a CAS.

The paper has established an “innovation continuum” linking incremental and discontinuous innovation within one holistic system. It establishes the use of evolutionary algorithms to improve the viability of incremental innovation, and generative design for discontinuous innovation. Finally, the concept of generative customization as an end to end product design and supply chain fulfillment solution for discontinuous innovation is established.

Urban typogenetics investigates the use of machine intelligence for the evaluation of performance measures as a decision support system (DSS) with a focus on urban aesthetics evaluation. This framework allows designers to address performance measures, urban measures and aesthetic criteria in an adaptive, interactive generative design approach. The purpose of this paper is to provide an understanding of the structure and the nature of the framework and the application of human-in-the-loop design systems to urban design.

Significant literature reviewed lead to the identification of an application potential in the decision-making process. This potential is situated around the use of AI for the evaluation of subjective performance criteria in a DSS. Recognising that the key decisions about urban aesthetics are based on the individual evaluation of the designer, an HITL approach for computational design software to support creative decisions is presented in this paper.

Urban typogenetics for interactive generative urban design allows the exploration of complex design spaces by using a human-in-the-loop design system in the context of urban aesthetics. Hybrid aesthetic evaluation allows the designer to analyse morphological features and urban aesthetics during exploratory search and reveal hidden aspects of the urban context by visualisation of the results of the aesthetic evaluation. Integrating performance measures and urban aesthetics in urban typogenetics addresses major criteria of urban design at the beginning of the creative process.

The use of a broad interactive approach to typogenetic design in an application to urban scenarios is a novel conceptual approach to the design of urban configurations. The suggested adaptive mechanism would allow the user of a typogenetic tool to subjectively evaluate solutions by sight and reason about aesthetic, social and cultural implication of the reviewed design solutions.

The integration and automation of the whole design and implementation process have become a pivotal factor in construction projects. Problems of process integration, particularly at the conceptual design stage, often manifest through a number of significant areas, from design representation, cognition and translation to process fragmentation and loss of design integrity. Whilst building information modelling (BIM) applications can be used to support design automation, particularly through the modelling, amendment and management stages, they do not explicitly provide whole design integration. This is a significant challenge. However, advances in generative design now offer significant potential for enhancing the design experience to mitigate this challenge.

The approach outlined in this paper specifically addresses BIM deficiencies at the conceptual design stage, where the core drivers and indicators of BIM and generative design are identified and mapped into a generative BIM (G-BIM) framework and subsequently embedded into a G-BIM prototype. This actively engages generative design methods into a single dynamic BIM environment to support the early conceptual design process. The developed prototype followed the CIFE “horseshoe” methodology of aligning theoretical research with scientific methods to procure architecture, construction and engineering (AEC)-based solutions. This G-BIM prototype was also tested and validated through a focus group workshop engaging five AEC domain experts.

The G-BIM prototype presents a valuable set of rubrics to support the conceptual design stage using generative design. It benefits from the advanced features of BIM tools in relation to illustration and collaboration (coupled with BIM's parametric change management features).

This prototype has been evaluated through multiple projects and scenarios. However, additional test data is needed to further improve system veracity using conventional and non-standard real-life design settings (and contexts). This will be reported in later works.

Originality and value rest with addressing the shortcomings of previous research on automation during the design process. It also addresses novel computational issues relating to the implementation of generative design systems, where, for example, instead of engaging static and formal description of the domain concepts, G-BIM actively enhances the applicability of BIM during the early design stages to generate optimised (and more purposeful) design solutions.

This study offers practical insights into how generative artificial intelligence (AI) can enhance responsible manufacturing within the context of Industry 5.0. It explores how manufacturers can strategically maximize the potential benefits of generative AI through a synergistic approach.

The study developed a strategic roadmap by employing a mixed qualitative-quantitative research method involving case studies, interviews and interpretive structural modeling (ISM). This roadmap visualizes and elucidates the mechanisms through which generative AI can contribute to advancing the sustainability goals of Industry 5.0.

Generative AI has demonstrated the capability to promote various sustainability objectives within Industry 5.0 through ten distinct functions. These multifaceted functions address multiple facets of manufacturing, ranging from providing data-driven production insights to enhancing the resilience of manufacturing operations.

While each identified generative AI function independently contributes to responsible manufacturing under Industry 5.0, leveraging them individually is a viable strategy. However, they synergistically enhance each other when systematically employed in a specific order. Manufacturers are advised to strategically leverage these functions, drawing on their complementarities to maximize their benefits.

This study pioneers by providing early practical insights into how generative AI enhances the sustainability performance of manufacturers within the Industry 5.0 framework. The proposed strategic roadmap suggests prioritization orders, guiding manufacturers in decision-making processes regarding where and for what purpose to integrate generative AI.

Generativity is defined in this chapter as the creation of new images, metaphors, physical representations, and so on that have two qualities: they change how people think so that new options for decisions and/or actions become available to them, and they are compelling images that people want to act on. Research and experiences that suggest “positivity,” particularly positive emotion, is not sufficient for transformational change, but that generativity is a key change lever in cases of transformational change, are reviewed. A model of different characteristics of generativity is offered and ways in which appreciative inquiry can be a generative process, increase generative capacity, and lead to generative outcomes, are discussed. Ways to increase the generativity of appreciative inquiry through generative topics, generative questions, generative conversations, and generative action are offered.

In addressing the future trajectory of knowledge management systems, this paper uses the psycho-social notion of generativity which recently stimulated contributions in technology and innovation for a holistic systemic knowledge management (KM) review. The purpose of this study is to identify current shortcomings and fixations together with their ramifying affordances, all enveloped within a novel KM concept and prototype-system-under-development.

It follows up on prior publications using design science research (DSR) methodologies in compliance with theory effectiveness, a principle expecting system designs to be purposeful in terms of utility and communication. The KM perspective taken prioritizes a decentralizing agenda benefiting knowledge workers while also aiming to foster a fruitful co-evolution with traditional organizational KM approaches.

The notions of generative fit and capacities in their technical, informational and social interpretations prove able to accommodate diverse KM models and to cumulatively synthesize a wide range of related concepts and perspectives. In the process, Nonaka’s renowned socialize, externalize, combine, internalize and Ba model is repurposed and extended to suggest a corresponding complementing seize, imbed, collate, encompass, effectuate workflow embedded in distinct digital ecosystems fully aligned to the diversity of the generative attributes introduced.

Although the prototype development is still in progress, the study conforms to the DSR practice to report on early visions of technology impact on users, organizations and society and also refers to and reflects on aspects of feasibility, suitability, acceptability and the system’s prospect as a general-purpose technology or disruptive innovation.

The paper transdisciplinarily integrates the well-established psychological notions of generativity into its newer digital and systemic KM dimensions. The resulting new insights transparently inform the concept and prototype design, present a holistic framework for individuals and organizations and suggest avenues for new KM applications and KM research directions inspired by the adopted and adapted novel generativity contexts.

Generative conversational artificial intelligence (AI) demonstrates powerful conversational skills for general tasks but requires customization for specific tasks. The quality of a custom generative conversational AI highly depends on users’ guidance, which has not been studied by previous research. This study uses social exchange theory to examine how generative conversational AI’s cognitive and emotional conversational skills affect users’ guidance through different types of user engagement, and how these effects are moderated by users’ relationship norm orientation.

Based on data collected from 589 actual users using a two-wave survey, this study employed partial least squares structural equation modeling to analyze the proposed hypotheses. Additional analyses were performed to test the robustness of our research model and results.

The results reveal that cognitive conversational skills (i.e. tailored and creative responses) positively affected cognitive and emotional engagement. However, understanding emotion influenced cognitive engagement but not emotional engagement, and empathic concern influenced emotional engagement but not cognitive engagement. In addition, cognitive and emotional engagement positively affected users’ guidance. Further, relationship norm orientation moderated some of these effects such that the impact of user engagement on user guidance was stronger for communal-oriented users than for exchange-oriented users.

First, drawing on social exchange theory, this study empirically examined the drivers of users’ guidance in the context of generative conversational AI, which may enrich the user guidance literature. Second, this study revealed the moderating role of relationship norm orientation in influencing the effect of user engagement on users’ guidance. The findings will deepen our understanding of users’ guidance. Third, the findings provide practical guidelines for designing generative conversational AI from a general AI to a custom AI.