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There are several studies showing that end-users' participation in the architectural design process of houses is critical to their satisfaction (e.g. Önder et al., 2010; Ammar et al., 2013). Housing that is not adjusted to inhabitants' needs leads to modification works (Davidson et al., 2007) that could be avoided if their design was defined from the beginning with their participation. Digital technologies, such as 3D interactive visualization, benefit co-design processes by helping non-specialists better understand space and design possibilities (Salter et al., 2009; Schroth et al., 2006). However, the available literature shows that existing co-design digital tools were not developed based on potential users’ requirements. This paper aims to define the user requirements of a co-design tool for housing customization.

Interviews were conducted to gather information on how participatory processes occur in housing cooperatives and identify how potential users can collaborate in the design definition of their houses using a digital tool.

The interviews were analyzed, and requirements were defined.

This work contributes to the advancement of knowledge since the tool is defined based on requirements collected from potential end-users. By using a user-centered approach, the tool can contribute to more effective and informed collaboration.

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.

The aim of this article is to map the use of AI in the user experience (UX) design process. Disrupting the UX process by introducing novel digital tools such as artificial intelligence (AI) has the potential to improve efficiency and accuracy, while creating more innovative and creative solutions. Thus, understanding how AI can be leveraged for UX has important research and practical implications.

This article builds on a systematic literature review approach and aims to understand how AI is used in UX design today, as well as uncover some prominent themes for future research. Through a process of selection and filtering, 46 research articles are analysed, with findings synthesized based on a user-centred design and development process.

The authors’ analysis shows how AI is leveraged in the UX design process at different key areas. Namely, these include understanding the context of use, uncovering user requirements, aiding solution design, and evaluating design, and for assisting development of solutions. The authors also highlight the ways in which AI is changing the UX design process through illustrative examples.

While there is increased interest in the use of AI in organizations, there is still limited work on how AI can be introduced into processes that depend heavily on human creativity and input. Thus, the authors show the ways in which AI can enhance such activities and assume tasks that have been typically performed by humans.

This paper examines the integration of artificial intelligence (AI) within family businesses, focusing on how AI can enhance their competitiveness, resilience and sustainability. The study seeks to provide insights into AI’s application in family business contexts, addressing the unique strengths and challenges these businesses face.

A systematic literature review was conducted to synthesize existing research on the adoption and integration of AI in family businesses. The review involved a comprehensive analysis of relevant academic literature to identify key trends, opportunities, challenges and factors influencing AI adoption in family-owned enterprises.

The review highlights the significant potential of AI for family businesses, particularly in improving operations, decision-making and customer engagement. It identifies opportunities such as analysing customer data, enhancing brand building, streamlining operations and improving customer experiences through technologies like Generative AI, Machine Learning, AI Chatbots and NLP. However, challenges like resource constraints, inadequate infrastructure, low customization and AI knowledge gaps inhibit AI adoption in family firms. The study proposes an AI adoption roadmap tailored for family businesses and outlines future research directions based on emerging themes in AI use within these enterprises.

This paper addresses the underexplored area of AI integration in family businesses, contributing to the academic understanding of the intersection between AI and family-owned enterprises. The study offers a comprehensive synthesis of existing research, providing valuable insights and practical recommendations for enhancing the competitiveness and sustainability of family businesses through AI adoption.

Generative artificial intelligence (GenAI) has progressed in its ability and has seen explosive growth in adoption. However, the consumer’s perspective on its use, particularly in specific scenarios such as financial advice, is unclear. This research develops a model of how to build trust in the advice given by GenAI when answering financial questions.

The model is tested with survey data using structural equation modelling (SEM) and multi-group analysis (MGA). The MGA compares two scenarios, one where the consumer makes a specific question and one where a vague question is made.

This research identifies that building trust for consumers is different when they ask a specific financial question in comparison to a vague one. Humanness has a different effect in the two scenarios. When a financial question is specific, human-like interaction does not strengthen trust, while (1) when a question is vague, humanness builds trust. The four ways to build trust in both scenarios are (2) human oversight and being in the loop, (3) transparency and control, (4) accuracy and usefulness and finally (5) ease of use and support.

This research contributes to a better understanding of the consumer’s perspective when using GenAI for financial questions and highlights the importance of understanding GenAI in specific contexts from specific stakeholders.

This paper analyzes the ethics of integrating Artificial Intelligence (AI), particularly regarding AI-generated educational content in academia. It attempts to explore how AI customization mimics human interaction and behavior in education, investigate ethical concerns in educational AI adoption, and assess ChatGPT’s ethical use for nurturing curiosity and maintaining academic integrity in education.

Fictional tales may help us think critically and creatively to uncover hidden truths. The narratives are analyzed to determine the affordances and drawbacks of Artificial Intelligence in Education (AIEd).

The study highlights the imperative for innovative, ethically grounded strategies in harnessing AI/GPT technology for education. AI can enhance learning, and human educators’ irreplaceable role is even more prominent, emphasizing the need to harmonize technology with pedagogical principles. However, ensuring the ethical integration of AI/GPT technology demands a delicate balance where the potential benefits of technology should not eclipse the essential role of human educators in the learning process.

This paper presents futuristic academic scenarios to explore critical dimensions and their impact on 21^st century learning. As AI assumes tasks once exclusive to human educators, it is essential to redefine the roles of both technology and human teachers, focusing on the future.

The study aims to understand the way metaverse might revolutionize the governance format – precisely the e-government concept – besides underlining the challenges leaving suggestive contours for further research in this realm.

An inductive research approach included the content analysis of relevant literature to identify patterns and generalize them into areas and approaches. Developing a conceptual framework seeks to systematically organize knowledge on metaverse government and provide an understanding of its contributions to enhancing the e-government maturity models.

The authors identified three general areas and approaches – content and data lifecycle management; platforms, tools and services; and infrastructure and computing resources – that shape how challenges of enterprise architecture layers should be resolved in a future research agenda. Integration of these government metaverse areas and approaches is linked with four enterprise architecture layers (technology, applications, data and business) that advance a new e-government meta stage of e-government maturity and the related challenges.

Hitherto, metaverse literature has hinged itself on the benefits and challenges of metaverse in the private sector. However, the exact dimensions have not been probed in the public sector and its e-government domain – the present study seeks to plug this gap.

This papers aims to study lattice structures in terms of geometric variables, manufacturing variables and material-based variants and their correlation with compressive behaviour for their application in a methodology for the design and development of personalized elastic therapeutic products.

Lattice samples were designed and manufactured using extrusion-based additive manufacturing technologies. Mechanical tests were carried out on lattice samples for elasticity characterization purposes. The relationships between sample stiffness and key geometric and manufacturing variables were subsequently used in the case study on the design of a pressure cushion model for validation purposes. Differentiated areas were established according to patient’s pressure map to subsequently make a correlation between the patient’s pressure needs and lattice samples stiffness.

A substantial and wide variation in lattice compressive behaviour was found depending on the key study variables. The proposed methodology made it possible to efficiently identify and adjust the pressure of the different areas of the product to adapt them to the elastic needs of the patient. In this sense, the characterization lattice samples turned out to provide an effective and flexible response to the pressure requirements.

This study provides a generalized foundation of lattice structural design and adjustable stiffness in application of pressure cushions, which can be equally applied to other designs with similar purposes. The relevance and contribution of this work lie in the proposed methodology for the design of personalized therapeutic products based on the use of individual lattice structures that function as independent customizable cells.

An AI concierge is a technologically advanced, intelligent and personalized assistant that is designated to an individual customer, proactively taking care of that customer’s needs throughout the service journey. This article envisions the idea of AI concierges and discusses how to leverage AI concierges in the customer journey.

This article takes a conceptual approach and draws insights from literature in service management, marketing, psychology, human-computer interaction and ethics.

This article delineates the fundamental forms of AI concierges: dialog interface (no embodiment), virtual avatar (embodiment in the virtual world), holographic projection (projection in the physical world) and tangible service robot (embodiment in the physical world). Key attributes of AI concierges are the ability to exhibit semantic understanding of auditory and visual inputs, maintain an emotional connection with the customer, demonstrate proactivity in refining the customer’s experience and ensure omnipresence through continuous availability in various forms to attend to service throughout the customer journey. Furthermore, the article explores the multifaceted roles that AI concierges can play across the pre-encounter, encounter and post-encounter stages of the customer journey and explores the opportunities and challenges associated with AI concierges.

This paper provides insights for professionals in hospitality, retail, travel, and healthcare on leveraging AI concierges to enhance the customer experience. By broadening AI concierge services, organizations can deliver personalized assistance and refined services across the entire customer journey.

This article is the first to introduce the concept of the AI concierge. It offers a novel perspective by defining AI concierges’ fundamental forms, key attributes and exploring their diverse roles in the customer journey. Additionally, it lays out a research agenda aimed at further advancing this domain.

This study aims to develop a production-oriented approach for optimal mass-customisation of floor panel layouts in cross-laminated timber (CLT) buildings. The study enables meeting building clients’ unique floor plan requirements at an optimal cost and simultaneously enhances manufacturers’ profit by minimising material and manufacturing process waste.

The present research uses a hybrid approach consisting of field data collection, mathematical modelling, development of a Genetic Algorithm (GA) and scenario analysis. Field data includes engineered timber production information, design data and building code requirements. The study adopts the Flexible Demand Assignment (FDA) technique to formulate a mathematical model for optimising the design of mass timber buildings and employs GA to identify optimal production solutions. Scenario analysis is performed to validate model outputs.

The proposed model successfully determines the load-bearing wall placement and building spans and specifications of floor panels that result in optimal production efficiency and the desired architectural layout. The results indicate that buildings made of a single category of thickness of panels but customised in various lengths to suit building layout are the most profitable scenario for CLT manufacturers and are a cost-effective option for clients.

The originality of the present study lies in its mathematical and model-driven approach towards implementing mass customisation in multi-storey buildings. The proposed model has been developed and validated based on a comprehensive set of real-world data and constraints.