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To meet their ambitious targets for a circular economy, multinationals need to speed up their innovation efforts. This requires experimentation capability. But it is not clear what this capability entails, and how companies can build it. The purpose of this paper is to give companies guidance on how they might develop experimentation capability for the circular economy.

We conduct in-depth interviews with innovators in frontrunner multinationals, H&M, IKEA and Philips. We use the Gioia method to analyse our data.

This article identifies novel institutional, strategic, and operational actions that build experimentation capability for a circular economy.

The identified actions help innovation managers experiment and speed up their innovation efforts for a circular economy.

To reverse environmental degradation, multinationals need to transform their dominant linear take-make-waste business models. This research provides actions that help them organize this transformation.

This article is based on extensive research with leading multinationals and reveals novel insights on how to innovate for a circular economy.

In the context of libraries adopting artificial intelligence (AI) technology, this paper aims to demonstrate the synergy between two different yet complimentary frameworks, need-based experimentation (NBE) and curiosity-based experimentation (CBE). It looks at how these frameworks interact and operate together to promote technological innovation and innovation in libraries.

The authors’ extensive professional experience in the AI adoption and innovation of libraries is drew upon in this paper. The methodology encompasses empirical observations of various libraries engaging in digital innovations through experimentations with AI technology adoption practices. Using the frameworks of NBE and CB), these observations are examined to find patterns, relationships and mutual reinforcement between the two methods. The analysis of this study is built on the authors’ observations and real-world case studies.

The research reveals that NBE and CBE work together to provide libraries with all-encompassing adoption methods for AI technology. This study indicates a dynamic interaction between NBE and CBE that boosts libraries’ methods for adopting AI technology. NBE acts as a catalyst for CBE by raising awareness of specific library needs, prompting librarians to explore AI technologies aligned with those needs. This synergy empowers librarians to creatively experiment with technology solutions that directly address pressing library challenges. Conversely, CBE fuels NBE by promoting group learning among diverse team members and fostering individual motivation to tackle library needs collaboratively. As they explore AI technology out of personal curiosity, librarians make important contributions that enhance NBE.

The novel aspect of this study is the recognition of the complementarity between NBE and CBE frameworks, which suggests that libraries should view them as intertwined rather than two separate approaches. Focusing on both methodologies increases the culture of experimentation and improves the problem-solving abilities of librarians. Innovation is fueled by controlled experimentation and innate curiosity in an atmosphere that is fostered by the mutual influence of NBE and CBE. This synthesis offers libraries a comprehensive strategy for adopting AI technology, empowering them to manage the shifting environment and realize the revolutionary promise of AI technologies.

This study aims to explore the serendipitous discovery of integrated reporting (IR) by Alpha, an Italian small and medium-sized enterprise (SME). Alpha piqued the curiosity when the authors discovered that it experimented with IR alongside other management accounting practices, such as the Balanced Scorecard. As the authors reflected on Alpha’s experiences, the authors had to opportunistically develop a new framework to understand the change that was taking place at Alpha fully. Thus, the authors developed the serendipitous drift framework. This study contributes to addressing the gap between management accounting research that sees change as a planned, ordered process versus research that sees it as an unmanageable drift.

The authors ground the research on a qualitative methodology based on a single case study. This methodology allows us to focus on understanding what has happened at Alpha to discover new themes and provide theoretical generalisations. The authors developed the framework using middle-range thinking and fleshed it out using empirical findings from the case study. Middle-range thinking implies going back and forth between the theory and the empirical material. Therefore, the authors develop the serendipitous drift framework from prior theories and use it to inform the empirical study. In turn, the empirical material collected in Alpha helps refine and flesh out the serendipitous drift framework. The framework explains how Alpha leveraged serendipity to steer change towards favourable outcomes for them.

The authors find that the search for change undertaken by Alpha’s managers was non-specific but purposeful. Their dispositions were sagacious enough to recognise the potential value found in management accounting practices, such as IR and the Balanced Scorecard. They chanced upon new and unforeseen practices through trial and error, iteration, internal engagement and networking.

Overall, the results indicate that Alpha’s managers shaped the disorder of management accounting changes, even though it followed unexpected, uncertain and messy paths. Indeed, appropriate informal controls can act as a frame of reference for choosing, adapting and implementing new management accounting practices to shape the disorder. Informal controls can both guide and bound the experimentation process towards desirable outcomes.

The authors contribute to management accounting change theory by developing a framework rooted in serendipity and drifting theories. The framework identifies how searching, sagacity and chance are essential for making positive, unexpected discoveries. Therefore, the authors provide novel insights on how and why IR and other management accounting practices are eventually translated and adopted in the case company. Moreover, the serendipitous drift framework has the potential to help managers frame cultural controls to actively seek opportunities for valuable serendipitous eureka moments through networking and experimentation.

Nearly 75% of EU buildings are not energy-efficient enough to meet the international climate goals, which triggers the need to develop sustainable construction techniques with high degree of resilience against climate change. In this context, a promising construction technique is represented by ventilated façades (VFs). This paper aims to propose three different VFs and the authors define a novel machine learning-based approach to evaluate and predict their energy performance under different boundary conditions, without the need for expensive on-site experimentations

The approach is based on the use of machine learning algorithms for the evaluation of different VF configurations and allows for the prediction of the temperatures in the cavities and of the heat fluxes. The authors trained different regression algorithms and obtained low prediction errors, in particular for temperatures. The authors used such models to simulate the thermo-physical behavior of the VFs and determined the most energy-efficient design variant.

The authors found that regression trees allow for an accurate simulation of the thermal behavior of VFs. The authors also studied feature weights to determine the most relevant thermo-physical parameters. Finally, the authors determined the best design variant and the optimal air velocity in the cavity.

This study is unique in four main aspects: the thermo-dynamic analysis is performed under different thermal masses, positions of the cavity and geometries; the VFs are mated with a controlled ventilation system, used to parameterize the thermodynamic behavior under stepwise variations of the air inflow; temperatures and heat fluxes are predicted through machine learning models; the best configuration is determined through simulations, with no onerous in situ experimentations needed.

With the rapid advancement of generative artificial intelligence (AI), it is important to consider how young people are making sense of these tools in their everyday lives. Drawing on critical postdigital approaches to learning and literacy, this study aims to center the experiences and perspectives of young people who encounter and experiment with generative AI in their daily writing practices.

This critical case study of one digital platform – Character.ai – brings together an adolescent and adult authorship team to inquire about the intertwining of young people’s playful and critical perspectives when writing on/with digital platforms. Drawing on critical walkthrough methodology (Light et al., 2018), the authors engage digital methods to study how the creative and “fun” uses of AI in youths’ writing lives are situated in broader platform ecologies.

The findings suggest experimentation and pleasure are key aspects of young people’s engagement with generative AI. The authors demonstrate how one platform works to capitalize on these dimensions, even as youth users engage critically and artfully with the platform and develop their digital writing practices.

This study highlights how playful experimentation with generative AI can engage young people both in pleasurable digital writing and in exploration and contemplation of platforms dynamics and structures that shape their and others’ literate activities. Educators can consider young people’s creative uses of these evolving technologies as potential opportunities to develop a critical awareness of how commercial platforms seek to benefit from their users.

This study contributes to the development of a critical and humanist research agenda around generative AI by centering the experiences, perspectives and practices of young people who are underrepresented in the burgeoning research devoted to AI and literacies.

Following the recent rise in generative artificial intelligence (GenAI) tools, fundamental questions about their wider impacts have started to reverberate around various disciplines. This study aims to track the unfolding landscape of general issues surrounding GenAI tools and to elucidate the specific opportunities and limitations of these tools as part of the technology-assisted enhancement of mechanical engineering education and professional practices.

As part of the investigation, the authors conduct and present a brief scientometric analysis of recently published studies to unravel the emerging trend on the subject matter. Furthermore, experimentation was done with selected GenAI tools (Bard, ChatGPT, DALL.E and 3DGPT) for mechanical engineering-related tasks.

The study identified several pedagogical and professional opportunities and guidelines for deploying GenAI tools in mechanical engineering. Besides, the study highlights some pitfalls of GenAI tools for analytical reasoning tasks (e.g., subtle errors in computation involving unit conversions) and sketching/image generation tasks (e.g., poor demonstration of symmetry).

To the best of the authors’ knowledge, this study presents the first thorough assessment of the potential of GenAI from the lens of the mechanical engineering field. Combining scientometric analysis, experimentation and pedagogical insights, the study provides a unique focus on the implications of GenAI tools for material selection/discovery in product design, manufacturing troubleshooting, technical documentation and product positioning, among others.

This study aims to emphasize the integration of lean start-up and design thinking approaches and investigate how they may be used together.

The report uses a systematic literature review methodology to analyze and summarize previous research on combining lean start-up and design thinking. Inferences were discovered and analyzed after relevant publications were chosen based on predetermined inclusion criteria.

The research shows that combining lean start-up and design thinking significantly impacts entrepreneurship. Start-ups can efficiently uncover consumer needs, reduce risks and improve their product or service offerings by combining the client-centricity of design thinking with the iterative and data-driven concepts of lean start-up. This integration promotes an innovative culture, gives teams the freedom to try new things and learn from mistakes and raises the possibility of start-up success.

The dependence on pre-existing literature, which might cover only some potential uses and circumstances, is a weakness of this research. It is advised that more empirical research be conducted to determine the precise circumstances in which the integrated strategy performs best. Future studies should also explore the difficulties and drawbacks of using these approaches to offer suggestions for overcoming them and maximizing their advantages.

The findings have significant ramifications for business owners and other professionals working in the start-up environment. The combination of lean start-up and design thinking emphasizes the relevance of early customer interaction and empathy-driven design. To foster creativity and hasten the expansion of start-ups, practitioners are urged to create a comprehensive strategy that integrates the advantages of both techniques. Through this integration, business owners may develop solutions that appeal to their target market, increasing adoption rates and market competitiveness.

This study is interesting in comparing lean start-up and design thinking, emphasizing the overlaps and benefits of their application to entrepreneurship. This study discusses successful start-up methods by offering suggestions for future research and practice. It also provides a basis for further developing and adopting the integrated approach.

The integration of industry 4.0 has become a priority for many organizations. However, not all organizations are suitable and capable of implementing industry 4.0 because it requires a dynamic and flexible implementation strategy. The implementation of industry 4.0 often involves overcoming several tensions between internal and external stakeholders. This paper aims to explore the paradoxical tensions that arise for health-care organizations when integrating industry 4.0. Moreover, it discusses how a paradox lens can support the conceptualization and proposes techniques for handling tensions during the integration of industry 4.0.

This qualitative and in-depth study draws upon 32 semi-structured interviews. The empirical case concerns how two health-care organizations handle paradoxical tensions during the integration of industry 4.0.

The exploration resulted in six recurring technology tensions: technology invention (modularized design vs. flexible design), technology collaboration (automation vs. human augmentation), technology-driven patient experience (control vs. autonomy), technology uncertainty (short-term experimentation vs. long-term planning), technology invention and diffusion through collaborative efforts among stakeholders (selective vs. intensive collaboration) and technological innovation (market maintenance vs. disruption).

A paradox theory-informed conceptual model is proposed for how to handle tensions during the integration of industry 4.0. To the best of the authors’ knowledge, this is the first paper to introduce paradox theory for quality management, including lean and Six Sigma.

The objective of this research is to propose an immersive framework that integrates virtual reality (VR) technology with directives international safety training certification bodies to enhance construction safety training, which eventually leads to safer construction sites.

The adopted methodology combines expert insights and experimentation to maximize the effectiveness of construction safety training. The first step was identifying key considerations for VR models such as motion sickness prevention and adult learning theories. The second step was developing a game-like VR model for safety training, with multiple hazards and scenarios based on the considerations of the previous step. After that, safety experts evaluated the model and provided valuable feedback on its alignment with international safety training practices. Finally, the developed model is tested by senior students, where the testing format followed the Institution of Occupational Safety and Health (IOSH) working safely exam structure.

An advanced immersive VR safety training model was developed based on extensive lessons learned from the literature, previous work and psychology-informed adult learning theories. Model testing – through focus groups and hands-on experimentation – demonstrated significant benefit of VR in upgrading and complementing traditional training methods.

The findings presented in this paper make a significant contribution to the field of safety training within the construction industry and the broader context of immersive learning experiences. It also fosters further exploration into immersive learning experiences across educational and professional contexts.

The purpose of this paper is to explore the approaches to education and training adopted by manufacturing organisations to identify and develop a set of learning principles for the successful transition to Industry 4.0.

A case study of a manufacturing ecosystem in Queensland, Australia was undertaken, that included semi-structured interviews with a total sample of 22 manufacturing industry representatives, an analysis of secondary data including organisational documents and government reports, and embedded cases of two manufacturing organisations.

Manufacturers successfully transitioning to Industry 4.0 are distinguished by a culture which values learning, management development to understand and lead innovation, experimental learning on the job and strong links to education and training providers through internships and upskilling pathways. These four principles inform approaches to creating tailored training solutions that respond to the unique needs of diverse manufacturing organisations.

The two case studies describe exemplary high performing companies only and not companies at earlier stages of adopting Industry 4.0. Therefore, future research could include a broader spectrum of companies across the adoption spectrum. Nevertheless, considered as a study of a total manufacturing ecosystem, there is strong alignment of views of government, industry, union and education stakeholders regarding the key factors of transition to Industry 4.0.

There is a strong need for leaders of manufacturing organisations to enable a broad strategy of capability development beyond simple acquisition of new technologies. Detailed consideration and resourcing of on-the-job training and experimentation, talent attraction through innovation workplace cultures and strong relationships with education providers are important.

Given that Industry 4.0 technologies such as robotics and AI are now rapidly diffusing into other industry sectors, the research has broader implications for education and training for the future of work. These technologies could produce stark differences between efficiency versus innovation-oriented adoption strategies. Whilst the former could displace workers, the latter can open pathways for upskilling, product and process innovation and cross sector employment.

Through the ecosystem level case approach, multiple stakeholder perspectives provide triangulated insights into advanced manufacturer's education, skills and training strategies, uncovering four learning principles that underpin the approach of manufacturers successfully transitioning to Industry 4.0. The findings have practical implications for policy makers and industry bodies supporting the transition to advanced manufacturing and provide manufacturing managers with insights into successful education and skill strategies that can be adapted to specific organisational needs.