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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.

Health care ecosystems instantiate different innovation trajectories, driven either by science-/techno-push or user-centric rationales. This article focuses on organization intermediaries (OIs), respectively, active in health care ecosystems driven by science- and techno-push versus user-centric innovation processes; it aims at characterizing their operation and intervention modes. The analysis elaborates on network and content brokerage. Innovation also needs to consider various challenges associated with physical vicinity. The authors check whether territorial anchoring plays a role in brokerage, depending on the innovation model.

The article offers an investigation of eight French organizations matching the definition of OIs and active in different areas of health care-related innovation. It follows a qualitative and abductive research protocol adhering to the precepts of grounded theory.

First, the authors show that content and network brokerage specialize in specific activities in each innovation model. On network brokerage, the authors show that OIs foster the development of communities of practice in the science-/techno-push model, while they nurture communities of innovation in the user-centric model. Services materializing content brokerage are typical consequences of activities performed in each model. The second contribution deals with physical vicinity. In the science-/techno-push model, OIs install a physical space (the “internal” dimension) to support the development of communities of practice, while the “external” dimension copes with agglomeration effects. In the user-centric model, OIs deliver services thanks to the “internal” space; communities of innovation create a leverage effect on the physical space to operate their activities that are supported by “external” network effects.

The originality of the article lies in the description of the alternative roles plaid by organization intermediaries in the science-/techno-push versus user-centric approaches of innovation. In these two approaches, (contents and network) brokerage and physical vicinity play different roles.

Follett's relational process theory illuminates key aspects of interdependence among organizations in the field of education that are essential to fostering capacities for interorganizational resilience. The article argues for the necessity of developing mutualism in systems of education as essential preparation for times of instability and crisis, as demonstrated through recent experiences with the COVID-19 pandemic.

This article provides an introduction to the relational process theory of Mary Parker Follett, a foundational theorist of organizing and administration. This theoretical review uses worked examples drawn from a collaborative, continuous improvement partnership focused on educational leadership preparation and development.

The author identifies four aspects of Follett's theory that connect most directly to collaborative, continuous improvement partnerships. These include mutualism as circular response, coactive power, embrace of difference through constructive conflict and integrating experience through learning. The article discusses how these offer an integrated framework of foundational concepts for nurturing and sustaining educational systems capable of adaptive change in the face of complex challenges.

Follett's relational process theory offers a perspective on partnering as a dynamic and evolving constellation of interactions and activity. The implications of Follett's core ideas for education resonate beyond improvement partnerships and offer guidance at all levels of educational systems seeking to orient towards an evolutionary logic.

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.

The purpose of this paper is to propose and validate a robust industrial control system. The aim is to design a Multivariable Proportional Integral controller that accommodates multiple responses while considering the process's control and noise parameters. In addition, this paper intended to develop a multidisciplinary approach by combining computational science, control engineering and statistical methodologies to ensure a resilient process with the best use of available resources.

Taguchi's robust design methodology and multi-response optimisation approaches are adopted to meet the research aims. Two-Input-Two-Output transfer function model of the distillation column system is investigated. In designing the control system, the Steady State Gain Matrix and process factors such as time constant (t) and time delay (?) are also used. The unique methodology is implemented and validated using the pilot plant's distillation column. To determine the robustness of the proposed control system, a simulation study, statistical analysis and real-time experimentation are conducted. In addition, the outcomes are compared to different control algorithms.

Research indicates that integral control parameters (K_i) affect outputs substantially more than proportional control parameters (K_p). The results of this paper show that control and noise parameters must be considered to make the control system robust. In addition, Taguchi's approach, in conjunction with multi-response optimisation, ensures robust controller design with optimal use of resources. Eventually, this research shows that the best outcomes for all the performance indices are achieved when Kp11 = 1.6859, Kp12 = −2.061, Kp21 = 3.1846, Kp22 = −1.2176, Ki11 = 1.0628, Ki12 = −1.2989, Ki21 = 2.454 and Ki22 = −0.7676.

This paper provides a step-by-step strategy for designing and validating a multi-response control system that accommodates controllable and uncontrollable parameters (noise parameters). The methodology can be used in any industrial Multi-Input-Multi-Output system to ensure process robustness. In addition, this paper proposes a multidisciplinary approach to industrial controller design that academics and industry can refine and improve.

Adopting lean principles can unleash several opportunities for firms seeking to increase the efficiency and effectiveness of their product development (PD) process. This study aims to investigate the implementation paths of lean tools in the innovation process of small and medium-sized enterprises (SMEs).

A set of 47 lean tools are identified from the literature and ascribed to the five lean thinking principles, i.e. Value, Map, Flow, Pull and Perfection. Their practical adoption – in terms of “when” and “how” – is then explored in a multiple case study of three SMEs in the manufacturing industry.

SMEs adopt multiple lean tools in different phases of their innovation process. They are still at the beginning of the holistic adoption of lean PD, but some core lean tools, such as A3 reports and visual management, are adopted systematically. Results reveal that specific sets of lean tools and supporting principles are more valuable in certain phases of SMEs innovation process. Specifically, the lean tools concerning the principle of Value and Map can enable the phases of Innovation inputs, Concept development and Solution implementation; the ones ascribed to Flow and Pull the phases of Concept development, Testing and experimentation, and Solution implementation; the Perfection tools to the final phases of Testing and experimentation, Solution implementation and Market introduction.

Results provide a reference for SMEs already adopting lean tools in their production process to be extended to the PD process, especially when the delivery of new products is pivotal. Innovative SMEs could evaluate the introduction of specific lean tools in one or more definite phases of their PD process.

The study contributes to the literature on the complementarity between lean and innovation by studying the context of SMEs with a process perspective, thus unveiling the potential paths of a widespread application of lean innovation in SMEs.