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Because of social interaction difficulties, cognitive characteristics and sensory sensitivity, people with autism spectrum disorder (ASD) may struggle with independence and active participation in urban contexts. Through understanding how people with ASDs experience and perceive urban spaces, the Personalized Interactive Urban Maps for Autism (PIUMA) project’s purpose is to create a useful platform to support persons with ASD in their movements in the city, helping them in managing their daily lives, promoting their autonomy and active participation in urban contexts, taking in to account their direct point of view. This paper aim to present a user-centered approach involving autistic people.

The authors involved researchers with different backgrounds applying mixed research techniques such as semi-structured qualitative interviews, participatory design, exploiting user-adapted systems techniques, human–computer interaction techniques and interviews. The authors involved people with autism (PWA) from the beginning of the project to gain user requirements, as well as to assess the acceptability and effectiveness of our solution in the final stage. In specific phases of the project, the authors involved a group of parents of PWA and a group of neurotypical individuals as a control group.

The authors collected various data and created an App taking into account PWA needs, suggestions and points of view.

Choosing different methodologies to allow participants to express their point of view, their needs and suggestions, the authors limited the quantitative data collected. The male-to-female ratio in the study, along with the sample size and participants’ age, can also be considered limitations.

The authors collected information about how autistic people move in urban contexts, represent the space of their cities, move independently and use technological devices. The data the author collected also provides suggestions about how to improve computer systems designed for them.

The results suggest how to improve computer systems designed for autistic people and how to encourage their full participation in community life according to their cognitive and sensory characteristics.

The PIUMA project takes into account the direct input of PWA in all the different stages from conception and design to deployment of the device in a human-centered design approach.

This study compares the fatigue performance and biocompatibility of as-built and chemically etched Ti-6Al-4V alloys in TPMS-gyroid and stochastic structures fabricated via Powder Bed Fusion Laser Beam (PBF-LB). This study aims to understand how complex lattice structures and post-manufacturing treatment, particularly chemical etching, affect the mechanical properties, surface morphology, fatigue resistance and biocompatibility of these metamaterials for biomedical applications.

Selective Laser Melting (SLM) technology was used to fabricate TPMS-gyroid and Voronoi stochastic designs with three different relative densities (0.2, 0.3 and 0.4) in Ti-6Al-4V ELI alloy. The as-built samples underwent a chemical etching process to enhance surface quality. Mechanical characterization included static compression and dynamic fatigue testing, complemented by scanning electron microscopy (SEM) for surface and failure analysis. The biocompatibility of the samples was assessed through in-vitro cell viability assays using the Alamar Blue assay and cell proliferation studies.

Chemical etching significantly improves the surface morphology, mechanical properties and fatigue resistance of both TPMS-gyroid and stochastic structures. Gyroid structures demonstrated superior mechanical performance and fatigue resistance compared to stochastic structures, with etching providing more pronounced benefits in these aspects. In-vitro biocompatibility tests showed high cytocompatibility for both as-built and etched samples, with etched samples exhibiting notably improved cell viability. The study also highlights the importance of design and post-processing in optimizing the performance of Ti64 components for biomedical applications.

The comparative analysis between as-built and etched conditions, alongside considering different lattice designs, provides valuable information for developing advanced biomedical implants. The demonstration of enhanced fatigue resistance and biocompatibility through etching adds significant value to the field of additive manufacturing, suggesting new avenues for designing and post-processing implantable devices.

Pursuing digital transformation is a valuable strategy to attain companies’ operational excellence and sustainable development. However, installing digital technologies and software is insufficient for achieving a successful digital transformation. Equally vital is providing digital solutions with reliable input data, which is a hard task in companies where information is gathered through manual or non-standardized processes. The lack of reliable data prevents technologies and software from operating at their best, hindering their ability to process information and derive correct insights for improvement. To avoid this, companies should embrace structured problem-solving approaches to evaluate current data retrieval processes, identify error sources and formulate countermeasures. This paper aims to provide an empirical study to substantiate A3 as a winning approach for advancing input data acquisition in companies.

A case study research is proposed, investigating the application of A3 in an ink manufacturing company, and checking how A3 improves data collection and company performance.

The case study corroborates A3 as an effective approach, allowing the removal of inefficiencies that previously went unnoticed and reaching a 10% productivity improvement.

Pursuing digital transformation is a valuable strategy to attain companies’ operational excellence and sustainable development. However, installing digital technologies and software is insufficient for achieving a successful digital transformation. Equally vital is providing digital solutions with reliable input data, which is a hard task in companies where information is gathered through manual or non-standardized processes.

The originality of this paper lies in the application scope and aim of the A3 approach. A3 is a mature paradigm but often coined for production and still endowed with unexplored potential. This paper proposes the application of A3 for improving companies’ data retrieval processes, focusing for the first time on information reliability and its importance in ensuring the functioning of digital technologies and software.

The current paper aims to discuss the implementation of Green Lean Six Sigma (GLSS) in the food industry to improve sustainable practices. The focus is more specifically on dairy processors to ascertain the current state of the literature and aid future research direction.

Utilising a systematic literature review (SLR), the paper addresses various terms and different written forms in the literature. The study characterises the current deployment of GLSS in the food industry and explains the reported benefits of this approach.

GLSS, a concept that has yet to be fully explored in the food industry, as in other sectors, holds significant potential to enhance the food industry’s sustainability practices. The dairy sector, a subsector of the food industry known for its high greenhouse gas emissions, is a prime candidate for the application of GLSS. In instances where it has been applied, GLSS has demonstrated its effectiveness in improving sustainability, reducing waste, lowering greenhouse gas emissions and minimising water usage. However, the specific tools used and the model for GLSS implementation are areas that require further study, as they have the potential to revolutionise food industry operations and reduce their environmental impacts.

Benchmarking of this research by the food industry sector and by academics can aid understanding of the practical application of GLSS tools and aid implementation of these practices to evolve the dairy processing sector in the next decade as sustainability champions in the sector.

This study extensively analyses GLSS in the food industry, with a particular focus on dairy processors.

This paper draws on the thinking of the nineteenth-century Italian philosopher and poet Giacomo Leopardi and scholars who studied his thoughts on the relationship between nature and humans. Leopardi's philosophy of nature recognizes the alienness of nature in relation to humankind, thus challenging human governance of the planet. The poet’s thoughts align with the dilemma identified in the Anthropocene literature: who speaks for nature? This dilemma has accounting implications in terms of the frameworks and disclosures to be adopted. Therefore, Leopardi’s thoughts can become the basis for a more articulated and complex understanding of some key concepts and issues at the roots of SEA.

The paper utilizes content analysis to examine four essays by Giacomo Leopardi, which serve as the source of our data.

Leopardi recognizes the alienness of nature with respect to humanity and the voicelessness of nature as a generative of conflict. He also warned of the consequences of human governance that does not take nature’s needs into account. These findings open a discussion on the complex accounting implications of the distance between humanity and nature. They can inspire SEA scholars to change the status quo by developing new accounting frameworks from the perspective of nature and adopting forms of governance of nature that recognize the need to protect it as a voiceless stakeholder.

Through Leopardi’s humanistic and poetic philosophy, the perspective of nature can be infused into SEA studies, thereby promoting the need for a multidisciplinary and complex approach to the discipline.

Acknowledging the importance of both lean thinking (LT) and Industry 4.0 (I4.0) for successful business performance and organisational success, the present study investigates the critical success factors (CSFs) for the concurrent implementation of both approaches, a field not yet significantly explored.

The study conducts two systematic literature reviews (SLRs), one on LT and the other one on I4.0 to map out the CSFs for the effective implementation of each approach. These CSFs are subsequently prioritised with the use of a Delphi Study. Finally, from the set of the common CSFs recognised through the two approaches, a more condensed list is put forward as the first step towards achieving a successful synergy between LT and I4.0.

The study’s findings suggest the most important CSFs and determine their definition in the context of a concurrent implementation of LT and I4.0. This can provide managers and practitioners with the awareness of crucial factors, enabling them to take the necessary steps for planning and implementing both approaches.

A concise set of CSFs for the concurrent implementation of both LT and I4.0 has been identified, which can be viewed as a starting point for providing top executives and managers with useful insights into enhanced business efficiency and performance. This study furthermore contributes to the overall body of knowledge on LT and I4.0.

The scholarly literature that explores a common set of CSFs for the concurrent implementation of LT and I4.0 is limited. This gap significantly enhances the importance of the present research, contributing to a better understanding amongst both academics and practitioners of the key supporting factors for the integration of the two approaches.

Many organizations currently transition towards digitalized process design, execution, control, assurance and improvement, and the purpose of this research is to empirically demonstrate how data-based operational excellence techniques are useful in digitalized environments by means of the optimization of a robotic process automation deployment.

An interpretive mixed-method case study approach comprising both secondary Lean Six Sigma (LSS) project data together with participant-as-observer archival observations is applied. A case report, comprising per DMAIC phase (1) the objectives, (2) the main deliverables, (3) the results and (4) the key actions leading to achieving the presented results is presented.

Key findings comprise (1) the importance of understanding how to acquire and prepare large system generated data and (2) the need for better large system-generated database validation mechanisms. Finally (3) the importance of process contextual understanding of the LSS project lead is emphasized, together with (4) the need for LSS foundational curriculum developments in order to be effective in digitalized environments.

This study provides a rich prescriptive demonstration of LSS methodology implementation for RPA deployment improvement, and is one of the few empirical demonstrations of LSS based problem solving methodology in industry 4.0 contexts.

This paper investigates how two characteristics of language, abstractness vs concreteness and narrativity, influence user engagement in communication exercises on innovation targeted to the general audience. The proposed conceptual model suggests that innovation fits well with more abstract language because of the association of innovation with imagination and distal construal. Moreover, communication of innovation may benefit from greater adherence to the narrativity arc, that is, early staging, increasing plot progression and climax optimal point. These effects are moderated by content variety and emotional tone, respectively.

Based on a Latent Dirichlet allocation (LDA) application on a sample of 3225 TED Talks transcripts, the authors identify 287 TED Talks on innovation, and then applied econometric analyses to test the hypotheses on the effects of abstractness vs concreteness and narrativity on engagement, and on the moderation effects of content variety and emotional tone.

The authors found that abstractness (vs concreteness) and narrativity have positive effects on engagement. These two effects are stronger with higher content variety and more positive emotional tone, respectively.

This paper extends the literature on communication of innovation, linguistics and text analysis by evaluating the roles of abstractness vs concreteness and narrativity in shaping appreciation of innovation.

This paper reports conceptual and empirical analyses on innovation dissemination through a popular medium – TED Talks – and applies modern text analysis algorithms to test hypotheses on the effects of two pivotal dimensions of language on user engagement.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

This paper has a dual purpose. The first is to provide a thorough analysis of developments in international marketing in relation to the coronavirus disease 2019 (COVID-19) pandemic; the second is to capitalize on these developments to set an agenda for future research in the field of international marketing.

This paper zooms in on and reviews the 18 papers published in International Marketing Review’s (IMR) Special Issue on “Covid 19: advancing international marketing theory and guiding practice” (2023, volume 40, issue 5). It also integrates recent research at the intersection of international marketing and the COVID-19 pandemic.

The paper highlights five areas that embody significant contemporaneous changes brought about by the COVID-19 pandemic and affect international marketing practice. These include (1) shifts in consumer behavior, (2) digitalization and artificial intelligence, (3) disruptions in supply chains, (4) communication and corporate social responsibility (CSR), and (5) international dynamic marketing capabilities. In order to advance international marketing theory in relation to pandemics and other external crises, the paper establishes research directions for each of these areas.

The paper provides a novel and comprehensive categorization of fundamental shifts caused by the COVID-19 pandemic and lays out a research roadmap to advance research in the field of International Marketing (IM). Important implications for practice are also discussed.