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Distribution network design involves a set of strategic decisions in supply chains because of their long-term impacts on the total logistics cost and environment. To incorporate a trade-off between financial and environmental aspects of these decisions, this paper aims to determine an optimal location, among candidate locations, of a new logistics center, its capacity, as well as optimal network flows for an existing distribution network, while concurrently minimizing the total logistics cost and gas emission. In addition, uncertainty in transportation and warehousing costs are considered.

The problem is formulated as a fuzzy multiobjective mathematical model. The effectiveness of this model is demonstrated using an industrial case study. The problem instance is a four-echelon distribution network with 22 products and a planning horizon of 20 periods. The model is solved by using the min–max and augmented ε-constraint methods with CPLEX as the solver. In addition to illustrating model’s applicability, the effect of choosing a new warehouse in the model is investigated through a scenario analysis.

For the applicability of the model, the results indicate that the augmented ε-constraint approach provides a set of Pareto solutions, which represents the ideal trade-off between the total logistics cost and gas emission. Through a case study problem instance, the augmented ε-constraint approach is recommended for similar network design problems. From a scenario analysis, when the operational cost of the new warehouse is within a specific fraction of the warehousing cost of third-party warehouses, the solution with the new warehouse outperforms that without the new warehouse with respective to financial and environmental objectives.

The proposed model is an effective decision support tool for management, who would like to assess the impact of network planning decisions on the performance of their supply chains with respect to both financial and environmental aspects under uncertainty.

Gazelle companies are characterized by rapid growth in a short time. Identifying the determinants of this exponential expansion is important as these firms have a significant impact on the economy. They generate increased employment and investment by investors interested in new opportunities. Previous studies have failed to reach a consensus about what fosters high growth in gazelle companies as each firm’s geographical, political and economic context is different. The present research uses cognitive mapping and interpretive structural modeling (ISM) to overcome the limitations of prior investigations and identify factors that can potentially accelerate growth in gazelle companies.

Two sessions were held with an expert panel with knowledge about and experience with these firms. In the first session, data were collected to create a group cognitive map, while the second meeting comprised ISM-based analyses of the high-growth determinants identified and the causal relationships between them. A final consolidation session was held to discuss the results with two members of the Committee for Central Region Coordination and Development (i.e. Comissão de Coordenação e Desenvolvimento Regional do Centro – a public entity that grants gazelle awards in Portugal).

The analysis system created was tested, and the results demonstrate that the dual methodology used can increase our understanding of the dynamics of high-growth determinants and lead to more informed and potentially better evaluations of gazelle companies. Indeed, once high-growth determinants in gazelle companies are understood, this information can help other firms implement the same business model to achieve similarly rapid growth. The strengths and shortcomings of this new structured analysis model are also analyzed.

The authors know of no prior work reporting the integrated use of cognitive mapping and ISM in this study context.

Our goals were to explore the pedagogies applied by instructors that supported Black, Indigenous and People of Color (BIPOC) student learning in a leadership course and the leadership behaviors BIPOC students identified as being applicable after the course.

Through survey research and qualitative data analysis, three prominent themes emerged.

High-quality, purposeful pedagogy created opportunities for students to learn. Second, a supportive, interactive community engaged students with the instructor, each other and the course material to support participation in learning. As a result, students reported experiencing big shifts, new growth and increased confidence during their leadership courses.

We discuss our findings and offer specific recommendations for leadership educators to better support BIPOC students in their leadership courses and classrooms and for further research with BIPOC students.

This study aims to explore an emerging collection of smart building technologies, known as smart workplace solutions (SWS), in the context of facilities management (FM).

This study is based on semi-structured interviews with facility managers in Finland, Norway and Sweden who have deployed SWSs in their organizations. SWS features, based on empirical data from a previous study, were also used to further analyse the interviews.

It analyses the benefits that SWSs bring from the facility management point of view. It is clear that the impetus for change and for deploying SWS in the context of FM is primarily driven by cost savings related to reductions in office space.

This research has been conducted with a focus on office buildings only. However, other building types can learn from the benefits that facility managers receive in the area of user-centred smart buildings.

SWSs are often seen as employee experience solutions that are only related to “soft” elements such as collaboration, innovation and learning. Understanding the FM business case can help make a more practical case for their deployment.

SWSs are an emerging area, and this study has collected data from facility managers who use them daily.

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 autoethnographic article presents the adaptation of collage—an arts-based method traditionally used in face-to-face settings—into an online research tool. It emphasizes the increased relevance of such a transition in the wake of the digital shift and the Covid-19 pandemic. The work aims to reveal how digital collages can facilitate in-depth participant responses in a time when conventional research settings are inaccessible.

The article incorporates autoethnographic vignettes, which are identified in italics, that offer insights into my personal reflections on the transition and adaptation to an online mode. Firstly, I review how collage can be a valuable tool to include in focus groups and for elicitation during semi-structured interviews. Secondly, I review the challenges I experienced when conducting focus groups online to create the collages. Thirdly, I explore, in more detail, three examples of collages that reflect the diverse ranges that were produced and the insightful discussions that emerged from the participants describing the visual elements of their collages. Finally, the reflective nature of my autoethnographic vignettes provides an insight into the world of the researcher during this turbulent time.

Findings show that collage, whether physical or digital, remains an effective tool for eliciting nuanced understandings from participants. The research contributes to the arts-based research narrative by showcasing how the digital adaptation of collage methods can yield profound insights into participants' perspectives, therefore enriching the data beyond what traditional interviews could unveil.

These observations can provide support for other researchers who are contemplating the adoption of online arts-based research methods. Understanding how traditionally face-to-face arts-based research methods can be adapted for the digitally evolving landscape is important for shaping the future of online research.

This paper introduces the YouWalk-UOS mobile application, a tool that revolutionises the assessment of urban open spaces (UOS). The paper demonstrates how integrating real-time, on-ground observations with users’ reactions into a digital platform can transform the evaluation of urban open spaces. It seeks to address the existing shortcomings of traditional UOS assessment methods and underscore the need for innovative, adaptable and inclusive approaches.

Emphasizing the necessity of UOS for mental and physical health, community interaction and social and environmental resilience in cities, the methodology involves a comprehensive analysis of a number of theoretical frameworks that have historically influenced urban open space conceptualisation, design and assessment. The approach includes a critical review of traditional UOS assessment methods, contrasting them with the capabilities of the proposed YouWalk-UOS application. Building on the reviewed theoretical frameworks, the methodology articulates the application’s design, which encompasses 36 factors across three assessment domains: functional, social and perceptual and provides insights into how technology can be leveraged to offer a more holistic and participatory approach to urban space assessment.

YouWalk-UOS application represents an important advancement in urban space assessment, moving beyond the constraints of traditional methods. The application facilitates a co-assessment approach, enabling community members to actively participate in the evaluation and development of their urban environments. Findings highlight the essential role of technology in making urban space assessment more user-centred, aligning more closely with community needs and aspirations.

The originality lies in the focus on the co-assessment approach and integration of mobile technology into urban open space assessment, a relatively unexplored area in urban design literature. The application stands out as an innovative solution, offering a new perspective on engaging communities in co-assessing their environments. This research contributes to the discourse on urban design and planning by providing a fresh look at the intersection of technology, user engagement and urban space assessment.

The concept of “participation” has become a buzzword in contemporary public governance models. However, despite the broad and significant interest, defining participation remains a debated topic. The aim of the current study was to explore how participants perceived and interpreted the meaning and scope of participation.

This study is part of a four-year (2019–2022) longitudinal research project investigating stakeholder participation in the context of developing and establishing a strategic regional plan in Region Skåne in southern Sweden. The research project has a qualitative approach and uses interviews with different stakeholder groups such as municipal politicians and public officials and a survey as empirical material.

The authors developed a participation spectrum including eight types of participation: to be open, to be informed, to be listened to, to discuss, to be consulted, to give and take, to collaborate and to co-create. The authors also identified four different purposes of participation: creating a joint network, creating a joint understanding, creating a joint effort and creating a joint vision. The spectrum and the purposes were related through four characteristics of participation, i.e. involvement, interaction, influence and empowerment.

The study rests on a single case, and so the results have limited transferatibility.

Researching participation in terms of the participants' perceptions contributes a new perspective to the existing literature, which has commonly focussed on the organizers' perceptions of participation. Moreover, in order to clarify what participation meant to the participants, the study puts emphasis on untangling this from the why question of participation.

Prefabricated building has been widely applied in the construction industry all over the world, which can significantly reduce labor consumption and improve construction efficiency compared with conventional approaches. During the construction of prefabricated buildings, the overall efficiency largely depends on the lifting sequence and path of each prefabricated component. To improve the efficiency and safety of the lifting process, this study proposes a framework for automatically optimizing the lifting path of prefabricated building components using building information modeling (BIM), improved 3D-A* and a physic-informed genetic algorithm (GA).

Firstly, the industry foundation class (IFC) schema for prefabricated buildings is established to enrich the semantic information of BIM. After extracting corresponding component attributes from BIM, the models of typical prefabricated components and their slings are simplified. Further, the slings and elements’ rotations are considered to build a safety bounding box. Secondly, an efficient 3D-A* is proposed for element path planning by integrating both safety factors and variable step size. Finally, an efficient GA is designed to obtain the optimal lifting sequence that satisfies physical constraints.

The proposed optimization framework is validated in a physics engine with a pilot project, which enables better understanding. The results show that the framework can intuitively and automatically generate the optimal lifting path for each type of prefabricated building component. Compared with traditional algorithms, the improved path planning algorithm significantly reduces the number of nodes computed by 91.48%, resulting in a notable decrease in search time by 75.68%.

In this study, a prefabricated component path planning framework based on the improved A* algorithm and GA is proposed for the first time. In addition, this study proposes a safety-bounding box that considers the effects of torsion and slinging of components during lifting. The semantic information of IFC for component lifting is enriched by taking into account lifting data such as binding positions, lifting methods, lifting angles and lifting offsets.