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The purpose of this paper is to explore and understand the perceptions of residents of Southeastern Nigeria about glocalizing tourism in the region to help improve their support for the sustainability of the industry. Emphasis is laid on their expectations and strategies to maximize the positive impacts while minimizing the negative aspects in a bid to address their specific local needs.

This paper adopts an ethnographic approach to explore the perspectives of key stakeholders in Southeastern Nigeria's tourism industry. These include traditional rulers, men, women and youth representatives, chief priests and local security agents. Decision-making theory is adopted to frame the study.

The findings identified residents' expectations from glocalizing tourism. They see tourism as an avenue for initiating community projects, creating jobs, patronizing farm produces, reviving cultural practices and addressing religious crises.

This research focused only on selected communities within Southeastern Nigeria. The implication is that the findings do not represent what obtains in other communities within the region. Future research should extend to these areas to have a deeper understanding of how residents perceive the glocalization of tourism.

As the government and developers continue to invest in the tourism industry in the study area, glocalization could be a good way to address specific local needs and gain residents' support in the future.

This paper represents a new research approach for understanding the perceptions of residents about the Nigerian tourism industry.

This paper aims to critically examine traditional approaches to paradoxes and propose a new approach and perspective that views “chiasmic” organizing as a intertwining combination of structure and processes that facilitate the handling of multiple interrelations for processing paradoxes and harness their creative potential in organizations.

Employing a cross-disciplinary approach, a literature review and a critical lens, along with conceptual work (typology), are used to identify problems and deficiencies in existing research on paradoxes. Specifically, it draws on Merleau-Ponty's process-oriented phenomenology and post-Cartesian ontology to gain a comprehensive understanding of post-dualistic forms of chiasmic organizing and its relationship with paradoxical phenomena.

The process-oriented phenomenology and post-Cartesian ontology used in this article offer valuable insights and a critical approach to comprehend post-dualistic forms of chiasmic organizing in relation to paradoxes. This understanding can help in tapping into the energizing and creative potential of paradoxes. The paper also highlights the significance of the “in(ter)-between” as a reversible principle in chiasmic organizing and proposes some implications.

Limitations and implications of this study are identified and discussed.

The paper offers practical implications for organizations in processing paradoxes.

This paper contributes to the existing literature by providing a conceptual critique and proposing a novel understanding of chiasmic organizing as an intertwining structure and mediating processes by employing a process-oriented phenomenology and post-Cartesian ontology. It also offers innovative ways to approach paradoxes and tap into their creative potentials, which can bring about change in organizations.

The purpose of this study is to assess the significance of employee work flexibility and the policy of the organization for survival during any crisis. This study also investigates the moderating role of leadership support (LS) during such turbulent conditions.

This study has used literature from the fields of organization performance, human resources and organization policy (OP), along with the theories of resource-based view (RBV) and dynamic capability view (DCV) to develop a conceptual model. Later, the conceptual model is validated using the structural equation modeling technique. The study used a survey method with a sample of 311 participants. These participants are employed as human resource managers (HRM) and other supportive workforce at different levels in the organizations.

The study shows that innovativeness and employee flexibility (EFL) are critical toward organizations’ survival during any crisis. Also, the study highlights the importance of OP and LS for the survival of organizations during and after any turbulent condition.

This study provides valuable inputs to the leadership teams of organizations, especially HRM. This research also provides food for thought for policymakers and researchers in the field of organizational performance. This study also contributes to the overall body of literature on organization analysis and extends the literature on RBV and DCV.

The study adds value to the overall body of literature on organization performance and capabilities along with human resource management. Few studies have nurtured issues on EFL during turbulent conditions. Also, there are limited studies in the areas of OP such as favorable and unfavorable policies toward employees. Thus, this study can be considered unique. Moreover, the study investigates the moderating role of LS which adds value toward the body of literature on organizational leadership capability.

This paper aims to discuss the practical dilemmas of institutional change to tackle the climate crisis in a UK university, identifying key assumptions and issues that block meaningful change. The research was part of an initiative to define a theory of change (ToC) to meet the university’s institutional climate commitments.

The findings are based on interviews with members of an inter-disciplinary ToC working group, a staff–student climate coalition and student representatives at the university. Interviewees were purposively selected to gain insights into assumptions about the nature of the university and its role in tackling the climate crisis, which must be addressed for the university to effectively implement its climate plan.

This paper identified tensions between the university’s role as a public and commercial institution, a lack of clarity over decision-making processes and the difficulties in balancing (and being transparent about) actions with commitments to tackle the climate crisis. A democratic and flexible approach to change is essential to mitigate these issues, providing an opportunity to reflect on the diversity of the university community and openly debate goals and commitments.

In setting out the initial steps of a ToC in a UK university, this paper offers practical insights for higher education institutions looking to change practices. By highlighting assumptions at a particular institution, this paper also contributes a level of granularity to a growing field of research on efforts in higher education institutions to tackle the climate crisis.

The global construction industry faces both challenges and opportunities from electronic waste (e-waste). This study aims to present a bibliometric analysis and comprehensive literature assessment on e-waste in concrete construction materials.

This study studies 4,122 Scopus documents to examine garbage generation in different countries and inventive ways to integrate e-waste into construction as a sustainable strategy. This study lists famous researchers and their cooperation networks, demonstrating a robust and dynamic area with a surge in research output, notably from 2018 to 2022. Data is visually represented using VOS Viewer to show trends, patterns and study interests throughout time.

The findings imply that e-waste can improve construction materials’ mechanical characteristics and sustainability. The results are inconsistent and suggest further optimization. e-Waste into construction has garnered scientific interest for its environmental, life cycle, and economic impacts. This field has great potential for improving e-waste material use, developing sophisticated prediction models, studying environmental implications, economic analysis, policy formulation, novel construction methods, global cooperation and public awareness. This study shows that e-waste can be used in sustainable building. It stresses this area’s need for research and innovation. This lays the groundwork for using electronic trash in buildings, which promotes a circular economy and environmental sustainability.

The findings underscore the critical role of ongoing research and innovation in leveraging e-waste for sustainable building practices. This study lays the groundwork for integrating e-waste into construction, contributing to the advancement of a circular economy and environmental sustainability.

The social implications of integrating e-waste into construction are significant. Using e-waste not only addresses environmental concerns but also promotes social sustainability by creating new job opportunities in the recycling and construction sectors. It fosters community awareness and responsibility towards sustainable practices and waste management. Additionally, this approach can reduce construction costs, making building projects more accessible and potentially lowering housing prices.

This research contributes to the field by offering a bibliometric analysis and comprehensive assessment of e-waste in concrete construction materials, highlighting its global significance.

Our study examines how artificial intelligence (AI) can enhance decision-making processes to promote circular economy practices within the utility sector.

A unique case study of Alia Servizi Ambientali Spa, an Italian multi-utility company using AI for waste management, is analyzed using the Gioia method and semi-structured interviews.

Our study discovers the proactive role of the user in waste management processes, the importance of economic incentives to increase the usefulness of the technology and the role of AI in waste management transformation processes (e.g. glass waste).

The present study enhances the circular economy model (transformation, distribution and recovery), uncovering AI’s role in waste management. Finally, we inspire managers with algorithms used for data-driven decisions.

Artificial intelligence (AI) can help in defining preventive strategies in taking decisions in complex situations. This paper aims to research how workers might deal with intervening AI tools, with the goal of improving their daily working decisions and movements. We contribute to deepening how workers might deal with intervening AI tools aiming at improving their daily working decisions and movements. We investigate these aspects within a field, which is growing in importance due to environmental sustainability issues, i.e. waste management (WM).

This manuscript intends to (1) investigate if AI allows better performance in WM by reducing social security costs and by guaranteeing a better continuity of service and (2) examine which structural change is required to operationalize this predictive risk model in the real working context. To achieve these goals, this study developed a qualitative inquiry based on face-to-face interviews with highly qualified experts.

There is a positive impact of AI schemes in helping to detect critical operating issues. Specifically, AI potentially represents a tool for an alignment of operational behaviours to business strategic goals. Properly elaborated information, obtained through wearable digital infrastructures, allows to take decisions to streamline the work organization, reducing potential loss due to waste of time and/or physical resources.

Being a qualitative study, and the limited extension of data, it is not possible to guarantee its replication and generalizability. Nevertheless, the prestige of the interviewees makes this research an interesting pilot, on such an emerging theme as AI, thus eliciting stimulating insights from a deepening of information coming from respondents’ knowledge, skills and experience for implementing valuable AI schemes able to an align operational behaviours to business strategic goals.

The most critical issue is represented by the “quality” of the feedback provided to employees within the business environment, specifically when there is a transfer of knowledge within the organization.

The study focuses on a less investigated context, the role of AI in internal decision-making, particularly, for what regards the interaction between managers and workers as well as the one among workers. Algorithmically managed workers can be seen as the players of summarized results of complex algorithmic analyses offered through simpleminded interfaces, which they can easily use to take good decisions.

By studying the green development efficiency (GDE) of 33 cities in the provinces of Jiangsu, Zhejiang, and Fujian in China, this study strives to conduct an analysis of the sustainable practices implemented in these developed regions, and derive valuable insights that can foster the promotion of green transformation.

First, the urban green development system (GDS) was decomposed into the economic benefit subsystem (EBS), social benefit subsystem (SBS), and pollution control subsystem (PCS). Then, a mixed network SBM model was proposed to evaluate the GDE during 20152020, with Moran’s I and Bootstrap truncated regression model subsequently applied to measure the spatial characteristics and driving factors of efficiency.

Subsystem efficiency presents a distribution trend of PCS > EBS > SBS. There is a particular spatial aggregation effect in EBS efficiency, whereas SBS and PCS efficiencies have no significant spatial autocorrelation. Furthermore, urbanization level contributes significantly to the efficiency of all subsystems; industrial structure, energy consumption, and technological innovation play a crucial role in EBS and SBS; external openness is a pivotal factor in SBS; and environmental regulation has a significant effect on PCS.

This study further decomposes the black box of GDS into subsystems including the economy, society, and environment. Additionally, by employing a mixed network SBM model and Bootstrap truncated regression model to investigate efficiency and its driving factors from the subsystem perspective, it endeavors to derive more detailed research conclusions and policy implications.

This study investigates and explores sustainable fashion based on social attitudes toward culture and sustainable fashion effects in sub-Saharan Africa (SSA), based on environmental knowledge and consumer satisfaction initiatives. It explicates sustainable fashion on the sustainable development agenda in addressing the gap of cultural value, environmental knowledge and sustainable fashion in SSA.

Using a quantitative approach, the study employed a web-based online cross-sectional survey to extract tangible information from 620 participants from SSA. The study integrated theory of planned behaviors (TPB) model and hypotheses. A structural equation model (SEM) was used to test all proposed hypotheses.

The results show that low environmental knowledge, influenced by geographical and cultural differences, affected fashion value, as which is predictively significant for sustainable fashion. However, attitude and cultural value results found statistical significance for consumer satisfaction in sustainable fashion. Furthermore, mediation was attained between consumer behavioral and environmental knowledge of sustainable fashion. The study recommends government policies on educational awareness and textile regulations for environmental garbage disposal possible harmful effects of climate change and finally, designing innovative initiatives for environmentally friendly fashion.

This study examines the environmental and social attitudes as well as behavioral effects, of an ecosystem that would most likely have a short life period, eliminate disposal dumps and foster an environmental control policy. Consequently, the study’s conceptual model and extended TPB contribute to how sustainable fashion supports environmental knowledge, consumer attitudes and cultural behaviors in fashion among Sub-Saharan Africans.

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.