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Violence toward frontline health-care workers (HCWs) from patients and visitors is a pervasive issue that ranges from verbal and psychological abuse to physical assault. The emergence of the COVID-19 pandemic has led to increased reports of escalated verbal workplace aggressions (VWPAs); however, most studies have been conducted internationally. Studies based in the USA have focused on physical violence experienced by nurses and paramedics in emergency situations. The purpose of this study is to learn about the experiences of different levels of frontline HCWs with VWPA from patients and visitors and discover ways to address this issue.

This qualitative descriptive study asked registered nurses, licensed practical nurses and patient care technicians from one health-care system about their experiences with patient and visitor VWPA using an anonymous, voluntary open-ended survey and in-person interviews. In all, 31 participants completed the survey and 2 were interviewed. Data were analyzed using content analysis.

Three themes emerged from the data: the experience, moving through and moving forward. Frontline HCWs described experiences of VWPA, indicating its forms, frequency and conditions. They used coping, along with personal and professional measures, to manage and move through the situation. Moving forward was captured as suggestions for the future and conveyed hope for a perfect state.

The experiences of frontline HCWs offered insight into how they perceive and cope with difficult encounters. Recommendations relate to not only implementing interventions that support frontline HCWs but also creating a culture where aggression is not tolerated and addressing perpetrator behavior is a priority.

This paper aims to explore the impact of COVID-19 on office space in Johannesburg. This study further explores the role of changing work practices in the office sector in South Africa because of the pandemic and its impact on future office space use planning and management.

To understand the footprints that the COVID-19 pandemic has left on the office space market in Johannesburg, this study uses semi-structured interviews, which were administered to corporate office users, and a thematic analysis was adopted to understand the views, perspectives and expectations of office users.

The study showed that space users perceive COVID-19-induced remote working as having benefits, opportunities and challenges. A notable shift in office space utilization has emerged, with employees increasingly opting for roles that permit remote work. This newfound flexibility, accommodating both on-site and remote work, often makes working from home more appealing than traditional office environments that may no longer align with users’ preferences for office spaces.

The study is limited to Johannesburg, South Africa, and may not apply to other African markets. Ten in-depth interviews were conducted, and analysis and results were deduced; this may be considered a limitation of this study.

The pandemic’s impact has brought about irreversible changes, compelling policymakers and business leaders to strategize and prepare. This proactive stance aims to prevent avoidable challenges for employees and companies during future pandemics. A thoughtful approach to the post-pandemic world can usher positive changes in the office and property sector. This includes the coexistence of both remote work and on-site working models.

This paper provides valuable insight into some of the outcomes of the COVID-19 pandemic in South Africa that are essential for future office space use planning and management. The insights from this study extend the literature and provide novel knowledge based on an office sector in the “global south.”

This study aims to examine the enablers of productivity of enterprise-level Agile development process using modified total interpretative structural modeling (TISM). The two main objectives of the current study are to determine the variables influencing enterprise-level agile development productivity and to develop modified TISM for the corresponding components.

To identify enablers of the productivity of enterprise-level agile software development process a literature review and opinions of domain experts were collected. A hierarchical relationship among variables that show direct and indirect influence is created using the modified TISM (M-TISM) technique with Cross Impact Matrix-Multiplication Applied to Classification analysis. This study examined and analyzed the relationships between the determinants within the enterprise using a M-TISM technique.

With the literature review, the study could identify ten enabling factors of the productivity of Agile development process at the enterprise level. Results depict that program increment (PI) planning and scalable backlog management, continuous integration and continuous delivery (CI/CD), agile release trains (ART), agile work culture, delivery excellence, lean and DevOps practices, value stream mapping (VMS), team skills and expertise, collaborative culture, agile coaching, customer engagement have an impact on the productivity of enterprise-level Agile development process. The results show that team collaboration, agile ways of working and customer engagement have a greater impact on productivity improvement for enterprise-level Agile development process.

The developed model is useful for organizations employing scaled Agile development processes in software development. This study provides a recommended listing of key enablers, that may enable productivity improvements in the Agile development process at the enterprise level. Strategists should focus on team collaboration and Agile project management. This study offers a modified TISM model to academicians to help them understand the effects of numerous variables on maintaining the productivity of an enterprise-level Agile. The identified characteristics and their hierarchical structure can help project managers during the execution of Agile projects at the enterprise level, more effectively, increasing their success and productivity.

The study addresses the gap in the literature by interpretative relationships between the identified enabling factors. The model validation is carried out by a panel of nine experts from several information technology organizations deploying Agile software development at the enterprise level. This unique method broadens the knowledge base in Agile software development at scale and provides project managers and practitioners with a practical foundation.

In developing countries like Tanzania, gems and jewellery industry mainly consists of disintegrated and unstable micro and small workshops which operate in a way that misalign value addition processes. This study is aimed to bridge gap by focussing on exploitation of industrial clusters in social normalisation and economic resilience to developing countries. The world economic shocks has been not only individually experienced but also globally shared while disrupted lives across all countries and communities and negatively affected global socio-economic growth.

Furthermore, the explorative design was adopted in this study in order to explore needs of respondents, and with the aim to direct the study towards a descriptive design. The sample frame consists of participants in gems and jewellery activities in Tanzania whereby sample was drawn from Dar es Salaam and Arusha. Semi-structured interview was used to collect quantitative data to establish evidence of Tanzanians’ SSJs linked to global value chains (GVCs).

Results revealed the benefits of exploitation of artisanal industrial clusters to Tanzanians’ SSJs when linked to global value chains (GVCs). Findings of the study demonstrate the importance of artisanal industrial clusters in facilitating Tanzanians’ SSJs to access GVCs. Further, insufficient education, trust and social protection directly affects inclusive GVCs, inferring that the impact of artisanal industrial clusters on inclusive GVCs in social normalisation and economic resilience.

Study findings reveals shortcomings in existing regulatory framework of linking Tanzanians’ SSJs to artisanal industrial clusters, for improvements to better support the inclusiveness in GVCs. Findings of this research invite interventions on institutional capabilities and entrepreneurial competencies to enhance the capabilities of small-scale jewellers (SSJs). Like other studies, this study involved cross-sectional data, limit targeted study population as representative of SSJs in industrial clusters and GVCs in economic crises at limited time.

The study findings makes important practical contributions to the Tanzania’s SSJs by examining mediating role of artisanal industrial clusters hence informing policymakers of mining sector how to improve accessibility on GVCs by focus on offering great institutional capabilities and entrepreneurial competencies. These findings will help SSJs and policy makers to get better understanding of the relationships in exploitation of artisanal industrial clusters when accessing GVCs. Therefore, they can make better decisions on implementing artisanal industrial clusters as well as management accessing GVCs, so that SSJs will attain the best possible performance.

This emphasises the importance of community empowerment in the GVCs process through artisanal industrial clusters. Study findings indicate the influence of industrial relations to social dynamics which are previously inadequately addressed and scantly researched. In actual fact study propose initiatives that ensure local communities benefit socially from the integration of SSJs into GVCs through artisanal industrial clusters. Findings suggest local communities that take into account inter-sectionality of artisanal industrial clusters and inclusive GVCs, by considering how factors like education, trust and social protection status intersect to influence the social inclusiveness of SSJs.

There is limited evidence of linking Tanzanians’ SSJs to GVCs in social normalisation and economic resilience and few researchers have explored this topic. This article leverages exploitation of industrial clusters in normalisation and economic resilience to developing countries such as Tanzania as way of improving shared prosperity, sustainability, inclusive growth, cohesion, value chain upgrading and financial inclusion to SSJs.

Supplier selection along with continuous evaluation of their performance is a crucial activity in healthcare supply chain management for effective utilization of scarce resources while providing quality service at an affordable price, and minimizing chances of stock-out, avoiding serious consequences on the illness or fatality of the patients. Presence of both qualitative and quantitative evaluation criteria, set of potential suppliers and participation of different stakeholders with varying interest make healthcare supplier selection a challenging task which can be effectively solved using any of the multi-criteria decision making (MCDM) methods.

To deal with various qualitative criteria, like cost, quality, delivery performance, reliability, responsiveness and flexibility, this paper proposes integration of grey system theory with a newly developed MCDM tool, i.e. mixed aggregation by comprehensive normalization technique (MACONT) to identify the best performing supplier for pharmaceutical items in a healthcare unit from a pool of six competing alternatives based on the opinions of three healthcare professionals.

While assessing importance of the six evaluation criteria and performance of the alternative healthcare suppliers against those criteria using grey numbers, and exploring use of three normalization procedures and two aggregation operations of MACONT method, this integrated approach singles out S5 as the most compromised healthcare supplier for the considered problem. A sensitivity analysis of its ranking performance against varying values of both balance parameters and preference parameters also validates its solution accuracy and robustness.

This integrated approach can thus efficiently solve healthcare supplier selection problems based on qualitative evaluation criteria in uncertain group decision making environment. It can also be deployed to deal with other decision making problems in the healthcare sector, like supplier selection for healthcare devices, performance evaluation of healthcare units, ranking of physicians etc.

Our proposed machine learning model contributes to improving the quality of Hire by providing a more nuanced and comprehensive analysis of candidate attributes. Instead of focusing solely on obvious factors, such as qualifications and experience, our model also considers various dimensions of fit, including person-job fit and person-organization fit. By integrating these dimensions of fit into the model, we can better predict a candidate’s potential contribution to the organization, hence enhancing the Quality of Hire.

Within the scope of the investigation, the competencies of the personnel working in the IT department of one in the largest state banks of the country were used. The entire data collection includes information on 1,850 individual employees as well as 13 different characteristics. For analysis, Python’s “keras” and “seaborn” modules were used. The Gower coefficient was used to determine the distance between different records.

The K-NN method resulted in the formation of five clusters, represented as a scatter plot. The axis illustrates the cohesion that exists between things (employees) that are similar to one another and the separateness that exists between things that have their own individual identities. This shows that the clustering process is effective in improving both the degree of similarity within each cluster and the degree of dissimilarity between clusters.

Employee competencies were evaluated within the scope of the investigation. Additionally, other criteria requested from the employee were not included in the application.

This study will be beneficial for academics, professionals, and researchers in their attempts to overcome the ongoing obstacles and challenges related to the securing the proper talent for an organization. In addition to creating a mechanism to use big data in the form of structured and unstructured data from multiple sources and deriving insights using ML algorithms, it contributes to the debates on the quality of hire in an entire organization. This is done in addition to developing a mechanism for using big data in the form of structured and unstructured data from multiple sources.

This study delves into the transformation of UAE-Israel relations, which transitioned from a long-term rivalry to a formal peace agreement in 2020. It aims to uncover the multifaceted elements that influenced both nations’ pursuit of bilateral negotiations, with a special emphasis on the role of unofficial collaboration.

Employing a case study approach, the research traces the evolution of the UAE-Israel ties, mapping their progression from covert collaborations to public accords. This exploration is set against a backdrop of political, economic, and societal factors that have historically characterized the broader Israel-Arab conflict. Real-world dynamics and theoretical constructs are analyzed in tandem to derive comprehensive insights.

Key drivers for the transformation of UAE-Israel relations included the threat from Iran, internal disturbances, economic stresses, and the strategic advantages of discreet diplomacy. Exogenous catalysts like the Covid-19 pandemic and Israel's annexation plans in 2019-2020 played pivotal roles, capitalizing on pre-existing covert collaborations and shared regional interests. Constructive strategies, notably inducements, effectively reshaped their adversarial relationship. The resultant U.S.-mediated agreement conferred strategic, security, economic, and diplomatic benefits to both parties. Importantly, the potency of conengagement conflict management strategy, especially when bolstered by exogenous factors and growing mutual interest, emerged as a game-changer in terminating longstanding rivalries.

This study offers a unique perspective on Israel-UAE relations, emphasizing the significance of covert engagements, inducements, and the innovative conengagement strategy in conflict resolution. By examining a relationship devoid of direct armed conflict, the research underscores the interplay of economic, political, and societal factors in reshaping rivalries. This case study serves as a testament to the potential for transformative change in enduring disputes when the right conditions and strategies align, supplementing conventional perspectives and offering valuable implications for policy and mediation initiatives in the Middle East.

This chapter investigates the potential of integrating multiple criteria decision-making (MCDM) techniques with decision support systems of digital supply chain management (DSCM) to achieve optimal outcomes. Digital supply chain (DSC) employs digital technologies (DTs) such as artificial intelligence (AI), Internet of Things (IoT), and big data analytics to provide extensive datasets and valuable insights pertaining to supply chain operations. MCDM techniques employ these realizations to facilitate informed decision-making through the assessment of multiple competing criteria. Usually MCDM approaches are used in the academic research with comparatively lesser application in industry. We argue that MCDM methodologies can play an instrumental role in DSCM, specifically in the areas of supplier selection, demand forecasting, and inventory management. Nevertheless, the integration of MCDM like AHP, ANP, DEMATEL, etc., with decision support systems presents several challenges, including concerns regarding the quality of data and the intricate task of assigning weights to various factors.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

Prefabricated technology is gradually being applied to the construction of subway stations due to its characteristic of mechanization. However, the prefabricated subway station in China is in the initial stage of development, which is prone to construction safety issues. This study aims to evaluate the construction safety risks of prefabricated subway stations in China and formulate corresponding countermeasures to ensure construction safety.

A construction safety risk evaluation index system for the prefabricated subway station was established through literature research and the Delphi method. Furthermore, based on the structure entropy weight method, matter-element theory and evidence theory, a hybrid evaluation model is developed to evaluate the construction safety risks of prefabricated subway stations. The basic probability assignment (BPA) function is obtained using the matter-element theory, the index weight is calculated using the structure entropy weight method to modify the BPA function and the risk evaluation level is determined using the evidence theory. Finally, the reliability and applicability of the evaluation model are verified with a case study of a prefabricated subway station project in China.

The results indicate that the level of construction safety risks in the prefabricated subway station project is relatively low. Man risk, machine risk and method risk are the key factors affecting the overall risk of the project. The evaluation results of the first-level indexes are discussed, and targeted countermeasures are proposed. Therefore, management personnel can deeply understand the construction safety risks of prefabricated subway stations.

This research fills the research gap in the field of construction safety risk assessment of prefabricated subway stations. The methods for construction safety risk assessment are summarized to establish a reliable hybrid evaluation model, laying the foundation for future research. Moreover, the construction safety risk evaluation index system for prefabricated subway stations is proposed, which can be adopted to guide construction safety management.