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Factors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.

In this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.

The findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.

The model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.

Although the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.

Previous research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

This study aims to identify opportunities and constraints of community forestry in the context of forest decentralization in Cameroon and what can be capitalized on for sound REDD+ design and implementation.

A qualitative approach to data collection was used through content analysis of 1994 forestry law, reports and publications related to decentralized forest management, community forestry and REDD+ in Cameroon. Principles that govern community forest and REDD+ were highlighted and opportunities and constraints of community forestry for REDD+ projects were discussed.

Community forestry was developed principally to protect forests in order to support the subsistence and income-generating extractive activities of forest-dependent communities. Community forestry governance arrangements were not designed with the objective of achieving verifiable emissions reductions or carbon stock values. Hence, existing community forestry institutions may not address all the specific demands of REDD+ programs. However, existing community institutions and practices can be strengthened or modified to align better with climate change mitigation goals and to achieve REDD+ objectives in community forestry sites. On the other hand, REDD+ was developed principally to mitigate climate change by reducing emissions from deforestation and forest degradation principally within developing countries where the livelihoods of forest-dependent people are a central component of all forest management policies. However, despite fundamental differences between community forestry and REDD+, there is substantial synergy between their objectives, and the dual forest conservation and livelihood development focus of both programs means that policies that strengthen and support existing community forestry institutions and sites will advance REDD+ objectives. As such, REDD+ will likely to be more successful if it builds on lessons learned from community forestry.

This paper demonstrates how REDD+ is more likely to succeed if it builds on the lessons learned from community forestry over the past 20-plus years in Cameroon. It also discusses how REDD+ can benefit from community forestry and how some of the many challenges related to community forestry can be directly addressed by the REDD+ mechanism. Further, this paper also argues how the congruence between community forestry and REDD+ can effectively facilitate the direct use of community forestry as a tool to achieve REDD+ goals.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

Healthcare organizations worldwide were badly hit by the “surprise” of the pandemic. Hospitals in particular are trying hard to manage problems it caused, searching for solutions to protect the health of citizens and reorienting operations. The implementation of resilience solutions in the coping phase and the ability to react promptly and redefine activities is essential. Integrating crisis management and resiliency literature, this paper discusses how health organizations were able to cope with adversity during the crisis.

The research is conducted through a case study of a large Italian hospital, the Gemelli Polyclinic Foundation, which was one of the leading hospitals in the Italian response to the pandemic.

The case reports actions taken in order to continue functioning and to maintain core activities despite severe adversity. The overall response of the Gemelli was the result of the three types of response: behavioral (effective leadership), cognitive (rapid resource reallocation) and the contextual reinforcement (multiagency network response). The authors highlight how an integrative framework of crisis management and resiliency could be applied to healthcare organizations in the coping phase of the pandemic. The experience of the Gemelli can thus be useful for other hospitals and organizations facing external crises and for overall improvement of crisis management and resilience. Responding to crisis brings the opportunity to make innovations introduced during emergencies structural, and embed them moving forward.

The paper focuses only on the coping phase of the response to the pandemic, whereas building long-term resilience requires understanding how organizations accumulate knowledge from crises and adapt to the “new normal.”

The paper responds to the call for empirical studies to advance knowledge of an integrative framework of crisis management and resiliency theories with reference to complex organizations such as healthcare.

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.

The purpose of this multi-voiced paper is to propose a prosocial paradigm for the field of performance management and management control systems. This new paradigm suggests cultivating prosocial behaviour and prosocial groups in organizations to simultaneously achieve the objectives of economic performance and sustainability.

The authors share a common concern about the future of humanity and nature. They challenge the influential assumption of economic man from neoclassical economic theory and build on evolutionary science and the core design principles of prosocial groups to develop a prosocial paradigm.

Findings are based on the premise of the prosocial paradigm that self-interested behaviour may outperform prosocial behaviour within a group but that prosocial groups outperform groups dominated by self-interest. The authors explore various dimensions of performance management from the prosocial perspective in the private and public sectors.

The authors call for theoretical, conceptual and empirical research that explores the prosocial paradigm. They invite any approach, including positivist, interpretive and critical research, as well as those using qualitative, quantitative and interventionist methods.

This paper offers implications from the prosocial paradigm for practitioners, particularly for executives and managers, policymakers and educators.

Adoption of the prosocial paradigm in research and practice shapes what the authors call the prosocial market economy. This is an aspired cultural evolution that functions with market competition yet systematically strengthens prosociality as a cultural norm in organizations, markets and society at large.

The purpose of this paper is to offer a critical examination of industrial relations in Denmark.

The approach is based upon available data and a mixture of Marxist theory and systems theory. The theoretical position is discussed in relation to the academic discourses on the main characteristics of Danish industrial relations and provides a review of the foundation and historical development of the Danish system.

From this basis, it is analysed how the stagnation or decline has taken place in recent years regarding representation of workers’ interest as well as the ability of the Danish system to maintain its key importance when challenged by decentralisation, decreasing union affiliation rates, cuts in unemployment insurance and social dumping due to labour migration.

It is an original paper which offers a critical analysis of the institutional decline and increasing inequality that are the result of the liberalist political-economic hegemony.

Human fascination in the unidentified flying objects (UFOs) and alien paranormal phenomenon is rich in history, explored widely in popular culture and many personal beliefs exist across society. The tourism industry offers a range of places where consumers can encounter such a phenomenon. Reports continue to highlight the growth in consumers participating at UFO and alien tourism attractions and locations. Significantly, the purpose of this paper is to shine a light on the relationship between UFOs, aliens and the tourism industry.

This paper takes a pragmatic philosophical approach by embracing a multi-disciplinary analysis. This study examines a range of secondary data information, statistics, reports and research studies.

By identifying the current impotence of the UFO and alien tourism markets and the growing consumer participation in it, this paper presents a theoretical starting point in the form of a model, which maps the current landscape of the industry from supply and demand perspectives. This study should be seen as a stepping stone towards further research into the UFO and alien tourism industry and provide researchers with a theoretical platform and novel ideas through which to explore the subject.

The phenomenon includes an established eclectic mix of attractions and likewise tourist motivations for visiting are wide and diverse. However, the subject lacks academic consideration. Thus, this paper presents original research and timely discussions on the topic.