The research is focused on the evolution of the enterprises, in the field of specialized professional services, medium-period, enterprises that implemented projects financed within Regional Operational Program (ROP) during the 2007–2013 financial programming period. The analysis of the economic performance of the micro-enterprises corresponds to general objectives, but there can be outlined connections between these performances and other economic indicators that were not considered or followed through the financing program. The study case is focused on the development of micro-enterprises in the services area, in the Central Region, Romania (one of the eight development regions in Romania). The scientific approach for this article was based on a regressive statistical analysis. The analysis included the economic parameters for the enterprises selected, comparing the economic efficiency of these enterprises, during implementation with the economic efficiency after the implementation of the projects, during medium periods, including the sustainability period. The purpose of the research was to analyse the economic efficiency of the selected micro-enterprises, after finalizing the projects’ implementation. The authors intend to point out the need for a managerial instrument based on the economic efficiency of companies that are benefiting from non-reimbursable funds. This instrument should be taken into consideration in planning regional development at the national level, regarding the conditions and results expected. Although the authors used regressive statistical analysis the purpose was to prove that there is a need for additional managerial instruments when the financial allocations are being designed at the regional level. This study follows the interest of the authors in proving that the efficiency of non-reimbursable funds should be analysed distinctively on the activity sectors.

This study aims to identify the causes of delays in public construction projects in Thailand, a developing country. Increasing construction durations lead to higher costs, making it essential to pinpoint the causes of these delays. The research analyzed 30 public construction projects that encountered delays. Delay causes were categorized into four groups: contractor-related, client-related, supervisor-related, and external factors. A questionnaire was used to survey these causes, and the Relative Importance Index (RII) method was employed to prioritize them. The findings revealed that the primary cause of delays was contractor-related financial issues, such as cash flow problems, with an RII of 0.777 and a weighted value of 84.44%. The second most significant cause was labor issues, such as a shortage of workers during the harvest season or festivals, with an RII of 0.773. Additionally, various algorithms were used to compare the Relative Importance Index (RII) and four machine learning methods: Decision Tree (DT), Deep Learning, Neural Network, and Naïve Bayes. The Deep Learning model proved to be the most effective baseline model, achieving a 90.79% accuracy rate in identifying contractor-related financial issues as a cause of construction delays. This was followed by the Neural Network model, which had an accuracy rate of 90.26%. The Decision Tree model had an accuracy rate of 85.26%. The RII values ranged from 68.68% for the Naïve Bayes model to 77.70% for the highest RII model. The research results indicate that contractor financial liquidity and costs significantly impact construction operations, which public agencies must consider. Additionally, the availability of contractor labor is crucial for the continuity of projects. The accuracy and reliability of the data obtained using advanced data mining techniques demonstrate the effectiveness of these results. This can be efficiently utilized by stakeholders involved in construction projects in Thailand to enhance construction project management.

eGovernment projects are capital intensive and have high probability of failure because of the dynamic and technological laden environment in which they operate. The number of skilled labour and technicalities required are often not available in quantity needed to sustain such project. There is always the need to have in place adequate risk assessment framework to guide the execution and monitoring of eGovernment projects. Several studies have been conducted on the critical success factors relating to risk assessment of eGovernment projects to understand the reasons for the high rate of failure. Therefore, there is need to review these articles and categorize them into different research domain in project risk assessment so as to reveal domain with more or less research and those that need to understand the future research directions in risk assessment for eGovernment projects. Using the positivism paradigm, this study utilized the Systematic Literature Review methodology to collect 147 articles from the following academic databases namely IEEE, Preprints, WorldCat Discovery, ArXiv. Ohio-state University databases, Science Direct, Scopus, ACM, NWU digital library, Usenix, Jise database, Sagepub, MDPI Academia published between 2013 to 2023. Different inclusion and exclusion criteria were applied pruning to 48 articles that were used for the study. The results show the classification of articles in risk assessment for eGovernment projects into those that discusses project analysis, review, framework, maturity and model tools, implementation, and integration, applied methodology and evaluation with the percentage of articles published in each domain with the past 10 years. The various critical success factors that should be considered in the development of a robust risk assessment framework were discussed and future research directions in eGovernment risk assessment were given based on the reviews.

Coordination and integration among farms within agri-food chains are crucial to tackle the issue of fragmentation within the primary sector, both at the European and national level. The Italian agri-food system still complains about the need to aggregate supply to support market dynamics, especially for niche and quality products that characterize the Made in Italy. It is well known that the Italian agri-food sector is closely linked to the relationship between agriculture on one hand and culture/tradition on the other, which is reflected in the high number of quality products that have obtained EU PDO (Protected Designation of Origin) and PGI (Protected Geographical Indication) recognition. The development of vertical forms of coordination has found significant support in recent years from the integrated supply chain design approach, which is increasingly becoming an essential tool for implementing rural development policies. In this context, the study provides a comparison between companies that have joined the Integrated Supply Chain Projects of the Rural Development Program and those that have not applied. The aim is to highlight any differences in order to understand policy impact. The analysis is based on the Emilia-Romagna region Farm Accountancy Data Network (FADN) data, and the sample consists of more than 2 thousand farms. The statistical analysis conducted compares treated and non-treated using the Welch-t-test for independent unmatched samples. The main results show higher values for treated farms when structural variables are analyzed, like the utilized agricultural area or the agricultural work unit. In general, higher balance sheet performances emerged for treated farms. In conclusion, this study shows that the Integrated Supply Chain Projects represent a worthwhile tool both to increase cooperation, food quality, and to enhance a competitive agricultural sector.

The rapidly growing construction industry often deals with complex and dynamic projects that pose significant safety risks. One of the state-owned companies in Indonesia is engaged in large-scale toll road construction projects with a high incidence of workplace accidents. This study aims to improve safety performance in toll road construction by implementing the Scrum framework. The study uses a System Dynamics approach to model interactions between the Scrum framework, project management, and work safety subsystems. Various scenarios were designed by modifying controlled variables and system structures, including introducing a punishment entity. These scenarios were evaluated based on their impact on reducing incidents and the incident rate over the project period. The results indicate that the combined scenario significantly reduces incidents and incident rates in different conditions. The study also finds a strong relationship between Scrum framework implementation and improved safety performance, demonstrating a reduction in incidents and incident rates by over 50% compared to existing conditions. This research underlines the effectiveness of the Scrum framework in enhancing safety in construction projects.

Sustainability in road construction projects is hindered by the extensive use of non-renewable materials, high greenhouse gas emissions, risk cost, and significant disruption to the local community. Sustainability involves economic, environmental, and social aspects (triple bottom line). However, establishing metrics to evaluate economic, environmental, and social impacts is challenging because of the different nature of these dimensions and the shortage of accepted indicators. This paper developed a comprehensive method considering all three dimensions of sustainable development: economic, environmental, and social burdens. Initially, the economic, environmental, and social impact category indicators were assessed using the Life cycle approach. After that, the Analytic Hierarchy Process (AHP) method and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) were utilized to prioritize the alternatives according to the acquired weightings and sustainable indicators. The steps of the AHP method involve forming a hierarchy, determining priorities, calculating weighting factors, examining the consistency of these assessments, and then determining global priorities/weightings. The TOPSIS method is conducted by building a normalized decision matrix, constructing the weighted normalized decision matrix, evaluating the positive and negative solutions, determining the separation measures, and calculating the relative closeness to the ideal solution. The selected alternative performs the highest Relative Closeness to the Ideal Solution. Lastly, a case study was undertaken to validate the proposed method. In three alternatives in the case study (Cement Concrete, Dense-Graded Polymer Asphalt Concrete, and Dense-Graded Asphalt Concrete), option 3 showed the most sustainable performance due to its highest Relative Closeness to the Ideal Solution. Integrating AHP and TOPSIS methods combines both strengths, including AHP’s structured approach for determining criteria weights through pairwise comparisons and TOPSIS’s ability to rank choices based on their proximity to an ideal solution.