This study investigates the impact of green innovation strategy on project management performance in the food sector of Saudi Arabia, examining the mediating roles of strategic quality planning and strategic leadership. Using a cross-sectional quantitative approach, data were collected in January 2025 from 341 employees through a convenience sampling technique. Structural equation modeling via SmartPLS was employed to analyze the data. Results indicate that green innovation strategy positively influences strategic quality planning (β = 0.655) and strategic leadership (β = 0.541), which in turn significantly enhance project management performance (β = 0.566 and β = 0.657, respectively). Additionally, strategic quality planning (β = 0.456) and strategic leadership (β = 0.505) partially mediate the relationship between green innovation strategy and project management performance. These findings highlight the critical role of green innovation and strategic management practices in improving project outcomes within Saudi Arabia’s food industry.

All projects require a structure to meet project requirements and achieve established goals. This framework is called project management. Therefore, project management plays an important role in national development and economic growth. Project management includes various knowledge areas such as project integration management, project scope management, project schedule management, etc. The article focuses on the resource-constrained project scheduling known as problem so- called the resource-constrained project scheduling problem (RCPSP). The RCPSP is a part of schedule management. The standard RCPSP has two important constraints, resource constraints and precedence relationships of activities during project scheduling. The objective of the problem is to optimize and minimize the project duration, subject to the above constraints. In this paper, we develop a convolutional neural network approach to solve the standard single mode RCPSP. The advantage of this algorithm over conventional methods such as metaheuristics is that it does not need to generate many solutions or populations. In this paper, the serial schedule generation scheme (SSGS) is used to schedule the project activities using an evolved convolutional neural network (CNN) as a tool to select an appropriate priority rule to filter out a candidate activity. The evolved CNN learns according to the eight project parameters, namely network complexity, resource factor, resource strength, average work per activity, etc. The above parameters are the inputs of the network and are recalculated at each step of the project planning. Moreover, the developed network has priority rules which are the outputs of the developed neural network. Therefore, after the learning process, the network can automatically select an appropriate priority rule to filter an activity from the eligible activities. In this way, the algorithm is able to schedule all project activities according to the given project constraints. Finally, the performance of the Convolutional Neural Network (CNN) approach is investigated using standard benchmark problems from PSPLIB in comparison to the MLFNN approach and standard metaheuristics.

Project management has a fundamental role in national development, industrial development, and economic growth. Schedule management is also one of the knowledge areas of project management, which includes the processes employed to manage the timely completion of the project. This paper deals with the Resource-Constrained Project Scheduling Problem (RCPSP), which is a part of schedule management. The objective of the problem is to optimize and minimize the project duration while constraining the resource quantities during project scheduling. There are two important constraints in this problem, namely resource constraints and precedence relationships of activities during project scheduling. Many methods such as exact, heuristic, and meta-heuristic have been developed by researchers to solve the problem, but there is a lack of investigation of the problem using methods such as neural networks and machine learning. In this article, we develop a multi-layer feed-forward neural network (MLFNN) to solve the standard single- mode RCPSP. The advantage of this method over evolutionary methods or metaheuristics is that it is not necessary to generate numerous solutions or populations. The developed MLFNN learns based on eight project parameters, namely network complexity, resource factor, resource strength, average work per activity, percentage of remaining work, etc., which are calculated at each step of project scheduling, and identified priority rules, which are the outputs of the developed neural network. Therefore, after the learning process, the network can automatically select an appropriate priority rule to filter out an unscheduled activity from the list of eligible activities and schedule all activities of the project according to the given project constraints. Finally, we investigate the performance of the presented approach using the standard benchmark problems from PSPLIB.

In Jordan, there are entrepreneurial companies in need of resources, and their resources available in the digital world are not sophisticated enough to utilize data analytics in effective decision-making processes. Thus, an understanding of the possible role which a digital human resource management can play to fill in some of these vacancies and to improve the project management in such situations becomes very important. In this study, it is aimed to examine whether digital transformation of human resource management acts as a mediation variable between business intelligence (direct) and project management (indirect) or not. In testing the hypothesis used method of data collection was the method of survey and the method of research used in the study was quantitative research. The Stratified random sampling technique was used and 400 questionnaires were distributed, however, 361 questionnaires were analyzed with business intelligence and digital HR proficient employees in entrepreneurial companies in Jordan. Data Analysis was carried out using the technique of 'Structural Equation Modeling (SEM)' and analysis by using SPSS and Smart PLS software. The results indicated that the impact of the business intelligence on the project management at beta level is strong (beta = 0.735, p < 0.001). Furthermore, business intelligence influences the Digital Transformation (beta = 0.697, p < 0.001) that consequently has a positive impact on project management (beta = 0.163, p < 0.001). Considering the obtained findings, it can be said that the Digital transformation of HRM can be a partial mediator between business intelligence and project management. This study makes a contribution to Resource Based-View as it considers digital transformation of HRM a capability that uses technologies as a way to achieve competitive advantage.

This study evaluates and prioritizes green logistics and supply chain management (GLSCM) practices in Jordan's industrial sector using Importance–Performance Analysis (IPA), while highlighting the role of project management in implementing economically effective green initiatives. Drawing on seven GLSCM practices: internal environmental management (IEM), green purchasing (GP), cooperation with customers (CWC), eco-design (ED), investment recovery (IR), green packaging (GPK), and reverse logistics (RL), the study examines managers' perceptions of importance and economic performance. Data were collected from 347 managers and decision-makers in Jordanian industrial firms. The findings show that GPK and GP fall within the "keep up the good work" quadrant, reflecting high importance and strong economic performance. IEM and ED are positioned in the "concentrate here" quadrant, indicating the need for stronger project planning, resource allocation, and performance monitoring. CWC and RL appear in the "low priority" quadrant, while IR is classified as "possible overemphasis." The study offers a practical framework for managing GLSCM projects and supports evidence-based decision-making in developing economies.

The purpose of this study is to analyze the relationship between project management and MSME sustainability, as well as the relationship between financial inclusion and MSME sustainability, and the relationship between digital transformation and MSME sustainability. The research method commonly used to analyze the role of financial inclusion and digital transformation in efforts to encourage MSME sustainability is a quantitative approach. The independent variables in this study are financial inclusion and digital transformation. The dependent variable is MSME sustainability. The respondents in this study were 453 MSME managers selected using a simple random sampling method. The research data was obtained from an online questionnaire distributed through social media. The questionnaire contained statement items designed using a Likert scale of 1 to 7. Data analysis used structural equation modeling (SEM) partial least squares (PLS) with the help of SmartPLS software. The stages of SmartPLS analysis in this study are: Structural Model Development, namely determining the hypothesis of direct and indirect relationships between variables, validity testing (convergent/discriminant) and reliability to ensure valid indicators measure the variables, path coefficient testing to measure influence and significance, using bootstrapping to see the p-value and T-statistics, to determine how strong the role of these variables. The results of this study are project management has a positive relationship with MSME Sustainability, Financial Inclusion has a positive relationship with MSME Sustainability, and digital transformation has a positive relationship with MSME Sustainability.

As a concept, PMS is becoming increasingly important for determining the sustainable success of an organization. PMS refers to how the organizations execute project tasks in a sustainable manner while maintaining the economic, environmental and social equilibrium of their activities. Importantly, the PMS includes actions that are geared toward project goal achievement and sustainable development. At the current time, there is increasing dynamism and availability of information among organizations. Under such conditions, BI emerges as a concept. Business intelligence can be defined as the set of methodologies and tools employed by organizations to capture, analyze and interpret data to gain information. With BI, organizations can achieve increased visibility and improved planning and performance of projects. Despite the importance of BI in helping organizations achieve sustainable success in project results, it cannot be emphasized enough that the sustainability of project results requires even better techniques of information analysis. In this connection, organizations need DM as an advanced form of BI that aims at identifying underlying information from large amounts of data.

Cybersecurity threats have become a structural risk to digital project management, challenging not only digital data integrity but also long-term organizational sustainability. While prior research has emphasized awareness and technical safeguards, limited attention has been given to the behavioral–cognitive mechanisms through which cybersecurity knowledge translates into sustainable organizational outcomes. Drawing on Protection Motivation Theory and the Theory of Planned Behavior, this study examines the moderated-moderation effect of Perceived Risk (PR) and Risk of Behavior (RB) on the relationship between Cybersecurity Knowledge of Threats (CKT) and Organizational Sustainability (OS). Using survey data collected from 235 project management staff across UK organizations, the results of structural equation modeling reveal a significant three-way interaction (CKT × PR × RB). The findings indicate that CKT enhances OS most strongly when both PR and RB are high, suggesting that knowledge translates into sustainability only when employees accurately perceive cyber threats and respond with appropriate protective actions. This joint amplification effect uncovers a previously underexplored mechanism linking individual-level cognition to macro-level organizational resilience.

This study's objective is to analyze the factors that influence whether or not small and medium-sized enterprises (SMEs) use business intelligence. Based on an exhaustive assessment of the literature, the study offers a model dependent on the diffusion of innovation and augmented with factors expressing the idea of project management maturity (PMM). The research applied structural equation modelling (SEM) to examine data obtained from 112 Jordanian company workers. The findings showed that the adoption of business intelligence has a positive and significant relationship to the complexity, compatibility, and relative advantage of business intelligence; the level of project management maturity has a significant effect on the level of relative advantage, compatibility, and complexity; and the level of project management maturity is significantly associated with the change management and knowledge sharing practices in SMEs. However, we contend that further study has to be carried out, particularly in the context of developing nations, in order to get a comprehensive understanding of how different SMEs may effectively deploy and make use of business intelligence.

This study uses the PMI® framework as a reference to analyse the impact of Project Management (PM) competencies on organisational competitiveness and productivity in southwestern Colombia. The research evaluates three key dimensions: Organisational Practices in PM (PMP), Skills, Attitudes and Values (HABI) and their influence on Business Competitiveness (COMPE) and Productivity and Innovation Indicators (PROD). A survey was conducted with 221 CESMAG University Project Management Postgraduate Programme graduates (2016–2023). Data were collected using Likert-type scales and analysed using structural equation modelling (PLS-SEM) to ensure the reliability and validity of the constructs. The results confirm that both organisational practices and individual competencies significantly enhance competitiveness and productivity. PMP was found to have a positive effect on strategic performance indicators, while HABI was found to have an even stronger influence on innovation and value creation. These findings emphasise the strategic importance of postgraduate training in project management (PM) for regional development. Beyond academic contributions, the study provides actionable insights for businesses, universities and policymakers, demonstrating how strengthening PM competencies can foster innovation, operational efficiency and sustainable competitiveness in emerging economies.