In a rapidly evolving digital economy, cyberpreneurship has emerged as a pivotal force driving innovation and economic growth. The study applies the Theory of Planned Behaviour in predicting entrepreneurial intention in the context of Malaysia, where the government has actively championed digital entrepreneurship. Drawing from a sample of 473 final-year university students in the Klang Valley region of Malaysia, the study investigates the impact of Individual Entrepreneurial Orientation (IEO) dimensions, namely innovativeness, risk-taking, and proactiveness, on the intention to engage in cyberpreneurship within the context of Digital Free Trade Zones (DFTZ). The study further examines the moderation effect of psychological characteristics incorporating visionary thinking, self-efficacy, opportunism, and creativity to provide a comprehensive understanding of the factors influencing cyberpreneurial intentions. With the moderating variable, the paper presents a comprehensive model to investigate the IEO and psychological characteristics contributing to cyberpreneurship intentions and its impact on engagement in DFTZ. An empirical examination of data and hypotheses found that risk-taking (RISK) and proactiveness (PRO) are significantly related to cyberpreneurial intention. Psychological characteristics significantly proved its moderating role in its interaction with innovatiness (INNO), risk-taking (RISK), and proactivness (PRO) in influencing cyberpreneurial intentions (CYBER_PI). Innovativeness (INNO) without the influence of the moderating variable is not significantly related to cyberpreneurial intentions. Engagement with the Digital Free Trade Zone (DFTZ) through the mediating role of cyberpreneurial intentions (CYBER_PI), the innovativeness (INNO) did not succeed. On the other hand, risk-taking (RISK) and proactiveness (PRO) are found to be significant. The paper contributes to the landscape of e-commerce and digital trade literature by advancing our understanding of the factors driving individuals’ intentions to participate in cyberpreneurship and engage in DFTZ. The findings of this study provide valuable insights for policymakers, educators, and entrepreneurs alike.

The application of optimization algorithms is crucial for analyzing oil and gas company portfolio and supporting decision-making. The paper investigates the process of optimizing a portfolio of oil and gas projects under economic uncertainty. The literature review explores the advantages of applying various optimizers to models that consider the mean and semi-standard deviations of stochastic multi-year cash flows and revenues. The methods and results of three different optimization algorithms are discussed: ranking and cutting algorithms, linear (Simplex) and evolutionary (genetic) algorithms. Functions of several key performance indicators were used to test these algorithms. The results confirmed that multi-objective optimization algorithms that examine various key performance indicators are used for efficient optimization in oil and gas companies. This paper proposes a multi-criteria optimization model for investment portfolios of oil and gas projects. The model considers the specific features of these projects and is based on the Markowitz portfolio theory and methodological recommendations for project assessment. An example of its practical application to oil and gas projects is also provided.

In this paper, we examine a possible application of ordered weighted average (OWA for short) aggregation operators in the insurance industry. Aggregation operators are essential tools in decision-making when a single value is needed instead of a couple of features. Information aggregation necessarily leads to information loss, at least to a specific extent. Whether we concentrate on extreme values or middle terms, there can be cases when the most important piece of the puzzle is missing. Although the simple or weighted mean considers all the values there is a drawback: the values get the same weight regardless of their magnitude. One possible solution to this issue is the application of the so-called Ordered Weighted Averaging (OWA) operators. This is a broad class of aggregation methods, including the previously mentioned average as a special case. Moreover, using a proper parameter (the so-called orness) one can express the risk awareness of the decision-maker. Using real-life statistical data, we provide a simple model of the decision-making process of insurance companies. The model offers a decision-supporting tool for companies.

Noise pollution in construction sites is a significant concern, impacting worker health, safety, communication, and productivity. The current study aims to assess the paramount consequences of ambient noise pollution on construction activities and workers’ productivity in Peshawar, Pakistan. Noise measurements have been recorded at four different construction sites in Peshawar at different times of the day. Statistical analysis and Relative Importance Index (RII) are employed to evaluate the data Risk variables, such as equipment maintenance, noise control, increased workload, material handling challenges, quality control issues, and client satisfaction. The results indicated that noise levels often exceeded permissible limits, particularly in the afternoon, posing significant worker risks. In addition, RII analysis identified communication difficulties, safety hazards, and decreased productivity as significant issues. The results show that noise pollution is directly linked with safety risks, decreased performance, and client dissatisfaction and needs immediate attention by authorities. This paper proposes a strategic policy framework, recommending uniform hand signals and visual communication methods without noise for workers, worker training about safety, and using wearable devices in noisy settings. Communication training for teams and crane operators, proactive quality control, and customer-oriented project schedules are also proposed. These recommendations aim to mitigate the adverse effects of noise pollution, enhance construction industry resilience, and improve overall operational efficiency, worker safety, and client satisfaction in the construction sector of Peshawar, aligning with policy and sustainable development objectives.

Hazards are the primary cause of occupational accidents, as well as occupational safety and health issues. Therefore, identifying potential hazards is critical to reducing the consequences of accidents. Risk assessment is a widely employed hazard analysis method that mitigates and monitors potential hazards in our everyday lives and occupational environments. Risk assessment and hazard analysis are observing, collecting data, and generating a written report. During this process, safety engineers manually and periodically control, identify, and assess potential hazards and risks. Utilizing a mobile application as a tool might significantly decrease the time and paperwork involved in this process. This paper explains the sequential processes involved in developing a mobile application designed for hazard analysis for safety engineers. This study comprehensively discusses creating and integrating mobile application features for hazard analysis, adhering to the Unified Modeling Language (UML) approach. The mobile application was developed by implementing a 10-step approach. Safety engineers from the region were interviewed to extract the knowledge and opinions of experts regarding the application’s effectiveness, requirements, and features. These interview results are used during the requirement gathering phase of the mobile application design and development. Data collection was facilitated by utilizing voice notes, photos, and videos, enabling users to engage in a more convenient alternative to manual note-taking with this mobile application. The mobile application will automatically generate a report once the safety engineer completes the risk assessment.

Throughout the course of a project cycle, the many phases of project management—including planning, execution, control and monitoring, and ending—are integrated and executed. In modern firms, project management has become the dominant tool for managing change. Best practices have emerged due to global project management practices and company evolution. The primary goal was to investigate how project management approaches affected project performance of the Saudi Arabia Small and Medium Sized Enterprises (SMEs). This study investigated the impact of various project management practices including risk management, communication, leadership, and stakeholder management, on project performance in manufacturing SMEs in Riyadh, Saudi Arabia. A quantitative research methodology was employed, with data collected from 250 employees (i.e., supply chain, finance and R&D managers/supervisors) across 8 SMEs. The results revealed that risk management, leadership practices, and stakeholder management significantly contribute to project performance. Surprisingly, no significant relationship was found between communication practices and project performance. The findings of this study emphasize the importance of effective risk management, strong leadership, and efficient stakeholder management in achieving successful project outcomes. Finance managers and R&D managers in Saudi manufacturing SMEs should lead and engage stakeholders to improve project performance. Supply chain managers must manage risk and maintain stakeholder relationships to avoid disruptions. Communication improvements, despite their small impact, are essential for departmental coordination. Global project management strategies tailored to local culture and business will improve project success.