This study analyzed the influence of project planning on the business growth of 313 SMEs in the Central Region of Peru (Tarma), evaluating the mediating effect of decision-making. Using a quantitative approach, a hypothetical-deductive method, and a non-experimental, cross-sectional, and explanatory design, data were collected using a Likert-scale questionnaire and processed with SmartPLS 4.0. The measurement model demonstrated optimal internal consistency and psychometric validity, with Cronbach's alpha and composite reliability values greater than 0.90, and a mean variance extracted (AVE) greater than 0.50. Structural equation modeling (PLS-SEM) validated all hypotheses (p ≤ 0.001; t ≥ 1.96). It was demonstrated that strategic planning (β = 0.412), resource and budget management (β = 0.356), and scope and quality (β = 0.438) have a direct and positive impact on decision-making. This, in turn, has a strong influence on business growth (β = 0.621), confirming a significant mediating effect of decision-making. The overall model fit was satisfactory (SRMR = 0.045; R² = 0.385), leading to the conclusion that the analytical professionalization of decision-making flows is a critical factor for the scalability of SMEs.

This research studies the impact of digital project management and sustainable performance enhancement by taking the role of business intelligence and the mediating role of big data analytics in destination management organizations in Jordan. The research aims to explore the main effects of business intelligence (BI) on digital project management and sustainable performance, with the role of big data analytics as a mediator. The research, using a quantitative approach, was employed by an online questionnaire aiming at employees and managers involved in digital project management in Jordanian Destination Management Organizations (DMOs). After collecting and analyzing 345 valid surveys through PLS-SEM, the results indicate that BI has an important positive effect on digital project management and sustainable performance, in addition to an indirect result through big data analytics (BDA). Furthermore, BI positively affects big data analytics skills, which sequentially give meaningfully to enhance digital project management. The intermediation analysis approves that BDA shows an important role in converting BI resources into real sustainable performance results. Generally, the research highlights the implication of supporting big data analytics talents to fully realize the revenues of BI systems and offers valuable theoretical and practical understandings for DMO managers looking to increase project results and provision digital transformation through data-driven decision-making.

This study proposes an innovative scientific framework for optimizing the placement of cross-docking facility projects within international logistics networks. The framework integrates an extended K-mean optimization and GIS based framework into a multi-project management to enable more accurate and spatially informed decision-making. A comprehensive review of the literature indicates that road freight transportation and inventory holding costs represent the largest components of total logistics expenditure. In international road freight operations, direct delivery is frequently impractical, thereby necessitating the use of intermediate transshipment hubs, such as cross-docking facilities. However, inefficient selection of these intermediary nodes may increase inventory storage requirements and transportation costs. Consequently, the accurate identification of optimal cross-docking locations has substantial potential to reduce transport distances and associated operational expenses across global logistics networks. To examine this proposition, two comparative scenarios were developed. The first scenario represents the Traditional Cross-Docking project (TCD) approach, in which cross-docking activities are conducted at national border points before international distribution. The second scenario applies the proposed K-mean optimization and GIS based framework into a multi-project management (KGMP) to identify optimal cross-docking locations beyond border regions across the wider international supply chain network. Both scenarios were assessed through numerical simulations and analytical evaluation to compare their effectiveness in minimizing transportation distances. Following this, the simulation results demonstrate that the proposed KGMP framework significantly reduces international transport routes compared with the conventional border-based configuration. These findings highlight the strategic importance of facility location decisions in international logistics planning, where optimized cross-docking placement can enhance transportation efficiency, operational cost reduction, and a greater administrative competitiveness in increasingly complex global markets.

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.

This study investigates the impact of sustainability dimensions (economic, environmental, and social) on financial performance. A quantitative research design was used, and data were collected from employees working in small businesses in Saudi Arabia. A convenience sampling technique was used to gather responses from 332 employees. Data analysis was performed with structural equation modeling. The results revealed that sustainability dimensions such as economic, environmental, and social practices have a significant and positive influence on financial performance. Social sustainability exerts a stronger influence on the financial performance of small businesses. Altogether, it explains 59.1 percent of the variance in financial performance, which signifies strong explanatory power. The study findings indicate that sustainability initiatives as part of a business strategy bring financial success. Moreover, small businesses can perform better with economic, environmental, and social practices. Thus, it leads to enhancing operational efficiency, stakeholder trust, and profitability in line with the national agenda under Vision 2030. Practitioners can take advantage of adopting sustainable business approaches in order to enhance organizational performance.

Under its Vision 2030, Saudi Arabia has heavily invested in mega construction projects such as NEOM and QIDDIYA, where logistics resilience is critical due to environmental, infrastructural, and operational uncertainties. This study proposes an IoT-enabled framework for managing project disruptions via real-time sensing, environmental stress indicators, and predictive modeling. Using a dataset of 1,000 logistics events, multiple statistical and machine learning models were applied to evaluate the impact of IoT deployment on delivery delays caused by disruptions, cost variability, and environmental stress. The findings indicate that IoT integration significantly reduces delay volatility and enhances cost predictability. However, disruptions like traffic and weather partially attenuate these benefits. Environmental stress had a minor but consistent influence on logistics risk exposure. A mediation analysis revealed no significant indirect effect of IoT on delay through environmental stress, suggesting a more direct intervention pathway. A decision dashboard is also proposed to visualize delay triggers and automate risk signaling. Theoretical implications extend the Unified Theory of Acceptance and Use of Technology (UTAUT) to construction logistics, emphasizing real-time sensing as the key performance enabler. Managerial implications suggest full IoT adoption in NEOM and QIDDIYA logistics and real-time SCM dashboarding. Future research may incorporate ensemble learning and edge-based IoT to improve predictive capacity. This study demonstrates the feasibility and strategic value of IoT-based disruption control in transforming mega-infrastructure supply chains.

The research paper at hand is devoted to the role of fintech practices in the municipal government institutions performance i.e. the role of organizational readiness on this performance, as seen by the municipal employees. The article quantifies the contribution of the digital innovations like automated payment systems on the efficiency and quality of administrative and services in a quantitative analytical scheme. The results show that fintech and positive institutional performance outcomes, such as lower operating costs, higher revenue collection, and higher levels of citizen satisfaction, have a strong positive association. Interestingly, the empirical findings indicate that preparedness in organizations consisting of technological infrastructure, workforce efficacy, and management support are some of the fundamental aspects in enhancing the association amid fintech and performance. Digital transformation will have a greater benefit on the more prepared departments, structure-wise and human resource-wise. On the same, but a different note, the inquiry warns against the necessity to create a compromise between innovation and high security measures to reduce the threat of fraud and data leaks. The government organizations should first coordinate their electronic infrastructure, use effective models, must focus on lifelong training of staff, not just buy software, and must also spend a specific budget on cybersecurity and fintech infrastructure to get such returns. This study offers a strategic road map through which the public institutions in the MENA region can use technology as a driving force in institutional excellence, and at the same time, remain financially stable on the national level.

The purpose of this study is to investigate the impact of social media marketing on value co-creation for tourism customers through the mediation effect of customer engagement capability. A quantitative research design was employed to conduct a survey-based study from employees working in tourism companies in Saudi Arabia. A convenience sampling technique was used to collect data from 297 employees from tourism companies in Saudi Arabia. Data analysis was performed with structural equation modeling. The study findings indicate that social media marketing significantly influences customer engagement capability and value co-creation for tourism customers. Additionally, customer engagement capability plays a significant mediating role between social media marketing and value co-creation for tourism customers. This indicates that customer engagement plays a vital role in transforming digital marketing activities into valuable customer outcomes. Moreover, the value of social media platforms is recognizable in terms of interaction and collaboration with valued customers. Hence, tourism companies in Saudi Arabia can take part in digital transformation while managing tourism activities in line with the priorities of Vision 2030. The study provides a better understanding of how digital engagement initiatives can add value to co-creation in the tourism sector.

The article's primary focus is on how IFRS S2 affects climate-related reporting on financial risk mitigation strategies in oil and gas companies in the Gulf Cooperation Council. This study takes advantage of the fact that there are varying levels in the regulatory needs across the six GCC states and has used a quasi-experimental design to establish the causality of this relationship. The United Arab Emirates and the Kingdom of Saudi Arabia (KSA), however, have chosen to adopt a regulated, mandatory framework. Additional GCC nations, like Bahrain, Oman, Kuwait, and Qatar, have kept these structures up to date and made them freely accessible. We estimate the difference-in-differences estimation and structural equation modeling results of 95 companies covering 2018 to 2024 (665 firm-year observations) to conclude that mandatory IFRS S2 Reporting significantly enhances climate disclosure quality by 34.2 percentage points. Furthermore, it has beneficial economic implications: a 180-basis-point lower cost of capital, a 31 percent higher firm valuation, and a 33 percent lower earnings volatility. Structural equation modelling shows that improved risk management performance is the mediating factor. When the analysis is cross-sectional, the effects are heterogeneous, and the benefits are magnified in firms with high carbon intensity. Causal interpretation has been supported using our extensive robustness tests. Findings from this research provide an innovative empirical basis for the influence of IFRS S2 on carbon-intensive industries. The research offers essential recommendations to policymakers and corporate executives seeking to address the challenges posed by financial risks and climate change in the context of the current energy transition, confirming the benefits of mandatory reporting systems over voluntary ones.

This study examines the association between Corporate Digital Transformation (CDT) and information asymmetry (ASY) among firms listed on the Stock Exchange of Thailand during 2017–2022. Drawing on agency theory, institutional theory, and resource dependence theory, the study conceptualizes CDT as a portfolio of organization-wide digital initiatives and transformation projects embedded in firms' governance and information infrastructures rather than as isolated technological adoption. CDT is measured using textual analysis of firms' annual reports based on a multidimensional keyword dictionary capturing disclosed digital transformation activities, while information asymmetry is proxied by the effective bid–ask spread. Using firm-level panel data and fixed effects regression models, the analysis documents a negative and marginally significant association between CDT and information asymmetry, suggesting lower information frictions among firms with higher levels of reported digital transformation activity. A series of robustness tests employing alternative measures, estimation strategies, and subsample analyses yield qualitatively consistent results. The study contributes to the project management literature by providing empirical evidence on how sustained digital transformation initiatives, implemented through multiple projects over time, are associated with changes in organizational information environments in an emerging market context. While the findings should be interpreted as indicative rather than causal, they highlight the relevance of managing digital transformation as an integrated project portfolio with implications for transparency and stakeholder communication.