Improving the competitiveness of tourism destinations is crucial for driving local economies and achieving income growth. In light of this evidence, numerous government departments strive to assess specific factors that impact the competitiveness of tourism destinations, enabling them to issue appropriate new tourism policies that promote more effective forms of tourism business. Therefore, the primary objective of this paper is to investigate how various elements such as tourism resources, tourism support, tourism management, location conditions, and tourism demand influence regional competitiveness in the Northern Bay region of Guangxi Province in China. To accomplish this goal, an online survey was conducted to collect data from 420 visitors who had experienced North Gulf Tourism; yielding an impressive response rate of 95 percent. The findings reveal that all aforementioned factors—namely: Tourism resources, tourism support, tourism management, location conditions and tourist demand—significantly impact destination competitiveness. Notably though, it was found that among these factors influencing destination competitiveness; it is primarily determined by effective local-level management (β = 0.345). Following closely behind are tourist demand (β = 0.133) as the second most influential factor affecting destination competitiveness; followed by location conditions (β = 0.116) ranking third; then comes tourist support (β = 0.03) as fourth in line impacting destination competitiveness; finally with least impact being exerted by available tourist resources (β = 0.016). Consequently, highlighting that regional competitiveness within Guangxi’s Northern Bay area predominantly hinges on efficient local-level management practices thus strongly recommending relevant authorities formulate novel work policies aimed at enhancing levels of local-level competitive advantage within the realm of regional touristic offerings.

Blockchain technology is poised to significantly transform the corporate world, heralding a new era of innovation and efficiency. Over the past few years, its impact has been noted by leaders, academics, and government representatives around the globe this growing interest underscores businesses’ need to evolve and reconsider traditional operational models. To remain competitive, organizations must embrace this change. Before introducing such ground-breaking technology, it is crucial to assess the motivations of primary stakeholders concerning its implementation. This study looks into what influences the use of Blockchain technology in the oil and gas sector, primarily using a quantitative survey of Iraqi oil and gas companies. A questionnaire was distributed among 250 top-level managers, senior executives, project managers, and IT managers for analyzing the data, the study employs the Structural Equation Modelling-Partial Least Squares (SEM-PLS) technique, with Smart PLS for data processing. The findings suggest that the intention to utilise blockchain technology is influenced by one’s attitude towards it. Competitive pressure (environmental factors), functional benefit, and privacy/security (technological factors) significantly affect blockchain adoption intention. Nevertheless, there was no discernible correlation between regulatory backing and the desire to use Blockchain. Additionally, cost concern and perceived risk (organizational factors) two factors contribute negatively to the perception of blockchain technology. Besides the direct relationship, the findings revealed that attitude toward blockchain technology mediate the relationship between cost concern, perceived risk, and intention to adopt Blockchain. Built upon the Technology-Organization-Environment (TOE) model and the Theory of Reasoned Action, this research offers a comprehensive framework for investigating the intention to adopt blockchain technology. The results enhance both theoretical understanding and practical implementation by providing valuable insights into the emerging area of blockchain adoption intentions.

Agriculture is an industry that plays an essential role in economic development towards eliminating poverty issues, but foreign direct investment (FDI) inflows to this sector remain modest in Vietnam. This study analyzed the determinants of foreign direct investment in the agricultural sector into the Southern Key Economic Zone (KEZ) of Vietnam, which is considered the foreign direct investment magnet of Vietnam, but its FDI inflows into the agricultural sector have been consistently low, and has shown a downward trend in recent years. The study was based on a sample of 129 foreign investors of a total of 164 multinational enterprises (MNEs) in the agricultural sector, including representatives of the Board of Directors and representatives at the department level. The Partial Least Squares Structural Equation modeling (PLS-SEM) approach was used to test the hypotheses. Findings indicated that FDI attraction policies have the strongest impact on FDI inflows. This was followed by infrastructure, regional agriculture policies, public service quality, natural conditions, and human resources. This study suggests policy recommendations to improve foreign direct investment inflows into the agricultural sector of the Southern Key Economic Zone (KEZ) of Vietnam.

Indonesia has ratified United Nations Convention on the Law of the Sea 1982 (UNCLOS 1982) through Law No. 17 of 1985 concerning the ratification of the 1982 Law of the Sea Convention, thus binding Indonesia to the rights and obligations to implement the provisions of the 1982 convention, including the establishment of the three Northern-Southern Indonesia’s Archipelagic Sea Lane (ALKI). The existence of the three ALKI routes, including ALKI II, has led to various potential threats. These violations not only cause material losses but, if left unchecked and unresolved, can also affect maritime security stability, both nationally and regionally. The maritime security and resilience challenges in ALKI II have increased with the relocation of the capital, which has become the center of gravity, to East Kalimantan. The research in this article aims to identify and analyze the factors influencing the success of maritime security and resilience strategies in ALKI II. The factors used in this research include conceptual components, physical components, moral components, command and control center capabilities, operational effectiveness, command and control effectiveness, and the moderating variables of resource multiplier management and risk management to achieve maritime security and resilience. This study employed a mixed-method research approach. The factors are modeled using Structural Equation Modeling (SEM) with WarpPLS 8.0 software. Qualitative data analysis used the Soft System Methodology (SSM). The results of the study indicate that the aforementioned factors significantly influence the success of achieving maritime security and resilience in ALKI II.

This study analysed the behaviour of both economic and financial profitability of credit unions belonging to segment 1 in Ecuador, as well as its determinants. For this purpose, data from the financial statements of a sample of 30 credit unions between 2016 and 2022 were used by means of a multiple linear regression methodology using panel data with fixed effects after applying the Hausman test. The findings of this research showed that current liquidity and non-performing loans have a negative and significant effect on both economic and financial profitability while the past due portfolio has a positive and significant impact on the generation of profitability of the financial institutions under study. In addition, it was revealed that the rate of outflow absorption has a negative relationship with economic profitability but a positive relationship with financial profitability. Unlike previous research in the Ecuadorian context, this research is pioneering in presenting results that indicate that the determinants traditionally considered for nonfinancial institutions and banks are also valid for credit unions, even though they are organisations with different characteristics from the rest.

This study evaluated the performance of several machine learning classifiers—Decision Tree, Random Forest, Logistic Regression, Gradient Boosting, SVM, KNN, and Naive Bayes—for adaptability classification in online and onsite learning environments. Decision Tree and Random Forest models achieved the highest accuracy of 0.833, with balanced precision, recall, and F1-scores, indicating strong, overall performance. In contrast, Naive Bayes, while having the lowest accuracy (0.625), exhibited high recall, making it potentially useful for identifying adaptable students despite lower precision. SHAP (SHapley Additive exPlanations) analysis further identified the most influential features on adaptability classification. IT Resources at the University emerged as the primary factor affecting adaptability, followed by Digital Tools Exposure and Class Scheduling Flexibility. Additionally, Psychological Readiness for Change and Technical Support Availability were impactful, underscoring their importance in engaging students in online learning. These findings illustrate the significance of IT infrastructure and flexible scheduling in fostering adaptability, with implications for enhancing online learning experiences.