Rapidly changing business environments and fierce competition are making it increasingly difficult for modern companies to maintain competitive advantage and accomplish business longevity. This study can fill the research gap in mission research and longevity research, and provides implications on what form and content of mission should be selected when determining the direction of a company’s corporate strategy. Although a company’s mission is a communication tool that represents the company’s strategic priorities and unique values, it has rarely been considered an important factor in business longevity. This study conducts a content analysis of the mission statements of 43 companies in the Henokiens Association to clarify the linkage between a company’s mission and business longevity and the configurations of long-lived firms’ missions. Our results show most long-lived firms have clear missions and perceptions of familism expansion. The firms’ past, present, and future additions to their concern for products, business growth, unique philosophy, and stakeholders are highlighted in their mission statements. Therefore, the main theoretical contribution of focusing on the corporate mission as a factor of business longevity in this study is not only a new approach to the longevity factor, but also the discovery of new values of the mission in strategic management research. The practical contribution of this study is that it reveals that companies seeking long-term competitive advantage in the market need to design, possess, and share a high-quality mission from a long-term perspective and instill the ideology of extended familyism. It can also provide hints about strategic priorities for small, family-run businesses facing threats to their survival.

This article aims to analyze the form of promotion and its policies in increasing tourists in Indonesia. Ecotourism is one of the nation’s vital sectors that can improve the economy, preserve nature and introduce local culture. Sadly, today, ecotourism has yet to be discovered by the public, which cumulatively causes much damage. Therefore, The Ministry of Tourism and Creative Economy is tasked with educating the public in order to create a collaborative synergy. This article uses qualitative research with a phenomenological approach. The primary data sources in this study are Twitter netizens’ tweets and The Ministry of Tourism and Creative Economy ‘s social media accounts. At the same time, the secondary data used in this study are articles, books, and reportage. Then the data will be analyzed through several procedures, namely, 1) data matrix, 2) data reduction, 3) coding, and 4) conclusion drawing. The results showed that the messages conveyed by The Ministry of Tourism and Creative Economy regarding ecotourism were good, and the intensity was relatively high. Public conversations about ecotourism have also been substantive in accordance with ideal ecotourism. Unfortunately, the intensity of The Ministry of Tourism and Creative Economy ‘s message is not accompanied by the intensity of ecotourism conversations in the community. However, the Ministry of Tourism and Creative Economy has issued a communication policy in promoting ecotourism in Indonesia. This aims to benefit the wider community, such as community productivity, economic improvement, and the introduction of local culture to the international community.

It is critical for urban and regional planners to examine spatial relationships and interactions between a port and its surrounding urban areas within a region’s spatial structure. This paper seeks to develop a targeted framework of causal relationships influencing the spatial structure changes in the Bushehr port-city. Hence, the study utilizes Fuzzy Cognitive Maps (FCMs), a computational technique adept at analyzing complex decision-making processes. FCMs are employed to identify concepts that act as drivers or barriers in the spatial structure changes of Bushehr port-city, thereby elucidating the causal relationships within this context. Additionally, the study evaluates these concepts’ relative significance and interrelationships. Data was collected through interviews with ten experts from diverse backgrounds, including specialists, academics, policymakers, and urban managers. The insights from these experts were analyzed using FCMapper and Pajek software to construct a collective FCM, which depicts the influential and affected concepts within the system. The resulting collective FCM consists of 16 concepts, representing the varied perspectives and expertise of the participants. Among these, the concepts of management and planning reform, economic growth of the city-port, and port development emerged as the three most central concepts. Moreover, the effects of all influential concepts on the spatial structure change in Bushehr port-city were evaluated through simulations conducted across four different scenarios. The analysis demonstrated that the system experiences the most significant impact under the fourth scenario, where the most substantial changes are observed in commercial and industrial growth and the planning of port-city separation policies.

Presently, there exists a burgeoning trend of female entrepreneurs worldwide, notably within the realm of small and medium-sized enterprises (SMEs), many of which manifest as family-run enterprises. The systematic literature review endeavors to construct an integrative framework concerning the practical ramifications of female involvement in family businesses by amalgamating extant global studies. The findings elucidate the practical implications inherent in female participation across global family businesses, concurrently furnishing a reservoir of prospects for prospective investigations. The deduction posits the imperative eradication of gender disparities, cognizant that gender parity underpins economic and financial advancement and is contingent upon female involvement. Furthermore, familial enterprises are urged to acknowledge and integrate women’s contributions in entrepreneurial decision-making processes.

The present study focuses on improving Cognitive Radio Networks (CRNs) based on applying machine learning to spectrum sensing in remote learning scenarios. Remote education requires connection dependability and continuity that can be affected by the scarcity of the amount of usable spectrum and suboptimal spectrum usage. The solution for the proposed problem utilizes deep learning approaches, namely CNN and LSTM networks, to enhance the spectrum detection probability (92% detection accuracy) and consequently reduce the number of false alarms (5% false alarm rate) to maximize spectrum utilization efficiency. By developing the cooperative spectrum sensing where many users share their data, the system makes detection more reliable and energy-saving (achieving 92% energy efficiency) which is crucial for sustaining stable connections in educational scenarios. This approach addresses critical challenges in remote education by ensuring scalability across diverse network conditions and maintaining performance on resource-constrained devices like tablets and IoT sensors. Combining CRNs with new technologies like IoT and 5G improves their capabilities and allows these networks to meet the constantly changing loads of distant educational systems. This approach presents another prospect to spectrum management dilemmas in that education delivery needs are met optimally from any STI irrespective of the availability of resources in the locale. The results show that together with machine learning, CRNs can be considered a viable path to improving the networks' performance in the context of remote learning and advancing the future of education in the digital environment. This work also focuses on how machine learning has enabled the enhancement of CRNs for education and provides robust solutions that can meet the increasing needs of online learning.

Accurate drug-drug interaction (DDI) prediction is essential to prevent adverse effects, especially with the increased use of multiple medications during the COVID-19 pandemic. Traditional machine learning methods often miss the complex relationships necessary for effective DDI prediction. This study introduces a deep learning-based classification framework to assess adverse effects from interactions between Fluvoxamine and Curcumin. Our model integrates a wide range of drug-related data (e.g., molecular structures, targets, side effects) and synthesizes them into high-level features through a specialized deep neural network (DNN). This approach significantly outperforms traditional classifiers in accuracy, precision, recall, and F1-score. Additionally, our framework enables real-time DDI monitoring, which is particularly valuable in COVID-19 patient care. The model’s success in accurately predicting adverse effects demonstrates the potential of deep learning to enhance drug safety and support personalized medicine, paving the way for safer, data-driven treatment strategies.