Finding the right technique to optimize a complex problem is not an easy task. There are hundreds of methods, especially in the field of metaheuristics suitable for solving NP-hard problems. Most metaheuristic research is characterized by developing a new algorithm for a task, modifying or improving an existing technique. The overall rate of reuse of metaheuristics is small. Many problems in the field of logistics are complex and NP-hard, so metaheuristics can adequately solve them. The purpose of this paper is to promote more frequent reuse of algorithms in the field of logistics. For this, a framework is presented, where tasks are analyzed and categorized in a new way in terms of variables or based on the type of task. A lot of emphasis is placed on whether the nature of a task is discrete or continuous. Metaheuristics are also analyzed from a new approach: the focus of the study is that, based on literature, an algorithm has already effectively solved mostly discrete or continuous problems. An algorithm is not modified and adapted to a problem, but methods that provide a possible good solution for a task type are collected. A kind of reverse optimization is presented, which can help the reuse and industrial application of metaheuristics. The paper also contributes to providing proof of the difficulties in the applicability of metaheuristics. The revealed research difficulties can help improve the quality of the field and, by initiating many additional research questions, it can improve the real application of metaheuristic algorithms to specific problems. The paper helps with decision support in logistics in the selection of applied optimization methods. We tested the effectiveness of the selection method on a specific task, and it was proven that the functional structure can help the decision when choosing the appropriate algorithm.

The complex interactions of industrial Policy, structural transformation, economic growth, and competitive strategy within regional industries are examined in this research. Using a dynamic capabilities framework, the study examines the mediating roles of organizational innovation and adaptability in the link between competitiveness and macroeconomic variables. A two-way fixed effects model is used in this study to examine the influence of structural transformation (ST) on Industrial Policy (IP). Using regional data covering the years 2010 to 2022, the research undertaken in this paper explores the dynamics of the Indonesian economy by empirically assessing the consequences of structural change on industrial Policy. In order to establish a comprehensive model that clarifies the mechanisms through which industrial policies and structural shifts impact the development of dynamic capabilities, ultimately influencing competitiveness strategies, this research draws on a large amount of empirical data and integrates insights from seminal works. Our research adds to our knowledge of strategic management in regional industries by providing detailed information on how economic development and policy interventions influence businesses’ ability to adapt and gain a competitive edge. In addition to advancing scholarly discourse, this study offers business executives and politicians valuable insights for managing the intricacies of global economic processes.

Since 2019, major travel destinations worldwide have issued travel-related restrictions against COVID-19. There is much research on tourism, but few studies have been conducted to explain the relevance of revisiting intention from the perspective of the epidemic or the dramaturgical theory. The purpose of the research is to explore the impact of customer experience on revisit intention during the period of COVID-19 slowdown by using dramaturgical theory. This study used a survey methodology, and the questionnaire was distributed on an online questionnaire platform. The URL of the questionnaire was published on social media (such as Facebook and LINE) to collect data from 389 samples of people who have foreign travel experience. The data was analyzed by employing partial least square structural equation model (PLS-SEM) methodology with the help of the statistical software “SmartPLS”. The research findings are as follows: 1) setting, audience, and performance are the three important elements of dramaturgical theory that impact the experience quality; 2) customer experience of tourists has a significant impact on the experience quality; 3) experience quality has a significant positive impact on the experience value and relationship quality; 4) experience value and relationship quality are important predictors of revisit intention. This study provides academic implications regarding the use of dramaturgical theory in relation to customer experience and relationship constructs in the context of tourism. Furthermore, it also provides some practical implications to tourism practitioners and managers, which would assist tourism industries in developing successful marketing strategies for the possible recovery of COVID-19.

Relational database models offer a pathway for the storage, standardization, and analysis of factors influencing national sports development. While existing research delves into the factors linked with sporting success, there remains an unexplored avenue for the design of databases that seamlessly integrate quantitative analyses of these factors. This study aims to design a relational database to store and analyse quantitative sport development data by employing information technology tools. The database design was carried out in three phases: (i) exploratory study for context analysis, identification, and delimitation of the data scope; (ii) data extraction from primary sources and cataloguing; (iii) database design to allow an integrated analysis of different dimensions and production of quantitative indicators. An entity-relationship diagram and an entity-relationship model were built to organize and store information relating to sports, organizations, people, investments, venues, facilities, materials, events, and sports results, enabling the sharing of data across tables and avoiding redundancies. This strategy demonstrated potential for future knowledge advancement by including the establishment of perpetual data updates through coding and web scraping. This, in turn, empowers the continuous evaluation and vigilance of organizational performance metrics and sports development policies, aligning seamlessly with the journal’s focus on cutting-edge methodologies in the realm of digital technology.

This study investigates the potential of developing a maritime tourism project within the blue economy of Pakistan and explores the factors influencing blue growth and maritime tourism. A quantitative research approach has been adopted. The research gathered primary data from diverse experts and stakeholders within the maritime sector and related industries. The study’s target population comprised on various entities involved in these sectors. A sample of around 250 individuals was selected using a convenient sampling technique. The collected data underwent analysis using the Statistical Package for the Social Sciences (SPSS) and the Partial Least Square (PLS) method. This approach was chosen to explore and understand the intricate relationships between variables in the context of the maritime industry. Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA) techniques were then employed to scrutinize the data further, allowing for a comprehensive examination of the interconnections among the variables identified in the study. This robust methodological approach enhances the study’s credibility and provides valuable insights into the dynamics of the maritime sector and its associated industries. The findings indicate that a balanced approach, valuing business sustainability, top management support, and enabling innovation structures positively impact blue growth. Additionally, uncertainty avoidance and promoting short-term goals have an appositive impact on the blue economy. Moreover, two potential barriers, Functional strategy, and weak competency, do not significantly affect the blue economy. This study lays the foundation for further exploration and implementation of strategies that promote sustainable growth and development in Pakistan’s blue economy. By integrating the insights gained from this study into policy and decision-making processes, stakeholders can work together to create a vibrant and sustainable maritime tourism sector that benefits both local communities and the environment.