Transit-oriented development is a concept that focuses on developing areas in and around transit nodes to create added value. The concept concentrates on integrating mass public transport networks with non-motorized modes of transport, minimizing the usage of motorized vehicles, and fostering the growth of dense, mixed-use areas with medium to high spatial intensity. This research examines the effects of altering the business model to create Transit Oriented Development (TOD) in Jakarta, contrasting it with PT Moda Raya Transports (PT MRT). We collected data by conducting in-depth interviews with experts and distributing questionnaires to seven respondents who work at this We used the Business Model Canvas (BMC) to identify business models and the internal resources needed for the implementation process. process. Therefore, six elements in BMC were used to conduct changes, and based on the results, RBV analysis was pe PT MRT needs to enhance its internal power to a competitive advantage level in order to effectively manage changes. We need to conduct further research on how the business model can influence the creation of transit-oriented development areas.

The article examines the issues of application and improvement of the methodology for evaluating industrial enterprises as recipients of state support within the framework of the implementation of industrial policy. The authors considered approaches to the content of industrial policy, investigated the factors influencing its efficiency, identified aspects of its imperfections that arise when applying an incomplete list of important parameters of economic development and ambiguity in the interpretation of previously applied estimates. The article presents proposals to improve the methodology for assessing potential recipients of state support based on the development of a comprehensive indicator for assessing enterprises (recipients of support), taking into account not only the classical parameters of the economic efficiency of industrial enterprises applying for state financial assistance, but also such aspects as the development of budgetary funds, belonging to priority sectors of the economy, characteristics of sustainable development and export and innovation potential. Combining the results of a comprehensive assessment of the recipient of state support with a map of the business demography of the territory allows making a decision not only about the fact of support and its efficiency, but also to predict the assessment of the life cycle of the enterprise and its subsequent development.

The major goal of decisions made by a business organization is to enhance business performance. These days, owners, managers and other stakeholders are seeking for opportunities of modelling and automating decisions by analysing the most recent data with the help of artificial intelligence (AI). This study outlines a simple theoretical model framework using internal and external information on current and potential clients and performing calculations followed by immediate updating of contracting probabilities after each sales attempt. This can help increase sales efficiency, revenues, and profits in an easily programmable way and serve as a basis for focusing on the most promising deals customising personal offers of best-selling products for each potential client. The search for new customers is supported by the continuous and systematic collection and analysis of external and internal statistical data, organising them into a unified database, and using a decision support model based on it. As an illustration, the paper presents a fictitious model setup and simulations for an insurance company considering different regions, age groups and genders of clients when analysing probabilities of contracting, average sales and profits per contract. The elements of the model, however, can be generalised or adjusted to any sector. Results show that dynamic targeting strategies based on model calculations and most current information outperform static or non-targeted actions. The process from data to decision-making to improve business performance and the decision itself can be easily algorithmised. The feedback of the results into the model carries the potential for automated self-learning and self-correction. The proposed framework can serve as a basis for a self-sustaining artificial business intelligence system.

The study aims to examine the labor market challenges and motivational factors for employee retention through the example of a small machinery company in Hungary. In recent years, Hungary’s labor market has faced significant difficulties, particularly due to the COVID-19 pandemic, which resulted in temporary unemployment followed by labor shortages. The research aims to identify the motivational, welfare, and financial factors that contribute to employee retention. Due to the small sample size, we did not investigate the relationships concerning loyalty, commitment, and performance. The research methods included comprehensive data collection at a machinery company employing 24 people located near the Austrian-Hungarian border. During the data collection, we conducted a questionnaire survey that included questions related to benefits, performance, commitment, and loyalty. The collected data were processed by calculating weighted averages and differences. The results indicate that flexible working hours and easy accessibility to the workplace are of utmost importance to employees. There is also a significant demand for performance-based pay and diverse, flexible benefit packages. Employees require both formal and informal professional recognition, such as praise and awards. The research has practical significance for both organizational management and employee well-being. Understanding employee opinions and implementing measures based on these can have four primary effects: improvement in employee performance, reduction in turnover, increase in organizational commitment, and enhancement of the company’s positive perception.

This study investigated the impact of social media on purchasing decision-making using data from a questionnaire survey of 257 randomly sampled students from the College of Business at Imam Muhammad Ibn Saud Islamic University. The study items were selected from the study community through a random sample, where several (257) students were surveyed. To achieve its objectives, the study follows the descriptive analytical approach in addressing its topic. The questionnaire was adopted as a tool for collecting data. The questionnaire collected data on the independent variable social media—and the dimensions of the dependent variables representing the stages of purchasing decision-making: Feeling the need for the advertised goods, collecting information about alternatives, evaluating available options, buying decisions, and post-purchase evaluation of the purchase decision. Then, the data were analyzed based on regression analysis using SPSS and AMOS. The important findings are summarized below: Social media use is directly related to feeling the need for and searching for information on advertised goods. Social communication and the evaluation of alternatives to advertised goods, in addition to the existence of a moral effect and a direct correlation between social media use and making the purchasing decision for advertised goods. Providing honest, sufficient, and accurate information via social media to the buyer can help them make the purchasing decision.