Scheduling strategies play a significant role in shaping retail assortment size, influencing both product availability and inventory management. This study examines the relationship between various scheduling methods—such as fixed, dynamic, and demand-based scheduling—and the size of product assortments offered in retail settings. By analyzing how different scheduling approaches impact inventory turnover, stockouts, and overstock situations, the research explores how retailers can optimize assortment size to meet consumer demand while minimizing operational costs. The findings suggest that flexible, demand-driven scheduling strategies lead to more efficient assortment planning, resulting in optimal product availability and improved customer satisfaction. In contrast, rigid scheduling methods may restrict assortment size, potentially leading to missed sales opportunities or excess inventory. This paper offers insights into how retailers can refine their scheduling practices to enhance inventory management and assortment optimization in a competitive market.

The COVID-19 pandemic has almost shaken the fact that some people think that resources, including the food system in the Pacific, may be vulnerable in terms of self-sustaining even with extensive impacts, including the acceleration in the number of COVID-19 cases verified in some of the most reliable countries of the world in terms of trade.  Key performance measures have been tact for Samoa and certainly, it affected remittances, international trade, and especially, the tourism industry which thus impacting on Samoa regarding it’s food systems and other means of support.  In a worst-case scenario, prioritizing what is meant as necessity, which is basically, access to food, water, and shelter regardless the loss of incomes, for instance, to pay for the next level of needs such as cash power and likewise should be considered first.  In this paper, we would like to share the fact that by living in the old traditional Samoan way in itemizing its Lands and Titles system of consuming locally produced and harvested fresh foods, not only it will contribute to sustaining Samoa’s market economy but will also contribute to bringing about a much healthier Samoa.

The article considers the possibility of using synthetic data to model a portfolio in conditions of instability in financial markets. Methods based on the analysis of historical data need to be revised to account for rare events that are missing from historical records. The use of generative models makes it possible to generate synthetic data, creating conditions for modeling situations that go beyond the known scenarios. The purpose of the article is to consider the impact of synthetic data on asset management methods in conditions of high market volatility. The methods of creating synthetic data using generative adversarial networks and their role in modeling situations with limited access to necessary information are described. The use of synthetic data in scientific papers confirms their effectiveness in adapting asset management strategies, which contributes to improving results. This underscores the need for their application and guarantees the stability of financial systems in the external conditions that determine the present. In conclusion, it is noted that generative models that create synthetic data increase the accuracy and flexibility of financial portfolio management strategies. The considered approaches open up opportunities for forecasting and decision-making. Due to this, the information contained in the work will be useful to investors and bank employees.

This article describes the crisis and its content, PR (Public Relationship) activities. It also discusses the emergence of the need for PR (Public Relationship) management in a crisis, its principles and levels. General conclusions on the area are formed, and appropriate recommendations for the effective use of principles are given.

This study investigates the relationship between personality traits and intrapreneurial behavior in organizational settings. Intrapreneurial behavior refers to the actions and mindset employees adopt when they engage in entrepreneurial activities within an established organization. The research examines how specific personality traits—such as openness to experience, conscientiousness, emotional stability, extraversion, and agreeableness—are associated with the likelihood of employees exhibiting intrapreneurial behaviors. Using a mixed-methods approach, including surveys and in-depth interviews with employees from various industries, the study analyzes the influence of these traits on the propensity to innovate, take risks, and drive change within organizations. The findings suggest that traits like openness to experience and extraversion significantly contribute to intrapreneurial actions, while traits such as conscientiousness and emotional stability moderate these behaviors. The study provides valuable insights into how organizations can identify and foster intrapreneurial talent, leading to enhanced innovation and organizational growth. This empirical exploration offers practical implications for HR practices and talent management strategies aimed at promoting intrapreneurship.

The article highlights the key areas of application of digital technologies to improve the level of service in the corporate banking segment for high-income customers. The study revealed how modern technologies — artificial intelligence, machine learning, blockchain, and cloud platforms — can improve service quality, accelerating the introduction of innovative products. Special attention is paid to their impact on customers' perception of services and the effectiveness of banks' internal processes, which is of current interest in the context of global changes and increasing competition in the banking sector.

The methodology includes comparative analysis and systematization of digital solutions for interaction in corporate banking. The emphasis is on a personalized approach to products developed based on data analysis using machine learning algorithms that allow segmentation of the customer base. The difficulties faced by banks in implementing modern technologies, such as compliance with regulatory requirements, cybersecurity issues, and the need to adapt IT infrastructure to new conditions, were also covered.

As a result, it was found that personalization improves the accuracy of recommendations, and automation of processes helps to reduce costs and accelerate service. The final part of the article describes the benefits for banks interested in implementing digital solutions to improve the efficiency of interaction with corporate customers. The research materials can be useful for customer service specialists, banking analysts, and managers responsible for transformational processes.

This article examines the changes brought by streaming services to the film industry, becoming a crucial component of the media economy. With the onset of digitalization and the growth of streaming, cinema has moved beyond traditional distribution channels. Platforms such as Netflix, Disney+, and HBO Max have transformed the content distribution model, offering users access to extensive libraries of movies and series through subscription-based or ad-supported models. The interdisciplinary approach of this article allows for an exploration of the economic, sociocultural, and technological aspects of this transformation. The study discusses innovations in recommendation algorithms, personalized content offerings, and original productions that have become defining features of successful streaming services. Examples of adaptive strategies include the creation of exclusive content and the incorporation of ads into subscription models, attracting new audiences and driving revenue growth. The research findings confirm that despite the audience's continued interest in traditional cinemas, streaming services retain significant potential for expanding their customer base, particularly in the context of globalization and shifting consumer habits. The article concludes that streaming has become an integral part of the modern film industry and a key driver of its future growth.

The cryptocurrency market is one of the most dynamic and volatile markets in the world's financial ecosystem, and investment landscapes in the US financial market have changed so much. In slightly over a decade, cryptocurrencies have moved from niche digital assets to mainstream investment opportunities such as Bitcoin, Ethereum, and many others. The prime objective of this research project was to investigate the effectiveness of various machine learning algorithms in the prediction of cryptocurrency prices within the volatile US financial market. This research pinpointed which Machine Learning techniques provide the most accurate and reliable predictions under different market conditions, with a full understanding of their strengths and limitations. The dataset gathered for analyzing and forecasting cryptocurrency prices entailed diverse and extensive data points, affirming a well-rounded foundation for machine learning algorithms. Particularly, current and historic price data from cryptocurrency exchanges such as Binance, Coinbase, and Kraken, together with trading metrics important for the definition of market dynamics. Aggregated data from financial databases such as Coin-Market-Cap, Crypto-Compare, and Yahoo Finance comes in structured form and presents historical consistency, hence perfectly fitting for machine learning applications. Models considered for the study ranged from simple, linear methods to complex ensemble and gradient-boosting algorithms. Precise performance evaluation is a proxy of its reliability and correctness of effectiveness in price predictions in a cryptocurrency market. Several measures of the effectiveness of prediction have been used here for assessing the different properties of models' performance: Precision, Recall, and F1-Score. Additional performance metrics were applied to evaluate the models in this study including Mean Absolute Error, Root Mean Squared Error, and R-squared. The gradient Boosting model did an excellent job as compared to other algorithms, as the values of accuracy, precision, recall, and F1-score for both classes were quite high. All three models have quite a relatively low MAE and RMSE, which means that each model is remarkably good at predicting the target variable. The application of machine learning models in the sphere of cryptocurrency price prediction might finally give very important implications to investors and stakeholders of the financial market in the USA, especially since recently, cryptocurrencies have been made integral parts of both individual and institutional investors' portfolios and trading strategies. To investors, it may provide indications of the entry and exit points, diversification of portfolios, and risk management by using machine learning models. Consolidation with the financial system will indeed mark a strategic shift toward data-driven decision-making in investment management and trading by integrating machine learning models into the financial systems.