Since revenue streams provide financial stability for business development, companies are tasked with conducting an individual revenue model, that ensures their healthy and sustainable growth. Therefore, it is important to take into account the manner of creating revenues. This initiative relates to the revenue generation mechanism which describes how revenue is generated from the offer. Our research aims to find out whether the number of revenue streams (scope of the revenue model) and other financial and production indicators can predict the amount of revenue, as well as which factors can predict the scope of the revenue model. Concurrently we have focused on analyzing the sources of revenue streams and have selected key business variables in the wine producing industry. This sector had been selected as there is a significant difference between revenue streams based on manufacturing and those based on tourism activities. This concept was created by using theoretical knowledge about the value, pricing and segmentation. The goal of our research article was to design the General Concept of the Revenue Model and to identify variables with a strong influence on it. Results of the multiple linear regression analysis confirmed the significant influence of particular predictors on the scope of the revenue model, whereby the model explains up to 80% of the variance.

Background: Data-driven decisions in each functional area of management, through all of the strategic levels, in the present time of dynamic changes in geopolitical and market conditions are necessary to achieve corporate (economic and social) goals, in line with securing future business success and sustainability. Because of this business need, we will focus in our research paper on price management, which can be seen as a supportive tool for strategic decisions, where competent decisions should be based on data-driven pricing decisions. Purpose: The aim of the research study is to identify what price consumers are prepared to pay for a new food product in a relatively saturated foreign market. The research study was conducted in the milk chocolate bar market segment. Study design: We applied the van Westendorp price sensitivity test to identify the range of acceptable prices for a product that is willing to enter a new foreign market. For this purpose, we used a milk chocolate bar product currently unknown in the Slovak market. Findings: In addition to the van Westendorp price sensitivity measurement, we used a non-parametric Mann Whitney U test to confirm the hypothesis that chocolate tasting will increase the likelihood of customers to pay a higher price for the tested product. The hypothesis mentioned above was statistically confirmed. Limitations: It is necessary to monitor customer reactions to a given price level and be prepared to optimize it. We did not address this part of the analysis in identifying a price that would be acceptable to consumers in terms of value perception, due to the scope of the study.

The aim of the paper is to present a mathematical programming model for optimizing the location of bio-belts in agricultural landscapes. The proposed model balances crop production and biodiversity conservation. Bio-belts represent an advanced form of vegetated field margins that improve ecosystem functions and reduce environmental impacts associated with intensive agriculture. The proposed approach uses a distance geometry formulation that enables the flexible placement of multiple bio-belts within irregularly shaped fields. The optimization problem aims to minimize the total unproductive area while ensuring that all parts of the cultivated land remain within an ecologically effective distance of the nearest bio-belt. The resulting nonlinear model is solved using a differential evolution algorithm. An illustrative case study shows the applicability of the proposed approach and illustrates the trade-off between cultivated and conservation areas. The results suggest that the model can serve as a decision-support tool for sustainable land management and may be integrated with precision agriculture and Agriculture 4.0 systems.