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The purpose of this literature research is expected to build a hypothesis regarding the influence between variables that can later be used for further research in the scope of marketing management. The research article literature review concept and application of canvas business model in startup companies: customer relationships, customer segments and market trends is a scientific literature article in the scope of marketing management science. The approach used in this literature review research is descriptive qualitative. The data collection technique is to use literature studies or conduct a review of relevant previous articles. The data used in this descriptive qualitative approach comes from previous research that is relevant to this research and is sourced from academic online media such as Thomson Reuters Journal, Springer, Taylor & Francis, Scopus Emerald, Elsevier, Sage, Web of Science, Sinta Journal, DOAJ, EBSCO, Google Scholar and digital reference books. In previous studies, 1 relevant previous article was used to review each independent variable. The results of this literature review article are: 1) Customer Relationships affect the Canvas Business Model Concept in Startup Companies; 2) Customer Segments affect the Canvas Business Model Concept in Startup Companies; 3) Market Trends affect the Canvas Business Model Concept in Startup Companies; 4) Customer Relationships Influence the Implementation of the Canvas Business Model in Startup Companies; 5) Customer Segments Influence the Implementation of the Canvas Business Model in Startup Companies; and 6) Market Trends Influence the Implementation of the Canvas Business Model in Startup Companies. [ABSTRACT FROM AUTHOR]

Transitioning fundamental and applied science to evidence-based real-world applications is challenging yet necessary, especially in environmental sciences. In this paper, a quantitative data analysis method is presented to support decision-making by augmenting qualitative customer discover methods used for new technology adoption. This method can be used to assess: i) customer segment (CS) and value proposition (VP) validity and ii) the ability of a new technology to address the VP. Two different environmental technologies were evaluated to demonstrate this quantitative approach. CS and VP(s) that were previously qualitatively identified were statistically (in)validated using binomial testing. In each of the presented cases, the CS was statistically validated but only one of the three VPs for each case was validated. The validated performance parameters for each technologywere tested against cost and performance data using linear optimization. This testing allowed for the identification of conditions under which the technology met minimum customer performance requirements and where further data or technology modifications were still needed. These identified conditions establish the features necessary to transition the technologies to the minimum viable product (MVP) phase of commercialization. One technology was found to be ready for MVP development while the other technology needed additional data and modification. Both findings demonstrate the effectiveness of this quantitative data analysis method and guide decision-making in the early stages of technology commercialization.

Globally, people started gravitating toward organic food as health consciousness rose. From the farm to the consumer's fork, organic food is produced using a comprehensive method. Organic farming has provided farmers and producers with many opportunities, but there are still difficulties concerning the cost of production and distribution. Due to environmental sustainability challenges, climate change, soil fertility, biological assortment, and consumer well-being, organic farming is attracting more attention than conventional farming. Organic farming can be done using standardised methods and has many common issues with conventional farming. Organic farming has some unique problems as well. With the right strategies, careful planning, and government assistance, many difficulties faced by farmers can be addressed. The downstream material flow of the supply chain, and in particular demand estimation, market price, and identifying customer segments, has been identified as a significant problem in organic farming. Massive losses have been incurred along with the entire supply chain a due to the inadequate demand estimation that has caused surpluses and shortages in the produce. A few farmers have resolved the cash flows and material information controlling problem. This article proposes various related hypotheses associated with identifying customer segments, forecasting demand for the product, and profitability as market price changes in the crop.

Globally, people started gravitating toward organic food as health consciousness rose. From the farm to the consumer's fork, organic food is produced using a comprehensive method. Organic farming has provided farmers and producers with many opportunities, but there are still difficulties concerning the cost of production and distribution. Due to environmental sustainability challenges, climate change, soil fertility, biological assortment, and consumer well-being, organic farming is attracting more attention than conventional farming. Organic farming can be done using standardised methods and has many common issues with conventional farming. Organic farming has some unique problems as well. With the right strategies, careful planning, and government assistance, many difficulties faced by farmers can be addressed. The downstream material flow of the supply chain, and in particular demand estimation, market price, and identifying customer segments, has been identified as a significant problem in organic farming. Massive losses have been incurred along with the entire supply chain a due to the inadequate demand estimation that has caused surpluses and shortages in the produce. A few farmers have resolved the cash flows and material information controlling problem. This article proposes various related hypotheses associated with identifying customer segments, forecasting demand for the product, and profitability as market price changes in the crop. [ABSTRACT FROM AUTHOR]

Using the RFM (Recency, Frequency, Monetary value) model can provide valuable insights about customer clusterswhich is the core of customer relationship management. Due to accurate customer segment coming from dynamic weighted applications, in-depth targeted marketing may also use type of dynamic weight of R, F and M as factors. In this paper, we present our dynamic weighted RFM approach which is intended to improve the performance of customer segmentation by using the factors and variations to attain dynamic weights. Our dynamic weight approach is a kind of Custom method in essential which roots in the understanding of the data set. Firstly, Analytic Hierarchy Process is used to calculate the subjective weight, then the entropy method is applied to calculate the objective weight. Finally, we use comprehensive integration weighting method to combine the subjective and objective weight to obtain the final weight of the index to calculate the individual user value and quantify the user value difference. The experiment shows that the dynamic weight we used in RFM model is vital, affects the customer segmentation performance positively. Also, this study indicates that customer segments containing dynamic weighted RFM scores bring about stronger and more accurate association rules for the understanding of customer behavior. At last, we discuss the limitations of RFM analysis. [ABSTRACT FROM AUTHOR]

In this paper, the value of information sharing is measured in a two‐stage supply chain consisting of a manufacturer and two retailers. These retailers compete in a market with multiple customer segments, including loyal, switching, and stockpiling ones, by using a promotional pricing strategy. With this setting, the differences in total supply chain profits are quantified under no information sharing, and information sharing with and without disclosure agreements. It is found that while sharing information with a nondisclosure agreement is always valuable, this might not be the case for a disclosure one. In addition, the conditions, where information sharing with the disclosure agreement is beneficial, have been analytically determined. Then, the measurements of information sharing value under varying model parameters are explored, both numerically and analytically. The results provide useful managerial insights on when an information sharing agreement offers the most benefits. [ABSTRACT FROM AUTHOR]

On the basis of the pattern of organization business-model the author puts forward business-models of the organization of the quadro-sector of economy of knowledge. Mainly, university is an organization of this sector and it carries out education and academic activity. Therefore, a typical business-model was designed for it and it was called 'mirror'. University activity as a business organization has always had certain distinguishing features. These features imply, first of all, that future specialist during the whole period of training acts both as a key resource and a customer and, finally as a value offer. The next important feature is connected with the fact that knowledge is not only transferred in the university but it is developed there. In economy of knowledge the university is assigned a part of a researcher and an impetus to development. At the same time, the university, which can train personnel for other industries and develop knowledge, can also in cooperation with other organizations develop knowledgeintensive products. In this case another business-model is necessary, which was called 'refraction'. Its own business-model was developed for the research organization. The article reveals the content and specific features of each block of advanced business-models. And finally, the author formulated the problem that should be resolved by economic science due to shaping of economy of knowledge.