Reasonable Price Research Articles

Research on the Purchase Intention of Intangible Cultural Heritage Creative Products based on Tourist Perceived Value

This study aims to explore in depth the purchasing intentions of tourists towards creative products of intangible cultural heritage, aiming to reveal the level of recognition and importance that tourists place on the intangible value carried by intangible cultural heritage during the purchasing process. By using a questionnaire survey, a total of 500 valid samples of tourists from different backgrounds and experiences were collected, allowing for a more comprehensive understanding of their purchasing preferences and decision-making factors. The statistical analysis and discussion of data reveal a series of factors that affect tourist purchase intention. Firstly, uniqueness is an important influencing factor, and tourists tend to purchase products with unique cultural characteristics and historical significance because these products can arouse their interest and sense of identity. Secondly, the expression of cultural symbols also has a significant impact on purchase intention. Tourists are more willing to purchase products that reflect local culture and traditions, as these products not only have aesthetic value but also convey deep cultural connotations. In addition, emotional identification is also one of the important factors affecting purchase intention. Tourists tend to purchase products that can evoke emotional resonance and identification, as these products can stimulate their emotional experiences and memories. Finally, although price factors also have a certain impact on purchase intention, they are not decisive factors. Tourists are more willing to pay reasonable prices for products with rich cultural connotations and emotional experiences. Overall, this study reveals that tourists pay more attention to the cultural connotations, emotional experiences, and emotional identity of intangible cultural heritage creative products when purchasing them, rather than simple material attributes. This provides important references for relevant enterprises and organizations to better understand the needs and preferences of tourists, thereby developing more attractive and competitive intangible cultural heritage creative products.

Research on Superstore Vegetable Pricing and Replenishment Prediction Based on ARIMA Time Series and Particle Swarm Algorithm

With the development of digitalization, it is important to provide accurate and reasonable pricing and replenishment strategies for superstores through market demand and data information. In this paper, we first preprocess the relevant data and draw line graphs to compare the correlation between multiple individual items through Spearman correlation analysis. Secondly, Random Forest feature importance analysis is used to explore the relationship between total sales and cost-plus pricing of each vegetable category, and finally, the target data are tested for grade ratio and a gray model is constructed, and model prediction is carried out by using spsspro, and then the particle swarm algorithm is used to derive the optimal pricing strategy. Meanwhile, by updating the pricing regularly and making accurate replenishment decisions, we can better adapt to market fluctuations, obtain better sales opportunities, and maximize the benefits gained by the superstore itself.

Optimization research on automatic pricing and replenishment decision method for vegetable commodities

This paper addresses the issues of replenishment and pricing of vegetable commodities in fresh food supermarkets. By analyzing sales data and loss rate data, mathematical models are established to solve the following problems. Spearman correlation analysis and cluster analysis methods are employed. Firstly, data preprocessing is conducted by excluding returned vegetable products from a certain supermarkets distribution, and then integrating sales transaction details and wholesale price-related data. Secondly, monthly data is used to succinctly present the sales distribution patterns of various categories and individual items of vegetables through line charts. Finally, the models of Spearman correlation analysis and cluster analysis are respectively applied to illustrate the relationship between the sales volume of 6 major categories of vegetables and 251 individual items. Linear regression models, time series analysis, and XG-Boost regression analysis models are used. This paper requires replenishment plans to be made by category, analyzing the relationship between the total sales volume and cost-plus pricing of various vegetable categories. Firstly, a linear regression model is applied to fit historical sales volume and cost-plus pricing, resulting in a linear functional relationship, indicating that only the sales volume of tomatoes is negatively correlated with cost-plus pricing. Based on historical sales data, the daily replenishment total for each vegetable category within the next week is predicted. Predictive models and time series analysis are employed. Meanwhile, for pricing strategies, the XGBoost algorithm is utilized to provide reasonable pricing strategies. Based on the optimal solution, replenishment plans and pricing strategies for the next week are formulated to ensure the maximization of sales revenue objectives.

Estimation of Soil Loss in the Nanoi River Basin using Geospatial Techniques

Objectives: The main purpose of this research is to identify severe soil loss areas in the Nanoi river basin of Assam, India to suggest appropriate soil management planning in the river basin. Methods: The Revised Universal Soil Loss Equation (RUSLE) integrates geospatial technologies to assess overall soil loss in the Nanoi river basin which provides a faster and more accurate estimation. It is possible to understand soil erosion patterns more thoroughly using RUSLE which makes sustainable soil management easier. Findings: According to the RUSLE equation, the Nanoi river basin experiences an estimated total soil loss of 18,562.5 tons per year, with an average annual soil loss of 0.32 t/h/y. This suggests that approximately 0.64 km2 of the area falls into the Moderate, high and Extreme soil erosion sensitivity zones. Furthermore, a final map is generated to display different areas with varying levels of soil loss rates. Novelty: The geospatial approaches used produce precise findings at a reasonable price and demonstrate the severity of soil loss in the river basin. If soil loss continues at the current rate, there is a high likelihood that certain areas on both sides of the river, particularly in the downstream region of the basin, will experience fluvial hazards, such as drainage congestion and floods. Hence, the results generated will be helpful in the management practices of the river basin. Keywords: Soil loss, Soil erosion, Geospatial, Revised universal soil loss equation, River basin

Genetic Algorithms Application for Pricing Optimization in Commodity Markets

The perishable nature of vegetable commodities poses challenges for superstores, as reselling them is often unfeasible due to their short freshness period. Reliable market demand analysis is crucial for boosting revenue. This study simplifies the pricing and replenishment decision-making process by making reasonable assumptions about the selling time, wastage rate, and replenishment time for vegetable commodities. A single-objective planning model with the objective of profit maximization was constructed by fitting historical data using the nonparametric method of support vector regression (SVR). The study reveals a specific relationship between sales volume and cost-plus pricing for each category and predicts future cost changes using an LSTM model. Combining these findings, we substitute the relationship between sales volume and pricing as well as the LSTM prediction data into the model, and solve it using genetic algorithms in machine learning to derive the optimal replenishment volume and pricing strategy. Practical results show that the method can provide reasonable pricing and replenishment strategies for vegetable superstores, and after careful accounting, we arrive at an expected profit of RMB 22,703.14. The actual profit of the supermarket was RMB 19,732.89. The method, therefore, increases the profit of the vegetable superstore by 13.08%. By optimizing inventory management and pricing decisions, the superstore can better meet the challenges of vegetable commodities and achieve sustainable development.

Charging station service scope division and real-time pricing strategy based on the strongest occupation method

With the rapid construction of charging stations (CSs), charging station operators need to enhance their core competitiveness by precisely planning their service areas and formulating reasonable and effective pricing strategies. However, the regional competition among multiple charging station operators is generally ignored. In the traditional model, the service scope of CSs appears as regular circles, which is inconsistent with the market distribution law. In response to the irregular relationship between the dynamic service scope of CSs and the real-time charging price, a charging station service scope (CSSS) model is proposed by introducing the variable service field strength (SFS). First, the competitiveness of CSs is evaluated quantitatively, and the SFS of CSs is defined to describe the service scope of CSs by the strongest occupation method. Second, the impact of the charging price on the charging demand is analyzed based on the CSSS division model. In addition, the revenue of charging station operators and the stability of the power grid are considered to establish a real-time pricing optimization model. Finally, the numerical simulation is operated in Furong District of Changsha. It is shown that the proposed method effectively achieves more profits for charging stations and decreases the average power deviation of the whole region.

A flow‐based ascending auction to compute buyer‐optimal Walrasian prices

AbstractWe consider a market where a set of objects is sold to a set of buyers, each equipped with a valuation function for the objects. The goal of the auctioneer is to determine reasonable prices together with a stable allocation. One definition of “reasonable” and “stable” is a Walrasian equilibrium, which is a tuple consisting of a price vector together with an allocation satisfying the following desirable properties: (i) the allocation is market‐clearing in the sense that as much as possible is sold, and (ii) the allocation is stable in the sense that every buyer ends up with an optimal set with respect to the given prices. Moreover, “buyer‐optimal” means that the prices are smallest possible among all Walrasian prices. In this paper, we present a combinatorial network flow algorithm to compute buyer‐optimal Walrasian prices in a multi‐unit matching market with truncated additive valuation functions. The algorithm can be seen as a generalization of the classical housing market auction and mimics the very natural procedure of an ascending auction. We use our structural insights to prove monotonicity of the buyer‐optimal Walrasian prices with respect to changes in supply or demand.

Farmer Application

The aim of the program is to provide a platform for farmers to rent modern agricultural machinery and equipment as well as rent storage space for crops. The app will also connect to government servers to track data on crops, fertilizer use and government funds. Records of machinery and equipment will be kept along with details of the crop stored. In India, agriculture is the main source of income for the majority of the population. However, many farmers are not aware of advances in agricultural technology and cannot understand the market value of their products. That's why they often sell products at very low prices. In addition, they also try to follow the government's agricultural plans and reports. To solve these problems, our initiative will create a platform where farmers can get the latest information on vegetables and fruits in every market in India. This will help them sell their products at reasonable prices and improve their financial situation. Additionally, farmers will also receive notifications regarding government agricultural programs and in-app notifications to benefit from these programs. Additionally, our application will help farmers prepare the required products by estimating the amount of vegetables and fruits according to the market. We will also include weather data to support farmers' planning for the next 2-3 days. Overall, our programs focus on providing farmers with new knowledge and technology that will improve their livelihoods and increase their productivity

Constraints of Sugarcane Farmers During Cultivation and Marketing in Kerala, India

The study analysed the constraints faced by farmers during cultivation and marketing of sugarcane in Kerala. A multi-stage random sampling technique was adopted for the data collection and information was obtained from 120 sugarcane farmers and 10 marketing intermediaries. The farmers ranked and used Garrett's ranking technique to analyze the main production and marketing constraints. The major production constraints identified were wild animal and rodent attack, followed by shortage of labour and high labour cost, high cost of inputs and delay in harvesting. Price fluctuations of sugarcane and absence of regulated markets were major marketing constraints. Valuable suggestions were implementing effective measures to mitigate wildlife damage, government should purchase farmers products at reasonable price.

A black-box adversarial attack on demand side management

Demand side management (DSM) contributes to the industry’s transition to renewables by shifting electricity consumption in time while maintaining feasible operations. Machine learning is promising for DSM with reasonable computation times and electricity price forecasting (EPF), which is paramount to obtaining the necessary data. Increased usage of machine learning makes production processes susceptible to so-called adversarial attacks. This work proposes a black-box attack on DSM and EPF based on an adversarial surrogate model that intercepts and modifies the data flow of load forecasts and forces the DSM to result in financial losses. Notably, adversaries can design the data modifications without knowledge of the EPF model or the DSM optimization model. The results show how barely noticeable modifications of the input data lead to significant deterioration of the decisions by the optimizer. The results implicate a significant threat, as attackers can design and implement powerful attacks without infiltrating secure company networks.

Synergizing Sustainability and Financial Prosperity: Unraveling the Structure of Business Profit Growth through Consumer-Centric Strategies—The Cases of Kosovo and Albania

This research investigates the synergistic relationship between sustainability and financial prosperity in businesses, specifically focusing on the impact of consumers on profit growth in Kosovo and Albania. The study aims to understand consumers’ perceptions of their purchases, the factors influencing their choice of businesses, and the types of businesses that effectively support consumers in these countries. Data were collected through a survey completed by 200 consumers and 200 businesses. The analysis, utilizing multivariate analysis of variance, descriptive analysis, and reliability analysis with SPSS, reveals that consumers significantly influence the sustainability of business profit growth. Moving forward, it is recommended that businesses prioritize offering reasonable prices, quality products/services, easy access to products/services, clear information about products/services, and convenient locations. The research has profound implications for businesses, consumers, and countries and suggests the need for further exploration of the impact of consumers on profit growth in diverse contexts.

Article: Open-Price Contracts Under the CISG: The Law in Action

The CISG is undoubtedly the most successful instrument of international trade law. While in former times the international sale of goods was regulated by domestic laws, today the international community is able to offer a truly international regime for these contracts. However, the CISG is not free from ambiguities and contradictions. One of the most famous is between Articles 14(1) and 55. This interplay has ignited the well-known academic controversy over the validity of sale of goods contracts without an agreed price under the CISG. The purpose of this article is to look at this contradiction from a different point of view. While in theory it is possible to argue that there is a stalemate between the supporters of validity of open-price contracts and their opponents, this stalemate is the law on the book. The law in practice offers a different landscape. It shows that in practice open-price contracts are regarded as valid under the CISG, although the adjudicators still struggle with the correct application of Article 55. Open-price contracts, Article 14, Article 55, agreements to agree, tentative prices, determinable price, contractual validity, market price, reasonable price.

A comparison of nutritional contents and price differential between dairy and plant-based milk in Fijian supermarkets

Plant-based milk import has been increasing in the Fijis supermarkets. While this milk may cater for vegans(1) and people with allergies from dairy milk, the question always remains that if the plant-based milk are equally nutritious and available at reasonable price in comparison to the dairy milk. Dairy milk is commonly consumed by the Fijian population while the plant-based milk is positioning itself into the market as alternatives. Therefore, this paper is aimed at comparing the nutrient content and price of milk from dairy cows and plant milk sources available in the supermarkets in Fiji. This study examines different brands of dairy and plant-based milk in 6 major supermarket chains in the central part of Fiji. There was 22 dairy milk, 6 soya milk, 5 almond milk and 4 oat milk sampled from these supermarkets. The median value of milk nutrient composition and price for dairy milks and different plant-based milks were calculated as the data was not normally distributed. The Kruskal- Wallis test was conducted to further analyse the difference between the nutrient composition and price of dairy milks and plant-based milk. The energy composition in the dairy milk was significantly higher (p < 0.01) when compared with plant-based milks soya almond milk and oat. There was a significant difference (p < 0.01) in dairy milk protein, fat and saturated fat when compared to plant-based milks. The result indicated that the protein, fat, and saturated fat are both significantly higher in cow’s milk. Milk carbohydrate analysis indicate higher composition in dairy milk therefore a significant difference (p < 0.01) was noted when compared to almond milk(except soya and oat milk). There is a significant difference (p < 0.01) in sugar indicating cow’s milk having higher sugar when compared to plant-based milk (except soya milk The sodium composition in the dairy milk and all the plant- based milk showed no significance difference (p > 0.05) in the composition. There was also significant difference (p < 0.05) in comparison of calcium composition of dairy milk and plant-based milk indicating almond milk with lower calcium. The phosphorus composition in dairy milk and plant-based milk indicates that there is a strong significant difference (p < 0.01) (except soya and oat milk). The riboflavin composition was significantly higher (p < 0.01) in dairy milk compared to soya and oat milk. Lastly, there was significant difference (p < 0.01) between price of dairy milk when compared to plant-based milk. The study concludes that there is more nutrient in dairy milk and the price is significantly lower than plant-based milk at which these nutrients are available in dairy milk.

Forces of energy welfare in Central Europe: The Russian war in Ukraine as a game changer

The Russian war in Ukraine has changed global geopolitical priorities and the policies of individual countries. The consequences of this ongoing war will be felt for decades to come, including the militarisation of states and the strengthening of political-military alliances. This article explores the effects of this conflict through the lens of energy welfare, which is used as an essential litmus test for the transforming economies of the Visegrad Group (V4) countries in Central Europe. Energy security and the energy transition have become critical topics, and energy security and its impact on social welfare affect the sensitive areas of policy choices that will determine the direction of development in this part of Europe. The V4 countries – Poland, the Czech Republic, Slovakia and Hungary – illustrate a wide range of domestic and geopolitical orientations that place individual countries in different decision-making situations. The ideological and political forces determine the scope for achieving energy welfare and are, therefore, the subject of this article. Based on macroeconomic and survey data results, the article’s primary research query examines how the V4 countries’ reliance on Russian fossil fuels affects their political attitudes and societal perceptions of the conflict in Ukraine, and the implications for their energy security and welfare in the face of the European Green Deal efforts. The different strategies adopted by the four countries considered translate into energy transition pathways to a low carbon economy described in the European Green Deal and strategies to provide energy at a reasonable price at the expense of a coherent European policy towards Russian aggression.

Research on pricing and replenishment based on optimal programming model

This paper studies the law and correlatoin of different categories of vegetable commodities, and makes the total amount of replenishment and pricing strategy of each vegetable category in the next week, so as to maximize the profit of supermarket. Firstly, the correlatoin between different categories of vegetable products was analyzed based on Pearson correlatoin analysis. The results showed that flower-leaf, cauliflower, pepper had strong correlatoin with edible fungi, while aquatic rhizome and solanaceae had relatively low correlatoin with other categories of vegetables. Then, the monthly and daily sales volume of each category and single vegetable were counted, and the monthly, weekly and daily distribution function of each category and single vegetable was obtained by fitting with Python software using the least square method. The vegetable category cluster analysis based on systematic clustering was used to cluster 6 categories of vegetables, and the 6 categories of vegetables were divided into 3 categories. Then the least square method was used to fit the processed data, and the fitting function expression between the sales volume and the cost plus pricing of each category of vegetables was obtained. The replenishment quantity decision model based on time series was established, and the daily replenishment quantity decision of each vegetable category in the next week was obtained. Finally, according to the correlatoin between sales volume and price, the optimal pricing model based on dynamic market demand is established, and the most reasonable pricing is obtained and the optimal replenishment decision is made to maximize the profit of supermarket.

Research on superstore vegetable replenishment decision-making based on time series and PSO particle swarm algorithm

Because vegetables are easy to wear and tear and not easy to store, supermarkets will replenish vegetables every day. In order to make reasonable replenishment and pricing decisions, this paper firstly correlates the total sales data with other data, and finds that the total sales data is negatively correlated with the average purchase price and average selling price, and positively correlated with the number of discounts, and then adopts the Feedforward Neural Network model, the Evenberg-Marquardt training algorithm, and the time series analysis prediction to predict the total daily replenishment and pricing policy of each vegetable in the coming week. Marquardt training algorithm, time series analysis and forecasting, predicting the total daily replenishment and pricing strategy of each vegetable in the coming week, and at the same time, adopting the method of dynamic planning, through the use of particle swarm algorithm of PSO to solve the total replenishment of each category and pricing strategy, and then iterated to get the total daily replenishment of each category of commodities and pricing strategy in the coming week.

A physics-informed graph learning approach for citywide electric vehicle charging demand prediction and pricing

A growing number of electric vehicles (EVs) is putting pressure on smart charging services. As a foundation of informing drivers of vacant charging facilities and rationalizing pricing, an effective approach for predicting citywide spatio-temporal EV charging demand that incorporates pricing information is required. Although many deep learning models have been carried out with the orientation of improving prediction accuracy and achieved good results, there is still a lack of prediction models that are economically interpretable and can consider the spillover effect of price adjustments on EV charging demand. To fill the research gaps, we propose a learning approach for accurate EV charging demand prediction and reasonable pricing, named PIAST, which enables the integration of convolutional feature engineering, spatio-temporal dual attention mechanism and physics-informed neural network training. On a dataset containing 18,061 EV charging piles, we demonstrate the state-of-the-art performance of the proposed model. The results empirically showed that the proposed approach achieved an average improvement of 14.78% in accuracy, compared to other representative models. Moreover, the model can uncover the price elasticity of demand during training, making the model interpretable. Finally, price adjustment experiments are conducted to demonstrate the self-influence and spillover effects of price.

Quality–Cost–Environment Assessment of Sustainable Manufacturing of Photovoltaic Panels

This paper aims to develop an integrated Quality–Cost–Environmental (QCE) indicator for the selection of photovoltaic panels (PVps) offered to customers, considering the following criteria: (i) quality satisfaction; (ii) cost-effectiveness; and (iii) environmental impact throughout the life cycle. The proposed QCE indicator was developed within a framework that incorporated an FAHP (Fuzzy Analytic Hierarchy Process), cost-effectiveness analysis (CEA), and life cycle assessment (LCA). The model test confirmed its effectiveness in choosing a PVp which combines environmental friendliness throughout its entire life cycle with satisfactory quality and a reasonable purchase price for customers. The proposed model can be utilized by individuals, businesses, and public entities for the selection of high-quality, cost-efficient, and environmentally friendly PVps—thereby promoting sustainable development.

Sustainable Economic Impact of Seed Replacement Rate on Production of Mustard Seed: A Case Study on Murshidabad District of West Bengal, India

"Quality Seeds are the critical determinant of different agro-climatic zones for sustainable planning in agricultural production. It is necessary to raise the supply of quality seeds of different crops in sufficient quantity at reasonable prices for farmers at the right time in the state. New seeds are either Certified Seeds (CS) or Hybrid Seeds. A better Seed Replacement Rate (SRR) presents a better application of Certified or Quality Seed or Hybrid Varieties Seeds (HYV). It plays a major role in agriculture to raise the production of crops. In this research paper, we have analysed the impact of Seed Replacement Rate on the production of Mustard Seeds during the period 2022-23 in the Jarur Gram Panchayat (GP) of Block Raghunathganj-I, Murshidabad District, West Bengal. The data have been collected by the stratified random sampling procedure. With the help of collected data, we have formed a Coob-Douglas Type of production function and which is estimated by the Ordinary Least Square Method. From the collected data, it has been observed that the average Seed Replacement Rate is 38.52 percent which is below the state average (53.2%). We have also noticed that 30.43 percent farmers have used farm-saved seeds and only 17.4 percent farmers have used hundred percent new seeds. The estimated result has proved that Seed Replacement Rate is a key determinant to raise both production, productivity and economic benefits. If Seed Replacement increases production also will increase and thereby there would be economic leverage. It has also been found that farm size with Seed Replacement Rate is not a significant factor in increasing the production of Mustard Seeds. Another interesting finding is that seventy percent of farmers have used inorganic fertilizers. They have applied excessive chemical fertilizers which is greater than the government’s prescribed norm. However, from the estimated result we have observed that the Seed Replacement Rate according to different farm sizes with excessive application of fertilizers does not influence both the production and productivity of Mustard Seeds. From an economic standpoint of view, new seeds are better than farm-saved seeds (old seeds) as their gross income increases."

Exploring Influential Factors on Customer Satisfaction with Clothing for Overweight and Obese Women: A Focus on Generations Y and Z in Sri Lanka

Purpose: At present, global trade in textiles and clothing is expanding across the world. The increase in the percentage of overweight and obese women creates tendency to focus on the demand for plus-size clothes. Prior studies confirmed that there is a low attention paid to women's plus-size clothing in Sri Lanka when compared to other countries such as the United Kingdom, Australia, and England. The main purpose of this study is to identify the factors affecting clothing satisfaction of overweight and obese women in Generation Y and Z in Sri Lanka.
 Design: Acknowledging Renoux’s theory of retail satisfaction, the study used three influencing factors; shopping system, buying system, and consuming system. Shopping system considers displaying plus-size mannequins, creating a separate section for plus-size cloth and providing proper and sufficient information. Buying system focuses on new clothing designs, maintaining a reasonable price range, high-quality materials, and adequate the space in the store. Consuming system focuses on body proportion, body image, and the length of time a garment is held after purchase. A self-administered questionnaire is used to gather data from 287 respondents via online survey. This study adopted quantitative analyses. Influencing factors were determined through the multiple regression analysis.
 Findings: Results revealed that the buying system and shopping system have statistically significant influences on overweight and obese women's clothing satisfaction and buying system recorded the highest influence. However, consuming system has an insignificant influence on overweight and obese women's clothing satisfaction.
 Research Implications: Under the shopping system, displaying plus-size mannequin and allocating a separate section for plus-size could stimulate clothing satisfaction of overweight and obese women. Offering a reasonable price range and providing an adequate fit-on-room are the significant factors to be considered in buying system. 
 Future Research Suggestions: Quantitative approach limited the ability to address the problems of overweight and obese women's clothing satisfaction in depth. Shopping mechanism, the ability to do online or offline shopping, store attributes and human attributes should focus on further studies.