Pricing Strategies Research Articles

A New Framework for Food Catering Price Determination

This article aims to propose a reasonable quantitative (mathematical) expressions for the price calculation of food-manufacturing products. Little research has been conducted on the validity of food product pricing; thus we have identified a need for a thorough, systematic investigation in this area. The suggested three methods are also applicable for developing the artificial intelligence system to solve raw food material (resources) quantity management and other inventory issues in collaboration with the food suppliers. We consider the option of an appropriate pricing strategy in the profit-making catering and other food production firms to be a relevant scientific issue, which we have have addressed and proposed solutions for in this article. By analyzing different scientific assumptions and attitudes, informed quantitative decisions were made. We also developed and described quantitative expressions for determining the price of food products using mathematical formulas. Also, we found that a simple three-digit rule, based on logic and proportional modeling of actions, as the main instrument is the most effective approach for formulating these pricing models. Original and innovative mathematical methods for the price accounting of food production have been created and explained. We have also explained how each of the three suggested methods works, along with their advantages and disadvantages in certain situations. Using these recommended methods of pricing will allow food companies to achieve more appropriate commercial results and to make less expenditure for decisions regarding the pricing of food products. In the article, we explained how to calculate the prices of food and catering companies using simple math. The application of any of the three mentioned techniques regarding existing circumstance will enable food production companies to ensure a straightforward yet accurate determination of the product price that aligns with the current situation.

Future Development Analysis Based on the Price Reduction Trend of Tesla

The electric vehicle (EV) market is developing rapidly, while the complexity of the market is growing along with the growing prosperity of the market, continuous technology innovation, and frequent policy adjustments. As a leading pioneer in the electric vehicle market, Tesla's development trends have had a significant impact on the market and other companies in the market. Tesla's price cut may especially affect the brand positioning of other companies in the industry, and they follow its price cut strategy, leading to a price war in the whole market. It urges them to adjust their strategies to maintain their market position and profitability. Therefore, the paper elaborates and focuses on Tesla's financial data from 2021 to 2023 and price reduction strategies, and uses key financial indicators, such as revenue growth, cash flow, and return on investment, to predict the future development trajectory of Tesla. This paper, through Tesla's financial analysis and future trends, provides reference and guidance for investors, markets, or companies seeking to follow suit.

Transactive energy management for CIES considering tri-level hybrid game-theoretic approaches with multiple weight allocation factors

In city integrated energy systems, to reduce the costs of energy production and usage, it is extremely important for different entities to design proper energy management approaches since they have diverse behaviours and benefits. The hierarchy of the system is naturally divided into three layers, and hybrid game theories are employed on the basis of the vertical master–slave and horizontal equal cooperation characteristics among energy providers, aggregators, and users. First, to reflect the interactions between different levels' entities, Stackelberg game theory is adopted vertically, and the model complexity is lessened by the KKT conditions. Then, for energy providers, the asymmetric bargaining game is improved by combining multiple weight allocation factors, which can help reduce renewable energy fluctuations and encourage the consumption of clean energy to reduce the carbon emissions of the system. Also, the results show that the proposed method can enhance the utility of renewable energy generation units by approximately 64 % to 69 %. Finally, the optimality and effectiveness of the proposed game theoretic approaches are verified in case studies and in strict mathematical proofs. In addition, the impact mechanisms on the demand response from the perspectives of the user load and price sensitivity are analysed. To explore pricing methods that are more suitable for user aggregators, two pricing strategies are designed for the user aggregator and compared in terms of computation time and revenue. The results show that a unified pricing strategy can increase the aggregator's revenue by 1.24 % and reduce the computation time by 24.47 %.

Design and Features of Pricing and Payment Schemes for Health Technologies: A Scoping Review and a Proposal for a Flexible Need-Driven Classification.

In a context of growing clinical and financial uncertainty, pricing and payment schemes can act as possible solutions to the problems of affordability and access to health technologies. However, a comprehensive categorization of the available schemes to help decision makers tackle these challenges is lacking. This work aims at mapping existing types of pricing and payment schemes, and proposes a new approach for their classification, in order to help decision makers and other stakeholders select the best type of scheme to meet their needs. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR)-compliant scoping literature review was performed between 2010 and 2023 in three databases (PubMed, Web of Science, Scopus). The search strategy was developed around two groups of keywords, "pricing/payment schemes" and "scheme innovativeness". Eligible studies were those illustrating the unique design and features of each scheme type, which were extracted by two independent reviewers, and synthesized using a narrative format, including a detailed tabular description of each type of scheme. A total of 70 unique types of pricing and payment schemes were identified. Around one third (33%) was only specified in principle, while two thirds (67%) had been implemented in practice. About half of the scheme types were proposed for drugs (34/70, 49%), and the vast majority were not designed for a specific therapeutic area (55/70, 79%). Each scheme type was categorized based on distinctive characteristics: the objectives, the outcome component, the timing/modalities of payments, and the evidence collection requirements. Instead of trying to fit the retrieved schemes into a rigid taxonomy, we propose a new approach that suggests a flexible need-driven use of the available scheme types, driven primarily by the specific objective that one might have, and allows leveraging of the other key characteristics of each type of scheme.

Pricing and Replenishment Decision System Based on LSTM

Fresh supermarket vegetables have a very short shelf life, and the quality of vegetables will also be affected by the storage time. If most of the goods are not sold on that day, they will not be sold on the next day. From the demand side, the sales volume of vegetables is related to the supply side of vegetables from April to October. At the same time, the sales space of the supermarket is limited, which makes a reasonable sales portfolio extremely important. The commercial overpricing replenishment decision system based on LSTM analyzes the relationship between daily sales volume and cost-plus pricing for each vegetable category and single product over three years. It predicts the sales volume and wholesale price of each category for the next week, determining replenishment volume and pricing strategy based on forecasted sales volume and average loss rate. The system considers limited sales space and uses the entropy method to calculate weights for key indicators like total profit. It employs the TOPSIS method to sort items, considering forecasted wholesale prices, to select items for pricing decisions. The system aims to maximize total profit while ensuring the supermarket meets minimum display conditions. By assisting supermarkets in formulating more reasonable pricing strategies, it aims to reduce overstock, backlog, or stock shortages, ultimately improving sales volume and operational efficiency.

Sharing channel choices for electric vehicle battery manufacturers when considering range anxiety among heterogeneous consumers

PurposeThis study aims to investigate the optimal pricing decisions and shared channel strategy selection of battery manufacturers considering heterogeneous consumers' range anxiety.Design/methodology/approachAmidst the rapid growth of the electric vehicle sector, countries are promoting upgrades in the automotive industry. However, insufficient driving range causes consumer range anxiety. The study utilizes the Stackelberg game model to assess how range anxiety influences battery manufacturers' pricing and channel strategy decisions across three strategies.FindingsWe find that electric vehicle battery manufacturers' decisions to cooperate with third-party sharing platforms (TPSPs) are primarily influenced by fixed costs and consumer range anxiety levels. As range anxiety increases, the cost threshold for joining shared channels rises, reducing cooperation likelihood. However, considering diverse consumer needs, especially a higher proportion of leisure-oriented consumers, increases the likelihood of cooperation. Furthermore, higher battery quality makes direct participation in shared channels more probable.Originality/valueIn the electric vehicle industry, range anxiety is a significant concern. While existing literature focuses on its impact on consumer behavior and charging infrastructure, this study delves into battery manufacturers' strategic responses, offering insights into channel options and pricing strategies amidst diverse consumer segments.

Does time of day affect consumers’ price sensitivity?

In the current economic downturn, consumers are showing increased price sensitivity. With mobile technology enabling anytime connection, understanding daily price sensitivity fluctuations is crucial. This study analyzes transaction data from two companies, covering both offline and online scenarios. Additionally, an online experiment was conducted to further reveal the causal relationship between time-of-day and consumers’ price sensitivity. The studies indicate that price sensitivity is highest in the morning, decreasing by approximately 0.5% per hour. These findings are interpreted using the circadian rhythms theory. This research deepens our understanding of how price sensitivity changes over time, offering invaluable insights for businesses to tailor their pricing and promotional strategies throughout the day.

Influence of Technology on Business Sustainability in Petroleum Firms in Nairobi, Kenya

Purpose: Sustainability is gaining more interest among several strategic management scholars. Technology provides a disruptive competitive advantage that is essential for businesses to thrive in a difficult environment where rules and strategies change from time to time. There is little competition among the oil marketers in Kenya because the government controls pricing strategy. The study's main aim was to determine the influence of technology on business sustainability in petroleum firms in Nairobi, Kenya. The study was underpinned by dynamic capability theory. This study adopted a descriptive research design in its investigation. Study design/methodology/approach: The study's total population was 60 oil marketing companies in Nairobi. The study selected heads of business development units from each oil marketing company. Since the population was small, a census was adopted because it was the most appropriate approach. The primary data was collected using questionnaires, where standard questionnaires with closed-ended questions were administered to capture important information about the respondents. Simple linear regression was employed to determine the effect of technology on business sustainability in petroleum firms in Nairobi. Findings: Technology had a positive and significant effect on the business sustainability of oil marketing companies. The study concluded that strengthening technology enhances competitiveness and thus results in business sustainability among oil marketing companies. Originality/value: The study recommends adopting improvised technology for all the processes and services of oil marketing companies.

Predicting smartphone prices using machine learning algorithms

This study investigates the prediction of smartphone prices using machine learning algorithms, motivated by the increasing demand for accurate pricing information in the highly competitive and rapidly evolving mobile phone market. With the proliferation of smartphones and the constant introduction of new models with varying specifications, it is crucial for consumers, retailers, and manufacturers to have a reliable method for estimating prices based on technical features. Accurate price prediction can assist consumers in making informed purchasing decisions, retailers in setting competitive prices, and manufacturers in optimizing production costs and pricing strategies. Utilizing Decision Tree Regression, Support Vector Regression (SVR), Random Forest Regression and Convolution Neural Network (CNN), the research investigates the efficacy of these methods in estimating prices accurately. Principal Component Analysis (PCA) is applied to reduce dimensionality and enhance model performance. Through rigorous experimentation and hyperparameter tuning, Random Forest Regression emerges as the most effective method, achieving the lowest Mean Squared Error (MSE) and the highest R-squared (R) score. The findings highlight the potential of ensemble learning techniques in addressing complex prediction tasks.

Automated pricing and replenishment forecasting for vegetable items

Firstly, the thesis extensively analysed and processed the vegetable sales data of a fresh food superstore. It integrated the product information and sales data of each vegetable category, and carried out data cleaning and analysis through Excel tools, including sorting out the outliers, listing the sales volume of each of the six categories, using pivot tables for statistical analysis, and establishing line graphs, heat maps, scatter plots, and other visual displays of the data characteristics and correlations. Secondly, the thesis investigated the relationship between sales volume and pricing, fitted the correlation between total sales volume and pricing using a random forest regression model, and predicted the daily replenishment volume for the coming week using an LSTM time series model. It also fitted quantitative fitting formulas for metrics for each vegetable category, classified profits into normal sales volume profits and discounted sales volume profits, and ultimately maximised profits through dynamic programming models and particle swarm optimisation algorithms. Finally, the thesis considered the impact of other factors such as weather, holidays, seasons and market environment on sales volume with specific data collection and analyses that help guide better pricing strategies. The findings will contribute to a better understanding of vegetable sales behaviour and optimise superstore operational strategies.

Adoption of Online Food Ordering Platforms: A Study on Consumer Behavior and Brand Influence of Swiggy and Zomato

The swift advancement of digital technologies alongside the evolving lifestyles of consumers has profoundly reshaped the food service sector, resulting in the extensive utilization of online food ordering platforms. This review manuscript seeks to investigate the principal determinants affecting the acceptance and expansion of these platforms, emphasizing consumer behavior, technological innovations, and branding methodologies. By synthesizing knowledge from prevailing literature and research concerning notable platforms like Swiggy and Zomato, the manuscript scrutinizes essential motivators such as convenience, pricing strategies, service excellence, user engagement, and promotional initiatives. Furthermore, it delves into the influence of trust, security, and social dynamics in molding consumer perceptions and preferences. Through a thorough examination of these variables, this manuscript endeavors to furnish a more profound comprehension of the mechanisms underlying consumer adoption and retention within the online food delivery sector. The conclusions underscore potential avenues for platforms to refine their strategies to align with the shifting expectations of consumers and uphold a competitive edge in an increasingly crowded marketplace.

Study on the Optimization Strategy of Chinese Film Ticket Price Pricing

Film, as an important cultural industry and entertainment mode, has developed over the years and formed a relatively complete industrial chain. However, in the long-term development process, movie ticket pricing has always been a hot issue that has attracted much attention. In recent years, China's film market has shown a trend of rapid development, and the box office has repeatedly reached a new high, but the level of movie ticket prices has also been rising, which has triggered widespread social concern and discussion.Research and optimization of movie ticket pricing strategy is of great significance to improve the competitiveness of the movie market and promote the sustainable development of the movie industry.This paper first reviews the current research status of the predecessors, summarizes the relevant theoretical basis needed for the study, analyzes the existing problems in movie ticket pricing, conducts an analysis of the change in domestic ticket prices and compares it with international prices, and finally concludes with an optimal strategy.

Trends and prospects of investigating cases of anticompetitive concerted actions in the form of similar actions in the light of antitrust reform

The article analyzes the peculiarities of the qualification and application by the Antimonopoly Committee of Ukraine (AMCU) and the courts of the institute of «similar actions» (parallel behavior) as a separate form of anticompetitive concerted actions, prosecution for which did not require direct evidence of a violation and the standard of application of which was the economic analysis of the price situation in the market (correlation analysis). It has been established that the institution of «similar actions» has traditionally been used by the AMCU in seasonally volatile markets which are characterized by the oligopolistic structure of the market or the obvious price leadership of several key market participants (core of the market) the pricing strategies of which were adjusted by all other market participants. It is analyzed that despite the established approaches of the AMCU and courts in the application of the institution of «similar actions» in practice such a standard of proof of «cartel conspiracies» was often criticized by practicing lawyers and economists and generally did not meet the standards of proof of cartel conspiracies in the EU. Despite this as can be seen from the author’s analysis the use of the AMCU of the institute of «similar actions» generally achieved the goals of the state’s antimonopoly policy which was to suppress sharp price disturbances in certain socially sensitive markets including in the markets of electricity and oil products by initiating appropriate investigations of cases of violation of economic competition with subsequent prosecution of participants in «price cartels». The author analyzes the further conditions and prospects for the investigation of anticompetitive concerted actions in the form of parallel behavior based on the results of further harmonization of Ukrainian competition legislation with the EU competition rules during the second stage of antitrust reform which is designed to raise the standards of application of the institution of anticompetitive concerted actions (and investigation of cases of the corresponding category) with the simultaneous exclusion of the institution of «similar actions» from national legislation.

Pricing Strategies for O2O Catering Merchants Considering Reference Price Effects and Unconditional Coupons

Coupon marketing is an important promotion model for the O2O catering supply chain. As a well-known catering O2O platform in China, Meituan provides its members with the opportunity to buy unconditional coupons at low prices, and it is very popular with consumers. It is worth studying how catering merchants develop pricing and promotion strategies considering consumers’ reference price effects in two scenarios: participating and not participating in coupon stacking (S-PICS/S-NPICS). In order to explore the pricing mechanism of meals, two models using Stackelberg games are established considering some exogenous variables, such as commission rate, reference price, and sensitivity of consumers to coupon prices. The results show that (1) the reference price has a great influence on the maximum profits of catering merchants, varying in different scenarios; (2) in S-NPICS, the merchants’ maximum profit increases with an increase in the reference price, while, in S-PICS, an increase in the reference price leads to a decrease in merchants’ maximum profit; (3) the profit of merchants in S-PICS is higher than that in S-NPICS, indicating that merchants can benefit from coupon stacking; (4) when the commission rate of the coupons increases, the merchants in both S-PICS and S-NPICS should raise their online meal prices to obtain higher profits. This study provides a balanced reference for pricing strategies and coupon promotion strategies in different scenarios through the use of deduction and simulation methods, which is conducive to the sustainable development of the O2O catering supply chain. As consumers enjoy using these coupons, including unconditional ones, it is suggested that merchants and Meituan should focus on coupon promotion and develop better dining pricing strategies.

Research on the Influencing Factors of House Prices in Chicago

This study provides a comprehensive analysis of the factors that influence housing prices in suburban Chicago. Using detailed regression analysis, several key variables were identified as significant predictors of property value. The study included variables such as the number of bedrooms, square footage, number of rooms, size of the lot, annual taxes, number of bathrooms, number of garages, and the condition of the house. The R-squared value of the model is 0.732, indicating that approximately 73.2% of the house price variance can be explained by the selected variable. Houses with more rooms and more square footage tend to cost more. In addition, characteristics such as the number of garages and bathrooms can also have a significant impact on house prices. These findings provide valuable insights for real estate professionals, buyers and sellers, enabling them to make informed decisions regarding real estate investment and pricing strategies. By understanding the key drivers of house prices, stakeholders can better navigate the suburban housing market and ensure more accurate assessments and strategic planning.

Temporal Convolutional Network for Carbon Tax Projection: A Data-Driven Approach

This study introduces a novel application of a temporal convolutional network (TCN) for projecting carbon tax prices, addressing the critical need for accurate forecasting in climate policy. Utilizing data from the World Carbon Pricing Database, we demonstrate that the TCN significantly outperformed traditional time series models in capturing the complex dynamics of carbon pricing. Our model achieved a 31.4% improvement in mean absolute error over ARIMA baselines, with an MAE of 2.43 compared to 3.54 for ARIMA. The TCN model also showed superior performance across different time horizons, demonstrating a 30.0% lower MAE for 1-year projections, and enhanced adaptability to policy changes, with only a 39.8% increase in prediction error after major shifts, compared to ARIMA’s 95.6%. These results underscore the potential of deep learning for enhancing the precision of carbon price projections, thereby supporting more informed and effective climate policy decisions. Our findings have significant implications for policymakers and stakeholders in the realm of carbon pricing and climate change mitigation strategies, offering a powerful tool for navigating the complex landscape of environmental economics.

Exploring impacts of electricity tariff on charging infrastructure planning: An activity-based approach

In the past decade, electric vehicles (EVs) have gained popularity for their efficiency and environmental benefits. Advances in battery technology and charging equipment have yielded long-range EVs and fast-charging. However, many major cities lack adequate charging infrastructure for daily EV use. This study addresses this gap by integrating activity-based modeling, charging behavior simulation, and charging infrastructure optimization. The research utilizes the POLARIS agent-based transportation model to accurately capture user activities, trip patterns, and traffic flows. Additionally, the study investigates the impact of fixed and spatiotemporal electricity rate distributions on optimal charging infrastructure deployment. The framework is applied to the Chicago regional area network and analyzed under various EV ownership scenarios. The results reveal significant impacts of the charging pricing strategy on user decision-making and charging demand distribution. There is also a need for consistent pricing policies in charging infrastructure planning and operational phases to avoid drops in service quality.

To What Extent Does Blind Box Marketing Affect Peoples Consuming Behavior?

Blind boxes, as an emerging industry, have gradually opened the Chinese market in recent years, and are sought after by consumers and various industries. It is a unique box that contains toys or other commodities, and people don't know what's inside until they open it. Meanwhile, continuous innovation has created a unique blind box marketing strategy, which uses consumers different motivations effectively to promote purchasing behavior. Based on the theory of consumer behavior and the strategy of blind box marketing +4p marketing theory (combined with AISAS model), this paper takes psychological motivation, demand motivation and investment motivation as intermediary paths to explore the impact on consumer purchasing behavior. Through the analysis of literature, questionnaire and tools such as SPSS25.0, the conclusions of relevant research are drawn: the effective combination of blind box marketing and product strategy can have a significant positive impact on consumers' purchasing behavior. The tremendous positive impact of price strategy on consumers' purchasing behavior is based on the consideration of different age groups. Place strategy and promotion strategy can only serve as the intermediary path between consumers' purchasing behavior, but cannot have a significant positive impact on them. Finally, through the above research, some suggestions for blind box marketing are provided.

Dynamic feed scheduling for optimised anaerobic digestion: An optimisation approach for better decision-making to enhance revenue and environmental benefits

Anaerobic digestion (AD) offers a sustainable solution for clean energy production, with the potential for significant revenue enhancement through enhanced decision-making. However, the complexity and limited flexibility of AD systems pose challenges in developing reliable optimisation methods. Changing feeding strategies provides opportunities for efficient feedstock utilisation and optimal gas production, especially in volatile gas markets.To provide better decision-making tools in AD for energy production, we propose an integrated site model for the dynamic behaviour of the AD process in a biomethane-to-grid system and optimise production based on predicted gas prices. The model includes methods for optimal feed co-digestion strategies and integrates these results into a scheduling model to identify the optimal feedstock acquisition, feeding pattern, and potential gas storage operation considering feedstock availability, properties, sustainability, and fluctuating gas demand under different pricing variations.The methodology was tested on a 150 tonnes per day farm-scale AD plant in the UK, processing energy crops and manure considering both environmental (global warming potential) and economic objectives. The results showed strong adaptability of the proposed feeding schedule to the general trend of gas prices over time. To address the challenge of immediate price peaks, typically unattainable due to the system's sluggish behaviour and high retention times, the impacts of on-site storage were explored, leading to annual revenue increases ranging from 2 % to 7.4 %, depending on the pricing scheme, which translates to a significant boost in terms of revenue.

Distributed Secondary Control of DC Microgrid with Power Management Based on Time-of-Use Pricing and Internal Price Rate

This paper presents a novel approach to manage distributed DC microgrids (DCMG) by integrating a time-of-use (ToU) electricity pricing scheme and an internal price rate calculation mechanism. The proposed power-management system is designed to effectively handle uncertainties such as utility grid (UG) availability, fluctuating electricity prices, battery state of charge (SOC) levels, and frequent plug-ins and plug-outs of electric vehicles (EVs). Uncertainties in DCMG systems often lead to inefficiencies, power imbalances, and inexact voltage regulation issues within DCMGs. In addition, to maintain the power balance and constant voltage regulation under various operational states, the proposed scheme also incorporates secondary control into the DCMG power-management system. Unlike the existing approaches that often fail to adapt dynamically to changing conditions, the proposed method is the first approach to consider the concept of internal price rate in designing the DCMG power management. To address this challenge, this approach proposes a more resilient power-management strategy to enhance the efficiency and adaptability of DCMG systems. Extensive simulations and experimental validations demonstrate the practicality and adaptability of the proposed control strategy under diverse test conditions, including operation transitions between grid-connected mode (GCM) and islanded mode (IM), low battery SOC condition, operation transition from the current control mode (CCM) to distributed secondary control mode (DSCM), and EV plug-in scenarios. The test results confirm that the proposed method enhances the reliability, efficiency, and economic viability of DCMG systems, making it a promising solution for future smart grid and renewable energy integrations.