Pricing Techniques Research Articles

A bibliometric analysis and typology of drug pricing policies across the globe.

Limited research exists on pricing policies from a bibliometric and visualization perspective, and there is a lack of understanding of their typology. This study aims to address these gaps in knowledge and provide a deeper understanding of the research topics and development trends in this field. A bibliometric study was conducted on drug pricing approaches in healthcare literature, published between 2000 and June 2023. The literature was identified through an extensive search of healthcare databases and was then classified based on the year of publication, research topics, corresponding authors, location of corresponding authors, and journal titles. The citation data analysis was conducted using Bibliometrix, which consisted of descriptive, geographical, and time-series analyses and visualization. Between 2000 and June 2023, 173 articles were disseminated across 98 distinct publication sources. During the analysis, we observed a significant and consistent rise in literature reports on drug pricing approaches in healthcare, especially in 2010. The research topics were distributed almost equally, discussing improvement or issues with drug pricing models and addressing drug pricing applications. Our analysis revealed that the top ten corresponding authors were responsible for 19% of the total articles, with those based in the United States being the most productive. Furthermore, the "Health Economics" journal ranked first among the top ten journals. These findings align with the overall publication trends of drug pricing methods reported in other fields. The current study offers a comprehensive overview of drug pricing techniques utilized in medicine through visualization and bibliometric techniques. Analysis of authors, journals, institutions, and countries could serve as a reference for new researchers and guide them differently. Researchers can also consider emerging trends when determining the focus of their studies.

Lifecycle Management of Generic product in US market

In the US Market, lifecycle management for generic products includes developing and implementing plans to maximize an item’s market potential from launch to decline. It starts with market research and approval of regulatory involving submission of an Abbreviated New Drug Application (ANDA) to demonstrate bioequivalence to the branded product followed by comprehensive market analysis to identify opportunities and assess rivals. Companies use aggressive pricing and marketing techniques to swiftly increase their market share after a product is approved. Continuous monitoring and adaptability to changes in the market, updates to regulations, and competitive actions are crucial throughout the lifecycle. This review focuses on Market entry strategy, Product launch, Market landscape of Generic Drugs in US.

Power Wheeling Pricing Considering Congestion in Power Network: A Review

Limited capacity of the power system infrastructure has a limiting effect on the quantity of electrical power that can be transmitted thereby resulting in the system been congested which also affects the quality of electrical energy supplied. Congestion is an undesired effect in power system with serious economic, reliability and security effect on power system infrastructure, which are yet to be adequately quantify in term of cost. Congestion occurrence and management on the power system network has been studied extensively, causes of congestion and various management techniques have been proposed which are at great financial consequences. While setting transmission wheeling prices, congestions are not adequately considered, nor are the management methods accurately financially compensated. Some wheeling pricing techniques however recognises the short coming and takes into consideration the financial cost of congestion and it’s management methods when setting their transmission wheeling costs. This paper is a review of wheeling methods that takes into consideration the cost of congestion and the management techniques when setting wheeling prices and makes a recommendation using financial and economic implication of the occurrence of congestion and the management techniques adopted in reducing the impact when setting wheeling prices, in order to encourage investment into the transmissions system infrastructures.

The Role of FPGAs in Modern Option Pricing Techniques: A Survey

In financial computation, Field Programmable Gate Arrays (FPGAs) have emerged as a transformative technology, particularly in the domain of option pricing. This study presents the impact of Field Programmable Gate Arrays (FPGAs) on computational methods in finance, with an emphasis on option pricing. Our review examined 99 selected studies from an initial pool of 131, revealing how FPGAs substantially enhance both the speed and energy efficiency of various financial models, particularly Black–Scholes and Monte Carlo simulations. Notably, the performance gains—ranging from 270- to 5400-times faster than conventional CPU implementations—are highly dependent on the specific option pricing model employed. These findings illustrate FPGAs’ capability to efficiently process complex financial computations while consuming less energy. Despite these benefits, this paper highlights persistent challenges in FPGA design optimization and programming complexity. This study not only emphasises the potential of FPGAs to further innovate financial computing but also outlines the critical areas for future research to overcome existing barriers and fully leverage FPGA technology in future financial applications.

ARTIFICIAL INTELLIGENCE ROLE IN OPTIMIZING ELECTRIC VEHICLE CHARGING PATTERNS, REDUCE COSTS, AND IMPROVE OVERALL EFFICIENCY: A REVIEW

The global popularity of electric cars (EVs) as a sustainable means of transportation, reliable and efficient charging infrastructure is essential. Traditional electric vehicle charging involves connecting the car to a power source and waiting for the battery to charge. However, AI has allowed us to improve charging patterns, reduce costs, and boost efficiency. This article examines how AI algorithms are changing electric car charging. If electric vehicle (EV) charging and discharging are not coordinated, the power supply infrastructure will be overrun. Demand response like dynamic pricing might encourage electric vehicle owners to participate in scheduling initiatives. Thus, EV charging and discharging scheduling and dynamic pricing model research are crucial. Artificial intelligence-based models for EV charging predictions and scheduling have been the focus of researchers. These models outperform linear, exponential, and multinomial logit optimization approaches. Due to the novelty and ongoing development of EVs returning electricity to the power grid, vehicle-to-grid (V2G) systems have received little attention. Thus, a complete analysis of EV charging and discharging research is needed to identify gaps and improve future studies. This study categorizes EV charging and discharging studies into forecasting, scheduling, and pricing techniques. The work links forecasting, scheduling, and pricing processes and identifies research gaps in EV discharge scheduling and dynamic pricing models.

Resource Optimization in H-CRN with Supervised Learning Based Spectrum Prediction Technique

Cognitive radio network shows potential means of granting intensifying demand for wireless applications. In this model, an efficient resource optimization scheme with Priority Pricing Technique (PPT) is proposed with supervised learning-based SVM to tackle limited spectrum availability and underutilization in Hybrid-Cognitive Radio Networks (H-CRN). H-CRN works under the principle of detection of PUs states (active/inactive). If spectrum sensing is made in favor of active PUs, then the CSI (Channel State Information) is estimated and works in underlay principle. If it is made in favor of inactive PUs, then the transmission is performed in overlay manner. In the proposed PPT the PUs and SUs with highest channel gain have the highest priority to use the spectral resources. SVM is used as an effective technique of spectrum sensing to provide higher probability of detection of PUs as soon as possible. The proposed method faces following challenges such as in order to enhance the CRN transmission performance, the PUs have to withstand more interference power and transmit power control is needed in improving the sum rates when the interference is severe in H-CRN. With Simulation outcomes, the assessment of the proposed (PPT) model among (Fixed Pricing Technique and Without Pricing Technique) indicates the proposed method's improved efficiency. The results reveal significant effectiveness in obtaining better classification accuracy with less computation complexity, increased throughput, spectral efficiency and energy efficiency of the network.

Data-Driven Techniques for Short-Term Electricity Price Forecasting through Novel Deep Learning Approaches with Attention Mechanisms

The electricity market is constantly evolving, being driven by factors such as market liberalization, the increasing use of renewable energy sources (RESs), and various economic and political influences. These dynamics make it challenging to predict wholesale electricity prices. Accurate short-term forecasting is crucial to maintaining system balance and addressing anomalies such as negative prices and deviations from predictions. This paper investigates short-term electricity price forecasting using historical time series data and employs advanced deep learning algorithms. First, four deep learning models are implemented and proposed, which are a convolutional neural network (CNN) with an integrated attention mechanism, a hybrid CNN followed by a gated recurrent unit model (CNN-GRU) with an attention mechanism, and two ensemble learning models, which are a soft voting ensemble and a stacking ensemble model. Also, the optimized version of a transformer model, the Multi-Head Attention model, is introduced. Finally, the perceptron model is used as a benchmark for comparison. Our results show excellent prediction accuracy, particularly in the hybrid CNN-GRU model with attention, thereby achieving a mean absolute percentage error (MAPE) of 6.333%. The soft voting ensemble model and the Multi-Head Attention model also performed well, with MAPEs of 6.125% and 6.889%, respectively. These findings are significant, as previous studies have not shown high performance with transformer models and attention mechanisms. The presented results offer promising insights for future research in this field.

Food security and the value of game animals—a study of Sweden

The food security value of wild meat is calculated by combining proxy methods for quantifying game animal abundance with shadow pricing techniques for assessing the unit values of food security. This study calculated the food security values of moose, roe deer, wild boar, and fallow deer for Sweden overall and for individual counties. The results showed that meat from these animal populations accounts for approximately 9% of meat consumption in Sweden and for 1.2% of the minimum energy food consumption during periods of crisis for the whole of Sweden, while in some counties it can be as much as 8%. The calculated unit value, or shadow price, of the minimum energy requirement ranged between € 0.1 and € 4.2/mcal, depending on the magnitude of the crisis scenario. At most, the total food security value of actual animal population sizes amounted to 0.50 billion euros, but this was unevenly distributed, with high values in counties that have an abundance of moose and wild boar.

A novel framework for photovoltaic energy optimization based on supply–demand constraints

Introduction: Distributed power supply has increasingly taken over as the energy industry’s primary development direction as a result of the advancement of new energy technology and energy connectivity technology. In order to build isolated island microgrids, such as villages, islands, and remote mountainous places, the distributed power supply design is frequently employed. Due to government subsidies and declining capital costs, the configured capacity of new energy resources like solar and wind energy has been substantially rising in recent years. However, the new energy sources might lead to a number of significant operational problems, including over-voltage and ongoing swings in the price of power. Additionally, the economic advantages availed by electricity consumers may be impacted by the change in electricity costs and the unpredictability of the output power of renewable energy sources.Methods: This paper proposes a novel framework for enhancing renewable energy management and reducing the investment constraint of energy storage. First, the energy storage incentive is determined through a bi-level game method. Then, the net incentive of each element is maximized by deploying a master–slave approach. Finally, a reward and punishment strategy is employed to optimize the energy storage in the cluster.Results: Simulation results show that the proposed framework has better performance under different operating conditions.Discussion: The energy storage operators and numerous energy storage users can implement master–slave game-based energy storage pricing and capacity optimization techniques to help each party make the best choices possible and realize the multi-subject interests of energy storage leasing supply and demand win–win conditions.

Analysis of Tesla's Marketing Strategy Model

A prominent American electric vehicle and renewable energy company, with a history of leadership and innovation in the automotive sector, Tesla has consistently pioneered advancements. This study explores Teslas synchronization of online and offline marketing tactics, and probes how Teslas dual marketing approaches, online and offline, complement each other through employing a case study method, which encompasses distinct brand positioning, emphasis on digital and regional strategies, active social media integration, seamless online-offline interplay, and varied advertising methods such as word-of-mouth and pricing techniques. Teslas robust collaborations within the supply chain further fortify its competitive standing. The findings sustain Teslas market leadership and hold broader implications. They contribute to heightened market competitiveness, shed light on marketing models for emerging markets, reveal the potential of digital marketing, and even inspire the transformation of traditional automotive business practices. Today, Tesla cars already occupy a very high market share. Compete in the market through several different strategic approaches. Now people need to study what kind of model and supply chain can make this brand popular and successfully pay for it.

Tax Haven Use and Related‐Party Transactions: Evidence from Australia

AbstractRelated‐party transactions (RPTs) and transfer pricing techniques are typically used by multinational enterprises to reduce their tax obligations by shifting the income to zero or to low‐tax countries. These techniques have thrived and have emerged as a significant concern for tax authorities. This study investigates the effect of tax haven utilisation on related‐party sales pricing in a sample of Australian listed firms. We find that tax haven use is positively and significantly associated with related‐party sales pricing. Moreover, we identify a positive and significant interaction between tax havens and earnings management with related‐party sales pricing. Ownership concentration and its interaction effect with tax havens are significant predictors of the increased use of related‐party sales pricing. Overall, the empirical findings demonstrate that earnings management, a high level of ownership concentration and the use of tax havens are substantial factors that enable firms to obtain tax benefits via the use of RPTs.

Optimal Dispatch Strategy for Electric Vehicles in V2G Applications

The overutilization of electric vehicles (EVs) has the potential to result in significant challenges regarding the reliability, contingency, and standby capabilities of traditional power systems. The utilization of renewable energy distributed generator (REDG) presents a potential solution to address these issues. By incorporating REDG, the reliance of EV charging power on conventional energy sources can be diminished, resulting in significant reductions in transmission losses and enhanced capacity within the traditional power system. The effective management of the REDG necessitates intelligent coordination between the available generation capacity of the REDG and the charging and discharging power of EVs. Furthermore, the utilization of EVs as a means of energy storage is facilitated through the integration of vehicle-to-grid (V2G) technology. Despite the importance of the V2G technology for EV owners and electric utility, it still has a slow progress due to the distrust of the revenue model that can encourage the EV owners and the electric utility as well to participate in V2G programs. This study presents a new wear model that aims to precisely assess the wear cost of EV batteries, resulting from their involvement in V2G activities. The proposed model seeks to provide EV owners with a precise understanding of the potential revenue they might obtain from participating in V2G programs, hence encouraging their active engagement in such initiatives. Various EV battery wear models are employed and compared. Additionally, this study introduces a novel method for optimal charging scheduling, which aims to effectively manage the charging and discharging patterns of EVs by utilizing a day-ahead pricing technique. This study presents a novel approach, namely, the gradual reduction of swarm size with the grey wolf optimization (GRSS-GWO) algorithm, for determining the optimal hourly charging/discharging power with short convergence time and the highest accuracy based on maximizing the profit of EV owners.

Sustainable Viticulture of Italian Grapevines: Environmental Evaluation and Societal Cost Estimation Using EU Farm Accountancy Data Network Data

Since the agriculture sector, such as Italian grapevine production, exert a pressure on the environment to some extent, this research aims to evaluate the environmental impacts and estimate the societal costs of four current grapevine production systems (i.e., vine grapes cultivated to produce common or quality wine using organic and non-organic agricultural practices), based on the Italian Farm Accountancy Network Data. For these purposes, the Life Cycle Analysis and Shadow Price techniques have been used. The results revealed that the levels of environmental impacts differed considerably between every cultivation system. Hence, the agricultural land occupation indicator induced the highest external costs, followed by climate change, terrestrial acidification, and freshwater eutrophication among the four grapevine cultivation systems. Accordingly, the assessment offers valuable insights into organic and non-organic viticulture practices to produce consistent and high-quality wine, as well as helping farmers make informed decisions that may improve environmental and societal impacts, leading to cost-effective management of their vineyards. We conclude that organic vineyard farming represents a promising sustainable viticulture production but is also important in exploring consumer perceptions and behavior towards this kind of grapevine production.

The influence of income tax rate, tunneling incentives, and return on equity on transfer pricing behavior of foreign direct investment enterprises in Vietnam

The utilization of competitive advantages in international trading has fortuitously put transnational manufacturing enterprises in the position of distorting transfer pricing techniques to maximize profits. The paper aims to explore the influence of the determinants on the transfer pricing behavior of foreign direct investment enterprises in Vietnam. The paper collects primary data from the financial statements of 96 foreign direct investment enterprises in Vietnam over six years from 2016 to 2021. The paper gets a final panel data of 576 observations to be processed by the fixed effects model estimation method using EViews 12. Supporting agency theory and positive accounting theory, the results show that the income tax rate negatively influences transfer pricing behavior, while tunneling incentives and return on equity positively affect transfer pricing behavior. The paper highlights that government agencies should reperform and implement fiscal policies synchronously to be able to monitor transfer pricing behaviors of foreign direct investment enterprises in Vietnam.

Pricing Asian and Barrier Options Using a Combined Heston Model and Monte Carlo Simulation Approach with Artificial Intelligence

The computation of fair values for exotic options often necessitates complex pricing techniques, which remain sparsely addressed in academic literature. Predominantly, the assessment of fair value for vanilla options relies on methodologies such as the Black-Scholes model or Monte Carlo simulations. This study proposes an innovative, dynamic approach to pricing, leveraging artificial intelligence in conjunction with the Heston model and a Monte Carlo simulation engine. This approach aims to furnish estimates of the prices for Barrier and Asian options. To enhance the accuracy of the model, calibration was performed employing a supervised machine learning algorithm, a continuous risk-free curve, and a dynamic implied volatility surface, derived from the current market data of vanilla options on S&P 500 futures. The amalgamation of these models yields instantaneous pricing for exotic option derivatives, contingent on the investor's determination of time to maturity and barrier levels. The efficacy of the model was evaluated by comparing the output prices to theoretical model predictions and a selection of over-the-counter traded options. Our findings indicate that the proposed dynamic, integrated approach substantially reduces the disparity between the theoretical models and current market prices. The prices calculated by our model demonstrate a marginal error of merely 0.33% in comparison to market prices, a significant improvement over the considerably larger error of 3.12% exhibited by traditional models.

Follow the money: Techniques for pricing rewards in crowdfunding projects

Crowdfunding, the practice of funding projects by appealing to the mass market, has grown in the past decade to become a significant path of raising money for various ventures. It works by offering people the ability to give a certain amount of money for some reward they will receive once the project is done. But while plenty of academic research investigates what makes projects successful, there is relatively little dealing with understanding how rewards should be priced and understood, and how they affects a project’s success. In this paper, we wish to examine if common pricing techniques used in the “regular” consumer space apply to crowdfunding as well. We constructed the largest dataset of crowdfunding projects, by collecting approximately 180,000 projects from Kickstarter, the largest crowdfunding platform. Using this extensive dataset, we explored several known pricing techniques used for consumers — such as putting a 9 as the last digit, bundle pricing, and anchoring prices — wishing to understand if they work for crowdfunding projects as well. Our results indicate that generally, they do with very high significance (with a few notable exceptions and changes). This helps to show that crowdfunding pricing in more akin to product pricing than to investment-type pricing modes.

Optimal pricing strategies and decision‐making systems in e‐commerce using integrated fuzzy multi‐criteria method

AbstractE‐commerce online stores are now virtual platforms for connecting with millions of potential clients worldwide in the age of digitization. The marketing teams develop digital marketing tactics to attract traffic to their e‐commerce sites and increase sales volume. With the vast amount of data provided by the cloud, decisions that were previously made with a significant level of intuition based on the knowledge and experience of decision‐makers can now be backed using artificial intelligence algorithms. To identify the variables influencing pricing decisions for products launched on e‐commerce shopping sites and to develop variable pricing techniques for each product found on an e‐commerce site. This paper introduces a novel approach that applies Fuzzy association rule mining (FARM) and Fuzzy TOPSIS MCDM methodology. A B2B e‐commerce marketing store based in Hong Kong has created and implemented Smart‐Quo, a pricing decision support system for B2B e‐commerce retail businesses. After a six‐month trial period, there has been a substantial advance in the effectiveness and efficiency of choosing prices for each product. The case study illustrates the viability and potential advantages of implementing artificial intelligence tools in marketing management in the digital era.

A Contract-Theory-Based Incentive Mechanism for UAV-Enabled VR-Based Services in 5G and Beyond

The proliferation of novel infotainment services such as Virtual Reality(VR)-based services has fundamentally changed the existing mobile networks. These bandwidth-hungry services expanded at a tremendously rapid pace, thus, generating a burden of data traffic in the mobile networks. To cope with this issue, one can use Multi-access Edge Computing (MEC) to bring the resource to the edge. By doing so, we can release the burden of the core network by taking the communication, computation, and caching resources nearby the end-users (UEs). Nevertheless, due to the vast adoption of VR-enabled devices, MEC resources might be insufficient in peak times or dense settings. To overcome these challenges, we propose a system model where the service provider (SP) might rent Unmanned Area Vehicles (UAVs) from UAV service providers (USPs) to serve as micro-based stations (UBSs) that expand the service area and improve the spectrum efficiency. In which, UAV can pre-cached certain sets of VR-based contents and serve UEs via air-to-ground (A2G) communication. Furthermore, future intelligent devices are capable of 5G and B5G communication interfaces, and thus, they can communicate with UAVs via A2G links. By doing so, we can significantly reduce a considerable amount of data traffic in mobile networks. In order to successfully enable such kinds of services, an attractive incentive mechanism is required. Therefore, we propose a contract theory-based incentive mechanism for UAV-assisted MEC in VR-based infotainment services, in which the MEC offers an amount reward to a UAV for serving as a UBS in a specific location for certain time slots. We then derive an optimal contract-based scheme with individual rationality and incentive compatibility conditions. The numerical findings show that our proposed approach outperforms the Linear Pricing (LP) technique and is close to the optimal solution in terms of social welfare. Additionally, our proposed scheme significantly enhanced the fairness of utility for UAVs in asymmetric information problems.

Green Pricing and Customer Patronage of Restaurants in Port Harcourt

The study examined the relationship between green pricing and customer patronage of restaurants in Port Harcourt. In order to address the objectives of the study, two research questions and two research hypotheses were formulated. The study was anchored on Institutional Theory; the study adopted correlational survey research design. The population of the study consists of 39 fast food restaurants gotten from www.businesslist.com.ng. The population of the study was adopted as the sample of the study since it is not too large for a study. To generate data for the study, one hundred and ninety-five (195) copies of questionnaire was given to the 39 fast food restaurants in the frame of five (5) copies per firm. A total of one hundred and ninety-five (195) respondents were used as the study subjects. The reliability of the instrument was determined using the Cronbach's alpha test instrument and it stood at 0.98 higher than the benchmark of 0.7. The data collected for this study were analyzed through descriptive and inferential statistics. The Spearman Rank-order correlation technique was employed to test the various hypothesis formulated. The result revealed a strong and significant relationship between green pricing and customer patronage of restaurants in Port Harcourt. The study concluded that; fast foods restaurant need to adopt green pricing techniques because environmental problems can be solved by going green. Based on the conclusion, the study recommends that: Fast food restaurants should adopt premium pricing as it would lead to customer patronage; they should start selling green products because green products would help preserve and sustain the environment which would in turn lead to repeat purchase from customers and also consumers should be willing and ready to pay the small premium that comes with green products as it would not only create market for sustainable products but would also ensure that the environment is protected against fu

Multi-agent based modeling and learning approach for intelligent day-ahead bidding strategy in wholesale electricity market

This work considers a restructured electricity market where the agents are considered as entities, and each agent has a learning capability. Also, the Independent System Operator (ISO) adopted the nodal pricing technique for managing transmission line congestion. In this multi-agent platform, the objective of each Generating Company (GenCo) agent is maximizing its net earnings. GenCo exhibits economic capacity withholding to enhance its daily net income ports updated cost parameters to the ISO. The basic model free policy-based reinforcement algorithms fail to calculate the probability that the rewards obtained are negative or zero. To overcome this drawback, the co-learning agent-based modeling approach implements an interactive learning algorithm applied to GenCo’s for reward consideration in all respects and thereby applies the strategic approach in improvising marginal pricing, commitments, and net incomes. The co-learning effect of GenCo is accomplished with the optimum value of the learning parameters by deploying a novel interactive Variant Roth – Erev (VRE) Reinforcement learning technique for efficient electricity market operation. The introduction of various parameters in learning helps to improve the mutual interaction among the agents and provides better experimentation implementable by GenCo’s meeting the social welfare and smart bidding. The result presented in this paper suggests that the learning capability of the agents is having an impact on the marginal pricing, which supports the operator in managing the congestion in the transmission line. The analysis showcases the effectiveness of the co-learning approach adopted by the agents in the electricity market which is tested on a standard IEEE system and the performance of the technique is validated on a realistic Indian grid network.