Initial Price Research Articles

Privacy Rights and Data Security: GDPR and Personal Data Markets

General Data Protection Regulation (GDPR)—the European Union’s data protection regulation—has two key principles. It recognizes that individuals own and control their personal (but not contractual) data in perpetuity, leading to three critical privacy rights, namely, the rights to (i) explicit consent (data opt-in), (ii) to be forgotten (data erasure), and (iii) portability (data transfer). It also includes data security mandates against privacy breaches through unauthorized access. We study GDPR’s equilibrium impact by including these features in a dynamic two-period model of forward-looking firms and consumers. Firms collect consumer data for personalization and price discrimination. Consumers trade off gains from personalization relative to potential losses from privacy breaches and price discrimination in their purchase, data opt-in, erasure, and transfer decisions. Though data security mandates impose fines on firms for privacy breaches, firms can benefit from higher opt-in given lower breach risk. Surprisingly, data security mandates can hurt consumers. The effect of privacy rights is nuanced. Since the right to opt in separates goods exchange from the provision of personal data, it prevents market failure under high breach risk. But it also reduces consumer opt-in and personal data availability. Erasure and portability rights reduce consumers’ hold-up concerns by disciplining firms to provide ongoing value by limiting price discrimination and not slacking off on data security; but they also reduce the incentive to offer lower initial prices that encourages opt-in. Overall, privacy rights always benefit consumers in competitive markets, but they can surprisingly hurt consumers under monopoly, as monopolists have less incentives to subsidize consumer opt-in. They raise (reduce) firm profit and social welfare when breach risk is high (low). Finally, privacy rights increase firm profit most at moderate levels of data transferability. This paper was accepted by Dmitri Kuksov, marketing. Funding: T. T. Ke acknowledges financial support from the General Research Fund of the Hong Kong Research Grants Council [Grant 14500421]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/mnsc.2022.4614 .

Fairness and the Coase conjecture

If a buyer has a private value for a good and a seller can make repeated offers to her over an infinite time horizon, the Coase conjecture predicts the good is sold almost immediately at the buyer’s lowest valuation when players are patient (they discount future payoffs only slightly relative to payoffs today). Initial prices are lower when players are more patient. However, experimental studies of this setting find considerable inefficient delay, with much higher initial seller prices that increase with players’ patience. In this paper, I show how preferences for fairness help explain those findings. They make the seller’s payoff from agreement depend on the buyer’s value, creating an adverse selection problem. If the seller strongly dislikes selling to a high value buyer at a low price, the unique equilibrium features high initial prices which fall only slowly, if at all, to a price acceptable to a low value buyer; initial prices can increase with players’ patience.

Total Cost of Ownership for Electric Vehicles Passenger cars in India and alternatives to reduce the operating cost

The primary barrier to Electric Vehicle (EV) Purchase from an Indian customer’s point of view is the price of the vehicle. With the recent advancements in battery technologies and power electronics, the prices have come down dramatically and are expected to reduce even further. Many Original Equipment Manufacturers (OEMs) in India, preparing to include EVs in their portfolio will be an added advantage due to scale benefit. Therefore, it is necessary to study the impact of higher Initial purchase price, maintenance, and fuel cost of EVs compared to Internal combustion Engine (ICE) vehicles before product launches. Total Cost of Ownership (TCO) is a critical measurable from the customer’s point of view. The aim is to create customer awareness of TCO and encourage EV Purchases using quantifiable parameters. This paper studies and analyses the TCO between EV & ICE models using various cost elements, contribution factors, and alternatives like battery swapping using case studies.

HTA119 Methodological Issues in Health Technology Assessment (HTA) for Re-Pricing in Japan

Health Technology Assessment (HTA) was introduced to the Japanese National Health Insurance (NHI) system in 2019. It was implemented not for coverage decision-making but for price revision in order to maintain the pre-existing rule of the Japanese NHI, covering almost all medications and avoiding delays to the initial pricing phase. We clarified issues with HTA in Japan from a methodological perspective.

전기철도 배전망을 활용한 팬터그래프 방식 충전시스템에 관한 연구

In the industrial fields have being accelerated the development of alternative energy resources because of restriction of environment pollution and increasing oil price at the world. Most of conventional vehicles are reason of environment pollutions. For these reason, interest in electric vehicles are increasing because of which is solution of reducing the environment pollution issues. Although a local autonomous entity had tried to replaced CNG buses for operating electrical buses, electrical buses have intrinsic problems such as charging/discharging sequences, connector issues and charging time. For these reason pantograph-type automatic charging technology is being developed for mating the environment of bus garages in nation. However, that has some problems such as initial price, room of charging infrastructure and electric power supply. In this paper, therefore, proposes methodology of using railway power supply system. It has been verified under suitability and balidity of charging and discharging scenario using electric bus charging system simulation model based on MATLAB Simulink and PSIM.

Menentukan harga jual warung makan Sunda Chicken berdasarkan metode full cost pricing

This study aims to help analyze the selling price at the Sunda Chicken food stall. In accordance with interviews conducted by the author, the Sunda Chicken food stall suffered losses due to the instability of raw material prices. This is because the Sunda Chicken food stall does not set a selling price based on production costs, but is adjusted based on market prices and consumer desires. Therefore, the author uses the full cost pricing method to calculate the new selling price as a form of solution offered to the Sunda Chicken food stall. Full cost pricing is one of determining selling price methods based on production costs and the percentage of expected profit. The author uses qualitative methods in the preparation of research journals, namely doing calculations based on data obtained from the owner of the food stall. The results of the research show that there is a difference in the selling price between the initial price of the Sunda Chicken food stall and the new price after calculating the full cost pricing method.

Chooser Option Pricing based on Black-Scholes Model and Monte-Carlo Simulations

Since February 2022, the intensification of the Ukrainian conflict has triggered significant impacts on financial market. Huge changes in supply and demand have led to a spike in stock volatility, which resulted in the growth in risk aversion among investors. The gold is regarded as a safe haven asset and has hedge function in the stock markets. In this study, a Monte-Carlo pricing simulation based on Black-Scholes model for chooser option was investigated using the underlying asset of gold. The gold price and volatility index were sourced from Yahoo Finance website and the Chicago Board Options Exchange (CBOE). According to the analysis, the present value of the average return for one-month chooser option is simulated as $58.91, using the initial gold price of $1965.10 and the strike price of $1977.3. The present value of the average return for one-month chooser option is significantly higher than traditional call or put options. According to the sensitivity analysis, the return of chooser option under the pricing model adopted is not sensitive to the changes in volatility and time, which indicates that this pricing model is able to resist to risks and play a better role in a high-volatility market. These results shed light on the optimization of investment portfolio under complex situations. The utilization of a new pricing method paved a path for the research of exotic options and the state-of-art applications of them in the financial innovation procedures.

Multi-period dynamic pricing model for deteriorating products in a supply chain with preservation technology investment and carbon emission

In this study, a multi-period dynamic pricing model in a two-echelon supply chain consisting of a retailer and a manufacturer is developed for a deteriorating product with a fixed shelf life. The demand at the retailer in each period is considered to be a function of the retail price, inventory on stock, reference price, and product freshness. Preservation technology investment (PTI) is considered to retard the rate of deterioration with carbon emission to keep the process green. Centralised and decentralised models are developed considering dynamic pricing for the retailer and static pricing for the manufacturer with a variable replenishment cycle length. Algorithms have been developed to obtain the optimal replenishment cycle length, retailer’s end of inventory level, retail price, PTI and discounted total profit for the retailer/manufacturer/supply chain. We study the generated trade-off between PTI, benefits of reduced deterioration rate and cost of carbon emission. The numerical studies suggest that supply chain profit can be greatly improved with efficient PTI and dynamic pricing policy. We also find that the initial reference price plays a key role in improving the profit.

Factors affecting short-term rental first price: A revenue management model

The aim of this paper is to conduct a revenue management study, generating a theoretical model that establishes the relationship between the factors of a Short-Term Rental apartment offered on the Airbnb marketplace or similar and its optimal rental price set when the property is first put on the market, considering not only the characteristics defined in the platform listing but also the sociodemographic characteristics of the area in which the apartment is located. The research process was structured in six phases as case study for the technology transfer model. First, research planning was conducted to estimate the time, cost, and suitability of the research topic. Second, the study design was determined to establish a technology transfer model focusing on the theory of mixed revenue management. Third, data collection about the city of Madrid was extracted from two technological databases, namely SeeTransparent based mainly on Airbnb (28 internal characteristics of the apartment) and Deskmind Research (9 sociodemographic variables of the area in which the apartment is located). Fourth, the data were prepared to create a new descriptive variable of the apartments based on geolocation. Fifth, the analysis of this study was applied to explore the correlation between the price charged per night, the 28 internal characteristics of the apartments, and the 9 sociodemographic variables of their surrounding areas. Sixth, with this integrated database, the information was transformed into multivariate inferential statistics through Exploratory Factor Analysis and Multiple Linear Regression, creating a technology transfer model (big data algorithm) that allows revenue managers to set the price of an apartment based on known information, prior to having a history of market reactions. This research process and model consider some of the factors affecting the psychological behavior of tourism consumers. Practical implications of the findings indicate that the size/capacity of the apartments used for Short-Term rentals largely determines the initial rental price set (72%). The equipment offered by the apartments has a moderate impact (18%), and the sociodemographic characteristics of the surrounding area have a minor influence (11%).

Analytical Formulas Using Affine Transformation for Pricing Generalized Swaps in Commodity Markets with Stochastic Convenience Yields

This paper presents analytical formulas for pricing generalized swaps, including the moment swap, gamma swap, entropy swap and self-quantoed variance swap. The formulas are based on closed-form formulas for the conditional expectations of the product of the price and its logarithm and the product of the price and the convenience yield obtained by solving a partial differential equation corresponding to the infinitesimal generator for the two-dimensional diffusion process. In this respect, the formulas obtained are combinatorial in nature and are solved via an affine transformation involving the complete Bell polynomials. The formulas are quite suitable for practical usage with symmetric and skew-symmetric properties, i.e., they are simpler and more compact compared with those existing in the literature. Moreover, for moments swaps, we show in general that the strike price does not depend on the initial spot price but depends only on the initial convenience yield, which highlights the resulting versatility in this respect.

Techno-economic analysis of photovoltaic rooftop system on car parking area in Rayong, Thailand

The intensity of solar radiation in Thailand is sufficient to produce electricity, thus the government has unconditionally promoted electricity production from solar energy, known as photovoltaic power system. Although it can reduce energy costs, it should also be environmentally friendly. This research aims to design and evaluate the photovoltaic rooftop system by concerning economic feasibility, cost of energy, and the compensation for carbon dioxide emissions. The research methodology also calculates the installation area and the solar power generation potential. Carbon dioxide emissions compensation and cost-effectiveness tools are used to evaluate the overall system. The results revealed that the photovoltaic rooftop system had the production capacity of 31.49 kilowatt-hours with an installation area of 184 m2. Solar panels orient to north direction with 11 degrees tilt from horizontal plane. The PV rooftop system generated the highest electricity around 137.103 kilowatt-hours per year and electricity production per installed system size is 4.354 kilowatt-hours per kilowatt-power. Within the period of 25 years and interest rate of 6.3145 percent, this project would be worthwhileness for the investment because the payback period is 6.83 years, the net present value is 1,523,571.70 baht, internal rate of return is 17.85 percent, benefit–cost​ ratio is 1.77, cost of energy is 2.31 baht per kilowatt-hour, in addition to the project can reduce carbon dioxide emissions by up to 643.77 tons of carbon dioxide equivalent. The promotion of electricity production from solar energy will increase the sustainability of energy and environment. In addition, the project sensitivity study indicated that initial cost, interest rate, and electricity price will be the important factors in making a decision to initiate a project.

Anchoring on Historical Round Number Reference Points: Evidence from Durable Goods Resale Prices

This paper examines how people price the resale of durable goods in systematically biased ways. We show across four studies that the anchoring effect of durable goods’ prior sales prices on subsequent valuations is discontinuous at psychologically salient round number reference points (e.g., $10,000 increments) because these numbers create qualitative differences in how people perceive values below them versus values at/above them. Resellers set disproportionately larger subsequent prices when previous prices move from just below round number thresholds (e.g., $349,000) to those at or just above these thresholds (e.g., $351,000). The findings show that buyers who pay a price just below a round number, therefore, may sacrifice money because they receive disproportionately less when reselling the good. Market forces only partially attenuate this pricing bias, but valuator experience seems to play a moderating role. Archival data show that home buyers who previously paid just under a $10,000 reference point subsequently listed their homes for about 1.8% (over $3,700) less on average than did buyers selling comparable homes who previously paid at or above a round number threshold. This drop is observable controlling for home characteristics and the general relationship between previous and current prices. Three experimental studies looking at housing and used car markets replicate these findings, highlight the mechanism, and increase confidence in causality. Market mechanisms and the negotiation process attenuate discontinuities by about 30%, but lower initial listing prices persist to final sales prices. We find additional weak evidence suggesting that valuator experience may attenuate intergenerational pricing bias. Conflict of Interest: This manuscript was submitted, revised, and accepted prior to the nomination and appointment of Lamar Pierce as Editor-in-Chief. Funding: This project was supported by the University of Southern California Marshall School of Business Behavioral Laboratory. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.1632 .

Does an initial public offering (IPO) issuer's Securities and Exchange Commission registration fee calculation method predict pricing revisions and IPO underpricing?

AbstractThis paper proposes a new proxy for the ex ante expectations of issuers and their underwriters about the direction of pricing revisions during the roadshows of an initial public offering (IPO): the way issuers elect to calculate the registration fees owed to the Securities and Exchange Commission. Consistent with fee‐minimizing decision‐making, I find that the choice of fee calculation method is associated with pricing revisions and IPO underpricing. This relationship suggests that issuers or their advisors may not incorporate economically significant private valuation information into the initial pricing range estimate and initial public offering price. The results provide empirical support for theoretical models of partial adjustment and IPO underpricing driven by the preferences of underwriters or managers of issuers for underpriced IPOs.

Signaling effects of recurrent list‐price reductions on the likelihood of house sales

Recurrent list-price reductions for a house may signal the impatience of sellers to conclude a sell transaction more quickly, leading to more visits and a higher likelihood of being sold (positive signal). Recurrent list-price reductions may also provide a market signal that the listing is problematic and thus harder to sell without a list-price reduction, leading to a lower likelihood of being sold (negative signal). Unlike standard survival analysis, we investigate which signal prevails using a joint frailty model that accounts for the interdependence among recurrent list-price reductions and the association between the recurrent reductions and the sold event. Our novel data set contains the time-dated recurrent list-price reductions for each house listed on the market. The results from the joint frailty model show time-varying negative impacts of list-price reductions on the likelihood of a house sale, supporting the dominance of the negative signaling effects of recurrent list-price reductions. Although listings with frequent list-price reductions are less likely to be sold, sold houses sell at a higher ratio of sold price to last list price, which incorporates current market conditions and fairer pricing, holding constant the initial list price and the aggregate list-price reduction from the initial list price.

An Economic Investigation of a Solar-Powered Adsorption Cooling System

In this paper, an economic investigation is performed to identify the economic feasibility of a solar-powered adsorption cooling system. In the first step, the system is mathematically formulated, and the solar fraction of the system is calculated. After that, the system is economically optimized for a cooling load related to a single-family house using particle swarm optimization. In order to identify the most efficient application of the system, the cost per 1 kW of cooling capacity is calculated over different cooling loads. The results demonstrated that the system under study is more suitable for small-scale applications. In addition, sensitivity to electricity price analysis demonstrates that the main bottleneck of cost-efficient system design is the initial price of the solar collector. Therefore, in the next step, the former sensitivity analysis to electricity price is jointly performed over different initial prices of the solar collector at two different cooling loads. Finally, by considering the cost of CO2 capture and the amount of avoided CO2 by utilizing solar energy, the environmental benefit of the system under investigation is calculated. The results show that the optimum solution is 13% more cost-efficient compared to the base design. In addition, taking the CO2 capture parameters into account, the environmentally friendly case has 21% more annual cost compared to the optimal solution one. However, this extra cost can be justified through CO2 capture cost.

A General Framework to Forecast the Adoption of Novel Products: A Case of Autonomous Vehicles

Due to the unavailability of prototypes, the early adopters of novel products actively seek information from multiple sources (e.g., media and social networks) to minimize the potential risk. The existing behavior models not only fail to capture the information propagation within the individual's social network, but also they do not incorporate the impact of such word-of-mouth (WOM) dissemination on the consumer's risk preferences. Moreover, even cutting-edge forecasting models rely on crude/synthetic consumer behavior models. We propose a general framework to forecast the adoption of novel products by developing a new consumer behavior model and integrating it into a population-level agent-based model. Specifically, we extend the hybrid choice model to estimate consumer behavior, which incorporates social network effects and interplay between WOM and risk aversion. The calibrated consumer behavior model and synthetic population are passed through the agent-based model for forecasting the product market share. We apply the proposed framework to forecast the adoption of autonomous vehicles (AVs) in Nashville, USA. The consumer behavior model is calibrated with a stated preference survey data of 1,495 Nashville residents. The output of the agent-based model provides the effect of the purchase price, post-purchase satisfaction, and safety measures/regulations on the forecasted AV market share. With an annual AV price reduction of 5% at the initial purchase price of $60,000 and 90% of satisfied adopters, AVs are forecasted to attain around 80% market share in thirty-one years. These findings are crucial for policymakers to develop infrastructure plans and manufacturers to conduct an after-sales cost-benefit analysis.

Adapted flower pollination algorithm for a standalone solar photovoltaic system

This Extraction of the maximum electrical power from a solar photovoltaic (PV) system under numerous weather conditions is required to reduce its payback time period, per unit energy price, and to compensate for the high initial price of the solar PV system. This could only be achieved by continuously operating the solar PV system at its maximum power point (MPP) under several weather conditions. Unlike under uniform weather conditions (UWC), identification of the real MPP (Global MPP) under partial shading condition (PSC) in a reasonable time is a challenging task due to the formation of multiple local MPP in the power-voltage (P-V) characteristic curve of a solar PV array. The nature-inspired MPP tracking algorithms have been proved suitable for global MPP tracking (MPPT) under PSC. In this research paper, a renowned nature-inspired flower pollination algorithm (FPA) is deeply reviewed, modified, and integrated with the random walk filter to improve its performance in terms of tracking speed, and efficiency. A comparison of the proposed ‘Adaptive Flower Pollination Algorithm (AFPA)’ and conventional FPA algorithm has been made under zero, weak, and strong PSCs for a 4S solar PV array. The proposed algorithm has produced remarkable results in tracking speed, and efficiency, for the global MPP (GMPP) tracking under different PSCs. The simulation is performed in MATLAB/Simulink software.

Procurement Mechanisms with Post-Auction Pre-Award Cost-Reduction Investigations

Integrating Cost Reduction and Supplier Selection Activities Manufacturers who outsource production may find ways to reduce suppliers’ cost to lower the contract price. Conventionally, this is done after supplier selection. However, in practice, a sizable portion of the degree to which the manufacturer can help a supplier reduce cost could be learned before the supplier starts production. This opens up a possibility to better integrate supplier selection and cost-reduction activities. In “Procurement Mechanisms with Postauction Preaward Cost-Reduction Investigations,” Chen, Beil, and Duenyas explore an alternative procurement process wherein, after suppliers’ initial pricing bids are collected, the manufacturer investigates suppliers’ cost-saving potential and then leverages this information to select the contract winner. They provide analytical and numerical evidence that this integrated procurement process not only helps the manufacturers reduce cost significantly, but also benefits the suppliers in many plausible scenarios.

The Role of Initial Return to Market Value and Entrepreneur Ownership in Indonesia

Initial pricing is done through negotiations, the company sets the offering price to achieve success in its IPO process. The difference in the ratio between the opening and closing prices is an initial return that can provide capital gains for shareholders. The gap between informed and uninformed investors makes stock prices a measure of market value for investors. The specific purpose of this research was conducted to determine the of the proportion of share ownership by entrepreneur ownership on the market value of equity and on the initial return. In addition, to determine the direct effect of the initial rate of return on market equity. As well as the effect of Initial Return in mediating the relationship between entrepreneur ownership and market value of equity. This research method is quantitative, with purposive sampling technique. While the data collection method used is a survey. The object of this research consisted of 228 companies that did their first listing for the 2008-2017 period on the Indonesia Stock Exchange. This study uses a simple linear regression analysis by doing the correlation test, F test, and t-test. The results of this research describe that all regression relationships have p values which are below the significance level of 0.05. Meanwhile, the findings on the role of initial return as a mediating variable made the value of the coefficient B decrease from 1.103 to 0.095. Thus it can be concluded that all hypotheses are supported with a significant and positive influence. On the effect of initial return in the relationship between entrepreneur ownership and market value, it has a significant effect with a partially mediating role.

A new empirical contribution to an old theoretical puzzle: what input–output matrix properties tells us about equilibrium prices and quantities

Eigenvalues of input-output matrices have significant implications for the structures of equilibrium prices and quantities. According to the Bródy Conjecture (BC), all subdominant eigenvalues of matrix would approach zero as matrix size approached infinity. Thus, any given initial quantity or price vector would converge to the corresponding equilibrium one in a single step. This paper adds significant empirical evidence to this theoretical discussion. We create a database of 307 different sizes matrices ranging over 30 years. Contrary to BC, we find that: the coefficient of variation and the subdominant eigenvalue moduli rise with matrix size; there’s a universal rank-size curve of eigenvalue moduli, but it is smooth and convex rather than L-shaped; the distribution of eigenvalue moduli is best fit by a Weibull probability distribution; the Weibull quantile function in turn yields a power law for eigenvalue moduli which is a better fit than a previously proposed exponential function.