Price Information Research Articles

Source-load cooperative multi-modal peak regulation and cost compensation mechanism in China’s ancillary service electricity market

To enhance the market participation initiatives from the power source and load sides, we propose a novel power system optimal scheduling and cost compensation mechanism for China’s peak regulation ancillary service market. Owing to China’s energy structure, thermal power accounts for nearly half of the country’s installed power generation capacity. Although the willingness of thermal power units to participate in peak regulation auxiliary services is low, we propose a peak regulation cost compensation and capacity-proportional allocation mechanism. This mechanism comprehensively considers the source-load initiative. From the source side, it encourages entities to participate in peak regulation, and the restriction of the peak regulation initiative is set to ensure that each entity benefits from the peak regulation transaction. From the load side, it takes the shiftable and sheddable load as the hybrid demand response and uses the price information to influence the power consumption behavior of the user side. Subsequently, a peak regulation scheduling model was constructed with the multi-objective minimum thermal power output fluctuation of the lowest system operating cost and minimum renewable energy abandonment. This was solved using a mixed-integer linear programming model and CPLEX. Finally, a power system consisting of wind-solar-hydro-thermal-storage and hybrid demand response with a modified IEEE 30-bus system was tested to verify the effectiveness. It was proven that the proposed method improves the utilization rate of renewable energy and optimizes the scheduling of the economic benefit system of each power generation entity.

Can rounding up price discounts reduce sales?

AbstractSome retailers round up price discounts, such as displaying a 7.7% discount as an 8% discount. In such instances, lay beliefs would suggest that displaying an 8% discount (vs. a 7.7% discount) would increase purchase intentions. In this research report, however, we show that displaying a rounded‐up, higher‐value discount (8%) versus a more precise but lower‐value discount (7.7%) reduces purchase intentions. Specifically, we show that using a more precise discount framing increases perceptions that the discount duration is shorter, in turn increasing purchase intentions. This research report presents a relevant and counterintuitive effect, and we propose contributions to work on both behavioral pricing and numerical information processing. Furthermore, this work has implications for practice, showing how to optimally display price discounts.

Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models

The ınvestment decisions of institutional and individual investors in financial markets are largely influenced by market uncertainty and volatility of the investment instruments. Thus, the prediction of the uncertainty and volatilities of the prices and returns of the investment instruments becomes imperative for successful investment. In this study we seek to identify the best fit model that can predict the volatility of return of Bitcoin, which is in high demand as an investment tool in recent times. Using the opening data of weekly Bitcoin prices for the period of 11.24.2013–03.22.2020, their logarithmic returns were calculated. The stationarity properties of the Bitcoin return series was tested by applying the ADF unit root test and the series were found to be stationary. After reaching the average equation model as ARMA (2.2), it was tested whether there was an ARCH effect in the ARMA (2,2) model. As a result of the applied ARCH-LM test, it is reached that the residuals of the average equation model selected have ARCH effect. Volatility of Bitcoin return series after detection of ARCH effect has been tried to predict with conditional variance models such as ARCH (1), ARCH (2), ARCH (3), GARCH (1,1), GARCH (1,2), GARCH (1,3), GARCH (2,1), GARCH (2,2), EGARCH (1,1) and EGARCH (1,2). While the obtained findings indicate that the best model is in the direction of GARCH (1,1) according to Akaike info criterion, it was found that GARCH (1,1) model does not have ARCH effect as a result of the applied ARCH-LM test. Thus, our empirical findings highlight an ample guide on appropriate modeling of price information in the Bitcoin market.

The dark side of surge pricing and the mitigating role of information disclosure

ABSTRACT This research paper investigates the impact of surge pricing, a dynamic pricing strategy that adjusts prices based on real-time demand and supply, on customers’ perception of price fairness. We aim to provide insights for both academic and managerial audiences. Our study focuses on how businesses using surge pricing can minimize negative effects on customer behavior by properly communicating price changes. We conducted two experiments to examine customers’ fairness perception of surge pricing policies. Our findings revealed that customers generally perceive surge pricing as less fair. However, we also discovered that aligning the amount of information shared with the price increase can effectively improve these fairness perceptions. Our research contributes to the literature on price communication by exploring factors that influence consumers’ understanding and acceptance of price changes. Additionally, our findings offer practical guidance for businesses on how to communicate pricing information in a way that customers perceive as fair, ultimately benefiting both companies and their customers.

Automated switching services

Automated switching services have recently emerged as online intermediaries that use algorithms to facilitate consumer switching. Unlike price comparison websites, these services (i) act on behalf of consumers by actively switching them to the cheapest deals, (ii) typically charge consumers directly, rather than charging suppliers commission, and (iii) tend to consider every supplier’s price. We offer the first theoretical analysis of such services. In an oligopoly model with imperfect price information, we characterize an equilibrium with an automated switching service, and analyze its impact on market outcomes and welfare. Among other results, we show how the service’s existence benefits all consumers, despite it only serving some consumers and charging them a fee.

PERILAKU DAN PENGARUH PENDIRI TERHADAP INFORMATIVITAS HARGA SAHAM: PERUSAHAAN PUBLIK KELUARGA DI INDONESIA

This study examines the relationship between founder as controller, age of founder, and dispersion of control on the informativeness of company stock prices. We argue that behavioral heterogeneity exists in family firms that are still majority owned and controlled by the founder. Using unbalanced panel data from 262 observations, first, this research finds support for the hypothesis that there is a convex relationship between founder voting rights and the informativeness of company stock prices. Second, older founders demonstrate more stewardship behavior than younger founders. Finally, high power dispersion in founding family firms creates agency conflicts between family owners that decrease the informativeness of firm stock prices. This finding is consistent with previous family business literature which reports that mixed behavior among family members can influence the informativeness of family company share prices.
 
 Keywords: Family Companies, Behavior Of Company Founders, Ownership Of Founders, Age Of Founders, Stock Price Informativeness

Context-aware driver risk prediction with telematics data

Driving risk prediction is crucial for safety and risk mitigation. While traditional methods rely on demographic information for insurance pricing, they may not fully capture actual driving behavior. To address this, telematics data has gained popularity. This study focuses on using telematics data and contextual information (e.g., road type, daylight) to represent a driver’s style through tensor representations. Drivers with similar behaviors are identified by clustering their representations, forming risk cohorts. Past at-fault traffic accidents and citations serve as partial risk labels. The relative magnitude of average records (per driver) for each cohort indicates their risk label, such as low or high risk, which can be transferred to drivers in a cohort. A classifier is then constructed using augmented risk labels and driving style representations to predict driving risk for new drivers. Real-world data from major US cities validates the effectiveness of this framework. The approach is practical for large-scale scenarios as the data can be obtained at scale. Its focus on driver-based risk prediction makes it applicable to industries like auto-insurance. Beyond personalized premiums, the framework empowers drivers to assess their driving behavior in various contexts, facilitating skill improvement over time.

Connecting the stocks of major energy firms in China to identify the systemic risk

This study mainly identifies systemic risks among energy firm stocks. By using a financial network approach on 198 major energy firms in China, the study finds that energy firms are accompanied with systemic risk due to extensive stock return and stock risk connections. Return connection is less severe and long-lasting than risk connection, but they are all buffeted to varying degrees by economic activities, such as stock market volatility, energy policy, and public event like COVID-19. Structural analysis further finds that heterogeneity and asymmetry exist in energy firms' bidirectional ability of price and risk information processing; relatively extreme asymmetry cases can be observed from risk connection ability. Multiple centrality indicators screen out some systemically important energy firms from exchanges of both return and risk information. All these characteristics could provide implications for policy making, corporate response, and portfolio construction.

Measuring Welfare and Inequality with Incomplete Price Information

Abstract We propose and implement a new approach that allows us to estimate income-specific changes in household welfare in contexts where well-measured prices are not available for important subsets of consumption. Using rich but widely available expenditure survey microdata, we show that we can recover income-specific equivalent and compensating variations from horizontal shifts in what we call “relative Engel curves”—as long as preferences fall within the broad quasi-separable class (Gorman 1970, 1976). Our approach is flexible enough to allow for nonparametric estimation at each point of the income distribution. We apply the methodology to estimate inflation and welfare changes in rural India between 1987 and 2000. Our estimates reveal that lower rates of inflation for the rich erased the real income convergence found in the existing literature that uses the subset of consumption with well-measured prices to calculate inflation.

When large traders create noise

We consider a market where large investors do not only trade on information about asset fundamentals. When they trade more aggressively, the price becomes less informative. Other investors who learn from prices, in turn, are less concerned about adverse selection and provide more liquidity, causing large investors to trade even more aggressively. This trading complementarity can engender three unconventional results: i) increased competition among large investors makes all investors worse off, ii) more precise private information reduces price informativeness, creating complementarities in information acquisition, and iii) multiple equilibria emerge. Our results have implications for competition and transparency policies in financial markets.

Dental tourism: factors influencing travelers’ revisit intention to the Mexican border

PurposeThe purpose of this study is to find out the factors that influence dental travelers’ revisit intention (RI) to Tijuana, Mexico.Design/methodology/approachBased on the push and pull model, five constructs were included: quality service, price, supporting services, cultural proximity and quality information. The information was obtained by applying an online survey to a sample of 384 dental tourists in Tijuana, Mexico, who were repeat patients of a dental clinic in Tijuana after the COVID-19 pandemic. Exploratory factor analysis, average variance explained and composite reliability were conducted to ensure the validity of each construct. Multiple regression analysis was done to identify predictors of travelers’ RI.FindingsThe results obtained show that cultural proximity, quality service, price and supporting services influenced the travelers’ revisit behavior after the COVID-19 pandemic lockdowns.Originality/valueThis study contributes to the body of knowledge on travel behavior in dental tourism after the COVID-19 pandemic lockdowns, which has been scarcely studied. Also, RI was analyzed, focusing on repeated travelers to propose a model mainly for borders or frontiers where developed and developing countries co-exist and interact.

Pricing information asymmetry in healthcare: stakeholder analysis and research insights

PurposeThis study aims to map and analyze health-care pricing information research. This work highlights current gap in pricing information research in health care and proposes future research avenues to academia and industry professionals.Design/methodology/approachA bibliometric method was adopted to analyze extant literature on pricing information asymmetry. Semistructured interviews were conducted with key stakeholders in health care to triangulate the findings.FindingsPricing information is crucial for all stakeholders including health-care consumers, providers and regulators. The popular research areas were the rising health-care cost, cost-saving, outcome-based pricing, price based on service supply and demand, insurance and out-of-pocket spending. Cost–quality perceived linkages, cost–demand correlation in health-care service and cost–price interlinked drivers were the dominant themes in extant literature. The study highlighted that pricing information asymmetry pushed patients from weaker sections into a debt trap due to unplanned out-of-pocket health-care expenses. The study suggests areas of research to minimize this pricing information asymmetry.Practical implicationsThe emerging themes in health pricing asymmetry will help key stakeholders to identify areas for improvement and take remedial actions in the health-care domain.Originality/valueThis study is a pioneering effort to summarize extant literature published in the health-care information pricing domain and analyze it from a bibliometric perspective. The study also triangulates the finding with primary data from key stakeholders and highlights emerging research areas.

Consumer Price Search Behaviors in Online Shopping

Aims: Simultaneously, the decelerating economy in the real world due to the covid-19 has intensified the competition in the virtual world, forcing online retailers to consider how to steering effective price promotion strategies to attract customers and to increase their purchase intentions. Online retailers are either struggling to find ways to formulate their online price promotion strategies or simply adopt the offline price promotion strategies. However, the Internet provides online shoppers with the benefit of searching and evaluate price information efficiently and effectively, greatly reduces consumers’ information search costs. Under this circumstance, the effect of offline price promotion strategies on consumers’ price perceptions and purchase decision may become a hinderance instead of an assistance. Overall, the price information provided by the Internet influences consumer perceptions and reactions to online shopping. The purpose of this research is to provide insight into consumers’ online price search behavior and consumers’ reactions to online price promotion.
 Methodology: An in-depth discussion of the literature review on online consumer price search behavior was applied. Specifically, the literature review covers the issues of price comparison search engine, price comparison refund policy, price promotion, and semantic cues related to consumer price perception and purchase intention.
 Conclusion: These research propositions can help online retailers to gain a deeper understanding of consumer price search behavior, response to price information, and promotions. Consequently, online retailers are able to better formulate price communication strategies effectively. This price scheme can influence consumer price search behavior and then purchase intention.

Algorithmic Personalized Pricing with the Right to Explanation

Abstract Personalized pricing by algorithms has been widely deployed by digital platforms. Although this strategy provides economic efficiencies and benefits consumers, it precipitates discriminatory, exploitative, and exclusionary effects on the market. If massive market power and certain information asymmetry exist, this pricing behavior could be illegal as the negatives could outweigh the positives. The negatives could nevertheless not be fully addressed by current ex-post and ex-ante antitrust, consumer protection, and anti-discrimination approaches because the sloping pricing information between platforms and consumers based on data will not be effectively rebalanced considering these approaches. Data protection measures should be underlined, despite the flaws of some data rights. This article shows how the right to explanation as a synthetic right of data protection mitigates the damaging effects by providing consumers with the access to the result of algorithmic decision-making to optimize the information distribution, despite the application limits due to technological and cognitive immatureness of ‘explanation’.

Healthcare price transparency in North America and Europe.

Healthcare price transparency is an effort to inform patient decision-making, but also to decrease prices and their variation across healthcare systems for equivalent medical services. The initiative is meaningful only for medical services that are shoppable-such as imaging examinations-for which patients incur out-of-pocket costs. Therefore, several countries in which patients commonly share a portion of their healthcare costs have been implementing mandates to improve healthcare price transparency. However, the provisional implementation has many issues, especially in the United States, including provider non-compliance and limited accessibility of price transparency tools by the general public. Many of the existing tools are not user-friendly, are difficult to navigate, focus on charges and health plan negotiated rates rather than patients' out-of-pocket costs, and disclose prices on the service level instead of per episode of care. As such, the disclosed amounts are often not reliable. Many price transparency tools also lack valid and measurable quality metrics, which can result in a selection of high-cost care as a proxy for high-value care, as well as an increase in healthcare prices when providers want to imply they offer high-quality care. Nevertheless, the impact of the initiatives on patients' decision-making and healthcare costs remains unclear. While transparency initiatives are patient-centric, efforts should be made to increase patient engagement, provide accurate patient-specific out-of-pocket cost information, compare available treatment and provider alternatives, and couple price information with quality metrics to enable making fully informed decisions.

Data Factor and Financial Market Equilibrium

ABSTRACT In this paper, we develop a novel asset pricing model in which data factor is incorporated into the fundamental value to explore its impact on financial market equilibrium. It is shown that high precision of data can attenuate the fundamental risk and increase the trading intensity, thus enhancing price informativeness and liquidity along with reducing the cost of capital. Furthermore, we discover that there is a positive relationship between real investment efficiency and the correlation coefficient of data factor with productivity. Our results emphasize the important role of data factor in stock pricing and real investment, as well as reveal the internal impact mechanism of data factor on equilibrium properties, which complement the existing empirical evidences and have significant implications on data application.

The Influence of Tourist Attraction Type on Product Price Perception and Neural Mechanism in Tourism Consumption: An ERP Study.

Tourism consumption is a topic with heated debates in tourism research, and pricing tourism products is a crucial task for tourism managers. Different types of tourist attractions offer different experiences to tourists, which affect their price perceptions and purchase decisions. This study combined questionnaires and event-related potentials (ERPs) measures to explore the magnitude of psychological conflict and the degree of emotional arousal that consumers experience when faced with different prices of goods in different scenic types. The questionnaire results showed that attraction type influenced consumers' price perceptions and that consumers were willing to pay higher prices for products in attractions. The ERP results implied that in the early stage of cognition, attraction type did not affect consumers' perceptual processing, while price information attracted consumers' cognitive attention. In the late stage of cognition, attraction type, and price information jointly influenced consumers' decision-making, and consumers tended to accept high prices of products in entertainment attractions and cultural attractions, but consumers were more sensitive to the price of products in cultural attractions and less tolerant to price increases. The study elucidated how price information influenced consumers' purchase decisions of tourism products at different stages of the dual-process theory, which can assist tourism managers in devising different pricing strategies and positioning strategies based on the attributes of attractions, to enhance product sales and revenues. This would further the vision of the World Tourism Organization (UNWTO) of "tourism fostering economic development".

Volatility analysis of chili price at Bengkulu Province using ARIMA approach

Chili is an inflation-forming commodity (volatile food). Prediction of chili price time series data using the ARIMA model approach has good performance for predicting chili prices in the future. The second highest average per capita consumption of chili for a month is Bengkulu Province, below West Sumatra Province. This shows that the level of consumption and demand for chili is relatively high in Bengkulu Province. High demand has an impact on the price volatility of chili. The volatility analysis illustrates the standard deviation or diversity of chili prices that fluctuate over a certain period. Based on the description above, it is necessary to conduct research on the analysis of chili price volatility in Bengkulu Province. This research uses data sourced from the National Strategic Food Price Information Center. chili price volatility analysis using the ARIMA model with the help of R studio software. ARIMA models are suitable for time series data. The stages in this study were stationarity test, building the ARIMA model, selecting the best model, evaluating the model, identifying the ARCH effect and building the ARCH GARCH model, and calculating the volatility value. If there is no ARCH effect, there is no need to create an ARCH GARCH Model. the output of this research was the form of an international conference. The results of the data forecast using the ARIMA model (1,1,2) show prices that are relatively stable but have an upward trend. This shows that the level of volatility can be controlled and this is reinforced by the results that the model has no ARCH effect. The absence of an ARCH effect means that the data is still considered to have relatively the same diversity of variance and is in line with a relatively small volatility.

Add to Cart!: Factors affecting Impulse Buying Behavior in E-Groceries

The study analyzes the digital factors, namely Promotional Strategies, Motivation, Price, Website Quality, and Product Information, and their correlation and significant effect on Filipino consumers' online impulse buying behavior in e-groceries. Studies of online impulse buying behavior in e-groceries have yet to be explored in the Philippines. It is vital for e-grocery business owners and the e-grocery industry to gain insights on how to drive online impulsivity, leading to an increased profit and market share. The study used a quantitative approach, and online survey questionnaires were used to collect data through a 4-point Likert scale, avoiding neutral responses. The study targeted Generations Z and Y due to them being the dominant online shoppers in the Philippines. The study's findings were analyzed using Pearson Correlation which showed that all digital factors have a weak positive relationship with online impulse buying behavior. Additionally, the researchers conducted a Multiple Regression Analysis concluding that Website Quality, Motivation, and Promotional Strategies significantly affect online impulse buying behavior among e-grocery customers. However, other digital factors, namely Price and Product Information, were found to be insignificant. The result suggests that as e-grocery retailers boost their strategies pertaining to Website Quality, Motivation, and Promotional Strategies, a rise in online impulse buying can materialize.

Early career experience of executives and stock price informativeness

This paper examines the impact of early career experience through economic recessions of executives on stock price informativeness. We find that firms with a greater percentage of executives who start their careers during recessionary periods are positively associated with stock price informativeness, as measured by firm-specific stock return variation. Moreover, we document that the association is more pronounced for firms with poor external monitoring. These findings indicate that the early career experiences of executives significantly affect the extent to which stock prices convey firm-specific information, and that external monitoring and executives’ experiences during economic downturns exhibit a substitutive relationship. This paper points out the importance of considering the heterogeneous characteristic of executives to better understand corporate transparency.