Online Price Research Articles

Differences between Online Prices and the Consumer Prices Index During Covid-19 in Germany

The purpose of this paper is to examine various online price indices with the consumer price index in Germany during the Covid-19 pandemic from 2019 to 2021. This investigation refers to 9,977 items of the product category stationery and drawing supplies, whose sales prices were automatically recorded and evaluated by Amazon.de and idealo.de on a daily basis through web scraping. The official consumer price index of the German Federal Statistical Office of the 3rd Stella level is available for this product category, which makes a direct comparison possible for this study. It was found that online prices are more dynamic and volatile and react directly to external market influences than the prices of stationary trade, which are reflected in the CPI of the German Federal Statistical Office. A deviation of up to 11.7% between the calculated online price indices (OPI) and the German consumer price index (CPI) was found. Especially the value-added increase on 01.01.2021 shows a deviation effect of 9.7% to the CPI. It could be determined that the online price indices and the CPI showed the same trends, but with different characteristics. While Amazon's OPI was still in line with the CPI in 2020, deviations of over 6% were observed in 2021. The prices of other online shops already showed significant deviations from the CPI within the first lockdown in April 2021, which was imposed by the government to contain the Covid 19 pandemic in Germany and forced the stationary trade to close down across the country.

An analysis of price strategies with price matching in the presence of showrooming

AbstractWith multi‐channel retail competition, some offline retailers implement price matching services to combat consumers’ showrooming behavior. With this service, offline retailers cut their prices to match the online price if it is lower. In this paper, we establish a game model that involves an offline and online retailer in the presence of showrooming to analyze the impact of price matching. Specifically, we capture consumers’ uncertainty of product value and heterogeneous awareness of price matching and assume that the online retailer provides a free return policy. The results indicate that price matching alleviates showrooming and relaxes competition. Moreover, we find that the online retailer adopts a randomized pricing strategy if the offline visiting cost is moderate. We further show that the price matching strategy does not necessarily benefit offline retailers and hurt online retailers. Subsequently, we identify the offline retailers’ optimal conditions for implementing price matching.

Online prices and inflation during the nationwide COVID-19 quarantine period: Evidence from 107 Chinese websites

Given the lack of activity in China's offline economy during the COVID-19 quarantine period, online prices provide new insights for analyzing the impacts of the pandemic on the economy. Using online prices from 107 websites in China and the DiD method to remove the Spring Festival effect, we show that the pandemic leads to a 0.4% surge in the overall inflation rate, a 20% decrease in the price change probability, and a 1% decline in the size of absolute price changes. Moreover, the pandemic had heterogeneous impacts on different sectors, leading to significant structural changes in inflation. Specifically, the pandemic hindered the price correction behavior after Spring Festival, and whether products could be consumed while customers stayed at home was an important factor affecting price adjustment and inflation dynamics.

Pricing and Promotions in Retail Tea Market in Argentina

Argentina has long been a tea provider with diverse types and variants dispatched to the internal market and abroad. The drink and its variants are a popular complement to other infusions such as yerba mate and coffee. We scraped an extensive online price database for analyzing the tea products offered in local supermarkets. We use a panel data modeling approach and test if attributes are associated with pricing and price discounting in the retail tea market. We control the estimations for time effects and exchange rate metrics because of the unstable macroeconomics. Time effects are highly significant and robust across estimations: we detect weekly, seasonal patterns, and yearly cycles. Attributes such as brand and variety play a weaker role in pricing but a stronger one in promotions.

Factors Affecting Online Purchase Intention of Prospects: A Study in Nepalese Context

Various factors influence potential buyers' purchase intention from stores. Attitude towards online shopping, perceived customer service, quality, and price fairness may also determine the prospects' intention to buy or not. Amid Covid-19, many traditional stores adopted digital platforms to reach and serve customers. In this context, this study aimed to examine how the attitude towards online shopping, prospects' perception of customer service of online stores, quality perception, and price fairness perception affect purchase intention. This study used the Ordinary Least Square model to examine the effect of independent variables on the dependent variable. 3 presents the Correlation between the variables attitude, perceived customer service, quality, price fairness, and purchase intention of the respondents. The correlation test revealed significant positive correlations between the stated variables. The study employed a survey method to collect data from 425 respondents, using a structured-self administered questionnaire. The collected data were coded, entered, and analyzed in Statistical Package for Social Science (25). The result of the study indicated a significant positive impact of attitude, perceived customer service, quality perception, and price fairness perception on purchase intention. The contribution of this study highlights the positive impact of the stated variables on the purchase intention of the prospects in the Nepalese social context. This information may be helpful for online stores to improve their marketing performance.

The Concentration-after-Personalisation Index (CAPI): Governing effects of personalisation using the example of targeted online advertising

Firms are increasingly personalising their offers and services, leading to an ever finer-grained segmentation of consumers online. Targeted online advertising and online price discrimination are salient examples of this development. While personalisation's overall effects on consumer welfare are expectably ambiguous, it can lead to concentration in the distribution of advertising and commercial offers. Constellations are possible in which a market is generally open to competition, but the targeted consumer is only made aware of one possible seller. For the consumer, such a market could effectively resemble a monopoly. We call such extreme cases ‘targeting pockets’. Competition-law metrics such as the Herfindahl–Hirschman Index and traditional means of public oversight of adverts would not detect this concentration. We, therefore, suggest a novel metric, the Concentration-after-Personalisation Index (CAPI). The CAPI treats every consumer as a separate ‘market’, computes a measure of concentration for personalised adverts and offers for each individual consumer separately, and then averages the result to measure the exposure experienced by an average consumer. We demonstrate how the CAPI can serve as a monitoring tool for regulators and auditors and thus help to enforce existing consumer law as well as proposed new regulations such as the European Union's Digital Services Act and its Artificial Intelligence Act. We further show how adding noise via randomly distributed non-personalised adverts can dilute the potential harm of overly concentrated personalisation. We demonstrate how the CAPI can identify the optimal degree of added noise, balancing the protection of consumer choice with the economic interests of advertisers.

Composite Contracts for Dual-Channel Supply Chain Coordination with the Existence of Service Free Riding

This paper aims to investigate how to coordinate a dual-channel supply chain composing of a manufacturer and a retailer when customers utilize the retailer’s service to conduct free-riding behavior. Specially, we consider the crucial role of service in affecting customers’ valuation for a kind of experience product and establish a channel choice model by employing utility theory. Then, we analyze the optimal pricing and service decisions under decentralized and centralized scenarios. To achieve overall optimization, we propose three contract mechanisms, namely price hike (Mechanism 1), price hike with service cost sharing (Mechanism 2) and price hike with service cost sharing and surplus compensation (Mechanism 3). We reveal the way of price difference and service provision in affecting customer free-riding behavior. Besides, we find that the three mechanisms can reduce free-riding behavior to some extent. However, the extent varies under different mechanisms and is related to the cost-sharing fraction and the degree by which the manufacturer increases his online price. Further, we find that Mechanism 3 can realize overall optimization and members’ win–win situations. Finally, we conduct numerical examples to explore how different mechanisms affect supply chain efficiency. The results also provide managerial insights for dual-channel firms in practice.

Individualized Out-of-Pocket Price Estimators for “Shoppable” Surgical Procedures: A Nationwide Cross-Sectional Study of US Hospitals

Price transparency for shoppable surgical services is a key requirement of several recent federal policies, yet the extent to which hospitals provide online OOP price estimators remains unknown. We reviewed a stratified random sample of 485 U.S. hospitals for the presence of a tool to allow patients to estimate individualized OOP expenses for healthcare services. We compared characteristics of hospitals that did and did not offer online price estimators and performed multivariable modeling to identify facility-level predictors of hospitals offering price estimator with and without surgical procedures. Nearly two-thirds (66.0%) of hospitals in the final sample (95% confidence interval 61.6%-70.1%) offered an online tool for estimating OOP healthcare expenses. Approximately 58.5% of hospitals included at least one shoppable surgical procedure while around 6.6% of hospitals included all 14 surgical procedures. The most common price reported was laparoscopic cholecystectomy (55.1%), and the least common was recurrent cataract removal (20.0%). Inclusion of surgical procedures varied by total annual surgical volume and health system membership. Only 26.9% of estimators explicitly included professional fees. Our findings highlight an ongoing progress in price transparency, as well as key areas for improvement in future policies to help patients make more financially informed decisions about their surgical care.

Does it pay to book direct?: Customers’ perceptions of online channel distributors, price, and loyalty membership on brand dimensions

To attract a transient market, hotels primarily use several distribution channels, such as the following: the hotel directly, central reservation offices, travel agents, and online booking systems. Yet, little attention has been paid to the revenue management implications with regard to the focus on distribution channels. The purpose of this study was to examine the effects of channels and prices on brand dimensions and to study the role of hotel loyalty membership in the relationships. A 2 (channel) × 2 (price) × 2 (loyalty membership) quasi-experiment was designed to test the research objectives. Multivariate Analysis of Variance was used to test significant differences. The findings provide implications for the industry.

Price discrimination under online–offline competition

Third-degree online price discrimination can improve welfare when the online–offline​ cross-demand parameter is large, even though the total outputs remain unchanged. However, if the online firm competes with a uniform-pricing chain store, online discrimination will reduce welfare. Extensive discussions on different slopes, a leader–follower game, and general multiple markets are provided.

THE EFFECT OF PRODUCT KNOWLEGDE, ONLINE CUSTOMER REVIEW, ONLINE CUSTOMER RATING AND PRICES ON CONSUMER BUYING INTEREST IN MEDIATION OF TRUST IN THE MARKETPLACE SHOPEE (STUDY AT MWCNU SUKODADI)

The era of increasingly sophisticated technology, it causes a shift in consumer behavior, especially in shopping. Online shopping has now become a habit. There are factors that consumers consider, namely safety or inappropriate goods. In this study there is a novelty, namely more variables that are examined and the object of research. This research is a quantitative study that has a sample of 165 respondents and the data analysis used is the SEM method with the Smart PLS version 3.0 tool. The test used is the outer model test, the structural model, mediation and hypotheses. The results show that The validity test has an outer load mg value > 0.7 and an AVE value > 05 then it is declared valid. The reliability test shows a composive reliability value and Cronbach's alpha > 07 then it is said to be reliable. said Partial Mediation Online customer review variable (X2) on buying interest (Y) mediated by trust (Z) said Non Mediation Online customer rating variable (3) on buying interest (Y) mediated trust (Z) said Non Mediation Price variable (X4) on buying interest (Y) mediated by trust (Z) is said to be Partial Mediation Based on the analysis of the test results The hypothesis can be concluded that the product knowledge variable (X1). Online customer review (X2), online customer rating (X3) and price (X4) on bell interest (Y) simultaneously have a positive and significant effect

Algorithmic pricing in hospitality and tourism: call for research on ethics, consumer backlash and CSR

PurposeThis viewpoint paper calls for research on the social impact that comes with implementing algorithmic pricing in hospitality and tourism, in particular online price discrimination. It seeks to broaden the literature on consumer backlash and corporate social responsibility (CSR) to include algorithmic pricing.Design/methodology/approachAs algorithmic pricing will become increasingly important in hospitality and tourism, the authors argue that scholarly attention should be directed to two topics.FindingsFirst, there is a need for research on how algorithmic pricing triggers consumer backlash and online firestorms, and how these can be detected, prevented, and mitigated. Second, the authors need to increase our understanding of how deception, misconduct, dishonesty, and injustice in algorithmic pricing impact CSR performance, especially when differential pricing is enticed by deceptive yet legal algorithmic applications of indirect behavioral “self-selection” mechanisms.Social implicationsAlgorithmic price discrimination has been criticized for its potential to harm consumers, and doubt is cast upon the current ability of legal frameworks to set minimum standards of behavior.Originality/valueAlgorithmic pricing includes a variety of computerized pricing applications aimed at increasing revenue and minimizing opportunity costs. With early use by airlines decades ago its diffusion has gradually extended to other sectors including hospitality and tourism. While algorithms are expected to increasingly impact pricing decisions, little research can be found on the topic, with the exception of a vigorous debate in the policy literature on its ethical implications and regulatory needs.

Optimal Online Service Strategy and Price Decision in Omnichannel Retail

As a new emerging sales promotion tool, various types of online services are increasingly adopted by firms to improve consumers’ satisfaction and then increase profit. This paper simulates a two-echelon supply chain where a supplier sells the product through an offline or online retailer. Online channel is characterised by direct selling and reselling. We consider three online service strategies: no online service and preemptive and reactive online service. Several results are obtained. We find that investing in online services can benefit all players in most scenarios more than no-service scenario. In the direct selling case, the offline retailer benefits the most from the reactive service strategy due to the webrooming effect, whereas the supplier performance is best in the preemptive service strategy. However, in the reselling case, we find that the supplier and the online and offline retailers benefit the most from reactive service strategy. Furthermore, we compare the prices and service levels of the three strategies and find that the webrooming effect coefficient can affect the optimal wholesale price, retail prices, and online service level. Finally, the findings indicate that the supplier’s choice of the two cases depends on the fixed cost of the online channel.

Relationship Between Hospital Compliance With Medicare's Price Transparency Rule, Proposed Cash Prices, and Consumer Ratings for MRI Lumbar Spine.

Since January 2021, CMS has mandated that hospitals publicly post gross and payer-specific negotiated service charges and discounted cash prices in an online machine-readable format and display such prices for 300 “shoppable” services in a consumer-friendly format or an online price estimator tool [1]. As of March 2021, however, only 17% of hospitals were compliant [2]. Of note, no hospital-based price estimators incorporated patient-focused quality metrics to permit comparison of cost versus quality.

Internet Retailing and Pricing Decisions: An Examination in the Italian Gambling Market

The rapid growth of e-commerce over the last few years has sparked a stream of research that analyzed the impact of Internet retailing on market prices. It has been theorized that the low entry and transaction costs associated with electronic commerce should lead to a reduction in prices, especially for homogeneous products. Some scholars have verified, through empirical research, whether the prices of specific products and services sold through the electronic distribution channel are, as hypothesized, lower than those practiced in brick and mortar stores. The results of this research, even if they mostly confirm the greater price efficiency of e-commerce compared to the physical sales network, are however not univocal. This paper intends to offer an original contribution in this field of study, examining a particular industry that in recent years has been affected by an exponential increase in online sales, that of gambling. The aim of this work is to analyze whether in the Italian sports betting market, in line with theoretical hypotheses, online prices are lower than offline prices and whether the development of Internet sales has been accompanied by greater market efficiency and, therefore, a generalized reduction in prices. From a methodological point of view, the study was developed by collecting and processing the official market data of the regulated sports betting sector in Italy provided by the Agency of Customs and Monopoly, for the years ranging from 2011 to 2019, thus carrying out a longitudinal analysis on price levels and related changes over time. The results of the analysis have shown that in the period analyzed in the Italian regulated sports betting market the average prices paid for the betting products offered by the gambling websites remained lower, in a range going from 38 to 48 percent, as compared to those practiced by betting shops. It also emerged that there was a general reduction in the average price levels of betting products equal to almost 50 percent. These results have interesting managerial implications for gambling operators and provide some indications for state institutions that are responsible for gambling policy and legislative changes.

Spread of E-commerce, prices and inflation dynamics: Evidence from online price big data in Korea

The so-called Amazon effect is generally defined as such that the increasing competition in the online market and between online and traditional retailers is reducing retail markups and putting downward pressure on prices. This paper investigates the existence of the Amazon effect using online price big data in Korea where e-commerce has been rapidly spreading. For this task, the direct comparison between the online and offline prices were conducted in terms of the levels, trends, inflations, and dynamic correlations for two prices. Online prices for products were collected and classified under the identical classification in the CPI for 14 items in two divisions: food and non-alcoholic beverages and clothing and footwear. Laspeyres formula with identical weights as for the CPIs was applied to compilation of the item- and division-level OPIs (online price indexes) from July 2018 to June 2019. The empirical analyses overall indicated that persistent decreasing trends in the online prices were found when compared to those in the offline prices represented by the CPIs, indicating the existence of the Amazon effect in Korea. More specifically, the OPI covering two divisions decreased by 1.8%, while the CPI increased by 3.6% for the period. In addition, the close dynamic correlations between on-month inflations of two indexes were also found in panel regression and VAR estimations, indicating that a 1% increasing shock to OPI inflation led to an around 0.3% increasing response in the CPI inflation. The dynamic correlations, however, were quite different across the divisions. Those were more pronounced for the division of food and non-alcoholic beverages while they were found little in the division of clothing and footwear.

Nowcasting food inflation with a massive amount of online prices

The consensus in the literature on providing accurate inflation forecasts underlines the importance of precise nowcasts. In this paper, we focus on this issue by employing a unique, extensive dataset of online food and non-alcoholic beverages prices gathered automatically from the webpages of major online retailers in Poland since 2009. We perform a real-time nowcasting experiment by using a highly disaggregated framework among popular, simple univariate approaches. We demonstrate that pure estimates of online price changes are already effective in nowcasting food inflation, but accounting for online food prices in a simple, recursively optimized model delivers further gains in the nowcast accuracy. Our framework outperforms various other approaches, including judgmental methods, traditional benchmarks, and model combinations. After the outbreak of the COVID-19 pandemic, its nowcasting quality has improved compared to other approaches and remained comparable with judgmental nowcasts. We also show that nowcast accuracy increases with the volume of online data, but their quality and relevance are essential for providing accurate in-sample fit and out-of-sample nowcasts. We conclude that online prices can markedly aid the decision-making process at central banks.

Online Pricing with Offline Data: Phase Transition and Inverse Square Law

This paper investigates the impact of pre-existing offline data on online learning in the context of dynamic pricing. We study a single-product dynamic pricing problem over a selling horizon of T periods. The demand in each period is determined by the price of the product according to a linear demand model with unknown parameters. We assume that before the start of the selling horizon, the seller already has some pre-existing offline data. The offline data set contains n samples, each of which is an input-output pair consisting of a historical price and an associated demand observation. The seller wants to use both the pre-existing offline data and the sequentially revealed online data to minimize the regret of the online learning process. We characterize the joint effect of the size, location, and dispersion of the offline data on the optimal regret of the online learning process. Specifically, the size, location, and dispersion of the offline data are measured by the number of historical samples, the distance between the average historical price and the optimal price, and the standard deviation of the historical prices, respectively. For both single-historical-price setting and multiple-historical-price setting, we design a learning algorithm based on the “Optimism in the Face of Uncertainty” principle, which strikes a balance between exploration and exploitation and achieves the optimal regret up to a logarithmic factor. Our results reveal surprising transformations of the optimal regret rate with respect to the size of the offline data, which we refer to as phase transitions. In addition, our results demonstrate that the location and dispersion of the offline data also have an intrinsic effect on the optimal regret, and we quantify this effect via the inverse-square law. This paper was accepted by Omar Besbes, revenue management and market analytics. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2022.4322 .

Economic Evaluation of Sintilimab Plus Bevacizumab Versus Sorafenib as a First-line Treatment for Unresectable Hepatocellular Carcinoma.

This study aimed to evaluate the cost-effectiveness of sintilimab plus bevacizumab versus sorafenib as a first-line treatment for unresectable hepatocellular carcinoma (HCC) in China to provide economic evidence to inform health decision making. We performed an economic evaluation from the perspective of the Chinese healthcare system using a partitioned survival model with three mutually exclusive health states: progression free, post-progression, and death. Efficacy data were obtained from the ORIENT-32 clinical trial and extrapolated to the lifetime horizon. Cost and utility values were derived from published studies and online price databases. The primary outcomes of the model were quality-adjusted life-years (QALYs), costs, and incremental cost-effectiveness ratios (ICERs). Sensitivity analyses were carried out to verify the robustness of the model results. Compared with sorafenib, sintilimab plus bevacizumab incurred a higher lifetime cost ($33,766 vs. $23,294) and yielded more QALYs (1.428 vs. 0.928 QALYs). The ICER for sintilimab plus bevacizumab was $20,968/QALY and lower than the willingness-to-pay threshold of $33,592. The results of sensitivity analysis showed that ICER values were most sensitive to the subsequent treatment cost of the sorafenib group after progression and the price of bevacizumab. In the scenario analysis, the ICER was $4191/QALY when a 7.5mg/kg dose of bevacizumab was applied in the model. Compared with sorafenib, the sintilimab plus bevacizumab combination is likely to be a cost-effective option for patients with unresectable HCC in China.

Online Pricing and Trading of Private Data in Correlated Queries

With the commoditization of private data, data trading in consideration of user privacy protection has become a fascinating research topic. The trading for private web browsing histories brings huge economic value to data consumers when leveraged by targeted advertising. And the online pricing of these private data further helps achieve more realistic data trading. In this paper, we study the trading and pricing of multiple correlated queries on private web browsing history data at the same time. We propose CTRADE, which is a novel online data CommodiTization fRamework for trAding multiple correlateD queriEs over private data. CTRADE first devises a modified matrix mechanism to perturb query answers. It especially quantifies privacy loss under the relaxation of classical differential privacy and a newly devised mechanism with relaxed matrix sensitivity, and further compensates data owners for their diverse privacy losses in a satisfying manner. CTRADE then proposes an ellipsoid-based query pricing mechanism according to a given linear market value model, which exploits the features of the ellipsoid to explore and exploit the close-optimal dynamic price at each round. In particular, the proposed mechanism produces a low cumulative regret, which is quadratic in the dimension of the feature vector and logarithmic in the number of total rounds. Through real-data based experiments, our analysis and evaluation results demonstrate that CTRADE balances total error and privacy preferences well within acceptable running time, indeed produces a convergent cumulative regret with more rounds, and also achieves all desired economic properties of budget balance, individual rationality, and truthfulness.