Online Price Research Articles

Online Pricing with Polluted Offline Data

Online Pricing with Polluted Offline Data

СПІВСТАВЛЕННЯ ПІДХОДІВ: НАУКОВИЙ ОГЛЯД ОПТИМІЗАЦІЇ РОЗДРІБНИХ ЦІН В ОНЛАЙН ТА ОФЛАЙН ТОРГІВЛІ

The aim of this paper is to compare online and offline retail price optimization and highlight the key differences. Online retail price optimization uses algorithms and data analysis to set the best price for an item on an e-commerce platform, considering product demand and competition. Offline retail price optimization involves manual methods, such as cost-plus pricing, market pricing, and psychological pricing, to price items in physical stores. The study involved a review of existing literature on retail price optimization and its application in online and offline retailing. The results showed that data availability is a significant difference between online and offline retail price optimization, with online retailers having access to more data. Online retailers can quickly adjust prices because of automation, while offline retailers need to manually change prices. The results of the study emphasize the importance of price optimization in both online and offline retailing and the benefits of using both methods together. The findings provide valuable insights for retail businesses and can inform future research in retail price optimization.

Online Learning and Pricing for Multiple Products with Reference Price Effects

Online Learning and Pricing for Multiple Products with Reference Price Effects

Research on financing strategies of retailers with capital constraints under the effect of showrooms

A supply chain made up of a financially constrained retailer and a well-financed manufacturer is constructed considering the showroom effect. The financing strategy choice of the retailer and the operational decisions of the supply chain members are investigated, in which the retailer can solve the capital constraint problem through a single financing strategy (ML and BL) and a combined financing strategy (MBL and BTL). Meanwhile, the impact of the showroom effect on the optimal decision and profit of supply chain members under different financing strategies and the optimal financing strategy decision is analyzed by numerical examples. The study shows that: (1) the increasing showroom effect has not inhibited retailers' enthusiasm to offer showroom services; on the contrary, it has prompted them to improve the level of showroom services. (2) The price of the products grows with the increases in the showroom effect, but the sales price of the online channel is more greatly affected. In addition, the showroom effect has a greater impact on the sales prices of online and offline channels and wholesale prices under the BL strategy than in other financing strategies. (3) When the showroom effect is small, the optimal strategy for retailers is to choose the ML strategy or the MBL strategy, while the BTL strategy is superior to the MBL strategy only when the equity financing ratio is very small. However, where the showroom effect exceeds a certain threshold, the BL strategy is the optimal funding strategy for retailers. (4) When the bank loan rate is too high, the ML and MBL strategies are better if the retailer's initial capital level is not too low.

Dynamic pricing and sales effort in dual-channel retailing for seasonal products

<p style='text-indent:20px;'>This paper studies the joint strategies of dynamic pricing and sales effort involving a seller and a third-party e-commerce platform (TPECP). The seller sells seasonal products through both offline and online channels, and the latter is opened on the TPECP. Considering a dual-channel setting, this paper fills a gap in the studies of joint strategies of dynamic pricing and sales effort. This paper formulates dynamic decision models and investigates them under both centralized and decentralized decision-making cases. Furthermore, a revenue-cost sharing contract is proposed to coordinate the two parties' decisions in the decentralized case, and it is validated theoretically and numerically. The key findings are as follows: For a given selling slot, the optimal online price decreases and the optimal sales effort level increases in the centralized case as the inventory level increases and remains below a threshold; however, when the inventory level exceeds the threshold, both the optimal online price and sales effort level remain fixed as the the inventory level increases in both cases. In addition, for a given inventory level below the threshold, the optimal online price decreases and the optimal sales effort level increases over time in both cases; however, when the inventory level exceeds the threshold, over time, the optimal online price increases and the optimal sales effort level decreases in the centralized case, and both the optimal online price and sales effort level increase in the decentralized case. Finally, a numerical experiment and some management implications are provided.</p>

Cost-effectiveness analysis of sintilimab plus pemetrexed and platinum versus chemotherapy alone as first-line treatment in metastatic non-squamous non–small cell lung cancer in China

ObjectiveIn frst-line treatment of advanced or metastatic nonsquamous non-small-cell lung cancer (NSCLC), the ORIENT-11 study demonstrated a signifcant progression-free survival and overall survival for sintilimab plus chemotherapy in comparison with chemotherapy alone. But the cost-effectiveness of the two treatment schemes is unclear in China. The objective of the current study was to evaluate the cost efectiveness of sintilimab plus chemotherapy versus Platinum-based chemotherapy for locally advanced or metastatic squamous NSCLC in China.MethodsWe 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. The circulation cycle of the model was 3 weeks and the study time limit was 10 years. Efficacy data were obtained from the ORIENT-11 clinical trial. 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). One-way sensitivity analysis and probability sensitivity analysis were used to verify the robustness of the base-case analysis results.ResultsSintilimab plus chemotherapy provided an additional 0.6 QALYs. The total cost per patient was CNY¥413,273.16 for sintilimab plus chemotherapy and CNY¥280,695.23 for Platinum-based chemotherapy. The ICER for sintilimab plus chemotherapy was CNY¥220,963.22/QALY. Sensitivity analyses found the results to be most sensitive to the cost of pemetrexed and utilities of PF state. In the probabilistic sensitivity analysis, sintilimab was cost-efective in 78.6% of the simulations, assuming a willingness-to-pay threshold (WTP) of CNY¥242,928 per QALY.ConclusionCompared with chemotherapy alone, the sintilimab plus chemotherapy is likely to be a cost-effective option as the first-line treatment for locally advanced or metastatic nonsquamous NSCLC in China.

The Multimodal Emotion Information Analysis of E-Commerce Online Pricing in Electronic Word of Mouth

E-commerce has developed rapidly, and product promotion refers to how e-commerce promotes consumers' consumption activities. The demand and computational complexity in the decision-making process are urgent problems to be solved to optimize dynamic pricing decisions of the e-commerce product lines. Therefore, a Q-learning algorithm model based on the neural network is proposed on the premise of multimodal emotion information recognition and analysis, and the dynamic pricing problem of the product line is studied. The results show that a multi-modal fusion model is established through the multi-modal fusion of speech emotion recognition and image emotion recognition to classify consumers' emotions. Then, they are used as auxiliary materials for understanding and analyzing the market demand. The long short-term memory (LSTM) classifier performs excellent image feature extraction. The accuracy rate is 3.92%-6.74% higher than that of other similar classifiers, and the accuracy rate of the image single-feature optimal model is 9.32% higher than that of the speech single-feature model.

Integrating online and offline business of a retailer: A customer utility based inventory model

Looking at the digital transformation in the retail industry many bricks and mortar (B&M) retailers are interested to run their business in an integrated way. In that direction, our study analyses a joint online and offline retail business of a retailer. Here, a utility based approach has been proposed to reflect the customers’ choice behaviour over the available alternatives, where we have incorporated product availability factor along with product valuation, price and hassle cost, as the observed factors. Moreover, keeping in mind the unobserved factors we have proposed the logit model structure for the demand estimation of the two channels. Categorizing the product based on the valuation, our findings reveal that for products like electronic and home appliances, the retailer should give more priority to low-valued and moderately premium items for his/her joint online and offline business, whereas for items like books and games, such business strategy would be the preferred choice irrespective of product premiumness. Examining the customers’ price sensitivities towards the two shopping platforms, we find that when the online price sensitivity is lower than offline, customers are more intended to buy the premium category items online. On the other side, under higher online price sensitivity, initially, for lower valued items customers prefer to buy from B&M store but when the product belongs to the premium category, a balanced shopping behaviour among the customers is noticed. We have also discussed that customers’ hassle costs in different platforms have many strategical importance on the joint online and offline business.

The Effect Of Online Consumer Review And Prices On Online Purchase Decisions

This study aims to determine the effect of online consumer reviews and prices on online purchasing decisions for students of the Faculty of Economics and Business, Makassar State University. The research method used is the quantitative method with the method of collecting data, namely through observation, questionnaires and documentation. In this survey, the majority of respondents are students who have made online purchases, and overall there are 97 respondents. The data collection methodology used a questionnaire instrument that was compiled based on a Liker scale and had gone through validation and reliability procedures. The analysis was then performed using multiple linear regression analysis. The results showed that: 1) Partially the online consumer review variable had a positive and significant effect on online purchasing decisions for students of the Faculty of Economics and Business, Makassar State University. 2) Partially, the price variable has a positive and significant effect on online purchasing decisions for students of the Faculty of Economics and Business, State University of Makassar. 3) Simultaneously online consumer reviews and prices have a positive and significant effect on online purchasing decisions for students of the Faculty of Economics and Business, State University of Makassar.

Pengaruh Online Customer Review, Online Customer Rating, dan Price Consciousness terhadap Keputusan Pembelian

This study aims to analyze whether there is an influence of online customer review, online customer rating and price consciosness on the study purchase decision of TikTok Shop customers. The population used in this study were TikTok Shop customers in Yogyakarta. This study uses a type of questionnaire that uses Google Form as a research tool. The research method used is a non-probability sampling technique with purpusive sampling method. The sample used is 100 respondents. The data obtained from the questionnaire was then processed and analyzed using Multiple Linear Regression analysis with model determination (classical assumption test), hypothesis testing using partial test (t) and determination test (R2) using SPSS 25 program. The results of this study indicate that the variable online customer review has no significant effect on purchasing decisions, online customer rating variable has no significant effect on purchasing decisions, price consciousness variable has a significant positive effect on purchasing decisions and online customer review, online customer rating, and price consciousness variables have a significant positive effect on purchasing decisions.
 Keywords: TikTok Shop, online customer review, online customer rating, price consciousness, purchase decision

Online Learning and Pricing for Service Systems with Reusable Resources

Revenue Management of Service Systems under Incomplete Information Revenue management with reusable resources finds many important applications in today's economy, such as cloud computing services, car/bicycle rental services, ride-hailing services, hotel management, project team management, and call center services. The existing literature predominantly assumes that the stochastic demand and service processes are given as an input to the models, and the pricing decisions are made with full knowledge of the distributional information. However, in practice, the decision maker may not know how demand or service rates react to price changes. Thus, the decision maker needs to learn the underlying mapping between prices and rates from past observations, while maximizing the total expected revenue on the fly. In “Online Learning and Pricing for Service Systems with Reusable Resources”, H. Jia, C. Shi, and S. Shen developed a series of online learning algorithms for revenue management problems with reusable resources and showed that they admit an optimal regret bound.

Let them stay or let them go? Online retailer pricing strategy for managing stockouts

A challenge for online retail firms is managing stockouts of products when substitutes are available. We develop a game‐theoretic model to analyze the competition between multiple‐product online firms and characterize the scenarios when they can use prices as stockout recovery mechanisms. Depending on the consumer costs of searching for unfamiliar substitute products on their sites and the consumer mismatch costs of shopping at backup compared to favorite firms, in our primary analysis, we demonstrate that the equilibrium of a pricing game can be one of three possible types: Following stockouts the firms retain their customers, the firms release their customers, or all consumers turn to one of the firms. We also examine a firm's combined use of prices and product recommender systems to manage stockouts. Results in the primary section of the analysis and a first extension combine to demonstrate a firm's strategic marketing complementarity of its prices and quality of its product recommender system. That is, when a firm uses a better product recommender system to retain consumers following stockouts, we show that it also raises prices. In our primary section, we also show that by implementing a better product recommender system, surprisingly a firm's sales may fall. However, implementing higher quality recommender systems may not be jointly optimal for the firms. In the first extension's game, depending on the cost of better product recommender systems, the firms, when choosing recommender systems, may engage in a prisoner's dilemma, where the price increase associated cannot fully compensate the implementation cost. In a second extension, we show that positively (negatively) correlated stockouts of a product between the competitors reduces (raises) each firm's profit and prices because the positive (negative) correlation reduces (raises) each consumer's expected utility of shopping experience.

PENGARUH HARGA DAN PROMOSI ONLINE MELALUI INSTAGRAM TERHADAP KEPUTUSAN PEMBELIAN DENGAN MINAT BELI SEBAGAI VARIABEL INTERVENING PADA TOKO RIFKHASIRA DI SITUBONDO (Studi kasus mahasiswa Universitas Abdurachman Saleh Situbondo Angkatan 2018)

Marketing is the beginning of a sale. In sales, performance is measured based on how many transactions were successfully closed and some value from the transaction. Judging from the timeframe, selling aims to get marketshare where products and prices become weapons in selling. The purpose of this study is to analyze and test the influence of Online Pricing and Promotion through Instagram on Purchasing decisions with Buying interest as an intervening variable in the Rifkhasira Store. The population in this study was students of Abdurachman Saleh Situbondo University class of 2018. The sampling technique uses random sampling techniques. This data analysis and testing uses the Structural Equation Model – Partial Least Square (PLS). 
 Based on the hypothesis test of direct influence using the Smart PLS 3.0 application, the results of the study showed that Price has a significant positive effect on buying interest, Promotion has a significant positive effect on buying interest, Price has a positive but insignificant effect on purchasing decisions, Promotion has a significant positive effect on purchasing decisions, buying interest has a significant positive effect on purchasing decisions, price has a significant effect on purchasing decisions. to the purchase decision through buying interest, promotion positively affects the purchase decision through buying interest.

A Study of Chinese Consumers’ Consistent Use of Mobile Food Ordering Apps

Mobile Food Ordering Apps (MFOAs) programs have changed the way consumers order and consume food. Mobile Food Ordering Apps (MFOAs) are widely recognized in the restaurant industry as an innovative method of serving customers; however, there are important questions about the impact of implementing MFOAs on customers’ willingness to reuse such applications, and several studies have examined the results of using MFOAs from a customer perspective. The main objective of this study was to identify and empirically test the main factors that predict Chinese users’ attitudes toward MFOAs, the satisfaction and re-use of such applications, and the moderating role of habits. This study proposes an integrated model based on the Technology Acceptance Model (TAM) and the Expectation Confirmation Theory (ECT), combined with the features of MFOA-added variables such as online reviews, price value and trust. A total of 212 valid questionnaires were obtained and collected from a sample of Chinese customers using MFOAs. This study uses SPSS 26.0 and smartPLS version 3.0 for a path analysis and empirical testing of the research model. There are 13 hypotheses, 6 of which are valid. H2(PE → SAT), H3(OR → SAT), H5(EOU → ATT), H6(PV → ATT), H7(TR → ATT), and H9(SAT → CI) are valid. However, H1(CON →SAT), H4(USF → ATT), H8(TR → CI), H10(ATT → CI) are not valid, and HAB does not play a moderating role between CI and SAT, CI and ATT, and CI and TR. Based on the research results, theoretical contributions and practical implications can be provided for scholars and practitioners in MFOA-related fields.

Has COVID-19 accelerated the E-commerce of agricultural products? Evidence from sales data of E-stores in China

We investigated the operation of e-stores specializing in food and agricultural products before and after the occurrence of COVID-19. A difference-in-difference (DID) method was employed to estimate the relationship between COVID-19 and the online sales of agricultural products using data from 164,002 food and agricultural product e-commerce stores (in short, e-stores) of two major Chinese e-commerce platforms in 120 prefectural-level or above cities. The results demonstrated that while COVID-19 and its control measures were associated with a substantial growth in the monthly sales of food and agricultural product e-stores, the growth varies considerably across store scales and with the type of food and agricultural product in which an e-store is specialized. Micro stores experienced much larger growth and played a more important role in maintaining the resilience of the supply chain of food and agricultural products than larger-scale stores; stores selling more essential food items experienced larger growth than those selling leisure food items. A mechanism analysis further revealed that the growth of online sales of agricultural products was mainly driven by changes in consumers’ food purchase behaviors from offline channels to online channels (i.e., an increase in the number of online customer orders and price per online order) starting with the onset of COVID-19. The results of this paper underscore the importance of e-commerce in maintaining the resilience of the agri-food supply chain and call for public support of the development of micro- and small-scale e-stores to meet consumers’ increasing demand for food supply from those types of stores during the pandemic period.

Dual-Channel Retailer’s Multi-Period Inventory and Pricing Policy with the Quantity-Sales Mode

This paper investigates a dual-channel retailer’s inventory and pricing integrated decision-making problem over multiple periods. The quantity-sales mode is adopted in the online channel while the unit-sales mode is used offline. The customer demand of each sales channel is stochastic, and depends on visit costs, customer return costs, sales modes and pricing levels. To achieve the long-term profit maximization, the dual-channel retailer needs to optimize the pricing levels of both channels and the ordering quantity. For this problem, we first formulate a stochastic dynamic programming model, and then prove the joint concavity and the supermodularity of the objective function. With these properties, the optimality of the base-stock policy is proved, and the optimum price policy is characterized. Specifically, if the retailer’s optimum stock is neither too low nor too high, both sales channels need to be operated simultaneously, otherwise, only operating one channel is optimal. The online price is invariant with the retailer’s stock level while the offline price is decreasing in the retailer’s stock level. Finally, numerical results show that the quantity-sales mode, dual-channel sales model, and the multi-period dynamic decisions could benefit the dual-channel retailer. In addition, influences of cost parameters on the total profit are verified numerically.

Beware the predatory shopper: exploring social vigilantism and proactivity in the exploitation of online pricing mistakes

PurposeThe authors provide an example of a group of online shoppers exploiting a pricing mistake and exploring the drivers of predatory shopping that may harm online retailers. This paper aims to examine the role of social vigilantism, proactivity and self-presentation in driving individual predatory shopping behaviors and delivers a broader understanding of how these behaviors develop in online communities.Design/methodology/approachThe authors use a mixed-methods sequential research model. In Study 1, the authors explore predatory shopping by using a netnographic textual approach to analyze an online forum engaging in predatory shopping. In Study 2, the authors empirically analyze the uncovered conceptual findings using the PROCESS macro.FindingsCustomers who engage in predatory shopping online exhibit social vigilantism when communicating their views to others and proactively seeking out pricing mistakes and opportunities. Customers engaging in predatory shopping adapt their presentation online to increase their chances of success; this effect is strengthened by the online disinhibition effect.Practical implicationsPredatory shoppers can actively seek out pricing mistakes online, encourage participation and exploit mistakes by adapting their self-presentation. Therefore, online retailers should be proactive and consistent when communicating with customers and collaborating to deter predatory shopping. In addition, online retailers should focus on building advocates in communities to prevent harm from predatory shoppers online.Originality/valueOnline predatory shopping is explored qualitatively and quantitatively to understand the propensities that can drive predatory behavior and provide warning signs for online retailers. In addition, the effects of predatory shopping drivers are analyzed in the presence of the online disinhibition effect.

LbSP: Load-Balanced Secure and Private Autonomous Electric Vehicle Charging Framework With Online Price Optimization

Nowadays, autonomous electric vehicles (AEVs) are increasingly popular due to low resource consumption, low pollutant emission, and high efficiency. In practice, Vehicle-to-Grid (V2G) networks supply energy power to EVs to ensure the usage of EVs. However, there are still certain security and privacy concerns in V2G connections, such as identity impersonation and message manipulation. Additionally, the widespread usage of EVs brings significant pressure on the power grid, leading to undesirable effects like voltage deviations if EVs’ charging is not well coordinated. In this article, to tackle these issues, we design a novel load-balanced secure and private EV charging framework named load-balanced secure and private framework (LbSP) for secure, private, and efficient EV charging with a minimal negative effect on the existing power grid. It assures reliable and efficient charging services by a lightweighted encryption technique. Also, it balances the energy consumption of power grids via an online pricing strategy that minimizes load variance by optimizing energy prices in real time. Moreover, it preserves users’ privacy while not affecting online pricing using an advanced differential privacy technique. Furthermore, LbSP deploys on an edge-cloud structure for fast response and more precise pricing, where clouds balance overall load consumption by online price optimization while edges gather data for clouds and respond to charging requests from EVs. The evaluation results show that the proposed framework ensures secure and private EV charging, balances energy load consumption, and preserves users’ privacy.

Laxity Differentiated Pricing and Deadline Differentiated Threshold Scheduling for a Public Electric Vehicle Charging Station

In this article, we study the online pricing and charging scheduling problem for a public electric vehicle (EV) charging station under stochastic electricity prices and renewable generation. We formulate the sequential decision making problem as a partially observable Markov decision process with continuous state and action spaces, with an objective of profit or social welfare maximization. The joint pricing and charging problem is challenging due to the curse of dimensionality (in both the system state and action spaces) and the unknown dynamics of system uncertainties. We propose a novel laxity differentiated pricing (LDP) scheme to tradeoff between electricity cost (associated with EV charging) and opportunity cost (associated with parking infrastructure occupancy). Shown to be optimal under arbitrary system dynamics, the characterized deadline-differentiated threshold charging (DTC) policy is integrated into a model-free soft actor critic (SAC) algorithm to reduce the action dimensionality. Numerical results demonstrate that the proposed SAC + LDP + DTC approach significantly outperforms alternative methods with various pricing and charging schemes.

A qualitative investigation of company perspectives on online price discrimination

Online price discrimination (OPD) or personalized pricing has triggered many debates in the existing literature, due to its potential adverse effects on (trust in) markets and certain consumer groups. The implicit assumption underpinning these debates is the actual use of OPD in practice, although empirical research has not provided evidence of its common use. The aim of this study is to explore company perspectives regarding OPD and possible explanations as to why this practice is not widespread. Semi-structured interviews were held with 14 data scientists, sales managers, marketing directors and policy experts from Dutch companies. The findings indicate that companies are reluctant towards using OPD, particularly selective price increases. They rather seek other forms of data application for personalized marketing. Next to economic, technological, legal, and ethical considerations, consumer backlash was another key factor in the decision to engage in OPD. While companies do not seem convinced that the current regulatory initiatives are sufficiently effective, they support self-regulation and ethical codes. However, as more covert or indirect forms of OPD are expected, continued attention and scrutiny by regulators is warranted.