Journal of Revenue and Pricing Management最新文献_第2页

Optimal pricing decision and capacity allocation of opaque selling in airline revenue management 航空公司收益管理中不透明销售的最优定价决策和运力分配

IF 1.6

Journal of Revenue and Pricing Management Pub Date : 2024-05-10 DOI: 10.1057/s41272-024-00483-9

Ben Li, Xiaolong Guo, Liang Liang

引用次数: 0

How to effectively present “book now, pay later”: the effects of appeal type, temporal distance, and traveler type on attitudes and purchase intentions 如何有效展示 "现在预订，稍后付款"：诉求类型、时间距离和旅行者类型对态度和购买意向的影响

IF 1.6

Journal of Revenue and Pricing Management Pub Date : 2024-05-02 DOI: 10.1057/s41272-024-00485-7

Yisak Jang, Yan Cao

{"title":"How to effectively present “book now, pay later”: the effects of appeal type, temporal distance, and traveler type on attitudes and purchase intentions","authors":"Yisak Jang, Yan Cao","doi":"10.1057/s41272-024-00485-7","DOIUrl":"https://doi.org/10.1057/s41272-024-00485-7","url":null,"abstract":"In today’s hotel industry, increasingly more hotels offer additional options on their booking websites, such as “book now, pay later.” Despite the prevalence of this phenomenon, methods to present this option more effectively have received limited attention. Using a 2 × 2 × 2 experimental design, this research examines how appeal type (attribute versus benefit appeals) and temporal distance (i.e., time of booking) jointly influence evaluations of the “pay later” option; it also investigates whether the joint effect has a boundary condition. The results demonstrated that leisure travelers planning a trip in the near future had more positive attitudes and greater purchase intentions when the “pay later” option was presented via an attribute appeal rather than a benefit appeal. However, leisure travelers planning a trip in the distant future did not exhibit such differences in attitudes and purchase intentions. Furthermore, this research revealed that the joint effect of appeal type and temporal distance was evident only for leisure travelers but not for business travelers.","PeriodicalId":46686,"journal":{"name":"Journal of Revenue and Pricing Management","volume":"21 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140831613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Strategic levers of revenue management: a three-dimensional model to categorize industries 收益管理的战略杠杆：行业分类的三维模型

IF 1.6

Journal of Revenue and Pricing Management Pub Date : 2024-05-02 DOI: 10.1057/s41272-024-00484-8

Henri Kuokkanen

引用次数: 0

Quantity surcharge, competition and package size: evidence from India 数量附加费、竞争和包装规模：来自印度的证据

IF 1.6

Journal of Revenue and Pricing Management Pub Date : 2024-05-02 DOI: 10.1057/s41272-024-00482-w

Dhruv Goel, Anushka Goyal, Ishaan Sand

引用次数: 0

Granger causality networks of price leadership in the retail tea market of Argentina 阿根廷茶叶零售市场价格领导力的格兰杰因果关系网络

IF 1.6

Journal of Revenue and Pricing Management Pub Date : 2024-04-09 DOI: 10.1057/s41272-024-00480-y

Juan M. C. Larrosa, Emiliano M. Gutiérrez, Juan I. Uriarte, Gonzalo R. Ramírez Muñoz de Toro

引用次数: 0

Sales prediction hybrid models for retails using promotional pricing strategy as a key demand driver 以促销定价策略为主要需求驱动因素的零售业销售预测混合模型

IF 1.6

Journal of Revenue and Pricing Management Pub Date : 2024-04-09 DOI: 10.1057/s41272-024-00477-7

Naragain Phumchusri, Nichakan Phupaichitkun

{"title":"Sales prediction hybrid models for retails using promotional pricing strategy as a key demand driver","authors":"Naragain Phumchusri, Nichakan Phupaichitkun","doi":"10.1057/s41272-024-00477-7","DOIUrl":"https://doi.org/10.1057/s41272-024-00477-7","url":null,"abstract":"The implementation of promotional pricing strategies constitutes a key component within the realm of retail revenue management. Nonetheless, the accurate prediction of sales in the presence of price discounts proves challenging due to the influence of various factors that contribute to demand uncertainty and high fluctuations. This study aims to find the most suitable prediction models for retail product unit sales while comprehensively accounting for the complex impacts of contributing factors. The dataset, sourced from a case study of a retail company, spans the temporal interval from January 2020 to December 2022. The predictive models, encompassing linear regression, random forest, XGBoost, artificial neural networks, and hybrid machine-learning models, are systematically developed. Then, the identification of the most suitable model is facilitated through the computation and comparative analysis of the Mean Absolute Percentage Error, with due consideration given to the weighting by the respective product’s revenue, thereby offering a comprehensive assessment of overall performance. Additionally, different types of feature selection are experimented. Factors used in machine learning models are either using all the independent variables or using significant factors from the stepwise method, and either considering or not considering exogenous factors of other products in the same cluster grouped by category, subcategory, or K-means method. The result shows that the series hybrid model of random forest and XGBoost outperformed others. Considering factors affecting sales, it is found that the promotion period factor was the most important, followed by discount percentage and price factors. This research provides analytics framework for sales prediction for retails using promotional pricing as a key demand driver.","PeriodicalId":46686,"journal":{"name":"Journal of Revenue and Pricing Management","volume":"54 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140591205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Trends and persistence in global olive oil prices after COVID-19 COVID-19 之后全球橄榄油价格的趋势和持续性

IF 1.6

Journal of Revenue and Pricing Management Pub Date : 2024-04-09 DOI: 10.1057/s41272-024-00481-x

Manuel Monge

引用次数: 0

Enhancing robustness to forecast errors in availability control for airline revenue management 增强航空公司收益管理可用性控制对预测误差的稳健性

IF 1.6

Journal of Revenue and Pricing Management Pub Date : 2024-04-09 DOI: 10.1057/s41272-024-00475-9

Tiago Gonçalves, Bernardo Almada-Lobo

{"title":"Enhancing robustness to forecast errors in availability control for airline revenue management","authors":"Tiago Gonçalves, Bernardo Almada-Lobo","doi":"10.1057/s41272-024-00475-9","DOIUrl":"https://doi.org/10.1057/s41272-024-00475-9","url":null,"abstract":"Traditional revenue management systems are built under the assumption of independent demand per fare. The fare adjustment theory is a methodology to adjust fares that allows for the continued use of optimization algorithms and seat inventory control methods, even with the shift toward dependent demand. Since accurate demand forecasts are a key input to this methodology, it is reasonable to assume that for a scenario with uncertainties it may deliver suboptimal performance. Particularly, during and after COVID-19, airlines faced striking challenges in demand forecasting. This study demonstrates, firstly, the theoretical dominance of the fare adjustment theory under perfect conditions. Secondly, it lacks robustness to forecast errors. A Monte Carlo simulation replicating a revenue management system under mild assumptions indicates that a forecast error of (pm 20%) can potentially prompt a necessity to adjust the margin employed in the fare adjustment theory by (-10%). Moreover, a tree-based machine learning model highlights the forecast error as the predominant factor, with bias playing an even more pivotal role than variance. An out-of-sample study indicates that the predictive model steadily outperforms the fare adjustment theory.","PeriodicalId":46686,"journal":{"name":"Journal of Revenue and Pricing Management","volume":"1 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140591772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Strategic-level perceived fairness of hotel dynamic pricing: the role of cues and the asymmetric moderating effect of inflation attribution 酒店动态定价的战略层面公平感：线索的作用和通货膨胀归因的非对称调节作用

IF 1.6

Journal of Revenue and Pricing Management Pub Date : 2024-04-08 DOI: 10.1057/s41272-024-00479-5

Rui Qi, Dan Jin, Han Chen, Xichen Mou, Faizan Ali

{"title":"Strategic-level perceived fairness of hotel dynamic pricing: the role of cues and the asymmetric moderating effect of inflation attribution","authors":"Rui Qi, Dan Jin, Han Chen, Xichen Mou, Faizan Ali","doi":"10.1057/s41272-024-00479-5","DOIUrl":"https://doi.org/10.1057/s41272-024-00479-5","url":null,"abstract":"This study examines consumers’ perceived fairness of hotel dynamic pricing, particularly in the evolving contexts of inflation and the post-pandemic phase. Instead of focusing solely on individual price points or price increases, this study develops a fairness model of dynamic pricing at the strategy level. It incorporates both social and physiological cues and broader contextual factors, given the inherent uncertainty surrounding the equality of outcomes. A sample of 579 U.S. consumers was recruited using Qualtrics consumer panel services. The study employs an orthogonalizing approach to eliminate the collinearity introduced by creating interaction terms. Rather than relying on internal price comparison, this study finds that consumers rationalize the pricing strategy based on two key cues: negative emotions and corporate social responsibility (CSR). Moreover, the study reveals an asymmetric effect of inflation attribution in moderating the cue-fairness linkage. Attributing dynamic pricing to inflation buffers the adverse effect of negative emotions while not enhancing the positive effect of CSR. Lastly, the study indicates that consumers’ perceived fairness of dynamic pricing increases consumer loyalty while decreasing revenge.","PeriodicalId":46686,"journal":{"name":"Journal of Revenue and Pricing Management","volume":"25 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140591230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Customer type discovery in hotel revenue management: a data mining approach 酒店收益管理中的客户类型发现：一种数据挖掘方法

IF 1.6

Journal of Revenue and Pricing Management Pub Date : 2024-04-05 DOI: 10.1057/s41272-024-00474-w

Hamed Sherafat Moula, S. Hadi Yaghoubyan, Razieh Malekhosseini, Karamollah Bagherifard

{"title":"Customer type discovery in hotel revenue management: a data mining approach","authors":"Hamed Sherafat Moula, S. Hadi Yaghoubyan, Razieh Malekhosseini, Karamollah Bagherifard","doi":"10.1057/s41272-024-00474-w","DOIUrl":"https://doi.org/10.1057/s41272-024-00474-w","url":null,"abstract":"Demand estimation is a fundamental component of revenue management systems. The demand for a product can be ascertained from the customers who purchase it. Identifying customer types in this context is a challenging endeavor, recently resolved using meta-heuristic and mathematical techniques. Meta-heuristics leverage the scarcity of data in the search space, commencing with random samples and employing the fitness function as a guide during operations. Our proposed approach generates the search space by incorporating supplementary data to identify valuable customer types. We employ a new period table with additional data to achieve this objective. Subsequently, we reduce the search space through data mining's clustering method and ultimately employ a greedy algorithm and fitness function to identify valuable customer types and construct our solution. To validate our approach, we compare our solution and the most recent research in this field, including genetic, memetic, and mathematical approaches. Compared to memetic methods, our results indicate that our solution has a smaller length, with a maximum reduction of 34%, and exhibits improvement in log value, with a maximum of 7%.","PeriodicalId":46686,"journal":{"name":"Journal of Revenue and Pricing Management","volume":"379 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140591207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0