{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T18:52:26Z","timestamp":1769712746762,"version":"3.49.0"},"reference-count":38,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,7,2]]},"abstract":"<jats:p>With the rapid development of O2O, offline experience and online purchase have become a method of purchase for more and more customers. Through offline experience, consumers can feel the quality of products directly. Such channel switching behavior of consumers will produce a \u201cshowroom\u201d effect and affect the return rate of online channels. This study adopts the multinomial logit model to maximize profits by considering the difference in quality between online and offline products, quality defects, and offline service. Then, a pricing decision model is developed to analyze the influence of returning goods due to quality problems on the retailers\u2019 optimal pricing and profit. The result shows that retailers can obtain the optimal profit when the offline service is maintained at a certain level. As the return rate increases, the optimal pricing increases, but the maximum profit decreases. The optimal pricing decreases with the increase in online product quality, but the maximum profit increases accordingly. In the omni-channel environment, customers can freely switch between channels according to utility and preference when purchasing products. Based on customer returns, retailers can dynamically adjust their service, control product quality, and set optimal product pricing, thus achieving maximum profits. This study can provide a theoretical basis and decision support for omni-channel retailers in platform operation and revenue management.<\/jats:p>","DOI":"10.3233\/jifs-230078","type":"journal-article","created":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T12:16:24Z","timestamp":1683288984000},"page":"659-673","source":"Crossref","is-referenced-by-count":0,"title":["Online product pricing considering product quality and return policy"],"prefix":"10.1177","volume":"45","author":[{"given":"Xuwang","family":"Liu","sequence":"first","affiliation":[{"name":"Institute of Management Science and Engineering, Henan University, Kaifeng, China"}]},{"given":"Yanyang","family":"Liu","sequence":"additional","affiliation":[{"name":"Business School, Henan University, Kaifeng, China"}]},{"given":"Wei","family":"Qi","sequence":"additional","affiliation":[{"name":"Institute of Management Science and Engineering, Henan University, Kaifeng, China"}]},{"given":"Xinggang","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Management, Hangzhou Dianzi University, Hangzhou, Zhejiang, China"}]}],"member":"179","reference":[{"issue":"02","key":"10.3233\/JIFS-230078_ref1","first-page":"120","article-title":"Analysis of E-commerce Customer Data Based on Deep Learning \u2013Comments on E-commerce Theory and Case Study","author":"Zhu","year":"2022","journal-title":"Journal of the Chinese Society of Education"},{"issue":"21","key":"10.3233\/JIFS-230078_ref2","first-page":"141","article-title":"Research on E-commerce customer Data Analysis based on Deep learning","volume":"23","author":"Liang","year":"2020","journal-title":"China Management Informationization"},{"issue":"10","key":"10.3233\/JIFS-230078_ref3","doi-asserted-by":"publisher","first-page":"5","DOI":"10.3969\/j.issn.1672-9528.2022.10.001","article-title":"On sentiment analysis of e-commerce reviews by combining multi-head attention mechanism and 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