Open Access
| Issue |
RAIRO-Oper. Res.
Volume 58, Number 6, November-December 2024
|
|
|---|---|---|
| Page(s) | 5441 - 5466 | |
| DOI | https://doi.org/10.1051/ro/2024196 | |
| Published online | 16 December 2024 | |
- Y. Zhang, S.H. Zhang and H. Feng, The impacts of money back guarantees in the presence of parallel importation. Eur. J. Oper. Res. 295 (2021) 170–182. [CrossRef] [Google Scholar]
- E. Crespo-Almendros and S. Del Barrio-García, Do online discounts and free gifts damage brand image of service? The moderating role of promotion-proneness. Serv. Bus. 10 (2016) 31–58. [CrossRef] [Google Scholar]
- K. Cao, X. Xu, Y. Bian and Y. Sun, Optimal trade-in strategy of business-to-consumer platform with dual-format retailing model. Omega 82 (2019) 181–192. [CrossRef] [Google Scholar]
- D.M. Hardesty and W.O. Bearden, Consumer evaluations of different promotion types and price presentations: The moderating role of promotional benefit level. J. Retail. 79 (2003) 17–25. [CrossRef] [Google Scholar]
- L. McAlister, E.I. George and Y.H. Chien, A basket-mix model to identify cherry-picked brands. J. Retail. 85 (2009) 425–36. [CrossRef] [Google Scholar]
- J. Tang, J. Zhou, C. Zheng and S. Jiao, More expectations, more disappointments: ego depletion in uncertain promotion. J. Retail. Consum. Serv. 66 (2022) 102916. [CrossRef] [Google Scholar]
- Y. Mei, K. Jing, L. Chen, R. Shi and Z. Song, An investigation of a frontal negative slow wave in a virtual hedonic purchase task. Front. Hum. Neurosci. 15 (2021) 674312. [CrossRef] [Google Scholar]
- S. Quach, M. Barari, P. Thaichon and D.V. Moudrý, Price promotion in omnichannel retailing: How much is too much? Asia Pac. J. Mark. Logist. 35 (2023) 198–213. [CrossRef] [Google Scholar]
- S. Song, W. Peng and Y. Zeng, Optimal add-on items recommendation service strength strategy for E-commerce platform with full-reduction-promotion. RAIRO:RO 56 (2022) 1031–1049. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
- P. Chen, R. Zhao, Y. Yan and C. Zhou, Promoting end-of-season product through online channel in an uncertain market. Eur. J. Oper. Res. 295 (2021) 935–948. [CrossRef] [Google Scholar]
- S.S. Sana, Sale through dual channel retailing system – a mathematical approach. Sustain. Analyt. Model. 2 (2022) 100008. [CrossRef] [Google Scholar]
- N. Feng, J. Chen, H. Feng and M. Li, Promotional pricing strategies for platform vendors: competition between first-and third-party products. Decis. Support Syst. 151 (2021) 113627. [CrossRef] [Google Scholar]
- Q. Zhou, K.F. Yuen and Y. Ye, The effect of brand loyalty and loss aversion on competitive trade-in strategies. Total Qual. Manag. Bus. Excell. 33 (2022) 1084–1112. [CrossRef] [Google Scholar]
- S.S. Sana, The effects of green house gas costs on optimal pricing and production lot size in an imperfect production system. RAIRO:RO 57 (2023) 2209–2230. [CrossRef] [EDP Sciences] [Google Scholar]
- B. Ma, X. Wang and L. Li, How does consumer privacy affect personalized pricing? Analysis based on intertemporal dynamic game model. J. Circuits Syst. Comput. 31 (2022) 2250251. [CrossRef] [Google Scholar]
- Q. Zhang and Y. Zheng, Pricing strategies for bundled products considering consumers’ green preference. J. Clean. Prod. 344 (2022) 130962. [CrossRef] [Google Scholar]
- B. Pal, A. Mandal and S.S. Sana, A closed-loop green supply chain with retailers’ competition and product recycling in the green environment under the cap-and-trade policy. Green Finance 6 (2024) 117–161. [CrossRef] [Google Scholar]
- S.S. Sana, A structural mathematical model on two echelon supply chain system. Ann. Oper. Res. 315 (2022) 1997–2025. [CrossRef] [MathSciNet] [Google Scholar]
- S.S. Sana, Price competition between green and non green products under corporate social responsible firm. J. Retail. Consum. Serv. 55 (2020) 102118. [CrossRef] [Google Scholar]
- Y. Tian and Y. Zhang, Pricing of crowdfunding products with strategic consumers and online reviews. Electron. Commer. Res. Appl. 54 (2022) 101169. [CrossRef] [Google Scholar]
- Y. Song, X. Zhao and W. Zhu, Decision biases of strategic customers with private product-value information: an experimental study. Prod. Oper. Manag. 28 (2019) 1305–1319. [CrossRef] [MathSciNet] [Google Scholar]
- J. Chen and Y. Zhao, High price or low price? An experimental study on a markdown pricing policy. Eur. J. Oper. Res. 284 (2020) 240–254. [CrossRef] [MathSciNet] [Google Scholar]
- H. Wang, C. Bai, Q. Wei and B. Lev, Inventory and pricing decisions when dealing with strategic consumers: a comprehensive analysis. Comput. Oper. Res. 136 (2021) 105473. [CrossRef] [Google Scholar]
- S. Shum, S. Tong and T. Xiao, On the impact of uncertain cost reduction when selling to strategic customers. Manag. Sci. 63 (2017) 843–860. [CrossRef] [Google Scholar]
- M. Wu, Y. Ran and S.X. Zhu, Optimal pricing strategy: How to sell to strategic consumers? Int. J. Prod. Econ. 244 (2022) 108367. [CrossRef] [Google Scholar]
- Y. Levin, J. McGill and M. Nediak, Dynamic pricing in the presence of strategic consumers and oligopolistic competition. Manag. Sci. 55 (2009) 32–46. [CrossRef] [Google Scholar]
- L. Xu, Y. Li, K. Govindan and X. Xu, Consumer returns policies with endogenous deadline and supply chain coordination. Eur. J. Oper. Res. 242 (2015) 88–99. [CrossRef] [Google Scholar]
- J. Lv and X. Liu, The impact of information overload of E-commerce platform on consumer return intention: Considering the moderating role of perceived environmental effectiveness. Int. J. Environ. Res. Public Health 19 (2022) 8060. [CrossRef] [Google Scholar]
- L.C. Dailey and M.A. Ülkü, Retailers beware: on denied product returns and consumer behavior. J. Bus. Res. 86 (2018) 202–209. [CrossRef] [Google Scholar]
- T. Suwelack, J. Hogreve and W.D. Hoyer, Understanding money back guarantees: cognitive, affective, and behavioral outcomes. J. Retail. 87 (2011) 462–478. [CrossRef] [Google Scholar]
- G. Walsh and M. Möhring, Effectiveness of product return-prevention instruments: empirical evidence. Electron. Mark. 27 (2017) 341–350. [CrossRef] [Google Scholar]
- X. Wan, D. Li, A. Chen and Y. Lei, Managing customer returns strategy with the option of selling returned products. Int. J. Prod. Econ. 230 (2020) 107794. [CrossRef] [Google Scholar]
- J. Heydari, T.M. Choi and S. Radkhah, Pareto improving supply chain coordination under a money-back guarantee service program. Serv. Sci. 9 (2017) 91–105. [CrossRef] [Google Scholar]
- F. Gao, V.V. Agrawal and S. Cui, The effect of multichannel and omnichannel retailing on physical stores. Manag. Sci. 68 (2021) 809–826. [Google Scholar]
- L. Nageswaran, S.H. Cho and A. Scheller-Wolf, Consumer return policies in omnichannel operations. Manag. Sci. 66 (2020) 5558–5575. [CrossRef] [Google Scholar]
- D. Jin, O. Caliskan-Demirag, F.Y. Chen and M. Huang, Omnichannel retailers’ return policy strategies in the presence of competition. Int. J. Prod. Econ. 225 (2020) 107595. [CrossRef] [Google Scholar]
- C. Li, M. Chu, C. Zhou and W. Xei, Is it always advantageous to add-on item recommendation service with a contingent free shipping policy in platform retailing? Electron. Commer. Res. Appl. 37 (2019) 100883. [CrossRef] [Google Scholar]
- M. Mussa and S. Rosen, Monopoly and product quality. J. Econ. Theory 18 (1978) 301–317. [CrossRef] [Google Scholar]
- B. Mantin, H. Krishnan and T. Dhar, The strategic role of third-party marketplaces in retailing. Prod. Oper. Manag. 23 (2014) 1937–1949. [CrossRef] [Google Scholar]
- Y. Zha, Y. Wang, Q. Li and W. Yao, Credit offering strategy and dynamic pricing in the presence of consumer strategic behavior. Eur. J. Oper. Res. 303 (2022) 753–766. [CrossRef] [Google Scholar]
- B. McWilliams, Money back guarantees: helping the low-quality retailer. Manag. Sci. 58 (2012) 1521–1524. [CrossRef] [Google Scholar]
- Y. Yan, R. Zhao and Z. Liu, Strategic introduction of the marketplace channel under spillovers from online to offline sales. Eur. J. Oper. Res. 267 (2018) 65–77. [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.