This purpose of the paper is to make an in-depth study on the selection of the optimal shopping add-on items recommendation service strength strategy of the e-commerce platform with full-reduction promotion based on consumers’ heterogeneity preferences for discount amount and add-on items recommendation. With respect to the optimal decision problem consisting of an e-commerce platform who maximizes the profits and consumers who make purchase decision based on their utility, we construct a Stackelberg game model that reflects the interaction between platform’s recommendation service strength and consumers’ purchase willingness. Furthermore, through the derivative function analysis method, we examine the effect of reservation price, recommended commodity price and discount amount on the platform’s optimal recommendation service strength strategy. The results show that the discount amount, reservation price and consumer preference have different effects on the optimal add-on items recommendation service strength and the profit of the platform. Additionally, appropriate recommendation services strength is beneficial to enhance consumers’ willingness-to-pay and then increase the profits of the platform. Therefore, it is an effective way to improve the performance of the platform to reasonably formulate the basic discount amount, full-reduction promotion threshold and add-on items recommendation service strength.
Accepté le :
Première publication :
Publié le :
DOI : 10.1051/ro/2022037
Keywords: Full-reduction promotion, add-on items recommendation service, consumer preference, Stackelberg game
@article{RO_2022__56_2_1031_0,
author = {Song, Sujuan and Peng, Wei and Zeng, Yuyang},
title = {Optimal add-on items recommendation service strength strategy for e-commerce platform with full-reduction-promotion},
journal = {RAIRO. Operations Research},
pages = {1031--1049},
year = {2022},
publisher = {EDP-Sciences},
volume = {56},
number = {2},
doi = {10.1051/ro/2022037},
mrnumber = {4407578},
zbl = {1487.90170},
language = {en},
url = {https://www.numdam.org/articles/10.1051/ro/2022037/}
}
TY - JOUR AU - Song, Sujuan AU - Peng, Wei AU - Zeng, Yuyang TI - Optimal add-on items recommendation service strength strategy for e-commerce platform with full-reduction-promotion JO - RAIRO. Operations Research PY - 2022 SP - 1031 EP - 1049 VL - 56 IS - 2 PB - EDP-Sciences UR - https://www.numdam.org/articles/10.1051/ro/2022037/ DO - 10.1051/ro/2022037 LA - en ID - RO_2022__56_2_1031_0 ER -
%0 Journal Article %A Song, Sujuan %A Peng, Wei %A Zeng, Yuyang %T Optimal add-on items recommendation service strength strategy for e-commerce platform with full-reduction-promotion %J RAIRO. Operations Research %D 2022 %P 1031-1049 %V 56 %N 2 %I EDP-Sciences %U https://www.numdam.org/articles/10.1051/ro/2022037/ %R 10.1051/ro/2022037 %G en %F RO_2022__56_2_1031_0
Song, Sujuan; Peng, Wei; Zeng, Yuyang. Optimal add-on items recommendation service strength strategy for e-commerce platform with full-reduction-promotion. RAIRO. Operations Research, Tome 56 (2022) no. 2, pp. 1031-1049. doi: 10.1051/ro/2022037
[1] , and , Internet recommendation systems. J. Market. Res. 37 (2000) 363–375. | DOI
[2] , and , Responsive pricing of fashion products: the effects of demand learning and strategic consumer behavior. Manage. Sci. 65 (2019) 2982–3000. | DOI
[3] , , Acceptance of recommendations to buy in online retailing. J. Retail. Consum. Serv. 17 (2010) 173–180. | DOI
[4] , and , The effect of specific discount pattern in terms of price promotions on perceived price attractiveness and purchase intention: an experimental research. J. Retail. Consum. Serv. 55 (2020) 102112. | DOI
[5] , and , Is this for me? How consumers respond to personalized advertising on social network sites. J. Interact. Advertising 15 (2015) 124–134. | DOI
[6] and , Two-period pricing and quick response with strategic customers. Int. J. Prod. Econ. 215 (2019) 165–173. | DOI
[7] and , Consumer decision making in online shopping environments: the effects of interactive decision aids. Market. Sci. 19 (2000) 4–21. | DOI
[8] , Customer retention and the mediating role of perceived value in retail industry. World J. Entrepreneurship Manage. Sustainable Dev. 14 (2018) 2–24. | DOI
[9] and , Buy four get 30% off: how consumers respond to missing a quantity discount. Eur. J. Market. 47 (2015) 1326–1342. | DOI
[10] , and , Boomerang effect of incentive reminders during shopping trips. J. Consum. Market. 36 (2019) 592–599. | DOI
[11] and , Online shopping recommendation mechanism and its influence on consumer decisions and behaviors: a causal map approach. Expert Syst. App. 35 (2008) 1567–1574. | DOI
[12] and , Coordinating a supply chain when facing strategic consumers. Decis. Sci. 48 (2017) 336–355. | DOI
[13] , , and , Is it always advantageous to add-on item recommendation service with a contingent free shipping policy in platform retailing? Electron. Commerce Res. App. 37 (2019) 100883. | DOI
[14] , , and , A 2020 perspective on “is it always advantageous to add on item recommendation service with a contingent free shipping policy in platform retailing?” Electron. Commerce Res. App. 40 (2020) 100960. | DOI
[15] , , and , Two-period discount pricing strategies for an e-commerce platform with strategic consumers. Comput. Ind. Eng. 147 (2020) 106640. | DOI
[16] and , Background music recommendation based on latent factors and moods. Knowl. Based Syst. 159 (2018) 158–170. | DOI
[17] , and , A bundling sales strategy for a two-stage supply chain based on the complementarity elasticity of imperfect complementary products. J. Bus. Ind. Market. 35 (2020) 983–1000. | DOI
[18] , and , A basket-mix model to identify cherry-picked brands. J. Retail. 85 (2009) 425–36. | DOI
[19] and , Incommensurate resource: not just more of the same. J. Market. Res. 40 (2003) 26–38. | DOI
[20] , and , Consumer perceptions of bonus packs: an exploratory analysis. J. Consum. Market. 14 (1997) 102–112. | DOI
[21] and , Dynamic pricing in the presence of social learning and strategic consumers. Manage. Sci. 63 (2016) 919–939. | DOI
[22] , , , and , Empirical analysis of the impact of recommender systems on sales. J. Manage. Inf. Syst. 27 (2010) 159–188. | DOI
[23] , and , Teleological sales and purchase approaches in complex business relationships – customers’ expectations before and perceptions after purchase. J. Bus. Ind. Market. 33 (2018) 523–538. | DOI
[24] , and , Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web. May 1–5, ACM Press, New York (2001) 285–295. | DOI
[25] , and , On the impact of uncertain cost reduction when selling to strategic customers. Manage. Sci. 63 (2016) 843–860. | DOI
[26] and , Strategic customer behavior, commitment, and supply chain performance. Manage. Sci. 54 (2008) 1759–1773. | Zbl
[27] , and , How consumers deal with missed discounts: Transaction decoupling, action orientation and inaction inertia. J. Econ. Psychool. 38 (2013) 104–110. | DOI
[28] , and , Marketing antecedents of industrial brand equity: an empirical investigation in specialty chemicals. Ind. Market. Manage. 34 (2005) 841–847. | DOI
[29] and , An empirical examination of the influence of biased personalized product recommendations on consumers’ decision-making outcomes. Decis. Support Syst. 110 (2018) 46–57. | DOI
Cité par Sources :
