Open Access
RAIRO-Oper. Res.
Volume 56, Number 5, September-October 2022
Page(s) 3545 - 3560
Published online 19 October 2022
  • B. Niu, H. Yue, H. Luo and W. Shang, Pricing for newly-launched experience products: Free trial or not? Transp. Res. Part E Logist. Transp. Rev. 126 (2019) 149–176. [CrossRef] [Google Scholar]
  • D. Liu and V. Mookerjee, Advertising competition on the internet: Operational and strategic considerations. Prod. Oper. Manag. 27 (2018) 884–901. [CrossRef] [Google Scholar]
  • J. Huang, M. Leng and L. Liang, Recent developments in dynamic advertising research. Eur. J. Oper. Res. 220 (2012) 591–609. [CrossRef] [Google Scholar]
  • M. Nerlove and K.J. Arrow, Optimal advertising policy under dynamic conditions. Economica 29 (1962) 129–142. [CrossRef] [Google Scholar]
  • B. Viscolani and G. Zaccour, Advertising strategies in a differential game with negative competitor’s interference. J. Optim. Theory App. 140 (2009) 153–170. [CrossRef] [Google Scholar]
  • A. Nair, R. Narasimhan, Dynamics of competing with quality- and advertising-based goodwill. Eur. J. Oper. Res. 175 (2006) 462–474. [CrossRef] [Google Scholar]
  • G. Bertuzzi and L. Lambertini, Existence of equilibrium in a differential game of spatial competition with advertising. Reg. Sci. Urban Econ. 40 (2010) 155–160. [CrossRef] [Google Scholar]
  • J. Pang and K.H. Tan, Supply chain quality and pricing decisions under multi-manufacturer competition. Ind. Manag. Data Syst. 118 (2018) 164–187. [CrossRef] [Google Scholar]
  • M.L. Vidale and H.B. Wolfe, An operations-research study of sales response to advertising. Oper. Res. 5 (1957) 370–381. [CrossRef] [Google Scholar]
  • Y.J. Chen and J.-B. Sheu, Environmental-regulation pricing strategies for green supply chain management. Transp. Res. Part E Logist. Transp. Rev. 45 (2009) 667–677. [CrossRef] [Google Scholar]
  • A. Prasad and S.P. Sethi, Competitive advertising under uncertainty: a stochastic differential game approach. J. Optim. Theory App. 123 (2004) 163–185. [CrossRef] [Google Scholar]
  • G.E. Kimball, Some industrial applications of military operations research methods. Oper. Res. 5 (1957) 201. [CrossRef] [Google Scholar]
  • M. Chalikias and M. Skordoulis, Implementation of F.W. Lanchester’s combat model in a supply chain in duopoly: the case of Coca-Cola and Pepsi in Greece. Oper. Res. 17 (2017) 737–745. [Google Scholar]
  • R. Jarrar, G. Martn-Herrán and G. Zaccour, Markov perfect equilibrium advertising strategies of Lanchester duopoly model: A technical note. Manag. Sci. 50 (2004) 995–1000. [CrossRef] [Google Scholar]
  • H.N. Zhou, X.G. Gu and L. Li, The dynamic investment strategy of online advertising based on spillover effect in duopoly competition market. Computing 100 (2018) 881–905. [CrossRef] [MathSciNet] [Google Scholar]
  • F. Rossi, Lower price or higher reward? Measuring the effect of consumers’ preferences on reward programs. Manag. Sci. 64 (2017) 4451–4470. [Google Scholar]
  • B.-D. Kim, M. Shi and K. Srinivasan, Reward programs and tacit collusion. Mark. Sci. 20 (2001) 99–120. [CrossRef] [Google Scholar]
  • S.S. Singh, D.C. Jain and T.V. Krishnan, Research note—Customer loyalty programs: Are they profitable? Manag. Sci. 54 (2008) 1205–1211. [CrossRef] [Google Scholar]
  • A. Gandomi and S. Zolfaghari, A stochastic model on the profitability of loyalty programs. Comput. Ind. Eng. 61 (2011) 482–488. [CrossRef] [Google Scholar]
  • J. Zhang and M. Wedel, The effectiveness of customized promotions in online and offline stores. J. Mark. Res. 46 (2009) 190–206. [CrossRef] [Google Scholar]
  • S. Lim and B. Lee, Loyalty programs and dynamic consumer preference in online markets. Decis. Support Syst. 78 (2015) 104–112. [CrossRef] [Google Scholar]
  • A. Bazargan, S. Karray and S. Zolfaghari, Modeling reward expiry for loyalty programs in a competitive market. Int. J. Prod. Econ. 193 (2017) 352–364. [CrossRef] [Google Scholar]
  • A. Nastasoiu and M. Vandenbosch, Competing with loyalty: How to design successful customer loyalty reward programs. Bus. Horiz. 62 (2019) 207–214. [CrossRef] [Google Scholar]
  • P.K. Chintagunta and D. Jain, A dynamic model of channel member strategies for marketing expenditures. Mark. Sci. 11 (1992) 168–188. [CrossRef] [Google Scholar]
  • J. Zhang, L. Lei, S. Zhang and L. Song, Dynamic vs. static pricing in a supply chain with advertising. Comput. Ind. Eng. 109 (2017) 266–279. [Google Scholar]
  • S.P. Sethi, Deterministic and stochastic optimization of a dynamic advertising model. Optim. Control Appl. Methods 4 (1983) 179–184. [CrossRef] [Google Scholar]
  • E. de Haan, P.C. Verhoef and T. Wiesel, The predictive ability of different customer feedback metrics for retention. Int. J. Res. Mark. 32 (2015) 195–206. [CrossRef] [Google Scholar]
  • O.S. Vaidya and S. Kumar, Analytic hierarchy process: An overview of applications. Eur. J. Oper. Res. 169 (2006) 1–29. [CrossRef] [Google Scholar]
  • V. Kumar, A. Lahiri and O.B. Dogan, A strategic framework for a profitable business model in the sharing economy. Ind. Mark. Manag. 69 (2018) 147–160. [CrossRef] [Google Scholar]
  • X. He, A. Krishnamoorthy, A. Prasad and S.P. Sethi, Retail competition and cooperative advertising. Oper. Res. Lett. 39 (2011) 11–16. [CrossRef] [MathSciNet] [Google Scholar]
  • X. He, A. Krishnamoorthy, A. Prasad and S. Sethi, Co-op advertising in dynamic retail oligopolies. Decis. Sci. 43 (2012) 71–103. [Google Scholar]
  • R.W. Buell, D. Campbell and F.X. Frei, How do customers respond to increased service quality competition? Manuf. Serv. Oper. Manag. 18 (2016) 585–607. [CrossRef] [Google Scholar]
  • S.R. Balseiro and O. Candogan, Optimal contracts for intermediaries in online advertising. Oper. Res. 65 (2017) 878–896. [CrossRef] [MathSciNet] [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.