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Allocating Spending Between Advertising and Information Technology in Electronic Retailing

Author

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  • Yong Tan

    (University of Washington Business School, Seattle, Washington 98195-3200)

  • Vijay S. Mookerjee

    (School of Management, University of Texas at Dallas, Richardson, Texas 75803-0688)

Abstract

This study examines coordination issues that occur in allocating spending between advertising and information technology (IT) in electronic retailing. Electronic retailers run the risk of overspending on advertising to attract customers but underspending on IT, thus resulting in inadequate processing capacity at the firm's website. In this paper, we present a centralized, joint marketing-IT model to optimally allocate spending between advertising and IT, and we discuss an uncoordinated case where marketing and IT make suboptimal advertising and capacity decisions. We show how these decisions can be coordinated either by reducing the value of a customer session or by designing an optimal processing contract between marketing and IT. Both the coordination methods can be implemented with only local knowledge of the IT function, yet they generate a solution that almost matches the quality of the centralized solution. We extend our basic model to consider demand uncertainty, lagged advertising effects, and uncertainties in the lead time to acquire IT capacity. With demand uncertainty, electronic retailers should reduce spending on advertising and increase IT capacity if there is potential for a demand upswing and the cost of IT capacity is relatively low. The value of a customer session should be further reduced when uncertainties exist. This is required to share the risk of excess or inadequate IT capacity.

Suggested Citation

  • Yong Tan & Vijay S. Mookerjee, 2005. "Allocating Spending Between Advertising and Information Technology in Electronic Retailing," Management Science, INFORMS, vol. 51(8), pages 1236-1249, August.
    • Handle: RePEc:inm:ormnsc:v:51:y:2005:i:8:p:1236-1249
    DOI: 10.1287/mnsc.1050.0424
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    3. Xingyue Zhang & Yuliang Yao, 2020. "How Much is Too Much? The Effect of Offline Call Intensity on Online Purchase of Digital Services," Production and Operations Management, Production and Operations Management Society, vol. 29(3), pages 509-525, March.
    4. Zibo Liu & Zhijie Lin & Ying Zhang & Yong Tan, 2022. "The Signaling Effect of Sampling Size in Physical Goods Sampling Via Online Channels," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 529-546, February.
    5. Fan, Ming & Kumar, Subodha & Whinston, Andrew B., 2009. "Short-term and long-term competition between providers of shrink-wrap software and software as a service," European Journal of Operational Research, Elsevier, vol. 196(2), pages 661-671, July.
    6. Yiting Xing & Ling Li & Zhuming Bi & Marzena Wilamowska‐Korsak & Li Zhang, 2013. "Operations Research (OR) in Service Industries: A Comprehensive Review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 300-353, May.
    7. Subodha Kumar & Yinliang (Ricky) Tan & Lai Wei, 2020. "When to Play Your Advertisement? Optimal Insertion Policy of Behavioral Advertisement," Information Systems Research, INFORMS, vol. 31(2), pages 589-606, June.
    8. Kumar, Subodha & Sethi, Suresh P., 2009. "Dynamic pricing and advertising for web content providers," European Journal of Operational Research, Elsevier, vol. 197(3), pages 924-944, September.
    9. Shengqi Ye & Goker Aydin & Shanshan Hu, 2015. "Sponsored Search Marketing: Dynamic Pricing and Advertising for an Online Retailer," Management Science, INFORMS, vol. 61(6), pages 1255-1274, June.
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    11. Li, Gang & Huang, Feng Feng & Cheng, T.C.E. & Zheng, Quan & Ji, Ping, 2014. "Make-or-buy service capacity decision in a supply chain providing after-sales service," European Journal of Operational Research, Elsevier, vol. 239(2), pages 377-388.

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