Traditional or drop-shipping? channel choice and product quality

Abstract

Compared to the traditional channel, where a retailer bears the inventory risk, the drop-shipping channel operates with a distinct approach: the retailer avoids stocking products while the manufacturer directly handles stocking and shipping to consumers. Due to the different inventory risk allocations, the two channels have different product qualities and profits for the manufacturer and retailer. In this study, we investigate product quality decision of the manufacturer and delve into the impact of product quality on the manufacturer's and retailer's profitability. First, we find that product quality can be weakly higher in the drop-shipping channel than that in the traditional channel. This is because the drop-shipping channel allows the manufacturer who undertakes demand uncertainty to charge a higher wholesale price, resulting in a higher product quality. Second, our study reveals that the traditional channel can concurrently yield higher profits for the manufacturer and retailer when product quality is considered, which is in contrast to the previous works. Third, in the traditional channel, as demand uncertainty rises, the retailer's production quantity increases, while the manufacturer's product quality decreases. Finally, our results indicate that the product quality is more sensitive to the shipping cost when demand uncertainty is low.

Keywords:

• Product quality
• drop-shipping
• channel strategy
• demand uncertainty
• supply chain management

Acknowledgements

The authors thank the constructive comments from the review team.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Geolocation information

The authors are located at the School of Management, University of Science and Technology of China, No.96, JinZhai Road Baohe District, Hefei, Anhui, 230026, P.R.China.

Data availability statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study. 

Notes

1 $q_1=\frac{2\delta (1-\lambda )}{k(1+\delta )}-\sqrt{\frac{1-2\delta +{\delta }^2-\lambda +4\mathrm{\delta \lambda }-3{\delta }^2\lambda -2\delta {\lambda }^2+2{\delta }^2{\lambda }^2}{k^2(1+\delta {)}^2}}$

2 ${\delta }_3=-4\sqrt{\frac{(1+12\mathrm{ks})(-1+\lambda {)}^2(4-5\lambda +4\mathrm{ks}(-4+3\lambda ){)}^2}{\begin{array}{c}\left(k^2{s}^2\right(40+3\lambda (-19+6\lambda ){)}^2+4(1-\lambda )(3-2\lambda )(16+3\lambda (-11+\\ 6\lambda \left)\right)+4ks(1-\lambda )(-292+3\lambda (221+12\lambda (-13+3\lambda )\left)\right){)}^2\end{array}}}+\frac{\begin{array}{c}-8(-1+\lambda {)}^2(-10+9\lambda )-k^2{\mathrm{s}}^2(-8+9\lambda \left)\right(40+3\lambda (-19+6\lambda ))\\ +4ks(-1+\lambda )(100+9\lambda (-17+6\lambda \left)\right)\end{array}}{\begin{array}{c}{k}^2{s}^2(40+3\lambda (-19+6\lambda ){)}^2+4(-1+\lambda \left)\right(-3+2\lambda \left)\right(16+3\lambda (-11+6\lambda ))-4ks\\ (-1+\lambda )(-292+3\lambda (221+12\lambda (-13+3\lambda )\left)\right)\end{array}}$

3 ${\delta }_1=\frac{4k+\lambda }{4k(-3+4\lambda )}-\frac{1}{4}\sqrt{\frac{\begin{array}{c}9+48k+64k^2-21\lambda -80k\lambda -64k^2\lambda \\ +16{\lambda }^2+32k{\lambda }^2-4{\lambda }^3\end{array}}{k^2(3-4\lambda {)}^2}}$.

4 ${\delta }_2=\frac{1+4k}{4k(-3+4\lambda )}-\frac{1}{2}\sqrt{\frac{1+8k+16k^2-\lambda -8\mathrm{k\lambda }-16k^2\lambda }{k^2(-3+4\lambda {)}^2}}$.

Additional information

Notes on contributors

Qingyi Wu

Qingyi Wu is a postgraduate student at the School of Management, University of Science and Technology of China, in Hefei, China. Her research interests include supply chain management and information sharing.

Shuang Xie

Shuang Xie is a postgraduate student at the School of Management, University of Science and Technology of China, in Hefei, China. His research interests include multi-product firm decision-making, customisation, and influencer marketing.

Buqing Ma

Buqing Ma is an associate professor at the School of Management, International Institute of Finance, University of Science and Technology of China, in Hefei, China. He received his Ph.D. degree from the University of Science and Technology of China. He has also been a joint-cultivated doctoral student at the University of Illinois at Urbana-Champaign. His papers have published in Production and Operations Management. His research area is the interface between marketing and OM (or finance), and the research interests are the platform’s services, including consumer installment, return policy, consumer information sharing, and influencer marketing.