Cause marketing decisions in supply chains: cause effect and marketing effect

Abstract

This paper studies a cause marketing (CM) design problem incorporating donation amount, marketing expenditure, and product pricing as decision variables in a manufacturer-retailer supply chain context. We investigate five CM decision models: centralized supply chain conducting CM (Model C), manufacturer conducting CM (Model M), retailer conducting CM (Model R), manufacturer leading CM collaboration (Model MC), and retailer leading CM collaboration (Model RC). The models incorporate not only the ‘cause effect’ of consumers' increased willingness to purchase but also the ‘marketing effect’, meaning the market-base expansion associated with marketing efforts in a CM campaign. A key insight is that CM should never be adopted in the absence of marketing effect but can be adopted in the absence of cause effect. Comparing different models reveals that there can be consistency or conflict in CM mode choice among supply chain members. It is also found that an influencing parameter in CM collaboration is the allocation proportion of marketing expenditure rather than that of donation amount. These results indicate that the marketing effect, which has long been neglected in the literature, is a driving factor for firms to conduct CM, and the impact of corresponding marketing efforts should be emphasised.

KEYWORDS:

• Cause marketing
• supply chain management
• corporate social responsibility
• game theory
• marketing and pricing strategy

Disclosure statement

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

Data availability statement

The data supporting the findings of this study are available from the corresponding author, [YJLI], upon reasonable request.

Notes

1 Here $\text{β}$ can be regarded as the average level of pro-sociality among consumers. For example, consider the consumers have two segments of prosocial levels ${\text{β}}_L$ and ${\text{β}}_H$, respectively, with proportion $\upsilon$ and $1-\upsilon$ among the total population. It can be seen that such a case can be reduced to our model with $\text{β}=\upsilon {\text{β}}_L+(1-\upsilon ){\text{β}}_H$, given each consumer segment still having heterogeneous product valuations.

2 The market base increment $\mathrm{\Delta }(t,s)=B(t,s)-1$ should be formulated as an increasing and concave function of donation amount and marketing effort. That is, ${\mathrm{\partial }}^2\mathrm{\Delta }(t,s)/\mathrm{\partial }{t}^2<0$ and ${\mathrm{\partial }}^2\mathrm{\Delta }(t,s)/\mathrm{\partial }{s}^2<0$ hold to characterize the law of ‘diminishing returns’ in marketing expansion. We further assume that this function is supermodular, with $\mathrm{\Delta }(0,s)=\mathrm{\Delta }(s,0)=0$ and $\underset{s\to \mathrm{\infty }}{lim}\mathrm{\partial \Delta }(t,s)/\mathrm{\partial t}=\underset{t\to \mathrm{\infty }}{lim}\mathrm{\partial \Delta }(t,s)/\mathrm{\partial s}=\mathrm{\infty }$. In other words, the cause effect and marketing effect are joint factors inseparable from each other, so no market base is expended if one factor does not contribute; and there is a large benefit from exerting more effort in one factor when the level of the other factor is high. In this sense, the Cobb-Douglas function $\mathrm{\Delta }(t,s)=\alpha t^m{s}^n$ with $m,n\in (0,1)$ satisfies all these assumptions and provides clear managerial implications.

3 This model corresponds to the case in which the retailer has considerable power or she regards CM as a long-term marketing tool. An alternative timeline is that the retailer makes CM related decisions after the manufacturer poses the wholesale price. In this case, computational study shows that the main results obtained from Model R carry over. Appendix B provides numerical examples.

4 The focus of this section is to identify the conditions under which mutual collaboration/participation of CM can be implemented. For $\lambda =\tau =0\text{ or }1$, the problem falls into the scope of Section 5 in which CM can be conducted unilaterally since a firm doesn’t need the other party’s consent to conduct CM if the whole expense of CM is borne by itself (from a logical perspective, implementing CM could be beneficial to the firm while detrimental to its supply chain partners, although according to the research of this article, this situation will not occur).

Additional information

Funding

This research was funded by the National Natural Science Foundation of China [grant numbers 72072094, 72132007, and 72261147707].

Notes on contributors

Xueqing Cui

Xueqing Cui is an Assistant Professor at School of Business, Tianjin University of Technology, China. She received her Ph.D. degree from the department of management science and engineering in Business School of Nankai University, China, in 2023. Her research focus is on the supply chain management and platform operations management. Her research appears in international journals such as International Journal of Production Economics and International Journal of Production Research.

Yongjian Li

Dr Yongjian Li is currently a professor of Operations and Supply Chain Management and serves as Chair of the Department of Management Science and Engineering at Business School of Nankai University, China. He received his Ph.D. degree from Nankai University, China in 2002. His research interests include Logistics and Supply Chain Management, Operations Management in Platform Economics, and interface of Marketing and Operations Management. He has published more than 100 papers in many well-known academic journals, including Production and Operations Management, Decision Sciences, International Journal of Production Research, IEEE Transactions, OMEGA-The International Journal of Management Science, and European Journal of Operational Research, etc.

Xiang Li

Xiang Li is currently a Professor of the Logistics Research Center in College of Economic and Social Development, Nankai University, China. He received the Ph.D. degree from the Department of Automation from Nankai University, China. His research area includes the method and application of operations research into the logistics and production system, supply chain management, platform economy, and marketing&OM interface. He was a visiting scholar at the Department of Industrial and Operations Engineering, the University of Michigan, the Department of Industrial Engineering and Logistics Management, Hong Kong University of Science and Technology, and the Department of System Engineering & Engineering Management, the Chinese University of Hong Kong. He has published papers in many journals, including Production and Operations Management, Decision Sciences, Naval Research Logistics, OMEGA-The International Journal of Management Science, European Journal of Operational Research, International Journal of Production Research, etc.

Xiaoqiang Cai

Professor Xiaoqiang Cai’s present research interests are mainly in supply chain management and intelligent transportation systems. He has published over 300 papers in journals, books, and conferences, including more than 150 papers in leading academic journals. He is Academician of the International Academy for Systems and Cybernetic Sciences, Fellow of the Hong Kong Institute of Engineers, and Fellow of Asia-Pacific Artificial Intelligence Association (AAIA). He received his Ph.D. from Tsinghua University in 1988, and has since worked at University of Cambridge, University of Western Australia, The Chinese University of Hong Kong, and The Chinese University of Hong Kong (Shenzhen).