Understanding Low Carbon Sustainable Development Behaviour and Its Complexity
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Cheng Che, Zhihong Zhang, Xiaoguang Zhang, Yi Chen, "TwoStage Pricing Decision for LowCarbon Products Based on Consumer Strategic Behaviour", Complexity, vol. 2021, Article ID 6633893, 12 pages, 2021. https://doi.org/10.1155/2021/6633893
TwoStage Pricing Decision for LowCarbon Products Based on Consumer Strategic Behaviour
Abstract
The development of information technology has changed the pricing strategy of retailers, and consumers have also made strategic consumption behaviours accordingly. At the same time, changes in the environment have caused changes in the retailer’s products and raised consumers’ environmental awareness. This paper uses a twostage pricing model to study the lowcarbon product pricing decisions of retailers based on strategic consumers with lowcarbon preferences in two situations. Through the analysis of lowcarbon and ordinary products in two situations, the following conclusions can be drawn: (1) In a market where retailers only sell lowcarbon products, product prices and profits increase as consumers’ green preference increases. (2) In the lowcarbon product and ordinary product markets, the price and profit of lowcarbon products increase with regard to consumers’ green preference . (3) In the second stage, when consumers’ intertemporal discount factor for ordinary products is larger than that of lowcarbon products, the retailer’s total profit is smaller. The research conclusion comprehensively analyses the impact of customer strategic behaviour on the twostage pricing decision of green differentiated products, which provides a very important reference for retailers to make pricing optimization decisions.
1. Introduction
With global warming and various environmental problems emerging in an endless stream [1, 2], the country has issued various related laws and policies to guide consumers and enterprises to conduct lowcarbon environmental protection behaviours. In response to the national call for energy conservation and emission reduction, enterprises have developed energysaving products. For consumers with lowcarbon awareness, they are more inclined to buy lowcarbon products [3]. Studies by Laroche et al. show that more consumers are willing to support higher prices for green products [4]. According to the report of the current situation of China’s public green consumption (2019 Edition), 83.34% of the respondents expressed support for green consumption behaviour [5]. But for ordinary consumers, the functions of lowcarbon products are the same as those of ordinary products [6], but the price is higher, so some consumers may not choose lowcarbon products because of the slightly higher prices.
On the other hand, the construction and improvement of various information platforms enable consumers to learn about product attributes and price changes through various channels. Therefore, consumers choose to purchase products based on their own utility maximization, which reflects the nature of consumers’ strategies [7, 8]. However, this behaviour will make retailers face the pressure of inventory and product updates. In order to alleviate the pressure, retailers will adopt measures such as price cuts and promotions, and customers will make strategic decisions based on the behaviour of the business. Therefore, retailers must take into account the strategic behaviour of consumers when making decisions to achieve their own maximum profits.
Previous research mainly focused on the coordinated pricing of a single lowcarbon product supply chain. It did not consider the pricing of a single retailer in the case of product differences, and did not consider the impact of consumers’ strategic behaviour on pricing. In the previous literature on the pricing of differentiated products, they generally focused on manufacturing and remanufacturing products and did not discuss the differential pricing of lowcarbon and ordinary products. In addition, consumers’ lowcarbon preferences will also have an important impact on retailers’ pricing. When retailers set prices for lowcarbon and ordinary products, they will be affected by consumer strategic behaviour and consumers’ lowcarbon preferences. The result will affect the retailer’s profit and sales, and its pricing cannot well guide consumers to choose the products the retailer wants to sell.
Therefore, based on the green differentiated products, this paper analyses the retailer’s pricing decisions under the lowcarbon preference and strategic behaviour of customers. Retailers make different price decisions according to the different needs of customers, in order to reduce the backlog of inventory or make the best order quantity.
2. Literature Review
2.1. Product Pricing Decisions in LowCarbon Supply Chain
There are many literature studies on lowcarbon products at home and abroad; most of them analyse the pricing of enterprises or supply chain from the aspects of lowcarbon policy and carbon emission reduction. Guo et al. analysed the impact of the carbon tax rate and consumer carbon sensitivity factor on product pricing and designed a coordinated supply chain of carbon emission reduction costbenefit sharing contract [9]. And mostly from a supply chain perspective, Su et al. have constructed a green supply chain pricing decisionmaking model with different power structures and different forms of subsidies under the context of consumer green preferences [10]. The existing literatures only make pricing decisions from the perspective of lowcarbon product supply chains. However, there are not only lowcarbon products but also ordinary products in the market. There are no literatures to analyze pricing decisions for green differentiated products [11, 12]. Consumers’ strategic behaviour also has an important impact on product pricing. Hu and Dai studied consumer behaviour under different lowcarbon product pricing strategies. It is found that incumbent manufacturers choose to produce lowcarbon products and retailers choose to sell lowcarbon products at high prices are the equilibrium strategy of the game between all parties [13]. Zhang et al. focus on the impacts of consumer environmental awareness (CEA) and retailer’s fairness concerns on environmental quality, wholesale price, and retail price of the green product in one manufacturer and oneretailer supply chain [14, 15]. Xu et al. analyse the renewable energy from the political, technical, economic, and social perspectives [16, 17]. In order to improve the utilization rate of resources, a fuzzy resource optimal allocation model for multistage stochastic logistics tasks was proposed [18].
2.2. Pricing Decisions of Differentiated Products
There are also many literature studies on the pricing of differentiated products. Because lowcarbon products have the same functions as ordinary products [19]. But lowcarbon products are priced much higher than ordinary products [20]. Yang et al. studied the differential pricing decision of remanufacturing closedloop supply chains [21]. Zhou et al. explored the influence of network externalities on the pricing strategy of quality differentiated products [22]. Kalnins studied price changes in the dualchannel supply chain and found that pricebased brand externalities have a significant impact on the choice of different quality brand sales channels [23]. Liu and Liu in an environment where lowcarbon products and ordinary products coexist, they consider that consumers have differences. Qualitative willingness to pay and consumption utility, research the supply chain’s ability to price products and supply chain coordination issues [24]. When one manufacturer produces the two kinds of products, its profit will increase with the increase incarbon trading price through alliance strategy [25]. Luo et al. have studied the location and pricing of products with the same but different sales functions based on the Hotelling model [26]. Li et al. established a secondary supply chain Stackelberg game model consisting of two manufacturers (ordinary product manufacturers and lowcarbon product manufacturers) and one retailer to make supply chain decisions [27].
2.3. Pricing Decisions Based on Strategic Consumers
There are also many literature studies that examine corporate pricing decisions based on strategic consumers. Both Nair [28] and Li et al. [29] provided empirical evidence for strategic consumers and their purchasing behaviour. Whether in reality or in academia, the impact of consumers’ strategic waiting and buying behaviour on business operations cannot be underestimated and ignoring consumers’ tactics will bring huge economic losses to the business [30, 31]. Du et al. found that the behaviour of strategic consumers would have adverse effects on enterprises [32]. Wu et al. considered a retailer’s markdown pricing and inventory decisions in multiple seasons where consumers can learn from reference prices to decide when to purchase [33]. Dong and Wu discussed the twoperiod pricing problem and concluded that when market demand is evenly distributed, strategic consumers may bring more benefits to manufacturers [34, 35]. But the above research ignores the lowcarbon preference factors of strategic consumers. Xinmin Liu et al. distinguished three types of strategic customers according to their different preferences to analyse the optimal pricing and greenness strategies in the sustainable supply chain in strategic customer scenarios [36]. Feng et al. analysed consumer buying habits and constructed a twostage game model between strategic consumers and retailers [37, 38]. Peng established a retailer optimization model facing homogeneous strategic consumers and used the stochastic optimal response equilibrium model to describe the limited rational behaviour of strategic consumers [39].
The abovementioned literature analyses the pricing decisions of enterprises and retailers from the aspects of supply chain coordination, differentiated products, consumer strategy, and consumer lowcarbon preference. However, there is no specific analysis on the pricing decisions of retailers selling green differentiated products under the lowcarbon preference of strategic consumers. Therefore, this article takes into account the practical significance and provides decision support for retailers to determine the optimal product sales price and obtain the maximum sales profit when facing strategic consumers with lowcarbon preferences. Based on the abovementioned literature, this article analyses the impact of retailer pricing under the consumer’s lowcarbon preference strategy behaviour in several aspects. One is the cost. The cost of lowcarbon products is much higher than that of ordinary products; the other is consumer demand. Strategic consumers will take into account product cost, patience, and preference for lowcarbon products, which will affect consumers’ purchasing behaviour, which in turn affects retailers’ pricing decisions.
3. Model Symbols and Assumptions
This article considers two situations. In the first model, the retailer only sells lowcarbon products. After a certain period of time, some products will not be sold. The retailer will carry out certain price discount activities according to the market to stimulate consumers to consume. This will reduce the overall utility of consumers, and some consumers will wait for the timing of this price adjustment to make a purchase, which is the degree of customer strategy .
In the second model, retailers will sell lowcarbon products and ordinary products at the same time. In the second stage of the sales period, retailers will adjust the prices of different products, thereby forming a price discount coefficient, which is the degree of consumer strategy. Consumer utility is affected by the price discount coefficient, and consumers' utility for ordinary products is lower than that of lowcarbon products. At the same time, which product the consumers choose is also based on consumers’ green preferences.
Figure 1 shows the game sequence of retailers in different markets. Faced with two situations, the retailer pursues how to adjust prices reasonably under the influence of the above factors, and better cater to consumers’ expectations, so as to maximize profits. So, this article will use the method of rational expectation equilibrium. Construct consumers’ purchasing decision and retailer’s pricing decision model so that retailers and customers form a game equilibrium.
In order to facilitate the analysis of the model, without loss of generality, this article is based on the following assumptions:
Assumption 1. A monopolistic retailer sells two alternative products L (lowcarbon products) and N (ordinary products) with different configurations. One order is placed at the beginning of the period. The goods are sold in two stages. The first stage is full price sales, and the second stage is discount promotion. Assuming that the total number of consumers in the market is a certain value N, they are all strategic consumers, and each person can only purchase 1 product at most.
Assumption 2. (, respectively, indicates the first and second sales period and indicates lowcarbon products and ordinary products, respectively) is the price of the products. and represent the unit generation costs of lowcarbon and ordinary products, respectively. indicates product sales.
Assumption 3. The consumer’s willingness to pay is . It obeys the uniform distribution on the interval , lowcarbon preference attributes , , and represents consumers’ preference for lowcarbon products, which means that consumers with lowcarbon preference are more inclined to choose lowcarbon products.
Assumption 4. Similar to the consumer strategy degree of Zhang [40] and Ma et al. [41] in the literature is . can represent the consumer’s degree of strategy; the larger the , the greater the consumer’s degree of strategy, and means the consumer will buy the product immediately. It can also be expressed as an interperiod discount factor. In the model where lowcarbon products are sold at the same time as ordinary products, consumers’ psychological strategies for ordinary products in the second stage are lower than those of lowcarbon products. Assuming that the degree of strategy is , satisfying.
The symbols and meanings of the parameter variables involved in the article are shown in Table 1.

4. Model
4.1. The Situation Where Retailers Only Sell LowCarbon Products (Model I)
Considering that there is only one lowcarbon product in the market, the price of the product changes over time, and the utility of consumers will also change as the price changes. The following uses reverse induction to analyse.
The consumer’s utility function is
In the case where the market only sells lowcarbon products, consumers' purchasing decisions will be affected by their own wishes, product green preference, and strategy. When and , that is, consumers will choose to purchase the product in the first stage. When and , that is, consumers will wait and see for a period of time and choose to buy in the second stage product.
Therefore, the demand function of lowcarbon products in the first and second stages is
Therefore, the total sales profit of the first and second stages is
Find the firstorder partial derivative with respect to and from equation (3) as follows:
Find the secondorder partial derivative of with respect to and as follows:
Through the above formula, the Hessel matrix of the secondorder partial derivatives of and can be obtained as follows:From formula (7), it can be seen that and . The Hesse matrix is negative definite, which proves that this point is a maximum point.
Let formulas (4) and (5) are equal to 0; the twostage retail prices of lowcarbon products are
According to formulas (8) and (9), the twostage optimal sales volume of lowcarbon products are
According to formulas (8) and (9), the optimal profit of lowcarbon products in a single product market is
Proposition 1. (1)In the first stage, the retail price of lowcarbon products decreases with the increase of consumer strategy , and the retail price of lowcarbon products in the second stage decreases with the increase in consumer strategy .(2)The retail price of lowcarbon products in the first stage increases with consumers’ green preference, and the retail price of lowcarbon products in the second stage increases with consumers’ green preference.(3)The optimal sales of lowcarbon products in the two stages are all about diminishing.
Proof. (1)Calculate the derivative of with respect to from formula (8), and obtain , so lowcarbon products are in The retail price in the first stage decreases with the increase of consumer strategy . Calculate the derivative of with respect to in formula (9), and obtain, lowcarbon products in the second stage. The retail price increases with the increase inconsumer strategy .(2)Calculate the derivative of with respect to in formula (8), and obtain , and calculate the derivative of with respect to in formula (9), and obtain , so the prices of lowcarbon products in both stages will increase with the increase inconsumers’ lowcarbon preference .(3)Since, , so the optimal sales of lowcarbon products in the two stages are all about decreasing.
4.2. The Situation Where a Retailer Sells LowCarbon Products and Ordinary Products at the Same Time (Model II)
In the case of lowcarbon preference consumers, considering that there are both lowcarbon products and ordinary products on the market, the two have formed a situation of mutual competition and substitution. Assuming that consumers have a preference for lowcarbon products , and consumers’ preference for ordinary products is less than their preference for lowcarbon products; the utility function of the first stage of consumers is:
In the first stage, when the market sells both lowcarbon products and ordinary products, consumers’ purchasing decisions will be affected by their own wishes and preference for green products. Through graph analysis, when consumers’ preference for lowcarbon products is within the range of , when and , consumers’ willingness to pay for lowcarbon products is , when, that is , consumers will choose to buy lowcarbon products. When and , that is , consumers will choose to buy ordinary products. Figure 2 is an analysis using the consumer utility function graph.
According to the consumer utility function, the market demand function is represents the proportion of consumers who bought the product in the first stage. The profit functions of retailers selling lowcarbon products and ordinary products in the first stage are
If , that is, , in this case, the consumer utility of lowcarbon products is always higher than that of ordinary products. Effectiveness: consumers will definitely choose to buy lowcarbon products, but this kind of situation does not match the actual sales situation, so we will not discuss it.
In the second stage, after a certain period of time sales, consumers will reduce their enthusiasm for the product and make more rational decisions. Strategic consumers will make strategic purchases. At the same time, strategic consumers will be affected by their patience, and their lowcarbon levels are different for different products. Thus, the utility function expression
When consumers’ preference for lowcarbon products is within the range of , when and, ; similar to the first stage, we do not consider the case of . When , that is , consumers will choose to buy lowcarbon products; when and that is , consumers will choose to buy ordinary products in the second stage.
Therefore, the consumer demand function is
The retailer profit function is
Combining formulas (15), (16), (20), and (21), the total sales profit of lowcarbon products and ordinary products in the first and second stages is
In order to maximize the profit, find the firstorder partial derivatives of , , , and for equation (22) as follows:
The twostage optimal retail prices of the two products obtained by the firstorder partial derivative are
Take the above formula into (13), (14), (18), and (19) to get the optimal sales volume as follows:
Substituting equations (24)–(31) into equation (22), the total profit of lowcarbon products and ordinary products in the two stages is
Analysing the optimal solution in this situation, we can get
Proposition 2. (1)In the first stage, the retail price of ordinary products has nothing to do with the customer’s greenness of lowcarbon products, while the retail price of lowcarbon products increases with the increase in . In the second stage, increases with the increase in . decreases as increases.(2)In the first stage, the sales volume of lowcarbon products increases as increases. The sales volume of ordinary products decreases with the increase in . In the second stage, the sales volume of lowcarbon products increases with the increase in , and the sales volume of ordinary products decreases with the increase in .
Proof. (1)From equation (25), has nothing to do with . Calculating the derivative of with respect to in equation (24) is, so increases as increases. Calculating the derivative of with respect to in equation (26) is ; find for equation (27). The derivative of is ; so in the second stage, increases with the increase in . decreases as increases.(2)Calculating the derivative of with respect to from equation (28) is . Therefore, the sales volume of lowcarbon products in the first stage increases with the increase in customers’ green preference. , so, the sales volume of ordinary products in the first stage decreases with the increase in customers’ green preference. In the second stage, the sales volume of lowcarbon products decreases with the increase in , and the proposition that the sales volume of ordinary products increases with the increase in will be verified in the numerical analysis in Chapter 5.
5. Numerical Analysis
5.1. Model I
5.1.1. The Influence of Parameter on Product Price
Assuming , , , , and , study the influence of parameter on the price of lowcarbon products. From Figure 3, it is found that given a value of , the price of lowcarbon products decreases with the increase in in the first stage, and increases with the increase in in the second stage. It shows that in the first stage, the greater the degree of customer strategy, the lower the price. In the second stage, the greater the degree of customer strategy, the greater the price. And the second stage is more affected by than the first stage.
5.1.2. The Influence of Parameters and on Total Profit
In the Figure 4, given the value of , study the effect of on total profit. It is found that the influence of customers’ lowcarbon preference on profit is positive, and the total profit will increase with the increase in . Given the value of , study the effect of on total profit and found that the impact of customer strategy on total profit is negative. The greater the customer strategy, the lower the total profit. This is because customers choose the time to purchase based on their own utility maximization. For retailers at this time, the price is lower and may cause a certain inventory cost.
5.2. Model II
Assuming , , , , and , . As the Figure 5 shows that given a value and a value to studies the influence of the price of each stage of parameter . It was found that consumers’ lowcarbon preference has a positive effect on lowcarbon products’ demand and has a slight negative effect on ordinary products’ demand. In the first stage, consumers' lowcarbon preferences have a more significant impact on demand, while the second stage is relatively flat.
Consider to study the influence of parameter on the total profit of the two stages. As the Figure 6 shows that given the values of and , the total profits of the two stages will increase as the difference in discount strength between the two products increases. When the value of is larger, the retailer’s total profit is smaller, and the two products are negatively correlated. That is, when the intertemporal discount coefficient of consumers in the second stage of ordinary products is larger, the retailer’s total profit is smaller. Therefore, for retailers, only two products with similar discount strength can increase total profit. When we give the value of , we find that the retailer's total profit increases as consumers’ lowcarbon preference increases. In other words, the greater the customer's green preference, the more beneficial to the retailer. Consumers’ green preference means that consumers are more willing to buy lowcarbon products. In the first stage, the price of ordinary products has nothing to do with green preference, while the price of lowcarbon products increases with the increase in , which leads to increased profits for retailers.
6. Conclusions and Discussion
This paper studies the twostage pricing model of green differentiated products based on customer strategic behaviour. First, it analyses the twostage sales market where there is only one lowcarbon product and finds the optimal pricing decision and the optimal sales volume at each stage. The study found(1)The price of lowcarbon products in the first stage decreases as the customer’s strategy degree increases, and the price of lowcarbon products in the second stage increases as the customer’s strategy degree increases.(2)The sales volume of lowcarbon products in the two phases is the same and decreases with the increase in customer strategy.(3)The total profit of twostage sales of lowcarbon products also decreases with the increase in customer strategy.
Secondly, the twostage pricing model with lowcarbon and ordinary products is studied. The study found(1)The retail price of ordinary products in the first stage has nothing to do with customers’ green preference , while the retail price of lowcarbon products increases with respect to . In the second stage, the retail prices of lowcarbon and ordinary products are all increasing.(2)The demand for lowcarbon products in both stages increases with the increase in consumers’ lowcarbon preference, while the demand for ordinary products is the opposite.(3)The total profit of the two stages increases with the increase in , and the greater the intertemporal discount factor of consumers for ordinary products in the second stage compared with lowcarbon products, the smaller the retailer’s total profit.
Therefore, there are the following inspirations for retailers:
In Model 1, consumers’ lowcarbon preference is positively correlated with retailers’ prices and profits, and the degree of consumer strategy has a greater impact on retailers. The higher the degree of consumer strategy, that is to say, the greater the retailer’s discount, the more consumers prefer to buy in the second stage. In order to maintain the retailer’s overall profit, the retailer’s price should be maintained at an appropriate level, and consumers should be encouraged to buy products in the first stage through advertising and other means. Secondly, do not discount or make the discount too large; because if the discount is too strong, the consumer’s strategy level will increase, which will reduce the retailer’s profit.
In Model II, consumers’ green preference has a huge impact on retailers’ profits. Retailers can improve consumers’ lowcarbon preference through the following points. First, increase publicity and promote lowcarbon knowledge through posters, advertisements, and publicity boards. Second, mark the carbon footprint of a product for consumers to understand and choose. Third, implement green packaging for products. Fourth, promote a lowcarbon economic model.
The greater the difference in discount strength between the two products, the smaller the retailer’s profit. Therefore, retailers should reduce the discount difference between lowcarbon and ordinary products, and the greater the green preference of consumers, the greater the total profit. When retailers are selling two products, they can promote consumers’ green preferences through advertising and so on, so as to encourage consumers to buy more lowcarbon products.
The research of this article can be expanded from the following aspects. This article studies the retailer’s pricing decision under the lowcarbon preference consumer strategy, but does not involve the perspective of the supply chain. The subsequent research can start from the coordination of the entire supply chain.
Data Availability
The data used to support the findings of this study are available from the corresponding author upon request.
Conflicts of Interest
The authors declare that there are no conflicts of interest regarding the publication of this paper.
Acknowledgments
This research was funded by the Qingdao Social Science Planning Research Project (QDSKL1901037), the Fundamental Research Funds for the Central Universities (19CX04010B), and the Shandong Soft Science Research Program General Project (2020RKE28013).
References
 F. Gao and X. Su, “Omnichannel retail operations with buyonlineandpickupinstore,” Management Science, vol. 63, no. 8, pp. 2478–2492, 2017. View at: Publisher Site  Google Scholar
 W. Zhang, M. Zhang, W. Zhang, Q. Zhou, and X. Zhang, “What influences the effectiveness of green logistics policies? a grounded theory analysis,” Science of the Total Environment, vol. 714, no. 714, Article ID 136731, 2020. View at: Publisher Site  Google Scholar
 T. T. Wang and D. P. Wang, “The dynamic coordination strategy of supply chain cooperation and lowcarbon publicity under government subsidies,” Operations Research and Management, vol. 29, no. 8, pp. 52–61, 2020. View at: Google Scholar
 M. Laroche, J. Bergeron, and G. Barbaro‐Forleo, “Targeting consumers who are willing to pay more for environmentally friendly products,” Journal of Consumer Marketing, vol. 18, no. 6, pp. 503–520, 2001. View at: Publisher Site  Google Scholar
 L. Wang, T. J. Xu, and L. H. Qin, “A study on supply chain emissionreduction level based on carbon tax and consumers’ lowcarbon preferences under stochastic demand,” Mathematical Problems in Engineering, vol. 2019, Article ID 1621395, 20 pages, 2019. View at: Publisher Site  Google Scholar
 F. Z. Zhang, “Application prospect of STF technology in braking field,” International Journal of Plant Engineering and Management, vol. 24, no. 2, pp. 109–114, 2019. View at: Google Scholar
 L. C. Liu and X. Zhai, “The optimal innovation and pricing strategy of enterprises facing strategic consumers,” Chinese Journal of Management Science, pp. 1–13, 2020, In press. View at: Google Scholar
 C. X. Wang, Q. P. Zhang, and W. Zhang, “Corporate social responsibility, green supply chain management and firm performance: the moderating role of bigdata analytics capability,” Research in Transportation Business & Management, vol. 37, no. 4, Article ID 100557, 2020. View at: Google Scholar
 J. H. Guo, L. Y. Sun, C. Zhang, M. Ni, and J. X. Zhu, “Supply chain pricing and coordination considering consumers’ lowcarbon preference under carbon tax policy,” Systems Engineering, pp. 1–14, 2020, In press. View at: Google Scholar
 C. Su and X. Liu, “Green supply chain decisions considering consumers’ lowcarbon awareness under different government subsidies,” Sustainability, vol. 12, no. 6, p. 2281, 2020. View at: Publisher Site  Google Scholar
 C. Q. Xu, D. Z. Zhao, and B. Y. Yuan, “Research on differential pricing and coordination mechanism of supply chain in low carbon environment,” Operations Research and Management Science, vol. 24, no. 1, pp. 19–26, 2015. View at: Google Scholar
 C. Che, W. Q. Ma, and S. F. Cao, “Research on time distance, social distance and the effect of online shopping decision framework,” Commercial Research, vol. 9, pp. 130–136, 2015. View at: Google Scholar
 P. Hu and Y. H. Dai, “Research on pricing strategy of lowcarbon supply chain based on consumer behaviour,” Soft Science, vol. 32, no. 8, pp. 73–90, 2018. View at: Google Scholar
 L. H. Zhang, Zhou, Y. Y. Liu, and R. Lu, “Optimal environmental quality and price with consumer environmental awareness and retailer’s fairness concerns in supply chain,” Journal of Cleaner Production, vol. 213, 2019. View at: Publisher Site  Google Scholar
 X. F. Xu, Z. Lin, and J. Zhu, “DVRPLS with variable neighbourhood region in refined oil distribution,” Annals of Operations Research, 2020, In press. View at: Publisher Site  Google Scholar
 X. Xu, Z. Wei, Q. Ji, C. Wang, and G. Gao, “Global renewable energy development: influencing factors, trend predictions and countermeasures,” Resources Policy, vol. 63, no. 10, Article ID 101470, 2019. View at: Publisher Site  Google Scholar
 X. Xu, J. Hao, and Y. Zheng, “Multiobjective artificial bee colony algorithm for multistage resource leveling problem in sharing logistics network,” Computers & Industrial Engineering, vol. 142, no. 4, Article ID 106338, 2020. View at: Publisher Site  Google Scholar
 X. Xu, J. Hao, L. Yu, and Y. Deng, “Fuzzy optimal allocation model for taskresource assignment problem in a collaborative logistics network,” IEEE Transactions on Fuzzy Systems, vol. 27, no. 5, pp. 1112–1125, 2019. View at: Publisher Site  Google Scholar
 Y. D. Li, L. J. Xia, and F. Z. Wang, “Game and coordination model of lowcarbon supply chain based on product substitution,” Chinese Journal of Management Science, vol. 27, no. 10, pp. 66–76, 2019. View at: Google Scholar
 M. W. Liu, K. L. Wu, H. Fu, and M. Z. Xu, “Retailerled supply chain cooperation and coordination of emission reduction under consumer lowcarbon preference,” Systems EngineeringTheory & Practice, vol. 37, no. 12, pp. 3109–3117, 2017. View at: Google Scholar
 A. F. Yang, A. Chen, X. J. Hu, and H. H. Yang, “The optimal pricing model of new products and remanufactured products in a twostage closedloop supply chain,” Mathematics in Practice and Theory, vol. 48, no. 12, pp. 1–10, 2018. View at: Google Scholar
 X. W. Zhou, D. Cai, S. G. Li, Y. J. Zhou, and X. H. Chen, “Product pricing strategy based on network externality and quality differentiation,” Journal of Management Sciences in China, vol. 22, no. 8, pp. 1–16, 2019. View at: Google Scholar
 A. Kalnins, “Pricing variation within dualdistribution chains: the different implications of externalities and signaling for high and lowquality brands,” Management Science, vol. 63, no. 1, pp. 139–152, 2017. View at: Publisher Site  Google Scholar
 J. L. Liu and M. W. Liu, “Lowcarbon supply chain pricing and coordination strategies for heterogeneous consumers,” Journal of Commercial Economics, vol. 3, pp. 47–49, 2019. View at: Google Scholar
 Y. Hao, C. Tian, and C. Wu, “Modelling of carbon price in two real carbon trading markets,” Journal of Cleaner Production, vol. 244, 2020. View at: Google Scholar
 Y. Luo, S. Tu, T. G. Peng, and J. Wei, “Research on location and pricing of differential products with purchase elasticity,” Acta Scientiarum Naturalium Universitatis Nankaiensis, vol. 4, pp. 78–82, 2007. View at: Google Scholar
 Y. B. Li, C. X. Wang, and D. D. Zhang, “Supply chain decisionmaking for competition between ordinary and lowcarbon products under fair preference,” Journal of Shanghai Maritime University, vol. 41, no. 3, pp. 66–72, 2020. View at: Google Scholar
 H. Nair, “Intertemporal price discrimination with forwardlooking consumers: application to the US market for console videogames,” Quantitative Marketing and Economics, vol. 5, no. 3, pp. 239–292, 2007. View at: Publisher Site  Google Scholar
 J. Li, N. Granados, and S. Netessine, “Are consumers strategic? structural estimation from the airtravel industry,” Management Science, vol. 60, no. 9, pp. 2114–2137, 2014. View at: Publisher Site  Google Scholar
 G. P. Cachon and P. Feldman, “Price commitments with strategic consumers: why it can be optimal to discount more frequently than optimal,” Manufacturing & Service Operations Management, vol. 17, no. 3, pp. 300–410, 2015. View at: Publisher Site  Google Scholar
 A. K. Parlaktürk, “The value of product variety when selling to strategic consumers,” Manufacturing & Service Operations Management, vol. 14, no. 3, pp. 371–385, 2012. View at: Publisher Site  Google Scholar
 J. Du, J. Zhang, and G. Hua, “Pricing and inventory management in the presence of strategic customers with risk preference and decreasing value,” International Journal of Production Economics, vol. 164, pp. 160–166, 2015. View at: Publisher Site  Google Scholar
 D. D. Wu, “Selling to the socially interactive consumer: order more or less?” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 45, no. 3, pp. 399–410, 2015. View at: Publisher Site  Google Scholar
 J. Dong and D. Wu, “Twoperiod pricing and quick response with strategic customers,” International Journal of Production Economics, vol. 215, pp. 165–173, 2017. View at: Publisher Site  Google Scholar
 C. Che, X. L. Qi, W. Q. Ma, and D. X. Shao, “An empirical study on the influencing factors of mobile social network marketing effectiveness,” Chinese Journal of Management Science, vol. 25, no. 5, pp. 145–149, 2017. View at: Google Scholar
 X. Liu, K. Lin, L. Wang, and L. Ding, “Pricing decisions for a sustainable supply chain in the presence of potential strategic customers,” Sustainability, vol. 12, no. 4, p. 1655, 2020. View at: Publisher Site  Google Scholar
 J. Feng, B. Liu, and Z. F. Liu, “Research on product dynamic pricing decision based on the purchase habits of strategic consumers,” Mathematics in Practice and Theory, vol. 49, no. 13, pp. 18–29, 2019. View at: Google Scholar
 C. Che, W. P. Luo, and X. L. Qi, “The influence of space and social distance on the online wordofmouth valence of virtual communities,” Soft Science, vol. 31, no. 4, pp. 117–121+144, 2017. View at: Google Scholar
 F. Peng, “Discussion on the influencing factors of retailers’ decisions: based on the perspective of strategic consumer behaviour,” Journal of Commercial Economics, vol. 24, pp. 28–31, 2019. View at: Google Scholar
 L. H. Zhang, Y. W. Kong, and J. Y. Wang, “Twostage production game equilibrium based on the lowcarbon preference of strategic consumers,” Computer Integrated Manufacturing System, pp. 1–14, 2020, In press. View at: Google Scholar
 P. Ma, W. J. Du, and H. Y. Wang, “Research on the twostage pricing model of differential products under the customer strategic behaviour,” Chinese Journal of Management Science, vol. 28, no. 2, pp. 136–144, 2020. View at: Google Scholar
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Copyright © 2021 Cheng Che et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.