Behaviour-based Pricing in the Green Product Supply Chain

We study the pricing strategies of supply chains of green products under behaviour-based 23 pricing. Considering consumer preferences for green product functional attributes and 24 environmental attributes, we construct a two-stage supply chain. The optimal behaviour pricing 25 of green products is solved, and the effects of green sensitivity and the cost coefficient on the 26 optimal price are analysed. We find that when consumers are less sensitive to the greenness, with 27 the increase in the market share of green products, green product retailers will increase the 28 loyalty price. An increase in greenness sensitivity and a decrease in the greenness cost coefficient 29 will increase the wholesale prices and retail prices of green products. Consumer attention to the 30 greenness and a decrease in the initial market share of green products will be conducive to 31 promoting the greenness and improving the environment. Consumers' emphasis on the greenness 32 of their products will lead to higher profits for the manufacturers and retailers of green products.


Introduction
technologies to obtain and record a substantial amount of customer information and use it to 60 implement differential pricing for loyal and new customers (Rhee and Thomadsen, 2017). For 61 example, when consumers log on to the shopping website or app of Amazon or JD, they may be 62 surprised to find that such Internet sellers show them commodities preferred by consumers 63 according to their purchase information and web-browsing records and that these sellers can 64 identify whether consumers have ever purchased such commodities before (Wang and Ng, 2018). 65 Currently, BBP and customer identification are used in many fields, such as commodity sales, 66 telecommunications services, and travel and housekeeping services. 67 Therefore, when green and nongreen products compete in the market, especially when 68 selling products through the Internet, an increasing number of enterprises adopt BBP for new and 69 old customers. Table 1 presents actual cases of BBP with respect to green products on the 70 Alibaba platform in 2020. And past evidence has proven that enterprises can increase profits by 71 using big data to implement BBP. For example, Netflix's profit increased 11.4% after using 72 differentiated pricing compared with pricing based only on customer display graphics (Shiller,73 2014). Therefore, we will discuss the application of BBP in GPSCs in this paper. 74 Most of the previous literature on GPSC pricing seldom applied behavioural pricing 75 (Jamali and Rasti-Barzoki, 2018; Sana, 2020), and the advantages of BBP and its successful 76 practices in other fields provide new ideas for the study of this paper. Some previous research 77 focused on the combination of BBP and organic product pricing strategies (Liu et al., 2019, 78 2020), but the model is designed to include only the retailers of the products. They did not 79 explore the influence of the green product initial market share and green degree sensitivity 80 coefficient on green product manufacturers' wholesale pricing, green degree decision and profit 81 after the implementation of BBP in the retail segment. The influence of the green sensitivity 82 coefficient and green cost coefficient on BBP, profit and the environment on the GPSC have not 83 been discussed. In this article, we answer the following questions: 84 (1) Given the different initial market shares of green products, how does sensitivity to 85 greenness affect the choice of BBP for green product supply chains? 86 (2) What are the effects of the initial market shares of green products, sensitivity 87 coefficients of greenness, and cost coefficients of greenness on wholesale and retail 88 pricing, greenness, environment, and profits of enterprises in the GPSC? 89 The rest of the paper proceeds as follows. After the introduction, we give a literature 90 review. In Section 3, we discuss the assumptions and the problem description of this paper. In

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Section 4, solutions to the problem are shown, and the applicable conditions for using BBP are 92 analysed. Section 5 conducts parametric sensitivity analysis. Section 6 is the numerical 93 simulation analysis. In Section 7, conclusions and future research suggestions are offered. parameters on the pricing, greenness, environment, and profit of green products after using BBP.

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Therefore, the main contributions of this paper are as follows.  163 In this section, we will first define the symbols used in the paper, as shown in Table 2.

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This paper considers a two-stage supply chain composed of a local nongreen product 167 manufacturer, a nonlocal green product manufacturer and a green product retailer, with M for the 168 manufacturer and R for the retailer. The main framework of the supply chain is shown in Fig.1.

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Suppose that two types of manufacturers produce two kinds of different degrees of products with 170 brand differences: a green product ( ) and a nongreen product (b). Non-green product 171 manufacturers are located locally and produce and sell their own products. Since the 172 requirements and complexity of green product production and sales are higher than those of 173 nongreen products, we assumed that the production and sales of green products are separated, 174 with green product retailers placing orders with green product manufacturers.

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Considering that there are multiple periods throughout the sales cycle, consumers' 176 purchasing behaviours will change in different periods due to price, environmental preferences, 177 diversified needs, and novelty-seeking. For convenience of analysis, the whole sales cycle is 178 standardized into two periods ( = 0,1). As green manufacturers dominate the supply chain, 179 before period ( = 0,1), green product manufacturers first determine green products' wholesale 180 price and greenness. Since the manufacturers themselves produce and sell nongreen products,

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there is no wholesale price. We assume that consumers in each period will not choose either 182 green or nongreen products of the same category.

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The order of the two-period game is as follows: In period = 0, the two types of 184 products are in competition to obtain the corresponding market share, and the green product    In period = 1, green retailers must price (loyalty price and poaching price) higher than the 220 wholesale price of green products because the profit of green product retailers must be positive.

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Meanwhile, the revenue of wholesale sales of green products should be greater than the 222 production cost of green products to ensure that the profit of wholesalers of green products is 223 positive. In addition, to ensure that nongreen product manufacturers do not lose money, the retail 224 price of nongreen products (loyalty price and poaching price) must be greater than 0 (Jamali and For consumers who purchase green productg in period = 0, in period = 1, due to the 232 influence of price and other factors, they may turn to buying nongreen products. Let 1 be given.

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Theorem 1 As the initial market share of green products increases, when consumers' green 275 sensitivity is relatively low ( < 2 √ ), green product retailers will increase the loyalty price of 276 green products. When consumers' green sensitivity is relatively high (2 √ < < √8 ), green 277 retailers will lower the loyalty price of green products.  Theorem 2 Increasing the initial market share of green products will reduce the 295 greenness of green products and have a negative impact on environmental improvement.

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Proof: The first derivative of the greenness of green products and the total impact of the 297 two types of products on the environment with respect to parameter 0 is derived.
We find that the initial market share of green products is not only negatively correlated 300 with the greenness of green products but also has a negative impact on environmental 301 improvement. As the initial market share of green products increases, the market share and < 0). Thus, green product manufacturers generate less revenue. To reduce the impact of 304 the decreased revenue from green products on their profits, green product manufacturers will 305 reduce the greenness of their products to reduce their costs. This decision will mitigate the 306 negative impact of the increase in the initial market share of green products on the profits of 307 green product manufacturers.

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On the other hand, the market share of green products in the second period * will 309 decrease with the increase in 0 ; when the greenness of green products and the market share in 310 the second period are reduced, because * = − * * , the total impact of the two types of 311 products on the environment increases. Hence, theorem 2 is proven. Therefore, when consumer sensitivity to product greenness increases, the profits of 341 manufacturers and retailers of green products also increase. As consumer preference for 342 greenness increases, manufacturers and retailers of green products increase the wholesale price 343 and retail price, respectively, of green products. Hence, the market share of green products in the 344 second period does decrease due to the increase in price, but rather, they occupy a larger market (− 2 +8 ) 2 > 0). Therefore, although the increase in the degree of greenness increases the 346 cost for green product manufacturers, the sales revenue of green products increases even more, 347 meaning that the profits of green product manufacturers will also ultimately increase.

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The growth rate of the green product retail price with the green sensitivity coefficient is 349 faster than that of the wholesale price with the green sensitivity coefficient . From theorem 3, 350 we know * = * > * > 0). Thus, the profits of green product retailers also increase with 351 the increase in the green sensitivity coefficient .  Proof: The first derivative of the prices of green and nongreen products (wholesale price, 357 loyalty price and poaching price) with respect to parameter is derived.
This means that the higher the unit cost is to improve the greenness of green products, the 360 lower the optimal greenness will be. Accordingly, manufacturers and retailers of green products 361 will reduce the wholesale price and the sale price (loyalty price, poaching price) to increase their 362 sales volume and maximize profits. As nongreen products narrow the gap between green and 363 nongreen products, their manufacturers have the confidence to raise their selling prices to obtain 364 more unit product profits.  nongreen products, respectively, corresponding to different initial market shares of green 373 products.

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As evidenced from Fig.3 and Fig.4, the increase in the initial market share of green 375 products results in a decrease in profits for green product manufacturers in the second period < 0). This indicates that when the initial market share of green products is 377 higher, the wholesale price of green products will be lower and the market share will be reduced 378 even though the production cost of green products is lower as the production cost is affected by < 0). Therefore, for manufacturers of green products, a lower initial share of 381 green products can actually be more beneficial.

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With the increase in the initial market share of green products, the profits of 8(− 2 +8 ) > 0). Therefore, the increased revenue from a 393 nongreen product's poaching price is less than the decreased revenue from its loyalty price.

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Hence, the profits of nongreen product manufacturers also decrease.

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In the second period, the profits of green retailers in the two intervals where 0< 0 <  The main conclusions of the study are as follows.
(1) As the initial market share of green 420 products increases, when consumers' green sensitivity is relatively low ( < 2 √ ), green 421 product retailers will increase the loyalty price of green products. When consumers' green 422 sensitivity is relatively high (2 √ < < √8 ), green retailers will lower the loyalty price of . Further, other equilibrium results in Table 3 can be 456 obtained.