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doi: 10.3934/jimo.2022021
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Product line extension with a green added product: Impacts of segmented consumer preference on supply chain improvement and consumer surplus

1. 

School of Management, Hefei University of Technology, Hefei 230009, China

2. 

Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education, Hefei 230009, China

3. 

College of Economics and Management, Anhui Agricultural University, Hefei 230036, China

4. 

School of Economics and Management, Xiamen University of Technology, Xiamen 361024, China

*Corresponding author: Rui Zhang

Received  June 2020 Revised  October 2021 Early access February 2022

Fund Project: The work was supported by the National Natural Science Foundation of China (71801076, 71802004, 61936009, 71690230, 71771076, 72071058), National Key Research and Development Program of China (2018AAA0101604), Philosophy and Social Science Project of Anhui Province (AHSKY2016D21, AHSKY2016D25), and Fundamental Research Funds for the Central Universities (JZ2020HGTB0066)

With the enhancement of environmental protection, more and more enterprises begin to develop green products. However, the high cost of green R&D leads to an increase of product price, which reduces the competitiveness of green products. In this paper, we model a supply chain which consists of one manufacturer and one retailer providing a primary product and a substitutable green added product in the market. In order to capture the impact of consumer behavior on the supply chain members' decision-making, we classify the market into two segments and assume that high-end green consumers have higher preferences for green products than ordinary consumers. Different to existing research, we assume ordinary consumers hold a positive but lower green preference compared to the green consumers. When analyzing the impacts of consumers' green preferences, we find that there exist specific boundaries of cost and market potential which define the optimal pricing strategy and product line design. Regarding profits, we find that when the green preferences of high-end and low-end consumers increase in the same proportion, the high-end market may not bring greater supply chain revenue. In particular, the marginal profit increase of the manufacturer is always greater than that of the retailer.

Citation: Xiaoxi Zhu, Kai Liu, Miaomiao Wang, Rui Zhang, Minglun Ren. Product line extension with a green added product: Impacts of segmented consumer preference on supply chain improvement and consumer surplus. Journal of Industrial and Management Optimization, doi: 10.3934/jimo.2022021
References:
[1]

M. A. Agi and X. Yan, Greening products in a supply chain under market segmentation and different channel power structures, International Journal of Production Economics, 223 (2020), 107523.  doi: 10.1016/j.ijpe.2019.107523.

[2]

J. Bull, Loads of green washing-can behavioural economics increase willingness-to-pay for efficient washing machines in the uk?, Energy Policy, 50 (2012), 242-252.  doi: 10.1016/j.enpol.2012.07.001.

[3]

H. K. ChanR. W. YeeJ. Dai and M. K. Lim, The moderating effect of environmental dynamism on green product innovation and performance, International Journal of Production Economics, 181 (2016), 384-391.  doi: 10.1016/j.ijpe.2015.12.006.

[4]

C. Chen and J. Zhang, Green product design with engineering tradeoffs under technology efficient frontiers: Analytical results and empirical tests, IEEE Transactions on Engineering Management, 60 (2013), 340-352.  doi: 10.1109/TEM.2012.2212199.

[5]

J. Chen and L. Liu, Customer participation, and green product innovation in smes: The mediating role of opportunity recognition and exploitation, Journal of Business Research, 119 (2020), 151-162.  doi: 10.1016/j.jbusres.2019.05.033.

[6]

Q. Cui, Quality investment, and the contract manufacturer's encroachment, European J. Oper. Res., 279 (2019), 407-418.  doi: 10.1016/j.ejor.2019.06.004.

[7]

S. DuW. TangJ. Zhao and T. Nie, Sell to whom? Firm's green production in competition facing market segmentation, Ann. Oper. Res., 270 (2018), 125-154.  doi: 10.1007/s10479-016-2291-4.

[8]

R. N. GiriS. K. Mondal and M. Maiti, Government intervention on a competing supply chain with two green manufacturers and a retailer, Computers & ndustrial Engineering, 128 (2019), 104-121.  doi: 10.1016/j.cie.2018.12.030.

[9]

M. R. GleimJ. S. SmithD. Andrews and J. J. Cronin Jr, Against the green: A multi-method examination of the barriers to green consumption, Journal of Retailing, 89 (2013), 44-61.  doi: 10.1016/j.jretai.2012.10.001.

[10]

M. Hafezi and H. Zolfagharinia, Green product development and environmental performance: Investigating the role of government regulations, International Journal of Production Economics, 204 (2018), 395-410.  doi: 10.1016/j.ijpe.2018.08.012.

[11]

J.-W. HoY.-S. Huang and C.-L. Hsu, Pricing under internal and external competition for remanufacturing firms with green consumers, Journal of Cleaner Production, 202 (2018), 150-159.  doi: 10.1016/j.jclepro.2018.08.109.

[12]

W. Li and J. Chen, Manufacturer's vertical integration strategies in a three-tier supply chain, Transportation Research Part E: Logistics and Transportation Review, 135 (2020), 101884.  doi: 10.1016/j.tre.2020.101884.

[13]

A. K. Moser, Thinking green, buying green? drivers of pro-environmental purchasing behavior, Journal of Consumer Marketing, 32 (2015).  doi: 10.1108/JCM-10-2014-1179.

[14]

S. SaberiJ. M. CruzJ. Sarkis and A. Nagurney, A competitive multiperiod supply chain network model with freight carriers and green technology investment option, European J. Oper. Res., 266 (2018), 934-949.  doi: 10.1016/j.ejor.2017.10.043.

[15]

S. Smith and A. Paladino, Eating clean and green? investigating consumer motivations towards the purchase of organic food, Australasian Marketing Journal, 18 (2010), 93-104.  doi: 10.1016/j.ausmj.2010.01.001.

[16]

C. S. Tang and R. Yin, The implications of costs, capacity, and competition on product line selection, European Journal of Operational Research, 200 (2010), 439-450.  doi: 10.1016/j.ejor.2009.01.012.

[17]

F. Testa and F. Iraldo, Shadows and lights of gscm (green supply chain management): Determinants and effects of these practices based on a multi-national study, Journal of Cleaner Production, 18 (2010), 953-962.  doi: 10.1016/j.jclepro.2010.03.005.

[18]

F. TestaF. Iraldo and M. Frey, The effect of environmental regulation on firms' competitive performance: The case of the building & construction sector in some eu regions, Journal of Environmental Management, 92 (2011), 2136-2144.  doi: 10.1016/j.jenvman.2011.03.039.

[19]

V. TiefenbeckL. GoetteK. DegenV. TasicE. FleischR. Lalive and T. Staake, Overcoming salience bias: How real-time feedback fosters resource conservation, Management Science, 64 (2018), 1458-1476.  doi: 10.1287/mnsc.2016.2646.

[20]

J. WeiJ. Lu and J. Zhao, Interactions of competing manufacturers' leader-follower relationship and sales format on online platforms, European J. Oper. Res., 280 (2020), 508-522.  doi: 10.1016/j.ejor.2019.07.048.

[21]

W. WenP. Zhou and F. Zhang, Carbon emissions abatement: Emissions trading vs consumer awareness, Energy Economics, 76 (2018), 34-47.  doi: 10.1016/j.eneco.2018.09.019.

[22]

J.-T. Wong, Dynamic procurement risk management with supplier portfolio selection and order allocation under green market segmentation, Journal of Cleaner Production, 253 (2020), 119835.  doi: 10.1016/j.jclepro.2019.119835.

[23]

X.-Y. WuZ.-P. Fan and B.-B. Cao, Cost-sharing strategy for carbon emission reduction and sales effort: A nash game with government subsidy, J. Ind. Manag. Optim., 16 (2020), 1999-2027.  doi: 10.3934/jimo.2019040.

[24]

X. XieJ. Huo and H. Zou, Green process innovation, green product innovation, and corporate financial performance: A content analysis method, Journal of Business Research, 101 (2019), 697-706.  doi: 10.1016/j.jbusres.2019.01.010.

[25]

A. Yenipazarli and A. Vakharia, Pricing, market coverage and capacity: Can green and brown products co-exist?, European Journal of Operational Research, 242 (2015), 304-315. 

[26]

Y. YuX. Han and G. Hu, Optimal production for manufacturers considering consumer environmental awareness and green subsidies, International Journal of Production Economics, 182 (2016), 397-408.  doi: 10.1016/j.ijpe.2016.09.014.

[27]

Y. YuG. Q. Huang and L. Liang, Stackelberg game-theoretic model for optimizing advertising, pricing and inventory policies in vendor managed inventory (vmi) production supply chains, Computers & Industrial Engineering, 57 (2009), 368-382.  doi: 10.1016/j.cie.2008.12.003.

[28]

Q. ZhangQ. ZhaoX. Zhao and L. Tang, On the introduction of green product to a market with environmentally conscious consumers, Computers & Industrial Engineering, 139 (2020), 106190.  doi: 10.1016/j.cie.2019.106190.

[29]

W. Zhu and Y. He, Green product design in supply chains under competition, European J. Oper. Res., 258 (2017), 165-180.  doi: 10.1016/j.ejor.2016.08.053.

[30]

E. ZimmerlingH. Purtik and I. M. Welpe, End-users as co-developers for novel green products and services–an exploratory case study analysis of the innovation process in incumbent firms, Journal of Cleaner Production, 162 (2017), S51-S58.  doi: 10.1016/j.jclepro.2016.05.160.

show all references

References:
[1]

M. A. Agi and X. Yan, Greening products in a supply chain under market segmentation and different channel power structures, International Journal of Production Economics, 223 (2020), 107523.  doi: 10.1016/j.ijpe.2019.107523.

[2]

J. Bull, Loads of green washing-can behavioural economics increase willingness-to-pay for efficient washing machines in the uk?, Energy Policy, 50 (2012), 242-252.  doi: 10.1016/j.enpol.2012.07.001.

[3]

H. K. ChanR. W. YeeJ. Dai and M. K. Lim, The moderating effect of environmental dynamism on green product innovation and performance, International Journal of Production Economics, 181 (2016), 384-391.  doi: 10.1016/j.ijpe.2015.12.006.

[4]

C. Chen and J. Zhang, Green product design with engineering tradeoffs under technology efficient frontiers: Analytical results and empirical tests, IEEE Transactions on Engineering Management, 60 (2013), 340-352.  doi: 10.1109/TEM.2012.2212199.

[5]

J. Chen and L. Liu, Customer participation, and green product innovation in smes: The mediating role of opportunity recognition and exploitation, Journal of Business Research, 119 (2020), 151-162.  doi: 10.1016/j.jbusres.2019.05.033.

[6]

Q. Cui, Quality investment, and the contract manufacturer's encroachment, European J. Oper. Res., 279 (2019), 407-418.  doi: 10.1016/j.ejor.2019.06.004.

[7]

S. DuW. TangJ. Zhao and T. Nie, Sell to whom? Firm's green production in competition facing market segmentation, Ann. Oper. Res., 270 (2018), 125-154.  doi: 10.1007/s10479-016-2291-4.

[8]

R. N. GiriS. K. Mondal and M. Maiti, Government intervention on a competing supply chain with two green manufacturers and a retailer, Computers & ndustrial Engineering, 128 (2019), 104-121.  doi: 10.1016/j.cie.2018.12.030.

[9]

M. R. GleimJ. S. SmithD. Andrews and J. J. Cronin Jr, Against the green: A multi-method examination of the barriers to green consumption, Journal of Retailing, 89 (2013), 44-61.  doi: 10.1016/j.jretai.2012.10.001.

[10]

M. Hafezi and H. Zolfagharinia, Green product development and environmental performance: Investigating the role of government regulations, International Journal of Production Economics, 204 (2018), 395-410.  doi: 10.1016/j.ijpe.2018.08.012.

[11]

J.-W. HoY.-S. Huang and C.-L. Hsu, Pricing under internal and external competition for remanufacturing firms with green consumers, Journal of Cleaner Production, 202 (2018), 150-159.  doi: 10.1016/j.jclepro.2018.08.109.

[12]

W. Li and J. Chen, Manufacturer's vertical integration strategies in a three-tier supply chain, Transportation Research Part E: Logistics and Transportation Review, 135 (2020), 101884.  doi: 10.1016/j.tre.2020.101884.

[13]

A. K. Moser, Thinking green, buying green? drivers of pro-environmental purchasing behavior, Journal of Consumer Marketing, 32 (2015).  doi: 10.1108/JCM-10-2014-1179.

[14]

S. SaberiJ. M. CruzJ. Sarkis and A. Nagurney, A competitive multiperiod supply chain network model with freight carriers and green technology investment option, European J. Oper. Res., 266 (2018), 934-949.  doi: 10.1016/j.ejor.2017.10.043.

[15]

S. Smith and A. Paladino, Eating clean and green? investigating consumer motivations towards the purchase of organic food, Australasian Marketing Journal, 18 (2010), 93-104.  doi: 10.1016/j.ausmj.2010.01.001.

[16]

C. S. Tang and R. Yin, The implications of costs, capacity, and competition on product line selection, European Journal of Operational Research, 200 (2010), 439-450.  doi: 10.1016/j.ejor.2009.01.012.

[17]

F. Testa and F. Iraldo, Shadows and lights of gscm (green supply chain management): Determinants and effects of these practices based on a multi-national study, Journal of Cleaner Production, 18 (2010), 953-962.  doi: 10.1016/j.jclepro.2010.03.005.

[18]

F. TestaF. Iraldo and M. Frey, The effect of environmental regulation on firms' competitive performance: The case of the building & construction sector in some eu regions, Journal of Environmental Management, 92 (2011), 2136-2144.  doi: 10.1016/j.jenvman.2011.03.039.

[19]

V. TiefenbeckL. GoetteK. DegenV. TasicE. FleischR. Lalive and T. Staake, Overcoming salience bias: How real-time feedback fosters resource conservation, Management Science, 64 (2018), 1458-1476.  doi: 10.1287/mnsc.2016.2646.

[20]

J. WeiJ. Lu and J. Zhao, Interactions of competing manufacturers' leader-follower relationship and sales format on online platforms, European J. Oper. Res., 280 (2020), 508-522.  doi: 10.1016/j.ejor.2019.07.048.

[21]

W. WenP. Zhou and F. Zhang, Carbon emissions abatement: Emissions trading vs consumer awareness, Energy Economics, 76 (2018), 34-47.  doi: 10.1016/j.eneco.2018.09.019.

[22]

J.-T. Wong, Dynamic procurement risk management with supplier portfolio selection and order allocation under green market segmentation, Journal of Cleaner Production, 253 (2020), 119835.  doi: 10.1016/j.jclepro.2019.119835.

[23]

X.-Y. WuZ.-P. Fan and B.-B. Cao, Cost-sharing strategy for carbon emission reduction and sales effort: A nash game with government subsidy, J. Ind. Manag. Optim., 16 (2020), 1999-2027.  doi: 10.3934/jimo.2019040.

[24]

X. XieJ. Huo and H. Zou, Green process innovation, green product innovation, and corporate financial performance: A content analysis method, Journal of Business Research, 101 (2019), 697-706.  doi: 10.1016/j.jbusres.2019.01.010.

[25]

A. Yenipazarli and A. Vakharia, Pricing, market coverage and capacity: Can green and brown products co-exist?, European Journal of Operational Research, 242 (2015), 304-315. 

[26]

Y. YuX. Han and G. Hu, Optimal production for manufacturers considering consumer environmental awareness and green subsidies, International Journal of Production Economics, 182 (2016), 397-408.  doi: 10.1016/j.ijpe.2016.09.014.

[27]

Y. YuG. Q. Huang and L. Liang, Stackelberg game-theoretic model for optimizing advertising, pricing and inventory policies in vendor managed inventory (vmi) production supply chains, Computers & Industrial Engineering, 57 (2009), 368-382.  doi: 10.1016/j.cie.2008.12.003.

[28]

Q. ZhangQ. ZhaoX. Zhao and L. Tang, On the introduction of green product to a market with environmentally conscious consumers, Computers & Industrial Engineering, 139 (2020), 106190.  doi: 10.1016/j.cie.2019.106190.

[29]

W. Zhu and Y. He, Green product design in supply chains under competition, European J. Oper. Res., 258 (2017), 165-180.  doi: 10.1016/j.ejor.2016.08.053.

[30]

E. ZimmerlingH. Purtik and I. M. Welpe, End-users as co-developers for novel green products and services–an exploratory case study analysis of the innovation process in incumbent firms, Journal of Cleaner Production, 162 (2017), S51-S58.  doi: 10.1016/j.jclepro.2016.05.160.

Figure 1.  Regions that define the product line choice in decentralized supply chain with $ c_o = 0.04 $ (All the regions are located with $ \frac{L}{H}>\frac{3+c_o}{4} $)
Figure 2.  Regions that define the product line choice in decentralized supply chain with $ c_o = 0.2 $ (All the regions are located with $ \frac{L}{H}>\frac{3+c_o}{4} $).
Figure 3.  Regions that define the product line choice in centralized supply chain with $ c_o = 0.04 $ (All the regions are located with $ \frac{L}{H}>\frac{1+c_o}{2} $)
Figure 4.  Regions that define the product line choice in decentralized supply chain with $ c_o = 0.2 $ (All the regions are located with $ \frac{L}{H}>\frac{3+c_o}{4} $).
Figure 5.  Market coverage and consumer choice ($ v_1 = p_o $, $ v_2 = \frac{p_g-p_o}{H x} $, and $ v_3 = \frac{p_g-p_o}{L x} $ with $ v_3>v2 $)
Figure 6.  Regions that define the relationship of $ \frac{\partial \Pi_{M(R)}^*}{\partial H} $ and $ \frac{\partial \Pi_{M(R)}^*}{\partial L} $
Table 1.  Product valuations in specific segments with $ H>L>0 $
Segments Product ($ o $) Product ($ g $)
Ordinary consumer ($ O $) $ \xi $ $ \xi+Lx\xi $
Green consumer ($ G $) $ \xi $ $ \xi+Hx\xi $
Segments Product ($ o $) Product ($ g $)
Ordinary consumer ($ O $) $ \xi $ $ \xi+Lx\xi $
Green consumer ($ G $) $ \xi $ $ \xi+Hx\xi $
Table 2.  Optimal results of the decentralized model
Optimums Case $ I $ Case $ \mathit{II} $ Case $ \mathit{III} $
Prices $ p_o^*=\frac{c_o+3}{4} $; $ w_o^*=\frac{c_o+1}{2} $ $ \frac{c_o+3}{4} $; $ w_o^*=\frac{c_o+1}{2} $ $ \frac{c_o+3}{4} $; $ w_o^*=\frac{c_o+1}{2} $
$ p_g^*=\frac{c_o}{4}+\frac{\Theta_1}{36 k (\alpha H-\alpha L+L)^2} $ $ p_g^*=\frac{c_o}{4}+\frac{\Theta_2}{4 k ((\alpha -1) L-\alpha H)} $ $ p_g^*=\frac{(c_o+3)\Theta_3}{4 k (\alpha H+(1-\alpha ) L)} $
$ w_g^*=\frac{c_o}{2}+\frac{\Theta_4}{18 k (\alpha H-\alpha L+L)^2} $ $ w_g^*=\frac{c_o}{2}+\frac{\Theta_5}{2 k (\alpha H-\alpha L+L)^2} $ $ w_g^*=\frac{\Theta_6}{2 k ((\alpha -1) L-\alpha H)^2} $
$ +\frac{H^2 c_o}{2 k}+\frac{\Theta_7}{2 k (\alpha H-\alpha L+L)} $
Greenness $ x^*=\frac{2 H L}{3 k (\alpha (H- L)+L)} $ $ x^*=x_{D_g^O} $ $ x^*=x_{D_o^G} $
Demands $ D_o^{O*}=\frac{\alpha}{12} \left(\frac{10 H}{\alpha H-\alpha L+L}-9-3 c_o\right) $ $ D_o^{O*}=\frac{\alpha}{4} \left(1-c_o\right) $ $ D_o^{O*}=\frac{\alpha \left(c_o+3\right) (H-L)}{4 L} $
$ D_g^{O*}=\frac{\alpha ((6 \alpha-5) H+6 (1-\alpha) L)}{6 (\alpha H-\alpha L+L)} $ $ D_g^{O*}=0 $ $ D_g^{O*}=\frac{\alpha \left(4 L-H c_o-3 H\right)}{4 L} $
$ D_o^{G*}=\frac{\alpha -1}{12} \left(3 c_o-\frac{10 L}{\alpha H-\alpha L+L}+9\right) $ $ D_o^{G*}=\frac{(\alpha -1) \left(H c_o+3 H-4 L\right)}{4 H} $ $ D_o^{G*}=0 $
$ D_g^{G*}=\frac{(1-\alpha) (6 \alpha H-6 \alpha L+L)}{6 (\alpha H-\alpha L+L)} $ $ D_g^{G*}=\frac{(1-\alpha) (H-L)}{H} $ $ D_g^{G*}=\frac{(\alpha -1) (c_o-1)}{4} $
Optimums Case $ I $ Case $ \mathit{II} $ Case $ \mathit{III} $
Prices $ p_o^*=\frac{c_o+3}{4} $; $ w_o^*=\frac{c_o+1}{2} $ $ \frac{c_o+3}{4} $; $ w_o^*=\frac{c_o+1}{2} $ $ \frac{c_o+3}{4} $; $ w_o^*=\frac{c_o+1}{2} $
$ p_g^*=\frac{c_o}{4}+\frac{\Theta_1}{36 k (\alpha H-\alpha L+L)^2} $ $ p_g^*=\frac{c_o}{4}+\frac{\Theta_2}{4 k ((\alpha -1) L-\alpha H)} $ $ p_g^*=\frac{(c_o+3)\Theta_3}{4 k (\alpha H+(1-\alpha ) L)} $
$ w_g^*=\frac{c_o}{2}+\frac{\Theta_4}{18 k (\alpha H-\alpha L+L)^2} $ $ w_g^*=\frac{c_o}{2}+\frac{\Theta_5}{2 k (\alpha H-\alpha L+L)^2} $ $ w_g^*=\frac{\Theta_6}{2 k ((\alpha -1) L-\alpha H)^2} $
$ +\frac{H^2 c_o}{2 k}+\frac{\Theta_7}{2 k (\alpha H-\alpha L+L)} $
Greenness $ x^*=\frac{2 H L}{3 k (\alpha (H- L)+L)} $ $ x^*=x_{D_g^O} $ $ x^*=x_{D_o^G} $
Demands $ D_o^{O*}=\frac{\alpha}{12} \left(\frac{10 H}{\alpha H-\alpha L+L}-9-3 c_o\right) $ $ D_o^{O*}=\frac{\alpha}{4} \left(1-c_o\right) $ $ D_o^{O*}=\frac{\alpha \left(c_o+3\right) (H-L)}{4 L} $
$ D_g^{O*}=\frac{\alpha ((6 \alpha-5) H+6 (1-\alpha) L)}{6 (\alpha H-\alpha L+L)} $ $ D_g^{O*}=0 $ $ D_g^{O*}=\frac{\alpha \left(4 L-H c_o-3 H\right)}{4 L} $
$ D_o^{G*}=\frac{\alpha -1}{12} \left(3 c_o-\frac{10 L}{\alpha H-\alpha L+L}+9\right) $ $ D_o^{G*}=\frac{(\alpha -1) \left(H c_o+3 H-4 L\right)}{4 H} $ $ D_o^{G*}=0 $
$ D_g^{G*}=\frac{(1-\alpha) (6 \alpha H-6 \alpha L+L)}{6 (\alpha H-\alpha L+L)} $ $ D_g^{G*}=\frac{(1-\alpha) (H-L)}{H} $ $ D_g^{G*}=\frac{(\alpha -1) (c_o-1)}{4} $
Table 3.  Optimal results of the centralized model
Optimums Case $ \tilde{I} $ Case $ \tilde{\mathit{II}} $ Case $ \tilde{\mathit{III}} $
Prices $ \tilde{p}_o^*=\frac{c_o+3}{4} $; $ \tilde{p}_o^*=\frac{c_o+3}{4} $; $ \tilde{p}_o^*=\frac{c_o+3}{4} $;
$ \tilde{p}_g^*=\frac{c_o}{2}+\frac{\tilde{\Theta}_1}{18 k (\alpha H-\alpha L+L)^2} $ $ \tilde{p}_g^*=\frac{c_o}{2}+\frac{\tilde{\Theta}_2}{2 k ((\alpha -1) L-\alpha H)} $ $ \tilde{p}_g^*=\left(c_o+1\right) (\frac{H^2 c_o}{k} $
$ \qquad\quad+\frac{\tilde{\Theta}_3}{2 k ((\alpha -1) L-\alpha H)}) $
Greenness $ \tilde{x}^*=\frac{2 H L}{3 k (\alpha (H- L)+L)} $ $ \tilde{x}^*=x_{D_g^O} $ $ \tilde{x}^*=x_{D_o^G} $
Demands $ \tilde{D}_o^{O*}=\frac{\alpha }{6} \left(\frac{4 H}{\alpha H-\alpha L+L}-3 c_o-3\right) $ $ \tilde{D}_o^{O*}=\frac{1}{2} \alpha \left(1-c_o\right) $ $ \tilde{D}_o^{O*}=\frac{\alpha \left(c_o+1\right) (H-L)}{2 L} $
$ \tilde{D}_g^{O*}=\frac{\alpha ((3 \alpha-2) H+3 (1-\alpha) L)}{3 (\alpha H-\alpha L+L)} $ $ \tilde{D}_g^{O*}=0 $ $ \tilde{D}_g^{O*}=\frac{\alpha \left(2 L-H c_o+H\right)}{2 L} $
$ \tilde{D}_o^{G*}=\frac{1}{6} (\alpha -1) \left(3 c_o-\frac{4 L}{\alpha H-\alpha L+L}+3\right) $ $ \tilde{D}_o^{G*}=\frac{(\alpha -1) \left(H c_o+H-2 L\right)}{2 H} $ $ \tilde{D}_o^{G*}=0 $
$ \tilde{D}_g^{G*}=-\frac{(\alpha -1) (3 \alpha H-3 \alpha L+L)}{3 (\alpha H-\alpha L+L)} $ $ \tilde{D}_g^{G*}=\frac{(1-\alpha) (H-L)}{H} $ $ \tilde{D}_g^{G*}=\frac{1}{2} (\alpha -1) \left(c_o-1\right) $
Optimums Case $ \tilde{I} $ Case $ \tilde{\mathit{II}} $ Case $ \tilde{\mathit{III}} $
Prices $ \tilde{p}_o^*=\frac{c_o+3}{4} $; $ \tilde{p}_o^*=\frac{c_o+3}{4} $; $ \tilde{p}_o^*=\frac{c_o+3}{4} $;
$ \tilde{p}_g^*=\frac{c_o}{2}+\frac{\tilde{\Theta}_1}{18 k (\alpha H-\alpha L+L)^2} $ $ \tilde{p}_g^*=\frac{c_o}{2}+\frac{\tilde{\Theta}_2}{2 k ((\alpha -1) L-\alpha H)} $ $ \tilde{p}_g^*=\left(c_o+1\right) (\frac{H^2 c_o}{k} $
$ \qquad\quad+\frac{\tilde{\Theta}_3}{2 k ((\alpha -1) L-\alpha H)}) $
Greenness $ \tilde{x}^*=\frac{2 H L}{3 k (\alpha (H- L)+L)} $ $ \tilde{x}^*=x_{D_g^O} $ $ \tilde{x}^*=x_{D_o^G} $
Demands $ \tilde{D}_o^{O*}=\frac{\alpha }{6} \left(\frac{4 H}{\alpha H-\alpha L+L}-3 c_o-3\right) $ $ \tilde{D}_o^{O*}=\frac{1}{2} \alpha \left(1-c_o\right) $ $ \tilde{D}_o^{O*}=\frac{\alpha \left(c_o+1\right) (H-L)}{2 L} $
$ \tilde{D}_g^{O*}=\frac{\alpha ((3 \alpha-2) H+3 (1-\alpha) L)}{3 (\alpha H-\alpha L+L)} $ $ \tilde{D}_g^{O*}=0 $ $ \tilde{D}_g^{O*}=\frac{\alpha \left(2 L-H c_o+H\right)}{2 L} $
$ \tilde{D}_o^{G*}=\frac{1}{6} (\alpha -1) \left(3 c_o-\frac{4 L}{\alpha H-\alpha L+L}+3\right) $ $ \tilde{D}_o^{G*}=\frac{(\alpha -1) \left(H c_o+H-2 L\right)}{2 H} $ $ \tilde{D}_o^{G*}=0 $
$ \tilde{D}_g^{G*}=-\frac{(\alpha -1) (3 \alpha H-3 \alpha L+L)}{3 (\alpha H-\alpha L+L)} $ $ \tilde{D}_g^{G*}=\frac{(1-\alpha) (H-L)}{H} $ $ \tilde{D}_g^{G*}=\frac{1}{2} (\alpha -1) \left(c_o-1\right) $
Table 4.  Sensitivity results on demands and profits when $ H>L>0 $
Cases $ D_o^{O*} $ $ D_g^{O*} $ $ D_o^{G*} $ $ D_g^{G*} $ $ \Pi_M^* $ $ \Pi_R^* $
$ \alpha $ $ (\uparrow) $ $ \uparrow(\downarrow)^{(1)} $ $ \uparrow(\downarrow)^{(2)} $ $ \uparrow(\downarrow)^{(1)} $ $ \uparrow(\downarrow)^{(2)} $ $ \downarrow $ $ \downarrow $
$ H $ $ (\uparrow) $ $ \uparrow $ $ \downarrow $ $ \downarrow $ $ \uparrow $ $ \uparrow $ $ \uparrow $
$ L $ $ (\uparrow) $ $ \downarrow $ $ \uparrow $ $ \uparrow $ $ \downarrow $ $ \uparrow $ $ \uparrow $
$"\uparrow"$ denotes increases and $"\downarrow"$ denotes decreases;
${(1)}$: $\uparrow (\downarrow)$ if $\frac{HL}{(\alpha H-\alpha L+L)^2} >(<)\frac{9+c_o}{10}$ and ${(2)}$: $\uparrow (\downarrow)$ if $\frac{HL}{(\alpha H-\alpha L+L)^2} >(<)\frac{6}{5}$.
Cases $ D_o^{O*} $ $ D_g^{O*} $ $ D_o^{G*} $ $ D_g^{G*} $ $ \Pi_M^* $ $ \Pi_R^* $
$ \alpha $ $ (\uparrow) $ $ \uparrow(\downarrow)^{(1)} $ $ \uparrow(\downarrow)^{(2)} $ $ \uparrow(\downarrow)^{(1)} $ $ \uparrow(\downarrow)^{(2)} $ $ \downarrow $ $ \downarrow $
$ H $ $ (\uparrow) $ $ \uparrow $ $ \downarrow $ $ \downarrow $ $ \uparrow $ $ \uparrow $ $ \uparrow $
$ L $ $ (\uparrow) $ $ \downarrow $ $ \uparrow $ $ \uparrow $ $ \downarrow $ $ \uparrow $ $ \uparrow $
$"\uparrow"$ denotes increases and $"\downarrow"$ denotes decreases;
${(1)}$: $\uparrow (\downarrow)$ if $\frac{HL}{(\alpha H-\alpha L+L)^2} >(<)\frac{9+c_o}{10}$ and ${(2)}$: $\uparrow (\downarrow)$ if $\frac{HL}{(\alpha H-\alpha L+L)^2} >(<)\frac{6}{5}$.
Table 5.  Notations
Parameters Descriptions
$ D_o^i $ The demand of product "o" on segment "$ i $", where $ i=O, G $
$ D_g^i $ The demand of product "g" on segment "$ i $", where $ i=O, G $
$ \alpha $ $ \alpha $ and $ 1-\alpha $ denotes the market size of segments "$ O $" and "$ G $"
$ k $ The cost factor related for the green added product
$ c_o $ The unit production cost of producing a primary product
$ H, L $ The consumers' preference of the added greenness of the product.
$ w_o, p_o $ Unit wholesale price and retail price of the primary product
$ w_g, p_g $ Unit wholesale price and retail price of the green product
$ x $ The green level of the products designed by the manufacturer.
$ D_o $ Total demand of product "o" across the two segments, $ D_o=\sum D_o^i $
$ D_g $ Total demand of product "g" across the two segments, $ D_g=\sum D_g^i $
$ \tilde{\Pi}_C $ The total profit of the integrated supply chain
$ \Pi_M $ The profit of the manufacturer under decentralized supply chain
$ \Pi_R $ The profit of the retailer under decentralized supply chain
Parameters Descriptions
$ D_o^i $ The demand of product "o" on segment "$ i $", where $ i=O, G $
$ D_g^i $ The demand of product "g" on segment "$ i $", where $ i=O, G $
$ \alpha $ $ \alpha $ and $ 1-\alpha $ denotes the market size of segments "$ O $" and "$ G $"
$ k $ The cost factor related for the green added product
$ c_o $ The unit production cost of producing a primary product
$ H, L $ The consumers' preference of the added greenness of the product.
$ w_o, p_o $ Unit wholesale price and retail price of the primary product
$ w_g, p_g $ Unit wholesale price and retail price of the green product
$ x $ The green level of the products designed by the manufacturer.
$ D_o $ Total demand of product "o" across the two segments, $ D_o=\sum D_o^i $
$ D_g $ Total demand of product "g" across the two segments, $ D_g=\sum D_g^i $
$ \tilde{\Pi}_C $ The total profit of the integrated supply chain
$ \Pi_M $ The profit of the manufacturer under decentralized supply chain
$ \Pi_R $ The profit of the retailer under decentralized supply chain
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