January  2017, 13(1): 297-312. doi: 10.3934/jimo.2016018

Service product pricing strategies based on time-sensitive customer choice behavior

1. 

School of Business, Anhui Provincial Key Laboratory of Regional Logistics Planning, and Modern Logistics Engineering, Fuyang Normal University, Fuyang, Anhui 236037, China

2. 

School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, Anhui 233030, China

* Corresponding author: Xuemei Zhang

Received  March 2015 Published  March 2016

Product pricing strategy has a significant impact on a service company's competitive edge. Considering the heterogeneous and time-sensitive customer choice behavior, monopoly service companies price their service products depending on cost parameters as well as time-sensitive customer choice behavior. According to the different time sensitivity, customers are classed into two groups (i.e., two market segments). By considering the impact of customer choice behavior, this paper investigates how a monopoly service firm decides its service product's response time and price under two product design strategies, i.e., offering two service products respectively to two market segments, or offering one standard service product to two market segments. Results indicate that, under the two strategies, the service firm adopts a segmented pricing strategy based on the customer perceived values and time-sensitive degrees. Besides, the service firm's profit under the strategy of offering two products is always higher than that under the other strategy. This indicates that, along with the individuation of customer demand, firms should firstly segment the market, and then, design targeted products for different customers. As a result, the degree of customer satisfaction can be increased, and firms can obtain higher profits.

Citation: Xuemei Zhang, Malin Song, Guangdong Liu. Service product pricing strategies based on time-sensitive customer choice behavior. Journal of Industrial & Management Optimization, 2017, 13 (1) : 297-312. doi: 10.3934/jimo.2016018
References:
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[2]

G. Allon and I. Gurvich, Pricing and dimensioning competing large-scale service providers, Manufacturing & Service Operations Management, 12 (2010), 449-469. doi: 10.1287/msom.1090.0280. Google Scholar

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G. Aydin and J. K. Ryan, Product Line Selection and Pricing Under the Multinomial Logic Choice Model, Working Paper, Purdue University, West Lafayette, IN, 2000.Google Scholar

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T. C. A. Bashyam, Service design and price competition in business information services, Operations Research, 48 (2000), 362-375. doi: 10.1287/opre.48.3.362.12434. Google Scholar

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A. BurattoL. Grosset and B. Viscolani, Advertising a new product in a segmented market, European Journal of Operational Research, 175 (2006), 1262-1267. doi: 10.1016/j.ejor.2005.06.035. Google Scholar

[7]

G. P. Cachon and P. Feldman, Pricing services subject to congestion: charge per-use fees or sell subscriptions?, Manufacturing & Service Operations Management, 13 (2011), 244-260. doi: 10.1287/msom.1100.0315. Google Scholar

[8]

G. P. Cachon and R. Swinney, The value of fast fashion: quick response, enhanced design, and strategic consumer behavior, Management Science, 57 (2011), 778-795. doi: 10.1287/mnsc.1100.1303. Google Scholar

[9]

J. M. Chaneton and G. Vulcano, Computing bid prices for revenue management under choice behavior, Manufacturing & Service Operations Management, 13 (2011), 452-470. doi: 10.1287/msom.1110.0338. Google Scholar

[10]

X. ChenL. Li and M. Zhou, Manufacturer's pricing strategy for supply chain with warranty period-dependent demand, Omega, 40 (2012), 807-816. doi: 10.1016/j.omega.2011.12.010. Google Scholar

[11]

Y. J. ChenB. Tomlin and Y. M. Wang, Coproduct technologies: product line design and process innovation, Management Science, 59 (2013), 2772-2789. doi: 10.1287/mnsc.2013.1738. Google Scholar

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S. Dewan and H. Mendelson, Information technology and time-based competition in financial markets, Management Science, 44 (1998), 595-609. doi: 10.1287/mnsc.44.5.595. Google Scholar

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M. Draganska and D. Jain, Product line length as a competitive tool, Journal of Economics and Management Strategy, 14 (2005), 1-28. doi: 10.1111/j.1430-9134.2005.00032.x. Google Scholar

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A. Federgruen and M. Hu, Multi-product price and assortment competition, Operations Research, 63 (2015), 572-584. doi: 10.1287/opre.2015.1380. Google Scholar

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H. S. Heese and J. M. Swaminathan, Product line design with component commonality and cost-reduction effort, Manufacturing & Service Operations Management, 8 (2006), 206-219. doi: 10.1287/msom.1060.0103. Google Scholar

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[19]

K. HosanagarR. KrishnanJ. Chuang and V. Choudhary, Pricing and resource allocation in caching services with multiple levels of quality of service, Management Science, 51 (2005), 1844-1859. doi: 10.1287/mnsc.1050.0420. Google Scholar

[20]

M. HuX. Li and M. Z. Shi, Product and pricing decisions in crowdfunding, Marketing Science, 34 (2015), 331-345. Google Scholar

[21]

Z. S. HuaX. M. Zhang and X. Y. Xu, Product design strategies in a manufacturer-retailer distribution channel, Omega, 39 (2011), 23-32. doi: 10.1016/j.omega.2010.02.001. Google Scholar

[22]

Z. M. HuangX. S. Li and V. Mahajan, An analysis of manufacturer-retailer supply chain coordination in cooperative advertising, Decision Science, 33 (2002), 469-494. doi: 10.1111/j.1540-5915.2002.tb01652.x. Google Scholar

[23]

S. Kekre and K. Srinivasan, Broader product line: A necessity to achieve success?, Management Science, 36 (1990), 1216-1223. doi: 10.1287/mnsc.36.10.1216. Google Scholar

[24]

S. W. Kim and P. Bell, Optimal pricing and production decisions in the presence of symmetrical and asymmetrical substitution, Omega, 39 (2011), 528-538. doi: 10.1016/j.omega.2010.11.002. Google Scholar

[25]

S. A. KrishnanM. F. Pac and S. Veeraraghavan, Quality-speed conundrum: Trade-offs in customer-intensive services, Management Science, 57 (2011), 40-56. Google Scholar

[26]

V. Krishnan and W. Zhu, Designing a family of development-intensive products, Management Science, 52 (2006), 813-825. doi: 10.1287/mnsc.1050.0492. Google Scholar

[27]

Z. LiT. Peng and X. Y. Li, Dynamic pricing in the presence of consumer inertia, Omega, 40 (2012), 137-148. Google Scholar

[28]

Q. Liu and Y. H. Tang, Construction of heterogeneous conjoint choice designs: A new approach, Marketing Science, 34 (2015), 346-366. doi: 10.1287/mksc.2014.0897. Google Scholar

[29]

C. Maglaras and A. Zeevi, Pricing and design of differentiated services: Approximate analysis and structural insights, Operations Research, 53 (2005), 242-262. doi: 10.1287/opre.1040.0172. Google Scholar

[30]

K. S. Moorthy, Market segmentation, self-selection, and product line design, Marketing Science, 3 (1984), 288-307. doi: 10.1287/mksc.3.4.288. Google Scholar

[31]

G. Rubera, Design innovativeness and product sales' evolution, Marketing Science, 34 (2015), 98-115. doi: 10.1287/mksc.2014.0875. Google Scholar

[32]

A. Rubinstein, Response time and decision making: An experimental study, Judgment and Decision Making, 8 (2013), 540-551. Google Scholar

[33]

A. N. SadighS. K. Chaharsooghi and M. Sheikhmohammady, A game theoretic approach to coordination of pricing, advertising, and inventory decisions in a competitive supply chain, Journal of Industrial and Management Optimization, 12 (2016), 337-355. doi: 10.3934/jimo.2016.12.337. Google Scholar

[34]

R. Saibal and E. M. Jewkes, Customer lead time management when both demand and price are lead time sensitive, European Journal of Operational Research, 153 (2004), 769-781. doi: 10.1016/S0377-2217(02)00655-0. Google Scholar

[35]

A. Sen, A comparison of fixed and dynamic pricing policies in revenue management, Omega, 41 (2013), 586-597. doi: 10.2139/ssrn.2101325. Google Scholar

[36]

W. X. Shang and L. M. Liu, Promised delivery time and capacity games in time-based competition, Management Science, 57 (2011), 599-610. doi: 10.1287/mnsc.1100.1292. Google Scholar

[37]

R. Stenbacka and M. M. Tombak, Time-based competition and the privatization of services, The Journal of Industrial Economics, 43 (1995), 435-454. doi: 10.2307/2950553. Google Scholar

[38]

A. SugandhaG. AnshuG. KannanP. C. Jha and M. Ieva, Effect of repeat purchase and dynamic market size on diffusion of an innovative technological consumer product in a segmented market, Technological and Economic Development of Economy, 20 (2014), 97-115. Google Scholar

[39]

W. H. Tsai and S. J. Hung, Dynamic pricing and revenue management process in Internet retailing under uncertainty: An integrated real options approach, Omega, 37 (2009), 471-481. doi: 10.1016/j.omega.2007.07.001. Google Scholar

[40]

G. Van-Ryzin and G. Vulcano, Computing virtual nesting controls for network revenue management under customer choice behavior, Manufacturing & Service Operations Management, 10 (2008), 448-467. doi: 10.1287/msom.1070.0210. Google Scholar

[41]

J. M. Villas-Boas, Product line design for a distribution channel, Marketing Science, 17 (1998), 156-169. doi: 10.1287/mksc.17.2.156. Google Scholar

[42]

C. WuS. M. Hsu and T. W. Tien, A new approach to evaluation the market share of service industries, Journal of the Operational Research Society, 49 (1998), 948-952. Google Scholar

[43]

G. XieW. Y. Yue and S. Y. Wang, Optimal selection of cleaner products in a green supply chain with risk aversion, Journal of Industrial and Management Optimization, 11 (2015), 515-528. doi: 10.3934/jimo.2015.11.515. Google Scholar

[44]

H. Xiong and Y. J. Chen, Product line design with deliberation costs: A two-stage process, Decision Analysis, 10 (2013), 225-244. doi: 10.1287/deca.2013.0273. Google Scholar

[45]

S. Xu and Y. Akcay, Joint Dynamic Pricing of Multiple Perishable Products Under Consumer Choice, Working Paper, Smeal College of Business, the Pennsylvania State University, 2008.Google Scholar

[46]

P. C. YangH. M. WeeS. L. Chung and Y. Y. Huang, Pricing and replenishment strategy for a multi-market deteriorating product with time-varying and price-sensitive demand, Journal of Industrial and Management Optimization, 9 (2013), 769-787. doi: 10.3934/jimo.2013.9.769. Google Scholar

[47]

T. F. Ye and S. H. Ma, Discount-offering and demand-rejection decisions for substitutable products with different profit levels, Journal of Industrial and Management Optimization, 12 (2016), 45-71. doi: 10.3934/jimo.2016.12.45. Google Scholar

[48]

J. X. ZhangZ. Y. Bai and W. S. Tang, Optimal pricing policy for deteriorating items with preservation technology investment, Journal of Industrial and Management Optimization, 10 (2014), 1261-1277. doi: 10.3934/jimo.2014.10.1261. Google Scholar

[49]

D. Zhang and W. L. Cooper, Revenue management for parallel flights with customer-choice behavior, Operations Research, 53 (2005), 415-431. doi: 10.1287/opre.1050.0194. Google Scholar

[50]

X. M. ZhangY. WeiJ. Q. Liu and G. Chen, Product design strategy with commonality by considering customer-choice behavior in supply chain, Asia-Pacific Journal of Operational Research, 32 (2015), 1-22. doi: 10.1142/S0217595915500372. Google Scholar

[51]

X. M. ZhangX. Y. Xu and P. He, New product design strategies with subsidy polices, Journal of Systems Science and Systems Engineering, 21 (2012), 356-371. Google Scholar

[52]

L. H. ZhangX. Zou and J. X. Qi, A trade-off between time and cost in scheduling repetitive construction projects, Journal of Industrial and Management Optimization, 11 (2015), 1423-1434. doi: 10.3934/jimo.2015.11.1423. Google Scholar

show all references

References:
[1]

P. AfècheO. Baron and Y. Kerner, Pricing time-sensitive services based on realized performance, Manufacturing & Service Operations Management, 15 (2013), 492-506. Google Scholar

[2]

G. Allon and I. Gurvich, Pricing and dimensioning competing large-scale service providers, Manufacturing & Service Operations Management, 12 (2010), 449-469. doi: 10.1287/msom.1090.0280. Google Scholar

[3]

M. Armony and A. Mandelbaum, Routing and staffing in large-scale service systems: The case of homogeneous impatient customers and heterogeneous servers, Operations Research, 59 (2011), 50-65. doi: 10.1287/opre.1100.0878. Google Scholar

[4]

G. Aydin and J. K. Ryan, Product Line Selection and Pricing Under the Multinomial Logic Choice Model, Working Paper, Purdue University, West Lafayette, IN, 2000.Google Scholar

[5]

T. C. A. Bashyam, Service design and price competition in business information services, Operations Research, 48 (2000), 362-375. doi: 10.1287/opre.48.3.362.12434. Google Scholar

[6]

A. BurattoL. Grosset and B. Viscolani, Advertising a new product in a segmented market, European Journal of Operational Research, 175 (2006), 1262-1267. doi: 10.1016/j.ejor.2005.06.035. Google Scholar

[7]

G. P. Cachon and P. Feldman, Pricing services subject to congestion: charge per-use fees or sell subscriptions?, Manufacturing & Service Operations Management, 13 (2011), 244-260. doi: 10.1287/msom.1100.0315. Google Scholar

[8]

G. P. Cachon and R. Swinney, The value of fast fashion: quick response, enhanced design, and strategic consumer behavior, Management Science, 57 (2011), 778-795. doi: 10.1287/mnsc.1100.1303. Google Scholar

[9]

J. M. Chaneton and G. Vulcano, Computing bid prices for revenue management under choice behavior, Manufacturing & Service Operations Management, 13 (2011), 452-470. doi: 10.1287/msom.1110.0338. Google Scholar

[10]

X. ChenL. Li and M. Zhou, Manufacturer's pricing strategy for supply chain with warranty period-dependent demand, Omega, 40 (2012), 807-816. doi: 10.1016/j.omega.2011.12.010. Google Scholar

[11]

Y. J. ChenB. Tomlin and Y. M. Wang, Coproduct technologies: product line design and process innovation, Management Science, 59 (2013), 2772-2789. doi: 10.1287/mnsc.2013.1738. Google Scholar

[12]

C. S. M. CurrieR.C. H. Cheng and H. K. Smith, Dynamic pricing of airline tickets with competition, Journal of the Operational Research Society, 59 (2008), 1026-1037. doi: 10.1057/palgrave.jors.2602425. Google Scholar

[13]

P. DesaiS. KekreS. Radhakrishnan and K. Srinivasan, Product differentiation and commonality in design: Balancing revenue and cost drivers, Management Science, 47 (2001), 37-51. doi: 10.1287/mnsc.47.1.37.10672. Google Scholar

[14]

S. Dewan and H. Mendelson, Information technology and time-based competition in financial markets, Management Science, 44 (1998), 595-609. doi: 10.1287/mnsc.44.5.595. Google Scholar

[15]

M. Draganska and D. Jain, Product line length as a competitive tool, Journal of Economics and Management Strategy, 14 (2005), 1-28. doi: 10.1111/j.1430-9134.2005.00032.x. Google Scholar

[16]

A. Federgruen and M. Hu, Multi-product price and assortment competition, Operations Research, 63 (2015), 572-584. doi: 10.1287/opre.2015.1380. Google Scholar

[17]

H. S. Heese and J. M. Swaminathan, Product line design with component commonality and cost-reduction effort, Manufacturing & Service Operations Management, 8 (2006), 206-219. doi: 10.1287/msom.1060.0103. Google Scholar

[18]

A. V. Hill and I. S. Khosla, Models for optimal lead time reduction, Production and Operations Management, 1 (1992), 185-197. doi: 10.1111/j.1937-5956.1992.tb00351.x. Google Scholar

[19]

K. HosanagarR. KrishnanJ. Chuang and V. Choudhary, Pricing and resource allocation in caching services with multiple levels of quality of service, Management Science, 51 (2005), 1844-1859. doi: 10.1287/mnsc.1050.0420. Google Scholar

[20]

M. HuX. Li and M. Z. Shi, Product and pricing decisions in crowdfunding, Marketing Science, 34 (2015), 331-345. Google Scholar

[21]

Z. S. HuaX. M. Zhang and X. Y. Xu, Product design strategies in a manufacturer-retailer distribution channel, Omega, 39 (2011), 23-32. doi: 10.1016/j.omega.2010.02.001. Google Scholar

[22]

Z. M. HuangX. S. Li and V. Mahajan, An analysis of manufacturer-retailer supply chain coordination in cooperative advertising, Decision Science, 33 (2002), 469-494. doi: 10.1111/j.1540-5915.2002.tb01652.x. Google Scholar

[23]

S. Kekre and K. Srinivasan, Broader product line: A necessity to achieve success?, Management Science, 36 (1990), 1216-1223. doi: 10.1287/mnsc.36.10.1216. Google Scholar

[24]

S. W. Kim and P. Bell, Optimal pricing and production decisions in the presence of symmetrical and asymmetrical substitution, Omega, 39 (2011), 528-538. doi: 10.1016/j.omega.2010.11.002. Google Scholar

[25]

S. A. KrishnanM. F. Pac and S. Veeraraghavan, Quality-speed conundrum: Trade-offs in customer-intensive services, Management Science, 57 (2011), 40-56. Google Scholar

[26]

V. Krishnan and W. Zhu, Designing a family of development-intensive products, Management Science, 52 (2006), 813-825. doi: 10.1287/mnsc.1050.0492. Google Scholar

[27]

Z. LiT. Peng and X. Y. Li, Dynamic pricing in the presence of consumer inertia, Omega, 40 (2012), 137-148. Google Scholar

[28]

Q. Liu and Y. H. Tang, Construction of heterogeneous conjoint choice designs: A new approach, Marketing Science, 34 (2015), 346-366. doi: 10.1287/mksc.2014.0897. Google Scholar

[29]

C. Maglaras and A. Zeevi, Pricing and design of differentiated services: Approximate analysis and structural insights, Operations Research, 53 (2005), 242-262. doi: 10.1287/opre.1040.0172. Google Scholar

[30]

K. S. Moorthy, Market segmentation, self-selection, and product line design, Marketing Science, 3 (1984), 288-307. doi: 10.1287/mksc.3.4.288. Google Scholar

[31]

G. Rubera, Design innovativeness and product sales' evolution, Marketing Science, 34 (2015), 98-115. doi: 10.1287/mksc.2014.0875. Google Scholar

[32]

A. Rubinstein, Response time and decision making: An experimental study, Judgment and Decision Making, 8 (2013), 540-551. Google Scholar

[33]

A. N. SadighS. K. Chaharsooghi and M. Sheikhmohammady, A game theoretic approach to coordination of pricing, advertising, and inventory decisions in a competitive supply chain, Journal of Industrial and Management Optimization, 12 (2016), 337-355. doi: 10.3934/jimo.2016.12.337. Google Scholar

[34]

R. Saibal and E. M. Jewkes, Customer lead time management when both demand and price are lead time sensitive, European Journal of Operational Research, 153 (2004), 769-781. doi: 10.1016/S0377-2217(02)00655-0. Google Scholar

[35]

A. Sen, A comparison of fixed and dynamic pricing policies in revenue management, Omega, 41 (2013), 586-597. doi: 10.2139/ssrn.2101325. Google Scholar

[36]

W. X. Shang and L. M. Liu, Promised delivery time and capacity games in time-based competition, Management Science, 57 (2011), 599-610. doi: 10.1287/mnsc.1100.1292. Google Scholar

[37]

R. Stenbacka and M. M. Tombak, Time-based competition and the privatization of services, The Journal of Industrial Economics, 43 (1995), 435-454. doi: 10.2307/2950553. Google Scholar

[38]

A. SugandhaG. AnshuG. KannanP. C. Jha and M. Ieva, Effect of repeat purchase and dynamic market size on diffusion of an innovative technological consumer product in a segmented market, Technological and Economic Development of Economy, 20 (2014), 97-115. Google Scholar

[39]

W. H. Tsai and S. J. Hung, Dynamic pricing and revenue management process in Internet retailing under uncertainty: An integrated real options approach, Omega, 37 (2009), 471-481. doi: 10.1016/j.omega.2007.07.001. Google Scholar

[40]

G. Van-Ryzin and G. Vulcano, Computing virtual nesting controls for network revenue management under customer choice behavior, Manufacturing & Service Operations Management, 10 (2008), 448-467. doi: 10.1287/msom.1070.0210. Google Scholar

[41]

J. M. Villas-Boas, Product line design for a distribution channel, Marketing Science, 17 (1998), 156-169. doi: 10.1287/mksc.17.2.156. Google Scholar

[42]

C. WuS. M. Hsu and T. W. Tien, A new approach to evaluation the market share of service industries, Journal of the Operational Research Society, 49 (1998), 948-952. Google Scholar

[43]

G. XieW. Y. Yue and S. Y. Wang, Optimal selection of cleaner products in a green supply chain with risk aversion, Journal of Industrial and Management Optimization, 11 (2015), 515-528. doi: 10.3934/jimo.2015.11.515. Google Scholar

[44]

H. Xiong and Y. J. Chen, Product line design with deliberation costs: A two-stage process, Decision Analysis, 10 (2013), 225-244. doi: 10.1287/deca.2013.0273. Google Scholar

[45]

S. Xu and Y. Akcay, Joint Dynamic Pricing of Multiple Perishable Products Under Consumer Choice, Working Paper, Smeal College of Business, the Pennsylvania State University, 2008.Google Scholar

[46]

P. C. YangH. M. WeeS. L. Chung and Y. Y. Huang, Pricing and replenishment strategy for a multi-market deteriorating product with time-varying and price-sensitive demand, Journal of Industrial and Management Optimization, 9 (2013), 769-787. doi: 10.3934/jimo.2013.9.769. Google Scholar

[47]

T. F. Ye and S. H. Ma, Discount-offering and demand-rejection decisions for substitutable products with different profit levels, Journal of Industrial and Management Optimization, 12 (2016), 45-71. doi: 10.3934/jimo.2016.12.45. Google Scholar

[48]

J. X. ZhangZ. Y. Bai and W. S. Tang, Optimal pricing policy for deteriorating items with preservation technology investment, Journal of Industrial and Management Optimization, 10 (2014), 1261-1277. doi: 10.3934/jimo.2014.10.1261. Google Scholar

[49]

D. Zhang and W. L. Cooper, Revenue management for parallel flights with customer-choice behavior, Operations Research, 53 (2005), 415-431. doi: 10.1287/opre.1050.0194. Google Scholar

[50]

X. M. ZhangY. WeiJ. Q. Liu and G. Chen, Product design strategy with commonality by considering customer-choice behavior in supply chain, Asia-Pacific Journal of Operational Research, 32 (2015), 1-22. doi: 10.1142/S0217595915500372. Google Scholar

[51]

X. M. ZhangX. Y. Xu and P. He, New product design strategies with subsidy polices, Journal of Systems Science and Systems Engineering, 21 (2012), 356-371. Google Scholar

[52]

L. H. ZhangX. Zou and J. X. Qi, A trade-off between time and cost in scheduling repetitive construction projects, Journal of Industrial and Management Optimization, 11 (2015), 1423-1434. doi: 10.3934/jimo.2015.11.1423. Google Scholar

Figure 1.  The impact of perceived values $v_{h}$ and $v_{l}$ on response time
Figure 3.  The impact of perceived values $v_{h}$ and $v_{l}$ on profit
Figure 2.  The impact of perceived values $v_{h}$ and $v_{l}$ on price
Figure 4.  The impact of perceived values $v_{h}$ and $v_{l}$ on response time
Figure 6.  The impact of perceived values $v_{h}$ and $v_{l}$ on profit
Figure 5.  The impact of perceived values $v_{h}$ and $v_{l}$ on price
Figure 7.  The optimal response times under SS and MS strategies
Figure 9.  The optimal profits under SS and MS strategies
Figure 8.  The optimal prices under SS and MS strategies
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