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doi: 10.3934/jimo.2021226
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Pricing and quality decisions in a supply chain with consumers' privacy concern

School of Management, Guangdong University of Technology, Guangzhou, Guangdong 510520, China

*Corresponding author: Rui Hou

Received  July 2021 Revised  November 2021 Early access January 2022

This study considers a supply chain consists of one manufacturer produces a product with a quality level and sells it through one retailer. A stylized model is developed to investigate the impacts of consumers' privacy concerns on pricing, quality decisions, and profitability through the relationship between product quality and personal information. When consumers' privacy concern is considered, the product quality level, the wholesale price, the payoffs of the manufacturer and retailer, and consumer surplus decrease with the personal information loss, whereas the selling price increases if this loss is low. Our results also show that the retailer prefers to charge a high selling price if the information benefit and the personal information loss are low, or the information benefit is relatively high. Moreover, a "win-win-win" outcome can be achieved among the manufacturer, retailer, and consumers if the personal information loss is sufficiently low. In the case of quality-differentiated products, however, although the manufacturer improves the product quality level, the wholesale prices are increased if the information benefit and the personal information loss are low, or the information benefit is high.

Citation: You Zhao, Zibin Cui, Jianxin Chen, Rui Hou. Pricing and quality decisions in a supply chain with consumers' privacy concern. Journal of Industrial and Management Optimization, doi: 10.3934/jimo.2021226
References:
[1]

A. AcquistiC. Taylor and L. Wagman, The economics of privacy, Social Science Electronic Publishing, 54 (2016), 442-492. 

[2]

S. BuehlerA. Schmutzler and M. A. Benz, Infrastructure quality in deregulated industries: Is there an underinvestment problem?, International Journal of Industrial Organization, 22 (2004), 253-267.  doi: 10.1016/j.ijindorg.2003.07.004.

[3]

R. Casadesus-Masanell and A. Hervas-Drane, Competing with privacy, Management Science, 61 (2015), 229-246. 

[4]

J.-H. CheahX.-J. LimH. TingY. Liu and S. Quach, Are privacy concerns still relevant? revisiting consumer behaviour in omnichannel retailing, Journal of Retailing and Consumer Services, (2020), 102242.  doi: 10.1016/j.jretconser.2020.102242.

[5]

J. ChenL. LiangD. Q. Yao and S. Sun, Price and quality decisions in dual-channel supply chains, European J. Oper. Res., 259 (2017), 935-948.  doi: 10.1016/j.ejor.2016.11.016.

[6]

V. ConitzerC. R. Taylor and L. Wagman, Hide and seek: Costly consumer privacy in a market with repeat purchases, Marketing Science, 31 (2012), 277-292. 

[7]

C. Conti and P. Reverberi, Price discrimination and product quality under opt-in privacy regulation, Information Economics and Policy, 55 (2021), 100912.  doi: 10.1016/j.infoecopol.2020.100912.

[8]

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.

[9]

K. Degirmenci, Mobile users' information privacy concerns and the role of app permission requests, International Journal of Information Management, 50 (2020), 261-272.  doi: 10.1016/j.ijinfomgt.2019.05.010.

[10]

De sai and S. Preyas, Quality segmentation in spatial markets: When does cannibalization affect product line design?, Marketing Science, 20 (2001), 265-283. 

[11]

S. -Z. Dong, L. Yang, B. Ding, C. -H. Wu and X. -F. Shao, Pricing strategy with customers' privacy concerns in smart-x systems, Enterprise Information Systems, (2020), 1–27. doi: 10.1080/17517575.2020.1802515.

[12]

F. Gao and X. Su, Omnichannel retail operations with buy-online-and-pick-up-in-store, Management Science, 63 (2016), 2478-2492.  doi: 10.1287/mnsc.2016.2473.

[13]

A. Ghose, B. Li and S. Liu, Nudging mobile customers with real-time social dynamics, Social Science Electronic Publishing.

[14]

A. GoliH. Khademi-ZareR. Tavakkoli-MoghaddamA. SadeghiehM. Sasanian and R. M. Kordestanizadeh, An integrated approach based on artificial intelligence and novel meta-heuristic algorithms to predict demand for dairy products: A case study, Network: Computation in Neural Systems, 32 (2021), 1-35.  doi: 10.1080/0954898X.2020.1849841.

[15]

A. Goli and B. Malmir, A covering tour approach for disaster relief locating and routing with fuzzy demand, International Journal of Intelligent Transportation Systems Research, 18 (2019), 140-152.  doi: 10.1007/s13177-019-00185-2.

[16]

A. GoliE. B. Tirkolaee and N. S. Aydin, Fuzzy integrated cell formation and production scheduling considering automated guided vehicles and human factors, IEEE Transactions on Fuzzy Systems, 29 (2021), 3686-3695.  doi: 10.1109/TFUZZ.2021.3053838.

[17]

A. GoliH. K. ZareR. Tavakkoli-Moghaddam and A. Sadeghieh, Application of robust optimization for a product portfolio problem using an invasive weed optimization algorithm, Numer. Algebra Control Optim., 9 (2019), 187-209.  doi: 10.3934/naco.2019014.

[18]

A. HaX. Long and J. Nasiry, Quality in supply chain encroachment, Manufacturing & Service Operations Management, 18 (2016), 280-298.  doi: 10.1287/msom.2015.0562.

[19]

Y. HeQ. XuB. Xu and P. Wu, Supply chain coordination in quality improvement with reference effects, Journal of the Operational Research Society, 67 (2016), 1158-1168.  doi: 10.1057/jors.2016.10.

[20]

J. JaisinghJ. BarronS. Mehta and A. Chaturvedi, Privacy and pricing personal information, European J. Oper. Res., 187 (2008), 857-870.  doi: 10.1016/j.ejor.2006.03.062.

[21]

A. Jeuland and S. Shugan, Managing channel profits, Marketing Science, 2 (1988), 239-272. 

[22]

E. Kim and B. Lee, E-service quality competition through personalization under consumer privacy concerns, Electronic Commerce Research and Applications, 8 (2009), 182-190.  doi: 10.1016/j.elerap.2009.04.001.

[23]

B. KohS. Raghunathan and B. R. Nault, Is voluntary profiling welfare enhancing?, MIS Quarterly, 41 (2017), 23-41.  doi: 10.25300/MISQ/2017/41.1.02.

[24]

X. LinY. W. Zhou and R. Hou, Impact of a "buy-online-and-pickup-in-store" channel on price and quality decisions in a supply chain, European J. Oper. Res., 294 (2021), 922-935.  doi: 10.1016/j.ejor.2020.03.064.

[25]

Y. LiuH. Shi and N. C. Petruzzi, Optimal quality and quantity provisions for centralized vs. decentralized distribution: Market size uncertainty effects, European J. Oper. Res., 265 (2018), 1144-1158.  doi: 10.1016/j.ejor.2017.08.030.

[26]

R. LotfiB. KargarS. H. HoseiniS. NazariS. Safavi and G. W. Weber, Resilience and sustainable supply chain network design by considering renewable energy, International Journal of Energy Research, 45 (2021), 17749-17766.  doi: 10.1002/er.6943.

[27]

R. LotfiN. Mardani and G.-W. Weber, Robust bi-level programming for renewable energy location, International Journal of Energy Research, 45 (2021), 7521-7534.  doi: 10.1002/er.6332.

[28]

R. LotfiM. NayeriS. M. Sajadifar and N. Mardani, Determination of start times and ordering plans for two-period projects with interdependent demand in project-oriented organizations: A case study on molding industry, Journal of Project Management, 2 (2017), 119-142.  doi: 10.5267/j.jpm.2017.9.001.

[29]

R. LotfiY. Zare MehrjerdiM. PishvaeeA. Sadegheih and G.-W. Weber, A robust optimization model for sustainable and resilient closed-loop supply chain network design considering conditional value at risk, Numer. Algebra Control Optim., 11 (2021), 221-253.  doi: 10.3934/naco.2020023.

[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.

[31]

M. Mussa and S. Rosen, Monopoly and product quality, J. Econom. Theory, 18 (1978), 301-317.  doi: 10.1016/0022-0531(78)90085-6.

[32]

N. J. OgbukeY. Y. YusufK. Dharma and B. A. Mercangoz, Big data supply chain analytics: Ethical, privacy and security challenges posed to business, industries and society, Production Planning & Control, (2020), 1-15.  doi: 10.1080/09537287.2020.1810764.

[33]

E. ÖzceylanC. CetinkayaN. Demirel and O. Sabirlioğglu, Impacts of additive manufacturing on supply chain flow: A simulation approach in healthcare industry, Logistics, 2 (2018), 1. 

[34]

S. M. PahlevanS. Hosseini and A. Goli, Sustainable supply chain network design using products' life cycle in the aluminum industry, Environmental Science and Pollution Research, (2021), 1-25.  doi: 10.1007/s11356-020-12150-8.

[35]

M. RodrigoS. Z. Wilfried and V. Tommaso, The value of personal information in online markets with endogenous privacy, Management Science, 65 (2019), 1342-1362. 

[36]

H. ShiY. Liu and N. C. Petruzzi, Consumer heterogeneity, product quality, and distribution channels, Management Science, 59 (2013), 1162-1176.  doi: 10.1287/mnsc.1120.1604.

[37]

Y. SongT. FanY. Tang and F. Zou, Quality information acquisition and ordering decisions with risk aversion, International Journal of Production Research, 59 (2021), 6864-6880.  doi: 10.1080/00207543.2020.1828640.

[38]

G. J. Stigler, The law and economics of privacy – an introduction to privacy in economics and politics, Journal of Legal Studies, 9 (1980), 623-644. 

[39]

S. WangQ. Hu and W. Liu, Price and quality-based competition and channel structure with consumer loyalty, European J. Oper. Res., 262 (2017), 563-574.  doi: 10.1016/j.ejor.2017.03.052.

[40]

X. Xu, Optimal price and product quality decisions in a distribution channel, Management Science, 55 (2009), 1347-1352.  doi: 10.1287/mnsc.1090.1023.

[41]

Y. YuY. Wang and Y. Liu, Product quality and quantity with responsive pricing, International Journal of Production Research, 59 (2021), 7160-7178.  doi: 10.1080/00207543.2020.1836418.

[42]

Q. ZhangW. TangG. Zaccour and J. Zhang, Should a manufacturer give up pricing power in a vertical information-sharing channel?, European J. Oper. Res., 276 (2019), 910-928.  doi: 10.1016/j.ejor.2019.01.054.

[43]

Z. ZhangK. Joseph and R. Subramaniam, Probabilistic selling in quality-differentiated markets, Management Science, 61 (2015), 1959-1977.  doi: 10.1287/mnsc.2014.1974.

show all references

References:
[1]

A. AcquistiC. Taylor and L. Wagman, The economics of privacy, Social Science Electronic Publishing, 54 (2016), 442-492. 

[2]

S. BuehlerA. Schmutzler and M. A. Benz, Infrastructure quality in deregulated industries: Is there an underinvestment problem?, International Journal of Industrial Organization, 22 (2004), 253-267.  doi: 10.1016/j.ijindorg.2003.07.004.

[3]

R. Casadesus-Masanell and A. Hervas-Drane, Competing with privacy, Management Science, 61 (2015), 229-246. 

[4]

J.-H. CheahX.-J. LimH. TingY. Liu and S. Quach, Are privacy concerns still relevant? revisiting consumer behaviour in omnichannel retailing, Journal of Retailing and Consumer Services, (2020), 102242.  doi: 10.1016/j.jretconser.2020.102242.

[5]

J. ChenL. LiangD. Q. Yao and S. Sun, Price and quality decisions in dual-channel supply chains, European J. Oper. Res., 259 (2017), 935-948.  doi: 10.1016/j.ejor.2016.11.016.

[6]

V. ConitzerC. R. Taylor and L. Wagman, Hide and seek: Costly consumer privacy in a market with repeat purchases, Marketing Science, 31 (2012), 277-292. 

[7]

C. Conti and P. Reverberi, Price discrimination and product quality under opt-in privacy regulation, Information Economics and Policy, 55 (2021), 100912.  doi: 10.1016/j.infoecopol.2020.100912.

[8]

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.

[9]

K. Degirmenci, Mobile users' information privacy concerns and the role of app permission requests, International Journal of Information Management, 50 (2020), 261-272.  doi: 10.1016/j.ijinfomgt.2019.05.010.

[10]

De sai and S. Preyas, Quality segmentation in spatial markets: When does cannibalization affect product line design?, Marketing Science, 20 (2001), 265-283. 

[11]

S. -Z. Dong, L. Yang, B. Ding, C. -H. Wu and X. -F. Shao, Pricing strategy with customers' privacy concerns in smart-x systems, Enterprise Information Systems, (2020), 1–27. doi: 10.1080/17517575.2020.1802515.

[12]

F. Gao and X. Su, Omnichannel retail operations with buy-online-and-pick-up-in-store, Management Science, 63 (2016), 2478-2492.  doi: 10.1287/mnsc.2016.2473.

[13]

A. Ghose, B. Li and S. Liu, Nudging mobile customers with real-time social dynamics, Social Science Electronic Publishing.

[14]

A. GoliH. Khademi-ZareR. Tavakkoli-MoghaddamA. SadeghiehM. Sasanian and R. M. Kordestanizadeh, An integrated approach based on artificial intelligence and novel meta-heuristic algorithms to predict demand for dairy products: A case study, Network: Computation in Neural Systems, 32 (2021), 1-35.  doi: 10.1080/0954898X.2020.1849841.

[15]

A. Goli and B. Malmir, A covering tour approach for disaster relief locating and routing with fuzzy demand, International Journal of Intelligent Transportation Systems Research, 18 (2019), 140-152.  doi: 10.1007/s13177-019-00185-2.

[16]

A. GoliE. B. Tirkolaee and N. S. Aydin, Fuzzy integrated cell formation and production scheduling considering automated guided vehicles and human factors, IEEE Transactions on Fuzzy Systems, 29 (2021), 3686-3695.  doi: 10.1109/TFUZZ.2021.3053838.

[17]

A. GoliH. K. ZareR. Tavakkoli-Moghaddam and A. Sadeghieh, Application of robust optimization for a product portfolio problem using an invasive weed optimization algorithm, Numer. Algebra Control Optim., 9 (2019), 187-209.  doi: 10.3934/naco.2019014.

[18]

A. HaX. Long and J. Nasiry, Quality in supply chain encroachment, Manufacturing & Service Operations Management, 18 (2016), 280-298.  doi: 10.1287/msom.2015.0562.

[19]

Y. HeQ. XuB. Xu and P. Wu, Supply chain coordination in quality improvement with reference effects, Journal of the Operational Research Society, 67 (2016), 1158-1168.  doi: 10.1057/jors.2016.10.

[20]

J. JaisinghJ. BarronS. Mehta and A. Chaturvedi, Privacy and pricing personal information, European J. Oper. Res., 187 (2008), 857-870.  doi: 10.1016/j.ejor.2006.03.062.

[21]

A. Jeuland and S. Shugan, Managing channel profits, Marketing Science, 2 (1988), 239-272. 

[22]

E. Kim and B. Lee, E-service quality competition through personalization under consumer privacy concerns, Electronic Commerce Research and Applications, 8 (2009), 182-190.  doi: 10.1016/j.elerap.2009.04.001.

[23]

B. KohS. Raghunathan and B. R. Nault, Is voluntary profiling welfare enhancing?, MIS Quarterly, 41 (2017), 23-41.  doi: 10.25300/MISQ/2017/41.1.02.

[24]

X. LinY. W. Zhou and R. Hou, Impact of a "buy-online-and-pickup-in-store" channel on price and quality decisions in a supply chain, European J. Oper. Res., 294 (2021), 922-935.  doi: 10.1016/j.ejor.2020.03.064.

[25]

Y. LiuH. Shi and N. C. Petruzzi, Optimal quality and quantity provisions for centralized vs. decentralized distribution: Market size uncertainty effects, European J. Oper. Res., 265 (2018), 1144-1158.  doi: 10.1016/j.ejor.2017.08.030.

[26]

R. LotfiB. KargarS. H. HoseiniS. NazariS. Safavi and G. W. Weber, Resilience and sustainable supply chain network design by considering renewable energy, International Journal of Energy Research, 45 (2021), 17749-17766.  doi: 10.1002/er.6943.

[27]

R. LotfiN. Mardani and G.-W. Weber, Robust bi-level programming for renewable energy location, International Journal of Energy Research, 45 (2021), 7521-7534.  doi: 10.1002/er.6332.

[28]

R. LotfiM. NayeriS. M. Sajadifar and N. Mardani, Determination of start times and ordering plans for two-period projects with interdependent demand in project-oriented organizations: A case study on molding industry, Journal of Project Management, 2 (2017), 119-142.  doi: 10.5267/j.jpm.2017.9.001.

[29]

R. LotfiY. Zare MehrjerdiM. PishvaeeA. Sadegheih and G.-W. Weber, A robust optimization model for sustainable and resilient closed-loop supply chain network design considering conditional value at risk, Numer. Algebra Control Optim., 11 (2021), 221-253.  doi: 10.3934/naco.2020023.

[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.

[31]

M. Mussa and S. Rosen, Monopoly and product quality, J. Econom. Theory, 18 (1978), 301-317.  doi: 10.1016/0022-0531(78)90085-6.

[32]

N. J. OgbukeY. Y. YusufK. Dharma and B. A. Mercangoz, Big data supply chain analytics: Ethical, privacy and security challenges posed to business, industries and society, Production Planning & Control, (2020), 1-15.  doi: 10.1080/09537287.2020.1810764.

[33]

E. ÖzceylanC. CetinkayaN. Demirel and O. Sabirlioğglu, Impacts of additive manufacturing on supply chain flow: A simulation approach in healthcare industry, Logistics, 2 (2018), 1. 

[34]

S. M. PahlevanS. Hosseini and A. Goli, Sustainable supply chain network design using products' life cycle in the aluminum industry, Environmental Science and Pollution Research, (2021), 1-25.  doi: 10.1007/s11356-020-12150-8.

[35]

M. RodrigoS. Z. Wilfried and V. Tommaso, The value of personal information in online markets with endogenous privacy, Management Science, 65 (2019), 1342-1362. 

[36]

H. ShiY. Liu and N. C. Petruzzi, Consumer heterogeneity, product quality, and distribution channels, Management Science, 59 (2013), 1162-1176.  doi: 10.1287/mnsc.1120.1604.

[37]

Y. SongT. FanY. Tang and F. Zou, Quality information acquisition and ordering decisions with risk aversion, International Journal of Production Research, 59 (2021), 6864-6880.  doi: 10.1080/00207543.2020.1828640.

[38]

G. J. Stigler, The law and economics of privacy – an introduction to privacy in economics and politics, Journal of Legal Studies, 9 (1980), 623-644. 

[39]

S. WangQ. Hu and W. Liu, Price and quality-based competition and channel structure with consumer loyalty, European J. Oper. Res., 262 (2017), 563-574.  doi: 10.1016/j.ejor.2017.03.052.

[40]

X. Xu, Optimal price and product quality decisions in a distribution channel, Management Science, 55 (2009), 1347-1352.  doi: 10.1287/mnsc.1090.1023.

[41]

Y. YuY. Wang and Y. Liu, Product quality and quantity with responsive pricing, International Journal of Production Research, 59 (2021), 7160-7178.  doi: 10.1080/00207543.2020.1836418.

[42]

Q. ZhangW. TangG. Zaccour and J. Zhang, Should a manufacturer give up pricing power in a vertical information-sharing channel?, European J. Oper. Res., 276 (2019), 910-928.  doi: 10.1016/j.ejor.2019.01.054.

[43]

Z. ZhangK. Joseph and R. Subramaniam, Probabilistic selling in quality-differentiated markets, Management Science, 61 (2015), 1959-1977.  doi: 10.1287/mnsc.2014.1974.

Figure 1.  Under Armour HOVR Phantom
Figure 2.  Supply chain structure
Figure 3.  Impacts of consumers privacy concern on firms' payoff and consumer surplus
Figure 4.  Firms' payoff and consumer surplus with general information benefit form
Figure 5.  Under Armour HOVR Sonic shoes
Figure 6.  Supply chain structure
Figure 7.  Impacts of consumers privacy concern on firms' payoff and consumer surplus in a vertically-differentiated supply chain
Table 1.  Comparison among this paper and representative literatures
Literatures multi Quality Privacy Supply chain
products decision concern management
[31] No Yes No No
[40, 36] No Yes No Yes
[20, 6, 22] No No Yes No
[7] No Yes Yes No
[43] Yes Yes No No
This paper Yes Yes Yes yes
Literatures multi Quality Privacy Supply chain
products decision concern management
[31] No Yes No No
[40, 36] No Yes No Yes
[20, 6, 22] No No Yes No
[7] No Yes Yes No
[43] Yes Yes No No
This paper Yes Yes Yes yes
Table 2.  Notations
Super(sub)scripts Explanation
S consumers share personal information
N consumers not share personal information
H high-quality product
L low-quality product
Parameters Explanation
$ \theta $ willingness to pay for product quality, $ \theta \sim U[0,1] $
$ b $ information benefit coefficient from product quality, $ b \in (0,1) $
$ c $ consumers' personal information loss, $ c > 0 $
$ X $ binary decision parameter for consumers's data sharing, $ X=0,1 $
Decision Variables Explanation
$ p $ product's selling price
$ q $ product's quality level
$ w $ product's wholesale price
$ \pi_{M} $ payoff of the manufacturer
$ \pi_{R} $ payoff of the retailer
$ CS $ consumer surplus
Super(sub)scripts Explanation
S consumers share personal information
N consumers not share personal information
H high-quality product
L low-quality product
Parameters Explanation
$ \theta $ willingness to pay for product quality, $ \theta \sim U[0,1] $
$ b $ information benefit coefficient from product quality, $ b \in (0,1) $
$ c $ consumers' personal information loss, $ c > 0 $
$ X $ binary decision parameter for consumers's data sharing, $ X=0,1 $
Decision Variables Explanation
$ p $ product's selling price
$ q $ product's quality level
$ w $ product's wholesale price
$ \pi_{M} $ payoff of the manufacturer
$ \pi_{R} $ payoff of the retailer
$ CS $ consumer surplus
Table 3.  The improvement of our work
Our work [7]
Utility function $ U_{i}=\theta q_{i}-p_{i}+(b q_{i}^{k}-c)X $, $ i=H,L $ $ U=\theta q-p+b q^{k}X $
Retailer's
objective function $ \pi_{R,i}^{N}=(p_{i}-w_{i})D_{i}^{N} $, $ i=H,L $ $ \pi_{R}=\int_{0}^{\bar{\theta}}P(\theta)d\theta+\int_{\bar{\theta}}^{1}P d\theta+\int_{\overline{\theta}}^{1}Pd\theta-C(q) $
Manufacturer's
objective function $ \pi_{M,i}^{N}=(w_{i}-q_{i}^2)D_{i}^{N} $, $ i=H,L $ None
Our work [7]
Utility function $ U_{i}=\theta q_{i}-p_{i}+(b q_{i}^{k}-c)X $, $ i=H,L $ $ U=\theta q-p+b q^{k}X $
Retailer's
objective function $ \pi_{R,i}^{N}=(p_{i}-w_{i})D_{i}^{N} $, $ i=H,L $ $ \pi_{R}=\int_{0}^{\bar{\theta}}P(\theta)d\theta+\int_{\bar{\theta}}^{1}P d\theta+\int_{\overline{\theta}}^{1}Pd\theta-C(q) $
Manufacturer's
objective function $ \pi_{M,i}^{N}=(w_{i}-q_{i}^2)D_{i}^{N} $, $ i=H,L $ None
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