Utility Function-based Pricing Strategies in Maximizing the Information Service Provider’s Revenue with Marginal and Monitoring Costs

Sitepu, Robinson; Puspita, Fitri Maya; Pratiwi, Anggi Nurul; Novyasti, Icha Puspita

doi:10.11591/ijece.v7i2.pp877-887

Cited by 13 publications

(7 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In previous work, most studies assumed that the willingness to pay was a linear function [18,20]. However, the linear value function does not accurately capture the estimates of actual data and consumer's self-selection behavior.…”

Section: Customer's Self-selection Behavior Analysismentioning

confidence: 99%

Personal Data Market Optimization Pricing Model Based on Privacy Level

Yang

Xing

2019

Information

View full text Add to dashboard Cite

In the era of the digital economy, data has become a new key production factor, and personal data represents the monetary value of a data-driven economy. Both the public and private sectors want to use these data for research and business. However, due to privacy issues, access to such data is limited. Given the business opportunities that have gaps between demand and supply, we consider establishing a private data market to resolve supply and demand conflicts. While there are many challenges to building such a data market, we only focus on three technical challenges:(1) How to provide a fair trading mechanism between data providers and data platforms? (2) What is the consumer's attitude toward privacy data? (3) How to price personal data to maximize the profit of the data platform? In this paper, we first propose a compensation mechanism based on the privacy attitude of the data provider. Second, we analyze consumer self-selection behavior and establish a non-linear model to represent consumers' willingness to pay (WTP). Finally, we establish a bi-level programming model and use genetic simulated annealing algorithm to solve the optimal pricing problem of personal data. The experimental results show that multi-level privacy division can meet the needs of consumers and maximize the profit of data platform.

show abstract

Section: Customer's Self-selection Behavior Analysismentioning

confidence: 99%

Personal Data Market Optimization Pricing Model Based on Privacy Level

Yang

Xing

2019

Information

View full text Add to dashboard Cite

show abstract

“…Some research [5,6] examined that three internet pricing schemes such as flat rate, usage based, and two-part tariffs in obtaining the profit of ISPs. If dealing with wireless internet pricing scheme then QoS attributes are introduced namely bandwidth, BER, and end to end delay [7,8].…”

Section: Introductionmentioning

confidence: 99%

Models of improved multilink reverse charging network by utilizing the bit error rate QoS attribute

Puspita

Rohania

Yuliza

et al. 2021

IJEECS

View full text Add to dashboard Cite

<span>In this paper, a modification model for single-link reverse charging of internet is formed on a multi-link wireless network. The pricing scheme also takes into account the base costs and quality of services provided by the service provider. Bit error rate (BER) was utilized as one of the well-known quality of service (QoS) attribute that can guarantee best performance for internet service provider (ISP) and users. The base price is determined as a decision variable to help ISP to maximize profit. This optimization model can be solved using the LINGO 13.0 program to gain optimal values. The computational results show that by setting costs as constants and service quality as variables, optimal results are obtained for ISPs. This can make ISP considerations in determining the base price that can benefit the ISP and according to the services provided</span>.

show abstract

“…One way for Internet service providers (ISPs) to enhance their quality of service is to perform the method of bundling and utility function [1], [2]. In wireless communication, one problem exists regarding with setting up the bundling or combining the service [3].…”

Section: Introductionmentioning

confidence: 99%

Improved incentive pricing-based quasi-linear utility function of wireless networks

Puspita

Rezky

Simarmata

et al. 2021

IJEECS

Self Cite

View full text Add to dashboard Cite

The model of the incentive pricing scheme-based quasi-linear utility function in wireless network was designed. Previous research seldom focusses on user’s satisfaction while using network. Therefore, the model is then attempted to be set up that is derived from the modification of bundling and models of reverse charging and maintain the quality of service to users by utilizing quasi-linear utility function. The pricing schemes then are applied to local data server traffic. The model used is known as mathematical programming problem that can be solved by LINGO 13.0 program as optimization tool to get the optimal solution. The optimal results show that the improved incentive pricing can achieve better solution compared to original reverse charging where the models will be obtained in flat fee, usage-based, and two-part tariff strategies for homogeneous consumers.

show abstract

Utility Function-based Pricing Strategies in Maximizing the Information Service Provider’s Revenue with Marginal and Monitoring Costs

Cited by 13 publications

References 10 publications

Personal Data Market Optimization Pricing Model Based on Privacy Level

Personal Data Market Optimization Pricing Model Based on Privacy Level

Models of improved multilink reverse charging network by utilizing the bit error rate QoS attribute

Improved incentive pricing-based quasi-linear utility function of wireless networks

Contact Info

Product

Resources

About