The Experts below are selected from a list of 16725 Experts worldwide ranked by ideXlab platform
Xiping Song - One of the best experts on this subject based on the ideXlab platform.
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comparison of the group buying auction and the fixed Pricing Mechanism
Decision Support Systems, 2007Co-Authors: Jian Chen, Xilong Chen, Xiping SongAbstract:With the development of electronic commerce, online auction plays an important role in the electronic market. This paper analyzes the seller's Pricing strategy with the group-buying auction (GBA), a popular form of online auction, which is designed to aggregate the power of buyers to gain volume discounts. Based on the bidders' stochastic arrival process and optimal strategy with independent private value model, this paper analyzes the sellers' optimal price curve of the GBA in the uniform unit cost case and in some supply chain coordination contracts. We find that the best discount rate is zero, which implies the optimal GBA is equivalent to the optimal fixed Pricing Mechanism (FPM). Then we compare the GBA with the FPM-in two special cases, the economies of scale and risk-seeking seller, and find that (1) when economies of scale are considered, the GBA outperforms the FPM; (2) when the seller is risk-seeking, the GBA also outperforms the FPM.
Yuyu Lihsieh - One of the best experts on this subject based on the ideXlab platform.
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examining cloud computing adoption intention Pricing Mechanism and deployment model
International Journal of Information Management, 2014Co-Authors: Yuyu LihsiehAbstract:Abstract Cloud computing is a new information technology (IT) paradigm that promises to revolutionize traditional IT delivery through reduced costs, greater elasticity, and ubiquitous access. On the surface, adopting cloud computing requires a firm to address many of the same concerns they face in adopting any enterprise IT. However, cloud technologies also offer new Pricing and deployment strategies that are unavailable in traditional enterprise solutions. It is unclear how previous research frameworks of enterprise IT adoption relate to these new adoption strategies. To bridge this gap in the literature, our study uses the technology–organization–environment (TOE) framework of innovation diffusion theory to develop a cloud service adoption model that deals with not only adoption intention, but also Pricing Mechanisms and deployment models. Our research model has been empirically tested using 200 Taiwanese firms. We found that: (1) Cloud adoption is still at its initial stage, since the adoption rates are very low; (2) the perceived benefits, business concerns, and IT capability within the TOE framework are significant determinants of cloud computing adoption, while external pressure is not; (3) firms with greater IT capability tend to choose the pay-as-you-go Pricing Mechanism; (4) business concern is the most important factor influencing the choice of deployment model, with higher concerns leading to private deployment options.
Yu Yu Li-hsieh - One of the best experts on this subject based on the ideXlab platform.
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Examining cloud computing adoption intention, Pricing Mechanism, and deployment model
International Journal of Information Management, 2014Co-Authors: Pei Fang Hsu, Soumya Ray, Yu Yu Li-hsiehAbstract:Cloud computing is a new information technology (IT) paradigm that promises to revolutionize traditional IT delivery through reduced costs, greater elasticity, and ubiquitous access. On the surface, adopting cloud computing requires a firm to address many of the same concerns they face in adopting any enterprise IT. However, cloud technologies also offer new Pricing and deployment strategies that are unavailable in traditional enterprise solutions. It is unclear how previous research frameworks of enterprise IT adoption relate to these new adoption strategies. To bridge this gap in the literature, our study uses the technology-organization-environment (TOE) framework of innovation diffusion theory to develop a cloud service adoption model that deals with not only adoption intention, but also Pricing Mechanisms and deployment models. Our research model has been empirically tested using 200 Taiwanese firms. We found that: (1) Cloud adoption is still at its initial stage, since the adoption rates are very low; (2) the perceived benefits, business concerns, and IT capability within the TOE framework are significant determinants of cloud computing adoption, while external pressure is not; (3) firms with greater IT capability tend to choose the pay-as-you-go Pricing Mechanism; (4) business concern is the most important factor influencing the choice of deployment model, with higher concerns leading to private deployment options. © 2014 Elsevier Ltd.
Jian Chen - One of the best experts on this subject based on the ideXlab platform.
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comparison of the group buying auction and the fixed Pricing Mechanism
Decision Support Systems, 2007Co-Authors: Jian Chen, Xilong Chen, Xiping SongAbstract:With the development of electronic commerce, online auction plays an important role in the electronic market. This paper analyzes the seller's Pricing strategy with the group-buying auction (GBA), a popular form of online auction, which is designed to aggregate the power of buyers to gain volume discounts. Based on the bidders' stochastic arrival process and optimal strategy with independent private value model, this paper analyzes the sellers' optimal price curve of the GBA in the uniform unit cost case and in some supply chain coordination contracts. We find that the best discount rate is zero, which implies the optimal GBA is equivalent to the optimal fixed Pricing Mechanism (FPM). Then we compare the GBA with the FPM-in two special cases, the economies of scale and risk-seeking seller, and find that (1) when economies of scale are considered, the GBA outperforms the FPM; (2) when the seller is risk-seeking, the GBA also outperforms the FPM.
Adam Wierman - One of the best experts on this subject based on the ideXlab platform.
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Greening multi-tenant data center demand response
Performance Evaluation, 2015Co-Authors: Niangjun Chen, Xiaoqi Ren, Shaolei Ren, Adam WiermanAbstract:Abstract Data centers have emerged as promising resources for demand response, particularly for emergency demand response (EDR), which saves the power grid from incurring blackouts during emergency situations. However, currently, data centers typically participate in EDR by turning on backup (diesel) generators, which is both expensive and environmentally unfriendly. In this paper, we focus on "greening" demand response in multi-tenant data centers, i.e., colocation data centers, by designing a Pricing Mechanism through which the data center operator can efficiently extract load reductions from tenants during emergency periods for EDR. In particular, we propose a Pricing Mechanism for both mandatory and voluntary EDR programs, ColoEDR, that is based on parameterized supply function bidding and provides provably near-optimal efficiency guarantees, both when tenants are price-taking and when they are price-anticipating. In addition to analytic results, we extend the literature on supply function Mechanism design, and evaluate ColoEDR using trace-based simulation studies. These validate the efficiency analysis and conclude that the Pricing Mechanism is both beneficial to the environment and to the data center operator (by decreasing the need for backup diesel generation), while also aiding tenants (by providing payments for load reductions).
