دانلود رایگان مقاله لاتین راهبرد قیمت گذاری اطلاعات بر اساس کیفیت دیتا از سایت الزویر
عنوان فارسی مقاله:
استراتژی قیمت گذاری اطلاعات بر اساس کیفیت داده
عنوان انگلیسی مقاله:
Data pricing strategy based on data quality
سال انتشار : 2017
برای دانلود رایگان مقاله راهبرد قیمت گذاری اطلاعات بر اساس کیفیت دیتا اینجا کلیک نمایید.
بخشی از مقاله انگلیسی:
2. Literature review
The value assessment of intangibles such as intellectual products is not a new challenge for entrepreneurs and scholars. The pricing of information products and information services has generated a substantial literature. We here review representative works on these methods, before selectively reviewing research on data pricing. Information-service markets involve three commonly used pricing schemes: ‘‘pure flat-fee” pricing, ‘‘pure usage-based” pricing, and ‘‘two-part tariff” pricing (Wu & Banker, 2010). Wu and Banker (2010) found that marginal and monitoring costs can influence a firm’s choice of pricing scheme. Huang, Kauffman, and Ma (2015) argue for the existence of service interruptions in cloud software, to which some consumers are sensitive. In such a market, it is sensible for a vendor of cloud-computing services to adopt a hybrid pricing strategy that mixes fixed-price reserved services with spot-price on-demand services. Mei, Li, and Nie (2013) constructed a pricing model based on the Stackelberg game and advocated adopting a pure-bundling strategy, instead of pure components, when device prices are high and consumers’ evaluations vary widely. Balasubramanian, Bhattacharya, and Krishnan (2015) considered differences in the use of frequencies and the psychological costs to consumers that are associated with a payper-use model. They concluded that two factors can affect a seller’s profit by analyzing two pricing mechanisms for information products, namely the fixed-fee and pay-per-use mechanisms. Sundararajan (2004) argued that administering usage-based pricing incurs transaction costs, which influence the optimal pricing of information goods when the available information is incomplete. On the other hand, versioning is a widespread differentiation strategy used in information-product markets. Under this scheme, a firm customizes information products according to the customers’ need and encourages them to pay the highest possible price for goods to maximize its overall revenue (Shapiro & Varian, 1998). Bhargava and Choudhary (2001) analyzed the optimal strategy for vertically differentiated information products in the context of a monopoly. They showed that the optimal product line of a firm depends on the benefit-to-cost ratio of qualities when the consumer’s valuation is a linear function of product quality and consumer type. Li, Feng, Chen, and Kou (2013) defined a nonlinear function to describe the ‘‘willingness to pay” and the utility to a consumer who has a specific quality requirement, and developed hybrid steady-state evolutionary algorithms. They observed that a monopoly can achieve more profit by using a multi-version strategy. Chen and Seshadri (2007) considered a two-stage development problem and found that versioning is an optimal strategy for sellers if the consumers have a convex-shaped reservation utility function. Because data and information products have many features in common, the pricing methods used for information products provide insight for our present research. However, these methods may not be compatible with the intrinsic characteristics of the data. Various authors have addressed the issues of data provision and data pricing, as summarized in Table 1. Tang et al. (2014) proposed a framework for the pricing of XML documents and devised PTIME algorithms. Heckman et al. (2015) combined qualitative and quantitative methods to determine the data value for buyers and sellers, in order to propose a grand pricing model. Query pricing is a common method used in data markets. Koutris, Upadhyaya, Balazinska, Howe, and Suciu (2015) proposed a ‘query-based pricing’ that satisfies arbitrary-free and discount-free. This pricing function allows the price of any query to be determined automatically. In 2013, they considered an updated database and overlapping information, and proposed a new pricing system that avoids recharging (Koutris, Upadhyaya, Balazinska, Howe, & Suciu, 2013). Bergemann and Bonatti (2015) proposed a model of data provision and data pricing for a single data provider selling individual consumers’ characteristics (web cookies) to individual firms (advertisers).
برای دانلود رایگان مقاله راهبرد قیمت گذاری اطلاعات بر اساس کیفیت دیتا اینجا کلیک نمایید.
کلمات کلیدی:
Data Quality Strategy - SAS Support Communities https://communities.sas.com/t5/SAS-Data.../Data-Quality-Strategy/td-p/301826 Sep 30, 2016 - and; provide cost-benefit-risk analysis scenarios and propose timeline for data quality improvement. Improve strategy based on leadership ... Why Data Quality Should be the 'Red Thread' of your Data Strategy ... https://www.talend.com › Blog Oct 26, 2017 - Talend has always been open source-based, but what many may not know is that data quality has also always been part of our Data Integration ... Searches related to strategy based on data quality data quality strategy example data quality improvement strategy data quality assurance procedures data quality assurance techniques data quality assurance processes and procedures
