Information Gatekeepers: Paid Placement and Competition

Information gatekeepers such as Internet search engines and shopbots play a crucial role in the information society. Recently, such gatekeepers have begun implementing a paid placement strategy, where some content providers are given, in return for a placement fee, prominent positioning in response to user queries. Generally, users have disutility toward the bias created by paid placement, and the search engine can manipulate the placement strategy to affect usersí disutility. We analyze the gatekeeperís tradeoff between revenue from paid placement and the potential loss in advertising revenue from the loss of credibility. In the optimal paid placement strategy, an increase in the gatekeeperís quality of service allows it to improve profits from paid placement, moving it closer to the ideal. However, an increase in the advertising rate motivates the gatekeeper to increase market share by reducing further its reliance on paid placement and fraction of paying providers. When there is competition between search engines of identical quality, they will choose the same bias level. For heterogeneous search engines with different qualities, the equilibrium outcome depends largely on the usersí cognitive or other limitations on the number of search results they effectively consider

Weekend effect in internet search advertising

Journal ArticleThis paper examines whether Internet search advertising exhibits a weekend effect, a substantial difference in the effectiveness of ad spending on weekends vs on weekdays. We employ a data set from a major hotel chain, consisting of daily spending on search ads for 13 months, across three search engines and two brands. We find that there is a strong weekend effect. Each dollar of ad spending on weekends delivers a lower sales return than the corresponding return on weekdays. The advertising elasticity (percentage change in sales for a percentage change in ad spending) is about 3:7% lower on weekends, which translates to a 10% reduction in sales return at the mean level of daily spending. The weekend effect is robust across the 6 combinations of search engines and brands. We show that the reduction in advertising elasticity is primarily attributable to an increase in the price of clicks on weekends rather than due to any differences in conversion rate of click-throughs to sales. Further, we find that the weekend effect is exacerbated for ad spending at the top-ranked paid search listings. Awareness of the weekend effect can help managers fine-tune the distribution of their advertising budget across time, and achieve greater sales returns from a given ad budget

Optimal Design of Contingency Pricing in IT-Intensive Commerce

We propose the use of quality contingent prices, where a firm announces quality-price pairs for various levels of quality instead of a single price, as a mechanism for mitigating quality uncertainty. Contingency pricing is especially applicable to IT-intensive commerce where quality uncertainty is prevalent. The modern IT infrastructure allows easy capture, verification, and dissemination of performance and quality data essential for the implementation of contingency pricing framework. Under very broad conditions, we show that when the market underestimates firm performance, it is optimal to design a full-rebate contingent contract. The optimal quality threshold is set at the quality level that maximizes the gap between market and actual performance probabilities, and the optimal market size is independent of the quality threshold. When contingency pricing is optimal, it is sufficient to consider two-part contingent contracts: two-part contract performs as well as any multipart contract. Use of contingent prices include IT-intensive settings such as ASP service levels, Internet connectivity, and transaction execution in financial services

Automated detection of naming conflicts in schema integration: Experiments with quiddities*

This paper discusses experiments involving a method for the automatic detection, prior to the integration of data base schemas, of conflicts in the naming of data elements within these schemas. The method relies on the representation of semantic information (called quiddity) about the data elements present in the various schemas. We develop several inference procedures which, utilizing this information, determine whether to distinctly named elements in fact represent the same object, or if elements with the same name actually represent different objects. The experiments are concerned with (a) examining the accuracy and consistency with which quiddities of data elements might be declared by different database designers, and (b) evaluating the accuracy and errors of these automated procedures. Our results indicate that the method has promise for use in detection of naming conflicts, and that certain inference procedures are superior to others in terms of their accuracy and error ratesNaval Postgraduate School, Monterey, CAhttp://archive.org/details/automateddetecti00bharO&MN Direct FundingNAApproved for public release; distribution is unlimited

On the integration of data and mathematical modeling languages

This paper examines ways in which the addition of data modeling features can enhance the capabilities of mathematical modeling languages, and demonstrates how such integration might be achieved as an application of the embedded languages technique proposed by Bhargava and Kimbrough. Decision making, and decision support systems, require the representation and manipulation of both data and mathematical models. Several data modeling languages as well as several mathematical modeling languages exist, but they have differences sets of capabilities. We motivate with a detailed example the need for the integration of these languages. We describe the benefits that might result, and claim that this could lead to a significant improvement in the functionality of model management systems. Then we present our approach for the integration of these languages, and specify how the claimed benefits are realized.This paper examines ways in which the addition of data modeling features can enhance the capabilities of mathematical modeling languages, and demonstrates how such integration might be achieved as an application of the embedded languages technique proposed by Bhargava and Kimbrough, [4]Decision-making, and decision support systems, require the representation and manipulation of both data and mathematical models. Several data modeling languages as well as several mathematical mod- eling languages exist, but they have differences sets of capabilities. We motivate with a detailed example the need for the integration of these languages. We describe the benefits that might result, and claim that this could lead to a significant improvement in the functionality of model management systems. Then we present our approach for the integration of these languages, and specify how the claimed benefits are realized.Naval Postgraduate School, Monterey, California.Approved for public release; distribution is unlimited

Unique Names Violations, a Problem for Model Integration, You Say Tomato, I Say Tomahto

The article of record as published may be found at https://doi.org/10.1287/ijoc.3.2.107The tomato-tomahto problem (known as the synonymy problem in the database literature) arises in the context of model management when different names are used in different models for what should be identical variables, and these different models are to be integrated or combined into a larger model. When this problem occurs, it is said that the unique names assumption has been violated. We propose a method by which violations of the unique names assumption can be automatically detected. The method relies on declaring four kinds of information and modeling variables: dimensional information, laws relating dimensional expressions, information (called the quiddity) about the intended interpretation of the variables, and laws relating quiddity expressions. We present and discuss the method and the principles and theory behind it, and we describe our (prototype) implementation of the method, as an additional function of an existing model management system

Competence in Endoscopic Ultrasound and Endoscopic Retrograde Cholangiopancreatography, From Training Through Independent Practice.

BACKGROUND & AIMS: It is unclear whether participation in competency-based fellowship programs for endoscopic ultrasound (EUS) and endoscopic retrograde cholangiopancreatography (ERCP) results in high-quality care in independent practice. We measured quality indicator (QI) adherence during the first year of independent practice among physicians who completed endoscopic training with a systematic assessment of competence. METHODS: We performed a prospective multicenter cohort study of invited participants from 62 training programs. In phase 1, 24 advanced endoscopy trainees (AETs), from 20 programs, were assessed using a validated competence assessment tool. We used a comprehensive data collection and reporting system to create learning curves using cumulative sum analysis that were shared with AETs and trainers quarterly. In phase 2, participating AETs entered data into a database pertaining to every EUS and ERCP examination during their first year of independent practice, anchored by key QIs. RESULTS: By the end of training, most AETs had achieved overall technical competence (EUS 91.7%, ERCP 73.9%) and cognitive competence (EUS 91.7%, ERCP 94.1%). In phase 2 of the study, 22 AETs (91.6%) participated and completed a median of 136 EUS examinations per AET and 116 ERCP examinations per AET. Most AETs met the performance thresholds for QIs in EUS (including 94.4% diagnostic rate of adequate samples and 83.8% diagnostic yield of malignancy in pancreatic masses) and ERCP (94.9% overall cannulation rate). CONCLUSIONS: In this prospective multicenter study, we found that although competence cannot be confirmed for all AETs at the end of training, most meet QI thresholds for EUS and ERCP at the end of their first year of independent practice. This finding affirms the effectiveness of training programs. Clinicaltrials.gov ID NCT02509416