8,673 research outputs found

    A Decision Technology System To Advance the Diagnosis and Treatment of Breast Cancer

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    Geographical variations in cancer rates have been observed for decades. Described spatial patterns and trends have provided clues for generating hypotheses about the etiology of cancer. For breast cancer, investigators have demonstrated that some variation can be explained by differences in the population distribution of known breast cancer risk factors such as menstrual and reproductive variables (Laden, Spiegelman, and Neas, 1997; Robbins, Bescianini, and Kelsey, 1997; Sturgeon, Schairer, and Gail, 1995). However, regional patterns also may reflect the effects of Workshop on Hormones, Hormone Metabolism, Environment, and Breast Cancer (1995): (a) environmental hazards (such as air and water pollution), (b) demographics and the lifestyle of a mobile population, (c) subgroup susceptibility, (d) changes and advances in medical practice and healthcare management, and (e) other factors. To accurately measure breast cancer risk in individuals and population groups, it is necessary to singly and jointly assess the association between such risk and the hypothesized factors. Various statistical models will be needed to determine the potential relationships between breast cancer development and estimated exposures to environmental contamination. To apply the models, data must be assembled from a variety of sources, converted into the statistical models’ parameters, and delivered effectively to researchers and policy makers. A Web-enabled decision technology system can be developed to provide the needed functionality. This chapter will present a conceptual architecture for such a decision technology system. First, there will be a brief overview of a typical geographical analysis. Next, the chapter will present the conceptual Web-based decision technology system and illustrate how the system can assist users in diagnosing and treating breast cancer. The chapter will conclude with an examination of the potential benefits from system use and the implications for breast cancer research and practice

    Supplier-Buyer Proximity and Production to Order Choice

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    We study the determinants of the firm-level choice to produce following an order placed by a downstream firm (production to order) or to produce in advance. We rationalize this choice through a simple theoretical model and apply it to a firm-level empirical analysis. Relying on a large panel of Italian manufacturing firms, we show that two main variables affect this choice: the distance between the supplier and the buyer and the degree of product differentiation in downstream industries where products are sold. The impact of proximity on the choice of producing to order crucially depends on the degree of product differentiation in downstream markets. We find that, in industries where average product differentiation is high, production to order prevails if the supplier is located close to the buyer. On the contrary, proximity is associated to production in advance in homogeneous sectors. We also find that, if suppliers are located in a different country from that of the buyers, they will tend to produce to order if product differentiation in downstream industries is low, and produce in advance if product differentiation is high. We also narrow the scope of our analysis to analyze the determinants of production to order originating from the same province where the supplier is located.Industrial Districts; Networks; Production to Order; Relationship-Specific Investments

    Relationship-Specificity, Spatial Clustering and Production to Order Choice

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    We study the determinants of the firm-level choice to produce following an order placed by a downstream firm (production to order) or to produce in advance. We rationalize this choice through a simple theoretical model and apply it to a firm-level empirical analysis. Relying on a large dataset of Italian manufacturing firms, we show that two main variables affect this choice: the extent of spatial clustering of the industry, and the degree of product complexity and relationship-specificity of the goods that are traded. The sign of the impact of clustering on the choice of producing to order crucially depends on product complexity. If product complexity is high, production to order prevails when firms are clustered together. On the contrary, clustering is associated to production in advance for sectors where goods are standardized.

    Procrastination in the Workplace: Evidence from the U.S. Patent Office

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    Despite much theoretical attention to the concept of procrastination and much exploration of this phenomenon in laboratory settings, there remain few empirical investigations into the practice of procrastination in real world contexts, especially in the workplace. In this paper, we attempt to fill these gaps by exploring procrastination among U.S. patent examiners. We find that nearly half of examiners’ first substantive reports are completed immediately prior to the operable deadlines. Moreover, we find a range of additional empirical markers to support that this “end-loading” of reviews results from a model of procrastination rather than various alternative time-consistent models of behavior. In one such approach, we take advantage of the natural experiment afforded by the Patent Office’s staggered implementation of its telecommuting program, a large-scale development that we theorize might exacerbate employee self-control problems due to the ensuing reduction in direct supervision. Supporting the procrastination theory, we estimate an immediate spike in application end-loading and other indicia of procrastination upon the onset of telecommuting. Finally, contributing to a growing empirical literature over the efficiency of the patent examination process, we assess the consequences of procrastination for the quality of the reviews completed by the affected examiners. This analysis suggests that the primary harm stemming from procrastination is delay in the ultimate application process, with rushed reviews completed at deadlines resulting in the need for revisions in subsequent rounds of review. Our findings imply that nearly 1/6 of the annual growth in the Agency’s much-publicized backlog may be attributable to examiner procrastination

    Business model synergy:a case-study at PostNL

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    The Internet as a Service Channel in the Public Sector : A substitute or complement of traditional service channels?

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    The Internet has been used as a channel for public service delivery since the mid 1990’s. During the first years of its existence it was believed to be the service channel of the future, making all other channels obsolete. But until now, the telephone and face-to-face contact remain being used more frequently and are rated higher. By comparing various studies that have recently been conducted in a number of countries, this paper suggests that the characteristics of the channel make it a suitable channel for basic transactions and simple information provision, and that the telephone and face-to-face contact remain prevalent for at least ambiguous and complex tasks. Therefore the Internet might be a complementary channel rather than a substitute of traditional channels. Research findings are interpreted by means of Media Richness Theory, the Social Influence model and Channel Expansion Theory

    The Use of Information Communication Technology (Ict) as the Technology Acceptance Model (Tam) of Mobile Banking

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    Information Technology services in banking business which is very dynamic and advanced makes the bankers must adjust to the presence of this technology. In this paper, the authors will explain the role of Mobile banking technology which is provided by CIMB Niaga in servicing its customers for banking transactions. The methodology used in this paper is quantitative with the purposive sampling one. Instrument used the Technology Acceptance Model (TAM) as developed by Parasuraman and Davis by modifying them to fit the state of the research object. This research was conducted in Jakarta and several other places. Research and data collection took 6 months from November 2015 to April 2016. The variables included trust, perceived use (PU), perceived ease of use (PEU), attitude toward use (ATU), Intent to use (ITU), and actual use (AU). After the data were collected, the regression analysis was performed. From the analysis result it was found that trust and perceived use (PU) variables did not positively and significantly influence the intent to use (ITU) variable, as well as the perceived use variables (PU) and perceived ease of use (PEU) which have no significant and positive effect on attitude variable on USAge (ATU). For that case, the authors propose an improvement strategy to maintain and grow this business forward
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