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This paper aims to address the effective selection of customers for direct marketing campaigns. It introduces a new method to forecast campaign-related uplifts (also known as incremental response modeling or net scoring). By means of these uplifts, only the most responsive customers are targeted by a campaign. This paper also aims at calculating the financial impact of the new approach compared to the classical (gross) scoring methods.

First, gross and net scoring approaches to customer selection for direct marketing campaigns are compared. After that, it is shown how net scoring can be applied in practice with regard to different strategical objectives. Then, a new statistic for net scoring based on decision trees is developed. Finally, a business case based on real data from the financial sector is calculated to compare gross and net scoring approaches.

Whereas gross scoring focuses on customers with a high probability of purchase, regardless of being targeted by a campaign, net scoring identifies those customers who are most responsive to campaigns. A common scoring procedure – decision trees – can be enhanced by the new statistic to forecast those campaign-related uplifts. The business case shows that the selected scoring method has a relevant impact on economical indicators.

The contribution of net scoring to campaign effectiveness and efficiency is shown by the business case. Furthermore, this paper suggests a framework for customer selection, given strategical objectives, e.g. minimizing costs or maximizing (gross or lift)-added value, and presents a new statistic that can be applied to common scoring procedures.

Despite its lever on the effectiveness of marketing campaigns, only few contributions address net scores up to now. The new χ²-statistic is a straightforward approach to the enhancement of decision trees for net scoring. Furthermore, this paper is the first to the application of net scoring with regard to different strategical objectives.

The scoring system for the National Board for Professional Teaching Standards (NBPTS) assessments was a groundbreaking undertaking that brought with it a host of unanticipated challenges. These, in turn, generated a complete revision of the approach to scoring and the design underwent a number of changes during the first decade. Beginning with an analytical model which was so ambitious that it was entirely too cumbersome and complex to be undertaken within a reasonable timeframe, assessment developers had to systematically redesign a scoring system that would be at once reliable, valid, and operationally feasible.

Nowadays there are a large number of XML documents on the web. This means that information retrieval techniques for searching XML documents are very important and necessary for internet users. Moreover, it is often said that users of search engines want to browse only relevant content in each document. Therefore, an effective XML element search aims to produce only the relevant elements or portions of an XML document. Based on the demand by users, the purpose of this paper is to propose and evaluate a method for obtaining more accurate search results in XML search.

The existing approaches generate a ranked list in descending order of each XML element's relevance to a search query; however, these approaches often extract irrelevant XML elements and overlook more relevant elements. To address these problems, the authors' approach extracts the relevant XML elements by considering the size of the elements and the relationships between the elements. Next, the authors score the XML elements to generate a refined ranked list. For scoring, the authors rank high the XML elements that are the most relevant to the user's information needs. In particular, each XML element is scored using the statistics of its descendant and ancestor XML elements.

The experimental evaluations show that the proposed method outperforms BM25E, a conventional approach, which neither reconstructs XML elements nor uses descendant and ancestor statistics. As a result, the authors found that the accuracy of an XML element search can be improved by reconstructing the XML elements and emphasizing the informative ones by applying the statistics of the descendant XML elements.

This work focused on the effectiveness of XML element search and the authors did not consider the search efficiency in this paper. One of the authors' next challenges is to reduce search time.

The paper proposes a method for improving the effectiveness of XML element search.

This paper aims to survey the credit scoring literature in the past 41 years (1976-2017) and presents a research agenda that addresses the challenges and opportunities Big Data bring to credit scoring.

Content analysis methodology is used to analyze 258 peer-reviewed academic papers from 147 journals from two comprehensive academic research databases to identify their research themes and detect trends and changes in the credit scoring literature according to content characteristics.

The authors find that credit scoring is going through a quantitative transformation, where data-centric underwriting approaches, usage of non-traditional data sources in credit scoring and their regulatory aspects are the up-coming avenues for further research.

The paper’s findings highlight the perils and benefits of using Big Data in credit scoring algorithms for corporates, governments and non-profit actors who develop and use new technologies in credit scoring.

This paper presents greater insight on how Big Data challenges traditional credit scoring models and addresses the need to develop new credit models that identify new and secure data sources and convert them to useful insights that are in compliance with regulations.

This chapter, completed in 1999, provides an overview and critical analysis of the validity research agenda undertaken by the National Board for Professional Teaching Standards (NBPTS) for its assessment to certify accomplished teachers at the end of its first decade of assessment development and implementation. The review is presented in three major sections: (a) an overview of the validity criteria underlying the review; (b) a description of the National Board's research agenda presented in its own terms, focusing first on the studies that were routinely carried out for each certificate and second on the “special studies” that were not part of the routine agenda; and (c) a series of six critical observations and explanations based on the validity issues described in the first section.

The purpose of this paper is to describe and illustrate an application of a tool for assessing the maturity and effectiveness of enterprise performance measurement systems.

Assessment elements were defined based on the literature, and the scoring approach was designed based on performance excellence frameworks such as Baldrige and EFQM. A research case application in a Belgian public sector organization is used to illustrate how the tool can be used and the types of insights generated.

The case application indicates that the tool can be used to generate useful and actionable feedback for leadership to increase the maturity of enterprise performance measurement systems. The tool can also be used to longitudinally track progress in performance measurement system effectiveness.

The current paper represents the pilot application of the tool. Future work is needed to further test and refine the tool, refine the assessment process, and collect additional assessment data in other organizations.

This tool provides a more holistic yet detailed assessment of performance measurement systems. It assesses measurement system design quality, as well as implementation/use Specific feedback on process gaps can be used to detect and address problem areas, providing value for practitioners. The tool provides researchers with a standard, structured approach for collecting and codifying detailed observations and artifacts when studying measurement systems.

Readability formulas have been criticised as a method for scoring accounting narratives because of their focus on word‐ and sentence‐level features and not on whole‐text aspects, their lack of regard for the interests and motivation of the reader, and their inappropriateness for evaluating adult‐based and technical accounting narratives. The literature of linguistics offers theoretical and practical validation for application of a texture index which addresses these criticisms. The paper shows how the general model drawn from applied linguistics can be tailored to the specific situation of an accounting narrative – the Operating and Financial Review. Rules which provide for objectivity in replication are specified and illustrated for a sample narrative. Illustrative empirical analysis shows that there is no evidence of association with the Flesch readability score. This suggests that the texture index is potentially a powerful tool for analysis of accounting narratives and association testing.

This article provides a review and conceptual comparison between self‐report and performance‐based measures of emotional intelligence. Analyses of reliability, psychometric properties, and various forms of validity lead to the conclusion that self‐report techniques measure a dispositional construct, that may have some predictive validity, but which is highly correlated with personality and independent of intelligence. Although seemingly more valid, performance‐based measures have certain limitations, especially when scored with reference to consensual norms, which leads to problems of skew and restriction of range. Scaling procedures may partially ameliorate these scoring weaknesses. Alternative approaches to scoring, such as expert judgement, also suffer problems since the nature of the requisite expertise is unclear. Use of experimental paradigms for studying individual differences in information‐processing may, however, inform expertise. Other difficulties for performance‐based measures include limited predictive and operational validity, restricting practical utility in organizational settings. Further research appears necessary before tests of E1 are suitable for making real‐life decisions about individuals.

This paper aims to assess how to select an appropriate intellectual property valuation method according to the valuation situation and context.

The article describes the difference between the quantitative and qualitative methods and principles. It reviews the principal approaches and methods used to evaluate an intellectual property asset and proposes a framework to help the evaluators to select an appropriate valuation method. The paper initiates a discussion on the parameters and requirements that influence the choice of an IP valuation method in order to reach the expected valuation result.

This paper provides useful guidelines for any evaluator who would be responsible for executing an IP valuation and who would be faced with the difficult task of choosing an appropriate IP valuation method. It is the intention of this paper to develop a synthesised and integrated procedure for the selection of an IP valuation method.

The limitation of this paper is that not all of the existing methods were described and taken into account in the final proposed procedure. The authors made a series of assumptions and a selection of the methods that may not be entirely shared by other researchers and practitioners. The authors are conscious that this constitutes a first proposal in the selection process of the most relevant IP valuation method. Further discussions and developments would be carried on in the future to enhance the proposed procedure.

This paper proposes a framework to orientate the choice of an appropriate IP valuation method according to the context and situation in which the valuation is to be implemented.

The migration approach to credit risk measurement is based on historic rates of movements of individual loans among the classes of a lender’s risk‐rating or credit‐scoring system. This article applies the migration concept to farm‐level data from Illinois to estimate migration rates for a farmer’s credit score and other performance measures under different time‐averaging approaches. Empirical results suggest greater stability in rating migrations for longer time‐averaging periods (although less stable than bond migrations), and for the credit score criterion versus ROE and repayment capacity.