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In the evaluation of most interventions in criminal justice settings, evaluators have no control over assignment to treatment and control/comparison conditions, which means that the treated and comparison groups may have differences that lead to biased conclusions regarding treatment effectiveness. Propensity score analysis can be used to balance the differences in the groups, which can be used in a number of ways to reduce biased conclusions regarding effectiveness. A review of propensity scoring studies was conducted for this chapter, where the limited number of evaluations of criminal justice interventions using these methods was identified. Due to the small number of these studies, research was also reviewed if propensity scoring had been employed to evaluate interventions that are similar to those in criminal justice systems. These studies are used as examples to demonstrate how the methods can be used to evaluate criminal justice interventions, the different ways propensity scores can be used to analyse treatment and comparison group differences, and the strengths and limitations of this approach. It is concluded that, while not appropriate for all interventions/settings, propensity score analysis can be useful in criminal justice arenas, at least to investigate the comparability of treatment and comparison groups, with suspected non-comparability being a common weakness of traditional quasi-experimental studies and frequently cited limitation in terms of drawing efficacy conclusions from such evaluations.

Scoring is a widely used, long-established, and universally applicable method of measuring risks, especially those that are difficult to quantify. Unfortunately, the scoring method is often misused in real estate practice and underestimated in academia. The purpose of this paper is to supplement the literature with general rules under which scoring systems should be designed and validated, so that they can become reliable risk instruments.

The paper combines the rules, or axioms, for coherent risk measures known from the literature with those for scoring instruments. The result is a system of rules that a risk scoring system should fulfil. The approach is theoretical, based on a literature survey and reasoning.

At first, the paper clarifies that a risk score should express the variation of a property’s yield and not of its quality, as it is often done in practice. Then the axioms for a coherent risk scoring are derived, e.g. the independence of the risk factors. Finally, the paper proposes procedures for valid and reliable risk scoring systems, e.g. the out-of-time validation.

Although it is a theoretical work, the paper also focuses on practical applicability. The findings are illustrated with examples of scoring systems.

Rules for risk measures and for scoring systems have been established long ago, but the combination is a first. In this way, the paper contributes to real estate risk research and risk management practice.

This study aims to explore credit scoring regulations, governance, variables and methods used by peer-to-peer (P2P) lending platforms in key players of the Association of Southeast Asian Nations (ASEAN) region’s P2P, Indonesia, Malaysia and Singapore.

This study explores the P2P Lending characteristics of the three countries using qualitative literature review, interview, focus group discussion and desk research.

This study concludes that the credit scoring variables used by the countries’ companies are almost the same. Key drivers of the differences are countries’ regulations, management/business core value and credit scoring data processing methods.

Ultimately, this research provides a comprehensive view for investors, businesses and researchers on the topic of ASEAN credit scoring governance and will help them navigate the complexities and improve their awareness on the importance of credit scoring governance in P2P lending companies.

This research provides an in-depth perspective on how P2P lending companies, credit scoring governance and regulations in the biggest three countries in Southeast Asia.

Previous researches on credit scoring mainly focussed on static modeling on panel sample data set in a certain period of time, and did not pay enough attention on dynamic incremental modeling. The purpose of this paper is to address the integration of branch and bound algorithm with incremental support vector machine (SVM) ensemble to make dynamic modeling of credit scoring.

This new model hybridizes support vectors of old data with incremental financial data of corporate in the process of dynamic ensemble modeling based on bagged SVM. In the incremental stage, multiple base SVM models are dynamically adjusted according to bagged new updated information for credit scoring. These updated base models are further combined to generate a dynamic credit scoring. In the empirical experiment, the new method was compared with the traditional model of non-incremental SVM ensemble for credit scoring.

The results show that the new model is able to continuously and dynamically adjust credit scoring according to corporate incremental information, which helps produce better evaluation ability than the traditional model.

This research pioneered on dynamic modeling for credit scoring with incremental SVM ensemble. As time pasts, new incremental samples will be combined with support vectors of old samples to construct SVM ensemble credit scoring model. The incremental model will continuously adjust itself to keep good evaluation performance.

The circular economy concept emerged as the resolution to the destructive linear economy practices. Nevertheless, the transition to a circular built environment is hindered due to the ambiguities of the economic value of the concept. Conversely, numerous decision-making tools are applied in the construction industry in assessing economic alternatives, even if there is a gap in utilising these tools in appraising circular economic practices. Hence, this study investigates the potential benefits of applying proven decision-making practices, particularly criteria scoring matrices, in developing circular built environments.

A qualitative approach was followed to achieve the aim of the study. A conceptual design of a criteria scoring matrix was developed with a comprehensive literature survey. Semi-structured interviews of a three-round Delphi expert survey were employed to assess the matrix qualitatively and develop the matrix further. Data were analysed using the content analysis method.

The lack of a value assessment tool in economically assessing the circular economy principles is a key barrier to transcending to a circular built environment. In addressing this issue, this study develops a criteria scoring matrix for circularity value assessment during the design stage of a construction project.

This research contributes to the theory by developing a criteria scoring matrix to measure the economic contribution of circular economy principles. Further, this research contributes to the practice by allowing construction alternatives to be selected, balancing the potential economic return options of a project with the project's contribution to a circular economy.

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.

Community detection is a significant research field in the study of social networks and analysis because of its tremendous applicability in multiple domains such as recommendation systems, link prediction and information diffusion. The majority of the present community detection methods considers either node information only or edge information only, but not both, which can result in loss of important information regarding network structures. In real-world social networks such as Facebook and Twitter, there are many heterogeneous aspects of the entities that connect them together such as different type of interactions occurring, which are difficult to study with the help of homogeneous network structures. The purpose of this study is to explore multilayer network design to capture these heterogeneous aspects by combining different modalities of interactions in single network.

In this work, multilayer network model is designed while taking into account node information as well as edge information. Existing community detection algorithms are applied on the designed multilayer network to find the densely connected nodes. Community scoring functions and partition comparison are used to further analyze the community structures. In addition to this, analytic hierarchical processing-technique for order preference by similarity to ideal solution (AHP-TOPSIS)-based framework is proposed for selection of an optimal community detection algorithm.

In the absence of reliable ground-truth communities, it becomes hard to perform evaluation of generated network communities. To overcome this problem, in this paper, various community scoring functions are computed and studied for different community detection methods.

In this study, evaluation criteria are considered to be independent. The authors observed that the criteria used are having some interdependencies, which could not be captured by the AHP method. Therefore, in future, analytic network process may be explored to capture these interdependencies among the decision attributes.

Proposed ranking can be used to improve the search strategy of algorithms to decrease the search time of the best fitting one according to the case study. The suggested study ranks existing community detection algorithms to find the most appropriate one.

Community detection is useful in many applications such as recommendation systems, health care, politics, economics, e-commerce, social media and communication network.

Ranking of the community detection algorithms is performed using community scoring functions as well as AHP-TOPSIS methods.

This chapter shows how to identify the characteristics of borrowers that are part of a credit scoring model. The credit risk scoring model is an important tool for evaluating credit risk associated with customer characteristics that affect defaults. This research was conducted at a financial institution, a subsidiary of a commercial bank in Indonesia, to answer the challenge of determining the feasibility of providing financing quickly and accurately. This model uses a logistic regression method based on customer data with indicators of demographic characteristics, assets, occupations, and financing payments. This study identifies nine variables that meet the goodness of fit criteria, which consist of WOE, IV, and p-value. The nine variables can be used as predictors of default probability: type of work, work experience, net finance value, tenor, car brand, asset price, percentage of down payment (DP), interest, and income. The results of the study form a risk assessment model to identify variables that have a significant effect on the probability of default.

The purpose of this paper is to analyze how local and central authorities choose between lowest price and more complex scoring rules when they design supplier-selection mechanisms for public procurements. Five hypotheses are tested: a high level of cost uncertainty and highly non-verifiable quality makes the use of the lowest-price supplier-selection method less likely. Organizational habits and transaction-cost considerations influence the choice of mechanism. Strong quality concerns make complex rules more likely.

The analysis departures from normative theory (rational choice) and is based on the regression analysis and survey data comprising a gross sample of 40 contracting authorities and detailed information about 651 procurements.

More complex scoring rules are used more often when the authority is uncertain about costs and about delivered quality. Authority effects are also found to directly and indirectly influence the choice of supplier-selection method, suggesting that tendering design is partly driven by local habits and institutional inertia.

The authors argue that, from a normative point of view, lowest price is an adequate method when the degree of uncertainty is low, for example, because the procured products are standardized and since quality can be verified. When there is significant cost uncertainty, it is better to use the so-called economically most advantageous tender (EMAT) method. (Preferably this should be done by assigning monetary values to different quality levels.) If there is significant uncertainty concerning delivered quality, the contracting authority should retain a degree of discretion, so as to be able to reward good-quality performance in observable but non-verifiable quality dimensions; options to extend the contract and subjective assessments of quality are two possibilities. The main findings are that EMAT and more complex scoring rules are used more often when the contracting authorities report that they experience substantial uncertainty concerning delivered quality and actual costs and that these factors tend to decrease the weight given to price, in line with the predictions. However, the authors also find that this result is mainly driven by variations between authorities, rather than by between-products variation for the same authority. This is from a training of professionals and regulation perspective of policy relevance.

Contract allocation based on habits rather than rational ground could implicate the waste of resources (tax payers money) as it adventures the matching of the preferences of the public sector (the objective, subject matter, of the procurement) and what the potential supplier offers in its tender.

Although the principles for supplier selection are regulated by law they give the contracting authority substantial freedom in designing the scoring rule and in choosing what quality criteria to use. The tension between different objectives and the more general question whether the choices made by authorities reflect rational decision making or institutional inertia together motivate the current study. While the design of the supplier-selection mechanism is an important consideration in procurement practice, it has attracted relatively little attention from the academic community.

This paper presents a method designed to measure the value of Knowledge Based Systems (KBSs) to the employees involved in their development, implementation and use at an organisation. The method is based upon the scoring approach to valuation. The major advantage of using this approach stems from the fact that many KBSs are typified by numerous intangible benefits and costs. Traditional cost benefit models are unable to account for the contribution of intangible benefits to the value of an evolving KBS project. The method presented here overcomes this difficulty by using managers, users, and experts involved in a KBS project to measure its perceived value from both tangible and intangible sources. It produces an overall measure of value which is separated into three critical categories ‐ time, finances, and quality. Time and finances are tangible, while quality is intangible. These categories are meaningful to decision makers at all organisational levels and are critical in making an informed investment decision. The paper applies the method to two KBS projects from a large manufacturing and sales organisation. Suggestions are made for practical uses to which the method can be applied.