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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.

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.

In September 2019, Booking.com changed from the smiley-based scoring system (2.5–10) to the purely 10-point evaluation system (1–10). The smiley-based service evaluation is based on the multi-dimensional (M-D) system, whereas the purely 10-point service evaluation is based on the single-dimensional (S-D) system. This paper aims to focus on how a change in review posting policies impacts service evaluations regarding review generation and distribution.

The authors exploit the natural experiment using Booking.com when the site changed its scoring system from a multidimensional smiley-based service evaluation system to an S-D scoring system. The authors collected online reviews posted on two travel agencies (Booking.com and Priceline.com) between September 2019 and October 2020. A quasi-experimental approach, Difference-in-Differences, was used to isolate the impacts of the new scoring system from the impacts of the change in the service evaluation environment, i.e. COVID-19.

The change in the scoring system considerably alters review distributions by decreasing the portion of positive reviews but increasing the portion of highly positive reviews. Using the theory of emotion work (Hochschild, 1979, 2001), DID is also the reason that the former M-D smiley-based system could have underrated, highly positive reviews of services. Using the information transfer theory (Belkin, 1984), the authors reason the asymmetric transfer of information when users consume reviews from the older (M-D) system but are required to generate reviews on a newer (S-D) system.

The findings would provide online review platform management with a deeper understanding of the consequences of changes in service evaluations when the scoring system is changed.

Though the change in the scoring system would affect how customers evaluate the services of hotels, the causal impacts of switching to the new S-D scoring system have not yet been thoroughly covered by prior hospitality and service evaluation literature, which this research aspires to do.

This paper aims to better jointly estimate Value at Risk (VaR) and expected shortfall (ES) by using the joint regression combined forecasting (JRCF) model.

Combining different forecasting models in financial risk measurement can improve their prediction accuracy by integrating the individual models’ information. This paper applies the JRCF model to measure VaR and ES at 5%, 2.5% and 1% probability levels in the Chinese stock market. While ES is not elicitable on its own, the joint elicitability property of VaR and ES is established by the joint consistent scoring functions, which further refines the ES’s backtest. In addition, a variety of backtesting and evaluation methods are used to analyze and compare the alternative risk measurement models.

The empirical results show that the JRCF model outperforms the competing models. Based on the evaluation results of the joint scoring functions, the proposed model obtains the minimum scoring function value compared to the individual forecasting models and the average combined forecasting model overall. Moreover, Murphy diagrams’ results further reveal that this model has consistent comparative advantages among all considered models.

The JRCF model of risk measures is proposed, and the application of the joint scoring functions of VaR and ES is expanded. Additionally, this paper comprehensively backtests and evaluates the competing risk models and examines the characteristics of Chinese financial market risks.

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 purpose of this paper is to minimize the volume of straight bevel gear and to develop resistance towards scoring failure in the straight bevel gear. Two evolutionary and more advance optimization techniques were used for performing optimization of straight bevel gears, which will also save computational time and will be less computationally expensive compared to a previously used optimization for design optimization of straight bevel gear.

The following two different cases are considered for the study: the first mathematical model similar to that used earlier and without any modification to show efficiency of the optimization algorithm for straight bevel gear design optimization and the second mathematical model consist of constraints on scoring and contact ratio along with other generally used design constraints. Real coded genetic algorithm (RCGA) and accelerated particle swarm optimization (APSO) are used to optimize the straight bevel gear design. The effectiveness of the algorithms used has been validated by comparing the obtained results with previously published results.

It has been found that APSO and RCGA outperform other algorithms for straight bevel gear design. Optimized design values have reduced the scoring effect significantly. The values of the contact ratio obtained further enhances the meshing operation of the bevel gear drive by making it smoother and quieter.

Low volume is one of the essential requirements of gearing applications. Scoring is a critical gear failure aspect that leads to the broken tooth in both high speed and low-speed applications of gears. The occurrence of scoring is hard to detect early and analyse. Scoring failure and contact ratio have been introduced as design constraints in the mathematical model. So, the mathematical model demonstrated in this paper minimizes the volume of the straight bevel gear drive, which has been very less attempted in previous studies, with scoring and contact ratio as some of the important design constraints, which the objective function has been subjected to. Also, two advanced and evolutionary optimization algorithms have been used to implement the mathematical model to reduce the computational time required to attain the optimal solution.

Achieving equal treatment of credit applicants has been a legitimate concern of legislators and the credit industry. However, measures taken to date in attempting to comply with anti‐discrimination laws arguably do not allow for the most effective use of credit scoring models, and could run counter‐intuitive to the intention of legislation through indirect discrimination. The purpose of this paper is to offer an alternative interpretation that preserves the intention of legislation and also retains the integrity and effectiveness of credit scoring models.

The paper makes a legal analysis of anti‐discrimination laws in the UK, with US law as a comparison, aiming to demonstrate that concerns in using information protected under anti‐discrimination laws as variables may be misplaced, because nothing in these laws precludes the inclusion of all relevant variables in modelling.

The inclusion of variables representing protected characteristics in credit scoring models may not contradict current anti‐discrimination laws.

Limitations exist from the perspectives of customer relationship and the need for further checks and balances. Conclusive validation of the findings will need to come from the courts. The paper provides a springboard for empirical research on whether the inclusion of variables representing protected characteristics in credit scorecards continues to produce better decision‐making models.

The findings benefit credit risk modelling as a whole in facilitating the development of credit scorecards that are in compliance with anti‐discrimination laws, without sacrificing their effectiveness.

The paper presents a fresh perspective and alternative solution to legal concerns regarding the use of protected characteristics in credit scoring, which will be useful to the credit industry.

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.

In recent years, the application of credit scoring in urban microfinance institutions (MFIs) became popular, while rural MFIs, which mainly lend to agricultural clients, are hesitating to adopt credit scoring. The purpose of this paper is to explore whether microfinance credit scoring models are suitable for agricultural clients, and if such models can be improved for agricultural clients by accounting for precipitation.

This study merges two data sets: 24,219 loan and client observations provided by the AccèsBanque Madagascar and daily precipitation data made available by CelsiusPro. An in- and out-of-sample splitting separates model building from model testing. Logistic regression is employed for the scoring models.

The credit scoring models perform equally well for agricultural and non-agricultural clients. Hence, credit scoring can be applied to the agricultural sector in microfinance. However, the prediction accuracy does not increase with the inclusion of precipitation in the agricultural model. Therefore, simple correlation analysis between weather events and loan repayment is insufficient for forecasting future repayment behavior.

The results should be verified in different countries and climate contexts to enhance the robustness.

By applying scoring models to agricultural clients as well, all clients can benefit from an improved risk assessment (e.g. faster decision making).

To the best of the authors’ knowledge, this is the first study investigating the potential of microfinance credit scoring for agricultural clients in general and for Madagascar in particular. Furthermore, this is the first study that incorporates a weather variable into a scoring model.